Three Essays on International Trade
by
Malick Diarrassouba Maurice
A dissertation submitted to the Graduate Faculty of
Auburn University
in partial fulfillment of the
requirements for the Degree of
Doctor of Philosophy
Auburn, Alabama
August 06, 2011
Keywords: Gravity Model, Spatial Econometrics, Heckman Sample Selection Model
Copyright 2011 by Malick Diarrassouba Maurice
Approved by
Norbert Wilson, Chair, Associate Professor of Agricultural Economics and Rural Sociology
Curtis Jolly, Professor of Agricultural Economics and Rural Sociology
Henry Thompson, Professor of Agricultural Economics and Rural Sociology
Asheber Abebe, Associate Professor of Mathematics and Statistics
ii
Abstract
This dissertation is composed of three essays. The first essay assesses the impact of
agricultural support policies on refined sugar exports. A theoretically consistent gravity model is
extended to include the spatial dependence across trade flows. The proposed methodology takes
into account spatial effects since it is based on the concept of location. The study provides
evidence of the distorting effects of market price support on exports of refined sugar.
The second essay uses balance panel data for 156 countries from 2006 to 2008 to
investigate how trade facilitation, defined as reducing time delays at borders affects trade. I
estimate a Heckman sample selection model, and find that a 10% reduction in relative delays is
associated with an increase of 8% in the volume of trade. Additionally, the simulations results
show that improved trade facilitation would be beneficial for all countries but especially for
developing countries.
The third essay develops a spatial panel simultaneous equation model to first investigate
the relationship between foreign direct investment and trade, and second to assess the presence
of complex foreign direct investment of multinational firms using 24 OECD countries from
19992009. The empirical results indicate a complementary relationship between foreign direct
investment and trade, as well as the presence of complex foreign direct investment with
agglomeration economies.
iii
Acknowledgments
Upon the completion of this dissertation, I would like to express my sincere appreciation
to my major advisor Dr Wilson for his moral support, guidance, encouragement, and valuable
critique during the whole work. I am grateful to Drs Jolly Curtis and Henry Thompson who
trusted me and encouraged me throughout the entire time I have been in the program. I would
like to thank Dr Asheber Abebe for his support and critical inputs by leading me towards new
scientific research in Applied Economics. I express my thanks to Dr Jackson for his willingness
to serve on my committee. I also thank Claudine Jenda for her contributions throughout the
completion of my dissertation.
Special thanks to my family, my wife Micaela Jo Diarrassouba who took care of my son
Callum Rayan Diarrassouba and daughter Claire Ann Diarrassouba and gave me the strength and
the courage to succeed. I really appreciate your patience while I was away from home. ?Thank
you Jo for taking care of our kids?. I thank my mother Ouattara Simone and uncle Kone Drissa
for their moral support and prayers. To Don and Cyndi Levi, I thank you for making difficult
times easy through your financial and moral support. I also would like to thank my brothers and
sisters in Ivory Coast and the United States for their encouragement.
I extend thanks to Mrs Delois Madox, Mrs Kathleen Dowdell, Mrs Dorothy Hughley, and
Mrs Madeleine Thompson for their moral support and encouragement when I had my surgery.
iv
Appreciations are extended to Carrie and Amanda for helping my wife throughout my study
period.
I also would like to express thanks to Belemlilga, Hodge, Touali, Nyangone,
Bouadoumou, Smith, Coulibaly, and Yessouffou families for all the assistance and support I
received from them. Finally, thanks to friends, Alassane, Urbain, Dr Giap, Cephas, Micheal,
Marcelle, Soro, Michel, Also, Nelson, Charles, Brice Steve, and others I have not mentioned.
v
In Memory of my father Adama Diarrassouba
vi
Table of Contents
Abstract ........................................................................................................................................... ii
Acknowledgments.......................................................................................................................... iii
List of Tables ................................................................................................................................. ix
List of Figures ................................................................................................................................. x
Introduction ..................................................................................................................................... 1
Chapter 1: Agricultural Policies and Refined Sugar Exports: A Theoretically Consistent Gravity
Model with Spatial Econometrics .............................................................................................. 5
1. Introduction ............................................................................................................................. 5
2.1. Overview of Agricultural Support ....................................................................................... 6
4.1. The Theoretical Foundation of the Gravity Model ............................................................ 11
4.2. Specification of the Gravity Model.................................................................................... 13
4.3. Spatial Dependence in the Gravity Model ......................................................................... 16
4.4. Specification of the Spatial Panels ..................................................................................... 17
5.1. Data Description ................................................................................................................ 20
5.3. Spatial Results .................................................................................................................... 22
6. Conclusion ............................................................................................................................ 24
vii
Chapter 2: Customs Procedures and Trade in a Heckman Sample Selection Model ................... 29
1. Introduction ........................................................................................................................... 29
2. Literature Review.................................................................................................................. 31
3. Methodology and data........................................................................................................... 34
4. Data Description ................................................................................................................... 38
6. Potential Benefits of improving customs procedures: Simulation Results ........................... 44
7. Conclusion ............................................................................................................................ 46
Chapter 3: Foreign Direct Investment and Trade in a Simultaneous Spatial Panel Model .......... 53
1. Introduction ........................................................................................................................... 53
2. Literature Review.................................................................................................................. 55
2.1. Relevant literature on FDI without space ...................................................................... 55
2.2. Related Literature on Spatial Patterns of FDI Theory ................................................... 58
3. Model and Empirical Specification ...................................................................................... 59
3.1. Importance of Spatial Econometrics in FDI Studies and Trade .................................... 59
3.2. Empirical Specification .................................................................................................. 63
4. Data and empirical results ..................................................................................................... 66
4.1. Data ................................................................................................................................ 66
4.2. Empirical Results ........................................................................................................... 68
5. Conclusion ............................................................................................................................ 71
viii
Appendix ....................................................................................................................................... 78
References ..................................................................................................................................... 79
ix
List of Tables
Chapter 1
Table 1 Descriptive Statistics of the variables from 19952007 ................................................... 25
Table 2 Regression Results of the different Gravity Model Specification ................................... 27
Table 3 Regression Results of the Spatial Econometrics.............................................................. 28
Chapter 2
Table 1 Regional Averages ........................................................................................................... 47
Table 2 OLS on LogLinear Model and the Heckman?s two step procedure ............................... 48
Table 3 Heckman?s two step procedure with disaggregated data ................................................. 49
Table 4 Conditional Marginal Effects of the full sample ............................................................. 50
Table 5 Conditional Marginal Effects of disaggregated data ....................................................... 51
Table 6 Conditional Marginal Effects of the Potential Benefits of Customs Procedure .............. 52
Chapter 3
Table 1 Summary of hypothesized spatial lag and the surrounding market potential variable .... 72
Table 2 Descriptive statistics of the variables from 1999 2009 .................................................. 74
Table 3 Spatial autoregressive model using data from 19992007 ............................................... 75
Table 4 Generalized spatial Two Stage Least Squares using data from 19992007 ..................... 76
Table 5 Generalized Spatial Two Stage Least Squares using data from 19992009 .................... 77
x
List of Figures
Chapter 1
Figure 1 Analysis of the Market Price Support ............................................................................ 26
Chapter 3
Figure 1 Export Platform FDI where the circle represents countries d, i and j ............................ 73
1
Introduction
Agriculture has faced the most severe protectionism due to government intervention
through domestic support and export subsidies for the sole purpose of transferring income to
farmers. As noted by Johnson (1991) these domestic programs create unwanted production and
increase income disparity within agriculture. In addition to domestic support, many countries
protect their agricultural markets through border protection by imposing tariffs and import
quotas. These instruments depress world prices and increase local prices that benefit farmers,
making the agricultural sector highly regulated and subsidized. This inherently leads to a
distortion of agricultural trade. As an example, Anderson et al. (2001) showed that domestic
support and border protection reduce agricultural growth rate which is much slower than that of
manufactured goods. As a result, agricultural trade policies and trade barriers have become one
of the contentious issues facing the World Trade Organization (WTO).
The challenge faced by policymakers is to identify which domestic support has the
biggest effect on agricultural trade. This challenge has inspired my desire to analyze the impact
of the market price support on refined sugar. An analysis of the topic requires the application of
spatial econometrics in order to assess the connectivity between market conditions of two
regions. As stated by Tobler?s (1979, p 8) first law of geography ?everything is related to
everything else, but near things are more related than distant things.?
In spite of the slow growth of agricultural trade, the volume of world merchandise trade
rose by 14 percent from 2007 to 2010 (WTO, 2010). One possible explanation for the increase
of international trade is a reduction of tariffs and nontariff barriers, as well as the decline in
2
transportation costs. A second possible explanation is the advancement of communication
technologies and improvement in infrastructure as a consequence of decreasing communication
and transport costs for differentiated goods (Tang, 2006). Nevertheless, Wilson, Mann, and
Otsuki (2003) argue that transaction costs associated with the movement of goods across borders
reduce international trade.
In a competitive environment, a cross border investment, known as foreign direct
investment (FDI) can be undertaken by profitable firms or multinational corporations (MNCs) to
establish affiliates in foreign markets and serve other markets through exports. MNCs have two
motives, the market access motive or the comparative advantage motive, which are called
horizontal FDI and vertical FDI. Horizontal FDI consists of MNC that have facilities producing
the final goods in several countries. Such investments are likely to occur between similar
countries. Vertical FDI by MNCs is the geographical fragmentation of the production into stages
on the basis of factor intensities, skilled?labor intensive, and skill?laborabundant (Markusen,
2002).
Previous work in theses area used the gravity model that explains bilateral trade and
between two countries as a function of their incomes (GDPs) and the distance between them.
However, the gravity model does not take into account the role of location to explain the
complexity of economic behavior in space. The purpose of this dissertation is to provide a
comprehensive economic analysis to examine three different areas of international trade. It
demonstrates how to incorporate the notion of relative space or location emphasizing the effect
of distance into the gravity model to understand how economic agents within a region may affect
its neighbors which is known as spatial effects.
3
This dissertation consists of three chapters. The first chapter examines the evidence of
the distorting effects of domestic support, specifically the market price support for raw sugar on
trade of refined sugar. A spatial regression analysis that incorporates spatial dependence is
compared with the theoreticallyconsistent gravity model. Spatial dependence is important in
explaining the interdependence across trade flows, while the theoreticallyconsistent gravity
model controls for omitted variables and endogenous policy variables. The results of this study
indicate the potential benefit in the reduction of the market price support, in particular OECD
countries where market price support is an important tool to protect their producers (Matthews,
2008).
The second chapter analyzes how trade facilitation, defined as reducing transactions costs
associated with the movement of goods across borders affect trade. A Heckman sample selection
model is applied to allow a complete decomposition of the volume of trade into the intensive and
extensive margins to investigate time as a trade impediment. The results indicate that a 10%
reduction in relative delays is associated with an increase of 8% in the volume of trade,
suggesting that more efficient customs regulations would increase trade.
The last chapter investigates the relationship between FDI and exports, specifically
whether FDI and exports are complements or substitutes applying a generalized spatial two stage
least squares (GS2SLS) model developed by Kelejian and Prucha (2004) extended to a
simultaneous spatial panel data. The results provide empirical evidence of complementary
between FDI and trade, as well as the presence of complex FDI with agglomeration economies.
The results from these studies will be useful for researchers and policymakers in
designing and implementing appropriate measures to increase international trade. As an
example, Chapter 1 results can be used to inform policymakers to encourage the debate at the
4
WTO that emphasizes reduction of the domestic support provided to agriculture through the
market price support. The results from Chapter 2 can be used to stimulate governments of the
need to improve their administrative procedures, improve physical infrastructure, and have a
network of communications in order to reduce the time required to export or import a good to
other markets. The results from Chapter 3 opens a door for future research on FDI to better
understand the complex strategies of MNCs in an interdependent world.
5
Chapter 1: Agricultural Policies and Refined Sugar Exports: A Theoretically Consistent
Gravity Model with Spatial Econometrics
1. Introduction
Sugar is an important agricultural crop in the world market with a total production of 160
million tons raw value, consumption of 159 million tons, and exports 51 million tons in 2009
(USDA, 2010). Sugar is produced in more than 100 countries, and is one of the heavily
regulated commodities, particularly in OECD countries with the worst offenders the European
Union (EU), the United States (US), and Japan through domestic support, export subsidies, and
import quotas for the purpose of transferring income to farmers (Elobeid and Beghin 2006). For
example, Japan protects its sugar market through a mix of producer price support and tariffs on
imports. The US tools are the loan program and import restrictions. The EU uses import
restrictions, limited market access, and subsidization of exports to protect its sugar producers.
While such policies achieve their goal of protecting producers, they have large effects on world
sugar markets by (1) depressing the world price, (2) increasing world price variability, and (3)
reducing the volume of international trade.
The present study examines the evidence of the distorting effects of domestic support,
specifically the market price support on trade of refined sugar. A spatial regression analysis that
incorporates spatial dependence is compared with the theoreticallyconsistent gravity model.
Spatial dependence is important in explaining the interdependence across trade flows, and
produces more consistent estimates than the theoreticallyconsistent gravity model even though
6
controlling for omitted variables and endogenous policy variables. The results of this study
indicate the potential benefit in the reduction of the market price support, in particular OECD
countries where market price support is an important tool to protect their producers (Matthews,
2008).
The rest of the paper is organized as follows. Section II provides an overview of
agricultural support, followed by an analysis of the market price support. The review of
literature is examined in Section III. Section IV introduces both the theory and empirical
specification of the gravity model and the spatial econometrics. Section V presents the data set
and empirical results, and Section VI concludes.
2.1. Overview of Agricultural Support
In 1994, the Uruguay Round Agreement on Agriculture (URAA) under the General
Agreement on Tariffs and Trade (GATT) mandated its members to reduce domestic support and
export subsidies, and to facilitate market access to lessen distortion in the world sugar market
and to increase export opportunities for more efficient producers. The URRA classified these
policies in three ?boxes? according to their impact on international trade. Those policies deemed
to have the least distorting trade effect are placed in the ? green? box and are exempt from
reduction; those policies that aggregate programs measured by the aggregate measure of
support (AMS) judged to be trade distorting are placed in the ?amber? box and are subject to
reduction; finally the ? blue? box refers to policies that provide support programs intended to
limit production and are not included in the AMS, making them exempt from reduction.
The AMS is based on the Producer Support Estimate (PSE) primarily used by the
Organization for Economic Cooperation and Development (OECD) countries to monitor and
7
evaluate agricultural policies by country and specific commodity. As mentioned by Legg
(2003), the PSE is defined since 1990 as ?an indicator of the annual monetary value of gross
transfers from consumers and taxpayers to agricultural producers, measured at the farm level,
arising from policy measures that support agriculture, regardless of their nature, objectives or
impacts on farm production or income. The PSE includes market price support, payments based
on output, payments based on input used, and payments based on historical entitlements (OECD
2001).
According to Oskam and Meester (2006), the major agricultural support in OECD
countries is the market price support. It is defined as the annual monetary value of gross
transfers from consumers and taxpayers to agricultural producers arising from policy measures
that create a gap between domestic market and border prices of the specific agricultural
commodity measured at the farm gate level (OECD 2001; Legg 2003). According to OECD
(2010), the market price support is based on the market price differential (MPD) which is the
difference of the domestic market price and the border price for a specific commodity.
