Deracination and its economic impact

AMERICAN UNIVERSITY IN BULGARIA DEPARTMENT OF ECONOMICS

Deracination and its Economic Impact on European Countries

Senior Thesis

Valeriia Tretiakova

5/6/2016

This paper was prepared with the help and advice of Prof. Jeffrey Nilsen, Ph.D., Economics.

Valeriia Tretiakova Senior Thesis 2016

Abstract

The paper investigates the effect of refugees into economies of Austria,

France, Germany, Switzerland, and the United Kingdom. The issue is widely

discussed in light of recent massive inflow of forced migrants in countries of

Europe; it raises various questions in media and influences contemporary political

views. The first part of the research compares obtained results with the ones

available in the literature. The second part introduces the augmented Solow growth

model along with econometric analysis used to examine the problem; both fixed

effects and random effects models results presented in the analysis of panel data.

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Table of Contents Abstract

Introduction

Literature Review

Economic Model

Data Description

Empirical Specification

Expected Signs

Results

Effect of Refugees on Economic Development Indicators

Motivation

Specification Results

Solow Model Results

Refugees as endogenous variable

Conclusion

References

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I. Introduction

Deracination, also known as forced migration, is a well-known process that

can be defined as a coerced movement of people from their original settlements due

to unexpected circumstances that are either a direct or indirect threat to their lives

and well-beings. Those, who have experienced deracination, in the contemporary

literature usually referred as forced migrants or refugees (Oliver 2006).

With an escalation of local and global conflicts the number of refugees has

dramatically increased over the recent decade. People seeking asylum move to host

countries in hope of peaceful and secure lives. The most recent European migrant

crisis has reached its escalation in April 2015, when more than twelve hundred

asylum-seekers were trying to reach Europe, but drowned or went missing in the

Mediterranean Sea (UNHCR 2015). As the number of refugees entering the

European Union increases on a daily basis, the topic attracts wider public attention

and urges an immediate reaction by policymakers. The pattern is illustrated on the

Graph 1.

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Do refugees hurt economies of host countries? In order to make proper

policies there is a need to research the issue. Based on her research, Sesay (2004)

presents evidence that refugees hurt economies of the host countries in the

short-run.

Austria, France, Germany, Switzerland, and the United Kingdom are the

major refugee host countries in Europe (Barberić 2015). Jointly they have accepted

over 445,000 forced migrants only in 2014 (UNHCR 2015b). Studying the impact of

refugees on the countries of Europe within last decades may show the pattern that

can be applicable to many other countries.

A preliminary analysis showed that economically developed countries tend to

worsen economic conditions by accepting forced migrants (Kuhlmann 1990).

Therefore, I expect to provide evidence that Austria, France, Germany, Switzerland,

and the United Kingdom incur losses by accepting a growing number of refugees.

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Graph 1. First-Time Asylum Applicants in Europe. Information Source: Eurostat

database.

Graph 2. Forced Migrants by Region. Information Source: Eurostat database.

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Graph 3. Refugees per GDP per capita (in US$). Information Source: Eurostat

database.

II. Literature Review

Sesay (2004) studied the relationship between the economic performance of

host countries and the number of refugees received. The presence of a conflict and

its proximity to a host country was of a particular interest for the author. She aimed

to explain cross-country differences by studying such variables as initial GDP per

capita, illiteracy rate, conflict situation, and etc. In broader terms, using the

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augmented Solow growth model that also includes several socioeconomic variables

the author argues that both conflict and forced migrants have a significant negative

influence into the economy of a host country.

She takes the sample of seventy two countries (5 from North Africa, 47 from

sub-Saharan Africa, 31 from Asia, the Pacific, Latin America, and the Caribbean)

and uses twenty seven different variables to study the issue. The empirical results of

her study showed that refugees tend to have negative effect on the economies

through growth variables.

Another point of view comes from Barro (1995), who argues that refugees

have a positive impact on the economies of host countries. The author believes that

refugees that received proper financial support for resettlement and adjustment to

their new environments contribute to cultural, social, and economic realities of the

host countries. In Australia refugees increase consumer markets for domestic

commodities, bring new skills, increase human capital, provide employment by

opening new businesses, and fill vacant employment positions requiring different

levels of education.

