# Fdi Inflows In Manufacturing Industry Of Malaysia Economics Essay

## CHAPTER 1

The US Bureau of Economic Analysis defines the foreign direct investment (FDI) as an acquisition of foreign assets (based on residence) with the intention to exert control, which, in practical terms, usually means ownership of more than 10 percent (De Santis, R.A., Anderton, R., Hijzen, A., 2004). Consequently, countries differ with regard to the minimum percentage of equity ownership that they consider direct as opposed to portfolio investment (Caves, 1996).

FDI can also classified by any subsequent transactions in financial assets or liabilities that occur between nonresident direct investors and resident companies that are linked by a foreign direct investment relationship. The transactions could be between the companies in Malaysia with its immediate parent, ultimate parent or fellow companies as shown in the diagram below.

C:\Users\My UiTM\Desktop\Untitled.jpg

Figure 1: The relationship of companies in one group within the

corporate structure and their share of equity ownership

In simple word, foreign direct investment (FDI) is defined as capital inflows from other countries that invest in the production capacity of the host economy.

Foreign direct investment (FDI) can be divided into several components which are equity capital, reinvested earnings and other capital associated with inter-company debt transactions. International Monetary Fund (IMF) describes FDI components as follows:

Equity capital comprises equity in branches, all shares in subsidiaries and associate companies (except non-participating preference shares).

Reinvested earnings consists of direct investors’ shares of earnings not distributed as dividends by subsidiaries or associates and earnings of branches not remitted to direct investors,

Other Capital consists of debt securities, trade credits, loans, deposits and others.

Normally, the are several motives why firms engaged in FDI. It might be due to access to resources, access to markets, efficiency gains, and acquisition of strategic assets (Dunning,1993).

## 1.2 Background of Study

## FDI Position in Malaysia

Over the recent decade, Malaysia attracted net FDI inflow averaging 3% of GDP per annum, which is more moderate compared with the 1990s (average of 6.4% of GDP), due mainly to the changing nature of investment. The trend in gross FDI has remained stable since 2000 after a moderation during the Asian financial crisis. A growing share of these investments have been channelled into less capitalintensive and higher value-added activity.

Furthermore, higher competition for FDI from emerging economies, particularly low labour cost countries such as China and Vietnam have also contributed to the lower net inflows. Nonetheless, the average share of foreign investment to total private investment has remained stable.

1990-1999

(% share)

2000-2010

(% share)

Net FDI to GDP ratio

6.4

3.0

Net FDI to total private investment ratio

27.0

28.5

Table : Average Net FDI Inflow

Three factors that make Malaysia attractive to FDI are undervalued currency, low cost of labour and fairly low inflation rate (Oti-Prempeh, 2003). The strength of Malaysia appeal to FDI is rooted in its Promotions of Investment Act no 327 (1986) which have been strongly observed in successive National Economic Plan (NEP).

FDI has contributed significantly to the growth and transformation of the Malaysian economy. The large presence of multinational corporations (MNCs) has provided direct benefits in the form of employment creation and capital formation. It also provided opportunities for local firms to perform the role of vendors by supplying parts and services to the large MNCs, which has benefited the small and medium enterprises (SMEs).

More importantly, FDI can also lead to the transfer of knowledge, technology and skills in developing local firms and human capital. Particularly, in the manufacturing sector, Malaysia has steadily climbed up the value chain with improved productivity levels. The real value-added per worker in the manufacturing sector has increased from RM49,013 in 2000 to RM78,707 in 2010 (equivalent to USD12,898 in 2000 to USD24,435 in 2010).In terms of sectoral distribution, net FDI inflows during 2000-2010 were channelled mainly into the manufacturing, services, and oil and gas sectors where the manufacturing sector remains the major recipient of the net inflows.

Thus, this study seeks to examining FDI inflows of Malaysia contributed by manufacturing industry over the period 1980 to 2010. Some of the macroeconomics factors influencing FDI are examined, including the market size and degree of openness of the economy, the currency value and economic stability.

This paper is structured as follows: This chapter discuss general idea about the topic discoverd in this study while the next chapter provide a review of the theoretical literature dealing with the determinants of foreign direct investment in manufacturing industry in Malaysia. In Chapter 3, estimated regression model and the hypotheses to be tested is developed and results obtained presented in Chapter 4. The results are then analysed too. Lastly, in Section 5, the conclusions of this study is presented and recommendation is given.