The benefit of calculating the value of price support transfers through an MPD is
that it captures in a single measure the combined impact on market prices of a
potentially complete set of price policies. Policies which raise the price received
by producers for a commodity without changing the market price (i.e. without
raising consumer prices) are included elsewhere within the PSE under category
A.2 Payments based on output (OECD p. 59).
The market price support represents the price differential between the domestic price and the
world reference price for the same commodity, a positive sign of the price gap implies the
market price support per unit of product, while a negative sign of the price gap suggests a tax on
agriculture that benefit consumers (Matthews,2008).
8
2.2. Analysis of market price support
Given that market price support to farmers increases both domestic producer and
consumer prices, a relevant question is what would be the effect of the market price on trade for
refined sugar. This point is illustrated in Figure 1a, 1b and 1c which show an analysis of the
market price support for an exporting large country.
Figure 1(a) depicts the raw sugar market with supply (S), demand (D), and the price of
raw sugar (Pr*). Figure 1(b) is the market for refined sugar with supply (Sd), demand (Dd), and
price (Pd). Figure 1(c) represents the international market with excess supply schedule (ES),
world excess demand (Dw), and the world price (Pw). In this analysis, I assume that Pd* and
Pw* are equal in order to evaluate how an increase in the market price support affects all
markets.
Consider the absence of market price support. In the raw sugar market, production is Q
and price Pr*. In the refined sugar market, the country produces S* and consumes D* at price
Pd, and exports the surplus equal to X* in the international market at price Pd* = Pw*.
If there is government intervention in agriculture through market price support, domestic
producers increase production of raw sugar from Q to Q?, while the price rises from Pr* to Pr?.
The high price raw sugar is transmitted to the refining sugar industries by increasing their
production costs. This is seen by the leftward shift of the refined sugar supply schedule from Sd
to Sd? increasing the price from Pd* to Pd?. As a result, the country?s production of refined
sugar decreases from S* to S? associated with the reduction of domestic consumption from D* to
D?. As the refined sugar supply schedule shifts leftward, the excess supply curve also shifts
9
leftward from ES to ES? in the international market. As a result, exports of refined sugar fall
from X* to X? and the world price increases from Pw* to Pw?. There is a distortion in the
country?s volume of trade due to an increase in the market price support in the form of lower
exports and higher domestic price as well as higher world price.
3. Literature Review
The literature reviewed in this section is twofold. First, I review studies that investigate
the impact of agricultural support on welfare (Gemmill, 1977; Boyd, Doroodian and Power,
1996; Tobarik, 2005). Second are studies that investigate the effects of agricultural support on
the sugar market with emphasis on world sugar price (Gemmill, 1977; Koo, 2002; Elobeid and
Beghin, 2006).
Gemmill (1977) employed a spatial equilibrium model to evaluate the effects of the sugar
program in the US, and found a welfare gain of $33 million in the US under free trade. Leu,
Schmitz and Knutson (1987) also found that the US would have a net social benefit of $1,888
million a year under free trade. Boyd, Doroodian and Power (1996) analyzed the removal effects
of the US sugar import quota system in a general equilibrium framework. They showed that
removing the US sugar quota import generates a net economic benefit estimated at $ 254,000
annually, and Borrell and Pearce (1999) argued that if the major sugar producers liberalize trade,
this would generate a global welfare gain of $ 6.3 billion a year.
More recently, Van der Mensbrugghe, Beghin and Mitchell (2003) used the global
computable general equilibrium linkage model to evaluate the effects of the tariff rate quota
(TRQ) in sugar markets in the US, the EU and Japan, as well as multilateral trade liberalization
by other countries. Their results suggest that full multilateral trade liberalization engender global
10
welfare gain of about $3 billion. Tokarik (2005) investigated the removal of agricultural support
in sugar markets in OECD countries using both partial and general equilibrium models. He
found that multilateral liberalization would result in an increase in welfare by over $2 billion in
the EU and by $166 million in the US.
Gemmill (1977) examined the effects of US sugar program on the US domestic sugar and
world sugar prices. He provided evidence that the world sugar price would increase by 32% and
the US domestic sugar price would decrease by 9% if the sugar program had been eliminated. In
evaluating the effects of trade liberalization under the Uruguay Round on the world sugar
market, Devadoss and Kropf (1996) argued that these provisions will contribute to more stable
world sugar price and consumers in countries where there are strong government subsidies and
other forms of intervention will enjoy lower domestic sugar price.
Koo (2002) analyzed how agricultural support in the US and EU affect the world sugar
price. He applied the global sugar policy simulation developed by Benirschka, Koo, and Loo on
17 sugar producing and consuming countries. He found that liberalizing the US and EU sugar
markets would lead to a 68.2% increase in the world sugar price, and a 4.7% decrease in the US
wholesale sugar price
Elobeid and Beghin (2006) examined the effects of domestic support and trade policies
on the sugar market in OECD and nonOECD countries within a partial equilibrium framework.
Their study showed that the removal of domestic policies and trade distortions would increase
the world sugar price by 48%. Additionally, their results indicated that the higher world sugar
price induces a lower domestic sugar price on average by 40% and 62% in the EU and Japan
respectively, and by 9% on average in the US.
11
While these studies have examined the impact of various agricultural and trade policies
on sugar trade, to the best of my knowledge, none of them has considered the role of spatial
relationship across geographically close countries in explaining the incidence of market price
support on trade of refined sugar, despite the presence of processing industries that process raw
sugar into white sugar. In addition, the application of the spatial econometrics method to trade
data is a new area for researchers as opposed to the ordinary least squares (OLS). This study
contributes to the literature by applying the spatial econometrics approach on a balance panel
data to assess the effect of market price support on trade of refined sugar. Taking into account
spatial lag dependence and spatial error autocorrelation are used to evaluate both the spillover
effects of the market price support across economies and generates consistent parameter
estimates.
4.1. The Theoretical Foundation of the Gravity Model
Tinbergen (1962) was the first to apply the gravity model to analyze international trade
flows analogous to Newton?s Law of Universal Gravity. The gravity model predicts that
bilateral trade flows between two countries is directly related to their economic ?masses?
measured as GDP and negatively related to the distance between them which is a proxy for
transportation costs. Linnemann (1966) added more variables such as population, and put forth a
theoretical justification of the gravity model of international trade flows in terms of the ?quasi
Walrasian? equilibrium system that determines total foreign supply and total foreign demand of a
country.
However, it was Anderson (1979) that developed the first theoretical foundation for the
gravity model based on the properties of the pure expenditure systems that assume products are
12
differentiated by place of origin, which implies that each country is completely specialized in the
production of its own good. Since then, several studies including Bergstrand (1990) have
contributed to improve the theoretical foundation of the gravity model with trade theories based
on models of imperfect competition and with HeckscherOhlin. Deardorff (1998) showed that
the gravity model can be derived from the HeckscherOhlin model both with frictionless trade
and with a trade impediment in which the bilateral export volume is influenced not only by the
geographical distance between two countries but also by their relative location to all other
countries.
Building upon the work of Anderson (1979) and Deardoff (1998), Anderson and
Wincoop (2003) refined the theoretical underpinnings of the gravity model to emphasize the
importance of the price indices called ?multilateral resistance? terms, because they depend on
transportation costs. They showed that multilateral resistance terms capture the fact that bilateral
trade between two regions depends on the bilateral barrier between them relative to average trade
barriers that both regions face with all their trading partners. Ignoring multilateral resistance
terms could bias the estimation.
Baier and Bergstrand (2007) argued that the theoretical gravity model developed by
Anderson and Wincoop (2003) suffers from an endogeneity problem that arises from unobserved
time?invariant heterogeneity in trade flows between country pairs since policy related barriers
such as tariffs and domestic policies are likely to generate the formation of regional trade
agreements (RTAs). As such, they proposed the theoretically motivated gravity model using
panel data with bilateral pair and countrybytimefixed effects to control for omitted variables
and endogenous policy variables which I apply in the present study.
13
4.2. Specification of the Gravity Model
The gravity model of bilateral trade explains the volume of trade (Xij) between two
countries as a function of their incomes (GDPs) and the distance between them. Sanso, Cuairan
and Sanz (1993) found empirical evidence that the log linear form of the gravity model is a fair
and ready approximation of the optimal form to analyze bilateral trade flows. Although incomes
and distance are important to predict the magnitude of bilateral trade flows, other variables such
as dummies may be added to the model to indicate membership to an economic area, protection
levels, historical ties and border effects. I also include market price support to represent
domestic support for farmers. The gravity model is specified in the natural logarithms as follows:
uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni006Cuni006Eg1876g3036g3037 g3404uni0020g2009g2868 g3397g2009g2869g1864g1866g1833g1830g1842g3036 g3397g2009g2870g1864g1866g1833g1830g1842g3037 g3397g2009g2871g1864g1866g1856g1861g1871g1872g3036g3037 g3397g2009g2872g1864g1853g1866g1859g3036g3037 g3397g2009g2873g1864g1853g1866g1856g1864g3036g3037
g3397uni0020g2009g2874g1855g1867g1866g1872g3036g3037 g3397g2009g2875g1864g1866g1839g1842g1845g3036 g3397g2009g2876g1864g1866g1839g1842g1845g3037 g3397g2009g2877g1840g1827g1832g1846g1827g3036g3037 g3397g2009g2869g2868g1831g1847g3036g3037 g3397g2013g3036g3037 uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020g4666uni0031g1853g4667
where subscript i denotes the exporter and j is the importer
The variables are defined as follows: Xij is the dollar value of country i exports to country j,
GDPi and GDPj are gross domestic products of countries i and j expressed as dollar value. Distij
is the distance between the economic centers of countries i and j, langij is a language dummy
variable taking the value of 1 if i and j share a common language and 0 otherwise, Contij is a
dummy variable assuming the value of 1 if i and j share a land border and 0 otherwise. Landlij
is the number of landlocked countries in the countrypair (0, 1,or 2) as in Rose 2004. EU and
NAFTA are dummies variables taking the value of 1 if i and j both belong to the same regional
trade agreement and 0 otherwise, MPSi and MPSj are market price support of the respective
countries. The error term ?ij captures any other shocks that may affect bilateral trade and
assumes to be normally distributed.
14
The exporting country?s GDP can be interpreted as its production capacity and importing
country?s GDP can be treated as its purchasing power. In fact, a high level of production in
country i increases the availability of goods for exports, while a high income in country j
suggests high demand for imports. Therefore, I expect the coefficients of GDPs to be positively
related to trade flows.
The distance variable is a proxy for natural resistance to trade which include transport
costs, transport time, and economic horizon (Linnemann 1966). As distance increase between
countries i and j, transaction costs also increase which reduce trade. It is hypothesized to have a
negative effect on trade flows. The common language variable is expected to act as an additional
stimulus to trade because trading partner speaks the same language which in turn facilitates
trade. Thus, it is expected to have a positive coefficient.
The border variable is expected to increase trade flows because it reduces transport cost
between trading partners. Therefore, it should be positively related to trade flows. The
landlocked variable is hypothesized to have a negative effect on trade flows because of high
transportation costs. The dummy variables for EU and NAFTA are used to capture the effects of
regional trade agreements. They are expected to stimulate trade among members? countries. EU
and NAFTA are hypothesized to be positively related to trade flows.
The coefficient estimate on the market price support variable need to be interpreted with
caution. The market price support is applied on raw sugar; however the bilateral trade data are
on exports of refined sugar. The coefficient on the exporter market price support variable is
expected to be negative because subsidies given to sugar growers have been reduced which is
transmitted to refining sugar industries via low cost of production, thereby increasing exports.
The importer market price support variable is hypothesized to be positive, implying that high
15
level of subsidies to sugar growers increase production cost of refining sugar industries that put a
break on their abilities to export, hence increasing imports.
By introducing multilateral resistance terms, and imposing unitary GDPs coefficients to
yield unbiased estimates in equation (1a) as suggested by Anderson and Wincoop (2003), the
theoreticallyconsistent gravity model is specified as follows:
g1864g1866g3436 g3051g3284g3285g3295g3008g3005g3017
g3284g3295g3008g3005g3017g3285g3295
g3440= g2009g2868 g3397g2009g3036g3047 g3397g2009g3037g3047 g3397g2009g2869g1864g1866g1856g1861g1871g1872g3036g3037 g3397g2009g2870g1864g1853g1866g1859g3036g3037 g3397g2009g2871g1864g1853g1866g1856g1864g3036g3037 g3397g2009g2872g1855g1867g1866g1872g3036g3037 g3397
uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020g2009g2873g1864g1866g1839g1842g1845g3036g3047 g3397g2009g2874g1864g1866g1839g1842g1845g3037g3047 g3397g2009g2875g1840g1827g1832g1846g1827g3036g3037 g3397g2009g2876g1831g1847g3036g3037 g3397?g2919g2920g2930 uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020g4666uni0031g1854g4667uni0020uni0020
where the subscript t denotes time; g2009g3036g3047 is the exporterbytime fixed effect and g2009g3037g3047 is the
importer by ?time fixed effect to control for the time varying multilateral resistance terms.
Even though correcting for time varying price terms, Baier and Bergstrand (2007) laid
out the theoreticallymotivated gravity model which takes the following form:
g1864g1866g3436 g3051g3284g3285g3295g3008g3005g3017
g3284g3295g3008g3005g3017g3285g3295
g3440= g2009g2868 g3397g2009g3036g3047 g3397g2009g3037g3047 g3397g2009g3036g3037 g3397g2009g2869g1864g1866g1839g1842g1845g3036g3047 g3397g2009g2870g1864g1866g1839g1842g1845g3037g3047 g3397g2009g2871g1840g1827g1832g1846g1827g3036g3037 g3397
uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020g2009g2872g1831g1847g3036g3037 g3397?g2919g2920g2930 uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020g4666uni0031g1855g4667
where g2009g3036g3047 and g2009g3037g3047 are the same as in equation (1b). g2009g3036g3037uni0020denotes bilateral fixed effects to account
for the variation in distance, language, and common border in the gravity equation. As the result,
the final equations have to be estimated using ordinary least squares (OLS), the twoway and
threeway fixed effects specification of a panel gravity model.
Extending the gravity model to the concept of location or relative space is particularly
useful to account for spatial interaction effects, in other words, spatial dependence across trade
flows (LeSage and Pace, 2008).
16
4.3. Spatial Dependence in the Gravity Model
According to Anselin (1988), data collected from observations located in geographic
space should incorporate spatial effects known as spatial dependence and spatial heterogeneity.
Spatial dependence is caused by the presence of spillover effects in two distinct ways. The first
is the measurement errors for observations in spatial units, that is the error of one observation in
unit i is likely to be related to the error in a neighboring unit j. This is called the spatial
autocorrelation or spatial error model (SEM). The second factor that may cause spatial
dependence is the structural dependencies across observations on the dependent variable in order
to access the processes of social and spatial interaction between spatial units or neighborhood
effects known as the spatial autoregressive model (SAR) or spatial lag model which is analogous
to the lagged dependent variable model in time series regressions (Anselin, 2009).
