Inflow determinants of foreign direct investment

Abstract

This article examines the determinants of the foreign direct investment (FDI) inflow to the Lao People’s Democratic Republic (Lao PDR). Namely, the study develops a static and dynamic gravity model that captures said determinants over the 1995 to 2015 time period. The results reveal that market size, trade openness, inflation rate, labor cost and exchange rate are primary FDI inflow attractants. And every year’s FDI inflow is itself a crucial precursor of next year’s foreign investor decision-making, while distance and border sharing among countries do not seem to support FDI inflow. Despite the study’s qualifications, exporting is a vital business component that affects a manufacturing firm’s working capital, with multiple implications for business practice and research.

Keywords

FDI gravity model determinants Lao PDR panel data GMM

Visansack Khamphengvong is a Ph.D. Candidate at Beijing Institute of Technology (BIT), Beijing China. He has completed his Master of Business Administration from Tianjin University (China), and a bachelor of Business Administration from the National University of Laos. He has ample insight on the topic of International Trade, Foreign Direct Investment, and Human resource management. He has several national and international publications.

Enjun Xia is a professor, Doctoral Advisor in Department of Business Administration in SME of Beijing Institute of Technology (BIT), Beijing China. Prof. Xia has reached more than 20 scientific research achievement, among which 3 won the second, the third and the fourth prize of provincial Scientific and Technological progress respectively. He has published over 100 papers in domestic and international periodical and conference and co-authored 4 books. He does strategy research, serves as consultant, trainer for several companies.

Khaysy Srithilat, is a Ph.D. Candidate at Dongbei University of Finance and Economics (DUFE), Dalian China. Before commencing his doctoral research, he worked as a lecturer of economics at National University of Laos (NUOL). He is the author of several publications and international conference proceeding. His area of interest includes monetary economics, International Economics, and Finance.

1 Introduction

Foreign direct investment is a significant assets source for social and economic growth in a developing country. The increase of FDI creates job opportunity, improves labor skill and technology spillover [1]. FDI in Lao PDR has been a source encourages economic development since the new economic policy introduced. The open-door paid good performance to promote the country, and it likely attracting from global, especially from ASEAN region [2]. In 1988, the first investment law encouraged and welcomed foreign investors in which it indicated the strong will of the government to provide an open policy to investors from all sectors. However, the number of FDI flows was just small and shared only by US$ 6 million in the early 1990s [3]. During the period, inward FDI in Lao PDR was mainly from the ASEAN countries such as Thailand, Malaysia, China, Vietnam, Japan, and Singapore, while the sources from the developed countries were likely rather small. Lao PDR has plenty of natural resources and abundant cultural heritages which became a central principal attracting foreign investment. Natural resources in the nation play an essential role and cover 80 percent of inward FDI. The sectors attracting investment were chiefly from hydropower, mining, petroleum, agriculture, industry & handicraft, service, garment, tourism, and wood industries [4]. Lao PDR’s FDI grew considerably for several years, e.g., to US$ 913 million in 2014, reaching the peak of US$ 1.079 billion in 2015 (Fig. 1). Following the purpose, with economic integration on the World Trade Organization (WTO) and ASEAN Economic Community (AEC), beneficial to the country promotes more tax and land policy to foreign investors which further to increase the number of FDI into Lao PDR [5]. Nevertheless, the Economic integration between China-ASEAN and intra-ASEAN have provided excellent opportunities supporting the amount of FDI inflows to the nation [6].

Fig.1

The inflow of foreign direct investment in Lao from 1990–2015. Source: World Development Indicate (WDI).

This article is managed as follows: Section 2 reviews the previous theory and literature on the determinants of foreign direct investment. Section 3 presents the data source, empirical models, and methodology of the article. Section 4 introduces the estimation of the result. The conclusion and policymaker issue are included in section 5.

