The effect of Economic Conditions on Net Greek Migration

The effect of economic conditions on Greek net
migration
by
Cristina Chivu 17967958
Scott Cislowski 18009196
Emily Marshall 17698623
Economic and Financial Modelling
Western Sydney University
May 2016
Researchteam
Cristina Chivu
Scott Cislowski
Emily Marshall
Advisors
Western Sydney University
Dr Gulay Avsar

Abstract
Each day, all over the world, individuals, couples and families make the excruciating decision
to gather their things and begin a new life in another country. Each situation is different to the
next, and each is motivated by different forces. Some are motivated by the prospects of a safe
lifestyle, fleeing their home due to war and destruction. Others are motivated by local
economic conditions, fleeing the stranglehold of unemployment for the chance to start over.
It is these economic migrants who are the focus of this paper. By using a series of
econometric techniques the paper attempts to show the relationship between a pool of key
economic data points and net migration for Greece, a nation that has experienced some of the
worst economic conditions post the Global Financial crisis. It will be using data from 1991 to
2014 to draw meaningful conclusions.
Contents
Abstract.......................................................................................................................................2
Introduction.................................................................................................................................3
Review of previous Literature........................................................................................................4
Specification of the Model ............................................................................................................ 5
Data Description........................................................................................................................... 6
Empirical Results.......................................................................................................................... 7
Conclusions and Recommendations............................................................................................... 9
References................................................................................................................................. 10
Appendices………………………………………………………………………………………………………………………………………11

Introduction
In the last decade the global economy has seen fundamental changes. From a period
of high growth during the first part of the 21st century (Dicken, 2013) to an catastrophic
global recession instigated by the Global Financial Crisis of 2008. Through these sweeping
changes saw a wave of economic refugees in search of a more stable and prosperous life.
Economic refugees are people whose long term prospects have been negatively impacted by
local economic performance and not necessarily by other factors like war or political unrest.
However it is possible that these external issues ultimately lead to economic problems.
Greece has been the most high profile country to feel the effects of the recent global
economic turmoil with growing debts totalling 175.1% of GDP in 2013 (Eurostat, 2013). As
a result of this, it is perfectly suited to studying the effects of economic performance on net
migration. The term net migration during this research is defined as total inflows minus out
flows (World Bank, 2014). It is expected that poor economic performance of a nation should
be reflected in the number of economic refugees that leave that particular nation. Though this
analysis, policies can be reviewed and adjusted as necessary to help mitigate an unsustainable
flow of migrants which will further impact their country of origin as they lose skilled workers
but also places unnecessary pressure on neighbouring nations.

Review of previous literature
To form the basis of this research paper, previous research was reviewed and the best
parts analysed and drawn upon. In addition to this, common issues were discovered and
compared. These issues tended to revolve around lack of empirical data points. In relation to
Greece some data is released semi-annually. This issue is seen in other nation’s economic
data. As an example, Romania, an ex-communist country has a lack of historic data sets
which makes drawing meaningful accurate conclusions difficult. (Rembar, 2015).
During her 2012 study of internal migration Daniella Bunea (2012), used panel data
from 2004-2008 (NUTS 3 level) from a dynamic point of view. This small sample size is
down to a sheer lack of data. Using real GDP per capita, nominal employment and
unemployment rate, degree of urbanisation, population density she attempted to model the
effects these had on internal migration. By taking some of these same variables we hope to
expand show how these changes effect migration out of Greece.
Martinho (2011) analysed the migration mobility in Portugal focusing on the
movements of the labour market. He used the cross section data to analyse the evolution of
migration from the availability of housing point of view. Also he tested the covariance of
migration between the costal and the continental region of Portugal.
Basile, Girardi and Mantuano (2010) used longitudinal data for 103 Italian regions
over the 1995-2007 to measure the dynamic of the unemployment rate. Their research
showed the negative impact of migration over unemployment rates. From this it can be
deduced that the rate of unemployment has a compounding effect on other economic
indicators. As such, it forms an important part of the economic model.

