Department of Economics
Lund University
Preferential trade agreements and bilateral
trade flow in Latin America
A study performed using the gravity model of trade
Bachelor Essay, Autumn 2013
Author: Axel Olsson
Mentor: Zouheir El-Sahli
Summary
After studying economic integration it doesn’t come as a shock that preferential
trade agreements (PTA) have an effect on the countries within the PTA, or outside
the PTA for that matter also. The majority of the Latin American countries have been
considered as developing countries for the better part of the 21st century, and have, for
the most part, been secluded from the world market.
Since the introduction of in particular ALADI (Asociación Latinoamericana de
Integración, 1980), but also Mercosur (Mercado Común del Sur, 1991), the Latin
American trade market has completely changed. The point of ALADI was to create a
stable socio-economic environment for the involved countries, as well as ensuring
development in a manner that eventually succumbed to a common Latin American
market. Mercosur was created a decade later, where the major countries within
ALADI had the goal to integrate further, by creating their own common market,
where they benefited more towards each other without the remaining ALADI
countries. To measure the exact effects that these PTAs have had on the different
countries, an econometric estimation must be performed. The regression that will be
executed here is the well-known gravity model of trade. A total of 22 countries are
included in the equation, which means that there are 462 country-pairs that are
examined over the time period of 1975-1995.
The study shows that there is a specific correlation between the entry of a
preferential trade agreement and the bilateral trade flow, especially concerning
Mercosur. Looking at the first estimation, it is evident that the membership of
Mercosur has led to an increase in bilateral trade flow with a rather substantial
margin. Since there is a problem of multicollinearity in the estimations, this has led to
it being hard to analyse ALADI in the specific estimations.
However, when looking at the statistics, the evidence shows that countries that
have entered a PTA (both ALADI and Mercosur) have increased their bilateral trade
flow. This has to do with several factors that will be presented throughout the essay.
Key Words: Latin America, Gravity Equation, Bilateral Trade Flow, Preferential
Trade Agreement, ALADI and Mercosur
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Table of Contents
1. Introduction……………………………………………………………………….5
2. Background………………………………………………………………………..6
2.1. ALADI…………………………………………………………………….6
2.2. Mercosur………………………………………………………………..6-7
2.3. Purpose of PTAs………………………………………………………..7-8
3. Previous Studies………………………………………………………………..8-10
4.The Gravity Equation…………………………………………………………10-11
5. Method……………………………………………………………………………11
6. Econometric equation……………………………………………………………11
6.1. Variables……………………………………………………………..11-12
6.2. External Countries………………………………………………………12
6.3. Logarithms…………………………………………………………...12-13
6.4. Time Interval…………………………………………………………….13
7. Fixed effect……………………………………………………………………….13
8. Data……………………………………………………………………………….14
8.1. Sources………………………………………………………………….14
8.2. Panel Data…………………………………………………………..14-15
9. Regression terminology…………………………………………………………15
9.1. Autocorrelation…………………………………………………………15
9.2. Heteroscedasticity……………………………………………………15-16
9.3. Null hypothesis…………………………………………………………..16
9.4. T-value……………………………………………………………….16-17
9.5. Multicollinearity…………………………………………………………17
9.6. Endogeneity…………………………………………………………..17-18
9.7. Zero Trade Flows………………………………………………………18
10. Equation Estimates…………………………………………………………….19
10.1. Fixed country specific and fixed period effects……………………..19-20
10.2. Fixed period effects……………………………………………………21
10.3. White test for heteroscedasticity……………………………………….21
11. Figures………………………………………………………………………..22-25
12. Analysis………………………………………………………………………26-27
13. Conclusion…………………………………………………………………...28-29
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14. Equation Estimate Figures……………………………………………………30
14.1. Fixed country specific and fixed period effects………………………30
14.2. Fixed period effects…………………………………………………..31
14.3. White test for heteroscedasticity……………………………………..32
15. References……………………………………………………………………..33
15.1. Websites…………………………………………………………….33-34
15.2. Authors/Studies……………………………………………………..34-35
16. Appendix……………………………………………………………………….36
16.1. Included Countries…………………………………………………....36
16.2. Included Variables……………………………………………………37
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1. Introduction
Preferential trade agreements have long been an important and influential part of
regional integration. Ever since the formation of the earliest preferential trade
agreements it has been obvious that PTAs have had a large impact on both the
members and the non-member parts of the PTA. Over the past decades PTAs (mainly
regional) have become immensely popular. Seeing to the beneficial possibilities that
PTAs can provide this is in no way a surprise. PTAs can vary a lot regarding the
geographical size, with them being as large as a continent to being an agreement
between two small countries. PTAs are not something solely associated with
prominent developed countries, as a matter of fact rather the opposite. Developing
countries often negotiate an agreement with a more developed country, e.g. Costa
Rica is involved in a PTA with Canada (URL 10). This then helps the less developed
country to gain comparative advantages towards similar under-developed countries.
Frequently asked questions revolving preferential trade agreements are such as:
what kinds of measurements are used to actually measure exact effects? Are PTAs
always beneficial? What effects do they have on the member countries towards their
bilateral trade flow? These questions are just a few that are to be answered in this
essay.
The goal of the essay will be, with the help of the gravity model of trade, to
determine the impact that a formation of a preferential trade agreement has on (both
member and non-member) countries’ bilateral trade flow. The focus field will be
Latin America, in particular the formation of the initial free trade agreement of
ALADI (Asociación Latinoamericana de Integración) in 1980 and also the more
recent and niched formation of MERCOSUR (1991) (see section 16.1 for full country
list).
There have been several similar studies regarding these questions, where the most
notable study regarding the Latin American countries is the one by Carillo & Li
(2002, Trade Blocks and the Gravity Model: Evidence from Latin American
Countries). Earlier theories and equations on this particular area will be brought up
later on in the essay.
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2. Background
2.1 ALADI
ALADI was formed in 1980 with the goal to integrate the Latin American
countries so that an eventual common Latin American market could be established.
The main reason for the formation of ALADI is to promote and in some ways also
regulate the reciprocal trade and eventually be able to reach a common market (URL
1). Large parts of the ALADI region are suffering from major poverty, with the
poverty scale varying quite a bit even domestically within the countries. Looking at
one of the main countries, Brazil, with just over 200 million inhabitants, where not
only the class-difference, but also the living standards vary immensely. The so-called
Favelas in Brazil are a prime example of this. Even though the domestic law
enforcements are trying to clear out the Favelas, the largest Favela, located in the
centre of Rio de Janiero, still has around 1 million inhabitants. The standards in the
Favelas are almost unimaginable. To have a finished roof over ones head is
considered to be a form of luxury in the Favelas (URL 2). Naturally the other ALADI
countries also have large variations between the rich and the poor, but Brazil’s
situation is probably the most prominent.
