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The impact of inward FDI flows to exports from China
Bachelor ThesisJune 2016
Faculty : Faculty of Economic and Business
Student Name : Rahaulia Sarah Miradiantri Bramasto
Student Number : 10827773
Specialization : Economics
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Statement of Originality
This document is written by Student Rahaulia Bramasto who declares to take full responsibility for the contents of this document.
I declare that the text and the work presented in this document are original and that no sources other than those mentioned in the text and its references have been used in creating it.
The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.
3 TABLE OF CONTENT 1. INTRODUCTION 4 2. LITERATURE REVIEW A. Theoretical Considerations 6 B. Empirical Evidence 7 3. METHODOLOGY 10
4. DATA AND RESULTS 13
5. LIMITATIONS 24
6. CONCLUSIONS 24
7. APPENDIX 25
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1. INTRODUCTION
Since China opened its economy, it has implemented an export promotion policy that was pursued with a number of radical reforms, which one of them includes encouragement of FDI (Yao, 2006). FDI is encouraged because it makes up the investment gap in the recipient economy which in turn will provide factors of production and that increase in production will push exports forward. Additionally, FDI is really encouraged after China’s economic reform where the government began to allow private entities to join economic activities in the country. China initiated a policy to support this transition by setting up four special economic zones where private and foreign investment firms were encouraged with preferential policies in the form of tax incentives and flexibility of employment that helped China become a main trading nation and the largest recipient of foreign direct investment (FDI) in the developing world (Yao & Zhang, 2001).
Both exports and FDI have such a big magnitude in China. With exports having a value of 23422.93 hundred millions US dollar (SAFE, 2015), China’s exports performance is very impressive. In 2004, China took Japan’s position as the leading Asian exporter and in 2007 China surpassed the United States and then Germany as well in 2009 to become the world’s leading exporter (WTO, 2015). The size of exports from China raises many questions about what could possibly be the determinant of such impressive performance. This phenomenon can be explained by FDI coming into the country because while China was not in the first place regarding the amount of FDI inflows in those years, China has always been in the top 5 of the biggest FDI recipient in the world and in 2014, FDI inflows into China came in at staggering amount of 289 billion USD, making China the biggest recipient of FDI in the world (World Bank, 2016). The rapid developments of China’s economy made it very attractive for other countries to invest.
This paper will try to confirm the hypothesis that inward FDI has a positive impact on exports from China. Based on the literatures and data mentioned above, it seems that FDI and exports have a great importance in China. The amount of FDI coming into the country and the value amount of exports are massive and seeing as China has implemented FDI as one of its tools to promote trade (Yao, 2006), there is a high possibility that FDI affects exports positively.
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Many scholars are interested in the connection between FDI and the exporting sector in China and there has been an increasing amount of literature dedicated to this topic. There are several studies that study the relationship between FDI and exports in China (Zhang & Song, 2000; Yao, 2006; Liu, Wang & Wei, 2001). However, most of the literature is focused on the impact of FDI and exports on China’s economic growth and looking at the province-level data. The purpose of this paper is to see whether there is an impact of inward FDI on China’s exports. Empirical observation will be conducted to investigate the relationship between FDI and exports using annual national level data from 1984-2014. To gain the quantitative results, OLS regression will be used to see the effect of inward FDI on exports while controlling several other variables such as exports in the previous period, GDP growth, gross capital formation (GCP), a manufacturing output share of GDP, trade agreements, labor costs and exchange rate. The findings will be used to support the belief that increased level of inward FDI will have a positive impact on exports. Some adjustments in the regression model need to be made by dropping several control variables namely GDP growth, labor costs, manufacturing output share of GDP and trade agreements, which resulting in the evidence that inward FDI has a positive impact on total exports from China. The adjustments are made because several of the conventionally relevant variables such as GDP growth, labor costs, manufacturing share of GDP and trade agreements are insignificant in the model. This may be caused by scarce data.
The sections of this thesis following the introduction are as follows: Section 2 provides literature review of the existing studies about the relationship between FDI and exports. In section 3, the methodology of this thesis will be explained with the description of the model. Section 4 provides information about the data that are being used for this study and the obtained results from the regression. Section 5 talks about the limitations of the study and section 6 will conclude.
