International Economics (INTECON)

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International Economics (INTECON) Term Paper Presented to the Economics Department De La Salle University-Manila

The Bidirectional Causality Relationship of Foreign Direct Investments to Trade and Economic Growth: The Case of the Philippines

In partial fulfillment of the course requirements in INTECON V25

Submitted to: Mr. Angelo Taningco

Submitted by: Castro, Francis Angelo Lorenzo A. Chua, Athena Bianca G. Diamada, Weddy Anne R. Tan, Jhann Erica O.

Date: December 10, 2014

Table of Contents I.

Introduction .................................................................................................................. 3 A. Background of the Study ............................................................................................................... 3 B. Significance of the Study ............................................................................................................... 4 C. Objectives of the Study ................................................................................................................. 5 D. Scope and Limitations ................................................................................................................... 5

II. Review of Related Literature ........................................................................................ 6 III. Data and Methodology ................................................................................................. 8 IV. Estimation and Results ................................................................................................ 8 A. Augmented Dickey Fuller (ADF) Test for Unit Root .............................................................. 8 B. Johansen Co-integration Test ....................................................................................................... 9 C. Granger Causality Tests ............................................................................................................... 10 V. Policy Implications and Conclusion…………………………………………… .......... 12

References: ......................................................................................................................... 14 Appendix:………………………………………………………………………………… .... 18

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I.

Introduction A. Background of the Study FDI According to the OECD (2012), Foreign Direct Investments or FDI’s are long term investments made by an entity in one country to another entity in another country. Investing entities are mostly made up of transnational corporations and enterprises because they have the size and capacity to acquire capitally-intensive assets that are used in the production of goods and services in the host countries. FDI’s provide many benefits for both source and host countries. The most important of these is the movement and transfer of technologies, knowledge and skills. As a result of the investments of huge transnational corporations and enterprises from the source countries, new and more efficient technologies and methods make their way through and benefit the host country. Spillovers of the transnational corporations are captured by local businesses and firms as a result of their link in the supply chain. The new methods and skills transferred also benefit local laborers as they become more in sync with the latest and most efficient techniques. Another benefit that FDI’s bring to the host country is that it provides additional funding for the national government. Since the large multinational corporations and enterprises operate factories that produce various products in the host country, taxes that result from these are used by the government to improve its services. Furthermore, taxes that result from the entry and establishment of these corporations in the host country also provide the government with the additional money for its projects (Feldstein, 2000). Finally, the “long-term” characteristic of FDI would mean that foreign investors could not as easily pull out from the country that they are investing in. The reason for this is because their investments are tied up to assets that are used in production, thus, cannot be easily withdrawn. Also, their decisions in investing in the host country are grounded on their long term expectations and considerations of the profits that could be realized in operating there. Thus, as compared to other investments that could be easily pulled out of the host country in times of uncertainty, FDI’s provide the stability and assurance needed for an economy to be conducive to growth and attract further FDI’s in the long-run (Hausmann & Fernandez-Arias, 2000). Trade & Comparative Advantage Trade occurs when various entities are engaged in exchange of goods and services. Ever since the dawn of mankind, trade was already being conducted. Barter trade or the direct exchange of products was the only form of trade then. However, sometime during the 2500 B.C. and 1200 A.D., coins and paper bills were already starting to take the place of barter trade. These coins and bills slowly became the medium of exchange and standardized throughout the countries (Beattie, n.d.). Today, trade is still very important and integral in the way the world works. International trade makes the world better off because both countries that engage in trade gain from it one way or the other. David Ricardo’s theory of comparative advantage explained this. According to his theory, a country will trade goods and services where it has a comparative advantage on (Salvatore, 2009). This means that even though a country has an absolute advantage on both goods, say it can produce more of both goods in a shorter span of time than the country it engages in trade with, it will export goods and services where it has the more absolute advantage on and import goods and services where it has the less absolute advantage on. This would mean that even if a country does not have any goods that it has an absolute advantage on relative to another country, it would still trade based on the good or service which it has a comparative advantage on, and realizing the gains that come with it (McDonald, 2009). 3

Heckscher-Ohlin Model The Heckscher-Ohlin Model states that in the course of trade, factor endowments, such as labor and capital, determine the advantage and flow of trade between countries. If a country is abundant in capital, then the model states that it must export capital intensive goods in exchange of goods that are labor intensive. On the other hand, if a country is abundant in labor, the model states that it must export labor intensive goods in exchange for capital intensive goods (McDonald, 2009). Even though one assumption of this model is that factor endowments are immobile or does not move from one country to another, the fact of the matter is, it does when the process of importation and exportation of goods and services occur. FDI’s are considered to be part of this movement of factor endowments, as such, it is considered to be a substitute to international trade in economies with barriers to trade exist (Liu et al. 2002). According to Zhang & Felmingham (2001), there may be no distinguishable direction either if FDI causes trade or trade causes FDI. In this case, policy makers must find other ways to encourage both FDI and trade in order to boost economic development and growth.

B. Significance of the Study In the past few decades, the Philippines, along with the other members of the ASEAN has been experiencing very high levels of trade activity as can be seen in Figure 1.1 and Figure 1.2 in the Appendix. Although the Philippines’ current policy on trade promotes openness and liberalization, this was not always the case. During the postwar period up until the early 1980’s the Philippines has adopted a protectionist policy, restricting exports and imports with its trading partners. This resulted in possible trade and investment activities to be diverted to its other ASEAN neighbors, which could have jumpstarted their high economic growth (Llanto, 2012). Following this template in the late 1980’s up to the current period, the Philippines has seen sporadic growth, however, for it to become as successful as its neighbors, it must improve its productivity. Also in the past few decades, FDI levels for the Philippines as well as for the other ASEAN economies have grown substantially as can be seen in Figure1.3 and 1.4 in the Appendix. Although FDI for the Philippines has increased in the past few decades, it is still largely behind its other more developed South East Asian neighbors. According to the current NEDA Director Arsenio M. Balisacan (2014), it is already not that surprising that the Philippines lags behind its neighbors when it comes to FDI numbers. For the past 25 to 40 years, the currently developed economies of the ASEAN were already growing at a very high rate as compared to the Philippines, which only started its strong growth for the past 3 years (Crismundo, 2014). This head start gave the developed ASEAN economies the advantage in providing investors with the needed economic confidence to continually attract FDI’s. In order to attract more FDI’s in the future, the Philippines needs to maintain the high levels of economic growth than it is currently experiencing. Since previous studies made on trade and FDI have focused more on how both of these individually relate to growth, it is now important to better understand the direct link and relationship between them. As seen from the trends above, both trade and FDI have positive links to economic growth and development. By knowing the link and causality between the two, policy makers will be able to strategize the best path for economic growth and development in the 4

Philippines. Also, by understanding the link and causality between the two in the case of the Philippines, policy makers would be able to provide new and better opportunities for everyone.