## Problem Statement

Foreign Direct Investment is increasingly important to Malaysia in their efforts to catch up and develop their economies. Malaysia received substantial amounts of annual FDI inflows in its manufacturing industry over the past decade. In 1980-1989, the average of FDI approved projects was 648.9 million US dollars. That average increased dramatically to 4752.7 million US dollars in 1990 – 1999. In 2002, the amount was 3,046.8 million US dollars. 2010?? The increasing amount of annual FDI inflows in manufacturing industry was not really affected even during financial crisis in 1997-1998. As evidence, in 1996, it was 12,353.6 million US dollars and increased to 12,829.9 million US dollars in 1997. In 1998, it was 8,274.1 million US dollars (MoF, various issues). Thus, FDI in manufacturing industry in Malaysia can be considered as stable every year.

As an important engine of economic growth, manufacturing industry contributed increasing percentage of the GDP every single year. For example, in 1987, it contributed 19.8 per cent of GDP, 24.6 per cent in 1990 and 44.8 per cent in 2001. Same goes to export orientation, namely exports of manufactured goods, machinery and transport equipment and miscellaneous manufactured articles which contributed 39.9 per cent of total export in 1987. In 1990, it increase to 53.6 and in 2002, 73.5 per cent of total exports were manufactured.

Today, manufacturing industry is also play an important role in transform the Malaysian economy from an agriculture based economy to an industry based economy in order to achieve a fully developed country by 2020 through Vision 2020.

Thus, the question we seek answer to this study are what are the factors that contributed to the movement of annual FDI inflows in manufacturing industry of Malaysia over the period 1980 to 2010.

## Objectives of Study

To investigate the determinants of FDI inflows in manufacturing industry of Malaysia.

To investigate the factor that most affected FDI inflows in manufacturing industry of Malaysia.

To investigate and highlights theoretical and empirical findings about determinants of FDI inflows in manufacturing industry of Malaysia.

To investigate the significant relationship between FDI inflows in manufacturing industry and economics variables.

To investigate the major determinants which are generally found in the literature to attract FDI inflows in manufacturing and compare result to this study.

## Scopes of Study

Scopes of study refer to what extend or degree the study covers. There are two types of scopes which are space dimension and time dimension.

## Space Dimension

This study only focuses on foreign direct investment inflows in Malaysia. The industry that has been chose to investigate is manufacturing industry.

## 1.4.2 Time Dimension

The range of data covered in this study is from 1980 to 2010. The mode of data is annually and the total number of observations (n) is 31.

## 1.6 Limitation of Study

There are several limitations that behind this study that might affect the result and findings of the study. The limitations are as follows:

## Variables

Variables that are used in this study is only limited to several macroeconomics factors. They are only gross domestic product (GDP), trade openness (TO), exchange rate (ER) and inflation rate (INF) that are used in this study.

## Types of data

Only secondary data are used in this study. All data that are published then used to determine the result of this study. Since there are much sources provided, thus each of information through articles, journals, newspaper and websites must be analyzed before that particular information is used in this study.

## Finding Information

Some problem during finding information regarding both independent and dependent variable occurred. Some data provided is not enough total observation needed and time must be spent on searching the internet in order to obtain data that met the requirements of study. Other than that, certain variable need to calculate by own before that particular data can be used. For example, data of CPI must be standardized their based year before it can be used in this study.

## Significance of Study

This study will give advantage and benefit to certain categories of people and organization. From this study, they will generate more inputs how foreign direct investment affected by macroeconomics variables. They can be categorized as follows:

## Government

Government will gain benefit from this study where they can accurately determine which factors attracted FDI in Malaysia. By doing so, they can focuses on that particular factors in order to increase FDI inflows in our country. As a result, government can make better decision such as determine which sub-sector of manufacturing industry should be invested most and at the same time risk will be reduced.

## Companies

This study will give advantage for companies too. They are able to determine what kind of FDI ways must be choose. For example is merger and acquisition or joint venture.

## Researchers

From this study, researchers in future would benefit by generate new knowledge regarding FDI’s inflows and factors that encourage FDI’s inflows. In future, new advanced research about the same field of study will be done and new variable will be finding in order to obtain more information about this study. The more study, the more result can be find.

## Potential investors

Which country or which industry should be invested in FDI is the main question for investors before any decision will be made. So, this study will give brief ideas about FDI and at the same time will encourage more potential investors to invest in FDI.

## CHAPTER 2

## LITERATURE REVIEW

## 2.0 Introduction

Literature review provide brief of past empirical study which investigate the determinants of FDI inflows across the world. Different variables with different methodology will result different outcomes. Financial development, wage rates, income, economic growth, market size, government spending on infrastructure, trade openness, exchange rate, inflation rate and corporate tax are among the variables commonly analyze in the FDI dynamic.

There are two parts of literature review which are the first part is theory of the study and second part is empirical study to test the theory. In each of these articles, the space and time dimension of that study, the methodology used the variables and the outcome of that study is identified.