The other component of spatial effects is spatial heterogeneity which is less prominent in
the spatial econometric literature, and describes the result of spatial processes that involve
structural instability of the functional form or varying parameters, and heteroskedasticity as a
consequence of omitted variables or other forms of misspecification (Anselin, 1988).
The absence of control of spatial dependence across trade flows in the gravity model
violates the Gauss Markov assumptions and provides biased and inconsistent ordinary least
squares (OLS) estimates. Therefore, inferences based on OLS estimates may be misleading.
These findings support the use of an estimation technique to overcome these problems by using
maximum likelihood techniques.
17
4.4. Specification of the Spatial Panels
This study follows Elhorst (2003, 2010) who reviewed the estimation techniques of
panel data models extending to the spatial error autocorrelation and a spatially lagged dependent
variable. Using a similar strategy, I will focus only on the spatial fixed effects model, because
Elhorst and Freret (2009) showed that the spatial fixed effects capture all space ? specific, time ?
invariant variables whose omission could bias the estimates in crosssectional data. The spatial
fixed effect model can be estimated using equation (1a) by incorporating the spatial specific
effect written as:
uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020g1851g3036g3037 g3404 g1850g2010g3397?g3397g2013g3047uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020g4666uni0032g1853g4667
where Yij =( lnX11, . . . . ., lnXNT )? is a ( nx1) vector of exports; X = ( lnGDPi, lnGDPj, lndistij,
,langij, landlij, lnMPSi, lnMPSj, NAFTA, EU)? is a (nxk) vector of independent variables; ? is (k x
1) matching vector of unknown fixed parameters; ?t=( ?1t??.. ?1t )? is (n x 1) vector and is
assumed to be independently and identically distributed (i.i.d) error terms; and ?= ( ?1 ???.
?N )? is (nx1) vector that captures the effect of the omitted variables of each spatial unit. I then
expand the model by including the spatial interaction effects.
As previously mentioned, the spatial dependence can either include autocorrelated error
terms and spatially lagged dependent variable. In the spatial lag form, the spatial dependence is
similar to having a lagged variable as an explanatory variable to capture neighborhood spillover
effects. In other words, a country? exports will be associated to those exports in its nearby
countries. The formulation for the spatial fixed effects including spatially lagged variable or
spatial lag and spatial fixed effects can be expressed as:
uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020g1851g3036g3037 g3404 g2025g1849g1851g3036g3037 g3397g1850g2010g3397?g3397g2013g3047 E (?t) =0, E (?t ?t?) =?2IN uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020g4666uni0032g1854g4667
18
where ? is the spatial autoregressive coefficient and assumed to lie between1 and 1. It measures
the degree of linear dependence between g1851g3036g3037 and the weighted average of neighboring countries?
exports. W is known as the ?connectivity matrix? or first order spatial contiguity matrix. This
matrix indicates the degree of interdependence between any two observations in the space in
which Wij =1 for any two countries sharing a common border, and 0 otherwise. W is a non
negative matrix of dimension N x N and by convention the main diagonal of the matrix consists
of zeros since a region i cannot be its own neighbor. The spatial weight matrix W is further row
normalized, which means that the elements of each rows are transformed so that each of the rows
sums to one, in order to keep its important property of symmetry to facilitate the interpretation of
the coefficient (Anselin,1988). I should stress that W is assumed to be constant over time for
estimation purposes. WYij is the spatially lagged dependent variable, g1851g3036g3037 X , ? , ?, ?t are the
same as in equation (2a).
In running the model, I begin by testing the statistical significance of the spatial
autoregressive coefficient as follows:
H0: ? =0
H0: ? ?0
Rejecting the null and running ordinary least squares (OLS) is equivalent to an omitted
variable error. The consequence is that OLS coefficients estimates are biased and inconsistent
and all statistical inferences are invalid. This is my preferred estimation strategy because it
allows determining if there is a spillover effect of the market price support due to the interaction
of countries through trade.
19
The second form of spatial dependence is the spatial error model which is the result of the
nonspherical error covariance matrix. The formulation for the spatial fixed effects including
spatial error autocorrelation is given by:
uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020g1851g3036g3037 g3404 g1850g2010g3397?g3397?g3047uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020g4666uni0032g1855g4667
and the spatial error autocorrelation is reflected in the following error term:
uni0020uni0020uni0020uni0020uni0020uni0020?g3047 g3404 g2019g1849?g3047 g3397g2013g3047uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni002Cg1831g4666g2013g3047g4667 g3404 uni0030uni002Cg1831g3435g2013g3047g2013g3047?g3439 g3404 g2026g2870g1835g3015uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020g4666uni0032g1856g4667
where g1851g3036g3037 X , W are defined in the same way as before, ? is the spatial autocorrelation
coefficient and assumed to lie between 1 and 1. The coefficient ? measures the effects of
neighboring shocks embodied in the error term (Bernat, 1996). Similarly, I run the model to test
the statistical significance of the spatial autocorrelation coefficient as follows:
H0: ? =0
H0: ? ?0
If the parameter ? is statistically different from zero, ignoring the spatial dependence invalidate
inferences based on OLS because coefficient estimates are no longer efficient but remains
unbiased.
Another approach to test the spatial dependence is to include the spatially lag dependent
variable and the spatially autocorrelated error term simultaneously, but the spatial weights matrix
of both the spatial lag model and the spatial error must be different, or adopt an unconstrained
spatial Durbin model to test whether the model can be simplified to one of the above
specification (Ehorst, 2009). Although Kelejian and Prucha (1998) propose a generalized spatial
two stage least squares procedure that use the same spatial weights matrix to estimate the
parameters of the linear regression model that include a spatially lagged dependent variable and
the correlated error terms, it is applicable on cross section data. Thus panel spatial econometrics
20
remains a topic of further research. As an example, Anselin, Le Gallo and Jayet (2008) pointed
out that apart from the routines in Matlab for panel data with spatial fixed effects developed by
Elhrost (see http:// www.spatialeconometrics.com), the situation is bleak for panel spatial
econometrics in general.
5.1. Data Description
The present study uses a balanced panel set for the gravity model and the spatial
econometrics in the estimation of bilateral trade flows of processed sugar (SITC Rev.3 code
062). I use annual export flows taken from the United Nations Commodity Trade Statistics
Database (UNCOMTRADE) that provides bilateral trade values and quantities of exports and
imports by commodities and by partner countries under the Standard International Trade
Classification system (SITC Revision.3) between 21 OECD countries and Brazil from 1995 to
2007. Brazil is included in the analysis because it is the major sugar exporting country (USDA
2008) and also has data on the market price support. GDP data are from the World Bank
Development Indicators, and information on distance, common language, contiguity, and
landlocked are obtained from the Centre d?Etudes Prospectives et d?Informations Internationales
(CEPII). The market price support (MPS) are drawn from the OECD Trade and Agriculture
Directorate. The MPS is the market price differential between domestic market price and border
price denominated in local currency units. The market price differential can be either positive or
negative. A positive market price differential implies that government intervenes in the sugar
market by stimulating production, hence raising domestic market price, while a negative sign of
the market price differential suggests that less government intervention in the sugar market,
resulting to lower domestic market price, thereby discouraging production as a consequence of
21
taxing producers (OECD, 2010). The MPS is the annual monetary value of gross transfers from
consumers and taxpayers to sugar growers expressed in million US dollar by using US$
exchange rate. For estimation purpose, Silva and Tenreyro (2006) point out that adding an
arbitrary positive constant number to all observations in the presence of zero in the dependent
variable lead to inconsistent estimators, because zero may be the result of rounding errors or
wrongly recorded. Since I do not have any zero in my dependent variable, I replace the negative
value of the MPS by zero and add one to all values. Table 1 presents the summary statistics of
the variables.
5.2. Empirical Results
The spatial lag and spatial fixed effects model is my preferred estimation to be compared
with the theoretically motivated gravity model, although, I also report the results of the
different specifications of the gravity model in Table 2, as well as the spatial error model in
Table 3. The first two columns in Table 2 report results with the standard gravity model with no
fixed or time effects and countryspecific fixed effects (theoretical gravity model). The
theoreticallymotivated gravity model is presented in column 3. In column 1, all coefficients of
the standard gravity model are statistically significant and have the correct signs with
explanatory power of 51%. Even though coefficient estimates of exporter and importer market
price support are statistically significant, the importer market price does not have the correct
sign. However, these estimates are not reliable because of the omission of the multilateral price
terms.
Column 2 shows a strong negative relationship between distance and trade which
confirms a priori expectations in the standard gravity model. The variables language, border and
landlocked have the expected sign and are statistically significant. The trade agreements are also
22
statistically significant and positive. While the model has a high explanatory power at 82%, the
importer market price support is positive and statistically significant. The results in column 3
take into account the endogeneity of a policy variable. The estimated coefficient of exporter
market price support is negative and statistically significant. This result suggests that a reduction
in the level of market price support provided to sugar growers benefits the processed sugar
industries through the induced reduction in market prices of raw sugar. Consequently, a one
percent decrease in the market price support in exporting countries increases refined sugar
exports by 1.7 percent. The estimated coefficient of importer market price support is positive
and statistically significant, indicating a higher market price support to sugar growers that
increase the price of raw sugar for processed sugar industries. Thus, a one percent increase in the
market price support in importing countries increases imports of refined sugar by 3.6 percent.
This result is in line with Jayasinghe and Sarker (2008) who focused on agricultural trade of raw
sugar rather than on refined sugar. However, the trade agreements are not statistically
significant. The next section discusses the results of the spatial econometric models
5.3. Spatial Results
The spatial econometrics approach accounts for the interdependence among observations.
The results for the spatial error model and the spatial lag model are presented in Table 3. The
results in column 12 indicate the presence of spatial dependence and all estimated coefficients
also are statistically significant with the correct sign except the exporter and importer market
price support. The estimated coefficient ? is positive and statistically significant in column 1.
This suggests that trade flow in one region is affected by the neighboring regions if these regions
trade are above or below ?normal? as predicted by the model (Bernat, 1996). In addition, the
23
statistical significance of the autocorrelation coefficient indicates the presence of the non
spherical errors, suggesting a good model specification as opposed to OLS. The same finding
holds in column 3 which included the spatial fixed effects, but the coefficients of exporter and
importer market price support are statistically significant and have the correct sign. The
estimated coefficient of ? is positive and statistically significant in column 2, suggesting that
trade in one region is affected by the performance of its neighbors exports.
The estimated coefficients in column 4 are compared with those of the theoretically
motivated consistent gravity model. For the spatial lag and spatial fixed effects model, the
estimated coefficient of ? is positive and statistically significant. This implies that a one percent
increase in trade in one region causes a 0.45 percent increase of weighted average of the
neighboring regions exports. This finding suggests that countries geographically close to each
other are likely to intensify trade. The coefficient estimated of the exporter market price support
is negative and statistically significant, indicating that a one percent reduction in the market price
support in exporting countries leads to 0.8 percent increases in exports. The coefficient
estimated of importer market price support is positive and statistically significant, suggesting that
a one percent increase in the market price in importing countries increases the volume of imports
of refined sugar by 0.6 percent. These findings suggest that the coefficients estimated of the
theoretically motivated gravity model overstates trade flows because they fail to account for the
spatial effects that capture the effects of EU and NAFTA, embodied in the spatially lagged
dependent variable (Porojan, 2001). Moreover, NATFA and EU are positive and statistically
significant. This indicates that being a member of the EU is associated with an average 122.58
percent ((exp(0.88)1) x 100) increase in refined sugar export relative to nonmembers, whereas
being part of NAFTA is associated with an average 2142 percent ((exp(3.11)1) x 100)
24
increase in refined sugar exports relative to nonmembers. This finding is consistent with Grant
and Lambert (2008) who found that the average effect of RTA increases agricultural trade of
members.
6. Conclusion
The primary purpose of this study is to access the effects of market price support on trade
of refined sugar using the theoretically motivated consistent gravity and spatial econometric
approaches in OECD countries over the period 19952007. The findings of this study suggest
that reduction in the market price support could have had statistically significant and positive
effects on refined sugar exports. Estimating the panel gravity model with bilateral pair and
country by time fixed effects generates a 1.7% increase in refined sugar exports, while the
spatial model lag increase refined sugar exports by 0.8%
It is evident that the presence of spatial dependency introduced in the form of spatially
autoregressive dependent variable changes the magnitude and statistical significance of the
estimated parameters. This finding justifies the use of appropriate spatial models in which the
structure of the spatial dependency is embodied in the weighted matrix to assess the effects of
regional trade agreements as well as policy questions. The empirical results of this study suggest
that any appropriate effort to reduce market price support, particularly in OECD countries that
heavily intervene in the sugar market will increase global trade of refined sugar.
25
Table 1. Descriptive Statistics of the variables from 19952007
Variables Mean Standard Deviation
Trade 1.13e+07 3.09e+07
GDPi 1.16e+12 2.15e+12
GDPj 1.93e+12 2.91e+12
MPSi 1.42 0.22
MPSj 1.40 0.24
Distance 5091.39 5018.97
Language 0.17 0.38
Border 0.16 0.37
Landlocked 0.07 0.26
NAFTA 0.02 0.15
EU 0.31 0.46
26
Figure 1. Analysis of the Market Price Support
Pw Pd
Pw*
Q S*
ES
ES?
Dw
Pw?
X* X? D? D* S?
P Pd?
Pd*
D
S Pr
Pr?
Pr*
Dd
Sd?
Sd
Q?
c b a
International Market Refined Sugar Raw Sugar
Q Q Q
27
Table 2. Regression Results of the different Gravity Model Specification
Variables
(1)
No fixed or time
effects
(2)
Countryspecific
fixed effects
(3)
Bilateral fixed and country
andtime effects
GDPi 0.59***
(24.14)
1.00a 1.00a
GDPj 0.50***
(20.61)
1.00a 1.00a
Distij 0.87***
(22.70)
0.77***
(15.69)
Langij 0.87***
(10.32)
0.74***
(7.81)
Borderij 0.42***
(4.10)
0.38***
(3.58)
Landlij 0.69***
(5.36)
2.37***
(7.72)
NAFTAij 0.66***
(3.34)
1.50***
(6.49)
0.37
(1.18)
EUij 0.41***
(4.82)
0.22*
(1.86)
0.31
(1.57)
MPSi 0.61***
(3.35)
0.61
(1.23)
1.71***
(2.94)
MPSj 1.21***
(6.06)
3.07***
(6.03)
3.65***
(6.66)
R2 0.51 0.82 0.94
Adj R2 0.50 0.78 0.92
N 2574 2574 2574
Notes: The numbers in parentheses are tstatistics; ***, **, * indicate significance at 1%, 5%, and 10% level,
respectively.
aIndicates unitary GDPs
Dependent variable in column 1 is log of (exports)
Dependent variable in column 2and 3 is exports divided by GDPs in log form
28
Table 3. Regression Results of the Spatial Econometrics
variables
(1)
Spatial error
(2)
Spatial
lag
(3)
Spatial error
and spatial fixed
effects
(4)
Spatial lag and
spatial fixed
effects
GDPi 0.45***
(25.66)
0.44***
(25.52)
1.00a 1.00a
GDPj 0.36***
(19.16)
0.36***
(19.18)
1.00a 1.00a
Distij 0.94***
(27.45)
0.93***
(27.23)
Langij 1.01***
(12.61)
1.00***
(12.50)
Landlij 1.01***
(7.94)
1.03***
(8.06)
NAFTAij 1.09***
(5.58)
1.08***
(5.51)
3.13***
(15.81)
3.11***
(15.75)
EUij 0.22**
(2.51)
0.21**
(2.50)
0.85***
(10.99)
0.84***
(10.92)
MPSi 0.72***
(3.92)
0.69****
(3.78)
0.76
(1.59)
0.82*
(1.78)
MPSj 0.83***
(4.17)
0.84***
(4.22)
0.62***
(3.78)
0.61***
(3.72)
? 0.33**
(2.53)
0.52***
(5.57)
?