C. Objectives of the Study There are similar previous studies made on the causality between FDI and trade (Majagaiya, 2010; Baldwin, 2003). However, these studies focus only on the relationship either between FDI and economic growth or trade and economic growth. Most of the time, theories and models that are created for FDI and trade are developed separately from each other (UNCTAD, 1996). Thus, the present researchers’ understanding of the relationship between FDI and trade, whether FDI causes trade or trade causes FDI, is limited. In order to address this gap in the case of the Philippines and to better understand the link between trade and FDI in general, the researchers aim to:  

Analyze and test the bidirectional causality between trade and growth to FDI in the Philippines throughout the years 1970-2013; and Provide brief policy implications in accordance with the results on causality

The main hypotheses on causality taken on this study are the following: i. ii. iii. iv. v. vi.

FDI does not cause Exports Exports does not cause FDI FDI does not cause Imports Imports does not cause FDI FDI does not cause growth Growth does not cause FDI

D. Scope and Limitations This study tests, analyzes, and presents the relationship and causality of trade and FDI for the case of the Philippines for the years 1970-2013. Generally, the relationship and causality between trade and FDI differs for each country, therefore, the results that come out will vary from one country to another. Also, since the relationship and causality between FDI and trade heavily depend on the type of data used, the results will also vary for each type of FDI and trade data used (Sharma & Kaur, 2013). Another limitation is that the researchers have no point of comparison in a sense that they had no other country to compare the results with via the same approach and time period. However, this limitation is addressed by looking at related studies of other countries that may have used similar approach and methodology. II.

Review of Related Literature Iqbal et al. (2010), Glass (n.d.) and Lui & Shu (2001) characterized horizontal FDI as an FDI that consists of the production of the same goods and services in different locations, while vertical FDI consists of the geographical division of the production progress by stages in order to reduce costs. 5

Dash & Sharma (2007) cited the models of Helpman (1984) and Helpman & Krugman (1985) where they integrated vertical FDI into international trade theory. They showed that FDI creates complementary trade flows of finished goods from foreign links to the home country. Iqbal et al. (2010) also coincided with this by stating that there is a complementary relationship existing between trade and FDI. On the other hand, in the models based on horizontal FDI such as those of Markusen (1983), Brainard (1993), Horstmann & Markusen (1992), Markusen (1995), and Markusen & Venable (1998), they stated that foreign investment is an “alternative modality”. Some of these models posited that there is a great chance of presence of horizontal FDI if two countries have a very high value of firm-level scale economies and tariffs and transport costs relative to that of plant-level scale economies due to having very similar technologies, factor endowments, and preferences. These models also stated that there is a substitution relationship between international trade and horizontal FDI given trade-off levels involving concentration and proximity (Dash & Sharma, 2007). This idea was concurred by the study of Iqbal, Shaikh & Shar (2010) on the causal relationship between FDI, trade and economic growth in Pakistan, that trade is proxied by FDI when it is horizontal as well. Sharma & Kaur (2013) referred to the studies of Ahmed et al. (2007), Liu & Graham (2008) and Iqbal et al. (2010) where they reviewed the relationship between FDI inflows, exports and economic growth in five Sub-Saharan African economies, Taiwan, South Korea and Pakistan. The assessment showed that there is a long run relationship amongst the three factors. The studies conducted by Sharma & Kaur (2013), Iqbal et al. (2010), Zhang & Felmingham (2001), PachecoLopez (2005) and Pourshahabi et al. (2012) also discovered that there exists bidirectional causality between FDI and exports. On the other hand, Dash & Sharma (2007) and Sharma & Kaur (2013) found that instead of bidirectional causality, their studies on FDI, trade, and growth in India and Pakistan concluded that there is a unidirectional causal relationship between FDI and exports. Furthermore, Sharma & Kaur (2013) stated that linkages between FDI flows and trade were stronger in developing than in industrialized countries. As for the causal relationship between imports and FDI, Dash & Sharma (2007), Sharma & Kaur (2013) and Pacheco-Lopez (2005) deduced that the causal relationship between one another is bidirectional, while Iqbal et al. (2010) gathered that their causal relationship is unidirectional. According to the findings of Pourshahabi et al. (2012) that studied the causality relationships between trade and FDI in 16 advanced European economies, FDI-Imports and FDIExports have a bidirectional causal relationship in the short run and a unidirectional causal relationship in the long run. The research conducted by Sharma & Kaur (2013) that compared China and India on the causal relationship between FDI and trade found that the causal relationship between FDI and exports and FDI and imports is unidirectional for China’s results and bidirectional for India’s. Sharma & Kaur (2013) explained that an increase in China’s FDI increases their imports, which subsequently raises their exports as well. Thus, FDI inflows can be viewed as efficiency seeking and as a consequence, boosts China’s degree of trade. On the other hand, they concluded that the FDI of India causes imports, which then causes exports, and these exports further augments FDI. In addition, India also showed that FDI causes exports, which consecutively leads to an increase in imports.

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According to the studies conducted by P. & Gu (2010) and Khaliq & Noy (2007) that analyzed the FDI and economic growth in Nepal and Indonesia respectively, they found that there is a positive long-run relationship between FDI and GDP. The study on India’s FDI, trade and growth created by Dash & Sharma (2007) stated that FDI can permanently affect the economic growth rate and they showed this in the perspective of how FDI affects growth through the factors in the production function. Through FDI, an increase in capital will further increase output. Dash & Sharma (2007) further stated that if domestic and foreign capitals are complementary of each another, FDI will have a greater impact on the aggregate output because as a result of these capitals, output will be larger. FDI can also have a significant impact on labor through the creation of more job opportunities and the expansion of human capital. However, despite the fact that FDI may only have a small impact on the increase of employment, FDI creates a much larger impact on human capital because this creates necessary knowledge transfers through the training of the labor force, skills acquisition, new management practices and organizational arrangements. Lastly, FDI can affect economic growth through the raising the levels of technology by a variety of mechanisms such as the variety of intermediate products and types of equipment, improve production methods, boost productivity of domestic research and development, and can overall increase productivity in the host country (Dash & Sharma, 2007; Kozenkow, 2014). Findlay (1978) also postulated that FDI expands that rate of technological progress in the host economy (P. & Gu, 2010). Behname (n.d.) cited a study by Blomstorm et al. (1988) that analyzed the causal relationship between GDP and FDI in all of the developed countries during 1960 to 1985 and found that the causal relationship between FDI and GDP is unidirectional in nature. Georgantopoulos & Tsamis (2011) also concluded from their study in the causal links between FDI and economic growth in Greece that there is a long-run equilibrium relationship and unidirectional causality from GDP to FDI. However, from the studies conducted by Dash & Sharma (2007), Alkhasawneh (2013) and Behname (n.d.), they inferred that there is a bidirectional causal relationship between GDP and FDI. They also found that there is a technological spillover effect from FDI to domestic output and international trade.

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III.

