4.2. Epistemology
4.2.2. Deductive Reasoning and Quantitative Research in Relation to the Study
deductive reasoning, which is the research approach used by this study. Pragmatism, on the other hand, places emphasis on investigating a research question with the aim of making positive contribution to knowledge, which is the
overriding objective of the chosen research topic. In line with the research aim of this study, which is to ascertain whether the development of local financial markets enhances the absorptive capacity of the local economy in terms of utilising the gains of FDI, the deductive approach provides the framework that will ultimately help in achieving this aim. It should be noted that though pragmatism allows the use of both quantitative and qualitative methods (i.e. mixed methods), the current study predominantly uses quantitative methods and as such only supports some aspects of pragmatism, which is the area of positive contribution to knowledge.
Deductive reasoning is associated with quantitative research. Quantitative research is a data collection and data analysis methods that uses or generates data in numbers (numerical data). On the contrary, qualitative research uses and generates non-numerical data. Bryman (2008) suggests that quantitative researchers are usually portrayed as being engrossed with the application of measurement processes to social life. Thus, quantitative variables or measures will range from measures of central tendency (mean, median and mode) to measures of spread (standard deviation and variance), as well as other statistical techniques such as regression analysis, correlation, and so on. Quantitative research as a research strategy is deductivist and objectivist in nature. The latter element means that quantitative research has combined the norms and practices of the natural scientific model and of positivism specifically and incorporates a social reality view as an objective reality.
This study on FDI and economic growth will analyse secondary (quantitative) data to ascertain the hypothesis that financial development brings about capital accumulation, which helps to enhance the linkages between FDI and economic growth. Therefore, unlike the inductive approach where theory formulation will follow data analysis, the deductive approach involves working with collected data to prove or disprove a given theory (Saunders et al., 2007:147). See Figure 4.1 below.
Figure 4.1: The Process of Deduction
Source: Adapted from Bryman (2008:10)
One of the issues often raised about the deductive process is that it appears very linear. That is, one step following the other in a clear and logical sequence (Bryman, 2008). However, this has proved not to be the case in many instances. A researcher’s view of the theory or literature could change because new theoretical ideas or findings could have been published by others before the researcher has summarised his or her findings or the relevance of a set of data for a theory may become obvious only after the data have been collected or even the analysis of the collected data. These all support the idea that observations or findings should precede theory (i.e. inductive approach).
These issues implicitly or explicitly manifest themselves in four main pre-
occupations that are for quantitative researchers: (1) measurement, (2) causality, (3) generalization, and (4) replication (Bryman, 2008:155). These pre-occupations reflect grounded epistemologically beliefs about what constitutes acceptable knowledge.
4.2.2.1. Measurement
From the perspective of quantitative research, measurement carries several advantages, which is (1) measurement allows us to delineate fine differences between research subjects in terms of their characteristics or variations, (2)
Theory
Hypothesis
Data Collection
Findings
Hypothesis Confirmed or Rejected
distinctions. A measurement device provides a consistent instrument for gauging differences, (3) measurement provides the basis for more exact estimates of the degree of relationship between concepts (e.g. through correlation analysis). Measurement problems tend to pose “reliability” and “validity” issues to quantitative researchers. Reliability, on the one hand, is basically concerned with issues of consistency of measures of concepts. Validity, on another hand refers to the question of whether an indicator (or set of indicators) that is devised to measure a concept actually measures that concept. One of the validity problems which might likely be encountered is the problem of which indicator to choose that accurately captures the balance of payment statistics on FDI. There are two measures that look at this: (a) net FDI flows and (b) gross FDI flows. Net FDI inflows, reported in the IMF’s international financial statistics (IFS) measures the net inflows of investment to gain a lasting management interest (10% voting stock or more) in an enterprise operating in an economy other than that of the investor. It is the sum of the reinvestment of earnings, equity capital, other short-term and long-term capital as shown in the balance of payments (Sghaier and Abida, 2013). On the other hand, gross FDI figures reflect the total sum of the absolute value of inflows and outflows accounted in the financial accounts of the balance of payments. The model adopted by this study focuses on the inflows to the Nigerian economy. Therefore, this study will use the net inflow measure.