6 2. LITERATURE REVIEW
2. A. Theoretical Considerations
International trade happens because there are differences in factor endowments of every country. Heckscher-Ohlin theory states that for trade to happen there must be a difference in factors of production or in other words, there needs to be an abundance of one type of endowments in one country and scarcity in the other. The Heckscher-Ohlin theory states that international trade can substitute the movement of factor of production and the exchange of endowment can be done indirectly. FDI can be seen as one of the tools to conduct indirect exchange of the factor of production. International mobility of factors of production, including FDI, is considered as substitute for international trade (Liu, Wang, & Wei, 2001)
Helpman (1984) suggest that FDI plays an important role in international trade. His study finds that multinational corporations possess factors of productions and that they are an active player in international trade. The size of the investing country and the factor endowments they possess will determine the direction of the trade and the decision to invest in the other country. The capital abundant country will tend to build manufacturing plants instead of exporting the differentiated goods to the labor-abundant country if there is a significant difference in endowments because of the relatively lower cost of labor. The labor abundant country will then continue the production and export the finished product in return. A complementary trade will be created as a result of FDI from the capital abundant country. However, Helpman’s model has the implication that FDI cannot happen between similar countries.
Markusen (1998) stated in his paper that the presence of the difference in factors of production and the restriction of liberal investment will determine the direction and volume of the trade. Investment liberalization will reverse the direction of the trade when countries are not very different in size but differ significantly in the factor of endowments. If the countries have different factor of endowments then the endowments that are abundant in each country will be relatively cheaper than in the other. If investment liberalization is implemented, the investors will take advantage of that and shift production to the other country. The investment liberalization encourages a country to shift production and import from the producing country instead of producing the goods themselves and exports the goods. A large country with unskilled
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labor is a perfect fit to be a dominant host country for manufacturing plants because of the relatively cheaper labor and spacious location to build the plants.
Every country has a different endowment and Heckscher-Ohlin theory proves that the differences in factors of production are the reason trade happens. In the case of FDI, the difference in factors of production can be seen as one of the reason there is a flow of investment from one country to the other and when that happen, the spillover from FDI will emerge as well and thus making the associated countries more similar. Kojima (1973) stated that in trade-oriented countries, foreign direct investment from the comparatively disadvantageous industry in the investing country will promote the industry condition in both countries and will accelerate trade activities between the countries. FDI will complement the capital, technological and managerial knowledge which are in shortage in the recipient country. FDI will also generate internationalization of production and marketing which will support economic development not only for the recipient country but for the investing country as well. Further empirical study is needed to predict the effect of inward FDI on export.
2.B. Empirical Evidences
Many empirical studies with various methods and models had been conducted in the past years. The impact of FDI on exports has been a point of interest for many scholars. However, most of the existing studies chose other countries than China as their subject or focus on the regional impact as opposed to the overall impact of FDI to the whole country’s exports which is the main topic of this paper.
Amighini & Sanfilippo (2014) study on the impact of the external flows, in which one of them is FDI, on the improvement of African exports by measuring exports diversification and unit values of exports. They separate external flows from other developing countries from those who originate in developed countries. The separation was implemented to test the assumption that each group will have a different impact on the ability of recipient economies to receive positive knowledge spillovers embedded in the inward flows. The paper confirms the importance of external flows (import and inward FDI) on the ability of African economies to improve their
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exports and that the result suggests that external flows have positive impact on exports. The impact of FDI from developing to developing countries bring more positive outcome because of the similarities in technology and more accessible goods and services. FDI from other developing countries can compensate for low domestic savings and contribute to capital accumulation in low-income countries. In the other hand, FDI from developed countries is less diversified because of the significant technology gap.
Leichenko & Erickson (1997) conducted an analysis of the impact of FDI on exports in the US focusing on the manufacturing industry exports during the period of 1980 to 1991. The result indicates that increased FDI will have a longer-term effect on the improvement of the state industrial competitiveness in the global market because of the global connection that foreign investors have which facilitates state industries with information about the global market. FDI has externality benefits as well such as introduction of new technologies, adoption of managerial innovations and increased competition between firms that may lead to positive impact on exports.