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On the other hand, the UN’s refugee agency gives several examples of

refugees being a negative factor for the host countries. According to its studies, as

soon as they arrive to a country, refugees start competing with local citizens for

scarce resources: housing, food, water, medical services, and land. As a result, they

may cause inflationary pressures, and decrease real wages. Additionally, when it

comes to rural areas, a flow of refugees creates a burden to local administrations that

are forced to redistribute resources and manpower that were once aimed to satisfy

local needs and contribute to development of the area (UNHCR 2015).

However, there are also positive impacts. According to the UNHCR agency,

host counties receive massive international attention in terms of funding. Besides

UNHCR international aid, there is an emergency assistance program introduced by

the World Bank in 1991 with a suggested amount of $25 million to be invested into

refugee-host countries. Also, international agencies may indirectly trigger economic

improvement through local food, supplies, and shelter materials purchases as a part

of relief programs (UNHCR 2015).

One of the fundamental objectives of this study is to scrutinize the effects of

refugees on host nations as a whole, instead of focusing solely on the economic

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welfare of refugees themselves. I will review literature not only in light of what the

effects of hosting refugees have been, but also include the magnitude of refugee

flow in Europe, the positive and negative effects of hosting refugees, and problems

associated with refugee assistance.

The refugee problem, how to handle them, is not a new issue. According to

Neumayer (Neumayer 2004), UNHCR together with several countries worked on

the development of system that would allow minimizing the effect that refugees

have on a host country. Today, Europe is seen bearing the huge burden of refugees

in the aftermath of the 2008 crisis. Even though growth mostly depends on

resources, capital formation, trade, infrastructure, and other economic variables,

refugees might also influence the GDP growth depending on a country’s state of

economy (Sesay 2004). According to UNHCR statistics, the UK accepted an

estimated 14,065 refugees in the country in 2014. Forced migrants mostly come

from devastated areas, where their lives are threatened by war and disruptions of

food supply.

Khasiana’s (Khasiana 1989) studied the issue of gender inequality among male

and female refugees. He came to conclusion that there is uneven distribution of

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scarcity resources. Massive inflow of forced migrants puts pressure on economies of

host countries. The poverty also becomes a major issue due to increase in

population. As a result, scarcity of resources problem leads to ill-suited support

programs for forced migrants, where women are discriminated.

Refugees are unequally distributed around the world. Therefore, it would be

deceptive to assume that Europe as a continent carries a heavy burden of hosting

forced migrants. Kibreab (Kibreab 1983) based on his research mentions that 18

countries of OAU host, approximately, 90% of refugees. At the same time, these

African countries are among the least developed economies. They also were

significantly affected by the 2008 crisis. Due to this situation, international society

introduced a concept of burden-sharing. It suggests that countries without forced

migrants should financially support refugee-host countries. Erikson et al (Erikson

1981) elaborates on the topic: if countries adhere to the principle, they will ease the

burden of hosting internally displaced persons.

The principle does not assume that refugees should be transferred from one

country to another. It should be in a form of financial contributions. Refugees

produce a complex range of effects on international economies: they affect

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themselves, their countries of origin, countries-recipients, and, finally, global

economy (Sesay 2004). Thus, refugees are a heavy burden for the economy.

However, at certain conditions, they might lead to economic growth (Erikson 1981).

III. Economic Model

Economic growth theories were founded several centuries ago. The most

notable contributors to the foundations of the topic were Adam Smith, Thomas

Malthus, and etc. The 20th century is characterized by the emergence of new

thoughts related to economic growth. They were formulated in the work known as

the Harrod-Domar model. Later on, as a response, there was created the neoclassical

model, developed by Ramsey (1928), Solow (1956), Swan (1956) and several others

notable economists (Sesay 2004).

The Solow growth model is considered to be one of the most influential

among others. It does not only provide useful insights into changes in economic

growth, but also creates a framework necessary for the assessment of certain

macroeconomics policies. On the other hand, there are many scholars, who question

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the feasibility of the growth theory. For instance, Douglass North (1974) argues that

it fails to explain observed trends and patterns.