In connection with (3) above, one justification for choosing the quantitative approach is the fact that it allows the “explanation of relationships between variables”, provided that the researcher is “independent of what is being observed”, and “if generalization about results is to be made then it is necessary to select samples of sufficient numerical size” (Saunders et al., 2007:145). The key data used in this study are indicators of FDI, financial development and measures of real economic growth and its sources. The sample will consist of time series data of 45 observations for the period 1970 to 2014. Though this sample size is not adequate, this study attempts to use auto-regressive distributed lags (ARDL) in order to enhance the power of the results.
4.2.2.2. Causality
Causality is a major concern in most quantitative studies because quantitative researchers don't just report or describe how things are but are obligated to explain why things are the way they are (Bryman, 2008: 156). With respect to the current
study, the issue of causality often arises between FDI and economic growth. That is, is it FDI that causes economic growth or economic growth causes FDI? To address this issue, this study uses the Granger Causality test to know whether FDI is the one that causes growth or whether growth is the one that causes FDI. The Granger Causality approach measures the precedence and information provided by a variable (X) in explaining the current value of another variable (Y) (Granger, 1969; Nwosa et al, 2011). It says that Y is said to be granger-caused by X if given the past values of Y, the past values of X helps in predicting the value of Y. The null hypothesis H0 tested is that X does not granger-cause Y and Y does not granger- cause X. Previous study by Omran and Bolbol (2003) found that the level of impact FDI has on growth may be subject to a minimum threshold level of financial development, so that it is appropriate to check whether FDI itself could contribute to financial development and in so doing, improve its chances to stimulate growth. An example of a study that examines the issue of causality in Nigeria, the country of study, is Umoh et al. (2012) who analysed the endogenous effects between FDI and economic growth and found evidence of a positive bi-directional causality (that is, there is a positive feedback from FDI to growth and from growth to FDI).
4.2.2.3. Generalization
Another distinctive preoccupation that can be discerned in quantitative research is generalisation. In quantitative research, the researcher is typically concerned with being able to say that his or her findings are generalizable beyond the boundaries of the context in which the research was carried out (Bryman, 2008:156). Therefore, if a study on bank lending to small businesses is carried out by a questionnaire with several entrepreneurs answering the questions, we would normally want to say that the results could apply to entrepreneurs apart from those whose response were used in the study. This concern divulges itself in business survey research in terms of the attention that is usually given to the question of how the researcher can create a representative sample.
However, in the case of the current research, which does not utilise a survey procedure, generalization will mean how widespread or universal the results obtained from the country of study (Nigeria) can be applied in studies on FDI, financial development and economic growth in other jurisdictions. The way this study responds to this is to compare the level of financial market development in the country of study with that in other countries before any conclusions or
There will be no use comparing the results of the study with what obtains in the UK, simply because both countries are at different stages of market development. For instance, in the study by Alfaro et al.(2004) which used panel data from a sample of 71 OECD and non-OECD countries, several countries where excluded when performing some of the regressions, for example, based on the non-existence of stock markets in certain less developed financial markets. It is inappropriate to generalise with a procedure for all subjects in a sample without taking due cognisance of the idiosyncratic characteristics of the subjects, in the same way it is inappropriate to generalize the findings of a research beyond the cases (for example, the subjects) that make up the sample. The outcome of the current study will only be compared to findings from similar studies on the economic effects of FDI in Nigeria and other emerging markets such as Latin America, Asia, Middle East and North Africa and Other Sub-Saharan African countries.
4.2.2.4. Replication
In the natural sciences, an experiment or research procedure should be capable of being replicated or reproduced. If an experiment is not capable of being reproduced, it will raise concerns about the validity of the research findings. Scientists therefore, often try to be highly clear about the procedures used in their research in order for an experiment to be capable of being replicated. In the same vein, quantitative researchers in the social sciences often see replication or more precisely, the ability to replicate as an imperative component of their research. This is because the possibility of a lack of intrusion and objectivity of the researcher’s values would seem to be much greater when examining the social world than when the natural scientist investigates natural phenomena (Bryman, 2008:157). Therefore, it is often regarded as imperative that a researcher clearly spells out his or her procedures so that others can replicate them, even if the research is not eventually replicated.
Having reviewed the philosophical approaches and the process of deduction associated with quantitative research of this nature, the rest of this chapter sets out the data collection process, describes the econometric methods and specifies, in a step-by-step manner, all the statistical and econometric procedures and tests that would be followed in this study on FDI, financial development and economic growth.