ALADI consists of 13 countries and operates from the Uruguayan capital
Montevideo. For the ALADI countries a main part of when the integration was
formed was to be able to include the country of Panama. The Panama Canal is central
to basically half a world’s shipping industry, with ships and fraters travelling through
the canal on a daily basis all year round. By including Panama, the remaining
countries were able to benefit from this advantage and gain a stronger bargaining
position when negotiating with countries outside of ALADI (GATT, 1982). Five of
the members of ALADI have since then formed an even more niched free trade
agreement known as the Mercosur.
2.2 Mercosur
Mercosur was formed in 1991 and consists of Argentina, Brazil, Paraguay,
Uruguay and Venezuela. Bolivia has since 2012 been considered as an acceding
member (URL 8), but since the time period for this essay is between 1975-1995, that
will not be taken into consideration. Quite like ALADI, Mercosur also has the
purpose of enhancing and promoting free trade, but amongst others, the main
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differences are that Mercosur is a full customs union and more integrated than
ALADI. A customs union is characterized by the countries having a common external
tariff, meaning that all the goods entering the countries must have the same import
quotas and preferences (URL 12).
Another main objective with Mercosur is to become an international “big shot” in
the trading market. With around 300 million inhabitants in the Customs Union, the
member-countries of Mercosur have gained a stronger negotiation position on the
world trade market (URL 9). By having a lot of natural resources, partly thanks to the
enormous Amazon rain forest, regional non-members obtain a form of dependency of
the Mercosur members, which leads to Mercosur gaining large advantages in the
world-trade market compared to the time before the formation of Mercosur.
Furthermore, this means that because it was the larger countries from ALADI that
formed Mercosur, they had more bargaining power towards the outside world than the
smaller ALADI-members countries, e.g. Peru, which in its turn made it even more
difficult for the non-Mercosur members.
Why is it these exact countries are part of the Mercosur? When analysing the
question, the answer will prove to be rather straightforward. Compared to the nonMercosur members, the members already had a large negotiating-power within Latin
America, and were in some ways the unspoken leaders within the region. Worth
noting in the analysis is also that the amount of inhabitants in the member countries
are largely superior to the non-members, which in its turn also leads to a stronger
market negotiation position.
2.3 Purpose of PTAs
The Mercosur members had, as when ALADI was formed, several reasons to
integrate further. One of the main, if not the main, reason was the possibility to
introduce free trade regarding produced goods, factors and services for the countries
involved. When enhancing their integration, the Mercosur countries decided to fixate
an external tariff, as well as adopt a trade policy, which was unanimous for the
member countries. This policy was both directed towards the countries that weren’t a
part of Mercosur, so that the actual members of Mercosur could receive greater
benefits, but also to ensure increased trade within Mercosur. A major role of
Mercosur was to ensure that the member countries were able to achieve free
competition within the PTA. By coordinating important policies, regarding things like
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foreign trade, the monetary system, customs, transports etc. this could be achieved.
Another main objective was the ability to be able to freely move manpower between
the different states (URL 11). The Asunción treaty regarding Mercosur wisely decided
to take a few small steps before targeting the free movement of manpower. By firstly
targeting free-trade zones and a unification for customs, the member states were able
to negotiate forward a common market, which in it’s turn led to the ability to freely
move manpower between the member states. Because of the Most Favoured Nation
(MFN) principle, any advantage that a Mercosur member may be able to achieve they
must extend this advantage towards the other members of the PTA (Persson, 2013).
3. Previous studies
One of the most notable previous studies is the work of Bergstrand & Baier (2006),
where they discuss the effect on the bilateral trade flow when entering the EEC
(European Economic Community) and the CACM (Central American Common
Market) during a time period of 40 years. Bergstrand and Baier, just like is intended
in this essay, used the gravity model of trade to find out the impact a PTA has on the
bilateral trade flow.
Bergstrand and Baier discuss the matter of CACM being noticed as “the most
advanced and successful regional integration scheme of Latin America in the 1960s.”
(Bergstrand & Baier, 2006). However the success of CACM was not to be
continuously successful when the 1970s came. A war between the member countries
of El Salvador and Honduras led to Honduras withdrawing from the PTA and the
eventual downfall (even though it took almost 30 years) of CACM. When Bergstrand
& Baier regarded the EEC, the results were opposite. After many hurdles the EEC has
eventually evolved into the European Union, which has to be seen as a largely
successful PTA as a whole.
A rather obvious conclusion that can be drawn from Bergstrand & Baiers study is
that there is no simple answer to whether an PTA automatically benefits the bilateral
trade flow of the involved countries; it generally depends from PTA to PTA and
around other, sometimes external, circumstances than just trade, e.g. the war in the
CACM case.
Another previous study is one provided by Celine Carrère (2004). The purpose of
Carrères study is to apply the gravity model to ex-post regional trade agreements. In
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other words Carrère applies the gravity model to actual results rather than predictions
of the regional trade agreements. Carrère emphasizes that she is applying the gravity
model with “proper specification”, meaning that Carrère is able to acknowledge the
effects on both trade creation and trade diversion, as well as being able to include the
unobservable characteristics and also take into account some of the explanatory
variables endogeneity (see section 9.6.). The data collected by Carrère is from over
130 countries and Carrère used panel data (see 8.2.) when performing the equation.
One main difference from Bergstrand and Baiers study is that Carrère draws the
conclusion that compared to earlier studies, regional integration has shown to increase
trade in a significant way between country-pairs, however this often leads to the entire
world-trade decreasing as a consequence.
A third previous study is the one that probably is the most relevant towards this
essay, which is the one mentioned in the introduction. Carlos Carillo and Carmen A
Li performed the study in 2002. Similar to this essay Carillo and Li also used the
gravity model to estimate their equations. The main goal of Carillo’s and Li’s study is
to research about the effects that the Andean Community and Mercosur had on both
intra-regional and intra-industrial trade from 1980-1997 (2002, Carillo & Li). Unlike
this essay, there are no external countries that are included, i.e. only members of
either Andean Community and/or the Mercosur are part of the equation. A general
consequence of the lack of the external countries can lead to less variation in the
equation results. However, in Carillo and Li’s study their goal is only to find out
internal matters, such as trade and industrialization, therefore this doesn’t matter in
their study. The authors divided up the timeline into two parts (1980-1989 & 19901997) as well as dividing up the goods into three different main categories;
Homogenous, reference price and differentiated. These main categories were later
split into four different sub-categories (agricultural intensive, mineral intensive,
labour intensive and capital intensive).
