MACROECONOMIC FACTORS OF FDI INFLOWS IN ASIAN ECONOMIES: A STUDY OF 14 ASIAN COUNTRIES.

The study conducted to analyze the relationship between foreign direct investment and macroeconomic factors, which were affecting foreign direct investment in Asian economies over the period of 2003 to 2017. The fixed effect model applied in order to anticipate the foreign direct investment inflow into the overall Asian economies and simple regression analysis organized for each economy individually to determine the foreign direct investment inflow. The result of the fixed effect model presented strong evidence that trade openness has a statistically significant and affirmative association with foreign direct investment inflow into different Asian economies. On the other hand, exchange rate found closer to significance with foreign direct investment inflow. However, the macroeconomic variables of the study jointly and significantly affected foreign direct investment inflow. The results of simple regression analysis found that GDP, trade openness, and exchange rate have a significant impact on foreign direct investment inflow in China, Indonesia, Jordan, Pakistan, and Vietnam. Meanwhile, labor cost and tax rate have positive significance to foreign direct investment in

The study conducted to analyze the relationship between foreign direct investment and macroeconomic factors, which were affecting foreign direct investment in Asian economies over the period of 2003 to 2017. The fixed effect model applied in order to anticipate the foreign direct investment inflow into the overall Asian economies and simple regression analysis organized for each economy individually to determine the foreign direct investment inflow. The result of the fixed effect model presented strong evidence that trade openness has a statistically significant and affirmative association with foreign direct investment inflow into different Asian economies. On the other hand, exchange rate found closer to significance with foreign direct investment inflow. However, the macroeconomic variables of the study jointly and significantly affected foreign direct investment inflow. The results of simple regression analysis found that GDP, trade openness, and exchange rate have a significant impact on foreign direct investment inflow in China, Indonesia, Jordan, Pakistan, and Vietnam. Meanwhile, labor cost and tax rate have positive significance to foreign direct investment in Hong Kong and Philippines. The conclusive remarksare that macroeconomic factors played a significant and decisive role to attract foreign direct investment in the Asian region and in each country as well.