Data and Methodology The variables in this empirical analysis employed in a time-series model include exports, imports, foreign direct investments (FDI) net inflows, gross domestic product growth of the Philippines from the year 1971 to 2013 in constant 2005 US dollars. These data were obtained from World Bank and iStat database. The FDI were originally obtained in current US dollars and then were deflated using the US CPI inflation as the base to obtain constant 2005 US dollar values. To examine the bidirectional causality of between trade and FDI in the Philippines, this study follows these three (3) empirical tests proposed by Sharma and Kaur (2013) and Cho (2013). First, the state of stationarity must first be tested before the actual causality test shall be applied. In order to fill such objective, the Augmented Dickey-Fuller (ADF) test shall be used. This test generates a negative coefficient wherein the higher the intensity of its magnitude in negativity, the stronger the rejection of the hypothesis that there exist unit root tests at differing level of confidences (Alezzee, 2010). Second, long-run relationship between these variables is tested by the means of Johannsen’s Cointegration Test both in natural logarithms and natural logarithms of the first differences. Third, the proper VAR Granger Causality Test shall then be applied depending on the results of the former and latter test on stationarity and long run-relationship. Cho (2013, p.24-25) has provided the empirical VAR Granger Equations on the basis of these aforementioned tests. The econometric model below shall be used, 𝑚

𝑋𝑡 = ∑

𝑖=1

𝑛

𝛽𝑖 𝑋𝑡−1 + ∑

𝑞

𝑗=1

𝛾𝑗 𝑌𝑡−𝑗 + 𝑢𝑡

𝑟

𝑌𝑡 = ∑ 𝑏𝑖 𝑌𝑡−1 + ∑ 𝑐𝑗 𝑋𝑡−𝑗 + 𝑣𝑡 𝑖=1

𝑗=1

where 𝑢𝑡 and 𝑣𝑡 represents the residual term from their corresponding equations. The equation follows the latter’s (p. 24) similar approach on the bidirectional causality while assuming that the both the level data on X and Y are stationary and have co-integration; if the aforementioned assumptions do not hold, an error correcting model (VECM) shall be applied. All of the tests were run using the Stata 10 software.

IV. Estimation and Results A. Augmented Dickey Fuller (ADF) Test for Unit Root The purpose of testing whether or not the variables are not stationary (in this case unit root) is to be able to avoid spurious results and conclusions when analyzing individual data on the bidirectional causality. According to the supplementary lecture provided by the University of Washington (2005), if the data are non-stationary, these time series variables show a trend behavior thus causes problems in making inferences in the time-series data. Rufino (2008) also stressed this characteristic of time-series data where they usually move together “..as certain common overriding forces of growth and decline impact their behavior” (p. 17). Consequently, tests for unit root are also necessary to employ the proper Granger Causality Test – that is whether to apply (1) 8

Unrestricted VAR Granger or (2) Error Correction VAR Granger. The null hypothesis taken in ADF Test is that there exists a unit root in the variables. Table 1 Summary of Results of Augmented Dickey Fuller (ADF) Root Tests Log (level) Log First-Difference FDI

-2.412

-5.321***

Exports

-1.142

-2.994*

Imports

-0.883

-3.243**

-5.657***

-6.007***

GDP Growth

Note: * statistically significant at 10%; ** at 5%; *** at 1% The table above shows the test statistics of the ADF results. Showed in Appendix B are the raw Stata results where these values are deemed statistically significant if the test statistic falls below the critical levels at 10%, 5%, and 1%. As can be observed, the log at level of the FDI, Exports and Import are not stationary. This therefore shall be considered in the Granger Test (part C) in this section to avoid spurious results. On the other hand, the logs at first-difference of all the concerned variables are statistically significant therefore the null hypothesis rejected. Hence, these variables are non-stationary. B. Johansen Co-integration Test Similarly, this test tends to cater the strictness of the Granger Causality Test by examining whether the data is co-integrated. In this case, however, the test examines also the long run relationship between the variables concerned. This co-integration test is also critical in choosing the type of Granger Test (part C) to apply, and avoiding spurious results in the time-series data when the data is not related in the long-run. Moreover, the null hypothesis taken in this test is that there is no co-integration. When the trace statistic is greater than the critical value, this null hypothesis is rejected. However, before running the command for Johansen Co-integration Test in Stata, the optimal number of lags must be known first. Appendix C shows the Stata results of obtaining these optimal lags via the Aikike Information Criterion (AIC) where the arbitrarily chosen maximum lag is 8. Table 2 shows the summary of the presence of co-integration in the level of the data. The level data on Export-FDI for zero co-integrating equation reject the hypothesis of no cointegration. On level Import-FDI, the null hypothesis is also strongly rejected which means that there exists a presence of co-integration among the variables. Lastly, the Growth-FDI rejects the null hypothesis of no co-integration both at zero and at most 1 co-integrating equations. Table 2. 9

Summary of Results of Johansen Co-integration Test Trace Statistic

5% Critical Value

Hypothesis of No Cointegration

None

18.6427

15.41

Reject

At most 1

1.2986

3.76

Accept

None

24.6209

15.41

Reject

At most 1

0.7558

3.76

Accept

None

24.3959

15.41

Reject

At most 1

9.6957

3.76

Reject

Maximum Rank

Export-FDI

Import-FDI

Growth-FDI Note: Full Stata Results of this test is on Appendix D C. Granger Causality Tests After the results obtained in part A and B in this section – following the same approach as Cho (2013) in his similar study on East Asian Countries – the framework shown in Table 3 is applied in the case of the bidirectional causality of the import, export, and growth to FDI in the Philippines. Again, this is to better utilize the Granger Causality Tests since this test can either be (1) Unrestricted VAR Granger or (2) Error Correction VAR Granger. Table 3 Utilization of Data and Granger Causality Test Methods Granger Causality Method / Conditionality Data Level X and Y data are Unrestricted VAR Granger / stationary and have coLevel Data integration Level X and Y are not Error Correction VAR Granger / stationary but have coLevel Data integration Level X and Y are stationary but have no co-integration

Unrestricted VAR Granger / First-Difference Data

Causality Applicability Growth-FDI Import-FDI Export-FDI None

Table 4 Granger Causality Test Results Null Hypothesis FDI does not cause Exports

Chi-Square

Probability

Inference

11.57

0.1155

Accept

10

Exports does not cause FDI

8.73

0.2729

Accept

FDI does not cause Imports

18.56

0.0097***

Reject

Imports does not cause FDI

4.85

0.6778

Accept

FDI does not cause growth

20.995

0.007***

Reject

Growth does not cause FDI

36.806

0.000***

Reject

Note: * statistically significant at 10%; ** at 5%; *** at 1%; Considering the tests on part A and B, the Granger Methods applied to the data are summarized on Table 3. Since Level Growth-FDI is stationary and co-integrated, VAR Unrestricted Granger is used for the bi-directional causality. On the other hand, both Exports-FDI and Imports-FDI are tested using an Error Correction VAR Granger since they are not stationary (see Table 1). In Table 4, the results of the main hypotheses of this study were summed up. The compete Stata results are shown in Appendix E. In all of the six (6) hypotheses, three (3) were rejected – (1) FDI does not cause Imports; (2) Growth does not cause FDI; (3) FDI does not cause Growth. All were rejected at 0.01 critical level. The rejected hypotheses imply a unidirectional causality from FDI to Imports, and bi-directional causality between Growth and FDI. Although the interesting results, it is noteworthy that the approach used is VECM on Import-FDI; therefore, this is only a short-run causality. In other words, the rejections of the null hypotheses denote that – FDI granger-cause Imports in the short run; and GDP and Growth granger-cause each other in the longrun. Hypotheses on the non bi-directional causality between Exports and FDI are accepted, as well as the non existence of unidirectional causality of Import to FDI.

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V.