After their estimation, it was proved that the Andean Community had a significant
effect on, in particular the capital-intensive goods, both concerning the differentiated
and the reference price products. As for the Mercosur, those agreements only had a
positive effect on the reference price products, specifically the capital-intensive
category. When comparing this study towards this essay, this essay will not be
divided into two different timelines and the study will especially not be divided up
into different categories like Carillo and Li’s is. Instead this essay will be analysed
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depending on how the variables in the gravity equation are affected by the different
PTAs.
4. The Gravity Equation
The Gravity Equation can be used within several different fields, in this essay the
focus will be on the gravity model of trade. The outline of the gravity model of trade
is the prediction of bilateral trade flows. Some tools used to analyse the bilateral trade
flows are the size of the country in mind, economically speaking, and also the
distance between, in this case the economic centres of the country-pairs in matter. A
main area where the gravity model of trade is used is to see how effective a trade
agreement actually is (Carrère, 2004), which makes this model very suitable for this
essay. The gravity model is built upon several different variables linked to the
different country-pairs. Some of the variables, such as if the countries share a border,
speak the same language and the bilateral distance between the countries are not
affected by time in any way. Other variables used in the gravity model, such as GDP
per capita, bilateral trade flow and if the countries are part of a PTA are timedependent. The bilateral trade flow represents the amount of trade between the
country-pairs and is represented by lnTFijt in the actual equation.
The gravity equation first originated in 1962 and was developed by the Dutch
economist Jan Tinbergen. Before Tinbergen had presented the gravity equation, it was
believed by the better part of the economic world that the Hecksher-Ohlin model was
enough when describing the world’s trade. The gravity model of trade has therefore
been revolutionizing when describing the worlds trade, since it is much more
sufficient then the Hecksher-Ohlin model and takes more contributing factors into
consideration (Frankel, 1998). Another economic-pair that in a way revolutionized the
model was Anderson and van Wincoop, who in 2003 used a system of equations that
was non-linear, so that the endogenous (see 9.6.) change from the trade liberalization
price terms could be taken into consideration (Anderson & van Wincoop, 2003).
The study will start by comparing a country pair (e.g. Argentina & Belize)
throughout a specific period of time, in this case 1975-1995, and then replicate this
process until the data concerning all 462 country-pairs has been calculated. When
estimating the gravity equation the regression that will be used is Ordinary Least
Squares (OLS). The OLS-method minimizes the sum of squares for the predicted
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responses of linear calculation with the observed responses from the data (Dougherty,
p.85).
Fixed country specific and fixed period effects will replace the dummy variables
when processing the actual estimation (see section 9 & 10).
5. Method
The gravity equation is a so-called multiple regression model. The main
characteristic that makes the gravity equation able to be represented by the multiple
regression models is the fact that it has several explanatory variables (Dougherty,
p.151-153). The multiple regression models equation is as following:
yi = β1 + β2x2i + β3x3i + ... + βKxKi + ei
In the equation, yi represents the dependent variable, β represents the intercept of the
explanatory variables that in their turn are x2i + x3i +...+ xKi .
6. Econometric equation
As earlier mentioned the essay will analyse the effect PTAs have on bilateral trade
flow by using the gravity equation with all its different variables. In this essay the
gravity equation is represented by the following regression:
lnTFijt = β0 + β1 ln(GDP per capita)it + β2 ln(GDP per capita)jt + β3 DISTij
+ β4 ADJij + β5 LANGij + β6 ALADIijt + β7 MERC ijt +
εijt
6.1. Variables
lnTFijt represents the bilateral trade flow between country i & j for a specific year,
lnTFijt and is also the dependent variable. ln(GDP per capita)it and ln(GDP per
capita)jt represent the GDP per capita for a specific country for a certain year. i & j
stand for the different countries (e.g. Argentina & Belize). The variables DISTij,
ADJij and LANGij are not correlated with time in any way. This means that the value
will be constant for the entire time period for these variables. DIST represents the
bilateral distance between the economic centres of the country-pairs, ADJ represents
if the country-pair share a geographical border or not and lastly LANG explains
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whether the country pair have the same official language as one another. The
variables ADJ and LANG are binominal, meaning that either the number 1 or 0
represents them. If the number is 1 between the country-pair this means that they in
fact share a border (or a language depending on the variable). The final two variables,
ALADIijt and MERCijt show if the country pair is part of the same preferential trade
agreement. These two variables are also binominal, also being represented by the
number 1 or 0. εijt is considered as the error term in this equation. All of the variables
mentioned, except the dependent lnTFijt variable, are therefor explanatory (see
Appendix 16.2.).
6.2. External Countries
When performing the equation, it is important not only to take in account the
countries that actually are a part of either of these preferential trade agreements, but to
also include regional non-members. This is partly to get a lot of variation and to
compare members to non-members. Furthermore, the benchmark is non-members and
the effects of the PTAs are then estimated in comparison to members. Why it is
important to choose specifically regional countries is that because of geographical
location, countries tend to trade more with nearby countries. The countries outside of
ALADI and Mercosur that have been taken into account are: Belize, Costa Rica,
Cuba, Dominican Republic, El Salvador, Guatemala, Guyana, Nicaragua and
Suriname. The goal was of course to try and include all of the Latin American
countries in the equation, but unfortunately there wasn’t enough data concerning Haiti
and Puerto Rico which means that the results will not be exactly correct, but as good
as.
6.3. Logarithms
Since the dependent variable in the gravity equation is using the logarithm of the
trade flow values this can cause problems. The main problem is that country-pairs that
have zero trade flow values cannot be defined, since the logarithm of zero is
indefinable. Earlier studies have had this problem as well and the easiest way to avoid
this obstacle is to assume the value 1 on the country-pairs that have zero trade flow,
since the logarithm of 1 is always defined as zero. This is considered to be much more
efficient than neglecting the values all together (Martin & Pham, 2008).
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6.4. Time Interval
The essay has, as earlier mentioned, analysed the time interval between 1975-1995.
This exact time period was chosen because by using these years it can clearly be
distinguished how the different preferential trade agreements have affected the
concerned countries, since ALADI was formed in 1980 and Mercosur in 1991.
Another factor was also the matter of CACM (Bergstrand & Baier, 2006) being
existent on a large scale before the years in this essays interval. To be certain that the
existence of CACM didn’t interfere with the results, it was best to focus on the
interval of 1975-1995.