0.01***
(3.09)
0.45***
(4.81)
R2 0.49 0.49 0.56 0.57
N 2574 2574 2574 2574
Notes: The numbers in parentheses are tstatistics; ***, **, * indicate significance at 1%, 5%, and 10%
level, respectively.
aIndicates unitary GDPs
Dependent variable in columns 1and 2 is log of (exports)
Dependent variable in columns 2 and 4 is exports divided by GDPs in log form
29
Chapter 2: Customs Procedures and Trade in a Heckman Sample Selection Model
1. Introduction
Trade costs as a determinant of international trade play an important role of getting a
good to the final consumer (Anderson and Wincoop, 2004). These trade costs can be linked to
domestic policy such as tariffs and limited market access, the physical distance (transport cost)
between regions, and nontariff barriers (NTB). Despite these trade costs, the volume of world
merchandise trade rose by 14 percent from 2007 to 2010 (WTO, 2010). One possible
explanation for the increase of international trade is a reduction of tariffs and nontariff barriers,
as well as the decline in transportation costs. For example, Baier and Bergstrand (2001) find
evidence that the growth in OECD trade from 1950 to 1980 is explained by tariff reductions of
about 25 percent, and transport cost declines of about 8 percent. A second possible explanation
is the advancement of communication technologies and improvement in infrastructure as a
consequence of decreasing communication and transport costs for differentiated goods (Tang,
2006). Nevertheless, the volume of international trade is reduced by hidden transaction costs
associated with borders constraints (Anderson and Marcouiller, 2002). McCallum (1995) finds
that border effect reduces the volume of trade between CanadaUS despite their similarities.
The border effect is the cost of moving goods across borders and the elasticity of
substitution between domestic and foreign goods that determine the gap between the price paid
by buyers and sellers of traded goods, as well as the impact of the price differential on the
30
amount imported as discussed by Pomfret and Sourdin (2010). Given the fact that crossing a
border has the adverse effect of reducing trade, it is important to identity factors related to border
procedures known as trade facilitation. Because of its importance to foster trade, trade
facilitation is one of the issues raised in the WTO meeting at the Singapore Ministerial Meeting
in 1996 and now in the Doha Development Round of negotiations. In a narrow sense, trade
facilitation includes the quality of infrastructure (maritime, air, roads, rail, and
telecommunication) and customs administration designed to evaluate the direct customs costs as
well as administrative transparency of customs and border crossings (Wilson, Mann, and Otsuki,
2005). With this in mind, trade facilitation involves reducing transaction costs associated with
the movement of goods across borders. For instance, transaction costs associated with crossing
borders affecting international trade in some Central and Eastern Europe is estimated to be about
six percent (Messerlin and Zarrouk, 2000).
The purpose of this chapter is to provide empirical evidence of the trade facilitation
argument, in particular customs procedures taken by the Doha Development agenda negotiations
on customs with the objective of increasing international trade. As the World Customs
Organization (2011) notes, customs procedure play an important role on the economic
competitiveness of nation to engage in international trade not only in providing reduction in
delays and indirect costs associated with the movement of goods across borders, but also secure
to tax revenues. Shorter processing time of goods at borders increase business opportunities by
eliminating inventory holding and depreciation costs on traders (Walkenhorst and Yasui, 2009).
For example, Engman (2009) argues that the lengthy waiting time to export is detrimental for the
competitiveness of businesses in export industries in developing countries. Djankov, Freund and
Pham (2010) estimate that if Uganda reduces its time to export from 58 to 27days, its export
31
would increase by 31%. Thus, customs procedures on the time required for the release of goods
is an important policy in support of trade facilitation to developing countries to be part of the
global marketplace.
The rest of this chapter proceeds as follows. Section 2 reviews the literature on trade
facilitation. Section 3 describes the methodology and data. Section 4 presents the estimation
results. Section 5 concludes.
2. Literature Review
The empirical literature on trade facilitation identifies many factors with adverse effects
of trade volume reduction such as cumbersome customs and port clearance procedures,
burdensome regulatory requirements, as well as poor infrastructure and institutions. For
example, Anderson and Marcouiller (2002) provide evidence that transaction costs associated
with poor institutions, specifically legal systems capable of enforcing commercial contracts,
transparency and impartiality, significantly reduce international trade. Building on the work by
Anderson and Marcouiller (2002), Berkowitz, Moenius and Pistor (2006) apply the gravity
model framework to evaluate the quality of institutions for 55 countries over the period 1982
1992. The authors show that strong legal institutions increase trade of complex products that are
difficult to stipulate in a contract by lowering the transaction costs. Helble, Shepherd and
Wilson (2009) provide evidence that the transparency of the trading environment through greater
predictability and simplification reduces transaction costs by increasing trade.
Subsequent studies such as those by Nord?s and Piermartini (2004), Limao and Venables,
(2001), and Freund and Weinhold (2004) relate trade facilitation to the quality of infrastructure
and information technology. Wilson, Mann, and Otsuki (2003) within the gravity model estimate
32
the impact of four measures of trade facilitation (port efficiency, customs environment,
regulatory environment, and service sector infrastructure) on trade over APEC members from
1989 to 2000. They find that improving all trade facilitation indicators positively impact trade
by reducing trade costs, but improvement in port efficiency has the strongest positive effect on
intraAPEC trade.
Nord?s and Piermartini (2004) use the gravity model to evaluate the quality of
infrastructure on trade, and provide evidence that better quality of infrastructure lowers
transportation costs with a positive impact on bilateral trade flows, in particular time sensitive
sectors such as clothing and automobiles. Limao and Venables (2001) within the gravity model
estimate the quality of infrastructure on trade. They find that poor infrastructure account for 40%
of transportation costs for coastal countries and 60% for landlocked countries. In addition, the
authors conclude that the quality of infrastructure account for much of SubSaharan poor export
performance because of the high transport costs. Using U.S. Department of Transportation data
on maritime transport costs, Clark, Dollar and Micco (2004) estimate the effect of port efficiency
on maritime transport costs, and find that the poor seaport infrastructure increase transport costs.
For instance, they show that if a country like Peru or Turkey decreases its seaport?s inefficiencies
to a level similar to Iceland or Australia, it would be able to increase trade by about 25%.
Freund and Winhold (2004) measure trade facilitation through the level of information
technology, focusing on the number of internet hosts per country. Using a model with imperfect
competition and fixed costs of entry in to a foreign market because they are important for a large
share of trade in goods. They find that a 10 percent increase in the number of web hosts in one
country would have led to about 1 percent increase in trade flows from 1997 to 1999. In
addition, the authors conclude that development in information technology explains trade growth
33
over this period because fixed costs associated with trade decrease. Furthermore, Wilson, Mann
and Otsuki (2005) take a broader approach to evaluate information technology which measures
the extent to which a country has the necessary network information (telecommunications,
financial intermediaries and logistic firms) on trade. Using a gravity model, they find that
improvement in information technology will generate $ 154 billion increase in global trade.
Other studies link trade facilitation to customs and administrative procedures such as the
time a good spends in transit (time to export and time to import) which is the focus of this paper.
Hertel, Walmsley and Itakura (2001) apply the Computable General Equilibrium Model to
evaluate the impact of removal of tariffs, common standard for ecommerce, and automating
customs procedure clearance for 17 regions, particularly trade between Japan and Singapore.
They find that customs automization has the biggest impact on trade between Japan and
Singapore as well as with the rest of world due to the reduction of cost of dispatching
information and documents to ensure security of associated documents. Hummels (2001)
examine time as a trade barrier using data on ocean shipping time for 1999 that include
information on modal choice (air versus ocean). In order to capture the shipping time, he posits
tariff as equivalent to an additional day?s travel time, and find that each day saved in shipping
time is equivalent to 0.8% tariff which means on average that 20 days of shipping by sea is
equivalent to a 16% tariff.
Djankov, Freud and Pham (2010) use a differencein difference gravity equation to
evaluate the effect of time delays on trade by choosing exporters with similar location and factor
endowments, and show empirically that each additional day a product is delayed prior to being
shipped reduces trade by more than 1%. Nord?s, Pinali and Geloso Grosso (2006) explore the
relation between time for exports and imports, logistics services and trade, and find that time
34
delays reduce the volume of international trade and the probability that firms will enter export
markets for time sensitive products. While these studies examine the importance of time as a
trade barrier, these studies have failed to accurately capture the presence of the zero valued trade
flows, hence ordinary least squares(OLS) results suffer from a downward bias. This study
differs from the above studies and contributes to the literature through the application of a
Heckman sample selection model that allows a complete decomposition of the volume of trade
into the intensive and extensive margins to investigate time as a trade impediment.
3. Methodology and data
In spite of the popularity of the gravity model to explain trade patterns, there are serious
concerns as to its correct specification. For instance, Feenstra (2002) shows that the use of fixed
effects for each exporting and importing country to take account of the unobserved price
indexes yield consistent estimates. However, Flowerdew and Aitkin (1982) argue that in the
presence of zero trade value, the conventional logarithm specification of the gravity model
violates the assumption that error terms are normally distributed with equal leading to biased and
inefficient estimates. They suggest the Poisson model to handle zero flows and avoid the bias in
the estimates of the logarithm transformation.
Silva and Tenreyro (2006) point out that to circumvent zero flows, either exclude
observations with zero flows from the data or add a positive small number (1) to all observations
in order to permit the loglinear formulation. However, this procedure leads to inconsistent
estimates due to the loglinearization in the presence of heteroskedasticity. They propose the use
of Poisson Pseudo Maximum Likelihood (PPML) method using Monte Carlo Simulations, and
find that PPML generates robust estimates in the presence of heteroskedasticity. Moreover, the
35
PPLM method provides a natural way to deal with zero flows. Similarly, Burger, van Oort and
Linders (2009) state that the zero inflated Poisson model specification circumvent the zero
valued trade flows, and gives both the probability of countries trading and the probability of
trade volume given specific factors.
In contrast, Linders and De Groot (2006) argue that the Heckman (1979) sample selection
model is appropriate both theoretically and econometrically because it allows zero flows and the
size of potential trade to be explained jointly. As Jayasinghe, Beghin and Moschini (2009) note,
the sample selection model takes into account the changes in exogenous variables on both the
likelihood of trade (extensive margin) and the existing volume of trade (intensive margin). This
approach is applied in the present chapter.
The standard gravity model posits that the volume of trade between two countries is
positively related to their levels of income reflecting the market size in both countries, and
negatively related to the distance between them which represent transport costs. As is usual in
the literature, I extend the gravity model with dummy variables that foster bilateral trade:
common language, regional trade agreement, contiguity, and colonial ties, as well as trade cost
dummy variable represented by landlocked countries. I also include variables of particular
interest, required time for exports and imports in multiplicative form. These are measures of
trade facilitation that I expect to be negatively related to trade. Finally, I use the country?
specific fixed effects of the gravity equation that interact sector level trade data and country fixed
effects to account for the unobserved price index at the sector level as in Disdier, Fontagne and
Mimoun (2008). Thus, the empirical gravity equation estimated is:
36
uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020g1864g1866g4678 g1850g3036g3037g3047g1833g1830g1842
g3036g3047g1833g1830g1842g3037g3047
g4679 g3404 g1858g1857g3036 g3397g1858g1857g3037 g3397g2009g2868 g3397g2009g2869g1864g1866g1856g1861g1871g1872g3036g3037 g3397g2009g2870g1864g1866g3435g1856g1853g1877g1871g3036g3037g3047g3439
uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020g3397uni0020g2009g2871g1864g1853g1866g1859g3036g3037 g3397g2009g2872g1855g1867g1866g1872g3036g3037 g3397g2009g2873g1864g1853g1866g1856g1864g3036g3037 g3397g2009g2874g1855g1867g1864g3036g3037 g3397g2009g2875g1844g1846g1827g3036g3037 g3397g2013g3036g3037g3047uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020g4666uni0031g4667
where j and i stand for the importer and exporter respectively, and t denotes a year. The
dependent variable has two components; Xij is bilateral import from country j to country i; and
GDPi and GDPj are the gross domestic product of exporting and importing countries. The
variable (daysijt) reflects the number of days to export multiplied with the number of days to
import (daysit*daysjt). The distance between pairs trading (distij), and dummies indicating
whether i and j: have the same language ( langij ), share a common land border(contij), have a
colonial relationship(colij), are both members of the regional trade agreement(RTAij) take the
value of 1, and 0 otherwise. Landlocked ( Landlij) equals 1 if one of the trade partner is
landlocked, 2 if both partners are landlocked, and 0 otherwise. The error term g2013g3036g3037g3047 is assumed to
be normally distributed, and g1858g1857g3036uni0020g1853g1866g1856uni0020g1858g1857g3037uni0020uni0020are the set of importer, exporter, and sector trade data
?fixed effects?.
