Wabash College

The European Union, Integration, and Recession

How does integration in the EU affect recovery and growth after economic recessions?

Carter D. Adams

5/9/2014

I would like to thank Dr. Byun, Dr. Howland, and Dr. Mikek for their help and input to prepare this paper. Also, I would like to thank Jocelyn Hopkinson for providing grammatical feedback and help.

Abstract: The European Union is the largest economy in the world. Countries across Europe are applying to join it reap the economic benefits. Do different levels of economic integration affect recovery and subsequent growth after economic recessions? By looking at the first difference of GDP per capita, the levels of integration cannot be concluded to result in different rate of recoveries. Further time and research on this subject will be needed to come to a significant conclusion.

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TABLE OF CONTENTS

I. Introduction………………………………………………………………………………………………………………………2

II. Background…………………………………………………………………………………………………………………….…3

III. Literature Review…………………………………………………………..…………………………………………………4

IV. Theoretical Analysis………………………………………………………….………………………………………………6

V. Empirical Analysis………………………………………………………………………………………………………………8

Data……………………………………………………………………………………………………………………………8

Results……………………………………………………………………..………………………………………………12

VI. Conclusion………………………………………………………………………………………………………………………17

VII. Appendix……………………………………………………………………………….………………………………………19

VIII. References……………………………………………………………………………………………………………………22

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Introduction

The European Union (EU) has developed into a global economic and political center.

Recently, the EU capital in Brussels, Belgium Harding (2014) proclaimed as “second only to

Washington as the world’s most important political capital.” Much research has been done on

the EU and potential economic benefits of joining it. Most scholarly results have mostly been

positive with some in disagreement, however many countries are still striving to become

members and reap the benefits. The EU’s website lists five official candidate countries and

three additional “potential candidates.”

With the overwhelming amount of literature in favor of EU membership and economic

integration, the desire to join makes sense. However, does it make sense when economy goes

into a recession? The economic benefits appear clear when the global economy is growing and

performing well, but what happens when the economy turns for the worse? Does integration

more adversely affect countries, or does it allow them to recover and grow quicker? Thus, the

basic question presented in this paper is as follows: Do different levels of economic integration

affect recovery and subsequent growth after economic recessions? This paper uses the 2008

global recession as an example of such economic downturn and the different levels of

integration into the EU.

The rest of the paper will seek to answer this question. First, this paper will provide a

literature review of similar scholarly research, building on and learning from previous analysis.

Next, there will be an empirical analysis of the data with multiple regressions and interpretation

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of the results. Finally, the conclusion will answer how integration affects economic recovery and

suggest further possible areas of research.

Background

The EU is a fascinating political and economic structure that is now being copied and

followed around the world. The African Union (AU) has been established and the Central Asian

Union is beginning the steps of formation. To better understand the complexity and intrigue of

the EU, it is necessary to understand the history of its formation and integration. The EU began

as the European Coal and Steel Community in 1950. It started as a way to improve trade

between the original six members: Belgium, France, Germany, Italy, Luxembourg, and the

Netherlands. Since then, the EU has grown in economic integration among members and

increased in member size. In 1993, the EU completed its integration into a single-market

economy, which allowed for the free movement of goods, services, people, and money within

it. In 2002, a common currency called the Euro was introduced and was made the official

currency of 12 member states, with some members opting out of the currency. The EU has also

seen three different expansions in the past 10 years. 2004 saw the accession of 10 countries to

the EU, most of whom were from the Eastern Block. In 2007, Bulgaria and Romania joined the

EU ranks and in 2013 Croatia became the newest member.

Today, the EU has a total of 28 members. It’s total GDP is larger than the United States’

as of 2012. On the global scale, the EU is responsible for 16.4% of total imports and 15.4% of

total exports, both of which are larger than the United States and China. The EU is a global

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powerhouse in almost every sphere and many of the members have benefitted from this

integrated, single-market economy.