…………………………………………………………………………………………………….... Introduction:-
Foreign direct investment can be define as per OECD Benchmark concept of Foreign Direct Investment (1996) that FDI is the long run association between foreign investor who is resident person in one economy and make direct investment in another economy, has meaningful control over the management of host country. In the foreign direct investment, initial transaction and succeeding capital transactions are involved (Chaudhuri and Mukhpoadhyay, 2014). Schwab (2018) revealed that, in the last 30 years, large economic changes have appeared and massive investment has taken place at the global level. It has all happened because of trade of goods and services between different countries around the world. Globalization and shifting of capital from one country to another country accelerate economic growth of the hosting country. Globalization enables poor and developing countries towards skilled labor, technology transfer, trade openness and inflow of capital. When domestic firms do business with multinational firms then trade and flow of capital increases.
(Organization for International Investment 2017 and FDIUS 2017) Foreign firms commence new investments every year, which provide advantages to the American Economy in different ways. Foreign companies construct new factories, invest in R&D, and begin well-established operation in US. Foreign investors provide many well-paid jobs to Americans. United States still attractive location for foreign investment and once again world prime destination for FDI. (Morrison 2018) explored that economists and financial expert say that main reason behind the rapid economic growth of China is the large-scale capital investment. It consists of two major determinants, foreign direct investment and large domestic savings. Chinese economic reforms boost national economy and enhance resources to gain further foreign investment. China is the world's largest manufacturer according to UNO and Global Manufacturing Competitiveness Index. However, US would overtake China again by 2020 to turn into the world's most manufacturing competitive economy because of its huge investment in R&D, top ranked universities, and large capital investment in advanced technologies. moderate-income countries. The gross domestic product, inflation, trade openness, infrastructure, population growth, and export have taken with respect to independent variables meanwhile foreign direct investment considered as a dependent variable. Unit root conducted to know the stationarity in the variables and run panel ordinary least square method for regression analysis. The conclusion made that GDP, trade openness and population growth of the country play a vital and significant role to attract foreign direct investment in the selected countries. Koojaroenprasit (2013) explored the factors that affect foreign direct investment in Australia. The panel data collected from 1986 to 2011 with the perspective of three leading foreign direct investing countries such as the United Kingdom, the United States, and Japan. Market size, labor cost, trade openness, customs duty, interest rate, inflation rate, corporate tax rate, research, and development treated as explanatory variables. The results showed that market size, research, and development factor positively affect foreign direct investment. In the meantime, appreciation in the exchange rate, increase in corporate tax rate, and customs duty negatively affect foreign direct investment. Ferrer and Zermeno (2015) probed the relationship between foreign direct investment and gross domestic product of China during 1995 to 2012. Vector autoregression model applied, unit root test with augmented dicky fuller and Johansen cointegration used. The results disclosed that foreign direct investment has a marginal impact on the economic growth of China. Makun (2018) analyzed the role of foreign direct investment along with other influencing determinants in Republic of the Fiji Islands. Annual quantitative data from 1980 to 2015 taken from World Bank about participating variables. Unit root test and cointegration analysis with ARDL estimator utilized for the long-run association between GDP, FDI, imports and remittances. The study drawn conclusion that FDI has a positive influence over economic growth of Fiji so government should make policies to attract more FDI to gain economic growth.
Rehman (2016) observed the determinants of foreign direct investment with the perspective of Pakistan during the period of 1984 to 2015 by using unit root test, Johansen cointegration and Vector Error Correction Model (VECM) to know the short-term and long-term influence of market size, trade openness, inflation and natural resources over foreign direct investment. The results revealed that all explanatory variables have a statistically significant and positive relationship with FDI in term of attracting factors. Sichei and Kinyondo (2012) studied in Africa about 45 African countries about foreign direct investment during the period of 1980 to 2009. A number of macroeconomic factors used as independent variables in the econometric fixed effect and random model to interrogate the impact of FDI. The outcomes discovered that agglomerate economies, growing economies and natural resources of the countries attract foreign direct investment positively. Musah et al. (2018) evaluated the role of foreign direct investment and its impact on the financial performance of different banks in Ghana over a period of ten years. Unit root, panel correlation, short run and long run estimation of financial indicator conducted to draw decisive research. The conclusion made that FDI has a positive association with the profitability of banks and economic growth in the short and long term.Gharaibeh (2015) conducted a study in Bahrain regarding the inflow of FDI and its influencing factors with respect to macroeconomics. For this purpose time series data taken from 1980 to 2013. The OLS (ordinary least square) regression model explored that macroeconomic factors play a significant role in the surge of foreign direct investment. Qamruzzaman (2015) examined factors that affecting FDI in Bangladesh. GDP, exchange rate, trade policy, and black market premium considered as explanatory variables. The study consisted of fourteen-year data from 2000 to 2013. The data analyzed with the help of fixed effect and random effect regression models. The study made the revelation that mostly determinants increase the inflow of FDI. Muraleetharan et al. (2018) observed determinants of FDI by applying time series data from 1978 to 2015 in Sri Lanka. Inflation, GDP, interest rate, exchange rate, infrastructure and international trade volume used as the explanatory variables. Augmented Dickey-Fuller test applied to check the stationarity in the data and ordinary least square regression model applied to know the relationship between variables. As per the results of this study, all attractive factors of FDI play a positive and significant role to increase foreign direct investment in Sri Lanka. Mitic and Ivic (2016) analyzed the export performance of 11 European countries with respect to foreign direct investment over the period of 1993 to 2013. The correlation analysis of the study showed that the export sector performed better in highly innovative and advanced countries and foreign companies have a significant impact on export of European countries.leitao and Rasekhi (2013) probed the association between economic growth and foreign direct investment about Portugal. Panel data statistical approach employed to prove that FDI and GDP have a significant and positive relationship in Portugal's perspectives. Akalpler and Adil (2017) asserted that foreign direct investment positively relates to the economic growth of Singapore however, in some way FDI effect adversely due to ineffective government policies.Dias and Hirata (2014) observed the relationship between foreign direct investment and productivity of Brazilian economy from 1992 to 2011. The study defined the relationship through the SVAR approach that long run productivity growth of the Brazilian economy attract FDI. Karthik and Kannan (2011) investigated the influence of FDI on the stock market of India. Based on ARDL and error correction model approaches, FDI plays a magnificent role in the development of the stock market in India.
Pandya and Sisombat (2017) examined the GDP growth of Australia with respect to foreign direct investment. As per regression results, FDI inflow makes the contribution in employment, export performance, and GDP growth rate, however, policymakers should make investment planning to attract more foreign direct investment. Sane (2016) Table 1, presents description of the study variables.

Methodology:
The study began with various econometric approaches to reveal the sensitivity of outcomes in the underlying models. The research methodology is based on the standard panel setting. A panel data technique has a big advantage over cross sections and in time series (Dellis et al., 2017). The study employs pooled OLS (Ordinary Least Squares), fixed effects and random effects models that consisted of macroeconomic variables along with foreign direct investment.