2 Theory and literature review

Numerous frameworks and theories have been applied in assessing the character and motivation of foreign direct investment, such as, for example, the production-cycle hypothesis, constructed by Vernon [7]. He suggests diving firms’ production function into four steps, including innovation, production growth, reach of maturity and decline. Similarly, Cave [8] focused on multinational enterprises (MNEs), and he recommended that foreign firms successfully launch their business when the MNEs have a higher potential than domestic companies do. Foreign direct investment by the MNEs can access to the host country which increases the monopoly power beyond the local enterprises. The advantages of multinational corporations can change monopoly power due to the recent technology spillover of MNEs, management skills, know how on cost reduction and other relevant characteristics. Significantly, the established eclectic framework which describes the principal of foreign direct investment inflows in a more specific way [9, 10]. Dunning [11] also suggests that motive determinants of FDI inflows are divided into three parts including the advantage of ownership, location advantage, and advantage of internalization. Later the framework concept has been reviewed to understand the complexities of MNEs activity.

The internalization theory is another way to explain the development of multinational corporations, and the motivations of firms investing abroad. Buckley [12] established the theory of internationalization, which sought to clarify the construction and effect of multinational corporations. Hymer [13] also developed the same theoretical framework, and two parts have recognized the primary determinants on FDI. One observed how deletion of the competition happened and another focused on how to maintain the decisive point of firm’s activates. Hennart [14] followed the same direction and tried to improve the theory in different ways by editing models of integration among two types including horizontal and vertical. Later Helpman [15] and Makusen [16] developed the same theory and realized that when firms invest abroad, they need to consider the two principal including market-seeking which can be called horizontal FDI. The foreign investors try to observe the domestic market via local production and capture the market environment in the host country. Another critical called horizontal FDI. It is defined as the resource-seeking activity of the investors to access the lower cost of labor, natural resource, infrastructure, and the external indispensable such as exchange rate.

The amount of empirical studies on the determinants of foreign direct investment has been conducted in the various period so that many countries have been applying the results on the different framework due to the circumstances of the countries. Ang’s [17] studies the main determinants of foreign direct investment in Malaysia, using time series data from 1960 to 2005. The results conclude that economic growth in the host country and the gross domestic product has a significant effect on FDI inflows to the state. Ahmed and Gabriel [18] utilized similar time-series data to investigate the determinants influence over 1970–2009 time period. The application of VECM model suggested that trade openness, exchange rate, and the inflation rate is an essential issue for foreign direct investment, whereas it does not support by GDP of the recipient country. Kalaivani [19] examined the same purpose and also applies the data from the period of 2004 to 2011 in India. However, the result from ARDL model found the negative interaction of some variable such as exchange rate and the market size in both short and long-term relationship. Ali and Guo [20] constructed the survey methodology from 22 multinational enterprises to find out the primary determinants of foreign direct investment in China. The authors found that only economic growth in the host country has a positive effect on FDI. Chidlow et al. [21] also used the survey methodology to observe the motivation for foreign capital inflows in Poland by creating the questionnaire and applying to multinomial logit model. They discovered that labor force, natural resource and geographic in Poland has a more significant influence on consideration of foreign investors. Yang [22] observed the determinants of FDI inflows in Australia by applying quarter data from 1985 to1994. The estimation of coefficient indicates that interest rate, labor cost, and more economic openness have catalysts to attract FDI into Australia. Similar, Kaur et al. [23] also used quarter data in India, but partitioned their data into two time periods: 1990-1991 and 2010-2011. The result from VECM framework suggests that trade liberalization; foreign exchange, long-term debt, and gross domestic product are a positive effect on FDI inflows to India, while inflation rate and exchange rate are insignificant.