As of right now, the EU has five official candidate countries attempting to ascend to

membership. These countries - Iceland, Montenegro, Serbia, The Former Yugoslav Republic of

Macedonia, and Turkey - are in the process of fulfilling the Copenhagen criteria, or the

accession criteria. After these criteria are met, accession negotiations are opened and the

applicant state must fulfill every chapter’s benchmarks, which prepare them for EU

membership. An Accession Treaty is then signed by all the member states. In addition to these

candidate countries, the EU has listed three “potential candidates” in Albania, Bosnia and

Herzegovina, and Kosovo. These countries are all striving to become EU members for the

perceived economic benefits.

Literature Review

The subject of integration and economic growth with the European Union (EU) has been

studied by a number of different scholars. Neoclassical growth theory says that integration can

only affect growth rate in the short run. This is due to the Solow-Swan model that says growth

in a steady state economy is only a result of technological growth. However, much research

shows that the single market of the EU has allowed for increased economic growth of member

countries and those who would join the EU in the long run. Since its final transformation into a

single-market economy in 1993, much of the EU research analyzes if further integration leads to

economic growth as these theories conclude.

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Henrekson et al (1997) was one of the first papers to do this examination. The authors

do not focus on the levels of integration. They only set one dummy variable for the country if it

is in the European Commission (EC) and the European Free Trade Association (EFTA). They use

initial GDP, education level, investment, and exchange rate, in addition to their dummy variable

as determinants of economic growth. One interesting variable that they take into account after

running their initial OLS regression is the development level of the country. They run another

regression with two dummy variables that count if the country’s GDP per capita level was above

4,000 US dollars and then above 6,000 US dollars. The authors conclude that membership in

the EC and the ETFA increase growth rates from .6% to .8% for members.

Similarly, Vanhoudt (1999) looks to examine the effects European unification had on

economic growth. Using the 23 Organization for Economic Co-operation and Development

(OECD) member countries, the author does a panel-data regression to determine the effect EU

membership had on economic growth compared to those not in the EU. When comparing

these two groups, Vanhoudt does not find that there is additional growth in the long run for EU

members. He does find that membership may increase trade flow, but he does not conclude

unification causes more an increase in growth rate.

More recently, Cuaresma et al (2008) analyzes the effects of EU membership on

economic growth and convergence. Unlike Vanhoudt and Henrekson, these authors focus solely

on EU members, and whether or not the EU integration has caused converging GDP per capita

numbers among members. The authors in this article decide to run the panel-data regression

model to look at the changes in log of GDP per capita of each country in the EU. They use

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determinants such as investment share, average education level, inflation rate, government

consumption, openness, and years in the EU. According to the authors’ model, EU membership

does have a positive and asymmetric effect on a country’s economic growth in the long run.

Meanwhile, Lejour and de Mooij (2005) examine the specific gains and losses from

Turkey joining the EU. Turkey and the EU entered into a Customs Union in 1995 and Turkey

finally became an official candidate country in 1999. The article uses the WorldScan Model to

compare growth of regions in the world and sectors within those regions. The authors find that

if Turkey becomes an EU member, GDP for Turkey would increase by .8% and consumption

would increase by 1.4%. If EU membership also caused an improvement in Turkish political

structures and corruption, GDP would increase by 5.6%. However, the free flow of labor would

decrease Turkey’s GDP from 1.8% to 2.2%.

Theoretical Analysis

This paper focuses on the changes in GDP per capita from 2004 to 2012 of the 36

countries in or associated with the EU. My hypothesis is that the countries on the outer most of

the EU integration circle (i.e. the “potential” candidates) would be able to recover and grow

quicker after a recession than those in the center of the EU circle. This is because those

countries who are further integrated would have to address all the struggles in the EU before

the recovery process startes. Countries like Portugal, Italy, Ireland, Greece, and Spain are still

struggling from the 2008 recession. For example, Spain’s GDP has yet to reach pre-recession

levels and Greece has had to be the recipient of multiple aid packages from the EU. I

hypothesize these countries hold back the EU from moving forward and recovering. Thus, those

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countries on the outer circle would have a quicker and a greater recovery than those at the

center.