## 2.1 Previous studies

Dunning (1973, 1981) was a father of research work for determinants of FDI. His classical model provides an analysis based on OLI paradigm (ownership, location and internationalism). Based on aggregate econometric approach made by Agarwal (1980), Schneider et al (1985) this empirical study was developed. Lucas (1993) then modified this study by examines the determinants of FDI inflows for select East and South Asian economies during 1960 to 1987 by using a model based on a traditional derived-factor of a multiple product monopolist. The study finds that FDI inflows are more elastic with respect to cost of capital than wages and also more elastic with respect to aggregate demand in exports than domestic demand.

Nursuhaili Shahrudin, Zarinah Yusof, and NurulHuda Mohd. Satar (2009) studied determinants of FDI in Malaysia from 1970-2008. The variable tested are GDP, growth, financial developemnt, trade openness, inflation,exchange rate and taxes. The causality and dynamic relationship between foreign direct investment (FDI) and its key determinants is identified using an autoregressive distributed lag (ARDL) framework. The result suggests that among the variables, financial development and economic growth contribute positively to the inflow of foreign direct investment in Malaysia.

Determinants of inward FDI in Korean service industry was done by Taek Dong Yeo, Youngman Yoon, Min Hwan Lee and Chang Yeal Lee using cross sectional time series during 1993 to 2006 from major 12 countries. Labour cost, market size, trade volume, regulation and agglomeration was confirmed are major determinants of FDI inflow into Korean service industry.

Tsen (2005) examined the long run relationship between FDI and its location-related determinants in the manufacturing industry in Malaysia over the period 1980 to 2002. By using this time series data, co-integration analysis is used to examined variables infrastructure, education, exchange rate and market size that are being tested. The result suggests that market size, good education and infrastructure have positive relationship while increase in exchange and inflation rate will result decrease in FDI.

Previous work has looked at the relationship of FDI with several macroeconomic variables. Thus, based on the discussed literature review above, this study gauges a set of potential determinant variables that influence the FDI flows in financial services industry in Malaysia and have been classified the variables into four broad categories, which are market size, trade openness, currency value, and economic stability.

## Market size

Larger host countries’ markets may be associated with higher foreign direct investment due to larger potential demand and lower costs due to scale economies. Usually measured by Gross Domestic Product (GDP), GDP per capita income and size of the middle class population, it is expected to be a positive and significant determinant of FDI flows (see: Lankes and Venables, 1996; Resmini, 2000; Duran, 1999; Garibaldi, 2002; Bevan and Estrin, 2000; Nunes et al., 2006; Sahoo, 2006).

For example, Resmini (2000), looking into manufacturing FDI, finds that countries in Central and Eastern Europe with larger populations tend to attract more FDI, while Bevan and Eastrin (2000) present similar results; transition economies with larger economies also tend to attract more FDI. In contrast, Holland and Pain (1998) and Asiedu (2002) capture growth and market size as insignificant determinants of FDI flow.

## Trade openness

Previous literature also considered trade openness as key determianant of FDI. It can be export oriented and may also require the import of complementary, intermediate and capital goods. In either case, volume of trade is enhanced and thus trade openness is generally expected to be a positive and significant determinant of FDI (see: Lankes and Venables, 1996; Holland and Pain, 1998; Asiedu, 2002; Sahoo, 2006). Trade openness is proxied as the ratio of the Export plus Import divided by GDP (Nunes et al. 2006; and Sahoo, 2006).

## Currency valuation

Exchange rate is used as proxy for currency valuation of the investing firm. Devaluation of a currency would result in reduced exchange rate risk. As a currency depreciates, the purchasing power of the investors in foreign currency terms is enhanced, thus we expect a positive and significant relationship between the currency value and FDI inflows.

The currency value can be proxies by the Real Exchange Rate, Real Effective Exchange Rate (REER) and Nominal Effective Exchange Rate (NEER).

Froot and Stein (1991) find evidence of the relationship between exchange rate and FDI inflows. A weaker host country currency tends to increase inward FDI within an imperfect capital market model as depreciation makes host country assets less expensive relative to assets in the home country. Blonigen (1997) makes a “firmspecific asset” argument to show that exchange rate depreciation in host countries tend to increase FDI inflows.

## Economic stability

A country which has a stable macroeconomic condition with high and sustained growth rates will receive more FDI inflows than a more volatile economy. The proxies measuring growth rate are: GDP growth rates, Industrial production index, Interest rates, Inflation rates (see: Duran, 1999; Dassgupta and Ratha, 2000).

Contradicting, when inflation is taken as proxy for the level of economic stability, then the classic symptoms of fiscal or monetary control will result in unbridled inflation. In connection with this, investors prefer to invest in more stable economies that reflect a lesser degree of uncertainty (see: Nonnenberg and Mendonca, 2004). Thus, GDP growth rate, Industrial production index, Interest rates would influence FDI flows are expected to be positively and the Inflation rate would influence positively or negatively.