Ceng [24] suggest that using panel data is conducive to investigate FDI inflows determinants, as panel data are more efficient than applying random weight to cross-section samples, offer many degree of freedom, and reduce multicollinearity. Cuyvers et al., [25] also use a panel data to investigate essential the determinants of foreign direct investment in Cambodia, which construct the basic of pooled OLS, fixed-effect and random effect. According to the results found that the gross domestic product from home countries, more trade liberalization, and the exchange rate has a significant impact on foreign direct investment. Rashid et al., [26] selected Oman, Brunei, and Malaysia as research subjects to examine the determinants of foreign direct investment in the agriculture sector. The authors tried to explain the result from fix and random effect models, and the STATA estimation indicates that growth of agriculture and poverty reduction in recipient countries is more significant to foreign direct investment inflows. Vijayakumar [27] established the determinants of FDI inflows from Brazil, Russia, India, and China (BRIC countries) by taking panel data from 1975 to 2007. The econometric framework concludes that when economic stability and economic growth in the host country it must be an excellent condition to support foreign direct investment inflows to the BRIC countries. Similar to that, Abdul [28] also proved that the size of gross domestic and the growth of economies in developing country had brought more FDI capital to host nations. Rjoub [29] investigated the same intent from the landlocked countries in Sub-Saharan Africa. He took the data from 1995 to 2013 and found that the natural resource, political stability, the economic growth, human capital in the countries is a critical issue contributing foreign direct investment. Demirhan and Masca [30] applied a cross-section framework from 38 developing counties during the period 2000 to 2004 to investigate determinants of FDI by taking macroeconomic components such as GDP per capita, infrastructure, inflation rate, labor cost, the trade openness and tax. The result suggests that almost all variables have played a significant effect on FDI inflows, accept tax and inflation rate. Bevan and Estrin [31] examined the flows of foreign direct investment from European Nation transition to central and eastern European countries by applying the gravity model cover the data from 1994 to 2000. The authors conclude that the country which has more incorporation with the EU nations is more efficient in considering for investment. The study of Levy et al. [32] also took a gravity model to find the reason of FDI inflows from developed countries to developing countries. The authors found that the interest rate cycle is significant to predispose foreign investors. Busse et al. [33] examined the main features of FDI flows to the host countries. The gravity model found the importance of bilateral investment treaties encouraging FDI inflows to the host countries. Likewise, Kahouli and Maktouf [34] constructed the static and gravity model to examine the main reason for FDI flows. They applied to panel data from 14 investment partnerships through 39 recipient countries. The authors concluded their result that GDP from home and host country is significant supported FDI inflows, while real exchange rate, currency in the recipient country and inflation also plays the vital role booster foreign direct investment.

3 Econometrics methodology and empirical study

3.1 Data

In this work, we take the data from different sources including Lao ministry of planning and investment, the central bank of Lao PDR, and the world development indicators (WDI). Panel data are appropriately used in this study covering the 1995–2015 time period. Given that this is a relatively short time period and that only thirteen countries share their FDI data, time-series estimates might prove inaccurate.

3.2 Methodology

Stock FDI is applying for a dependent variable because the stock has several efficient and ideal than other uses. Moreover, the stock is more suitable to estimate the capital ownership [35]. This study follows the gravity model developed by [36 –38] to capture the determinants of foreign direct investment in Lao PDR. The gravity model is widely used along the lone of economic to solve the problem of international trade and foreign direct investment in their countries. The authors have modified and developed extensively about the specific economic condition in each period. The existing empirical studies based on the gravity model suitable for panel data, applying static and dynamic specification. According to [39] the dynamic model is appropriate to capture the contribution of the previous stock from FDI in the partner country. The prior FDI has established a large number of networks and services in partner countries. The performance of previous FDI therefore indirectly provides an essential activity for the current year of FDI. Meanwhile, the static gravity model ignored the prior activity of FDI which might lead to bias estimation. This study has employed both the static and dynamic gravity analysis by utilizing the fixed effect (FE), random effect (RE) and Generalized Method of Movement (GMM) provided by [40, 41]. The fixed effect method offers consistent estimation across the considering grouped countries regardless the correlation between individual effect and explanatory variable. Nevertheless, the fix effect approach is not sufficient for non-time series variables. Therefore, the random effect method needs to be taken in to account to deal with non-time series variable distance and border [42]. Both, fixed effect and random approach are performed under the Ordinary Least Square (OLS) Equation (1).