Conclusion and Policy Implications

Conclusion While the Philippines, in present time, cannot compare to Singapore in terms of trade and FDI, it has been steadily growing in recent times. According to Dash and Sharma (2007), in an open or free trade environment, FDI can have beneficial effects on GDP or growth because of the inflow of capital from the investment. Moreover, they maintain that FDI has an effect on the exports sector of a country as it is considered to bolster the growth of a country. Thus, exploring the relationship of trade, FDI and economic growth may shed light on possible policy implications in the Philippines. In our paper, we wished to examine the causality relationship of trade and foreign direct investment (FDI) in the Philippines. Looking at various studies, there seems to be a difference in the findings of different researches studying different situations of countries. There is no general consensus on the nature of trade and FDI’s relationship. Dash and Sharma (2007) even goes on to say that the connection between them is ambiguous. For example, studies differ whether trade and FDI are complementary or substitutes; they also differ whether the causality is bidirectional or unidirectional. According to Sharma and Kaur (2013), studies seem to show that the relationship depends on the country—that is why it varies. This is further supported by Fontagné (1999), who states that when the relationship between them is viewed from the micro, macro and industry degree, the relationship varies and there are circumstances that affect it. We first examined whether the variables are stationary or not using Augment Dickey Fuller (ADF) Test in order to not generate spurious results. After determining that the variables are nonstationary-- this will be taken into account while solving for Granger Causality--, we looked at data’s co-integration characteristic. This will once again aid in solving for which type of Granger causality to utilize, as well as observe the short run relationship between the variables. Results show there is co-integration between imports and FDI, and between growth and FDI. Lastly, we used Granger Causality to observe trade and FDI’s relationship; more specifically, the causality relation of FDI, exports and imports. The results show that there is unidirectional causality from FDI to imports in the short run, and a bi-directional causality between GDP growth and FDI. The results gathered differ from other articles or literature we read up on, but as stated by Sharma and Kaur (2013), results may differ because they are based on a country the research studies. Therefore, while the other studies may show bidirectional causality between exports and FDI or imports and FDI, it is not surprising that there is no causality between FDI and exports, as Cho (2013) finds trade and FDI’s causality non-existent in Korea-India study, and the like.

Policy Implications The results could reveal the policy implications for the Philippines; given that we have discovered that exports and FDI do not cause each other, but FDI causes imports and GDP growth causes FDI and vice versa. The government could focus on attracting FDI to boost imports and growth. Furthermore, the examination of what causes FDI and growth and the direction of causality between them, the government can better know what areas to develop and fiscal policies to implement.

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According to Cho (2013), countries must develop and stimulate trade and attract FDI at the same time regardless of the ambiguity of the causality link of trade and FDI. They must be dealt with together, instead of just dealing with one aspect at a single time. Free trade agreements with other countries are to be improved to better facilitate more trade between countries. And because of a somewhat lacklustre relationship between trade and FDI, countries could improve FTA and simplify the process by making a larger scale of production line, using trade and FDI, fit for at least two countries (Cho, 2013). Policy implications on unidirectional causality from FDI to imports are just as interesting to examine. As Pourshahabi, Soderjani, and Mahmoudinia (2013) state that a reason why FDI causes imports is that FDI helps the economy to grow, thus causing imports to increase. The country has more ability to buy goods from other countries with more capital and a larger national income. More than that, a more inviting and engaging FDI policy of the Philippines could then encourage more trade, and perhaps have a positive effect on the exports of the country (Pourshahabi et al., 2013). Fontagné (1999) supports FDI and imports’ relationship and adds policy implications saying both carry positive effects for the country. Currently, a reason why there is a non-existent causality between exports and trade could be because exports of the country need to be further developed. If Philippine exports were improved, a stronger relationship could be found between exports and FDI. In that event, investors may be attracted to invest if there is more export action in the country— since there is a lot movement in the economy, they may find it a thriving economy with plenty to do business in (Pourshahabi et al., 2013). Other than that, the Philippines is a labor-abundant country and a growing economy, investors could be enticed to invest in transportation as well as in export businesses in the country. This could potentially help the country’s vulnerable sectors to grow.

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International Conference on Business and Economics, 1, 372-79. Retrieved from http://www.ipedr.com/vol1/79-B00002.pdf Alkhasawneh, M. F. (2013). “The Granger Causality Relationship between Foreign Direct Investment (FDI) and Economic Development in the State of Qatar”. An International Journal: Applied Mathematics & Information Sciences. Vol. 7, No. 5. Retrieved from http://www.naturalspublishing.com/files/published/m1h1tgd3v48q68.pdf Baldwin, R. (2003). Openness and Growth: What’s the Empirical Relationship? National Bureau of Economic Research, 9578. doi: 10.3386/w9578 Beattie, A. (n.d.). The History Of Money: From Barter To Banknotes. Retrieved from: http://www.investopedia.com/articles/07/roots_of_money.asp Behname, M. (n.d.). “The Relationship between Growth, Foreign Direct Investment and Trade in Mena Countries: A Causality Test”. Retrieved from http://ssrn.com/ abstract=1867805 Blomstrom, M., Lipsey, R. E. & Kulchycky, K. (1988) “U.S. and Swedish direct investment and exports”. R. E. Baldwin (ed.): Trade policy issues and empirical analysis, The University of Chicago Press, Chicago, 259-297. Brainard, S.L. (1993) “Simple Theory of Multinational Corporations and Trade with a trade-off between Proximity and Concentration”. NB ER Working Paper, No. 4269. Cho, C. (2013). The Causal Relationship between Trade and FDI: Implication for India and East Asian Countries. Korea Institute for International Economic Policy (KIEP). Retrieved from http://www.kiep.go.kr Chow, P.C.Y. (1987). “Causality between export and industrial development”. Journal of Developmental Economics, 26, 55-63. Dash, R. & Sharma, C. (2007). FDI, trade, and growth in India: a modified causality analysis. Journal of Indian School of Political Economy. Vol. 19 (3). Findlay, R. (1978). “Relative backwardness, direct foreign investment and the transfer of technology: A simple dynamic model.” Quarterly Journal of Economics, 92: 1-16. Fontagné, L. (1999). Foreign Direct Investment and International Trade: Complements or Substitutes? OECD Science, Technology and Industry Working Papers, 1999/03, OECD Publishing. Retrieved from http://dx.doi.org/10.1787/788565713012 Georgantopoulos, A. G. & Tsamis, A. D. (2011) “The Causal Links between FDI and Economic Development: Evidence from Greece”. Greece, EuroJournals Publishing. Glass, A. (n.d.). “Vertical versus Horizontal FDI.” World Economy. Retrieved from 14