There are several papers that can prove that not all spillovers from FDI have a positive effect. Haddad & Harrison (1993) and Konings (2001) find that FDI affects efficiency and productivity of domestic firms negatively. The rate of growth of productivity is higher in domestic-owned firms because foreign firms lag behind domestic firms in the protected market (Haddad & Harrison, 1993). In the study by Konings (2001) about the effect of FDI in three emerging market economies: Bulgaria, Romania, and Poland, there is evidence that foreign firms do not perform better than domestic ones except in Poland because Poland is the more advanced economy so the condition of their economy is better and FDI can be used more effectively. Foreign firms in the other two countries are slower because it takes time for ownership effect to change performance due to lags in restructuring. Competition effect also occurs in Bulgaria and Romania where the lag in ownership effect dominates a technological spillover effect, which otherwise would hold under the increasing return to scale assumption. Technology effect will be dominated by competition effect if the technological gap is too large, which would apply to less advanced countries such as Bulgaria and Romania.
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Several studies also find that the impact of FDI on exports might not be the same in different areas in the same country. Zhang & Felmingham (2001) try to explain the causal relationship between FDI and export in China’s cities and provinces. The research of each area generates different results. In the middle area of China, the causality actually goes from exports to FDI while in the west area FDI affects exports significantly. The causality of FDI and export seem to depend on the condition of the economy in the area and the potential for exporting in each province as implied by Lucas (1993). He finds that FDI is responsive to the economic indicators such as price and wage. The increase in price and wage in the host country will deter the inflow of FDI. If the condition in the receiving country is unstable with the existence of conflicts such as industrial and political dispute, it can deter the entrance of FDI as well.
According to Aitken, Hanson and Harrison (1997) capital is one of the factors that can influence the exports behavior. Because FDI can be seen as capital provided by foreign countries, FDI can also be seen as a catalyst for exports. The study finds that the probability a domestic plant exports is positively correlated to proximity to multinational firms. Foreign-owned enterprises have more information about foreign markets and technology so they can distribute domestic goods more effectively.
Coughlin and Fabel (1998) have done a cross-section analysis to analyze the connection between endowment and exports on state exports in the United States and the result was that the increase of capital, whether it is physical or human, enhances the country’s ability to exports goods and services. During 1993-1996 periods, China has received 13 percent of global FDI inflows, making it the second biggest recipient in the world behind the US (Noorbakhsh, Paloni, & Youssef, 2001). Based on the findings of Coughlin and Fabel (1998), a conclusion could be drawn that the impact of FDI on China’s exports will be positive. Further empirical research is needed to support that hypothesis.
The papers mentioned above can be good guidelines to analyze the impact of FDI on export in China, but they cannot predict the result of this paper’s research question because they study the effect of FDI in other countries or using other methods. Zhang & Song (2000) paper is the most similar regarding the research topic but they estimate a panel data model, in contrast to this thesis. The variables they used for the time series model are FDI in the previous period, exports
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in the previous period and domestic investment in the previous period among other variables. For the panel data analysis, they compare the result from several provincial areas and divided them into two regional, the Coastal and Inland areas. The result shows that FDI play an important role in promoting exports and there is a strong link between the two. Increased level of FDI positively affects provincial manufacturing exports performance. The reason for this positive impact is because there is evidence that FDI provides assistance in the exporting sector by creating spillovers such as the teaching of marketing strategies, methods, procedures, and channels of distribution.
3. METHODOLOGY
This paper will examine the relationship between inward FDI and exports from China where changes in FDI is expected to have a positive impact on exports from in China. To evaluate, I use an Ordinary Least Square (OLS) regression.
To make sure that the result from the regression is reliable, some assumptions need to be made. OLS approach is assumed to have exogeneity and all regressors are linearly related to the dependent variable. Other assumptions that need to be made:
1. Conditional distribution of error where the means of is zero 2. and are identically and independently distributed
3. There is little to no probability of the existence of large outliers
The form of OLS multiple regression model with variables (Stock and Watson, 2012) is as follows:
where is the dependent variable, is the independent variables, and is the error term.
The model used for this paper adapts the structure of the OLS model mentioned above. This paper studies the relationship between inward FDI and exports in China from 1984 to 2014 using the adopted version of the model that had been constructed by Zhang & Song (2000) with
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additional control variables (labor costs and a dummy variable trade agreements). The model is as follows:
stands for the dependent variable, that China’s exports in year , where . is the inward FDI to China in period . Other control variables of this model are: for exports in period , for gross capital formation in period t, for GDP growth in period t, for share of manufacturing output of GDP in period t, for the dummy variable trade agreements of China, for exchange rate of China’s renminbi (RMB) against US dollar in period and for labor cost in period . The term represents the difference from the value in the previous period and the term represents the error.