Estimating the above loglinear formulation of the gravity equation in the presence of
zerovalued trade flows using ordinary least squares (OLS) will bias the results because the
logarithm of zero is undefined as a consequence of excluding zero flows from the effective
sample. As Felbermayr and Kohler (2006) remark, zero bilateral trade flows contain valuable
information which may reflect misreporting and mismeasurement, particularly small and poor
countries. To address this issue, I use the Heckman two ?step procedure that posits two
equations, the selection equation and the trade equation. Let g1851g3047uni0020denotes the vector of the LHS of
equation (1), anduni0020g1852g3036g3037g3047 g3404 g1864g1866g3436 g3025g3284g3285g3295g3008g3005g3017
g3284g3295g3008g3005g3017g3285g3295
g3440. The Sample selection model of bilateral trade is
specified as follows:
37
The selection equation:
uni0020uni0020uni0020uni0020uni0020uni0020uni0020g1845g3036g3037g3047 g3404 g1858g1857g3036 g3397g1858g1857g3037 g3397g2009g2868 g3397g2009g2869g1864g1866g1856g1861g1871g1872g3036g3037 g3397g2009g2870g1864g1866g3435g1856g1853g1877g1871g3036g3037g3047g3439
uni0020g3397g2009g2871g1864g1853g1866g1859g3036g3037 g3397g2009g2872g1855g1867g1866g1872g3036g3037 g3397g2009g2873g1864g1853g1866g1856g1864g3036g3037 g3397g2009g2874g1855g1867g1864g3036g3037 g3397g2009g2875g1844g1846g1827g3036g3037 g3397g2009g2876g1864g1866g3435g1855g1867g1871g1872g3036 g1499g1855g1867g1871g1872g3037g3439g3397?g3036g3037g3047uni0020uni0020uni0020g4666uni0032g4667
where
uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020g1845g3036g3037g3047 g3404 uni0031uni0020g1861g1858uni0020g1852g3036g3037g3047 g3408 uni0030uni0020g1853g1866g1856uni0020g1845g3036g3037g3047 g3404 uni0030uni0020g1861g1858uni0020g1852g3036g3037g3047 g3409 uni0030
and the trade equation:
uni0020uni0020uni0020uni0020uni0020uni0020uni0020g1851g3364g3036g3037g3047 g3404 g1858g1857g3036 g3397g1858g1857g3037 g3397g2009g2868 g3397g2009g2869g1864g1866g1856g1861g1871g1872g3036g3037 g3397g2009g2870g1864g1866g4666g1856g1853g1877g1871g4667
uni0020uni0020uni0020g3397g2009g2872g1864g1853g1866g1859g3036g3037 g3397g2009g2873g1855g1867g1866g1872g3036g3037 g3397g2009g2873g1864g1853g1866g1856g1864g3036g3037 g3397g2009g2874g1855g1867g1864g3036g3037 g3397g2009g2875g1844g1846g1827g3036g3037 g3397g2013g3036g3037g3047uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020g4666uni0033g4667
where
uni0020uni0020uni0020uni0020uni0020uni0020uni0020g1851g3036g3037g3047 g3404 g1851g3364g3036g3037g3047uni0020g1861g1858uni0020g1845g3036g3037g3047 g3404 uni0031uni0020g1853g1866g1856uni0020g1851g3047 g3404 g1866g1867g1872uni0020g1867g1854g1871g1857g1870g1874g1857g1856uni0020g1861g1858uni0020g1845g3036g3037g3047 g3404 uni0030
where uni0020?g3036g3037g3047g1853g1866g1856uni0020g2013g3036g3037g3047uni0020uni0020have a bivariate normal distribution with zero means, standard deviation ??
and ??, and ? the correlation between uni0020?g3036g3037g3047uni0061uni006Euni0064uni0020g2013g3036g3037g3047. Costi and costj represent exporter and
importer costs associated with completing the procedure to export or import. Now let us assume
that trade is observed, thus the sample selection model to be estimated is:
uni0020uni0020uni0020uni0020g1851g3036g3037g3047 g3404 g1850g3047g2010g3397g2025g2026? g2038g4666g1849g3047g2011g4667uni0424g4666g1849
g3047g2011g4667
g3397uni0020g2021g3047uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020g4666uni0034g4667
where g1849g3047uni0020g1853g1866g1856uni0020g1850g3047 represent the exogenous variables, and ? and ? are unknown parameter vectors
of equations (2) and (3) respectively; g2038uni0020uni0061uni006Euni0064uni0020uni0424 are the standard normal density function and the
standard normal distribution function of equations (2) and (3); g2038g4666g1g1849g3047g2011g4667g1 uni0424g4666g1g1849g3047g2011g4667g1uni2044 is the
selectivity regressor called the inverse Mills ratio uni0077uni0069uni0074uni0068uni0020uni0063uni006Funi0065uni0066g976uni0069uni0063uni0069uni0065uni006Euni0074uni0020uni0020g2025g2026? (Davidson and
Mackinnon, 1993).
38
The Heckman?s two step procedure works as follows. In the first stage a probit model is
used to produce consistent estimates of the parameters of the selection equation to determine the
probability that trade occurs between two countries. In the second stage, the inverse Mills ratio
is included as an additional explanatory variable in the trade equation estimated by ordinary least
squares to generate consistent estimates (Hoffman and Kassouf, 2005). The trade equation
assesses the amount of trade that occurred between two countries. In order to implement the
Heckman?s two step procedure, Helpman, Melitz and Rubinstein (2008) argue that the model
need to be overidentified , achieved by including at least one variable in the selection model that
affects fixed trade costs but not the volume of trade, and does not appear in the outcome
equation or trade equation. Their work points out that costs faced by both exporting and
importing countries satisfy this condition, therefore, the interaction term costs will be my
excluded variable in my estimation.
4. Data Description
The data used in this chapter are from the World Bank survey called ?Doing Business? in
the section Trading Across Borders that generates a metric of customs and administrative
procedures for 183 countries. The survey put together procedural requirements for exporting and
importing a standardized cargo of goods by contacting local freight forwarders, shipping lines,
customs brokers, and port official on the necessary documents, costs, and time to cross the
border.
The documents include customs and clearance, as well as official documents signed
between exporter and importer. The time to cross the border (number of days) is the number of
calendar days for a product to cross the border. In the 2006 survey, Doing Business introduced
39
costs measures that include all costs associated with completing the procedures to export or
import a product in a 20foot container in U.S. dollars, but do not include tariffs or trade taxes.
The survey also makes assumptions about the traded goods to make it comparable across
countries. The traded product travels in a dry cargo, 20foot full container load, is not hazardous,
and does not require refrigeration or any special environment. It also does not require any
special phytosanitary or environmental safety standards other than accepted international
standards. The goods are coffee, tea, cocoa, spices and manufactures thereof (SITC, 07), textile,
yarn, fabrics, and madeup articles (SITC, 65), and articles of apparel and clothing accessories
(SITC, 84). Trading Across Borders indicators are available annually, however, it should be
noted for instance that data on Doing Business 2008 reflects survey conducted from June 2006 to
May 2007.
The trade data are bilateral import taken from the United Nations Commodity Trade
Statistics Database (UNCOMTRADE). GDP data are from the World Bank Development
Indicators, and information on distance, colony, common language, contiguity, common land
border, and landlocked are obtained from the Centre d?Etudes Prospectives et d?Informations
Internationales (CEPII). GDP data are not available for 27 of 183 countries for which I have
data on documents, costs, and time to cross the border, so these data cover all trade from 156
countries from 2006 to 2008.
Table1 presents summary statistics of customs and administrative procedure for the
regions of the world. In SubSaharan Africa, on average it takes 34.6 days and $ 1805 to export
a standard container of goods, while in OECD countries it takes on average 11.1 days and $ 1015
to export an identical good. For all these indicators, Sub Sahara Africa is worst of all
40
continents, indicating trade passes through excessive and inefficient customs and administrative
procedures.
5. Empirical Results
The estimation results are displayed in Table 2. Model (1), column1 estimates come
from the standard ordinary least squares (OLS) of the loglinear specification for which trade
flows are nonzero. Model (2) reports results with the Heckman?s two step procedure estimated
by ordinary least squares (OLS) to account for zerovalued trade flows to correct for sample
selection bias in OLS estimators.
Columns 23 present the results for the outcome and selection equations, while columns
45 report the conditional marginal effects evaluated at the sample means. The conditional
marginal effects provide the elasticities of trade given that trade takes place (intensive margin),
and the unconditional marginal effects represent the elasticities on both intensive and extensive
margins of trade for all countries trading and not trading in the sample (Jayasinghe, Beghin and
Moschini, 2009). However, I will focus on the conditional marginal effects to estimate the
potential gain of trade facilitation.
Beginning with model (1), parameter estimates of the standard gravity variables are
statistically significant at the 1% level and have the correct signs except the variable landlocked
that does not have the correct sign although it is statistically significant. These findings are in
line with the existing literature. Distance exerts a negative effect on bilateral trade flows,
whereas common language, colonial relationship, RTA, and common land border positively
affect bilateral trade flows. The coefficient estimate on days is negative and statistically
significant which suggests that relative delays decrease the amount of trade flows.
41
Results from the selection equation show that all parameters have the correct sign and are
statistically significant at the 1% level except for the variable landlocked, implying that being a
landlocked country does not affect the probability of trading. On the other hand, distance, days,
and costs decrease the likelihood of trading while common language, common land border, RTA,
and colony increase the probability of trading.
The comparison between columns (1) and (2) indicates some similarities. For instance,
the variable landlocked although statistically significant has the opposite sign. However, the
magnitude of the coefficient estimates are significantly affected in absolute value by the choice
of the estimation procedure in the presence of zero flows. As evidence, the statistically
significance of the inverse Mills ratio ( g2019g4632) from the selection equation illustrates the sample
selection bias in the OLS estimates in the outcome equation. The coefficient estimate of distance
increases in absolute value from 1.11 in OLS to 1.4 in Heckman, and is statistically significant
at the 1% level. The coefficient estimates of dummies for common language, RTA, colony, and
common land border are larger in the Heckman than in OLS, and are statistically significant at
the 1% level. The coefficient estimate of days increases from 1.00 in the OLS to 1.7 in the
Heckman, and is statistically significant at the 1% level. These findings confirm that OLS
underestimates the true value of the parameter estimates when dealing with zerovalued trade
flows.
Columns 45 report the conditional marginal effects of the outcome and selection
equations. All of the coefficient estimates in those equations have the correct sign and are
statistically significant at the 1% level except the variable landlocked that does not have the
expected sign. On the magnitude of trade effects, the statistical significance of days implies that
a 10% reduction in relative delays increases relatively the amount of trade by about 8%. This
42
finding is consistent with Djankov, Freud and Pham (2010) who find that 10% reduction in
relative delays increases relative exports by 4% for exporters that are similar in location and
factor endowments, and face the same trade barriers in foreign markets.
To further investigate the benefit of trade facilitation, I use disaggregate data of the three
products, since the time at the border depends on the product and the export destination. In
addition, some goods are more time sensitive than others because of the higher costs associated
with long time delays at the border, particularly textile and apparel (Nord?s, Pinali and Grosso,
2006). To that end, I run the sample selection model including only the exporter and importer
fixed effects to derive the conditional marginal effects.
Table 3 presents the results of the conditional marginal effects. The parameter estimates
for days is negative and statistically significant at the 1% level, as well as provide information
about the magnitude of the coefficient estimates. Comparing across the three products at the
intensive margin, days has the strongest impact on coffee, tea, cocoa, spices and manufactures
thereof, suggesting the most time sensitive of all three products. A 10% reduction in relative
delays increases agricultural trade (column1) by about 7.3% compared to about 7.1% in trade of
articles of apparel and clothing accessories (column3). This consistent with Engman (2009) who
states that the perishability of agricultural products generate losses associated with delays.
Additionally, being landlocked reduces the likelihood of trading. Textile, yarn, fabrics, and
madeup articles (column2) are timeinsensitive because it has the lowest statistical significance,
and a 10% reduction in relative delays increases relative trade by about 4.5%, suggesting a
greater reduction in relative delays to promote trade. This result is in line with Wilson (2009)
who finds that to achieve a 10% increase in trade of textile yarn, fabrics, and madeup articles,
43
Brazil has to reduce its time at the border by 4.04 compared to 2.86 for trade of coffee tea and
spices.
Distance impacts the trade volume and the probability of trading of textile, yarn and
madeup articles more than the others. Similarly, being a landlocked country also decreases the
likelihood of trading of textile, yarn and madeup articles. Interestingly, having colonial ties
increases the probability of trading of coffee, tea, cocoa, spices and manufactures thereof.
Finally, on the magnitude of trade effects, the coefficient estimate of distance is bigger in
absolute value than days across all three products, implying reduction in relative delays may
compensate for geographical distance. Overall, these results provide evidence of the benefit of
trade facilitation.
As robustness check of my results, I run the sample selection model without the
interaction of export time and import time on the whole sample and the disaggregated data
(SITC, 07). The same result holds for the other two commodities. Table 4 and 5 present the
conditional marginal effects of this alternative approach.
In Table 4, all parameters of the standard gravity model have the anticipated signs and are
statistically significant at the 1% level for the trade equation. The coefficient on export time is
negative and statistically significant at the 1% level, implying that 10% reduction in the time to
export increases exports by about 25%. Surprisingly, the coefficient on import time does not
have the correct sign although statistically significant. In column 2, the coefficient estimate on
import time is negative and statistically significant, implying that required time for import affect
the likelihood of trading. The combined results from the trade and the selection equations
suggest that time to export significantly impact the volume of trade as well as the probability of
trading, while the time to import decrease the likelihood of trading.
44
The results of the disaggregate data in Table 5 show that both export time and import
time significantly decrease the amount of trade. Additionally, most of the signs of the other
variables remain unchanged and are statistically significant. Thus, I can conclude that the model
specification is adequate.
6. Potential Benefits of improving customs procedures: Simulation Results
Results from the sample selection model show that relative delays reduce the volume of
trade. From a policy point of view, it is important to improve customs procedure to increase
trade. To provide evidence, I reestimate the sample selection model on the subsets of country
bilateral trade, particularly intra Sub ?Sahara Africa (SSA) trade, intra South Asia and Latin
America and Caribbean trade (SA&LAC), SSA OECD trade, SSA SA& LAC trade, and
SA&LACOECD trade. Table 6 provides the conditional marginal effects of the sub panels.
In column 1, relative delays for intra ?SSA reduce the value of trade by about 1.18
percent. Distance has a significant negative impact on trade, while the remaining variables have
no impact on trade. In column 4, relative delays reduce the amount of trade by about 0.88
percent, whereas sharing a common border, having a colonial relationship, and belonging to an
RTA boost intra SA& LAC trade. The common language and landlocked variables have the
opposite sign. Comparing column 1 and 4, for example a 10% reduction in relative delays would
increase intraSSA trade by about 11.8% and intraSA& LAC by about 8.8 %, suggesting that
poor customs procedures have a bigger impact on intra ?SSA trade. This finding is in line with
Wilson, Mann and Otsuki (2003) who find that improvement in customs procedures increase
intraAPEC trade.
45
In column 2, distance has a statistically and negative effect on trade, so does the time to
export which reduces the volume of export by about 1.05%. However, the statistical significance
of the coefficient estimate on import time is unexpected, because it takes less time for OECD to
import, suggesting a better customs procedures than SSA. The remaining variables have no
impact on trade. In column 3, time to export has a significant and negative effect on reducing
SSA exports by about 1.69%. Surprisingly, the estimated coefficient on import time is
statistically significant. Distance and landlocked negatively impact the volume of trade, and the
common language and colony variables are positively associated with trade. Comparing column
2 and 3, the biggest loss of SSA export is toward SA& LAC countries because of the large
absolute value of time to export compared to OECD countries.
In column 5, the coefficient estimate of the time to export for SA& LAC has a
statistically and negative impact on reducing trade by about 3.19 percent. A 10% reduction in
required time for export would increase trade by about 31.9% toward OECD countries. The
coefficient estimate of the time to import is statistically insignificant, indicating that OECD
countries had already reduced the required time for import from these countries. The variables
colony and RTA are statistically significant and positively affect trade. The coefficient estimate
of common language is statistically significant but it has the opposite sign. The variable
landlocked is negative and statistically significant, indicating that being landlocked as well as
distance reduce the amount of trade. These results suggest that Sub ?Saharan Africa and South
Asia and Latin America and Caribbean can enhance trade by improving their customs procedures
with the greatest required improvement in this area is in Africa.