Specification of the Model
Drawing from the conclusions of previous research it is clear that some economic
variables form the key to qualifying economic effects on migration.
The most common being the Gross Domestic Product (GDP) of the nation in question.
Real GDP (Which takes inflation into account over time) is a basic look at the overall
performance of an economy. The Consumer Price Index (CPI) or inflation shows how the
cost of living increases or falls over a period of time. As the inflation rate grows year on year
it makes it more difficult for the population to go about their day to day lives if inflation is
not met with a wage increase as well. Interest rates set by the central bank effect how
expensive it is for companies to borrow money in order to invest in new technology or
upgrade their capital to make their business more productive. The overall unemployment rate
of Greece is also a necessary variable when assessing migration. A high unemployment rate,
effects government debt as their social security burdens increase. It also increases the pool of
people who consider migrating to another part of the world. Penultimately, Greece’s highly
publicised economic crisis in the early 2010s would have contributed greatly to an outflow of
population, as they were trying to escape financial and economic ruin. We have included this
as a dummy variable, which will take the form of 1 during the years when the economic crisis
was occurring. Finally, the effect that the European Union has had on the Greek economy
cannot be under stated. Using a dichotomous or dummy variable for data taken when Greece
was both in and out of the Eurozone will highlight the effects, if any that this has had overall.
In addition to this a random variable is added to take into account any external factors that
will affect the results but are not quantified by the variables.
As such, the model follows a linear function of:
𝑌( 𝑚𝑖𝑔𝑟𝑎𝑡𝑖𝑜𝑛)
= 𝛽1 + 𝛽2( 𝐺𝐷𝑃) + 𝛽3( 𝐼𝑛𝑓𝑙𝑎𝑡𝑖𝑜𝑛) + 𝛽3( 𝑈𝑛𝑒𝑚𝑝𝑙𝑜𝑦𝑚𝑒𝑛𝑡)
+ 𝛽4( 𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡 𝑅𝑎𝑡𝑒) + 𝛿( 𝐸𝑐𝑜𝑛𝑜𝑚𝑖𝑐 𝑐𝑟𝑖𝑠𝑖𝑠) + 𝛿( 𝐸𝑈) + 𝑒
These key economic indicators will model their ultimate effects –positively and
negatively - on net migration.

Data Description
For the purposes of this paper, all data points were taken from reputable data sources
all over the world. For Greece, most of the data required was available through government
data agencies. Eurostat is the European body tasked with sourcing and keeping key statistical
information about all its member nations. Statistics on Gross Domestic Product and
Unemployment and inflation were all sourced from this government agency. In addition to
Eurostat, Trading Economics, a data sourcing website provided data on migration rates as
well as interest rates that were in effect in Greece through the time period in question.
In the case of real GDP this data takes the form of United States dollars. This is to
remain consistent with other global statistics. Inflation, unemployment and interest rates are
noted in percentage points. Finally the explanatory variable refers to before and after Greece
joined the European Union. This is noted by a 0 for no and a 1 for yes.
Some weaknesses in the data that has been sourced are in relation to the frequency of
the data points being updated. This was most prevalent with migration data, where, in recent
times, new data sets are only updated every four to five years, which is in line with other
European nations. As such, the points of data that fall in between may be estimates with an
error built into the statistic. The remove this, it would require government agencies to
conduct more thorough analysis more frequently.
All data used in this model was time-series.