http://econweb.tamu.edu/aglass/VerticalVsHorizontalFDI.pdf Helpman, E. (1984) “Simple Theory of International Trade with Multinational Corporations”. Journal of Political Economy. Vol. 92, No. 3. Helpman, E. & Krugman, P.R. (1985) “Market Structure and Foreign Trade, Increasing Returns, Imperfect Competition and International Economy”. MIT Press, Cambridge, Mass. Horstmann, I.J. & Markusen, J.R. (1992) “Endogenous Market Structures in International Trade”. Journal of International Economics, Vol. 32. Iqbal, M. S., Shaikh, F. M. & Shar, A. H. (2010). “Causality Relationship between Foreign Direct Investment, Trade and Economic Growth in Pakistan”. Asian Social Science. Vol. 6 No. 9. Khaliq, A. & Noy, I. (2007). “Foreign Direct Investment and Economic Growth: Empirical Evidence from Sectoral Data in Indonesia”. Retrieved from http://www.economics.hawaii.edu/research/workingpapers/WP_07-26.pdf Kozenow, J. (2014). “The Pros & Cons of Foreign Direct Investment International Businesses.” Chron.com. Retrieved from http://smallbusiness.chron.com/pros-cons-foreign-directinvestment-international-businesses-56164.html Krismundo, K. (2014, August 24). It's not surprising that PH lags in FDI among ASEAN, NEDA chief explains why. Philippine News Agency. Retrieved from: http://www .interaksyon.com/article/93872/its-not-surprising-that-ph-lags-in-fdi-amongasean-nedachief-explains-why Liu, X. & Shu, C. (2001), “Determinants of Exports Performance and the Effects of the WTO Entry on Labour Intensive Exports: Evidence from China’s Manufacturing”. Retrieved from http//: cisinfo/boro.ac.uk.01/c1/wr0013.hpa Liu, X.; Wang, C. and Wei, Y. (2002), “Causal Links between Foreign Direct Investment and Trade in China”, China Economic Review, 12(2):190-202. Llanto, G. (2012). Philippine Productivity Dynamics in the Last Five Decades and Determinants of Total Factor Productivity. Philippine Institute for Development Studies, 2012-11. Retrieved from: http://dirp3.pids.gov.ph/ris/dps/pidsdps1211.pdf Majagaiya, K. (2010). A Time Series Analysis of Foreign Direct Investment and Economic Growth: A Case Study of Nepal, International Journal of Business and Management, 5 (2): 144-8 Markusen, J. R. (1995) “Boundaries of Multinational Enterprises and Theory of International Trade”. Journal of Economic Perspectives. Vol.9. 15

Markusen, J.R. (1983) “Factor Movements and Commodity Trade as Complements”. Journal of International Economics. Vol. 14. Markusen, J. R. & Venable, A. J. (1998) “Multinational Firms and New Trade Theory”. Journal of International Economics. Vol.46. Martin Feldstein, 2000, "Aspects of Global Economic Integration: Outlook for the Future," NBER Working Paper No. 7899 (Cambridge, Massachusetts: National Bureau of Economic Research). McDonald, B. (2009). Back to Basics: Why Countries Trade. Finance & Development, 46 (4). Retrieved from: http://www.imf.org/external/pubs/ft/fandd/2009/12/basics.htm

OECD. (2013). OECD Factbook 2013: Economic, Environmental and Social Statistics. Retrieved from: http://www.oecd-ilibrary.org/sites/factbook-2013 en/04/02/01/ index.html?itemId=/content/chapter/factbook-2013-34-en Pacheco-Lopez, P. (2005). “Foreign Direct Investment, Exports and Imports in Mexico.” The World Economy, Vol. 28, No. 8, pp. 1157-1172. P., M. K. & Gu, Q. (2010). “A Time Series Analysis of Foreign Direct Investment and Economic Growth: A Case Study of Nepal”. International Journal of Business and Management. Vol. 5 No. 2 Pourshahabi, F., Soderjani, E. S. & Mahmoudinia, D. (2012). “Panel Causality Relationship among FDI and Trade (Evidence from 16 Advanced Europe Countries)”. Iranian Economic Review. Vol. 17, No. 1, 2013

Ricardo Hausmann and Eduardo Fernández-Arias, 2000, "Foreign Direct Investment: Good Cholesterol?" Inter-American Development Bank Working Paper No. 417 (Washington). Salvatore, D. (2009). International Economics (10th ed.). Upper Saddle River, NJ: Prentice Hall Inc. Sharma, R., & Kaur, M. (2013). Causal Links between Foreign Direct Investments and Trade: A Comparative Study of India and China. Eurasian Journal of Business and Economics, 6(11), 75-91.

16

UNCTAD. (1996). “World Investment Report” United Nations Conference on Trade and Development, New York and Geneva.

Unit Root Tests. (2005). Retrieved November 2014, from University of Washington: http://faculty.washington.edu/ezivot/econ584/notes/unitroot.pdf Zhang, Q. and Felmingham, B. (2001), “The Relationship between Inward Direct Foreign Investment and China’s Provincial Export Trade”, China Economic Review, 12(1): 8299.

17

Appendix A Figure1.1: FDI inflows in the ASEAN from 1970-2013

Source: UNCTAD. Retrieved from: http://unctadstat.unctad.org/wds/TableViewer/chartView.aspx

Figure 1.2: FDI outflows in the ASEAN from 1970-2013

Source: UNCTAD. Retrieved from: http://unctadstat.unctad.org/wds/TableViewer/chartView.aspx

18

Figure 1.3: Total Trade in Goods and Services (Exports) in the ASEAN from 1980-2013

Source: UNCTAD. Retrieved from: http://unctadstat.unctad.org/wds/TableViewer/chartView.aspx

Figure 1.4: Total Trade in Goods and Services (Imports) in the ASEAN from 1980-2013

Source: UNCTAD. Retrieved from: http://unctadstat.unctad.org/wds/TableViewer/chartView.aspx

19

Appendix B Augmented Dickey Fuller Tests for Level and First-difference FDI, Imports, Exports, and GDP Growth . dfuller

lnfdi

Dickey-Fuller test for unit root

Number of obs

=

40

---------- Interpolated Dickey-Fuller --------Test 1% Critical 5% Critical 10% Critical Statistic Value Value Value -----------------------------------------------------------------------------Z(t) -2.412 -3.648 -2.958 -2.612 -----------------------------------------------------------------------------MacKinnon approximate p-value for Z(t) = 0.1383 . dfuller

lnexports

Dickey-Fuller test for unit root

Number of obs

=

42

---------- Interpolated Dickey-Fuller --------Test 1% Critical 5% Critical 10% Critical Statistic Value Value Value -----------------------------------------------------------------------------Z(t) -1.142 -3.634 -2.952 -2.610 -----------------------------------------------------------------------------MacKinnon approximate p-value for Z(t) = 0.6981 . dfuller

lnimports

Dickey-Fuller test for unit root

Number of obs

=

42

---------- Interpolated Dickey-Fuller --------Test 1% Critical 5% Critical 10% Critical Statistic Value Value Value -----------------------------------------------------------------------------Z(t) -0.883 -3.634 -2.952 -2.610 -----------------------------------------------------------------------------MacKinnon approximate p-value for Z(t) = 0.7934 . dfuller

lngdpgrowth

Dickey-Fuller test for unit root

Number of obs

=

34

---------- Interpolated Dickey-Fuller --------Test 1% Critical 5% Critical 10% Critical Statistic Value Value Value -----------------------------------------------------------------------------Z(t) -5.657 -3.689 -2.975 -2.619 -----------------------------------------------------------------------------MacKinnon approximate p-value for Z(t) = 0.0000 . dfuller

lndiffdi

Dickey-Fuller test for unit root

Number of obs

=

11

---------- Interpolated Dickey-Fuller --------Test 1% Critical 5% Critical 10% Critical Statistic Value Value Value -----------------------------------------------------------------------------Z(t) -5.321 -3.750 -3.000 -2.630 -----------------------------------------------------------------------------MacKinnon approximate p-value for Z(t) = 0.0000 . dfuller