3.3 Static gravity equation:

$\begin{matrix} \ln {FDI}_{ijt} & = & a + β_{1} \ln {Market}_{it} + β_{2} \ln {Market}_{jt} \\ + β_{3} \ln {CGDP}_{it} + β_{4} \ln {CGDP}_{jt} \\ + β_{5} \ln {REE}_{ijt} + β_{6} \ln {TRADE}_{it} \\ + β_{7} \ln {INFL}_{it} + β_{8} \ln {DIST}_{ij} \\ + β_{9} \ln {BORD}_{ij} + β_{10} \ln {LANG}_{ij} \\ + β_{11} \ln {ASIA}_{ijt} + ɛ_{ijt} \end{matrix}$ (1)

Where FDI is the inflow of foreign direct investment from country i to the host country j in the time period t, Market_it and Market_jt are the market size of home country i and host country j which represented by gross domestic product of both countries, CGDP_it and CGDP_jt are GDP per capita as the proxy variable of labor cost of home country i and host country j [43], RER is real bilateral exchange rate between host country j and home country i, TRADE is trade openness of the host country j measured by the proportion of total export and import as percentage of GDP, INFL is inflation rate of the host country j, DIST_ij is the geography distance between capital city of the host country j and home country i, BORD_ij is the length of sharing a border with neighboring country, LANG_ij is the sharing of common language, ASIA determined the dummy variable for Asian financial crisis from 1997–1999 and ɛ is an error term.

To eliminate the fix effects of the OLS model, [40] introduced the first different variable, which widely known as the standard or difference GMM. However, since the difference GMM has been introduced on panel analysis frameworks, it contains the problem of correlation between lagged dependent variable and error term. To solve this problem, [41] developed a system GMM estimator which combined a system with the regression in first difference and levels. However, most of the empirical studies are included in the fundamental explanatory variable related to distance and geography information between home and host countries. Therefore, the observed model on the determinants of foreign direct investment in Lao PDR can be formulated as below:

3.4 Dynamic gravity equation:

$\begin{matrix} \ln {FDI}_{ijt} & = & a + β_{1} \ln {FDI}_{ij, t - 1} + β_{2} \ln {Market}_{it} \\ + β_{3} \ln {Market}_{jt} + β_{4} \ln {CGDP}_{it} \\ + β_{5} \ln {CGDP}_{jt} + β_{6} \ln {RER}_{ijt} \\ + β_{7} \ln {TADE}_{it} + β_{8} \ln {INFL}_{it} \\ + β_{9} \ln {DIST}_{ij} + β_{10} \ln {BORD}_{ij} \\ + β_{11} \ln {LANG}_{ij} + β_{12} \ln {ASIA}_{ijt} + ɛ_{ijt} \end{matrix}$ (2)

4 Result and discussion

This study employs the static and dynamic gravity model to estimate coefficients (Table 1). According to the Hausman testing, the result indicates that the random effect models have been rejected and instead accept the fixed effect model at 5 percent significance. Normality and heteroskedasticity have been examined via the Breush-Pagantest. Regarding the result, the heteroskedasticity is present, so it is better to apply heteroskedasticity fixed and random effect models. Moreover, the constructed the correlation matrix between the independent variable to estimate multicollinearity.

Table 1
The results of the static and dynamic Gravity model

Variables Dependent: FDI

Static estimations Dynamic estimations

FE RE Dif. GMM Sys. GMM

Sfdi – – 0.3040679 (0.004) 0.9965839 (0.000)

LnMarket_it 0.0676913 (0.327) 0.0558953 (0.396) –0.004577 (0.950) 0.018923 (0.337)

LnMarket_jt 29.86697 (0.000) 29.88078 (0.000) –0.750117 (0.000) –1.751357 (0.000)

LnCGDP_it –0.043158 (0.527) –0.037823 (0.572) –0.1352991 (0.217) 0.0170829 (0.115)

LnCGDP_jt –25.74338 (0.000) –25.75623 (0.000) –2.0082681 (0.018) –2.1005001 (0.012)

LnRER_ijt –0.0160163 (0.004) –0.0162133 (0.003) 0.0003174 (0.866) –0.0033318 (0.043)

TRADE_jt 0.0012028 (0.088) 0.0012037 (0.085) 0.102971 (0.000) 0.199459 (0.000)