According to economic theory, the Solow growth model shows county’s

output, Y, as a function of capital, K, labor, L, and knowledge or the effectiveness of

labor, At.. It features a Cobb-Douglas production function for diminishing returns in

labor, physical and human capital. The model also includes constant returns to scale

along with the steady state growth path. Savings here equal total investment in

physical and human capital. The Solow growth model is presented below:

(t) K(t) [A(t)L(t)] , where 0 (1)Y = α 1−α < α < 1

The model takes the rates of saving, population growth and technological

progress as exogenous growing at rates n and g:

In order to determine an empirical effect of deracination on economic

growth, we further augment the Solow growth model to include refugees as a

determinant of economic growth:

(t) H [A(t) (t)] , where 0 (2)Y = K(t)α β * L 1−α−β < α + β < 1

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are the elasticities of the output with respect to physical and human and βα

capital respectively. Y(t) is aggregate real output at time t, K is real physical capital

stock, L is labor, H is human capital, A(t) is a technology parameter embodied in

labor and is the output elasticity of effective units of labor A(t)L(t).1 α )( − − β

Labor is assumed to grow at a constant rate n, technical progress growth at the

exogenous rate g and both human and physical capital depreciates at the same rate

of δ. Given the above, we divide both sides of the Eq. (2) by effective labor AL

given an expression in terms of income per effective worker (y=Y/AL), which is

equal to:

(t) h(t) (3)y = k(t)α β

Quantities per effective worker are presented in the form of and .k = KAL h = H

AL

In order to complete the model we need the transition equations for physical and

human capital that account for the growth of capital per capita ( and human) k

capital per capita ( . By assuming that equations of motion for labor (L),)h

labor-augmented technology (A), physical (K) and human (H) capital are:

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(t) L(t) (4)L = n

(t) A(t) (5)A = g

(t) Y (t) K(t) (6)K = sk − δ

(t) Y (t) K(t) (7)H = sH − δ

The transition equations of physical and human capital are derived as

following:

(t) y(t) n ) k(t) (8)k = sk − ( + g + δ

and

(t) y(t) n ) h(t) (9)h = sh − ( + g + δ

Therefore, the equation for the steady state of physical and human capital per

capita, where . It allows the economy to converge to a steady state:h = k = 0

(10)k*= ( )n+g+δ

s s k1−β

hβ 1

1−α−β

and

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(11)h*= ( )n+g+δ

s s kαh1−α 1

1−α−β

We can substitute it into the production function in equation (3), the steady

state level of output per effective worker is:

y ( ) (12) * = A(t)( )skn+g+δ

1−α1−α−β sh

n+g+δα

1−α−β

We can also express the production function as in equation (13).

(t)k h (13)L(t)Y (t) = A α β

The steady state per capita GDP estimates require taking logs of the equation above:

n t lns ln(n ) (14)l L(t)Y (t) = c + g + α

1−α−β k − α1−α−β + g + δ

Here, I assume for simplicity A is set to be constant. GDP invested in

physical and human capital has a positive effect on steady-state per capita GDP. At

the same time, growth of labor, n, depreciation, labor-embodied technical change

have a negative effect on steady state per capita GDP.

IV. Data Description

With a recent refugee crisis in Europe, the region became a leader in hosting

refugees. Therefore, the representative domain for the thesis includes five countries

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of the European Union (Austria, France, Germany, Switzerland, and the United

Kingdom) that claim to attract the biggest amount of forced migrants (UNHCR

2015). The major focus is on refugees that might be a reason for potential growth in

the region.

In the model we use monthly observations for each series for the period

January 1990 – December 2014. For the number of refugees as a percentage of

country’s population, which is the variable of the primary interest, we have taken

monthly data from the UNHCR (the UN’s refugee agency). It was calculated based

on the absolute number of refugees divided by the total population of a country for

each year. The data for the variable is extracted from the World Bank database

system. We also obtained information for the rest of the variables from the World

Development Indicators provided by the World Bank.

The following Table 1 shows means and standard deviations alongside with

minimums and maximums of the variables used in the model:

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Variable Mean St. Dev. Min Max

GDPcapgrowth 0.0127 0.0180 -0.0537 0.0464

Refugee per capita 0.0058 0.0038 0.0007 0.0174

Popgrowth 0.0048 0.0038 -0.0169 0.0127

Trade 0.7022 0.2229 0.3957 1.3249

Netenrollment 0.9597 0.0431 0.8255 0.9998

Grosscapform 0.2245 0.0308 0.1528 0.3322

Lifexpect 79.01 1.96 75.17 82.96

Table 1. Descriptive Statistics for used variables.

Here, in the study, population growth, school enrollment rate, gross capital

formation, government expenditure, trade, and refugees are independent variables,

while GDP per capita growth rate is an independent variable.