lndifexports

20

Dickey-Fuller test for unit root

Number of obs

=

25

---------- Interpolated Dickey-Fuller --------Test 1% Critical 5% Critical 10% Critical Statistic Value Value Value -----------------------------------------------------------------------------Z(t) -2.994 -3.750 -3.000 -2.630 -----------------------------------------------------------------------------MacKinnon approximate p-value for Z(t) = 0.0354 . dfuller

lndifimports

Dickey-Fuller test for unit root

Number of obs

=

27

---------- Interpolated Dickey-Fuller --------Test 1% Critical 5% Critical 10% Critical Statistic Value Value Value -----------------------------------------------------------------------------Z(t) -3.243 -3.736 -2.994 -2.628 -----------------------------------------------------------------------------MacKinnon approximate p-value for Z(t) = 0.0176 dfuller

lndifgdpgrowth

Dickey-Fuller test for unit root

Number of obs

=

11

---------- Interpolated Dickey-Fuller --------Test 1% Critical 5% Critical 10% Critical Statistic Value Value Value -----------------------------------------------------------------------------Z(t) -6.007 -3.750 -3.000 -2.630 -----------------------------------------------------------------------------MacKinnon approximate p-value for Z(t) = 0.0000

21

Appendix C Aikike Information Criterion for Optimum Lag . varsoc

lnexports lnfdi, maxlag(8)

Selection-order criteria Sample: 1979 - 2013, but with a gap +---------------------------------------------------------------------------+ |lag | LL LR df p FPE AIC HQIC SBIC | |----+----------------------------------------------------------------------| | 0 | -48.8451 .171269 3.91116 3.93903 4.00794 | | 1 | -4.80833 88.073 4 0.000 .007889* .83141* .915014* 1.12174* | | 2 | -2.9809 3.6549 4 0.455 .009395 .99853 1.13787 1.48241 | | 3 | -.488032 4.9857 4 0.289 .010737 1.11446 1.30954 1.7919 | | 4 | 1.87422 4.7245 4 0.317 .01258 1.24044 1.49126 2.11143 | | 5 | 2.75713 1.7658 4 0.779 .016872 1.48022 1.78677 2.54476 | | 6 | 7.08255 8.6508 4 0.070 .017893 1.45519 1.81748 2.71329 | | 7 | 14.2387 14.312* 4 0.006 .015928 1.21241 1.63043 2.66406 | | 8 | 17.9197 7.3621 4 0.118 .019718 1.23695 1.7107 2.88215 | +---------------------------------------------------------------------------+ Endogenous: lnexports lnfdi Exogenous: _cons . varsoc lnimports lnfdi, maxlag(8) Selection-order criteria Sample: 1979 - 2013, but with a gap +---------------------------------------------------------------------------+ |lag | LL LR df p FPE AIC HQIC SBIC | |----+----------------------------------------------------------------------| | 0 | -48.5095 .166905 3.88535 3.91321 3.98212 | | 1 | .295225 97.609 4 0.000 .005328* .438829* .522433* .729159* | | 2 | .841366 1.0923 4 0.895 .007002 .70451 .843851 1.18839 | | 3 | 2.71915 3.7556 4 0.440 .008389 .867758 1.06283 1.54519 | | 4 | 6.5726 7.7069 4 0.103 .008764 .87903 1.12984 1.75002 | | 5 | 8.97543 4.8057 4 0.308 .010457 1.00189 1.30844 2.06643 | | 6 | 12.2913 6.6317 4 0.157 .011986 1.05452 1.4168 2.31261 | | 7 | 18.7475 12.912* 4 0.012 .01126 .865575 1.2836 2.31722 | | 8 | 23.0484 8.6018 4 0.072 .01329 .84243 1.31619 2.48763 | +---------------------------------------------------------------------------+ Endogenous: lnimports lnfdi Exogenous: _cons . varsoc lngdpgrowth lnfdi, maxlag(8) Selection-order criteria Sample: 1979 - 2013, but with a gap +---------------------------------------------------------------------------+ |lag | LL LR df p FPE AIC HQIC SBIC | |----+----------------------------------------------------------------------| | 0 | -22.6793 1.64328 6.16982 6.03587 6.18968 | | 1 | -16.0854 13.188 4 0.010 .925447 5.52136 5.11951 5.58094 | | 2 | 20.7273 73.625 4 0.000 .000362 -2.68182 -3.35157 -2.58252 | | 3 | . . 4 . 0* . . . | | 4 | 490.353 . 4 . . -118.588 -119.66 -118.429 | | 5 | 492.106 3.506 4 0.477 . -119.026 -120.098 -118.868 | | 6 | 499.298 14.384 4 0.006 . -120.825 -121.896 -120.666 | | 7 | 508.182 17.769 4 0.001 . -123.046 -124.117 -122.887 | | 8 | 514.937 13.509* 4 0.009 . -124.734* -125.806* -124.575* | +---------------------------------------------------------------------------+ Endogenous: lngdpgrowth lnfdi Exogenous: _cons

22

Appendix D

Johansen Tests for Co-integration . vecrank

lnexports lnfdi, lag(1)

Johansen tests for cointegration Trend: constant Number of obs = 42 Sample: 1972 - 2013 Lags = 1 ------------------------------------------------------------------------------5% maximum trace critical rank parms LL eigenvalue statistic value 0 2 -32.145342 . 18.6427 15.41 1 5 -23.473291 0.33831 1.2986* 3.76 2 6 -22.823975 0.03045 ------------------------------------------------------------------------------. vecrank

lnimports lnfdi, lag(1)

Johansen tests for cointegration Trend: constant Number of obs = 42 Sample: 1972 - 2013 Lags = 1 ------------------------------------------------------------------------------5% maximum trace critical rank parms LL eigenvalue statistic value 0 2 -29.894662 . 24.6209 15.41 1 5 -17.962084 0.43347 0.7558* 3.76 2 6 -17.584208 0.01783 ------------------------------------------------------------------------------. vecrank

lngrowth lnfdi, lag(8)

Johansen tests for cointegration Trend: constant Number of obs = 35 Sample: 1979 - 2013 Lags = 8 ------------------------------------------------------------------------------5% maximum trace critical rank parms LL eigenvalue statistic value 0 30 -64.320419 . 24.3959 15.41 1 33 -56.970293 0.34296 9.6957 3.76 2 34 -52.122449 0.24196 ------------------------------------------------------------------------------.