## CHAPTER 3

## RESEARCH METHODOLOGY

## 3.0 Introduction

This chapter discusses ‘how to carry out the study’ or ‘how to achieve the objective of the study’. In simple words, it discusses the tools used to perform the task. Several criteria in research methodology have been analyzed in this chapter including research framework, regression model, variables and data descriptions, and statistical tests that are used in this study.

## 3.1 Theoretical Framework

A theoretical framework is a conceptual model of how dependent variable influence by independent variables. In this study, theoretical framework shows that which independent variables tested (GDP, TO, ER, INF) will influence FDI inflows in manufacturing industry in Malaysia.

Gross Domestic Product

Foreign direct investment in manufacturing industry per RM millions

Trade openness

Exchange rate

Inflation rate

Dependent variable

Independent variable

Figure 1: Theoretical Framework Model

## 3.2 Variables and data descriptions

There are two types of variable that are being used in this research. They are independent and dependent variable. Dependent variable is Foreign Direct Investment (FDI) inflows in manufacturing industry sector per RM millions. Other than that, independent variables included in this paper are gross domestic product, trade openness, exchange rate and inflation. Table presents the dependent and independent variables used, and the corresponding sources of data.

Table 1: Summary Variable and Sources of Data

## VARIABLE

## SOURCES

## DEPENDENT VARIABLE

Foreign direct investment inflows in manufacturing industry (RM million)

Ministry Industrial Development Authority (MIDA), Ministry of Finance (MoF)

## INDEPENDENT VARIABLE

Gross Domestic Product per capita (RM millions)

Department of Statistic (DoS), Bank Negara Malaysia (BNM)

Trade Openness

(Import + Export)/GDP

Unit Perancang Ekonomi (UPEN)

Exchange rate (RM/US)

Bank Negara Malaysia (BNM)

Inflation rate (%)

Bank Negara Malaysia (BNM)

Dependent variable is FDI inflows per capita in manufacturing industry of Malaysia. The data were obtained from MIDA and MoF websites, which contains data from 1980 until 2010 in Malaysia.Currently, MIDA is the only government agency in Malaysia that compiles the FDI data in the manufacturing industries. The data also published by Ministry of Finance thourgh its Economic Report. FDI is expressed by value of foreign investment in approved projects in the manufacturing industry.

On other side, independent variables that are being used in this study are macroeconomics variables. They are split into four groups which are market size, trade openness, currency value and economic stability.

## 3.2.1 Market size

In order to determine market size of a country, Gross Domestic Product (GDP) (RM million) or growth in GDP normally used in previous studies. But in this research, we only used GDP (RM million) in Malaysia at current prices from 1980 to 2010 which data is obtained from Department of Statistic and Bank Negara Malaysia through Monthly Statistical Bulletins.

## Trade openness

As a proxy for trade openness, sum of imports and exports as a percentage of GDP is calculated. This is the standard measure of openness in the FDI literature, and Chakrabarti (2001) finds that it is the variable most likely to be correlated with aggregate FDI besides market size. Data of imports and exports in Malaysia was found in Unit Perancang Ekonomi website. The formula of trade openness is as follows:

## 3.2.3 Currency value

Data of exchange rate was obtained from Bank Negara Malaysia through their Monthly Statistical Bulletins. The data of annual exchange rate that used in this study is Ringgit Malaysia per unit of US Dollar by the end of period from 1980 until 2010.

## 3.2.4 Economic stability

The last macroeconomic variable that is used in this paper is inflation rate. This inflation rate is measured by annual changes of total Consumer Price Index for each year from 1980 to 2010. Data provided by Bank Negara Malaysia is in different based year, so all indexes must be standardized in same year. So, in this study, each year of Consumer Price Index then standardized into the latest based year which is in 2005 before annual inflation rate have been calculated. Annual inflation calculated by changes of annual CPI. The formula used is

## 3.3 Hypothesis

The purpose of this study is to determine which hypothesis developed is correct and accepted. The symbol is recognized as H0 and H1. H0 considered as null hypothesis and H1 represent alternate hypothesis.

## Hypothesis 1 – Market size (GDP)

Q: Do market size have significant relationship with FDI inflows in manufacturing industry of Malaysia?

H0 There is no significant relationship between market size and FDI inflows in food manufacturing industry of Malaysia.

H1 There is significant relationship between market size and FDI inflows in food manufacturing industry of Malaysia.

## Hypothesis 2 – Trade openness

Q: Do trade openness have significant relationship with FDI inflows in manufacturing industry of Malaysia?