This study uses economic data from 2004 to 2012. This will require a time series and a

test for autocorrelation. The autocorrelation from the time series will violate the Classical

Econometric Model (CEM), the process by which the regressions are run. This makes the

Ordinary Least Squares (OLS) models not BLUE, which will be explained later in the results.

Therefore, this paper will have to use the Feasible Generalized Least Squares (FGLS) models on

the first difference of GDP per capita in each of the countries. Also, the regressions will come

from a data-panel because it is a study of list of countries.

Before examining the different groups of regressions, the error term needs to be

considered. Barreto and Howland (2006) define the error term as “an element of a linear

regression equation which encapsulates the effects of measurement error, omitted variables,

and luck” (Barreto and Howland, 752). Measurement error is the inaccuracy of the measured

variables. The chance results from differences in regressions and data. In this case, the error

could be due to not enough years being taken into account or Omitted Variable Bias (OVB).

OVB is the disregard of important variables in the regression that find their way into the

error term. OVB is defined as, “any variable not included as an independent variable in the

regression that might influence the dependent variable” (Barreto and Howland, 490).

Observational studies, such as this one, are subject to the threat of OVB. In this case, OVB could

be from the exclusion of important economic factors, the differences in human capital, or the

differences in economic diversity in the panel of countries. All of these would be included into

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the error term which would skew the regressions’ coefficients. Thus, the error term cannot be

disregarded when interrupting these different regressions. Both the error term and OVB could

lead to inaccurate results and inaccurate conclusions.

Empirical Analysis

A. Data

This analysis will measure the recovery process and then potential growth of the

countries in the EU as well as those on the outside in different levels of integration with it.

Some of the economic factors that will be taken into account are change in inflation rate,

imports, exports, change in unemployment, and government debt. There will also be some

dummy variables for the countries on their level of integration: EU membership level (member,

candidate, possible candidate), and use of the Euro within the EU.

The data for this research was gathered from Worldbank.org. Worldbank provides a

comprehensive country-by-country dataset of many economic variables over time, some dating

are back to 1960. This paper will focus on 36 countries and their official level of relation to the

EU. The dummy variables that help illustrate integration and relationship with the EU were

derived from Europa.eu, the official EU website. These 36 countries come from the 28 current

EU members, the five official listed candidates, and the three listed “potential” candidates.

Table 1 has descriptions of all the different variables used.

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Variable Description

GDPPCDetermines GDP Per Capita level in 2005 US dollars at the end of each respective period.

DummyEUMemberDetermines if Country was an EU member (value of 1), or not (value of 0) by the end of each respective period.

DummyEUCandidateDetermines if Country was an EU Candidate (value of 1), or not (value of 0) by the end of each respective period.

DummyEuroDetermines if Country's offi cial currency was the Euro (value of 1), or not (value of 0) by the end of each respective period.

InflationPercentChangeDetermines the percent change of the Country's Inflation rate from the previous year in each respective period.

UnemploymentPercentChange

Determines the percent change of the Country's Unemployment rate from the previous year in each respective period.

PercentChangeInFDIofGDP

Determines the percent change of the Country's Foreign Direct Investment as a percent of GDP from the previous year in each respective period.

PercentChangeInImportsofGDP

Determines the percent change of the Country's Imports as a percent of GDP from the previous year in respective to each period.

PercentChangeInExportsofGDP

Determines the percent change of the Country's Exports as a percent of GDP from the previous year in respective to each period.

difGDPPCDetermines the first difference in the Country's GDP Per Capita Levels from the previous year to the next.

Table 1: Variable Description

Note: Year Dummy Variables in Table like (ex. Dummy05) is determined by if the year is 2005 (value of 1) or not (value of 0).

In Table 2 there is a comprehensive list of all the different variables means, standard

deviations, minimums, maximums, and the number of observations for each variable. There

are somes holes in the data where it was missing. These missing observations were simply not

included into the regression process when they occurred. Thus, not all observations are equal

to 324 (36 countries x 9 year period).