The control variables are used to prevent the omitted variable bias. and reflect the short-run elasticity of exports with respect to FDI and exports variables (Zhang & Song, 2000). The purpose of the lag in the model is to see the impact of those variables, especially FDI, on exports. The lag in FDI variable accounts for the delayed response of exports to changes in FDI while the lag in the exports variable accounts for the effect of the previous exports performance on current exports.
Current exports are chosen as the dependent variable because the aim of this paper is to see the impact of FDI on exports. The result of the constructed model will reflect the impact of the chosen variables on exports, to see whether there is going to be a difference caused by the change in variables and whether the impact is going to be positive or negative. The exports variable includes exports of goods and services.
Inward FDI is the explanatory variable. The estimate of the FDI effect will determine the answer to the research question. The changes in value from previous period are used in the regression to see the effect of FDI on exports and to make sure that the changes in the dependent variable are caused by the changes in the explanatory variable. To ensure that the impact of FDI can be fully analyzed, one period lag is implemented.
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Total exports with one period lag are included in the control variable because of the strong relationship between the past and future performance of exports (Zhang & Song, 2000). Past performance of exports can influence the future performance by affecting the economic condition and as a predictor of future performance. Gross capital formation (GCP) is included to separate the effect of domestic investment with foreign investment. The inclusion of this variable is intended to hold constant of the others investment factors. GDP growth represents the economic performance of China. Economic performance is expected to have an impact on exports because when there is a good economic condition, it can support the production of goods and services and the impact on production will be reflected on exports.
Share of manufacturing output of GDP is included in the model because Zhang and Song (2000) see a pattern in the increase of exports and manufacturing output between 1984 and 1997 where exports grew 16% annually and manufacturing exports grew 21% annually while the share of manufacturing goods in total exports during 1986-1997 reached 75% with rising tendency, so it seems like there is a relationship between manufacturing goods and exports. Manufacturing goods hold such a big proportion of total exports so the fluctuation in manufacturing goods’ production is expected to have an impact on exports in general. Exchange rate is considered to be an important variable because the relative strength of the currency will affect exports at least in the short term. Low exchange rate will increase exports demand from abroad because of relatively cheaper goods and services. When the exchange rate of RMB against Dollar is low, China’s assets are considered to be cheaper and investment in China increases as well.
Additional control variables are added to the model to prevent omitted variable bias. Trade agreements are one of the added control variables and it is the form of a dummy variable. The value of the dummy variable is 1 if trade agreement happens and 0 if there are no trade agreements. The trade agreements that counts for this variable is only trade agreements that China make with major trade partners such as European Union, World Trade Organizations, and ASEAN because it is expected that agreements with such enormous partners have a more noticeable impact on exports. The existence of trade agreements is expected to have an impact on exports because it can open up the way for free trade between the countries involved. The other additional control variable is labor costs because the conventional factor-proportion model
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suggested that there might be a negative effect of labor cost on exports and China is expected to exports labor-intensive products because it is a labor-abundant country (Zhang K. H., 2005).
4. DATA AND RESULTS
The data for this paper is obtained from various sources. The first source is used to obtain the data for FDI (NBS, 2014). The second source provides data for the exchange rate, GDP growth and manufacturing share of GDP (World Bank, 2016). Data for the value of total exports can be obtained from the third source (SAFE, 2015) and the other source is capable of providing data for gross capital formation (GCP) (OECD, 2016). The list of trade agreements can be obtained from two different sources (WTO, 2011; CFTA, 2016) while labor costs come from another (MOHRSS, 2016). The data used for labor costs is the average wage in China. Annual data is used from the period 1984-2014 that generates 31 observations. The data is adjusted to real values by converting the data to US dollar if the data is only available in Yuan and then adjusting it for inflation using GDP deflator in order to see the real growth.
To check for the presence of unit root in the time-series, the Augmented Dickey-Fuller test is conducted. The results for all the variables are significant with p-value more than 0.05 thus the null hypothesis for the test is rejected. Unit root is seen to be present in the model so the data needs to be calculated in a difference form.