V. Empirical Specification

Sesay (2004) employs the Augmented Solow growth model in order to study

the consequences that refugees have on developing countries. In my work I apply

the same model placed under different conditions in Austria, France, Germany,

Switzerland, and the United Kingdom.

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DPcapgrow β Popgrowth Lifexpect Refugee β Grosscapform G = α + 1 + β2 + β3 + 4

(15)Trade Netenrollmen + β5 + β6 + ε

Where:

= GDP per capita growth rate for my sample of countriesDPcapgrowthG

averaged over 1990-2014. It is a dependent variable.

= Population growth rate; estimated by calculating total population inopgrowth P

the country within 1990-2014 time span. It may capture unregistered or self-settled

refugees. Population growth rate might explain both GDP per capita, as well as a

number of forced migrants’ growth rates.

= Life expectancy variableifexpectL

= Percentage of refugees hosted is a key variable. It reflects percentage ofefugeesR

forced migrants per population of the country, and should depend on country’s

economic situation: years with a small number of refugees should be associated with

recession and otherwise (Sesay 2004).

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= Gross capital formation (physical capital). It captures investmentrosscapformG

rate as a percentage of GDP. Gross capital formation is a sum of fixed assets and a

change in a net level of inventories.

= Trade variable is able to describe international trade openness of therade T

country and show the potential impact on GDP growth. It is measured as sum of

exports and imports of goods and services as a share of GDP (World Bank 2015).

= Net school enrolment is a human capital variable. It shows theetenrollment N

ratio of total enrollment into secondary schools without age consideration.

= error term ε

, = parameters to be estimatedα βs

As an assumption, all variables in the model are independent and identically

distributed (i.i.d.), which means that each of them has the same probability as the

others and together they are mutually independent.

The particular specification includes key variables in the augmented Solow

model – population growth rate and investment. The percentage of refugees hosted

is the key variable introduced in the traditional Solow growth model.

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The GDP per capita growth rate is a dependent variable, which serves as

GDP per capita growth rate for the period of time selected (1990-2014). It is used

for estimation of both bivariate and growth regressions in the study.

The refugee as a percentage of country’s population variable is responsible for

negative (or positive) effects on growth within a country. It is calculated as a

percentage of country’s population:

, where t is a year of interest.00)( RefugeestPopulationt * 1

However, it would be naïve to assume that forced migrants directly affect

GDP growth through increase in population. Therefore, several other variables that

align with augmented Solow growth model are included in the study.

The next variable of interest is population growth rate. It is included to

capture the possible effects of unregistered refugees. This variable might be able to

explain GDP per capita growth and it will be related to refugees as well. Population

growth rate is also a key variable in the Solow model, where it is expected to have a

negative effect on growth.

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The trade variable measures openness of countries towards international

trade, as a sum of exports and imports of goods measured as a share of gross

domestic product (WDI 2002). It also affects GDP growth. The variable is expected

to have a positive effect on growth.

The World Bank (2002) measures “the foreign direct investment variable as

the net inflows of investment to acquire a lasting management interest (more than

10%) in an enterprise operating in an economy other than that of the investor”. It

includes equity capital, retained earnings, and another long and short-term capital

from the balance of payments (Sesay 2004). It is expected that foreign direct

investment should have a positive influence on growth.

Net school enrolment rate variable is used to represent the value of human

capital accumulation in a calculation. According to WDI (2002), it is the ratio of

children of official school age, who are enrolled in school to the population of the

corresponding official school age. Secondary education is more subject- and

skill-oriented; it aims to cultivate a lasting learning. However, the variable does not

include university education that might have a higher impact on growth.

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The gross capital formation variable aims to capture the effect of investment

rate as a percentage of GDP. Gross capital formation consists of the fixed assets of

the economy and changes in the level of inventories. Fixed assets include land

improvement, plant, machinery, and equipment purchases, and etc. Inventories are

necessary for the firms to have extra stocks that might be used during fluctuations of

demand (WDI 2002). The variable is significant for the study since refugees might

be a reason for the diversion of capital to non-economic activities and, as a result,

reduce capital formation. Gross capital formation is expected to have a positive

effect on growth and to be reduced by increased deracination.

General government final consumption expenditure includes all government

current expenditures for purchases of goods and services in a country, as well as

national defense expenditure without military spending. This variable is necessary for

the assessment of whether it fluctuates with refugee inflow. It should have negative

effect on growth.