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Variable Obs Mean SD Min MaxCountryNumber 324 18.5 10.4044 1 36Year 324 2008 2.8598 2004 2010GDPPC 324 22550.86 18429.58 2070.92 87716.73DummyEUMember 324 0.7314 0.4439 0 1DummyEUCandidate 324 0.1111 0.3148 0 1DummyEuro 324 0.4012 0.4909 0 1InflationPercentChange 324 10.1023 365.0345 -3927.39 4300.684PercentChangeInFDIofGDP 319 0.0326 11.9523 -129.776 85.8518PercentChangeInImportsofGDP 312 2.394 9.1731 -30.0414 41.6506PercentChangeInExportsofGDP 312 3.191 8.9615 -23.0176 50.9963UnemploymentPercentChange 308 3.475 24.7349 -100 150.9091difGDPPC 288 148.603 998.19 -6234.26 4140.82

Table 2: Summary of Data

The dependent variable in the regression is GDP Per Capita, “GDPPC”. GDP Per Capita is

obtained it by taking the overall GDP level of a country divided by the population of that

specific country. This will allow for comparison of economic growth with controlling for

differences in population and population growth. However, “GDPPC” as the dependent variable

leads to heavy autocorrelation. For this reason, the dependent variable becomes the first

difference of GDP Per Capita, which is listed as “difGDPPC”.

The independent variables included are hypothesized to be contributing factors to a

country’s GDP Per Capita level. The three dummy variables are where most of the focus of the

paper will occur. The three dummy variables measure the level of integration into the EU. The

inner most circle will be the Euro dummy variable, the next will be the EU membership, then

the EU candidate, and, lastly, in the outer circle are the “potential” candidates. The EU

membership dummy variable will show the benefits or costs of integration into the EU with

membership. The EU candidate dummy variable will show the benefits or costs of association to

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the EU with candidate status. The Euro dummy variable will show how further integration into

the EU with the use of the Euro as the official currency affects GDP Per Capita.

“InflationPercentChange” is an independent variable that reports the change of inflation

in relation to the previous year. Simply using Inflation would cause a dependency on the

previous Inflation levels. By using the percent change of Inflation, the variable stands more

individually as simply the relative change inflation in that specific year.

“UnemploymentPercentChange” is an independent variable that reports the change of

unemployment in relation to the previous year. In a similar fashion to

“InflationPercentChange”, this variable needs to show its relative change to the previous year

instead of being dependent upon it. “PercentChangeInImportsofGDP” and

“PercentChangeInExportsofGDP” simply reflect the relative change the imports and exports,

respectively, of a specific country as a percentage of its total GDP from the previous year.

Finally, “PercentChangeInFDIofGDP” measures the percent change of Foreign Direct Investment

(FDI) from the previous year as a percent of GDP.

In addition to these variables, there are variables given to each country as a designation,

resulting in a list from 1 to 36 with the variable “Country Number”. Additionally, the “Year”

variable covers for the change over different years. In this study, the time series will begin in

2004, four years before the recession, and end with the most recent complete data available in

2012. Starting in 2004 is also important as it will allow for the inclusion of all the current

members that joined in that year into the “DummyEUMember” variable. Not listed in Table 2

are the dummy year variables for the years 2004 to 2012. These will be used in Model 6 to

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control for the fluctuations in each year. For example, “Dummy05” will be 1 for the data in 2005

and 0 for every other year.

B. Results

This section interprets the regressions. It should be noted that these regressions are

attempting to find the significance of the integration into the EU. Thus, a dummy variable for

potential candidates is left out of the regressions. When the regressions include the

“DummyEUMember”, “DummyEUCandidate”, and “DummyEuro”, their coefficients report the

change in the dependent variable in comparison to the potential candidates. It should also be

noted that the first difference in GDP is in the terms of 2005 constant US dollars, so all the

interpretations are in 2005 US dollars.

Finally, all of the models have heteroskedasticity. Heteroskedasticity is when the

residuals of the model have a pattern, meaning that they have different error boxes. This, like

the autocorrelation, violates the CEM as the errors are not drawn from the same box with the

same standard deviation. The requirements for the CEM can be found in the Appendix. A

Beusch-Pagan test (BP test) tests for heteroskedasticity. Every model, when run through the BP

Test, results in p values below .05. This means the null hypothesis that there is no

heteroskedasticity is rejected. With heteroskedasticity, the OLS regression is no longer BLUE

because it is no longer the most accurate. In order to fix this, robust SEs are used. This makes

the OLS regressions BLUE again. Therefore, all of the reported SEs in Table 3 and Table 4 are

robust SEs.