Some econometric issues are present in this study. There is a possibility that there is reverse causality between FDI and exports. To minimize this problem, current exports is used as the dependent variable while FDI with one period lag is used as the independent variable. This decision is implemented to represent the sequence of causality which starts with investment in the previous period that will determine production and eventually the production will affect exports in the current period. For example, in 1983 there was a 206 million US$ increase of inward FDI from 1982 and in 1984, exports increased as well by 2654 million US$ from 1983. Moreover, Zhang and Song (2000) stated that they have not seen any study that shows exports as a major determinant of FDI so the probability that reverse causality might happen is low.
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Annual data on FDI inflow and total exports
Based on the data plot in Figure 1.a, there seems to be a correlation between inward FDI and the real value of total exports. When FDI increases, exports on the following year increase as well and when FDI falls in 2009, total exports in 2010 decrease significantly. The delayed effect of FDI can be seen on the data plot where the movement of FDI inflows will be followed by exports in the same direction in the next period.
Figure 1.b
Annual data on GCP and total exports
Based on the data plot, GCP constantly increases and total exports constantly increase as well. GCP and total exports seem to move in the same direction except in 2008 where exports drop but GCP continues to rise. The drop in exports can be caused by the global financial crisis. In 2010 exports performance recovered and continues to rise.
0 1000000 2000000 3000000 4000000 5000000 6000000 GCP in millions of US$ Total exports in millions of US$
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Annual data on labor costs and total exports
In this data, labor costs are represented by average annual wage. Annual wage and total exports also seen to be moving in the same direction with rising tendency except in 2008 where exports drop. Global financial crisis can be held accountable for the drop. Apart from 2008-2010 period, the data plot show that there might be a positive relationship between exports and labor costs. Figure 1.d
Annual data on GDP growth, manufacturing share and total exports
The manufacturing share is relatively constant with no significant spikes and fluctuations over the years but it has a slightly declining tendency while exports rise continuously. The data plot implied that manufacturing share and exports move in the opposite directions while the correlations matrix (see Appendix Table A.1) show otherwise. However, the correlation
0 5000 10000 15000 20000 25000 19 84 19 86 19 88 19 90 19 92 19 94 19 96 19 98 20 00 20 02 20 04 20 06 20 08 20 10 20 12 20 14
Average annual wage in US$ Total exports in hundreds millions of US$ 0 500 1000 1500 2000 2500 0 5 10 15 20 25 30 35 40 19 84 19 86 19 88 19 90 19 92 19 94 19 96 19 98 20 00 20 02 20 04 20 06 20 08 20 10 20 12 20 14 GDP growth in % Manufacturing share in % Total exports in billions of US$
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coefficient between manufacturing share and exports is low albeit positive. The relatively stable manufacturing share may not affect the rising tendency of exports significantly, hence the positive coefficient. Menawhile, GDP growth experiences some fluctuations. The upward spikes in GDP growth in 1987, 1992, 2007 and 2011 are accompanied by the rise in exports, but when GDP growth experiences downturn, exports still has rising tendency, except in 2008-2010 where both exports and GDP growth decreases. This period is when financial crisis happened so it may be the cause of the downturn. In 2010, exports and GDP growth both recovered but exports continue to rise in the following years while GDP growth continues to slow down. The fluctuations in GDP growth makes it hard to predict the effect based on data plot only. Empirical observation needs to be done.
Figure 1.e
Annual data on exchange rate and total exports
Exchange rate experienced some fluctuations over the years. There is increasing trend in the beginning of the plot followed by the increase in exports but in 1995 exchange rate has no remarkable movements and relatively constant until 2008 while exports continue to rise. There is a sharp drop in exports in 2008 but it could be due to the global economic crisis. From 2005 onward, there is declining trend in exchange rate while exports continue to rise significantly. Based on the observations from 2005 until 2014, there might be a negative relationship between exports and exchange rate. The correlation between variables can be seen on the correlation matrix in Appendix Table A.1
Table 1
China’s trade agreements 0 5 10 15 20 25
Exchange Rate US$/RMB Total exports in hundreds billions of US$
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Year Trade Agreements
1987 China joined the Working Party of GATT
1995 China joined the Working Party of WTO
2001 China joined WTO
2002 China signed Framework Agreement with
ASEAN
2003 China signed Information Technology
Agreements with WTO
2004 China signed Agreements on Trade in Goods
with ASEAN
2007 China signed Agreements on Trade in Services
with ASEAN
2009 China signed Agreements on Investment with
ASEAN
Table 1 shows the list of major trade agreements by China. Because trade agreements variable is translated into a dummy, the number 1 will represent the existence of trade agreements and 0 means that there are no trade agreements happening in that year. Note that trade agreements are effective on the year the agreements are made and will be in place in the following years onward unless stated otherwise, thus number 1 will be used for the year the agreement is signed and for the remaining periods.