Empirical Results
Upon regressing the model, many new details came to light about it. It was worth
noting the R2 for the model as 0.8137, meaning that 81.37% of variation in the dependent
variable (migration) was explained by the independent variables in the model. This is a
relatively good outcome for a first-stage model.
Coefficients were predicted for each variable, with the negative signs being expected
for inflation, unemployment, and interest. This means that for a one-unit increase in any of
these variables, there would be a further outflow of people. The nature of these variables
makes this a predictable and likely conclusion. However, there was a surprising negative sign
for the GDP coefficient, meaning that as GDP increases more people would want to leave.
This does not seem likely in reality. The positive coefficient for the economic crisis dummy
variable was also unexpected, as it would be more likely that people would want to leave
Greece when faced with economic downfall. The negative coefficient for the other dummy
variable of joining the Eurozone could be considered either likely or unlikely as the decision
to leave Greece due to this factor would be very much based on personal preference of each
citizen.
We were able to assess the errors of the regression for normality and
heteroscedasticity. Through use of the Jarque-Bera test, it became apparent that the errors
were not normal due to high values for skewness and kurtosis. The White test was then used
to determine that the errors did indeed have a constant variance, making them homoscedastic
– a desirable outcome.
Using the p-value test for individual significance, we were able to ascertain that the
variables GDP and unemployment were statistically significant at 5%, while the other four
variables were not. At 1%, only GDP remained significant. The significant variables in the
model had some power in explaining the outcome of migration (the dependent variable).
Although this result was rather disappointing, use of the F-test for overall significance
implied that the model as a whole was significant, meaning that the combination of variables
was successful at explaining at least part of the dependent variable.
We conducted assessments for correlation of both the errors and the variables. The
Durbin-Watson test was used to test for a relationship between the errors, with the d-statistic
landing in the no decision zone between dupper and dlower. This meant that we could not be sure
whether or not there is any positive autocorrelation. The Breusch-Godfrey test was used to
examine the presence of serial correlation between the variables at 3 lags. This resulted in the
conclusion that there was no serial correlation at any of the lags for this model.
The Ramsey RESET test was used to check that the correct functional form was used,
and that there were no omitted variables. At 5%, we had to reject H0, meaning that the model
either had incorrect functional form or was missing explanatory variables. This was an issue
as the model would not have been providing a good or reliable description of the
circumstances, and was not the best it could be.
The final test conducted on the model was the variance inflation factor (VIF) test for
multi-collinearity. We determined that there was an issue with multi-collinearity for the

variables GDP and inflation, meaning that these were more highly correlated with other
explanatory variables than with the dependant variable. This can cause large variance in the
OLS estimators, and an abnormally high R2 and F-stat. It can also result in the individual
significance of these variables being quite low. None of the other variables had this
characteristic.
To try and remedy the problems in model 1 of incorrect functional form/omitted
variables and multi-collinearity, we attempted to transform the model from linear to log-log
the only difference being that migration and GDP were taking their log forms. Model 2 can
be transcribed as:
𝑙𝑛𝑌( 𝑚𝑖𝑔𝑟𝑎𝑡𝑖𝑜𝑛)
= 𝛽1 + 𝛽2( 𝑙𝑛𝐺𝐷𝑃) + 𝛽3( 𝐼𝑛𝑓𝑙𝑎𝑡𝑖𝑜𝑛) + 𝛽3( 𝑈𝑛𝑒𝑚𝑝𝑙𝑜𝑦𝑚𝑒𝑛𝑡)
+ 𝛽4( 𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡 𝑅𝑎𝑡𝑒) + 𝛿( 𝐸𝑐𝑜𝑛𝑜𝑚𝑖𝑐 𝑐𝑟𝑖𝑠𝑖𝑠) + 𝛿( 𝐸𝑈) + 𝑒
After regressing the new model, we re-ran the Ramsey RESET test and found that we
were then able to accept H0 and deem the model to be in correctional functional form and
have no omitted variables at 1% and 5%.
The VIF test for multi-collinearity was also conducted again with Model 2. This time
lnGDP was the only variable to display any multi-collinearity, while inflation no longer did.
This can be seen as an achievement, with the new mean value being 39.94 as compared to
11.47 from Model 1. The new model far exceeded the decision rule cut-off of 10.
Other impacts of the model transformation were an increase in the R2 to 0.9991,
meaning that the independent variables were then accounting for approximately 18% more of
the variation in migration. A re-conduction of the Jarque-Bera test also showed the errors had
become normal in the new model. The Breusch-Godfrey test demonstrated a negative impact
of model 2, with serial correlation becoming present in all 3 lags. All other tests maintained
the same results, with slightly different values used for calculation purposes.

Conclusions and Recommendations
From this study, we have been able to conclude that migration is greatly affected by
the variables we included, to varying extents for each individually.
The fact that the EU variable had a negative coefficient could provide an indication
that the Greek authorities may need to reconsider their membership. If this coefficient is true,
it is clear that people living in Greece do not like being a part of the EU. The economic crisis
also occurred at similar time to EU membership, so it would be worth considering the
relationship between these 2 variables in order to stabilise Greek society.
The negative net migration in Greece requires some attention to bring it back to a
positive figure. The government could consider moving away from austerity measures to
boost investment. This would in turn entice people to stay in, or migrate to Greece.
As a result of this study, we are confident to say that if Greece can reclaim control
over its economic position, the whole society will be able to achieve a greater state of calm.
Almost all of the variables in the model would be improved upon if Greece is able to
strengthen itself.
In future studies on this topic, it would be beneficial to further reduce levels of multi-
collinearity, and to eliminate the serial correlation that became prevalent in model 2. These
objectives could be achieved by transforming the model again, or by omitting any
insignificant variables.