INFL –0.0006026 (0.001) –0.0006034 (0.000) –0.00685 (0.000) 0.0081439 (0.000)

LnDIST_ijt – 0.0015651 (0.998) – 0.000000115 (0.856)

BORD_ijt – 0.0399065 (0.947) – 0.0101804 (0.285)

LAND_ijt – 1.555776 (0.058) – 0.0200397 (0.236)

ASIA_ijt –0.0659095 (0.000) –0.0658683 (0.000) 0.3933784 (0.000) 3.631128 (0.000)

Constant –204.0555 (0.000) –204.1688 (0.000) – –

R 0.8918 0.9399 – –

N/Group Number 273/21 273/22 247/19 260/20

F-statistic 32405.76^* 261009.68^* 24.4^* 15.7^*

Breush-Pagan Test 576^* 2522^* – –

Normality Tests 0.000 0.000 – –

AR(2) – – 0.123 0.123

Hansen Test – – 106 149

Variables	Dependent: FDI
Sfdi	–	–	0.3040679 (0.004)	0.9965839 (0.000)
LnMarket_it	0.0676913 (0.327)	0.0558953 (0.396)	–0.004577 (0.950)	0.018923 (0.337)
LnMarket_jt	29.86697 (0.000)	29.88078 (0.000)	–0.750117 (0.000)	–1.751357 (0.000)
LnCGDP_it	–0.043158 (0.527)	–0.037823 (0.572)	–0.1352991 (0.217)	0.0170829 (0.115)
LnCGDP_jt	–25.74338 (0.000)	–25.75623 (0.000)	–2.0082681 (0.018)	–2.1005001 (0.012)
LnRER_ijt	–0.0160163 (0.004)	–0.0162133 (0.003)	0.0003174 (0.866)	–0.0033318 (0.043)
TRADE_jt	0.0012028 (0.088)	0.0012037 (0.085)	0.102971 (0.000)	0.199459 (0.000)
INFL	–0.0006026 (0.001)	–0.0006034 (0.000)	–0.00685 (0.000)	0.0081439 (0.000)
LnDIST_ijt	–	0.0015651 (0.998)	–	0.000000115 (0.856)
BORD_ijt	–	0.0399065 (0.947)	–	0.0101804 (0.285)
LAND_ijt	–	1.555776 (0.058)	–	0.0200397 (0.236)
ASIA_ijt	–0.0659095 (0.000)	–0.0658683 (0.000)	0.3933784 (0.000)	3.631128 (0.000)
Constant	–204.0555 (0.000)	–204.1688 (0.000)	–	–
R	0.8918	0.9399	–	–
N/Group Number	273/21	273/22	247/19	260/20
F-statistic	32405.76^***	261009.68^***	24.4^***	15.7^***
Breush-Pagan Test	576^***	2522^***	–	–
Normality Tests	0.000	0.000	–	–
AR(2)	–	–	0.123	0.123
Hansen Test	–	–	106	149

Notes. Dif GMM and Sys GMM applying robust standard errors. The value in parentheses is p-statis. ^***denote as significant at 1% level; ^**at the 5% level and ^*at 10% level.

4.1 Estimation of static models

The empirical result of the fixed and random effect models presented that the market size of the host country is significant for consideration of foreign investors. The result of the static model gives a positive effect on the market size and statistic meaningful for both, fixed and random effect models. The result corresponding to [31, 44] argued that the vast market size can affect the inflows of foreign direct investment compared to a small market in the host countries. Thus, the foreign investors would prefer a large market when investing abroad. The GDP per capita expected to be positive benefits to both home and host country. Evenett and Keller [43] suggest that when the coefficients statistic of GDP per capita has a negative sign, it is a proxy ratio of labor costs. Though, lower labor rate with high skill employees brings more chance contributing FDI inflows to the host country [45].