Figure 1.f
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The figure above shows exports and trade agreements. Based on the figure the existence of trade agreements in China began in 1987 and because trade agreements are still in place in the years following the signing of the agreements, the dummy variable has a value of 1 until the end of observations. After the first agreement was made, exports in China continue to rise significantly. Thus, it is highly possible that trade agreements support the rising tendency of exports performance. However, even though there is a trade agreement in 2009, exports take a sharp downward turn. The global financial crisis needs to be taken into consideration at that time. The negative effect of the crisis might overpower the positive effect of the trade agreement.
The figures mentioned above provide insight into the trend of exports and the explanatory variables. However, it is not enough to predict the answer to this thesis’ research question. Further empirical observation needs to be done. This paper uses an OLS regression to study the effect of inward FDI on exports from China. There are 31 observations with annual data from 1984 to 2014, all of them using the same time-series regression model. The result of the regression can be seen in Table 2.a.
Table 2.a
Determinants of China’s exports (dependent variable: )
Independent Variable Coefficients t value
Constant 2.06e+10 0.36 FDI(-1) 1.156 1.08 X(-1) -0.525* -1.86 0 5 10 15 20 25 Trade agreements Total exports in hundreds billions of US$
19 GCP 0.149 0.56 GDPGR 4.11e+11 0.61 MS 2.42e+12 0.83 TA 6.07e+09 0.10 LC 2.27e+08 1.38 R -5.24e+09 -0.15 Adjusted 0.3784 N 31 Prob > F 0.0128 *p<0.10, **p<0.05,***p<0.001
The result shows that exports from previous period are the only significant variable in the model with p-value less than 10% significant level. This can be caused by limited data for the regression. Limited data can lead to estimate bias and make the result of the regression unreliable. Because there are concerns about the presence of endogeneity in this model due to possible reverse causality between FDI and exports, I try to do two-stage OLS as well with FDI as the instrumented variable and GDP growth and FDI with 2 lag period as the instruments. The second stage model is as follows:
(1)
with as the endogeneous variable. is regressed with and as the instruments variables. The first stage model is as follows:
(2)
And then we plug the fitted value of on equation (2) into equation (1). FDI with 2 period lags is chosen as one of the instruments to prevent reverse causality between FDI and exports because the FDI that already took place on the previous period cannot be affected by exports from the next period, in this case, the FDI with 2 period lags cannot be affected by exports with one period lag because FDI already happened. Another reason for 2 lags is because it can predict the performance of FDI with one period lag as well. The second instrument is GDP growth. It is chosen as one of the instrument because according to Sun, Tong and Yu (2002),
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GDP growth is one of the determinants of export because it captures the demand and size effect of FDI. The larger the GDP growth is expected to attract more FDI because it means that the country is doing well and the investment given to that country will be utilized effectively.
Table 2.b
Determinants of 2 stage OLS (Dependent Variable : )
Independent variable Coefficients z value
Constant - - FDI(-1) 1.483 1.05 X(-1) -0.509 -1.58 GCP 0.543*** 5.27 MS - - LC - - TA - - R - - 0.438 N 31 Prob > 0.000 *p<0.10, **p<0.05,***p<0.001
Based on the results on Table2.b, the only significant variable is the gross capital formation and FDI is still not significant. Furthermore, many variables are omitted in this regression so it seems that there are dependencies between the variables. To check whether FDI is actually endogeneous or not, I conducted endogeneity test and the result is that FDI is exogenous with the resulting Durbin score and Wu-Haussman score probability amounting to 1. These results mean that there is no need to do 2 stage OLS regression.