7. Fixed Effect
When performing an equation that is of the gravity equations calibre, the model
can allow the parameters to change for both individuals (in our case countries) and for
the time period. A way to deal with this is by adding a fixed effect. The fixed effect
treats the observed quantities as if they were non-random, regarding the explanatory
variables. This ensures that the coefficient of slope will stay constant throughout the
regression, depending on if one chooses to use it on time or countries, which is the
coefficient of slope that will remain constant. It is important to distinguish the
difference between a random and fixed effect. Compared to the random effect the
fixed effect is a cause of non-random quantities, whereas the random effect is, by
definition, a consequence of random causes. The fixed effect replaces the dummy
variable, leading to the use of only the variation within the countries. The lack of
using fixed effect means that one must include the variation between the countries, as
well as the variation within the countries (Dougherty, p. 525). When fixed effects are
applied, an important part is to ensure that every parameter that may be correlated
with the regression gets time independent effects added. The fixed effects that are
included in the estimations in this essay are both country specific as well as period
fixed effects.
8. Data
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8.1. Sources
The goal was to be able to determine, with as much accuracy as possible, how the
bilateral trade flow is affected by the formation of the respective preferential trade
agreements. To be able to go through with this it was very important to have a
sufficient amount of data. Therefor, it was important to have a large amount of
country pairs, since there weren’t really that many variables (both dependent and
explanatory). The total amount of country pairs came to a total of 462, with there
being 22 countries in total (see section 16.1 for more information).
The data used in the gravity equation comes mainly from two different sources.
These are the World DataBank and the website www.cepii.fr (URL 7), where the
latter is an acknowledged website which has specified in, amongst other things, the
gravity equation. Cepii allowed us to find the data concerning the dependent variable
(bilateral trade flow) in the equation. The GDP per capita for the individual countries
was easily accessible from the World DataBank’s website (URL 6). The GDP per
capita for the various countries are all measured in US dollars. The bilateral distance
between the different countries’ economic centres was also accessible on
www.cepii.fr (by using the “geodist” tool) and is measured in kilometres. The rest of
the data is very easy to find with a quick search on the Internet, since it has come to
be very public information. To see if the country-pairs are adjacent, what their official
languages are and to see if they are part of either ALADI and/or Mercosur, this
information was accessible from Wikipedia. Since the information is acknowledged
and well known, it is considered sufficient to use Wikipedia as a source.
When deciding if there should be data for every year or only take data for every
five years within the interval, the decision was rather easy. Since the trade can vary
quite a lot from year to year and the goal is to get as accurate an answer as possible,
the best decision was to use data from every year.
8.2. Panel Data
The gravity equation is built, as earlier mentioned, on one dependent variable and a
couple of explanatory variables. Before performing the actual regression, with the
help of Eviews, the data had to be sorted. In this case, the choice was to use panel
data, partly because it was the kind that was best suitable for the process of the gravity
equation, but also because it was rather simple to sort and perform in Excel. What is
meant by the last sentence is that panel data can be both balanced and unbalanced.
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When the panel data is unbalanced, it means that there necessarily doesn’t have to be
data for every specific country for every year (Dougherty, p. 515). Since there are a
few years where this occurs for different countries, e.g. Argentina during the Falkland
war in 1982-1983, the choice of panel data became even more beneficial. When panel
data is balanced it points towards all countries showing data for every single specific
year.
9. Regression terminology
9.1. Autocorrelation
When observations are dependent on one another this shows that there is a serial
correlation between the disturbance terms. If this assumption is fulfilled then the
regression is known to have autocorrelation, or in other words, the covariance is
something else than zero. Autocorrelation has come to be apparent most often when
performing a regression analysis with time-series data. In the case of this essay, it is
more than likely that there is autocorrelation since we have chronological time-series
data. When autocorrelation is apparent the OLS-estimate doesn’t have the lowest
variance amongst all the expected value estimates. The most common version of
autocorrelation is positive autocorrelation, where the characteristics of positive
autocorrelation can be seen when analysing the Durbin-Watson (DW) statistic
(Dougherty 2011, p. 429-430). If the DW-value is any number below two, there is
considered to be autocorrelation.
9.2. Heteroscedasticity
When the random variables have different variances for all the observations this
indicates that heteroscedasticity is existent for the random variable, and naturally
when the random variables have the same variances, homoscedasticity is existent.
Similar to autocorrelation, the consequences of heteroscedasticity mean that the OLSestimate doesn’t have the lowest variance amongst all of the expected value estimates.
The point of defining if there is either hetero- or homoscedasticity is to see how
efficient the chosen estimates are (Dougherty 2011, p. 283). If heteroscedasticity is
apparent this means that the variance concerning the regression coefficient is larger,
which in its turn leads to worse estimates than if the regression indicated
homoscedasticity. Heteroscedasticity can also be the cause of the standard errors
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showing wrongful results, which means that the F- and T-tests will have results that
are easily interpreted wrongfully. To see if heteroscedasticity is apparent the most
common way to detect heteroscedasticity is to perform the White test (White, 1980).
9.3. Null hypothesis
The null hypothesis (often written as: H0) represents, in a statistical model filled
with data, the fact whether there is a correlation between the two (or more) measured
parameters (Dougherty, p. 37). The point of the null hypothesis is to determine
whether the experiment in matter is false or not. When deciding whether or not to
reject the null hypothesis or not, the following algebra is good to start from:
Reject H0 if | t | > t α/2, n-2
Don’t reject H0 if | t | < tα/2, n-2
Which in other words means the following; α assumes the variable for the
significance level, where the significance level represents the probability that the null
hypothesis may actually be correct. If a lower significance level had been selected,
then that would obviously mean that the probability of the null hypothesis being
correct would be larger than previously.
In this essay the significance level of 10%, 5% and 1% will be assumed. The
purpose of choosing these levels is to minimize the risk of type II errors, which means
that the null hypothesis isn’t rejected even though it is correct (Dougherty, p.42).
Type I errors are characterized by the opposite of type II, meaning that the null
hypothesis is rejected even though it is correct (Dougherty, p. 38).
9.4. T-value
When a regression has been executed, T-values will appear, where they play the
part of deciding whether or not the null hypothesis is to be rejected (Westerlund, p.
116). Looking at the algebra in the previous paragraph; tα/2, n-2, this represents the
critical value, with the sub-variables, (α/2 & n-2) representing the fact that a twotailed hypothesis test is being performed and n represents the amount of observations
(in this case 9701). By using degrees of freedom as well as the significance level, it is
possible to interpret the critical value, which will be presented later on in the results
section.
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9.5. Multicollinearity
When the equation one is trying to estimate has more than one explanatory
variable, there is always a risk for multicollinearity. This phenomenon occurs when
two or more of the explanatory variables are dependent on one another (Westerlund,
2005). When there is perfect multicollinearity, the estimation will be impossible to
perform in the computer software program (in this case Eviews) leading to that at
least one of the variables must be eliminated. After the elimination of the variable(s)
there may still be multicollinearity and the problem with this is that it is hard to see
which of the variables in matter is the one affecting the Y-term (in this case lnTFijt).