Model Specification:
Based on the literature review and past studies, there is a model being probed in this research that consisted of macroeconomic factors and each country influencing variables on foreign direct investment. Different variables are exercised to express a couple of factors probably have influence over foreign direct investment as per empirical models and past empirical researches. These influencing variables computed for regional countries cumulatively and for each country separately over the period under the study. Equations run on STATA (Software for Statistics and Data Science) to explore the influencing factors that affect foreign direct investment in different Asian countries in terms of integrated and discrete analysis. Where FDI i.t, presents percent inflow of foreign direct investment of i'th country against its GDP and performance in year t. Where β is the constant and GDP i.t is the country annual logarithm of nominal GDP. Trade openness is denoted by TRADE i.t . Where LABOR i.t represents the annual wages and salaries of male in these countries. XR i,t denotes each country exchange rate against us dollar on annual basis and TR i.t signify the annual corporate tax rate of each country. Where e i.t , is a disturbance term. Empirical Results:-Asian Regional Level Results: The Table 2, displays the regression results regarding our data set. The study conducted on standardized panel evaluation techniques such as pooled OLS (ordinary least square), fixed effect and random effect. Breusch-Pagan Lagrange multiplier test (1980) and Hausman test carried out to reject and accept the following models. Firstly, the Breusch-Pagan Lagrange Multiplier test performed to reject the pooled OLS model over the random effect model based on the outcomes. The Breusch-Pagan employed to test for the existence of heteroscedasticity. The probability value of the test is 0.00. Therefore, Alternative hypothesis is accepted and reject the null hypothesis. Heteroscedasticity is a difficulty because OLS regression supposes that residuals are taken from data that has a constant variance. It also causes of biased results and estimation. The study has chosen the random effect model over pooled OLS to make valid outputs.

Hausman Test:
Null Hypothesis : Random effect model is appropriate Alternative Hypothesis : Fixed effect model is appropriate The Hausman test applied to determine the appropriate model between the random effect model and the fixed effect model. The probability value of the Hausman test is less than 5%. Consequently random effect model is not consistent over the fixed effect model. The study rejects the null hypothesis in favor of the fixed effect model.

Regression Analysis:
The regression analysis employed to determine the relationship between macroeconomic FDI influencing factors and FDI in order to know the inflow of FDI in different Asian countries. Coefficient (β) of variables, standard error, t-Statistics, probability values, R2, Adjusted R2 and the probability of F-Statistics considered making the decisions. The Table 2, presents regression results of pooled OLS but which was rejected over the random effect model based on heteroscedasticity problem. The results of the fixed effect model and random effect model revealed in the Table  3. However, the study rejected the random effect model based on the Hausman test. Therefore, the study focused to express the fixed effect model concisely.
Talking about GDP, that is insignificant and having a positive association with FDI of selected Asian countries. The coefficient of GDP (3.6943) is one of the macroeconomic independent variable among others that has the highest positive relationship with FDI inflow. The probability value of TRADE (0.000), the t-Statistics value (4.780) are highly significant and the coefficient of TRADE (0.0669) is favorably associated with FDI in all countries. In the Asian region, if any country wants to accelerate its FDI, It should make sure trade openness in their county to attract foreign direct investment. The model shows that LABOR, XR, and TR are insignificant as per their probability values and t-Statistics values. However, the coefficient of labor cost (0.149) and tax rate (0.140) are positively associated with foreign direct investment into the different Asian economies. XR is the key determinant of a macroeconomic explanatory variable to foreign direct investment. It also has a slightest significant impact on FDI asprobability value (0.069) and t-Statistics (-1.83). XR is expected to be significant as per regression analysis. Overall, almost 85.58% influence on FDI is explained by explanatory variables based on R 2 . Consequently, the probability value of F-Statistics (0.00) determines that macroeconomic indicators jointly and significantly affect foreign direct investment into different Asian's economies.

States LevelRegression Results: Factors Influencing FDI in Bangladesh:
Taking into consideration the Table 4 (a) and (b). The Table 4 (a) and (b), report the outcomes of OLS between independent macroeconomic variables and FDI of each country. There is no significant relationship exist between FDI and macroeconomic variables as per regression analysis in Bangladesh. However, the value of R-Square is 0.6332, which indicates that independent variables have been influenced by 63.32% over FDI. Meanwhile, if it is necessary to incorporate more related independent variables in the same regression model then adjusted R-Square would be adjusted at the value of 0.4294. The probability of F-Statistic is 0.0666, which means F-Statistics is insignificant at the 5% level of significant so macroeconomic variables mutually neglect the FDI.

Factor Influencing of FDI in China:
The regression model reporting the influence of FDI in China is displayed in the Table 4(a) and (b). Only TRADE has a significant and positive relationship with FDI at the 5% level of significance. Macroeconomic variables contributing 89.95% dominance over FDI as per R-Square. All independent jointly affect FDI because the probability value of F-Statistic is 0.0003 in Chinese economic perspective.