Expected Signs

Economic models also provide necessary information about the signs and

magnitudes of different parameters. The Table 2 below shows expected signs of the

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variables based on economic theory and common sense; justification is provided

along with variables description above.

Variable Expected Sign

Lifeexpectancy -

Popgrowth -

Trade +

Netenrollment -

Grosscapform +

Refugees +/-

Table 2. Predicted signs for the Model. Dependent variable is GDP per capita growth rate

VI. Results

To assess refugees’ effect on growth and development two types of estimates

presented in this part: bivariate regressions and general growth regressions using

panel data estimates.

According to Barro (1995) growth and development might be two distinct

notions. Therefore, only studying the effect of deracination on economy by solely

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concentrating on growth does not imply anything about the degree of wealth

available to a society. Thus, I estimate bivariate regressions having refugees as the

only independent variable and several dependent ones, which are determinants of

economic development. The main goal is to study the relationship between refugee

and various determinants of welfare.

The variables of economic development are the annual growth rate of GDP

per capita, the average value of GDP per capita, the growth rate of the population,

growth capital formation, average life expectancy, and trade as a percentage of GDP.

The variable on life expectancy is an important indicator of well-being. It is also

crucial for the augmented Solow growth model. Forced migrants might both directly

and indirectly affect growth by influencing its determinants. This relationship can be

observed by estimating bivariate regressions between the number of refugees and

growth determinant variables.

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Effect of Refugees on Economic Development Indicators

Motivation

The effect of refugees on determinants of economic development is observed

in Table 3, which presents only significant results at the 1% level on the effect of

number of refugees in a country and their effect on growth enhancing variables.

Specification Results

In the table there are few significant variables which show interesting results.

Refugees alone significantly reduce school life expectancy, and trade as a difference

between exports and imports in the host countries. Both results are significant at the

1% level. Forced migrants also marginally increase gross capital formation. The

results are all according to the common expectations. Refugees require large amount

of support in terms of food, supplies, shelter materials, and etc., which leads to

increase in imports. Trade, or net exports, decline as a result. The relationship can be

seen from the equation below.

Trade xports mports↑↓ = E − I

Deracination process also significantly reduces life expectancy through

crowding on meagre health facilities of the hosts. What is surprisingly interesting is

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that refugees have a positive impact on gross capital formation, even though, having

in mind diversion of capital to non-economic activities, it was expected to be

otherwise. Thus, further studies might be needed to prove the relationship. I also

include a pairwise correlation results in Table 4 to account for problems with

bivariate regressions. (Appendix I).

Variable Coefficien

t

Constant R2 Number of

observations

Significanc

e (p-value)

Trade -28.07* .8663 0.2227 125 0.000

Grosscapform 2.24* .2114 0.1580 125 0.001

Lifeexpect -258.17* 80.52 0.1254 125 0.000

Table 3. The results of bivariate regressions * - significance at the 1% level

Solow Model Results

Panel data analysis was used in the research to capture short term effects that

forced migrants might have on the dependent variable. In the study, GDP per capita

was regressed on independent variables using fixed-effects (FE) and random-effects

(RE) models. In both cases the same variables were used for each of the models.

26


FE type of regressions applies within-group estimator and allows to eliminate

omitted variable bias. It also diminishes the effect of time-invariant specifics to

evaluate the net effect of the predictors on the outcome variable. To compare the

obtained fixed-effect results, we also use Random Effects (RE) model. The RE

model

The results of the fixed-effects and random-effects estimations are presented in

Table 5 below.

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Variable FE Coefficients (SE) RE Coefficients (SE)

Lifeexpectancy -.0066482*

(.0018)

-.0040043*

(.0012)

Popgrowth -1.053435**

(.5092)

-.7694447***

(.4651)

Trade .1073053*

(.0299)

.0223937**

(.0114)

Netenrollment .0498581

(.0587)

.1378822*

(.0523)

Grosscapform .3181896*

(0.599)

.1477094**

(.0737)

Refugees .4609994

(.8011)

-.8619813**

(.4451)

Constant .3458099

(.1409)

.156691

(.0952)

Number of observations

125 125

Wald chi-squared/F statistics

0.000 0.006

Table 5. The results of FE and RE regressions * - significance at the 1% level ** - significance at the 5% level

28


Assessing goodness of fit by analyzing the R2 estimations, RE performs worse

than the within FE estimator but much better in terms of between and overall

estimators, which aligns with econometrics theory. F-statistics test in FE model

shows the probability that the coefficients on the regressors are all jointly significant.