Perfect multicollinearity is apparent in the first estimation that is performed (see
section 10.1.) and the solution that has been used to deal with it is also presented in
the same section.
9.6. Endogeneity
When there is correlation between the variable and the error term in a model, in
our case the gravity model, it is said to be endogenous. There are four different types
of ways that endogeneity can come to surface; measurement error, simultaneity,
omitted variables or autoregression (where the errors are auto correlated). What
distinguishes an endogenous variable from an exogenous variable is that an
endogenous variable has values that are determined from interactions within the
model, whereas an exogenous variable is determined from external interactions
(Dougherty, p. 332).
When performing econometric equations, problems with endogeneity often occur.
When defining problems with endogeneity it is important to revise the four abovementioned ways as to how endogeneity can arise. First of all we must define what the
different areas actually mean (Wooldridge, 2013). A measurement error is defined as
the difference between a quantity’s true value and the measured value of the quantity
(Dougherty, p.85) To find out if there is a measurement error in the essay, the DurbinWu-Hausman test is the best way to test if there in fact is a measurement error. When
a model neglects at least one important factor from the equation this is referred to as
omitted-variables bias. When the assumed specification in a regression analysis is
incorrect, the bias that shows from the estimates is the omitted-variables bias
(Dougherty, p. 252). A random process that often describes different time varying
17
processes within e.g. economics is often referred to as an autoregressive model. When
a dependent variable is determined together with an explanatory variable simultaneity
occurs. Usually an equilibrium mechanism is used to perform this (Dougherty, p.
335).
Typically, when a problem occurs with endogeneity, the cause of this is because
the error term in the regression model is correlated with the dependent variable,
instead of the explanatory variable. (Sørensen, 2012). For every independent
regression in matter, the problem of endogeneity is unique. Initially there was a large
problem with endogeneity in this essay, where the GDP-data was measured in actual
GDP without considering the geographical and demographical size of the countries.
This made the estimation results to be rather obscure and unrealistic. However, when
the GDP-data was changed to GDP per capita that specific endogeneity problem
disappeared.
9.7. Zero Trade Flows
The dependent variable that is part of the performed equation in this essay is based
on how much different country-pairs trade with each other. Since there are 22
different countries, for several obvious reasons some country-pairs will not trade
every year between 1975-1995. Some critics may say that a large reason as to why the
trade flow is zero is because of the lack of reporting the trade between different
country-pairs, but generally the trade between the countries really is non-existent.
This leads to the trade flow between various country-pairs assuming the value zero.
With slightly over 40% of the country-pairs (all 21 years included) obtaining the
value zero, therefore not trading with one another, this clearly has an affect on the
results (Martin & Pham, 2008). If the zero values of the trade flow are excluded,
heteroscedasticity can create large biases in different samples (Hurd, 1979). Therefore
it is very important to include the zero trade flows when compiling ones data in the
gravity mode, since this will neglect the problems that are created when excluding
zero trade flows (Arabmazar & Schmidt, 1981).
10. Equation Estimate Results
In this section of the essay the results from the econometric equations will be
presented. As earlier mentioned (see section 4.), the method that has been used whilst
performing the different equations is Ordinary Least Squares. When executing the
equation in Eviews, it was important to choose various different settings so that the
18
most accurate results could be calculated and compared with one another. The
different versions of the calculations will be presented separately in this section. For
all of the estimations a significance level of 10%, 5% and 1% will be assumed,
meaning that with the probability of 90%, 95% and 99% that the correct value will be
identified within the confidence interval (Dougherty, p. 40). Since the amount of
observations is exactly the same for all the different equations (as well as the
significance level), this means that the critical value will be the same for each and
every one of the equations.
Since the test is two-tailed, this means that the top column in the table (Dougherty,
p. 533) is the one to use. Because the amount of observations is superior to 600, that
means that the “infinity level” in the t-table must be identified and used in the
equations. For example when the significance level is 5%, the critical value of all the
equations will be 1.96.
10.1. Fixed country specific effects and fixed period effects (for stats see 14.1.)
The first equation is characterized by having fixed country specific effects as well
as fixed period effects. When looking at the Durbin-Watson statistic, it is noticeable
that it is way below the value of two (see section 8.1.) and rather close to zero,
meaning that there is apparent autocorrelation.
When considering the variables in the equation, it is noticeable that there are a few
variables missing (see section 14.1.). The reason for the lack of variables is because of
multicollinearity. When performing the estimation with all of the variables the
equation suffered from perfect multicollinearity, which is acknowledged as a socalled dummy variable trap. The reason for the dummy variable trap occurring is that
when using fixed effects alongside panel data (which is used in this study), dummies
are created leading to an overflow of dummies. The solution to this is to remove one
or two dummy variables in order for the equation to be performed (Dougherty p. 235236). In 14.1 the variables LANG and ADJ had to be removed for the possibility to
perform the equation, which unfortunately led to somewhat deceptive results. The
most important part is however that the variables concerning the preferential trade
agreements are included in the results, which they are.
When analysing the results the first thing that is noticeable is that only two
variables are significant, no matter what significance level works as guidelines. These
two variables are ln(GDP per capita)it and the variable concerning Mercosur
19
(MERC), meaning that they are separated from zero. When applying these results
practically, it is important to see what kind of effect these variables have on the
bilateral trade flow (lnTFijt). If ln(GDP per capita)it increases by one percentage this
will lead to the bilateral trade flow increasing with the coefficient of ln(GDP per
capita)it, in this case 20.22333. Looking at the MERC variable the conclusion that
can be drawn is that for the countries that are members of Mercosur their difference
will be a positive of 371728 (coefficient) concerning the bilateral trade flow
compared to not being a member.
Regarding the other preferential trade agreement variable ALADI, it is hard to
say anything about it, because of the presence of multicollinearity, the variables value
must be assumed as 0 and more advanced conclusions than that would be unwise to
draw.
Since this regression is built on time-series data, the possibility of spurious
regression is large and it is important to try and avoid it. Looking at R2 and its value
(0.600697), the conclusion is that the value isn’t nearly high enough to be concerned
about spurious regression. In short, spurious regression means that two variables that
do not have any correlation, appear to have correlation, which can often be caused by
a third so-called unseen factor (Dougherty, p. 475).
20
10.2. Fixed period effects (for stats see 14.2.)
Another estimation that was also to be performed was testing with only fixed
period effects and neglecting the country (fixed and random) effects. The purpose of
this was to get some diversification in the results and see if there could be any large
difference compared to when there was fixed effects for both countries and period.
Another reason was also that the possibility of multicollinearity decreases a lot when
not including fixed country-specific effects, which allows us to exclude less variables.