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Model 1 (GDPPC)

Model 2 (difGDPPC)

Model 3 (difGDPPC)

Model 4 (difGDPPC)

Model 5 (difGDPPC)

Model 6" (difGDPPC)

Constant21513.48*** (2526.539)

144.222 (141.076)

137.786 (159.344)

103.744 (170.961)

76.795 (206.783)

-13.245 (217.323)

DummyEUMember591.007

(728.806)14.779

(164.232)138.792

(188.028)246.791(18

4.928)201.618

(218.494)-59.511

(206.496)

DummyEUCandidate16.158

(181.562)14.107

(202.806)-17.051

(238.297)-228.283 (234.850)

DummyEuro-212.121 (169.301)

-177.180 (146.048)

-160.758 (145.191)

-72.347 (117.869)

InflationPercentChange .189 (.134) .139 (.126) .058 (.103)

UnemploymentPercentChange-18.755***

(2.637)-15.790***

(3.059)-5.437* (2.850)

PercentChangeInFDIofGDP-8.880 (8.000)

-9.497 (6.447)

PercentChangeInImportsofGDP20.8875* (10.928)

-.259 (7.236)

PercentChangeInExportsofGDP5.392

(10.607)-10.245 (8.176)

R_Squared 0.119 0.0042 0.0098 0.2365 0.3015 0.4999Observations (n) 324 288 288 276 263 263Rho 0.9976 0.25 0.2454 0.2322 0.2452 0.2167

" Model 6 includes dummy variables for years 2005, 2006, 2007, 2008, 2009, 2010, and 2011, which are not shown on the chart. Model Equations and ful l table (Table 4) can be found in the Appendix.

Note: *p<.10, **p<.05, and ***p<.01

Table 3: OLS Regression Predicting GDPPC and FGLS Regressions Predicting difGDPPC

Table 4 contains the six different regressions used in this paper. Model 1 is different

from the rest of the regressions on the table because it is an OLS regression. Model 1 is a simple

bivariate regression of GDP per Capita based on the dummy EU membership variable. The

regression does not provide a statistically significant result in terms of the EU dummy variable

and it also has a low R2 value of .1190. This means that regression is only 11.9% better at

predicting the GDP per Capita than using the sample average. In addition to these two

problems, Model 1 has a high level of autocorrelation. This can be seen in the rho (ρ) value

of .9976. This violates the CEM as the error terms are no longer independent of each other.

Autocorrelation causes the OLS regressions to become biased, which cannot be fixed if the

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observations increase. Instead, the Generalized Least Squares (GLS) model becomes BLUE.

However, we can never know the true value of ρ, so the rest of the models run FLGS

regressions.

Models 2 through 6 are FGLS regressions, which changes each of model’s equation. In

order to lower autocorrelation, the regressions are on the difference of GDP per Capita levels

from year to year. For example, Austria in 2004 and 2005 had a GDP per Capita level of

$36,455.77 and $37,067.32, respectively. In the model, it would be Yt – Yt-1 = YDif. Thus,

$37,067.32 - $36,455.77 = $621.55, the difference of GDP per Capita. This allows the data to be

more independent of each other and not dependent on the previous period, which reduces

autocorrelation. However, as can be seen in the rest of the ρ values between .2500 and .2167,

there is still some autocorrelation. These levels are less intense and their regressions can still be

used by changing the model equation. Models 2 through 6 take the ρ into account. This full

change be seen in the Appendix in the Model Equations section.

Moving on to Models 2 to 6, these regressions are FGLS. It can be noted that they also

have fewer observations. This is due to taking the first difference of GDP Per Capita from the

data, meaning 2004 did not have a value. As more variables are added to the regression, there

are less observations due to holes in the data. These holes were rare and random, thus they

were ignored in the regressions.