H0 There is no significant relationship between trade openness and FDI inflows in manufacturing industry of Malaysia.

H1 There is significant relationship between trade openness and FDI inflows in manufacturing industry of Malaysia.

## Hypothesis 3 – Exchange rate

Q: Do exchange rate have significant relationship with FDI inflows in manufacturing industry of Malaysia?

H0 There is no significant relationship between exchange rate and FDI inflows in manufacturing industry of Malaysia.

H1 There is significant relationship between exchange rate and FDI inflows in manufacturing industry of Malaysia.

## Hypothesis 4 – Inflation

Q: Do inflation have significant relationship with FDI inflows in manufacturing industry of Malaysia?

H0 There is no significant relationship between inflation and FDI inflows in manufacturing industry of Malaysia.

H1 There is significant relationship between inflation and FDI inflows in manufacturing industry of Malaysia.

## Regression model

Regression model refers to techniques for the modeling of numerical data consisting of values of a dependent variable (FDI inflows in manufacturing industry of Malaysia) and independent variables (GDP, TO, ER and INF). Regression model can be used for prediction including forecasting of time-series data, inference, hypothesis testing, and modeling of causal relationships.

These uses of regression model rely heavily on the underlying assumptions being satisfied. There are two different types of regression model which are linear and multiple. Linear regression model will be used if there is only one independent variable tested in the study. Meanwhile, multiple regression model will be used if there are more than one independent variables used in that particular study. Thus, in this study, multiple regression model is used since there are four independent variables tested in this study (GDP, TO, ER, INF).

## 3.4.1 Multiple Regression Analysis

Estimation model: LFDI = α + β1 LGDP + β2 LTO+ β3 LER + β4 INF + ε

(Equation 1)

Where L refer to logarithm form;

FDI = FDI inflows in manufacturing industry.

α = Intercept

β1,2,3.4 = Estimated parameters

GDP = Gross domestic product per capita (RM million),

TO = Trade openness

ER = Exchange rate (RM/USD)

INF = Inflation rate(%)

ε = Error term

## Research Analysis

In research analysis, several statistical tests have been done in order to determine which hypothesis tested is correct. It included Coefficient of Determination, Pearson Correlation Coefficients, Regression Coefficient, T-statistic, F-statistic, and Durbin Watsons.

## 3.5.1 Coefficient of determination

Coefficient of determination or R2 is a statistical measure of how well the regression line fit of a model. It determine the proportion of the total variation in the dependent variable (FDI inflows) that is explained by the variations of independent variables (GDP, TO, ER, INF).

An R2 of 1.0 indicates that the regression line perfectly fits the data and values of R2 will be 0.0 if there are no correlation between dependent and independent variables in regression model. The higher value of R2, the higher explanatory power of estimated regression model.

Coeeficient of determination can be measure by using this formula;

## 3.5.2 Pearson Correlation Coefficients

Pearson correlation coefficients is a numerical value that can measure the strength and direction of relationship that exits between two variables which are dependent variable (FDI) and independent variables (GDP, TO, ER, CPI).

The correlation coefficient would take on values ranging from -1 to 1. A value of -1 and 1 would mean perfect negative correlation and perfect positive correlation respectively. A value of 0, however would indicate the existence of no relationship between the variables. Figure shows the relationship between diffent types of correlation and the values.

Negative correlation Positive correlation

-1 0 1

Negative No correlation Positive

Perfect Perfect

Correlation Correlation

Figure 2

According to Pearson correlation analysis, the result can be ranked as follows :

Less than 0.30 - weak relationship

0.30-0.49 - moderate relationship

0.50-0.69 - strong relationship

0.70-0.99 - very strong relationship

1.00 - perfect relationship

## 3.5.3 Regression Coefficient

Regression coefficient is a techniques for modelling to focus on the relationship between a dependent variable and one or more independent variables.

More specifically, regression analysis helps to know the strength of relationship between two variables; how much of the variance of the dependent variable (FDI inflows in manufacturing industry) will be explained when several independent variables (GDP, TO, ER, INF) are theorized to simulatanously influence it.

When the variance of DV (FDI) is expected to be explained by four IV (GDP, TO, ER, INF), it should be noted that not only are the four independent variables correlated to the dependent variable in varying degrees but they might also be intercorrelated among themselves.

## 3.5.4 T-statistic

The t-statistic is sometimes also referred to as a t-test or t-ratio. It is a statistical hypothesis test in which the test statistic follows a t- distribution, if the null hypothesis is supported. Thus, the t-statistic measures how many standard errors the coefficient is away from zero. It is the regression coefficient of a given independent variable (GDP, TO, ER, INF) divided by its standard error.