Regarding the results the variables ln(GDP per capita)it, ADJ, LANG, ALADI
and MERC are all significant at all the three testing-levels (10%, 5%, 1%), and the
DIST variable is significant when testing for all the levels except at 1%. The
conclusion of this is that when ln(GDP per capita)it increases by a percentage, the
bilateral trade flow will increase with its coefficient (16.51978). The members of
ALADI and Mercosur will have a difference (compared to prior-membership) of their
coefficients (45619.93 respectively 467151.5). The same goes for ADJ and LANG, if
the country-pair in matter (e.g. Argentina and Paraguay) in fact share a border or the
same language the bilateral trade flow difference will be their respective coefficients
(98434.58 & -24548.95).
10.3 White test for heteroscedasticity (for stats see 14.3.)
The purpose of this regression is mainly to test if there is heteroscedasticity. When
calculating the existence or not of heteroscedasticity, the degrees of freedom must be
identified. This is done in the following way: the amount of regressors in the equation
minus one. In this case the amount of regressors is 8, so the degrees of freedom is 7.
The next step is to read from the chi-squared statistic table, which in this case shows
(at a 5% significance level) a result of 14.067. To see if there is apparent
heteroscedasticity, the test statistic must be calculated. By multiplying R2 (0.243968)
with the amount of observations (9701 minus 2), the test statistic is 2366,73357. Since
the test statistic is so much larger than the chi-squared statistic, this means that there
is a lot of heteroscedasticity (Dougherty, p. 286). This means that the null hypothesis
for heteroscedasticity is rejected.
.
21
11. Figures
Figure 1.1.
250000
Bilateral trade flow 1975 & 1985 for Colombia(US $)
200000
150000
100000
1975
50000
Colombia & Argentina
Colombia & Belize
Colombia & Bolivia
Colombia & Brazil
Colombia & Chile
Colombia & Costa Rica
Colombia & Cuba
Colombia &…
Colombia & Ecuador
Colombia & El Salvador
Colombia & Guatemala
Colombia & Guyana
Colombia & Honduras
Colombia & Mexico
Colombia & Nicaragua
Colombia & Panama
Colombia & Paraguay
Colombia & Peru
Colombia & Suriname
Colombia & Uruguay
Colombia & Venezuela
0
1985
Figure 1.1 shows the bilateral trade flow between Colombia and the rest of the
included countries for the years 1975 (blue line) and 1985 (red line). These exact
years were chosen since they are five years either side of the formation of ALADI,
which makes it easy to see the impact of the preferential trade agreement. It is
immediately evident that Colombia’s bilateral trade flow has increased immensely
towards all of the members of ALADI and the bilateral trade flow is rather unchanged
towards the non-members. This shows that the introduction of ALADI definitely has
had a positive impact on the trade between Colombia and the remaining members of
ALADI.
22
Figure 1.2.
Bilateral trade flow 1975 & 1985 for Guatemala(US $)
1975
Guatemala & Argentina
Guatemala & Belize
Guatemala & Bolivia
Guatemala & Brazil
Guatemala & Chile
Guatemala & Colombia
Guatemala & Costa Rica
Guatemala & Cuba
Guatemala &…
Guatemala & Ecuador
Guatemala & El Salvador
Guatemala & Guyana
Guatemala & Honduras
Guatemala & Mexico
Guatemala & Nicaragua
Guatemala & Panama
Guatemala & Paraguay
Guatemala & Peru
Guatemala & Suriname
Guatemala & Uruguay
Guatemala & Venezuela
90000
80000
70000
60000
50000
40000
30000
20000
10000
0
1985
Figure 1.2 shows the bilateral trade flow between Guatemala and the rest of the
countries included in the equation. The years are the same as in figure 1.1 and the
colour of the lines represent the same years. The reason that Guatemala was chosen in
this figure is to see how a non-member of the preferential trade agreement ALADIs
bilateral trade flow was affected by the introduction of ALADI. Guatemala’s bilateral
trade flow hasn’t increased with any of the country-pairs, as a matter of fact rather the
opposite. It is most noticeable on the countries that joined ALADI, but also on e.g. El
Salvador and to some extent Honduras (that aren’t a part), that the bilateral trade flow
has decreased rather drastically.
23
Figure 1.3.
Bilateral trade flow 1986 & 1995 for Argentina (US $)
4500000
4000000
3500000
3000000
2500000
2000000
1500000
1986
1000000
1995
0
Argentina & Belize
Argentina & Bolivia
Argentina & Brazil
Argentina & Chile
Argentina &…
Argentina & Costa…
Argentina & Cuba
Argentina &…
Argentina & Ecuador
Argentina & El…
Argentina &…
Argentina & Guyana
Argentina &…
Argentina & Mexico
Argentina &…
Argentina & Panama
Argentina &…
Argentina & Peru
Argentina &…
Argentina & Uruguay
Argentina &…
500000
Figure 1.3 shows the difference of the bilateral trade flow between Argentina and
the rest of the countries included in the equation. The blue line represents five years
before Argentina entered Mercosur and the red line represents four years after
Argentina had entered the Mercosur. The first noticeable thing is obviously that the
bilateral trade flow has increased immensely between Argentina and Brazil (both
Mercosur members) during this decade. The other two country-pairs that have very
noticeable differences are Argentina & Mexico, also Argentina and Uruguay (both
Mercosur members). Overall, from this chart, it is evident that (at least for Argentina)
the bilateral trade flow has increased with its fellow members of Mercosur since the
formation of the PTA (even a slight increase between Argentina and Paraguay). This
chart then shows that, just as the estimations showed, if there is a correlation between
the entry of a PTA and bilateral trade flow, the trade can increase (and in Argentina’s
case has) between the fellow PTA members. The purpose of choosing Argentina
when showing this chart was partly because Argentina is a member of Mercosur, but
also the fact that Argentina is geographically placed very well when looking at the
Mercosur members, sharing a border with all of the countries (except Venezuela) and
also speaking the same language as all of the involved countries, excluding Brazil that
is.
24
Figure 1.4.
6000000
Bilateral Trade Flow 1986 & 1995 for Brazil (US $)
5000000
4000000
3000000
2000000
1986
0
1995
Brazil & Argentina
Brazil & Belize
Brazil & Bolivia
Brazil & Chile
Brazil & Colombia
Brazil & Costa Rica
Brazil & Cuba
Brazil & Dominican Republic
Brazil & Ecuador
Brazil & El Salvador
Brazil & Guatemala
Brazil & Guyana
Brazil & Honduras
Brazil & Mexico
Brazil & Nicaragua
Brazil & Panama
Brazil & Paraguay
Brazil & Peru
Brazil & Suriname
Brazil & Uruguay
Brazil & Venezuela
1000000
Figure 1.4 shows the bilateral trade flow between Brazil and the other countries
included in the equation. Just like figure 1.3 the blue line represents 1986 and the red
line represents 1995. The choice of including this figure (in addition to figure 1.3.)
was to show for another Mercosur country how the trade has been affected because of
Mercosur. The results are very similar to figure 1.3 but this figure shows that trade
has generally increased with other large countries (Mexico & Chile), meaning that it
necessarily isn’t the entry of Mercosur that has increased the bilateral trade flow
between the different countries.