Model 2 does not provide a clear picture of the data. In this model, the first difference

of GDP is regressed based on the EU membership dummy variable. However, it does not give a

very clear picture of the data at all. The R2 value is only .0042, meaning the regression is

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only .42% better than the sample average at predicting the dependent variable. Neither the

constant nor the EU dummy variable were close to being statistically significant.

Autocorrelation is still present with a ρ value of .2500 in Model 2.

Model 3 only does slightly a better job but is still too far from being able to draw any

conclusions. Model 3 adds in two additional dummy variables: EU candidate status and use of

the Euro. The R2 of .0098 leads to a .98% better at predicting the dependent variable than the

sample average, barely twice as much as Model 1. Again, none of the variables are found to be

statistically significant. However, the ρ value did decrease slightly in comparison to Model 2

with a value of .2454.

Model 4 begins to bring in other economic factors. Model 4 only includes the

“InflationPercentChange” and “UnemploymentPercentChange”. These were added first to

examine how the change in national economic factors affected the dependent variable. It works

as Model 4 begins to do a better job. With a respectable R2 value of .2365, Model 4 is 23.45%

better at predicting the difference of GDP per Capita than the sample average. This is over 24

times more effective than Model 3. Although, none of the dummy variables are still statistically

significant, the first statistically significant variable is found in “UnemploymentPercentChange”.

With a value of -18.755, this means if the unemployment rate’s percent change increases by 1%

in relation to the previous year, the difference in GDP per Capita will decrease, on average, by

about $18.76, ceteris paibus. And the ρ value continues to decrease, as well, with a value

of .2322.

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Model 5 continues this improvement. Model 5 includes variables that depend on

international relationships, including “PercentChangeInFDIofGDP”,

“PercentChangeInImportsofGDP”, and “PercentChangeInExportsofGDP”. These would be

expected to change due to EU member, so it is necessary to take them into consideration. The

R2 has increased again to .3015, leading to 30.15% better predictions than the sample average.

However, none of the three dummy variables continue to be statistically significant. Again,

“UnemploymentPercentChange” is extremely significant. At a value of -15.790, an increase of

1% of Unemployment rate percentage compared to the previous period will result in a decrease

in the difference of GDP per Capita by about $15.79, holding everything else constant.

Additionally, “PercentChangeInImportsofGDP” is reported as slightly significant. When Imports

as a percent of GDP increase by 1% in relation to the previous period, difference of GDP per

Capita will increase by $20.89 on average, ceteris paribus. However, autocorrelation is slightly

higher in this model with a ρ value of .2452.

Model 6 includes dummy variables for the year. This allows the model to control to

fluctuations that naturally occurred in the economy. Model 6 is the best regression in

comparison to the others with the highest R2 of the group at .4999. This means Model 6 is

49.99% better at predicting the dependent variable than the sample average. This is over twice

as much as Model 4 and over one-and-a-half times better than Model 5. Again, however, the

three dummy variables are not found to be statistically significant. Besides every year dummy

variables, only “UnemploymentPercentChange” is found to be slightly significant. This means

that if Unemployment rate increases by 1% in relation to the previous period, difference of GDP

per Capita will decrease by $5.44 on average, ceteris paribus. Additionally, Model 6 contains

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the smallest ρ value of the group at .2167 meaning it has the smallest amount of first-order

Autocorrelation.

After Model 6, I also a few ran hypothesis tests to confirm the results. When testing if

“DummyEUMember” = “DummyEUCandidate” = “DummyEuro” = 0 results in a p value of .6246.

The null hypothesis, that the dummy variables do not matter, cannot be rejected. Similarly,

testing the “InflationPercentChange” = “UnemploymentPercentChange” =

“PercentChangeInFDIofGDP” = “PercentChangeInImportsofGDP” =

“PercentChangeInExportsofGDP” = 0, which results in a p value of .1348. Thus, the null

hypothesis, that these economic variables do not matter, cannot be rejected.