References
Basile, R, Girardi, A & Mantuano, M 2010, 'Interregional migration and unemployment
dynamics: evidence from Italian provinces', viewed 12/05/2016,
<http://www.aiel.it/page/old_paper/Basile_Girardo.pdf>.
Bunea, D 2012, 'Modern gravity models of internal migration. The case of Romania ',
Theoretical and Applied Economics, vol. XIX, no. 4(569), pp. 127-44.
Chindea, A, Majkowska-Tomkin, M, Mattila, H & Pastor, I 2008, Migration in Romania: a
country profile 2008, International Organization for Migration, Geneva.
Dicken, P. (2013) Global shift: Reshaping the global economic map in the 21st century. (4
Vols). New York: Guilford Publication
Eurostat. 2016. Eurostat Statisticshttp://ec.europa.eu/eurostat/. [Accessed 8 May 2016].
Martinho, VJPD 2011, 'Analysis of net migration between the Portuguese regions', viewed
15/05/2016, <https://arxiv.org/ftp/arxiv/papers/1110/1110.5564.pdf>.
Obrzut, R 2015, 'Personal remittances statistics', viewed 14/05/2016,
<http://ec.europa.eu/eurostat/statistics-explained/index.php/Personal_remittances_statistics>.
Rembar, ØHS 2015, 'Economic effects of labor migration. A Romanian case study', viewed
15/05/2016,
<https://www.duo.uio.no/bitstream/handle/10852/49103/thesis.pdf?sequence=1&isAllowed=
y>.
Trading Economics. 2016. www.tradingeconomics.com. Accessed 8 May 2016,
http://www.tradingeconomics.com/

Appendices
Table 1: Regression Results (Summary Table)
Dependent Variable: Yt migration
Independent Variables Model 1 Model 2
Constant
502179.8
(3)
11.84186
(31.37)
X1 t gdp
-8.87e-07
(-3.82)
3.96e-13
(1.04)
X2t inf
-2574.504
(-0.76)
0.236979
(3.64)
X3t unem
-163301
(-2.48)
-0.0086463
(-0.37)
X4t interest -3113.404
(-0.40)
0.000765
(0.10)
X5t eco 98402.49
(1.13)
-3.879072
(-10.86)
X6t eu -7461.726
(-0.10)
-0.4852211
(-0.65)
Diagnostic Tests
R2
0.8137 0.9991
Adjusted R2
0.7480 0.9986
F(6,17) (6,11)
12.38*** 2006.46***
White’s Test
0.4038  >0.05,
homoscedastic.
0.3888  >0.05 Fcv,
homoscedastic.
JB Test
7.83  not normal 5.04  normal
DW (7,24) (6,18) at 5%
0.8140166  no
decision
1.980807  no
decision
***,**,* significant at 1%, 5% and 10% respectively

Project Diagnostic Tests –Working Table (Appendix)
Test Test
Statistic
Formula Ho H1 Decision Rule Desired Result
Individual
Significance
P-test From p-
value table.
Variable not
significant
Variable
significant
If p < ∝  do not
accept H0. Reject
Overall
Significance
F-test (SSER-
SSEU)/J
SSEU/(N-K)
Model is not
significant
Model is
significant
If fcalc>fcv reject
H0. Reject
Normality of
errors
Jarque-
Bera
test
N/6 x [s2 +
(K-3)2
4]
Normally
distributed
Not normally
distributed
JB > 5.99  do
not accept H0. Accept
Constant
variance of
errors
White
test
From p-
value table.
Homoscedastic Heteroscedastic If p < ∝ do not
accept H0. Accept
Errors are
not related
Durbin-
Watson
test
From DW
table find
upper and
lower limits
for N and K.
𝜌 = 0. Errors
not
correlated.
𝜌 ≠ 0. Errors
correlated.
If dcalc falls
between dl and
du or 4-versions,
no decision. If
left, positive
correlation, if
right negative
correlation.
Between du and
4-du  no
correlation.
Accept
X’s are
weakly
related
VIF test VIFx =
1
(1-R2
)
X’s are weakly
correlated 
no multi-
collinearity
X’s are strongly
correlated 
multi-
collinearity
If VIF > 10, reject
H0, there is
multicollinearity.
Accept
Correct
Functional
Form/No
omitted
Variables
Ramsey
RESET
test
From F-
value table.
Model has no
omitted
variables/has
correct
functional
form.
Model has
omitted
variables/incorr
ect functional
form.
If Fcalc > Fcv do not
accept H0. Accept