25
12. Analysis
In this following section, the results from this essay will be compared with
previous studies. It will also be determined whether or not there is a specific
correlation between the bilateral trade flow of country-pairs in Latin America and the
explanatory variables mentioned in the essay.
The study performed in this essay shows through the econometric estimations that
the entry in specifically Mercosur leads to an increase concerning the bilateral trade
flow. When looking at the figures presented in the previous sections it is evident that
the membership of ALADI has also contributed to an increased bilateral trade flow
for the countries involved, but because of the multicollinearity problem this has been
hard to show econometrically.
Even though there is proven correlation between a few of the explanatory variables
and the dependent variable, there is far too much data in comparison to the amount of
years the time interval is spread over to get exact results. This meaning that it is hard
to generally point out that a country-pair will definitely trade more with each other
just because they share a language or a border. The only proven part is, as earlier
mentioned, that an entry in Mercosur will definitely lead to an increased bilateral
trade flow.
The study that Cèline Carrère performed (see section 3) came to the conclusion
that when preferential trade agreements are formed, the consequence is that trade
between the country-pairs that are in matter increases, however at the cost of the
entire world trade decreasing. After receiving the results from the estimation in this
essay, a conclusion that has to be drawn is that the results are rather similar to the
ones of Carrères’ study. In both of the first two performed regressions the entry of a
PTA who’s an increased bilateral trade flow, however it is hard to see just from the
estimations that this will affect the rest of the world negatively. When looking at the
figures however (section 11) it is apparent that the non-members of the PTAs are
affected negatively from the formation of the two preferential trade agreements. The
first regression is considered as the trust worthiest because of the presence of the
fixed effects regarding both the countries and the period. The fixed effects have
replaced the dummy variables in the two different estimations.
When looking at the study performed by Carillo & Li (see section 3), in some ways
the results resemble one another, when comparing to this essay. Carillo & Li regarded
26
both the Andean community and Mercosur. However, they didn’t focus on the
bilateral trade flow between country-pairs, instead they focused on intra-trade and
intra-industrialization, meaning that they not only had a different focus field but they
also left out non-members of the preferential trade agreements. The conclusion that
Carillo & Li managed to reach was that the entry of Mercosur proved to enhance the
capital-intensive reference price products. Since the gravity equation wasn’t used in
their study, the results can be slighter harder to compare than with Carrères study.
Nevertheless, the results from the study performed in this essay shows the correlation
between the bilateral trade flow and the MERC variable, which means that the entry
of Mercosur as a matter of fact also had an effect on trade (in this essay’s estimation).
Even though the effect from the entry of Mercosur only had a slight positive effect in
Carillo & Li’s study, the effect still was evident, leading to the conclusion of their
study’s results being in a way rather similar by the results in this essay.
27
13. Conclusion
That the formation of ALADI and Mercosur has increased the bilateral trade flows
for the members and decreased the same dependent variable for the non-members has
been proven, not least in the analysis. Earlier studies have also proven that an
introduction of preferential trade agreements has increased the trade between the
member countries at the expense of the “outside world”.
The point of this study was to see how an introduction of a PTA affected both the
members and non-members, as well as find out if there is any correlation between any
of the chosen variables (from the gravity model of trade) and the bilateral trade flow
between the chosen country-pairs. When the regressions had been performed, it was
evident that the variables that had a correlation with the bilateral trade flow of the
country-pairs were ln(GDP per capita)it and MERC. This then meant that there was
correlation between the membership of Mercosur and the bilateral trade flow.
However, by looking at section 11 the figures show slightly more correlation. In all
four cases it is shown that an entry of a PTA, regardless of if it’s ALADI or Mercosur,
increases the bilateral trade flow between the members and that it decreases for the
non-members.
Since there isn’t any direct correlation for both of the PTAs (only for Mercosur)
and bilateral trade flow, other factors must be taken into consideration. The bilateral
trade flow has increased for all of the countries that have included in ALADI and
basically for all of the Mercosur countries. Since the bilateral trade flow levels have
increased, the conclusion that the countries have grown economically must be drawn.
A lot of the countries in Latin America have been affected by the fact that Brazil has
grown rapidly since 1975, meaning that the amount of goods and services traded
automatically increase, which is reflected when looking at the figures of the four
sample-countries.
The main conclusion to be drawn from this study is that not one factor affects the
bilateral trade flow of a country-pair; instead there are a number of factors that come
into play and together they affect the trade. An introduction of a PTA has shown that
the bilateral trade flow increases for the members, specifically Mercosur. The
statistics in this essay show that the trade in fact has increased for both Mercosur and
ALADI. Another factor that also affects the trade flow is time. With time countries
have managed to grow, increasing their consumption, leading to a larger trade market,
28
which then has lead to the countries reaching higher GDPs and eventually larger
values of bilateral trade flow.