Conclusion

This paper looks to examine the effects recessions have on countries in or associated

with the EU. Do countries that are fully integrated into the EU, like Spain, recover quicker after

an economic recession? Or are those countries on the outside of the circle, like Albania, able to

recover faster? To answer these question, I set up regressions with three dummy variables that

measured the level of integration into the EU. The innermost circle are the countries who are in

the EU and use the Euro. The next circle include all of the EU members. The third circle include

the candidate countries to the EU. All of these are compared to the “potential” countries, who

maintain the outermost circle.

However, the analysis of the data does not provide a statistically significant result for

any of these three dummy variables. Therefore, according to this study, the level of integration

does not matter when looking at economic recovery after a recession. If another recession were

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to occur, a conclusion about who would recovery faster between Spain and Albania, for

instance, could not be made. This means that countries looking into joining the EU should not

worry about future recessions in terms of economic recovery. The only slightly significant result

in the final model is “UnemploymentPercentChange”. In this model, if there is a one percent

change of unemployment rate from the previous period, the GDP Per Capita will decrease by

approximately $5.347, ceteris paribus.

The results about the integration dummy variables do not go against any previous

results from different research as there has not been much research on the topic. Most of the

research has shown an increase in GDP growth rates for those countries who do join the EU

which cannot be rejected by this paper. Further research on this subject might provide a clearer

picture and significant results. One area of further research could be by including more years

before and after. If there had been more time after the recession, the results might be

different. This is because some of the countries are still struggling from the recession. If enough

time was allowed after the recession to where all the countries had fully recovered, there

would be a more whole picture of the recession and subsequent recovery. This paper, as it only

goes to 2012, is working with an incomplete picture. Additionally, adding more variables, not

just economic, will provide a more complete description of the situation. This could include

education levels or healthcare quality. This would control for differences in the capital make-up

of the countries and their abilities.

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Appendix

Requirements of the Classical Econometric Model*1. The model must be linear in the parameters, and it contains an additive error.2. The Xs are fixed in repeated sampling3. The error terms have a mean of zero.4. The error terms are independent of one another.5. Each error term draw comes from the same box with the same SD.6. The error terms are independent of the X’s.7. A technical requirement is that the X’s, including the intercept tem, cannot have an exact linear relationship.

* Barreto and Howland (330-331)

Model Equations

Model 1: Predicted GDP Per Capitait = b0 + b1 DummyEUMemberit + Ԑit

Model 2: Predicted difGDP Per Capitait* = b0 (1 – ρ) + b1 DummyEUMemberit* + νit

Model 3: Predicted difGDP Per Capitait* = b0 (1 – ρ) + b1 DummyEUMemberit* + b2 DummyEUCandidateit* + b3 DummyEuroit* + νit

Model 4: Predicted difGDP Per Capitait* = b0 (1 – ρ) + b1 DummyEUMemberit* + b2 DummyEUCandidateit* + b3 DummyEuroit* + b4 InflationPercentChangeit* + b5 UnemploymentPercentChangeit* + νit

Model 5: Predicted difGDP Per Capitait* = b0 (1 – ρ) + b1 DummyEUMemberit* + b2 DummyEUCandidateit* + b3 DummyEuroit* + b4 InflationPercentChangeit* + b5 UnemploymentPercentChangeit* + b6 PercentChangeInFDIofGDPit* + b7

PercentChangeInImportsofGDPit* + b8 PercentChangeInExportsGDPit* + νit

Model 6: Predicted difGDP Per Capitait* = b0 (1 – ρ) + b1 DummyEUMemberit* + b2 DummyEUCandidateit* + b3 DummyEuroit* + b4 InflationPercentChangeit* + b5 UnemploymentPercentChangeit* + b6 PercentChangeInFDIofGDPit* + b7

PercentChangeInImportsofGDPit* + b8 PercentChangeInExportsGDPit* + b8 Dummy05it* + b9

Dummy06it* + b10 Dummy07it* + b11 Dummy08it* + b12 Dummy09it* + b13 Dummy10it* + b14

Dummy11it* + νit

* Yit* = Yit - ρYi(t-1) and Xit* = Xit - ρXi(t-1)

Adams 20

Full Regression Table

Model 1 (GDPPC)

Model 2 (difGDPPC)

Model 3 (difGDPPC)