23

Appendix E Granger Causality Tests VECM on Exports-FDI and Imports-FDI, and VAR on Growth-FDI . vec lnfdi lnexports, trend(constant) lags(8) Vector error-correction model Sample:

1979 - 2013

Log likelihood = Det(Sigma_ml) =

No. of obs AIC HQIC SBIC

8.573571 .0021003

= = = =

35 1.395796 1.902022 2.862267

Equation Parms RMSE R-sq chi2 P>chi2 ---------------------------------------------------------------D_lnfdi 16 .977197 0.6816 40.67307 0.0006 D_lnexports 16 .10001 0.6168 30.58789 0.0152 --------------------------------------------------------------------------------------------------------------------------------------------| Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------D_lnfdi | _ce1 | L1. | -.8776271 .4399604 -1.99 0.046 -1.739934 -.0153205 | lnfdi | LD. | .3104646 .3892611 0.80 0.425 -.4524732 1.073402 L2D. | .1715486 .3327451 0.52 0.606 -.4806198 .8237171 L3D. | .3675701 .3250078 1.13 0.258 -.2694336 1.004574 L4D. | .1250638 .260671 0.48 0.631 -.3858419 .6359695 L5D. | .2473131 .2406245 1.03 0.304 -.2243023 .7189286 L6D. | .2173864 .2312127 0.94 0.347 -.2357822 .670555 L7D. | .4770554 .1711103 2.79 0.005 .1416854 .8124254 | lnexports | LD. | -.4921919 2.130534 -0.23 0.817 -4.667963 3.683579 L2D. | .1815554 2.064113 0.09 0.930 -3.864031 4.227142 L3D. | 1.351123 2.003546 0.67 0.500 -2.575755 5.278001 L4D. | -4.900125 2.142226 -2.29 0.022 -9.09881 -.7014394 L5D. | 1.246957 2.195903 0.57 0.570 -3.056935 5.550848 L6D. | -3.4569 1.94318 -1.78 0.075 -7.265463 .3516636 L7D. | .7278383 2.235251 0.33 0.745 -3.653173 5.10885 | _cons | .0237453 .655214 0.04 0.971 -1.26045 1.307941 -------------+---------------------------------------------------------------D_lnexports | _ce1 | L1. | .0901674 .045027 2.00 0.045 .0019162 .1784187 | lnfdi | LD. | -.0659525 .0398383 -1.66 0.098 -.144034 .0121291 L2D. | -.0803247 .0340542 -2.36 0.018 -.1470698 -.0135796 L3D. | -.047356 .0332624 -1.42 0.155 -.1125491 .017837 L4D. | -.0433983 .0266779 -1.63 0.104 -.0956861 .0088894 L5D. | -.0440806 .0246263 -1.79 0.073 -.0923473 .0041861 L6D. | -.0099368 .0236631 -0.42 0.675 -.0563155 .036442 L7D. | .0091759 .017512 0.52 0.600 -.025147 .0434987 | lnexports | LD. | -.3818315 .2180459 -1.75 0.080 -.8091936 .0455306 L2D. | -.2746351 .2112481 -1.30 0.194 -.6886737 .1394035 L3D. | -.2738987 .2050495 -1.34 0.182 -.6757883 .1279909 L4D. | -.3372771 .2192424 -1.54 0.124 -.7669843 .0924301 L5D. | -.2568332 .2247359 -1.14 0.253 -.6973076 .1836411 L6D. | -.5390812 .1988715 -2.71 0.007 -.9288621 -.1493003 L7D. | -.1962055 .2287629 -0.86 0.391 -.6445726 .2521615 | _cons | .2311204 .0670567 3.45 0.001 .0996916 .3625492

24

-----------------------------------------------------------------------------Cointegrating equations Equation Parms chi2 P>chi2 ------------------------------------------_ce1 1 118.9415 0.0000 ------------------------------------------Identification:

beta is exactly identified

Johansen normalization restriction imposed -----------------------------------------------------------------------------beta | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------_ce1 | lnfdi | 1 . . . . . lnexports | -1.815288 .1664481 -10.91 0.000 -2.14152 -1.489056 _cons | 23.01252 . . . . . -----------------------------------------------------------------------------. test ([D_lnfdi]: LD.lnexports L2D.lnexports L3D.lnexports L4D.lnexports L5D.lnexportsL6D.lnexports > L7D.lnexports) lnexportsL6D: operator invalid r(198); . test ([D_lnfdi]: LD.lnexports L2D.lnexports L3D.lnexports L4D.lnexports L5D.lnexports L6D.lnexport > s L7D.lnexports) ( ( ( ( ( ( (

1) 2) 3) 4) 5) 6) 7)

[D_lnfdi]LD.lnexports = 0 [D_lnfdi]L2D.lnexports = 0 [D_lnfdi]L3D.lnexports = 0 [D_lnfdi]L4D.lnexports = 0 [D_lnfdi]L5D.lnexports = 0 [D_lnfdi]L6D.lnexports = 0 [D_lnfdi]L7D.lnexports = 0 chi2( 7) = Prob > chi2 =

8.73 0.2729

. test ([D_lnexports]: LD.lnfdi L2D.lnfdi L3D.lnfdi L4D.lnfdi L5D.lnfdi L6D.lnfdi L7D.lnfdi) ( ( ( ( ( ( (

1) 2) 3) 4) 5) 6) 7)

[D_lnexports]LD.lnfdi = 0 [D_lnexports]L2D.lnfdi = 0 [D_lnexports]L3D.lnfdi = 0 [D_lnexports]L4D.lnfdi = 0 [D_lnexports]L5D.lnfdi = 0 [D_lnexports]L6D.lnfdi = 0 [D_lnexports]L7D.lnfdi = 0 chi2( 7) = Prob > chi2 =

11.57 0.1155

. FDI & IMPORTS . vec lnfdi lnimports, trend(constant) lags(8) Vector error-correction model Sample:

1979 - 2013

Log likelihood = Det(Sigma_ml) =

No. of obs AIC HQIC SBIC

14.44881 .0015013

Equation Parms RMSE R-sq chi2 P>chi2 ---------------------------------------------------------------D_lnfdi 16 1.02085 0.6525 35.67825 0.0032

25

= = = =

35 1.060068 1.566294 2.526539

D_lnimports 16 .084486 0.7178 48.33054 0.0000 --------------------------------------------------------------------------------------------------------------------------------------------| Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------D_lnfdi | _ce1 | L1. | -2.483161 .8247621 -3.01 0.003 -4.099665 -.8666575 | lnfdi | LD. | 1.509008 .7081751 2.13 0.033 .1210105 2.897006 L2D. | 1.389859 .6131362 2.27 0.023 .1881344 2.591584 L3D. | 1.250855 .5111319 2.45 0.014 .2490551 2.252655 L4D. | .7574487 .4040507 1.87 0.061 -.0344762 1.549374 L5D. | .7947181 .349877 2.27 0.023 .1089717 1.480464 L6D. | .6931098 .2952145 2.35 0.019 .1144999 1.27172 L7D. | .4866068 .1921761 2.53 0.011 .1099485 .8632651 | lnimports | LD. | 2.358492 2.470382 0.95 0.340 -2.483367 7.200351 L2D. | 1.751619 2.32777 0.75 0.452 -2.810726 6.313965 L3D. | 2.152053 2.238556 0.96 0.336 -2.235436 6.539542 L4D. | -1.312734 2.555296 -0.51 0.607 -6.321023 3.695555 L5D. | .9108708 3.111279 0.29 0.770 -5.187124 7.008865 L6D. | 1.895484 2.899348 0.65 0.513 -3.787132 7.578101 L7D. | 3.942039 3.082574 1.28 0.201 -2.099696 9.983774 | _cons | .0046328 .4198826 0.01 0.991 -.818322 .8275877 -------------+---------------------------------------------------------------D_lnimports | _ce1 | L1. | .1217502 .0682573 1.78 0.074 -.0120318 .2555321 | lnfdi | LD. | -.0890411 .0586086 -1.52 0.129 -.2039118 .0258297 L2D. | -.0504822 .0507432 -0.99 0.320 -.149937 .0489726 L3D. | -.0387071 .0423013 -0.92 0.360 -.1216161 .044202 L4D. | -.0383325 .0334393 -1.15 0.252 -.1038722 .0272073 L5D. | -.0117478 .0289558 -0.41 0.685 -.0685002 .0450046 L6D. | .0287321 .024432 1.18 0.240 -.0191537 .0766179 L7D. | .0224017 .0159045 1.41 0.159 -.0087706 .053574 | lnimports | LD. | -.0671453 .2044489 -0.33 0.743 -.4678578 .3335672 L2D. | -.2077794 .1926464 -1.08 0.281 -.5853593 .1698005 L3D. | -.1894471 .185263 -1.02 0.307 -.5525558 .1736617 L4D. | -.3197129 .2114764 -1.51 0.131 -.7341991 .0947733 L5D. | -.4617065 .2574896 -1.79 0.073 -.9663768 .0429638 L6D. | -.2152909 .2399501 -0.90 0.370 -.6855845 .2550027 L7D. | .0164595 .255114 0.06 0.949 -.4835547 .5164737 | _cons | .0944887 .0347495 2.72 0.007 .0263809 .1625965 -----------------------------------------------------------------------------Cointegrating equations Equation Parms chi2 P>chi2 ------------------------------------------_ce1 1 609.9054 0.0000 ------------------------------------------Identification:

beta is exactly identified

Johansen normalization restriction imposed -----------------------------------------------------------------------------beta | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------_ce1 | lnfdi | 1 . . . . . lnimports | -1.611532 .0652541 -24.70 0.000 -1.739428 -1.483637

26

_cons | 18.88521 . . . . . -----------------------------------------------------------------------------. test ([D_lnfdi]: LD.lnimports L2D.lnimports L3D.lnimports L4D.lnimports L5D.lnimports L6D.lnimport > s L7D.lnimports) ( ( ( ( ( ( (

1) 2) 3) 4) 5) 6) 7)

[D_lnfdi]LD.lnimports = 0 [D_lnfdi]L2D.lnimports = 0 [D_lnfdi]L3D.lnimports = 0 [D_lnfdi]L4D.lnimports = 0 [D_lnfdi]L5D.lnimports = 0 [D_lnfdi]L6D.lnimports = 0 [D_lnfdi]L7D.lnimports = 0 chi2( 7) = Prob > chi2 =

4.85 0.6778

. test ([D_lnimports]: LD.lnfdi L2D.lnfdi L3D.lnfdi L4D.lnfdi L5D.lnfdi L6D.lnfdi L7D.lnfdi) ( ( ( ( ( ( (

1) 2) 3) 4) 5) 6) 7)

[D_lnimports]LD.lnfdi = 0 [D_lnimports]L2D.lnfdi = 0 [D_lnimports]L3D.lnfdi = 0 [D_lnimports]L4D.lnfdi = 0 [D_lnimports]L5D.lnfdi = 0 [D_lnimports]L6D.lnfdi = 0 [D_lnimports]L7D.lnfdi = 0 chi2( 7) = Prob > chi2 =

18.56 0.0097

. . var lngrowth lnfdi, lags(1/8) Vector autoregression Sample: 1979 - 2013 Log likelihood = -52.12245 FPE = .562376 Det(Sigma_ml) = .0673853

No. of obs AIC HQIC SBIC

= = = =

35 4.921283 5.442849 6.432192

Equation Parms RMSE R-sq chi2 P>chi2 ---------------------------------------------------------------lngrowth 17 .569211 0.5966 51.75311 0.0000 lnfdi 17 .967052 0.7763 121.4643 0.0000 --------------------------------------------------------------------------------------------------------------------------------------------| Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------lngrowth | lngrowth | L1. | .0315491 .1744442 0.18 0.856 -.3103553 .3734535 L2. | .1544899 .1493368 1.03 0.301 -.1382049 .4471846 L3. | .0926635 .1549632 0.60 0.550 -.2110587 .3963857 L4. | -.1421401 .1704307 -0.83 0.404 -.4761782 .191898 L5. | -.259378 .2045731 -1.27 0.205 -.6603339 .141578 L6. | -.147622 .2090127 -0.71 0.480 -.5572794 .2620355 L7. | .4903989 .2258012 2.17 0.030 .0478367 .932961 L8. | .0317934 .2117778 0.15 0.881 -.3832834 .4468703 | lnfdi | L1. | .2036245 .0954852 2.13 0.033 .0164769 .3907722 L2. | -.0467424 .0981608 -0.48 0.634 -.2391339 .1456492 L3. | -.1303892 .0825242 -1.58 0.114 -.2921337 .0313553 L4. | -.1333488 .0862783 -1.55 0.122 -.3024511 .0357536 L5. | .2136747 .0879942 2.43 0.015 .0412093 .3861402 L6. | .2105899 .0880195 2.39 0.017 .0380748 .383105 L7. | -.0469065 .0748619 -0.63 0.531 -.1936333 .0998202 L8. | -.0783812 .0731904 -1.07 0.284 -.2218318 .0650694

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| _cons | -2.979709 1.679621 -1.77 0.076 -6.271706 .3122882 -------------+---------------------------------------------------------------lnfdi | lngrowth | L1. | .2416506 .2963695 0.82 0.415 -.339223 .8225241 L2. | -.2681445 .2537136 -1.06 0.291 -.7654141 .2291251 L3. | .1884322 .2632725 0.72 0.474 -.3275724 .7044367 L4. | -1.255453 .2895509 -4.34 0.000 -1.822963 -.6879441 L5. | .8756916 .3475566 2.52 0.012 .1944932 1.55689 L6. | -1.31455 .3550992 -3.70 0.000 -2.010532 -.6185684 L7. | .4135917 .3836217 1.08 0.281 -.338293 1.165476 L8. | -.6720392 .3597969 -1.87 0.062 -1.377228 .0331498 | lnfdi | L1. | .4299397 .1622233 2.65 0.008 .1119879 .7478916 L2. | -.1776675 .1667688 -1.07 0.287 -.5045284 .1491934 L3. | .3842944 .1402034 2.74 0.006 .1095008 .659088 L4. | -.3390275 .1465813 -2.31 0.021 -.6263215 -.0517335 L5. | .3778006 .1494965 2.53 0.011 .0847928 .6708084 L6. | .0643594 .1495395 0.43 0.667 -.2287327 .3574515 L7. | .2156115 .1271856 1.70 0.090 -.0336677 .4648908 L8. | -.228455 .1243458 -1.84 0.066 -.4721684 .0152584 | _cons | 7.91395 2.853569 2.77 0.006 2.321058 13.50684 -----------------------------------------------------------------------------. vargranger Granger causality Wald tests +------------------------------------------------------------------+ | Equation Excluded | chi2 df Prob > chi2 | |--------------------------------------+---------------------------| | lngrowth lnfdi | 20.995 8 0.007 | | lngrowth ALL | 20.995 8 0.007 | |--------------------------------------+---------------------------| | lnfdi lngrowth | 36.806 8 0.000 | | lnfdi ALL | 36.806 8 0.000 | +------------------------------------------------------------------+ .

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