The standard error is essentially one estimated standard deviation of the data set for the relevant variable. To have a very large t-statistic implies that the coefficient was able to be estimated with a fair amount of accuracy. If the t-static is more than 2, the variable in model has a significant impact on the dependent variable.

T-value is calculated by dividing the value of coefficient (b) by the standard error coefficient (Se).

Meanwhile, to determine critical t-value from t-distribution table, we need to find the degree of freedom of this study by using this formula:

Where; n = number of observations

k = number of independent variables

## .

If the calculated t-statistic is greater than critical t-value, the variable is said to statistically significant. Therefore, accept H1.

t-statistic > critical t- value = reject H0, accept H1

If the calculated t-statistic is lower than critical t-value, the variable is said to statistically insignificant. Therefore, reject H1.

t-statistic < critical t- value = accept H0, reject H1

## 3.5.5 F-statistic

F-statistic is used to test the hypothesis that the variations in the independent variables explained a significant portion of the variation in the dependent variable. In other words, it is used to test the significant of the overall model tested in the study.

An F-statistic is a statistical test in which the test statistic has an F-distribution under the null hypothesis. It is also used in analysis of variance (ANOVA), where it tests the hypothesis of equality of means for two or more groups. For instance, in an ANOVA test, the F-statistic is usually a ratio of the Mean Square for the effect of interest and Mean Square Error.

The F-statistic is very large when MS for the factor is much larger than the MSE. In such cases, reject the null hypothesis that group means are equal. The F-statistic can be also used in Simple Linear Regression to assess the overall fit of the model.

The value of F-statistic is given by:

Where;

F= critical value

k = number of independent variables

n = number of observations

If the calculated F-value is greater than F-distribution table, the variable is said to statistically significant. Therefore, accept H1.

F-value > F- distribution = reject H0, accept H1

If the calculated F-value is less than F-distribution table, the variable is said to statistically insignificant. Therefore, reject H1.

F-value < F- distribution = accept H0, reject H1

## 3.5.6 Durbin Watsons

Durbin Watson (D-W) is the test that takes order on autocorrelation of residuals. It is used to test the presence of autocorrelation that will appear when time series data are used. Autocorrelation give a downward bias to the standard error of estimated coefficient (t-value are exaggerated) and hence, the estimated coefficient are concluded to be significant when in reality they are not.

There are 3 possibilities, where the autocorrelation problem might arise:

When the IV are duplicated

When some of the IV are mis-specified

When some important variables are found missing in the model

dL dv 2 4-du 4-dL

Diagram above shows graph of D-W. If D-W is between1.9-2.1, thus it indicate that there are no autocorrelation exits, if D-W is lower than 1.9, it indicates that there are positive autocorrelation and if D-W is greater than 2.1, it indicates that there are negative autocorrelation.

## CHAPTER 4

## EMPIRICAL RESULT AND INTERPRETATION

## 4.0 Introduction

This chapter provides all the result obtained from the variables tested trough Statistical Package of Social Science software (SPSS) to analyzed and interpret the result. The interpretation and analysis of correlations and regression in this study then be converted into meaning form.

The data will be interpret including examined how well independent variables chosen fit the model, examined the relationship between each independent variable using coefficient relationship, examined the significant relationship of each variables using t-statistic, examined the combination of independent variables which can be predictors to the dependent variable using F-statistic and examined the auto correlation between dependent and independent variables using Durbin Watson.

## 4.1 Coefficient of Determinations

## STATISTICAL TEST

## SYMBOL

## RESULT

Correlation coefficients

(R)

0.906

Coefficient of determination

(R2)

0.821

Adjusted R2

0.794

Interpretation:

Correlation coefficients (R) for this study are 0.906. Thus, it represents a strong positive relationship between FDI inflows in manufacturing industry of Malaysia (DV) and GDP, TO, ER and CPI (IV).

Coefficient of determination (R2) is 0.821. It means that 82.10% of total variation in dependent variable is explained by independent variables. The remaining balance of 17.9% is due to randomness or variables that might not be included in this study.

The result has an adjusted R2 of 0.794. This means that 79.4% of the variations of FDI inflows in manufacturing industry can be explained by the variation GDP, TO, ER and INF.

## 4.2 Pearson Correlation Coefficients

Table below shows result of Pearson Correlation between FDI and all variables tested in this study.