χ2 in random-effects demonstrates that results are also jointly significant. Thus,

fixed-effects and random-effects models are both significant. However, when it

comes to estimated coefficients, random-effects has more significant explanatory

variables, including refugee that was insignificant under the fixed-effects. Therefore,

we take a closer look at the RE model.

The random-effects regression allows making conclusions on whether the

independent variables have positive or negative influence on the dependent variable,

which is GDP per capita in our case. In the model refugees negatively influence

GDP per capita in Austria, France, Germany, Switzerland, and the United Kingdom.

One of the major problems in macroeconomic panels with time series of more

than twenty years is cross-sectional dependence, or contemporaneous correlation.

The impact of this type of correlation varies depending on the nature of the

cross-sectional dependence and the magnitude of correlation. Breusch-Pagan LM

29


test of independence, with the null hypothesis stating that residuals across entities

are not correlated, is used to check for cross-sectional dependence. Based on the

results contemporaneous correlation is present in the model.

To check for heteroscedasticity we carried out the Breusch–Pagan Lagrange

Multiplier (LM) test with H0 hypothesis that variances across entities are zero. There

is no evidence of heteroscedasticity; homoscedasticity or constant variance is present

in the model.

The Wooldridge test for autocorrelation in panel data has a null hypothesis that

there is no first-order autocorrelation. Based on the p-value=0.001 the null

hypothesis should be rejected; there is an AR(I) type of serial correlation present in

the model due to the error term depending only on its previous value. The result is

expected as the research analyzes information coming from sample nations, which

are all parts of the European Union. Therefore, there should be some degree of

correlation among the counties of interest. As a consequence, R2 becomes inflated,

true variance is increased, a decrease in standard error and increase in t-statistics

making coefficients look more accurate that they are in reality. Prais–Winsten

regression, which stands for the generalized least-squares (GLS) method and

30


estimates the parameters in the model in which errors are serially correlated, takes

the problem into account. Results of the regression are improved from biases, but

less powerful than the ones coming from the cross-sectional time-series feasible

generalized least squares (FGLS) regression. Therefore, both types of the estimates

are included in Table 6.

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Variable Prais-Winsten

Coefficients (SE)

FGLS

Coefficients (SE)


(.0017)

-.0034*

(.0009)

Popgrowth -.5966

(.5293)

-.76859*

(.2849)

Trade .0357*

(.0106)

.0210*

(.0049)

Netenrollment .1022**

(.0519)

.0548**

(.0243)

Refugees -.7111***

(.4426)

-.7683*

(.2269)

Constant .2889

(.1327)

.2282

(.0714)

Number of observations

125 125

Wald chi-squared statistics

0.0005 0.0000

Table 6. The results of Prais-Winsten and FGLS regressions * - significance at the 1% level ** - significance at the 5% level ***- significance at

the 10% level

Both models confirm that refugees have negative influence on GDP per capita in

sample nations. Prais–Winsten and the cross-sectional time-series feasible

32


generalized least squares regressions have similar signs of the coefficients with

higher magnitudes in the FGLS model. All of the signs are as predicted in the

research.

Refugees as endogenous variable

As an addition to deracination reducing GDP per capita in host countries,

refugees are attracted by economic growth in a host country. Opponents usually

claim that forced migrants use conflict situations in their countries as means for

moving to well-developed ones, where they will receive substantial financial support.

In order to test the validity of the argument there is a need to assess if refugee

variable is endogenous. The following can be checked with extended instrumental

variables regression and refugees in the lagged form. The results of the regression

are presented in Table 7 below.

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Variable Extended instrumental variables regression

Coefficients (SE)


-.8479***

.0234*

.1546*

-.8421*

.1344

(.0012)

(.4941)

(.0094)

(.0461)

(.4235)

(.0902)

Popgrowth

Trade

Netenrollment

Refugees

Constant

Number of observations 120 Diagnostic tests:

● Hansen J Statistics ● Underidentification ● Endogeneity

0.000

0.0000 0.3735

Table 6. The results of extended instrumental variables regression and diagnostic tests.

* - significance at the 1% level ** - significance at the 5% level ***- significance at the 10% level

The regression results align with previous models; refugee as percentage of

population is a significant variable, which has negative influence on host countries.