The result from Table 1 indicates the statistics of the real exchange rate is significant for all models, and the estimated coefficient has shown a negative sign which means that host country‘s currency depreciation enlarge inward FDI to the host country. Consistent with the previous studies [46, 47] confirms that the currency depreciation of host countries can take a good effect on the home country by absorbing the low currency. This result causes an impact to increase inward FDI in the host country. However, the sign of the estimated coefficient of inflation rate is negative with statistics significant at 1 percent in the host country. The negative sign indicates the meaning of the recipient country that low inflation rate in the country will take effect to bring FDI flows to the state. Apparently, foreign investors will invest in the country where economic stability and lower of the inflation rate present to preserve the price of goods [48, 49]. In the same way, trade openness in the host country is one significant point to attract FDI, and trade openness might take effect to support FDI [17 , 51]. However, The estimation cannot get the confirmation for the evaluation of the coefficient in this study.

The distance variable is measuring the geographic distance between the host country and investment partnerships to capture the activities of investment including the cost of transportation, communication between two countries and other information. According to the result, the coefficient does not contribute for FDI and is not significant at any models. The previous studies also confirm that the distance is not supported to FDI [25, 52]. Similar to the border variable it is not essential for the host country to increase the number of FDI. The sharing of commend border does not take effect to foreign investors [53]. However, the coefficient of language indicates the positive of the statistic model with 10% significantly. The result notes that sharing a common language might cause to increase FDI flows in the recipient country [54, 55]. Importantly, the coefficient of dummy crisis is negative and significant. This result shows that during the Asian Financial Crisis between 1997 to 1999 has had less impact on foreign investment. It was because the government attached much attention on improving policy and laws to promote investment, as well as the situation of the economy, had been quickly recovered. As a result, the influx of investment had been steadily [2, 56].

4.2 Estimation of GMM dynamic models

The dynamic model applied the GMM which takes lagged FDI to view the environment of the past year investments in the host country. The coefficient result of FDI is confident in both different GMM and system GMM models which is statistically significant at 5 and 1 percent respectively. This finding is consistent with the previous studies of [39 , 58] which found the positively and significance of lagged FDI variables could be useful information for foreign investors to decide on new investments. In the same order, the coefficient result of market size is significant in the host country. The finding supports the ideas that the market size in the host country influences is attracting FDI [17]. The estimation results from GDP per capita (proxy as wages) has shown the importance of wages encourage FDI in the host country. The studies from [20, 44] also recommend that cheap labor cost in the host country has a positive effect on FDI. The coefficient result on the exchange rate is positive for the system GMM model with statistically significance at 5 percent. Accordingly, the result is consistent with the empirical study from [59] which highlights that depreciation of the currency in the host country and stability of exchange rate make a proper stimulation for foreign investors.

According to the result of the coefficient on openness variable related to the evidence of [30 , 60] which display that more open trade in the recipient country increases FDI for the country inwards. The inflation variable can be given in both positive and negative in the host country. The high inflation rate might reduce the number of FDI flows [61, 62]. On the other hand, the low inflation rate and the stability of economic would attract foreign direct investment [63, 64]. The estimates of distance variables reveal the insignificant from dynamic models which capture that the distance is not the determinants motive for foreign investors [33, 65]. Also, the border between host and the original country does not play an important role to promote inward FDI [55]. However, the result of dummy crisis is positive and statically significance. This finding suggests that there were some increases in foreign investment during the financial crisis. Because Laos became the member of ASEAN in 1997 and also the government spent more budget on infrastructure development. For this reason, much more foreign investment was from ASEAN countries [56].

5 Conclusion

This article has captured the main influences which most attract the flow of foreign direct investment in Lao PDR. This work applied the panel data during the period from 1995 to 2015. The static and dynamic models encourage analyzing the FDI impact on a macroeconomic from investment partners and Lao PDR. The FDI stock has been chosen with the control variables associated with market size, GDP per capita, real exchange rate, inflation, distance, border, language, and crisis. According to the result from the gravity model, it concludes that FDI in the previous years is an essential for their investment decision in Lao PDR. Moreover, the effect of the market size, lower labor cost, real exchange rate, inflation rate played an essential role in supporting FDI flows to Lao PDR. While the gravity approach reveals that the result from distance and border between two countries is not influenced, that encourages inward FDI into Lao PDR This identified that the sharing of FDI flows to Lao PDR during the period is mainly from the ASEAN region. Language carries a positive sign only for a random effect model while other models are not favorable.