Data
year migration gdp inf unem interest eco eu
1991 220000 $105,143,232,379.88 19.47285123 7.699999809 8.06614829 0 0
1992 200000 $116,224,673,042.55 15.86589902 7.800000191 12.11643093 0 0
1993 180000 $108,809,058,858.50 14.41449365 9 12.34793398 0 0
1994 175000 $116,601,802,106.74 10.92278719 8.899999619 14.62448428 0 0
1995 175000 $136,878,366,230.33 8.937057107 9.100000381 12.07572842 0 0
1996 172000 $145,861,612,825.60 8.196219484 9.699999809 12.37752289 0 0
1997 160000 $143,157,600,024.96 5.538972981 9.600000381 11.60290398 0 0
1998 152000 $144,428,172,835.24 4.766225587 10.80000019 12.80143467 0 0
1999 145000 $142,540,728,958.02 2.636782729 11.69999981 10.97950811 0 0
2000 135000 $130,495,109,913.40 3.166082521 11.10000038 10.55257994 0 0
2001 140000 $136,191,353,467.56 3.37396641 10.19999981 4.938642413 0 1
2002 140000 $153,830,947,016.75 3.629362936 10.30000019 3.932859534 0 1
2003 145000 $201,924,270,316.03 3.530650791 9.699999809 3.221018743 0 1
2004 147000 $240,521,260,988.33 2.89884797 10.5 2 0 1
2005 148000 $247,783,001,865.44 3.54507305 9.800000191 2 0 1
2006 140000 $273,317,737,046.80 3.19594597 8.899999619 2 0 1
2007 150000 $318,497,936,901.18 2.89500102 8.300000191 3.5 0 1
2008 0 $354,460,802,548.70 4.15279636 7.699999809 4 0 1
2009 -20000 $330,000,252,153.38 1.210073956 9.5 1 0 1
2010 -70000 $299,379,400,264.90 4.712981576 12.5 1 0 1
2011 0 $287,779,921,184.32 3.329870174 17.70000076 1 1 1
2012 -45000 $245,670,666,639.05 1.501519795 24.20000076 1 1 1
2013 -51000 $239,509,850,570.45 -0.921271918 27.20000076 0.75 1 1
2014 2320 $235,574,074,998.31 -1.312242411 26.29999924 0.25 1 1

Model 1 STATA
. predict e1, residuals
_cons 502179.8 167351.7 3.00 0.008 149098.5 855261.1
eu -7461.726 73779.19 -0.10 0.921 -163122.2 148198.8
eco 98402.49 87115.55 1.13 0.274 -85395.26 282200.2
interest -3113.404 7788.889 -0.40 0.694 -19546.52 13319.72
unem -16301 6583.621 -2.48 0.024 -30191.22 -2410.774
inf -2574.504 3399.524 -0.76 0.459 -9746.873 4597.865
gdp -8.87e-07 2.32e-07 -3.82 0.001 -1.38e-06 -3.98e-07
migration Coef. Std. Err. t P>|t| [95% Conf. Interval]
Total 1.8647e+11 23 8.1075e+09 Root MSE = 45200
Adj R-squared = 0.7480
Residual 3.4731e+10 17 2.0430e+09 R-squared = 0.8137
Model 1.5174e+11 6 2.5290e+10 Prob > F = 0.0000
F(6, 17) = 12.38
Source SS df MS Number of obs = 24
. regress migration gdp inf unem interest eco eu
eu 24 .5833333 .5036102 0 1
eco 24 .1666667 .3806935 0 1
interest 24 6.172383 5.057937 .25 14.62448
unem 24 12.00833 5.749096 7.7 27.2
inf 24 5.402498 5.143006 -1.312242 19.47285
gdp 24 2.02e+11 7.95e+10 1.05e+11 3.54e+11
migration 24 105846.7 90041.77 -70000 220000
Variable Obs Mean Std. Dev. Min Max
. summarize migration gdp inf unem interest eco eu
delta: 1 unit
time variable: year, 1991 to 2014
. tsset year