29
14. Equation Estimate Figures
14.1. Fixed country effects and fixed period effects
Dependent Variable: LNTFIJT
Method: Panel Least Squares
Date: 01/15/14 Time: 14:54
Sample: 1975 1995
Periods included: 21
Cross-sections included: 462
Total panel (unbalanced) observations: 9701
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LN_GDP_PER_CAPITA_IT
LN_GDP_PER_CAPITA_JT
DISTIJ
ALADIIJT
MERCIJT
-231088.1
20.22333
1.24E-05
69.71369
-1343.134
371728.0
226163.7
1.670049
9.73E-05
70.27487
4461.086
11542.71
-1.021774
12.10942
0.127243
0.992014
-0.301078
32.20458
0.3069
0.0000
0.8988
0.3212
0.7634
0.0000
Effects Specification
Cross-section fixed (dummy variables)
Period fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.600697
0.579636
95553.56
8.41E+13
-124761.0
28.52106
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
30
31869.01
147378.5
25.82166
26.18210
25.94385
0.716794
14.2. Fixed period effects
Dependent Variable: LNTFIJT
Method: Panel Least Squares
Date: 01/15/14 Time: 14:57
Sample: 1975 1995
Periods included: 21
Cross-sections included: 462
Total panel (unbalanced) observations: 9701
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LN_GDP_PER_CAPITA_IT
LN_GDP_PER_CAPITA_JT
DISTIJ
ADJIJ
LANGIJ
ALADIIJT
MERCIJT
-12221.58
16.51978
-1.41E-05
1.617029
98434.58
-24548.95
45619.93
467151.5
4033.145
1.290513
3.78E-05
0.756883
4207.109
2871.529
3859.097
13393.66
-3.030286
12.80094
-0.373991
2.136433
23.39720
-8.549087
11.82140
34.87857
0.0024
0.0000
0.7084
0.0327
0.0000
0.0000
0.0000
0.0000
Effects Specification
Period fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.247880
0.245781
127992.0
1.58E+14
-127832.2
118.0735
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
31
31869.01
147378.5
26.36021
26.38093
26.36723
0.504328
14.3. White test for heteroscedasticity
Dependent Variable: LNTFIJT
Method: Least Squares
Date: 01/19/14 Time: 11:08
Sample: 1 9701
Included observations: 9701
White heteroskedasticity-consistent standard errors & covariance
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LN_GDP_PER_CAPITA_IT
LN_GDP_PER_CAPITA_JT
DISTIJ
ADJIJ
LANGIJ
ALADIIJT
MERCIJT
-14219.70
17.93427
-1.51E-05
1.566157
99511.99
-23708.40
39636.20
471033.0
3075.039
2.440599
3.04E-06
0.541237
5657.859
3318.584
3569.714
94893.82
-4.624234
7.348306
-4.988334
2.893665
17.58828
-7.144132
11.10347
4.963790
0.0000
0.0000
0.0000
0.0038
0.0000
0.0000
0.0000
0.0000
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
Prob(Wald F-statistic)
0.243968
0.243422
128192.0
1.59E+14
-127857.3
446.8410
0.000000
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
Wald F-statistic
32
31869.01
147378.5
26.36127
26.36719
26.36328
0.505789
115.2291
15. References
15.1. Websites
URL 1:
http://www.aladi.org/nsfweb/sitioIng/
Viewed: 2013-11-15
URL 2:
http://www.macalester.edu/courses/geog61/chad/thefavel.htm
Viewed: 2013-11-15
URL 3:
http://www.sunat.gob.pe/customsinformation/tradeagreements/aladi.html
Viewed: 2013-11-15
URL 4:
http://www.itfglobal.org/itf-americas/ALADI.cfm/languageID/8
Viewed: 2013-11-15
URL 5:
http://www.eclac.org/publicaciones/xml/8/4228/cap1.htm
Viewed: 2013-11-15
URL 6:
http://data.worldbank.org/region/LAC
Viewed: 2013-11-20
URL 7:
http://cepii.fr/CEPII/en/bdd_modele/bdd.asp
Viewed: 2013-11-21
URL 8:
http://en.mercopress.com/2012/12/08/bolivia-signs-mercosur-incorporation-protocoland-becomes-sixth-member
Viewed: 2014-01-06
33
URL 9:
http://www.reuters.com/article/2012/06/30/us-mercosuridUSBRE85S1JT20120630
Viewed: 2013-12-17
URL 10
http://www.international.gc.ca/trade-agreements-accords-commerciaux/agracc/costarica/index.aspx
Viewed: 2014-01-13
URL 11
http://www.lni.unipi.it/stevia/Organizzazioni/page03.html
Viewed: 2014-01-15
URL 12
http://www.businessdictionary.com/definition/customs-union.html
Viewed: 2014-01-21
15.2. Authors/Studies
Anderson, James E & van Wincoop, Eric (2003) “Gravity with gravitas: A solution to
the border puzzle”, Volume 93, Number 1, The American Economic Review
Arabmazar, A. and P. Schmidt (1981) "Further evidence on the roubstness of the
Tobit estimator to heteroscedasticity." Journal of Econometrics, p. 258.
Bergstrand, Jeffrey H. & Baier, Scott L. (2006) “Estimating the effects on free trade
agreements on international trade flows using matching econometrics”, Clemson
University, South Carolina, USA.
Carillo, Carlos & Li, Carmen A (2002) “Trade Blocks and the Gravity Model:
Evidence from Latin American Countries” University of Essex, England
Carrère, Cèline (2004) “Revisiting the effects of regional trade agreements on trade
flows with proper specification of the gravity model”, Université d’Auvergne, France.
Cyrus, Teresa L. (2010) “Income in the gravity model of bilateral trade: Does
endogeneity matter?” The International Trade Journal, 16:2, 161-180
Devlin, Robert & Ffrench-Davis, Ricardo (1999) “Towards an Evaluation of Regional
Integration in Latin America in the 1990s”. Blackwell Publishers Ltd.
34
Dougherty, Christopher (2011) “Introduction to Econometrics” 4th Edition, New
York: Oxford University Press Inc.
Frankel, Jeffrey A. (1998) “The Regionalization of World Economy” University of
Chicago, Illinois, USA
GATT: General agreement on tariffs and trade (1982) “Latin American Integration
Association” Limited Distribution, Montevideo, Uruguay
Hurd, M. (1979) "Estimation in truncated samples when there is heteroscedasticity."
Journal of Econometrics” 11: 247-58.
Martin, Will & Pham, Cong S. (2008) “Estimating the gravity model when zero trade
flows are frequent” Deakin University, Melbourne, Australia
Persson, Maria (2013) “Lecture Notes: Economic Integration” Lund University,
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AB
White, Halbert (1980) “A heteroscedasticity Consistent Covariance Matrix Estimator
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(Fifth international ed.). Australia: South-Western. p. 82–83
35
16. Appendix
16.1. Included Countries:
Country
Language
ALADI
Mercosur
Argentina
Spanish
Yes
Yes
Belize
English
No
No
Bolivia
Spanish
Yes
No
Brazil
Portuguese
Yes
Yes
Chile
Spanish
Yes
No
Colombia
Spanish
Yes
No
Costa Rica
Spanish
No
No
Cuba
Spanish
No
No
Dominican Republic
Spanish
No
No
Ecuador
Spanish
Yes
No
El Salvador
Spanish
No
No
Guatemala
Spanish
No
No
Guyana
English
No
No
Honduras
Spanish
No
No
Mexico
Spanish
Yes
No
Nicaragua
Spanish
No
No
Panama
Spanish
Yes
No
Paraguay
Spanish
Yes
Yes
Peru
Spanish
Yes
No
Suriname
Dutch
No
No
Uruguay
Spanish
Yes
Yes
Venezuela
Spanish
Yes
Yes
36
16.2. Included Variables
Variable
Bilateral Trade Flow
Source
www.cepii.fr
(Gravity Model)
Unit of measurement
US $
GDP
World DataBank
US $
Adjacent Countries
Wikipedia
Binominal (0/1)
Language
Wikipedia
Binominal (0/1)
ALADI/Mercosur
Wikipedia
Binominal (0/1)
Bilateral Distance
www.cepii.fr
(Geodist tool)
37
Kilometres