As in generally know, FDI is essential for developing country like Lao PDR; it is necessary for the state to have a good strategy promoting the country itself to increase the number of foreign direct investment. One crucial of this framework is to have an excellent suggestion to the policy makers to encourage further development. It is interesting to capture that the government should pay attention to economic structure in the country due to the FDI inflows were driven by economic structure. The GDP growth and the economic stability in the country motivate foreign investors’ investment to Lao PDR. The findings of this work also indicate that trade openness is a critical aspect in catalyzing FDI flows. Regarding further sustain of this sector, Lao PDR needs to enhance trade liberalization with more cooperate and international trade. The government should develop its infrastructure seriously which is fundamental to development, upgrade human resources with a concentrate on labor skills, promote laws clearly and friendly for foreign investors to increase the investment in the country.

Footnotes

Appendix

Appendix B: The list of variables hypothesis description

Variable	Description
Sfdi	FDI in previous study
LnMarketit	Gross domestic product in home country i
LnMarketjt	Gross domestic product in host country j
LnCGDPit	The proxy of labor cost of home country i
LncGDPjt	The proxy of labor cost of host country j
LnRERijt	The real exchange rate
LnTRADEjt	The real trade liberalization of the country j
LnINFLjt	The Inflation rate of the country j
LnDISTijt	Distance between country I and country j in kilometers
Dummy variables
LnBORDijt	The dummy of border, takes 1 for the countries sharing common border, and 0 for others
LnLANGijt	The dummy of language, take 1 if the country sharing a common language, and 0 for others
LnASIAijt	The dummy of crisis. Takes one during the Asian financial crisis from 1997 to 1999, and 0 for others

Acknowledgments

The authors are thankful to Prof. Phouphet Kyophilavong, Visasacksith Snookphone, Chanthasone Phetvixay, and for their valuable suggestions and immense support in drafting the manuscript.

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Variables	Dependent: FDI
	Static estimations		Dynamic estimations
	FE	RE	Dif. GMM	Sys. GMM
Sfdi	–	–	0.3040679 (0.004)	0.9965839 (0.000)
LnMarket_it	0.0676913 (0.327)	0.0558953 (0.396)	–0.004577 (0.950)	0.018923 (0.337)
LnMarket_jt	29.86697 (0.000)	29.88078 (0.000)	–0.750117 (0.000)	–1.751357 (0.000)
LnCGDP_it	–0.043158 (0.527)	–0.037823 (0.572)	–0.1352991 (0.217)	0.0170829 (0.115)
LnCGDP_jt	–25.74338 (0.000)	–25.75623 (0.000)	–2.0082681 (0.018)	–2.1005001 (0.012)
LnRER_ijt	–0.0160163 (0.004)	–0.0162133 (0.003)	0.0003174 (0.866)	–0.0033318 (0.043)
TRADE_jt	0.0012028 (0.088)	0.0012037 (0.085)	0.102971 (0.000)	0.199459 (0.000)
INFL	–0.0006026 (0.001)	–0.0006034 (0.000)	–0.00685 (0.000)	0.0081439 (0.000)
LnDIST_ijt	–	0.0015651 (0.998)	–	0.000000115 (0.856)
BORD_ijt	–	0.0399065 (0.947)	–	0.0101804 (0.285)
LAND_ijt	–	1.555776 (0.058)	–	0.0200397 (0.236)
ASIA_ijt	–0.0659095 (0.000)	–0.0658683 (0.000)	0.3933784 (0.000)	3.631128 (0.000)
Constant	–204.0555 (0.000)	–204.1688 (0.000)	–	–
R	0.8918	0.9399	–	–
N/Group Number	273/21	273/22	247/19	260/20
F-statistic	32405.76^***	261009.68^***	24.4^***	15.7^***
Breush-Pagan Test	576^***	2522^***	–	–
Normality Tests	0.000	0.000	–	–
AR(2)	–	–	0.123	0.123
Hansen Test	–	–	106	149