e1 24 0.6466 0.5192 0.66 0.7194
Variable Obs Pr(Skewness) Pr(Kurtosis) adj chi2(2) Prob>chi2
joint
Skewness/Kurtosis tests for Normality
. sktest e1
Note: N=Obs used in calculating BIC; see [R] BIC note.
. 24 -307.3365 -287.1688 7 588.3376 596.584
Model Obs ll(null) ll(model) df AIC BIC
Akaike's information criterion and Bayesian information criterion
. estimates stats
99% 91579.57 91579.57 Kurtosis 3.066398
95% 50581.59 50581.59 Skewness .1914938
90% 48279 48279 Variance 1.51e+09
75% 14457.09 47369.92
Largest Std. Dev. 38859.53
50% 2123.644 Mean .0000331
25% -29069.48 -37517.98 Sum of Wgt. 24
10% -43523.34 -43523.34 Obs 24
5% -60772.93 -60772.93
1% -80025.98 -80025.98
Percentiles Smallest
Residuals
. summarize e1, detail
Mean VIF 11.47
inf 3.44 0.290587
gdp 3.83 0.260836
eco 12.38 0.080761
eu 15.54 0.064341
unem 16.13 0.062004
interest 17.47 0.057233
Variable VIF 1/VIF
. estat vif
Durbin-Watson d-statistic( 7, 24) = .8140166
. estat dwatson

H0: no serial correlation
3 12.439 3 0.0060
2 11.314 2 0.0035
1 11.100 1 0.0009
lags(p) chi2 df Prob > chi2
Breusch-Godfrey LM test for autocorrelation
. estat bgodfrey, lags(1 2 3)
Total 32.77 30 0.3328
Kurtosis 0.01 1 0.9131
Skewness 8.75 6 0.1879
Heteroskedasticity 24.00 23 0.4038
Source chi2 df p
Cameron & Trivedi's decomposition of IM-test
Prob > chi2 = 0.4038
chi2(23) = 24.00
against Ha: unrestricted heteroskedasticity
White's test for Ho: homoskedasticity
. estat imtest, white
Prob > F = 0.0382
F(3, 14) = 3.68
Ho: model has no omitted variables
Ramsey RESET test using powers of the fitted values of migration
. estat ovtest

Model 2 STATA
_cons 11.84186 .3774632 31.37 0.000 11.01107 12.67265
eu -.0485211 .0748971 -0.65 0.530 -.2133684 .1163262
eco -3.879072 .3571477 -10.86 0.000 -4.665149 -3.092995
interest .000765 .0079057 0.10 0.925 -.0166353 .0181654
unem -.0086463 .0234156 -0.37 0.719 -.0601837 .0428911
inf .0236976 .0065046 3.64 0.004 .0093811 .0380141
gdp 3.96e-13 3.83e-13 1.04 0.323 -4.46e-13 1.24e-12
lnMIGRATION Coef. Std. Err. t P>|t| [95% Conf. Interval]
Total 17.1713351 17 1.01007854 Root MSE = .03775
Residual .015675381 11 .001425035 R-squared = 0.9991
Model 17.1556597 6 2.85927662 Prob > F = 0.0000
F(6, 11) = 2006.46
. regress lnMIGRATION gdp inf unem interest eco eu
. generate lnGDP = ln(gdp)
(6 missing values generated)
. generate lnMIGRATION = ln(migration)
eu 24 .5833333 .5036102 0 1
eco 24 .1666667 .3806935 0 1
interest 24 6.172383 5.057937 .25 14.62448
unem 24 12.00833 5.749096 7.7 27.2
inf 24 5.402498 5.143006 -1.312242 19.47285
gdp 24 2.02e+11 7.95e+10 1.05e+11 3.54e+11
migration 24 105846.7 90041.77 -70000 220000
. summarize migration gdp inf unem interest eco eu
delta: 1 unit
time variable: year, 1991 to 2014
. tsset year