To generate more reliable model, I compute correlation coefficients to see which variable needs to be dropped (see Appendix A table A.1). The result is that labor costs are highly correlated with the gross capital formation which indicates multi correlation problem. In addition to that, the p-value of labor costs is 0.181 which is larger than 10% significant level so the variable needs to be dropped. I decided to drop GDP growth, manufacturing share and trade agreements
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as well because the p-value of those variables is really high (0.548, 0.415 and 0.92 respectively) which make those variables highly insignificant compared to the p-value of other variables. The new regression model is as follow:
The new regression, which includes only inward FDI in the previous period, total exports in the previous period, gross capital formation and exchange rate, generate a new result that can be seen in Table 2.c below.
Table 2.c
New determinants of China’s exports (dependent variable: )
Independent Variable Coefficients t value
Constant 3.3e+10 1.41 FDI(-1) 1.724* 1.76 X(-1) -0.599** -2.22 GCP 0.473*** 4.05 R -2.8e+10 -0.90 Adjusted 0.399 N 31 Prob > F 0.0015 *p<0.10, **p<0.05,***p<0.001
The table above presents coefficients and standard errors. The number of observations, R-squared and Prob > F also mentioned above. The focus of our study is which is the coefficient of FDI variable. The regression shows that FDI has both positive and significant effect. The regression has a low p-value of 0.0015 and FDI has a p-value of 0.091 thus FDI is significant with the p-value less than 10% significant level.
Several tests are needed to check the validity of this new regression. To check for autocorrelation, Durbin-Watson test is used and the resulting test statistic is 1.1.757 which means that the regression is not completely free from autocorrelation but the presence of autocorrelation is relatively low. The VIF test is then conducted to check for multi correlation. The mean VIF of this regression is 2.31 with VIF values of all individual variables less than 5 which means that
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there are no multi correlation in this model. The next test is Breusch-Pagan/Cook-Weisberg test for heteroscedasticity. The probability resulting from this test is 0.0921 which is bigger than 5% significant level. It means that there is no heteroscedasticity in this regression.
The model predicts that increased inward FDI will have a positive effect on exports. The coefficient of FDI is 1.724 which can be interpreted as for every 1 US$ change in FDI, it will change the value of exports by 1.724 US$ in the same direction and thus prove that there is a positive relationship between inward FDI and exports. The result implies that the rise in FDI will provide funds for China to obtain more capital and labor and thus will increase the production of goods and services that they can exports. The spillovers effect can be taken into account as well as one of the factors of FDI that promotes exports. Technological spillovers, knowledge and global connection that emerge from the presence of FDI in the country are proven to have a positive impact on exports (Amighini & Sanfilippo, 2014; Leichenko & Erickson, 1997).
The significance of FDI variable in this paper is larger in coefficient compared to the significance of FDI variable in Zhang and Song (2000) literature. In this paper, FDI is significant with a coefficient of 1.724 while FDI variable from the other literature is significant with 0.0334%. The difference in estimation can be caused by the amount of control variables that is used in this literature. Zhang and Song use exports from the previous period, domestic investment, GDP growth, manufacturing share and exchange rate as their control variable which is notably more than control variables that are used in this paper’s new model which only consists of exports from previous periods, gross capital formation and exchange rate. The smaller number of control variable used can cause overestimation in FDI variable because the variable tried to compensate for omitted variable bias.
There are other significant explanatory variables as well in this model, namely exports from the previous period and gross capital formation. Exports from previous period are significant with p-value 0.035 which is lower than 5% significant level. However, the coefficient of this variable is -0.599 which means that for every 1 US$ increase in exports from previous periods, exports in the current period will decrease by 0.599 US$ . This is a bit strange because exports performance from previous year usually associated with current exports in positive ways, as what is found by Zhang and Song (2000). The reason for this unusual result can be explained by the demand and supply theory, where price will adjust supply and demand until equilibrium is reached, the
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negative relationship can emerge from the increasing price of exported goods and services due to the excess demand from the previous period that resulting in the rise of total exports value in previous period. In the next periods, the resulting high price will discourage demand and price will go down as well, thus the decrease in total value of exports.
Meanwhile, Zhang and Song (2000) find that exports from previous period are significant with positive coefficient 0.946 which means that every 1 percent increase in exports from the previous period will increase current exports by 0.946%. The difference in estimation between Zhang and Song’s paper and this paper can be caused by the different method of calculation used in their literature because whereas this literature only use time series, Zhang and Song’s literature use both panel data and time series. Moreover, the big coefficient that Zhang and Song have in their paper make them worried that there is overestimation bias in exports from previous periods variable because the big coefficient in this variable can cause the other variables to be underestimated. Meanwhile, the implication of this occurrence is that the smaller estimate generated in this paper means that there is less risk of overestimation.