Table : Pearson Correlation Coefficients

## Correlations

logFDI

logGDP

logER

INF

logFDI

Pearson Correlation

1

.876**

.851**

.690**

-.264

Sig. (2-tailed)

.000

.000

.000

.152

N

31

31

31

31

31

logGDP

Pearson Correlation

.876**

1

.869**

.827**

-.343

Sig. (2-tailed)

.000

.000

.000

.059

N

31

31

31

31

31

Pearson Correlation

.851**

.869**

1

.823**

-.212

Sig. (2-tailed)

.000

.000

.000

.252

N

31

31

31

31

31

logER

Pearson Correlation

.690**

.827**

.823**

1

-.395*

Sig. (2-tailed)

.000

.000

.000

.028

N

31

31

31

31

31

INF

Pearson Correlation

-.264

-.343

-.212

-.395*

1

Sig. (2-tailed)

.152

.059

.252

.028

N

31

31

31

31

31

**. Correlation is significant at the 0.01 level (2-tailed).

*. Correlation is significant at the 0.05 level (2-tailed).

Interpretation:

A value of 0.876 indicates that a very strong positive correlation between FDI and GDP.

A value of 0.690 indicates that a moderate positive correlation between FDI and ER.

A value of 0.851 indicates that a very strong positive correlation between FDI and TO.

A value of – 0.264 indicates that a weak negative correlation between FDI and INF.

## 4.3 Regression analysis

The following regression result are obtained from SPSS:

## Variables

## Coefficients

## Standard error

## T-statistic

## Constant

11.852

3.156

3.756

## GDP

1.011

0.285

3.550

## TO

2.379

0.893

2.664

## ER

-1.912

1.097

-1.743

## INF

-0.208

0.061

-0.462

The result of the study shows coefficients, standard error and t-statistic value indicated by variables tested. Thus, a new equation can be developed by adding those values into equation 1. The multiple regression equation now can be written as:

Equation 1:

LFDI = α + β1 LGDP + β2 LTO+ β3 LER + β4 INF + ε

Convert into specific equation;

Equation 2:

LFDI = 11.852 + 1.011 LGDP + 2.379 LTO – 1.912 LER – 0.208 INF + ε

(Se) (3.156) (0.285) (0.893) (1.097) (0.061)

(T-stat) (3.756) (3.550) (2.664) (-1.743) (-0.462)

Where L refer to logarithm form;

FDI = FDI inflows in manufacturing industry.

GDP = Gross domestic product per capita (RM million),

TO = Trade openness

ER = Exchange rate (RM/USD)

INF = Inflation (%)

ε = Error term

Interpretation of Regression Coefficient

## 4.3.1 The relationship between GDP and FDI inflows in manufacturing industry.

Table above shows a positive relationship between GDP and FDI inflows in manufacturing industry of Malaysia. It indicates that if GDP increase by 1 unit, FDI inflows in manufacturing industry in Malaysia will increase by 1.011%.

## 4.3.2 The relationship between TO and FDI inflows in manufacturing industry.

Same goes to trade openness, which is a positive relationship between TO and FDI inflows in manufacturing industry of Malaysia. In value 2.379, it indicates that if TO increase by 1 unit, FDI inflows in manufacturing industry in Malaysia will increase by 2.379%.

## 4.3.3 The relationship between ER and FDI inflows in manufacturing industry.

Meanwhile, ER has negative relationship to FDI inflows in manufacturing industry of Malaysia. If ER increases by 1 unit, FDI inflows in manufacturing industry in Malaysia will decrease by 1.912%.

## 4.3.4 The relationship between INF and FDI inflows in manufacturing industry.

There is negative relationship between INF and FDI inflows in manufacturing industry of Malaysia. By increases in 1 unit of INF, FDI inflows in manufacturing industry in Malaysia will decrease by 0.208%.

## 4.4 T-Statistic

Degree of freedom : n-k-1

Where :

n = number of observations

k = number of independent variables

For this study, n = 31,k = 4, thus degree of freedom is 26 (31-4-1).

Since the degree of freedom is 26, the critical t-stats from t-distribution table with 95% confident interval and 5% significant level is 1.706.

Variables

Calculated t-stats

Sign

Critical

t-stats

Outcomes

GDP

3.550

## >

1.706

Significant

TO

2.664

## >

1.706

Significant

ER

1.743

## >

1.706

Significant

INF

0.462

## <

1.706

Insignificant

Table shows the outcomes found after calculated t-stats are compared to critical t-stats.

Interpretation:

There is a significant relationship between GDP and FDI inflows in manufacturing industry. Due to the calculated t- stats is greater than critical t-stats (3.550 > 1.706), thus in hypotheis, H1 will be accepted meanwhile H0 will be rejected.

Significant outcomes also goes to TO and ER which indicates that there are significant relationship between TO and FDI inflows in manufacturing industry and ER and FDI inflows in same industry respectively. Since the values of t-stats for both TO and ER is greater than critical t-stats (TO: 2.664 >1.706, ER: 1.743 >1.706), thus, H1 for both hypothesis will be accepted and H0 will be rejected.