46
7. Conclusion
The present study uses the Heckman sample selection model to estimate the benefit of
trade facilitation measures as time to export and import based on the sample of 156 countries
over the period 20062008. The results show that OLS underestimates the parameters in the
presence of zerovalued trade flows which can result in misleading conclusions.
I find that a 10% reduction in relative delays is associated with an increase of 8% in the
volume of trade, suggesting that more efficient customs regulations would speed the process of a
product crossing a border. However, I show that relative delays vary across industries. My
results indicate that a 10% reduction in relative delays yield about 7.3% increase in bilateral
trade on timesensitive products.
Finally, as a policy implication, these results support the importance of trade facilitation
reform as a key element to promote regional and global trade. The simulation analysis suggests
that governments need to improve administrative procedures, improve physical infrastructure,
and have a network of communications to reduce the time required to export or import. In
particular, African countries would especially benefit as it takes on average 34.6 days to export
and 41 days to import a standardized cargo.
47
Table 1. Regional Averages
Region
Document
to export
Time to
export
Cost to
export
Document
to import
Time to
import
Cost to
import
Regional Averages
SubSahara Africa
7.8
34.6
1805
8.82
41
2190
East and South
Asia
7.4
26.2
1028
8.1
27
1113
Latin America and
Caribbean
6.9
20.5
1190
7.3
23.61
1371
OECD
4.4
11.1
1015
5
11.9
1075
Middle East
6.3
18.6
890
7.7
21.9
1094
Eastern Europe and
Central Asia
6.8
31.6
1647
8.2
33.3
1852
World Summary Statistics
Average
6.7
24.9
1344
7.6
27.9
1550
Standard Deviation
2.2
16.3
778.2
2.4
19.0
961.9
48
Table 2. OLS on LogLinear Model and the Heckman?s two step procedure
(2) Heckman?s two step method
Coefficient estimates Marginal effects
Model
OLS
(1)
Trade
equation
(2)
Selection
equation
(3)
Trade
equation
(4)
Selection
equation
(5)
Constant
19.80***
(0.193)
24.44***
(0.244)
9.42***
(0.137)
ln (distance)
1.11***
(0.164)
1.40***
(0.191)
0.44***
(0.006)
0.77***
(0.021)
0.37***
(0.006)
ln (days) 1.01***
(0.023)
1.70***
(0.032)
0.62***
(0.007)
0.82***
(0.033)
0.50***
(0.007)
Common border
1.02***
(0.063)
1.16***
(0.662)
0.44***
(0.030)
0.55***
(0.077)
0.49***
(0.031)
Common language
0.60***
(0.035)
0.78***
(0.363)
0.26***
(0.011)
0.41***
(0.039)
0.25***
(0.010)
Landlocked
0.29***
(0.058)
0.26***
(0.573)
0.02
(0.153)
0.23***
(0.061)
0.03***
(0.010)
Colony
1.09***
(0.069)
1.45***
(0.730)
0.63***
(0.374)
0.59***
(0.087)
0.75***
(0.045)
RTA
1.03***
(0.049)
1.06***
(0.507)
0.46***
(0.022)
0.41***
(0.059)
0.35***
(0.014)
ln(cost exporter*
cost importer)
0.18***
(0.009)
0.03***
(0.001)
Mills ratio ( g2019g4632)
1.7***
(0.052)
R2
0.66
Observations
77272 77272 143156
Notes: Fixed effects not reported. Standard error appear in parentheses and ***,**, *denotes significance at 1% , 5 %, and 10%
level.
49
Table 3. Heckman?s two step procedure with disaggregated data
SITC 07 SITC 65 SITC 84
Variables
Trade
equation
Selection
equation
Trade
equation
Selection
equation
Trade
equation
Selection
equation
ln(distance)
0.871***
(0.0340)
0.015***
(0.0013)
1.202***
(0.0295)
0.09***
(0.0045)
0.920***
(0.0280)
0.061***
(0.0035)
ln(days)
0.729***
(0.1753)
0.002
(0.0022)
0.453***
(0.1439)
0.0005
(0.0087)
0.716***
(0.1389)
0.011*
(0.0066)
Common border
0.327***
(0.1092)
0.027***
(0.0052)
0.381***
(0.1026)
0.027***
(0.0104)
0.361***
(0.1076)
0.045***
(0.0109)
Common language
0.392***
(0.0642)
0.021***
(0.0022)
0.402***
(0.0551)
0.094***
(0.0065)
0.701***
(0.0550)
0.085***
(0.0063)
Landlocked
7.808***
(1.2977)
0.032***
(0.0046)
0.446
(0.7396)
0.118***
(0.0122)
1.394**
(0.6522)
0.640***
(0.0395)
Colony
0.877***
(0.1229)
0.033***
(0.0074)
0.687***
(0.1223)
0.016
(0.0121)
0.802***
(0.1293)
0.013
(0.0105)
RTA
0.211**
(0.0883)
0.014***
(0.0016)
0.211***
(0.0809)
0.081***
(0.0072)
0.183**
(0.0839)
0.104***
(0.0143)
ln(cost exporter*
cost importer)
0.002
(0.0018)
0.008
(0.0071)
0.004
(0.0058)
Notes: Fixed effects not reported. Standard error appear in parentheses and ***,**, *denotes significance at 1% , 5 %, and 10% level. SITC 07: coffee, tea,
cocoa, spices and manufactures thereof; SITC 65: textiles, yarn, fabrics and madeup articles; SITC 84: articles of apparel and clothing accessories.
50
Table 4. Conditional Marginal Effects of the full sample
Variables
Trade equation
Selection equation
ln(distance)
0.77***
(0.02)
0.07***
(0.0012)
ln(export time)
2.56***
(0.04)
0.11***
(0.0018)
ln( import)time
0.65***
(0.03)
0.11***
(0.0018)
Common border
0.57***
(0.07)
0.11***
(0.088)
Common language
0.41***
(0.03)
0.05***
(0.003)
Landlocked
0.42***
(0.06)
0.002
(0.0028)
Colony
0.57***
(0.08)
0.156***
(0.012)
RTA
0.35***
(0.06)
0.11***
(0.007)
ln(cost exporter*cost importer)
0.31***
(0.001)
Notes: Fixed effects not reported. Standard error appear in parentheses and ***,**, *denotes significance at
1% , 5 %, and 10% level.
51
Table 5. Conditional Marginal Effects of disaggregated data
Notes : Fixed effects not reported .Standard error appear in parentheses and ***,**, *denotes significance at 1% , 5 %, and 10% level. SITC 07: coffee, tea,
cocoa, spices and manufactures thereof; SITC 65: textiles, yarn, fabrics and madeup articles; SITC 84: articles of apparel and clothing accessories.
SITC 07 SITC 65 SITC 84
Variables
Trade
equation
Selection
equation
Trade
equation
Selection
equation
Trade
equation
Selection
equation
ln(distance) 0.871***
(0.0339)
0.015***
(0.0006)
1.202***
(0.0295)
0.089***
(0.0029)
0.921***
(0.027)
0.059***
(0.0023)
ln(export time) 0.733***
(0.2756)
0.005
(0.0033)
0.453*
(0.1439)
0.014
(0.0131)
0.654***
(0.2157)
0.004
(0.0106)
ln( import time)
0.737***
(0.2666)
0.00002
(0.0029)
0.471**
(0.2098)
0.135
(0.124)
0.785***
(0.2072)
0.018*
(0.01001)
Common border 0.327***
(0.1092)
0.027***
(0.005)
0.381***
(0.1027)
0.027***
(0.0103)
0.361***
(0.1076)
0.035***
(0.01001)
Common language 0.392***
(0.0642)
0.022***
(0.0017)
0.401***
(0.0551)
0.094***
(0.0058)
0.701***
(0.0549)
0.084***
(0.0053)
Landlocked 12.030***
(1.2851)
0.327***
(0.0223)
5.860***
(0.5285)
0.708***
(0.0138)
5.966***
(1.1928)
0.639***
(0.0164)
Colony 0.877***
(0.1228)
0.033***
(0.0072)
0.687***
(0.1224)
0.016
(0.0121)
0.802***
(0.1293)
0.015
(0.0107)
RTA 0.211**
(0.0883)
0.013***
(0.0013)
0.210***
(0.0809)
0.081***
(0.0072)
0.183**
(0.0839)
0.067***
(0.0054)
ln(cost exporter*
cost importer)
0.003
(0.0017)
0.008
(0.0071)
0.005
(0.006)
52
Table 6. Conditional Marginal Effects of the Potential Benefits of Customs Procedure
Variables IntraSSA
(1)
SSAOECD
(2)
SSA
SA&LAC
(3)
Intra
SA&LAC
(4)
SA&LAC
OECD
(5)
lndistance 0.755***
(0.211)
1.089***
(0.200)
2.198***
(0.493)
0.602***
(0.843)
0.259***
(0.132)
ln(days) 1.176***
(0.343)
0.878***
(0.179)
ln exporttime 1.055***
(0.227)
1.690***
(0.569)
3.198***
(0.137)
lnimporttime 0.482**
(0.188)
2.416***
(0.710)
0.543
(0.144)
Common
border
0.270
(0.359)
0.556**
(0.235)
Common
language
0.003
(0.266)
0.219
(0.165)
1.838***
(0.393)
0.749
(0.197)
0.943***
(0.181)
Landlocked 0.535
(0.378)
0.153
(0.231)
0.406
(1.368)
1.595***
(0.418)
0.231***
colony 1.313
(1.913)
0.087
(0.087)
0.63***
(0.374)
7.030***
(1.720)
1.371***
(0.254)
RTA 0.298
(0.300)
1.636***
(0.229)
3.141***
(0.930)
Notes: Fixed effects not reported. Standard error appear in parentheses and ***,** ,* denotes significance at 1% , 5 %, and 10%
level
53
Chapter 3: Foreign Direct Investment and Trade in a Simultaneous Spatial Panel Model
1. Introduction
Foreign direct investment (FDI) and international trade are often seen to promote
economic growth. Foreign direct investment (FDI) has grown at a faster rate than most
international transactions, in particular bilateral trade flows between countries (Blonigen, 2005).
According to UNCTAD (2009) world FDI inflows reached a historic high of $1.9 trillion in
2007, but with a sharp decline estimated to 15% in 2008 due to the global economic slowdown in
a number of major economies.
FDI is a cross border investment of ?lasting interest? undertaken by multinational
corporations in an existing enterprise when the direct investor owns at least 10% of the voting
power (OECD, 2010). It is a valuable source of capital allowing the introduction of new
technology, and stimulating domestic investment as well as facilitating improvements in the
competitiveness of domestic firms by providing advanced managerial skills (Balasubramanyan et
al. 1996).
Multinational corporations (MNCs) making investment decisions in a foreign country can
be explained either by the market access motive or the comparative advantage motive. The first
motive is known as the proximity concentration tradeoff and refers to horizontal FDI in which a
MNC production facility is designed to serve customers in the foreign market to avoid higher
transport costs and trade barriers (Brainard, 1997). The second motive of MNC arises to exploit
international factor price differentials by engaging in unskilled laborintensive production in an
54
unskilled laborabundant host country, referred as vertical FDI (Baltagi, Egger, and Pfaffermayr,
2007). Thus, the motivation for horizontal and vertical FDI depends on country characteristics
such as factor endowments as well as trade and investments costs. However, Markusen and
Maskus (2002) find that horizontal FDI is more important in the world economy than vertical
FDI because most FDI flows are from high income countries to other similar high income
countries.
Additionally, both types of MNC investment decisions have implications on the host
country. For example, host countries benefit from horizontal FDI through higher productivity by
raising output, employment, and exports (Girma, Greenaway and Wakelin, 2001). Moreover,
host countries take advantage of the spillover effects related to backward and forward linkages
between MNC and domestic firm via labor training (Blomstrom and Kokko, 1998). On the other
hand, vertical FDI may compress the skilled wage differential and change the income
distribution on host countries (Aizenman and Marion, 2004).
Direct investment by MNCs may also be a hybrid of both horizontal and vertical FDI
known as complex FDI which is a function of parent and host countries characteristics such as
the level of transport cost, the factor intensity of production, and the cost of investing abroad, as
well as host neighbors policies and characteristics (Yeaple, 2003). Complex FDI strategies
fragment production between parent and host country to serve the home market or ?third
market?.
The purpose of this chapter is to analyze the relationship between FDI and exports,
specifically whether FDI and exports are complements or substitutes, and identify the presence
of complex FDI. The proximity?concentration trade off hypothesis states that firms invest
abroad when the gains from avoiding trade costs outweigh the advantage from production scale
55
economies (Brainard 1997). Therefore, direct investment as a consequence of distance substitute
trade. On the other hand, complementary of FDI and trade suggest that the spillover effects on
MNC on the productivity of local firms in host countries resulting from vertical FDI.
Given the surge in spatial econometric techniques is fairly recent in FDI literature, few
studies have tested the importance of third country effects (Blonigen et al., 2007, Baltagi, Egger
and Pfaffermayr, 2007; Garretson and Peeters, 2009). However, no study to date has analyzed
the relationship between FDI, trade, and the presence of complex FDI in a simultaneous equation
framework. The present chapter contributes to the existing literature through the development of
the application of the Generalized Spatial Two Stage Least squares (GS2SLS) model developed
by Kelejian and Prucha (2004) extended to a simultaneous spatial panel data. Before doing so, I
investigate the spatial dependence of outward FDI stock to test the presence of complex FDI by
applying the spatial autoregressive technique and compare it to Blonigen et al. (2007).
The remainder of this chapter proceeds as follows. Section 2 presents the review of
relevant literature. Section 3 introduces the model and the empirical specification. Section 4
presents the data and empirical results. Section 5 concludes.
2. Literature Review
2.1. Relevant literature on FDI without space
A large body of literature exists concerning the relationship between FDI and trade, yet
the debate is inconclusive as to whether FDI complements or substitutes with trade. For example
Pfaffermayer (1994) applies a time series technique to study the relationship between outward
FDI and exports of Australian firms, and finds a complementary relationship between FDI and
56
exports. Clausing (2000) empirically investigates the relationship between U.S. multinational
activity and trade in 29 host countries from 1977 to 1994. He provides evidence of a
complementary relationship between multinational activity and trade. He concludes that
government actions to discourage one activity may simultaneously discourage the other.
Brainard (1997) applies the gravity model to test the proximityconcentration tradeoff
hypothesis between multinational sales and exports with bilateral FDI flows and exports with
three digit SIC data. He finds a complementary relationship between multinational activities and
exports because affiliate sales and exports are increasing in market size and in intellectual
property advantages. Co (1997) applies a probit model to analyze the relationship between
Japanese FDI and trade over the period 19741992. Co finds that FDI and trade are
complements. Jenson (2002) explores the relationship between FDI and exports in Poland, and
finds that FDI and trade are complements in the labor intensive sector. Wilson (2006) applies the
gravity model to investigate the relationship between FDI and trade in OECD countries. He finds
a complementary relationship between imports and inward FDI.
Marchant, Saghaian, and Vicker (1999) use a simultaneous equation to examine U.S.
agricultural food exports and FDI for the Chinese processed food market. They find that exports
and FDI are complementary, consistent with the literature for developing countries.