Trade, population growth, life expectancy, and net enrollment also have similar signs

and significant at the 1% and 5% levels.

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Hansen test with H0 stating that the model has over-identifying restrictions

included in the regression. Based on the p-value, the null hypothesis is not rejected;

the over-identification restrictions are valid.

Endogeneity test in the model checks if refugee variable is endogenous. The null

hypothesis says that regressor is exogenous. Alternative one presents a regressor as

endogenous variable. The null hypothesis is accepted, according to p-value results.

Thus, refugee is exogenous variable and the model has valid robust results.

The results of the extended instrumental variables regression do not show

refugee as endogenous variable and present no evidence that forced migrants tend to

choose host countries based on its economic conditions. However, having in mind

limited sample used in the research, it would be naïve to assume that the same

conclusion might be applicable to other countries. In order to answer the question

further studies should be implemented.

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VII. Conclusion

In the beginning the research includes a statement that it is expected that

refugees should have a negative impact into the host-countries of interest. Further

research showed that there are many scholarly debates around the topic: they include

different opinions both for and against the statement. Therefore, further tests were

required to affirm the assumption.

Based on the data collected from the World Bank, UNHCR, and Eurostat

databases, I found a panel dataset and used both Fixed Effects (FE) and Random

Effects (RE) models. Since the RE model was more efficient and had better

explanatory power for the explanatory variable, we chose it as our main model.

Further tests were implemented in order to correct results for heteroscedasticity and

serial correlation. Random Effects, Feasible Generalized List Squares (FGLS), as

well as Prais-Winsten regression all confirmed that refugees, indeed, have a negative

impact on GDP per capita in countries of our interest, as it was predicted in the

beginning of the research process.

36


Bivariate regressions showed that refugees have a negative influence on such

growth development indicators as trade and life expectancy. Nevertheless, forced

migrants increase gross capital formation.

Even though there is an evidence of negative impact of deracination on growth

and development, it might be the case that the results are only applicable to the

countries of interest in this study, and the picture may be radically different in other

regions of the World, including Europe. There might be deviations depending on

the economic situation of the country. For instance, developed countries might have

negative implications of rapid refugee inflow, while developing ones might enjoy

some benefits in terms of increase in international support provided by such

organizations as UNHCR, etc. Further studies should be implemented to confirm

the results.

37


VIII. References

Barberić, Jasna. "Asylum in the Republic of Croatia One Year after Accession to the

European Union." UNHCR News. N.p., n.d. Web.

Barker, Alex. "Greece Warned EU Will Reimpose Border Controls - FT.com."

Financial Times. Web.

Barro, Robert J., and Xavier Sala-i-Martin. "Economic Growth." New York:

McGraw-Hill, 1995. Print.

De, La Grandville Olivier, and Solow, Robert M. "Economic Growth: A Unified

Approach." Cambridge, UK: Cambridge UP, 2009. Print.

"Economic Impact of Refugees in the Cleveland Area" (n.d.): n. pag., Oct. 2013.

Web.

Kuhlmann, Tom. "The Economic Integration of Refugees in Developing Countries:

A Research Model." ResearchGate. N.p., Aug. 1990. Web.

Price, Matthew. "Living in the Poorest Part of the EU." BBC News. Web.

Neumayer, Eric. "Asylum Destination Choice: What Makes Some West European

Countries More Attractive Than Others?", Centre for the Study of Civil war,

London School of Economics. (2004): 155-80. Web.

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Neumayer, Eric. "Bogus Refugees? The Determinants of Asylum Migration to

Western Europe.", SSRN Electronic Journal SSRN Journal (n.d.): N.p. Web.

Oliver-Smith, Anthony. "Disasters and Forced Migration in the 21st Century."

Disasters and Forced Migration in the 21st Century. N.p., 11 June 2006. Web.

"Social and Economic Impact of Large Refugee Populations on Host Developing

Countries." UNHCR News. N.p., 6 Jan. 1997. Web.

Solow, Robert M., Gertrude Himmelfarb, and Amy Gutmann. "Work and Welfare."

Princeton, NJ: Princeton UP, 1998. Print.

The World Bank. World Development Indicators(WDI). 2002. Web.

Treviranus, Barbara, Törngren, Sayaka Osanami. "A Socio-Economic Review of

Japan's Resettlement Pilot Project." UNHCR News. N.p., 24 June 2015. Web.

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Deracination and its economic impact