On other side, INF resulted that there is no significant relationship to FDI inflows in manufacturing industry of Malaysia. Their calculated t-stats is lower than critical t-stats at 95% confidence level ( 0.462 < 1.706) which means that the alternate hypotheis will be rejected. Thus, null hypothesis is accepted which indicates FDI inflows in manufacturing will not influenced by INF.

All of the actual sign is consistent with reseacher expectation obtained from most of journals in the same field of study. In previous studies, GDP, TO and ER have significant relationship with FDI inflows in manufacturing industry while INF is not. This study also produce the same result.

## 4.5 F-statistic

Degree of freedom for numerator = k - 1

Degree of freedom for denominator = n - k

Thus,value of F-distribution table will be:

Where; k = independent variables

n = number of observations

For this study, value of F-distribution table is

Since there is no value for degree of denominator 27, so researcher used degree of numerator 4, degree of denominator 28 at 95% confidence level. Thus, the value of F-distribution table is 2.98.

## Calculated F-stats

Sign

Critical

F-stats

Outcomes

## 29.825

## >

2.98

Significant

Interpretation:

Calculated F-stats for this overall model of study is 29.825. It is greater than critical F-stats which is 2.98. It indicates that the independent variables are statistically significant with dependent variable. So, the model appears to be useful for predicting FDI inflows in manufacturing industry of Malaysia.

## 4.6 Analysis of Variance (ANOVA)

The two hypotheses that will assume are:

H0, β=0 indicates the model should be rejected

H1, β≠0 indicates the model should be accepted

Hence our ANOVA table for the whole regression model is constructed:

## ANOVAb

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

42.142

4

10.535

29.825

.000a

Residual

9.184

26

.353

Total

51.326

30

a. Predictors: (Constant), [email protected], INF, logER, logGDP

b. Dependent Variable: logFDI

Interpretation:

The F-value is calculated to test whether there is significant relationship between the independent variables and dependent variables. From the table above, F-value (29.825) is significant at 0.000. Thus, the significant value is less than 0.05 which indicates that there is statistically significant between independent variables and dependent variable. Thus, the null hypothesis in this model should be rejected.

## 4.7 Durbin Watsons

Since the value of D-W stat is 0.716, shows that t-value may be exaggerated because and hence the estimated coefficient may be insignificant due to the presence of autocorrelation. Since D-W is lower than 1.9, it indicates that there are positive autocorrelation.

## CHAPTER 5

## CONCLUSION AND RECOMMENDATIONS

## 5.0 Introduction

This final chapter provides to the readers the conclusion and recommendation throughout this study. In conclusion, it wills reviews of the overall study from the beginning of this paper including analysis of result obtained from SPSS. In addition, the recommendation will focuses on giving the readers suggestion on how to conduct further researcher about the same field of this study in future.

## 5.1 Conclusion

In this study, we seek to find between four variables mentioned earlier, what are the determinants of FDI inflows in manufacturing industry of Malaysia. The SPSS test result shows that only INF is insignificant where as GDP, TO and ER is statistically significant. The empirical results are strong, with all variables having the theoretically expected signs and all coefficients being significant at the 5% level and 95% confidende level and it is also consistent to the researcher expectation.

## 5.2 Recommendations

Based on result obtained through SPSS, several recommendations are given as guidelines for future researchers in order to generate more accurate results. The recommendations are as follows:

Since this study limited to four macroeconomics variable, thus in future, research should be done by taking more variables that are being used in past studies or others variable that are not used before. Critical factors that have potential to attract foreign direct investment inflows in manufacturing industry of Malaysia are human capital, labour cost, infrastructure facilities, money supply, financial market development, country risk, cultural differences, government incentive policies or political stability. Thus, all of the potential variable listed above can be used in future research in foreign direct investment.

The different modes of data also influence the accuracy and reliability of result. Annually, quarterly, monthly or daily mode will give different result due to different figure of each tested variable used. Thus, researcher in future should use more number of observations than this study because more total observations will lead more accuracy of the result.

Instead of focusing on manufacturing sector, future researchers are recommend to consider sub - manufacturing sector that are not done before in any study, or other sector that contributes to the growth of FDI inflows in Malaysia such as services, construction, mining and quarrying or agriculture sector.

Other than that, research also can obtained more correlation and accurate result by doing more test through other software such as LIMDEP or E-VIEWS instead of only SPSS. Various methods to investigate the variables tested have power impact on the result obtained. Thus, in future, researchers can do their study by using more tests such as Generalized Method of Moments (GMM) dynamic estimator based on the Arellano-Bond methodology. Blundell and Bond (1988) developed the system GMM dynamic model, which combines the regression in first differences above with an estimation run in levels, using both lagged levels and lagged differences as instruments. By doing so, result will be more efficient and readers will get better understanding about FDI inflows and their determinants of FDI inflows movements. .

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