Subsequent literature on FDI and trade find that FDI and trade are substitutes, or there is
a presence of both substitute and complementary relationship between FDI and trade. For
example, Belderbos and Sleuwaegen (1998) analyze Japanese manufacturing FDI and electronics
exports in Europe using data from 19821991. They use a logit model to provide evidence of the
strong substitution effect between exports and FDI for Japanese manufacturers. Gopinath, Pick
and Vasavada (1999) use a four equation system to investigate the relationship between FDI and
57
trade in U.S. food industries. The authors indicate that FDI and exports are substitutes in the
U.S. processed food industries.
Ma, Morikawa, and Shone (2000) investigate Japanese outward FDI into developed and
developing countries with data from 19751990. Their error correction model shows that trade
and FDI are substitutes in developed countries and complements in developing countries for
Japanese FDI. Blonigen (2001) utilizes industry level FDI inflow from Japan to the U.S. from
19801990, and finds substantial evidence of the presence of both substitute and complementary
relationships between trade and FDI.
Egger and Pfaffermayr (2004) utilize bilateral industry level exports and outward stocks
of FDI from the U.S. and Germany to other OECD and non OECD countries between 1989 and
1999 to investigate the relationship between distance, trade and FDI. In a seemingly unrelated
regression HausmanTaylor model, they show that exports and FDI may be substitutes or
complements with respect to distance, depending on relative factor endowments. They conclude
that exports and outward FDI are complementary with respect to distance in the U.S. but are
substitutes in Germany.
Pain and Wakelin (1998) analyze manufacturing exports and inward and outward stocks
of FDI among 11 OECD countries from 1971 to 1992 data. In an augmented export demand
model, they provide evidence of the heterogeneity in the linkages between FDI and exports.
Their findings suggest that outward FDI has a negative impact on home country export
performance, while inward FDI has a positive effect on the host country export.
More recently, the literature on FDI has recognized the importance of third country
effects to explain multinational investment decisions. That is to say a home country invests in a
particular host country with the intention of serving ?third markets? with exports of final goods
58
from the affiliate in the host country (Blonigen et al. 2007). As pointed out by Neary (2009), the
two country models cannot explain the relationship between FDI and trade due to trade
liberalization and falling trade costs. He argues that the spatial as well as the temporal
dimensions of FDI must be taken into account to understand why FDI falls rather than rises with
distance.
Garretsen and Peeters (2009) recommend that geography or spatial interdependencies
have to be included in the analysis of FDI to take into account how agglomeration economies
may arise with FDI patterns captured by the market potential variable that includes not only
market size (GDP) of the host country but also the distance weighted GDPs of other locations.
The market potential variable has been put forth by Harris (1954) and it is important to identify
additional market demands that are not included in two country models. In their study of
Japanese manufacturing FDI flows into the European Union, Head and Mayer (2004) derive a
market potential measure for country pairs, and find that the spatial distribution of Japanese
investment is correlated with the market potential.
2.2. Related Literature on Spatial Patterns of FDI Theory
There are a few empirical studies that incorporate spatial econometrics in FDI studies to
investigate the importance of third country effects. Coughin and Segev (2000) are the first to use
spatial econometric techniques to examine US FDI flows into 29 Chinese provinces from 1990
1997 (Blonigen et al. 2007). They test both the spatial error model and the spatial autoregressive
model. Their spatial autoregressive model indicates that increased FDI in one province has
positive effects on FDI in nearby provinces.
59
Baltagi, Egger, and Pfaffermayr (2007) use bilateral outward FDI stocks and foreign
affiliate sales (FAS) at the industry level over the period 19891999 to investigate third country
effects. They augment their spatial autocorrelation with spatially weighted exogenous variables
to capture the third country effects using a spatial panel data with spatially correlated error
components suggested by Kapoor, Kelejian, and Prucha (2007). They find evidence of the
presence of complex FDI, leading to the importance of third country effects.
Blonigen et al. (2007) utilize industry level U.S. outbound FDI data into 35 host
countries from 19831998 applying the spatial autoregressive model to test the importance of
spatial interactions in empirical FDI studies estimated by maximum likelihood methods. They
argue that ignoring the spatial interdependence in cross country FDI estimations causes omitted
variable bias, and provide evidence of exportplatform FDI for most industries within the
developed European countries. Gerretsen and Peeters (2009) analyze Dutch outbound FDI into
18 OECD host countries for the period 19842004 to investigate the third country effects. In a
spatial autoregressive model, they find support of the presence of complex vertical FDI with
agglomerations economies.
3. Model and Empirical Specification
3.1. Importance of Spatial Econometrics in FDI Studies and Trade
Spatial econometrics has been extensively applied in studies on crosssectional data to
identify the presence of externalities or spillover effects as well as to generate unbiased and
consistent estimates (Anselin, 1988). As noted by Anselin (2009) ignoring spatial econometrics
in empirical studies results either in omitted variables bias, leading to biased and inconsistent
60
estimates, or to inefficient estimates. However, the empirical literature to expand these models
to panel data that incorporate the role of space and time in explaining FDI patterns is not well
documented (Blonigen et al. 2007).
Blonigen et al. (2007) mention that the importance of spatial econometrics, in particular
the estimated coefficient of the spatial autoregressive term in FDI studies, assesses the
cotemporaneous correlation between one region?s FDI and the other geographically proximate
region?s FDIs. In addition, the underlying decisions to invest in a particular country depends on
the size of the proximity markets it will be serving through exports, what is known as the
surrounding market potential (the market potential variable). The market potential variable for
region i is the accessibility of market j to goods shipped from country i (Head and Mayer, 2004).
Moreover, the omission of third country effects of the determinants of FDI may lead to biased
parameter estimates (Baltagi, Egger, and Pfaffermayr, 2007). Finally, the interpretation of the
coefficient of the spatial autoregressive term and the market potential variable identify MNC
investment strategies: vertical FDI, horizontal FDI, export platform FDI, and complex vertical
FDI (Garretson and Peeters, 2009). The results are summarized in Table1.
Horizontal FDI by MNC is motivated by the market access motive known as the
proximityconcentration tradeoff hypothesis which predicts that firms expand production
horizontally across borders to avoid high trade costs and take advantage of production of scale
economies (Brainard 1997). In this situation, there is no spatial relationship between FDI and
market potential because MNC make independent decisions about which markets to enter
through exports or affiliate sales, with zero entries for the spatial lag coefficient and the market
potential variable (Blonigen et al., 2007).
61
A vertical FDI decision by MNC arises from the international difference in relative
factor endowments due to factor prices difference between home and host countries by shifting
activities to the lowest cost locations (Helpman, 1984). Since vertical FDI is driven by factor
cost differences between countries, the spatial lag coefficient is expected to be negative because
FDI from home country d to host country i will be at the expense of FDI going into other regions
(Blonigen et al. 2007). On the other, the market potential variable should not be relevant since
goods produced in host countries are more likely to be shipped to the home country (Baltagi,
Egger, and Pfaffermayr, 2007).
Export platform FDI refers to a situation where a MNC located in home country d set up
a production plant in country i, with exports from i to the third market j, arising from the lower
transport costs between i and j , and a high transport cost between d and i (Baltagi, Egger, and
Pfaffermayr, 2007). As pointed out by Neary (2009) exportplatform is always a gain for MNCs
because the decision to locate a production plant depends not only on the size of the host country
market but also on the size of the intended market to serve. The expectation is a positive sign for
the potential market variable. The coefficient of the spatial autoregressive is expected to be
negative since MNC serving a third country j through exports from i is more efficient from a
single location. This suggests that an increase in FDI in the third country j would result in a
decrease in FDI to country i (Garretsen and Peeters, 2009).
Complex vertical FDI is driven by the difference in relative factor endowments and the
involved production plant in host country i and third country j with exports from j to home
country d (Baltagi, Egger, and Pfaffermayr, 2007). Due to several firms located to each other,
positive externalities or spillover from FDI into countries i and j produce agglomeration
economies, suggesting a positive sign of the coefficient of autoregressive (Blonigen, et al. 2007).
62
The market potential variable would be positive if it captures the agglomeration effects, and
otherwise 0 if it takes into account demand or market size (Garretsen and Peeters, 2009). The
demand of the market potential is the attractiveness of the third country j relative to country i for
MNCs in country d, measured in terms of GDP (Baltagi, Egger, and Pfaffermayr, 2007). Figure
1 illustrate export platform FDI.
On the other hand, the importance of the spatial autoregressive model in the trade
equation is to capture the cross sectional interdependence across trade flows, controlling for
multilateral resistance as in Anderson and Wincoop (2003) to generate unbiased and consistent
parameter estimates (Behrens, Ertur, and Koch, 2007).
As noted above, recent empirical studies on the determinants of FDI support evidence of
interdependencies across countries. However, the point of departure of this study is that I
investigate the relationship between FDI stocks and exports simultaneously controlling for
spatial dependence. This approach yields consistent parameter estimates and is superior to the
ordinary least squares that uses instrumental variables because of the difficulty of finding
appropriate instruments, that is exogenous variables that have a direct effect on FDI but do not
belong in the export equation. Also it provides support of whether FDI and exports are
complements or substitutes and captures the third country effects of FDI and trade flows among
host countries as well. Therefore, the proposed modeling addresses the empirical challenges of
the two stage least squares that relies on instrumental variables which fail to simultaneously
explain the relationship between FDI and trade when there is a spatial dependence between
observations. Additionally, this approach demonstrates the weakness of the gravity model by
using the weighted distance in the sample to understand the spatial distribution of FDI.
63
3.2. Empirical Specification
Following Blonigen et al. (2007), I use the spatial autoregressive model to test the
presence of complex FDI. Next I develop a simultaneous system of equations to analyze the
relationship between U.S. outward FDI stock and exports, as well as to test third country effects.
The third country effects are important if trade costs are reduced between countries i and j and
the distance between i and j is small (Baltagi, Egger, and Pfaffermayr, 2007). Thus, I construct
an inverse distance weight matrix based on the smallest distance between i and j. Also, the third
country effects are captured by the market potential variable that includes not only the GDP of
FDI host country but also the inverse distance weight matrix weighted by the GDPs of other
locations (Garretsen and Peeters, 2009). However, Blonigen et al. (2007) argue that the market
potential measure of Head and Mayer (2004) that includes host GDP may influence the results
by increasing the size of the significance of the estimated coefficients. I will come back to the
construction of the inverse distance weight matrix and the market potential variable.
A modified gravity model specification is applied since it is a commonly used empirical
specification of FDI and trade (Brainard, 1997), suggesting the standard gravity model variables
for the analysis. While the negative effect of distance on trade in the gravity model has been
confirmed, the impact of distance on FDI is ambiguous. As noted by Markusen (2002) the
theory of multinational firms does not offer much about the prediction of distance because it is
an element in both export cost and investment and monitoring costs by raising transactions costs
of investments and export costs. I expect host GDP to be positively related to FDI because of the
convergence in income level between the U.S and its trading partners (Markusen and Venables,
2000). Host country?s population is expected to be negatively related to FDI because an
increased in population reduces GDP per capita discouraging FDI. I include skilled labor
64
endowments which I expect to be positively related to FDI because MNCs build plants in skilled
labor abundant countries that require skilled labor intensive activities (Markusen and Maskus,
2002). With regard to host countries trade costs, if FDI motive is to exploit factor differences
between parent and host countries, then higher trade costs reduce FDI. By way of contrast, if
FDI motive is to avoid higher transport costs, then higher trade costs encourage FDI. Therefore,
the sign of host countries trade costs is ambiguous. Dummy variables indicating whether home
and host country: have the same language (lang), have a colonial relationship (col) take the value
of 1, and 0 otherwise, are expected to stimulate FDI. Following Baltagi, Egger and Pfaffermayr
(2008), I include NAFTA, a dummy variable taking the value of 1 if both home and host
countries are member of the regional trade agreement and 0 otherwise which I expect to be
positively correlated with FDI.
An inverse distance matrix g1849g3052g3435g1856g3036uni002Cg3037g3439 identifies the geographical relationship among host
countries by dividing each observation by the shortest bilateral distance. The matrix g1849g3052 is
symmetric and time invariant. The shortest distance in my sample is 173.033 km, separating
Belgium and Netherlands that receives a weight of unity and all other distances within the
sample a weight that declines as follows:
uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020g1849g3052g3435g1856g3036uni002Cg3037g3439 g3404 uni0031uni0037uni0033uni002Euni0030uni0033uni0033g1856
g3036uni002Cg3037
uni0020uni0020g1482uni0020g1861 g3405 g1862uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020g4666uni0031g4667
where g1849g3052 is a matrix of all g1849g3052g3435g1856g3036uni002Cg3037g3439 defined as:
uni0020g1849g3052 g3404 g3438
uni0030 g1849g3052g3435g1856g3036uni002Cg3037g3439 g1849g3052g3435g1856g3036uni002Cg3038g3439
g1849g3052g3435g1856g3037uni002Cg3036g3439 uni0030 g1849g3052g3435g1856g3037uni002Cg3038g3439
g1849g3052g3435g1856g3038uni002Cg3037g3439 g1849g3052g3435g1856g3038uni002Cg3037g3439 uni0030
g3442uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020g4666uni0032g4667
As is standard in spatial econometrics, the inverse distance matrix is row standardized so
that each row sums to unity. The market potential variable is defined as the row sum of the
65
product inverse distance weight matrix and the vector of all host GDP countries in the sample
(Blonigen et al, 2007). It should be noted that the inverse distance matrix does not need to be
standardized to compute the market potential variable. Combining all elements, I specify the
following spatial autoregressive model where all variables are in natural logs except dummy
variables:
g1832g1830g1835 g3404 g2009g2868 g3397g2025g1849g3052g1832g1830g1835g3397g2009g2869g1833g1830g1842g3397g2009g2870g1830g1861g1871g1872g3397g2009g2871g1842g1841g1842g3397g2009g2872g1860g1867g1871g1872uni0020g1871g1863g1861g1864g1864g1857g1856g3397g2009g2873g1846g1870g1853g1856g1857uni0020g1855g1867g1871g1872g1871
g3397g2009g2874g1865g1853g1870g1863g1857g1872uni0020g1868g1867g1872g1857g1866g1872g1861g1853g1864g3397g2009g2875g1840g1853g1858g1872g1853g3397g2013uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020g4666uni0033g4667
and the spatial simultaneous system of equation as follows:
g1857g1876g1868g1867g1870g1872g1871 g3404 g2009g2868 g3397g2025g3435g1835g1620g1849g3052g3439g1857g1876g1868g1867g1870g1872g1871g3397g2009g2869g1832g1830g1835g3397g2009g2870g1833g1830g1842g3397g2009g2871g1856g1861g1871g1872g3397g2009g2872g1864g1853g1866g1859g3397g2009g2873uni0020g1855g1867g1864
g3397uni0020g2020g2869uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020g4666uni0034g4667
g1832g1830g1835 g3404 g2009g2868 g3397g2025g3435g1835g1620g1849g3052g3439g1832g1830g1835g3397g2009g2869g1831g1876g1868g1867g1870g1872g1871g3397g2009g2870g1833g1830g1842g3397g2009g2871g1842g1867g1842g3397g2009g2872g1865g1853g1870g1863g1857g1872uni0020g1868g1867g1872g1857g1866g1872g1861g1853g1864
g3397g2009g2873g1856g1861g1871g1872g3397g2009g2874g1840g1853g1858g1872g1853g3397g2020g2870uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020g4666uni0035g4667
where
g2020g2869 g3404 g1847g3365g2869g1844g3397g1831g2869uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020g1853g1866g1856uni0020uni0020g2020g2870 g3404 g1847g3365g2870g1844g3397g1831g2870uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020uni0020g4666uni0036g4667
where g2013 is the error term; g1849g3052 is the inverse distance weight matrix of dimension nxn which is
the same in all equations; g2025uni0020uni0020is the spatial autoregressive coefficient to be estimated assumed to
lie between 1 and 1; g2020g2869uni0020g1853g1866g1856uni0020g2020g2870 are the disturbance of the spatial error; R is taken to be a
diagonal matrix; g1847g3365g2869uni0020g1853g1866g1856uni0020g1847g3365g2870 are the spatial lag of the spatial error that follows an autoregressive
process in the disturbances; and g1831g2869uni0020g1853g1866g1856uni0020g1831g2870 are the error terms.uni0020I is the identity matrix of
dimension T, and g1620 is the Kronecker product. Since the g1849g3052uni0020uni0020uni0020is row standardized, thenuni0020uni0020g1849g3052g1832g1830g1835
and g3435g1835g1620g1849g3052g3439g1832g1830g1835 is interpreted as rowsums being a proximityweighted average of FDI into
alternative countries (Blonigen et al., 2007), and uni0020g3435g1835g1620g1849g3052g3439g1857g1876g1868g1867g1870g1872g1871 is the weighted average of
neighboring countries exports (Porojan, 2001).