Gross capital formation variable is very significant with p-value 0.000 which is even lower than 1% significant level. It means that there is evidence that 1 US$ change in gross capital formation will change exports by 0.473 US$ in a positive manner. Gross capital formation represents the domestic investment in China and it is added to the model to separate the effect of investment from foreign and domestic. The positive relationship between gross capital formation and exports is to be expected and in line with Coughlin and Fabel (1988) findings, that physical and human capital from within the country is positive determinants of exports. The abundance of capital can be a comparative advantage for the country and based on the Heckscher-Ohlin theory, the abundance of a factor of production can promote trade and exports.
However, the estimates from their literature are very large with a coefficient of 9.99 while the coefficient of gross capital formation from this paper is relatively small with a coefficient of 0.473. This can be caused by the different concentration area of both studies because Coughlin and Fabel study the effect of capital in the United States while this paper studies the effect of gross capital formation in China. The magnitude of domestic capital as exports determinant is higher in the United States because of the large state endowments that they have in both physical and human capital.
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Exchange rate is not significant with p-value more than 1% significant level while Zhang and Song (2000) found that exchange rate variable is significant with coefficient -0.0417. The limited amount of available data can be the cause of the insignificance of this variable. The reason why exchange rate is still used in the regression model even though it is insignificant is because it can be used as a control variable and it is expected to have an impact on exports because the movement of exchange rate means that the goods and services will be relatively cheaper or more expensive than in the importing country and thus will determine the demand for exported goods and services.
5. LIMITATIONS
There are some limitations to this study. The most important limitation is the limited data that are available. The data provided by each source’s organizations reports are annual data and the data for FDI only started from 1982 so only small amount of observations can be found. Estimate bias can emerge from this problem. Quarterly data is much more preferable if available because it can generate more observations and the confidence in the reliability of the model will increase.
6. CONCLUSIONS
This paper studies the effect of inward FDI on exports from China using annual data from 1984 to 2014. An OLS regression was conducted with current exports as the dependent variable, FDI from previous period as the independent variable and several other control variables such as exports from previous period, GDP growth, GCP, manufacturing output share of GDP, trade agreements, labor costs and exchange rate is used as well. These variables are chosen because this paper adopts the model from Zhang and Song (2000) and Zhang (2005) where they used those variables and found significant effects from all of them. After checking for robustness by conducting heteroscedasticity test, autocorrelation test and multi correlation matrix, several variables needs to be dropped because of high correlation between variables and insignificance of the variables (GDP growth, labor costs, manufacturing output share of GDP and trade agreements) and a new regression model is made with FDI from previous period, exports from previous period, gross capital formation and exchange rate as the explanatory variables.
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The result from the new regression confirms the hypothesis that there is positive impact of inward FDI on exports from China. For every 1 dollar change of FDI in the previous period, there is 1.724 dollar change of exports in a positive manner in the year after. The results also prove that FDI is a significant determinant of exports in China. The findings imply that FDI can promote exports of the nation as a whole and that the trade promotion policy using FDI as mentioned by Yao (2006) is effective. However, because the new model uses less explanatory variables, there is still a possibility that omitted variable bias is present in this model. The limited data provides limited insight into this topic and in the future studies with more data available in yearly or quarterly manner, a deeper insight of the relationship between inward FDI and total exports can be seen.
7. APPENDIX Table A.1 Correlation matrix FDI(-1) X(-1) GCP GDPGR MS TA LC R X FDI(-1) 1 X(-1) 0.7496 1 GCP 0.4056 0.6591 1 GDPG R -0.1306 -0.1818 -0.1523 1 MS 0.0801 -0.0614 -0.0609 0.0674 1 TA 0.1129 0.1942 0.2355 0.064 0.1465 1 LC 0.4742 0.6805 0.9397 -0.1409 -0.0346 0.2643 1 R -0.1375 -0.3581 -0.4092 0.0724 -0.1634 -0.1993 0.0724 1 X 0.279 0.2643 0.6133 0.0299 0.1562 0.2033 0.6133 -0.3056 1
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