Factor Influencing of FDI in Hong Kong:
The regression model presenting the influence of FDI in Hong Kong is obtained from the Table 4 (a) and (b). LABOR cost controls FDI as per probability value (0.03) and t-statistics (2.61). Other independent variables and LABOR cost have 82.12% influence over FDI. The probability value of F-Statistic (0.0035) revealed that macroeconomic variables have a significant impact on FDI inflow into Hong Kong economy.

Factor Influencing of FDI in India:
The regression results from the Table 4 (a) and (b) shows that independent variables have insignificant relationship with FDI. In the meantime, macroeconomic variables participating 37.89% deviation based on its R-Square value. The probability result of F-Statistic (0.4241) is greater than 5% at the 5% level of significance. Therefore, FDI inflow does not affect by any independent variable in Indian perspective.   (2). It also indicates the significance of both variables with FDI. The R-Square value found 0.8376, which reflects that macroeconomic variables with strong influence over the inflow of FDI into the Indonesian economy. The p-value of F-Statistics (0.0023) determined that all independent variables jointly affect the inflow of FDI in Indonesia.

Factor Influencing of FDI in Jordan:
As per regression analysis from the Table 4 (a) and (b), TRADE is one of the macroeconomic estimator positively and significantly effects FDI inflow to Jordanian economy. The t-Statistics value of TRADE coefficient is 2.26 and its probability value is 0.05 at the 5% level of significance. Both values determine the significant relationship of TRADE with the inflow of FDI. R-Square valued at 0.7810. This implies that 78.10% disparities of FDI have been explicit by the deviation of macroeconomic factors of the study. All Independent variables aggregately have a significant impact on the inflow of FDI into the Jordanian economy based on the p-value of F-Statistics (0.0083).

Factor Influencing of FDI in Malaysia:
The regression outcomes are presented in the

Factor Influencing of FDI in Philippines:
Taking into consideration of regression results of the Table 4 (a) and (b). XR has a significant relationship with FDI however, the probability value of TR (0.055) and its t-Statistic value (2.60). Therefore, TR is closer to statistical significance. Investors need to contemplate on both macroeconomic indicators before investing capital in Philippines as per regression outcomes. The value of R-Square is 0.8041, which means that independent variables have been influenced by 80.41% over FDI inflow into Philippines. Meanwhile, the probability value of F-Statistics found significant, which is less than 5% at 5% level of significance. Its means that all independent indicator mutually affect FDI.

Factor Influencing of FDI in Singapore:
According to regression outcomes of the Table 4 (a) and (b), there is no significant relationship exist between FDI and macroeconomic indicators. However, macroeconomic variables contributing 26.03% dominance over FDI as per R-Square. The probability of F-Statistic is 0.6801, which means that F-Statistics is insignificant at the 5% level of significant so macroeconomic variables cumulatively neglect the inflow of FDI in Singapore.

Factor Influencing of FDI in South Korea:
The regression outcomes are presented in the

Factor Influencing of FDI in Sri Lanka:
In accordance with regression results from the Table 4 (a) and (b), display that there is no significant relationship exist between FDI and other independent factors of the study. In the meantime, R-Square value found (0.5773) which means the macroeconomic variables have 57.73% dominance over the changes of FDI.

Factor Influencing of FDI in Turkey:
In the case of Turkey as per the Table 4

Factor Influencing of FDI in Vietnam:
The Table 4  Conclusive remarks, the investor need to have a look on GDP, XR, and TRADE before investing capital in Vietnam.

Conclusions:-
The core objective of the study was to determine the macroeconomic indicators that affect foreign direct investment in different Asian countries by establishing panel data regression approaches. Firstly, Simple regression analysis use for each Asian economy to deduce the macroeconomic factors, which have an impact on foreign direct investment in a particular country. The study found that the macroeconomic indicators significantly affect the inflow of foreign direct investment in 07 Asian countries such as China, Hong Kong, Indonesia, Jordan, Pakistan, Philippines, and Vietnam. In these countries, the GDP significantly and positively enhancing FDI in Indonesia, Pakistan, and Vietnam. Trade openness brings significant, positive and favorable impact on FDI in China, Indonesia, and Jordan. Labor cost plays a significant role to derive FDI in Hong Kong. Exchange rate giving the potential advantage and has a significant impact on FDI in Pakistan, Philippines, and Vietnam. While Tax rate plays a significant role to boost FDI inflow into the Philippines economy. Secondly, for all countries pooled OLS model, random effect model and fixed effect model applied. The result of regression analysis revealed that trade openness is a decisive economic indicator to attract foreign direct investment into the selected Asian economies. Trade openness significantly and positively allows bringing foreign direct investment so it is necessary for the policymakers of Asian economies to take initiatives to improve collaboration among different countries. Meanwhile, the exchange rate is another economic indicator of little bit importance and point of interest with trade openness for offering foreign direct investment because it also closer to significance as per results.