66
Equation (3) is estimated by the maximum likelihood technique since the dependent
variable appears in the exogenous variable, while estimating equations (4) and (5) require an
instrumental variable since FDI and trade are endogenous. To circumvent this issue, Kelejian and
Prucha (2004) point out that the inverse distance weight matrix defined above as well as the
exogenous variables in each equation represent an instrument matrix for estimation purposes.
Moreover, in a linear simultaneous equation model, Greene (2003) states that the order
condition, which is a necessary but not a sufficient condition, requires that the number of
exogenous variables excluded from one equation must be at least as large as the number of
dependent variables included in that equation. Thus the order condition of my system is fulfilled
because (4) excludes two variables, while (5) excludes three variables.
Finally, the simultaneous system of equation is estimated by a generalized spatial two
stage least squares in three step procedure. In the first step the equations are estimated by two
stage least squares (2SLS) using the inverse distance weight matrix as an instrument. In the
second step, the autoregressive parameteruni0020g2025 is estimated by the generalized method of moments
procedure introduced in Kelejian and Prucha (1999). In the third step, the estimate for ?
accounts for the spatial autocorrelation in a Cochran?Orcutt transformation.
4. Data and empirical results
4.1. Data
The empirical analysis is performed with a panel of annual data on U.S. Direct
Investment Abroad into 25 OECD host countries1 taken from the U.S Bureau of Economic
1 Austria, Belgium, Canada, Denmark, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Japan,
Korea,rep, Luxembourg, Mexico, Netherlands, New Zealand, Norway, Poland, Portugal,
Spain, Sweden, Switzerland, Turkey, United Kingdom
67
Analysis (BEA) covering the period 19992007. The sample period is chosen in order to test the
presence of complex FDI and compared to Blonigen at al. (2007). However, it should be noted
that data on host country investment costs measured as operations risk index, political risk index
and remittance and repatriation factor index developed by Business Environment Risk
Intelligence are not included due to monetary constraints. Next, I investigate the relationship
between FDI and trade as well as to assess the presence of complex FDI using data from 1999
2009 for 24 OECD countries, because of missing data on GDP for New Zealand. The U.S.
Direct Investment Abroad is U.S. outward direct investment stock measured at the historical cost
basis expressed in millions of dollars of operations of parent companies and their foreign
affiliates (BEA, 2011).
Human capital or host country skill is from Barro and Lee dataset on educational
attainment in the World from 1950 to 2010, and measured average years of schooling for those
25 years and older reported every five years. I use a linear interpolation method for the missing
years. Trade costs are measured by the inverse of openness which itself equal to exports plus
imports divided by GDP obtained from the Penn World Tables (Blonigen et al., 2007). Trade
data are total exports from the United Nations Commodity Trade Statistics Database
(UNCOMTRADE) under the Standard International Trade Classification system (SITC
Revision.3). Host countries GDPs and population data come from the World Bank?s World
Development Indicators (WDI). Distance, language, and colony data are drawn from the Centre
d?Etudes Prospectives et d?Informations Internationales (CEPII).
I chose U.S. outward direct investment to OECD countries for two reasons. First, this
allows me to test the third country effects by isolating vertical FDI, since horizontal FDI is more
prevalent among industrialized countries (Aizenman and Noy, 2006). Second, as noted by
68
Blonigen and Wang (2004), pooling rich and poor countries in empirical FDI studies is
inappropriate, leading to misleading results. Table 2 provides summary statistics of the variables.
4.2. Empirical Results
The estimation result of the spatial autoregressive model from equation (3) is presented in
Table 3. In column 1, I add a time trend and a time trend square as in Blonigen et al. (2007) for
comparison, while column 2 is augmented with NAFTA. The inclusion of NAFTA in the model
is motivated by the fact that a regional trade agreement can stimulate FDI since transactions
costs such as taxes and trade protection barriers have been reduced among countries. In
coulumn1, the variables GDP, population, distance, and trade costs have the expected sign and
are statistically significant at the 1% level. The coefficient of the spatial lag is positive and
statistically significant, implying the presence of complex FDI. The coefficient of the market
potential is negative and statistical significant. This finding is in line with Blonigen et al. (2007)
and suggests the importance of spatial distribution of U.S. FDI into OECD countries. This result
corroborates the validity of the specifications of the spatial econometric in empirical FDI studies.
Turning to column 2, I augmented the spatial autoregressive model with NAFTA. The
variables of the standard gravity model have the correct sign and are statistically significant at
the 1% level. The positive and statistically significant of the spatial lag suggests the presence of
complex FDI which is consistent with Blonigen et al. (2007). However, the market potential
comes up statistically insignificant. The variable NAFTA is statistically significant at the 1%
level, suggesting that being part of the regional trade agreement fosters FDI. Trade costs have a
negative and statistical significance on FDI. As noted by Blonigen et al. (2007) the inclusion of
the host GDP in determining the market potential may overstate the coefficient estimates. I reran
69
the regression with the market potential without the host GDP. The coefficients on the market
potential and NAFTA variables are statistically insignificant while the coefficients on other
variables did not change. The results are displayed in Appendix.
Next I investigate the relationship between FDI and trade and test for the presence of
complex FDI using data from 1999 to 2007 and the same variable as in the spatial autoregressive
model estimated by the generalized spatial two stage least squares. This is done to assess the
robustness of the findings in the simultaneous framework. The estimation results are displayed
in Table 4. From this Table, I conclude that the results do not hold since the standardization of
the weight matrix bound the coefficient of the autoregressive term by 1 and 1 (Dubin, 2009).
This leads me to my preferred system of simultaneous equation specification of equations (4) and
(5) using 24 OECD countries from 19992009, and variables I believe to be important in
examining the relationship between FDI and trade, and the spatial interdependence of FDI as
well. Results are presented in Table 5. Column1 displays the result of the FDI equation, while
column 2 shows the results of the export equation. Beginning with column (1), the coefficient on
GDP and population have the expected sign and are statistically significant at the 1% level. The
positive sign of GDP indicates that the larger the economic size of an economy, the greater
potential to attract FDI, while a negative coefficient on population suggests that lower capital
stock per worker available for production, ceteris paribus, discourages FDI.
The market potential is positive and statistically significant at the 1% level, suggesting
that FDI from the U.S. into OECD countries is a function of the surrounded countries (distance
weighted GDP matrix) with relatively large GDP levels. A positive and statistical significant
spatial autoregressive coefficient indicates the presence of the spatial interdependence in the
data, implying positive externalities of FDI. This result means that an increase of FDI in one
70
region will increase FDI of the proximity regions. As I discussed above, the testable hypotheses
based on the coefficient of the spatial autoregression and the market potential confirm the
presence of complex FDI with agglomeration economies. This result is in line with Garretsen
and Peeters (2009) who support this finding for Dutch FDI into OECD countries. However, this
finding is in contrast with Blonigen et al. (2007) who found export platform FDI for OECD
countries. The coefficient estimate of export is positive and statistically significant at the 1%
level. This result suggests a 1% increase in exports causes FDI to increase by 0.59%. There is a
complementary relationship between U.S. FDI and exports for OECD countries. This result is
consistent with findings in Clausing (2000) who finds a complementary relationship between
U.S. FDI and exports to OECD countries.
Column 2 presents the result for the export equation. With regard to the relationship
between FDI and exports, the positive statistical significance of FDI reinforces result from
column 1. The parameter estimate suggests that a 1% increase in FDI causes a 0.25% increase in
exports, a complementary relationship between FDI and exports. The coefficient estimate of the
spatial autoregressive term is positive and statistically significant at the 1% level. This implies
that a 1% increase in export causes a 0.72% increase of the proximity weighted average exports
of host countries. The parameter estimate on GDP positively influences exports while distance
negatively impacts exports. This is consistent with the standard gravity model. Language has
clear strong effect of attracting U.S. exports. The coefficient estimate of colony is statistical
significant, but has the opposite sign. Overall the estimation results in Table 5 provide evidence
of the importance of spatial interdependence in the data because the spatial lag coefficient is
statically significant in both equations.
71
5. Conclusion
The present chapter assesses the implications of spatial dependence of U.S. FDI into
OECD countries to test the importance of third country effects. I examine the relationship
between U.S. FDI and exports to OECD countries. I also identify the presence of complex FDI
by estimating a generalized spatial two stage least squares model developed by Kelejian and
Prucha (2004) extended to a panel data. The use of the spatial econometrics points out the
importance of third country effects to understand the determinants of FDI. Both estimation
procedures confirm the presence of complex FDI by MNCs which are neither purely horizontal
nor purely vertical integration.
However, the results of the simultaneous equation suggest that agglomeration economies
are the result of complex vertical FDI that account for difference in relative factor endowments.
This implies that investment by MNCs is a function of country characteristics as well as
characteristics of its neighbors in attracting FDI. The empirical results for the FDI equation
show a complementary relationship between FDI and trade. The empirical results for exports
also indicate complementary relationship between FDI and trade. This finding indicates that host
countries will benefit from attracting FDI to gain from spillover effects in order to improve
productivity.
72
Table 1. Summary of hypothesized spatial lag and the surrounding market potential
variable
FDI motivation Sign of spatial lag Sign of surrounding market
potential variable
Horizontal FDI 0 0
Vertical FDI  0
Export Platform  +
Complex FDI + 0/+
Source: Baltagi et al. (2007) and Blonigen et al. (2007)
73
Figure 1. Export Platform FDI where the circle represents countries d, i and j
Exports
FDI
d i
j
74
Table 2. Descriptive statistics of the variables from 1999 2009
Variable Mean Std. Deviation Minimum Maximum
FDI (in millions) 60384.11 85157.61 760 449521
Trade (in thousands) 2.23e+07 3.88e+07 274241.2 2.22e+08
GDP(in millions) 685205 1015066 18691 5201164
POP (in millions) 34101.63 34430.4 430 127773
Distance(in km) 6407.889 2128.246 548.39 14546.24
MP(in millions) 1894279 1234849 165960.4 5942495
Language 0.13 0.34 0 1
Colony 0.12 0.32 0 1
NAFTA 0.83 0.27 0 1
Notes: MP is the market potential
75
Table 3. Spatial autoregressive model using data from 19992007
Variables
(1)
(2)
GDP 1.54***
(12.66)
1.54***
(12.74)
Host country skill
0.01
(0.02)
0.08
(0.22)
Trade cost
0.80***
(3.82)
0.71***
(3.15)
Pop 0.81***
(7.51)
0.86***
(7.48)
Distance 0.82***
(8.09)
0.70***
(5.22)
Market Potential 0.17*
(1.75)
0.14
(1.33)
NAFTA _ 0.62**
(2.02)
Time
0.03
(0.29)
0.02
(0.25)
Time2
0.001
(0.16)
0.00008
(0.07)
? 0.43***
(4.24)
0.37***
(3.38)
Notes: T statistics are in parentheses. ***, **,* Significant at the 1%, 5% and 10% level
1. R2/loglikelihood 0.64/ 311.75
2. R2/loglikelihood 0.63/ 312.59
76
Table 4. Generalized spatial Two Stage Least Squares using data from 19992007
Variables
FDI
Export
Constant
8.51***
(3.36)
5.22***
(2.64)
GDP 0.75***
(4.57)
0.91***
(22.69)
Pop
0.64***
(6.19)

Trade costs 0.24
(1.18)

Host country skill
0.07
(0.21)

Distance 0.38**
(2.09)
0.53***
(6.97)
Market potential 0.02
(0.24)

Language
 0.62***
(5.23)
Colony
 0.76***
(5.68)
NAFTA 0.51
(1.01)

Export
0.54***
(5.77)

FDI
 0.21***
(6.02)
? 1.04***
(8.19)
0.77***
(6.02)
Notes: T statistics are in parentheses; ***,**, * significant at the 1%, 5% and 10% level.
77
Table 5. Generalized Spatial Two Stage Least Squares using data from 19992009
Variables FDI
(1)
Exports
(2)
Constant 9.24***
(3.78)
3.91**
(2.37)
GDP 0.67***
(4.72)
0.89***
(25.22)
Population 0.66***
(7.11)

Distance 0.38**
(2.09)
0.64***
(8.22)
Language  0.32***
(2.52)
Market potential 0.20**
(2.26)

Colony  0.74***
(6.09)
NAFTA 0.17
(0.35)

Exports 0.59***
(6.41)

FDI  0.25***
(8.10)
g2025 0.81***
(6.88)
0.72***
(7.00)
Notes : Tstatistics are in parentheses. ***, **,* Significant at the 1%, 5% and 10% level.
78
Appendix
Spatial autoregressive model with market potential without GDP
Variables
(1)
(2)
GDP 1.50***
(12.96)
1.49***
(12.94)
Host country skill
0.03
(0.07)
0.06
(0.16)
Trade cost
0.67***
(3.88)
0.54***
(2.72)
Pop 0.81***
(7.35)
0.85***
(7.36)
Distance 0.87***
(9.24)
0.74***
(4.92)
Market potential 0.11
(1.26)
0.03
(0.36)
NAFTA  0.50
(1.19)
Time
0.02
(0.21)
0.01
(0.15)
Time2
0.00009
(0.008)
0.0007
(0.06)
? 0.35***
(3.28)
0.33***
(3.01)
Notes: Asymptotic T statistics are in parentheses. ***, **,* Significant at the 1%, 5% and 10% level
1. R2/loglikelihood 0.64/ 312.64
2. R2/loglikelihood 0.63/ 313.44
79
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