(6 missing values generated)
. predict e1, residuals
eu 24 .5833333 .5036102 0 1
eco 24 .1666667 .3806935 0 1
interest 24 6.172383 5.057937 .25 14.62448
unem 24 12.00833 5.749096 7.7 27.2
inf 24 5.402498 5.143006 -1.312242 19.47285
lnGDP 24 25.95803 .3965428 25.37859 26.59386
lnMIGRATION 18 11.74023 1.005027 7.749322 12.30138
. summarize lnMIGRATION lnGDP inf unem interest eco eu
_cons 8.94984 2.142284 4.18 0.002 4.234704 13.66498
eu -.0379583 .0710922 -0.53 0.604 -.1944312 .1185147
eco -3.96006 .327945 -12.08 0.000 -4.681862 -3.238257
interest .0026245 .0075873 0.35 0.736 -.014075 .019324
unem -.0030762 .0215154 -0.14 0.889 -.0504312 .0442787
inf .0262252 .0064432 4.07 0.002 .012044 .0404065
lnGDP .1112137 .0736694 1.51 0.159 -.0509316 .273359
lnMIGRATION Coef. Std. Err. t P>|t| [95% Conf. Interval]
Total 17.1713351 17 1.01007854 Root MSE = .03599
Residual .014250032 11 .001295457 R-squared = 0.9992
Model 17.1570851 6 2.85951418 Prob > F = 0.0000
F(6, 11) = 2207.34
. regress lnMIGRATION lnGDP inf unem interest eco eu

Note: N=Obs used in calculating BIC; see [R] BIC note.
. 18 -25.11672 38.73142 7 -63.46283 -57.23023
Model Obs ll(null) ll(model) df AIC BIC
Akaike's information criterion and Bayesian information criterion
. estimates stats
99% .0328447 .0328447 Kurtosis 3.135686
95% .0328447 .030139 Skewness -1.101726
90% .030139 .0289666 Variance .0008382
75% .0209338 .0228601
Largest Std. Dev. .0289523
50% .0038047 Mean -9.70e-11
25% -.0013256 -.0087052 Sum of Wgt. 18
10% -.0581304 -.0528594 Obs 18
5% -.0598598 -.0581304
1% -.0598598 -.0598598
Percentiles Smallest
Residuals
. summarize e1, detail

Mean VIF 39.94
lnGDP 8.31 0.120344
inf 16.36 0.061141
eu 17.34 0.057672
interest 17.90 0.055865
eco 78.41 0.012754
unem 101.32 0.009870
Variable VIF 1/VIF
. estat vif
Durbin-Watson d-statistic( 7, 18) = 1.980807
Number of gaps in sample: 1
. estat dwatson
H0: no serial correlation
3 3.447 3 0.3277
2 0.851 2 0.6534
1 0.001 1 0.9755
lags(p) chi2 df Prob > chi2
Breusch-Godfrey LM test for autocorrelation
Number of gaps in sample: 1
. estat bgodfrey, lags(1 2 3)

Total 31.15 24 0.1496
Kurtosis 0.04 1 0.8447
Skewness 13.11 6 0.0413
Heteroskedasticity 18.00 17 0.3888
Source chi2 df p
Cameron & Trivedi's decomposition of IM-test
Prob > chi2 = 0.3888
chi2(17) = 18.00
against Ha: unrestricted heteroskedasticity
White's test for Ho: homoskedasticity
. estat imtest, white
Prob > F = 0.4207
F(2, 9) = 0.95
Ho: model has no omitted variables
Ramsey RESET test using powers of the fitted values of lnMIGRATION
(note: predicted lnMIGRATION^2 dropped because of collinearity)
. estat ovtest
e1 18 0.0295 0.4302 5.22 0.0734
Variable Obs Pr(Skewness) Pr(Kurtosis) adj chi2(2) Prob>chi2
joint
Skewness/Kurtosis tests for Normality
. sktest e1

The effect of Economic Conditions on Net Greek Migration

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