Model 4 (difGDPPC)

Model 5 (difGDPPC)

Model 6" (difGDPPC)

Constant21513.48*** (2526.539)

144.222 (141.076)

137.786 (159.344)

103.744 (170.961)

76.795 (206.783)

-13.245 (217.323)

DummyEUMember591.007

(728.806)14.779

(164.232)138.792

(188.028)246.791(18

4.928)201.618

(218.494)-59.511

(206.496)

DummyEUCandidate16.158

(181.562)14.107

(202.806)-17.051

(238.297)-228.283 (234.850)

DummyEuro-212.121 (169.301)

-177.180 (146.048)

-160.758 (145.191)

-72.347 (117.869)

InflationPercentChange .189 (.134) .139 (.126) .058 (.103)

UnemploymentPercentChange-18.755***

(2.637)-15.790***

(3.059)-5.437* (2.850)

PercentChangeInFDIofGDP-8.880 (8.000)

-9.497 (6.447)

PercentChangeInImportsofGDP20.8875* (10.928) -.259(7.236)

PercentChangeInExportsofGDP5.392

(10.607)-10.245 (8.176)

Dummy05851.172*** (161.152)

Dummy06998.967*** (142.880)

Dummy07913.952*** (143.523)

Dummy0881.301

(150.646)

Dummy09-1160.823***

(276.658)

Dummy10503.718** (199.493)

Dummy11502.818*** (140.253)

R_Squared 0.119 0.0042 0.0098 0.2365 0.3015 0.4999Observations (n) 324 288 288 276 263 263Rho 0.9976 0.25 0.2454 0.2322 0.2452 0.2167Note: *p<.10, **p<.05, and ***p<.01

Table 4: OLS Regression Predicting GDPPC and FGLS Regressions Predicting difGDPPC with Dummy Years

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FINAL.do

set mem 400muse N:\eco253\cdadams15\FINAL.dtasumsort countrynumber yeartsset countrynumber yearreg GDPPC dummyeumemberprais GDPPC dummyeumembergen laggdppc =l1.GDPPCgen difGDPPC = GDPPC - laggdppcreg difGDPPC dummyeumemberestat hettestprais difGDPPC dummyeumember, robustreg difGDPPC dummyeumember dummyeucandidate dummyeuroestat hettestprais difGDPPC dummyeumember dummyeucandidate dummyeuro, robustreg difGDPPC dummyeumember dummyeucandidate dummyeuro InflationPercentChange

UnemploymentPercentChangeestat hettestprais difGDPPC dummyeumember dummyeucandidate dummyeuro InflationPercentChange

UnemploymentPercentChange, robustreg difGDPPC dummyeumember dummyeucandidate dummyeuro InflationPercentChange

UnemploymentPercentChange PercentChangeInFDIofGDP PercentChangeInImportsofGDP PercentChangeInExportsofGDP

estat hettestprais difGDPPC dummyeumember dummyeucandidate dummyeuro InflationPercentChange

UnemploymentPercentChange PercentChangeInFDIofGDP PercentChangeInImportsofGDP PercentChangeInExportsofGDP, robust

reg difGDPPC dummyeumember dummyeucandidate dummyeuro InflationPercentChange UnemploymentPercentChange PercentChangeInFDIofGDP PercentChangeInImportsofGDP PercentChangeInExportsofGDP Dummy05 Dummy06 Dummy07 Dummy08 Dummy09 Dummy10 Dummy11

estat hettestprais difGDPPC dummyeumember dummyeucandidate dummyeuro InflationPercentChange

UnemploymentPercentChange PercentChangeInFDIofGDP PercentChangeInImportsofGDP PercentChangeInExportsofGDP Dummy05 Dummy06 Dummy07 Dummy08 Dummy09 Dummy10 Dummy11, robust

test InflationPercentChange = UnemploymentPercentChange = PercentChangeInFDIofGDP = PercentChangeInImportsofGDP = PercentChangeInExportsofGDP = 0

test InflationPercentChange = UnemploymentPercentChangetest dummyeumember = dummyeucandidate = dummyeuro =0

Adams 22

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