Appendices
Table 1. – Descriptive statistics
Variable Description Mean Standard deviation
Estatei Evolution of house prices in home
country (%) 1.759444 1.046878
Childi Child benefits per month in home
country 0.013642 0.004773
INCi Monthly average incomes in home
country 0.385839 0.302593
INCj Monthly average incomes in host
country 2.59697 1.212091
GINIi GINI coeffcient in home coutry 0.298463 0.033774 GINIj GINI coeffcient in host coutry 0.315509 0.028789 JOBi Job satisfaction in home country 0.649037 0.079907 JOBj Job satisfaction in host country 0.816762 0.099404 Rurali Rural population in home country
(thousands) 9009.019 5162.3
UMPi Unemployment rate in home
country 11.5188 4.35102
UMPi Unemployment rate in host country 9.667593 3.237177 UBENi Unemployment benefits in home
country 0.079503 0.062103
UBENj Unemployment benefits in host
country 0.567118 0.420029
Table 2. CORRELATION MATRIX
CHILDI ESTATEI GINII GINIJ INCI INCJ JOBI JOBJ RURALI UBENI UBENJ UMPI UMPJ
Table 3. Regression results I
Case 1 Case 2 Case 3 4
Dependent variable log
(Imfloij) Coeff Prob. Coeff Prob. Coeff Prob. Coefficient Prob.
23.4854 70.10936 -10.70797 -7.648056 CHILDI (0.821628) 0.4134 (2.032396) 0.0449 (-0.855418) 0.3946 (-0.596347) 0.5526 -0.524444 -0.320465 0.054152 0.059446 ESTATEI (-2.823141) 0.0058 (-1.408769) 0.1622 (0.59193) 0.5554 (0.622079) 0.5356 -8.986073 -2.362586 -0.369182 -0.993977 GINII (-2.600666) 0.0108 (-0.572507) 0.5683 (-0.202142) 0.8403 (-0.546499) 0.5862 7.161338 11.65588 0.498349 2.154318 GINIJ (2.362654) 0.0202 (3.171591) 0.002 (0.301253) 0.7639 (1.217193) 0.2271 3.908773 INCI
(7.200357) 0 n/a n/a n/a n/a n/a n/a
0.70484 0.683197 0.091541 0.170661 INCJ (5.103079) 0 (3.993062) 0.0001 (0.319095) 0.7504 (0.582513) 0.5618 -14.65397 -12.02206 -2.218268 -1.915291 JOBI (-6.604074) 0 (-4.43328) 0 (-1.576432) 0.1185 (-1.44844) 0.1514 -4.844451 -4.282564 -2.607776 -2.008669 JOBJ (-4.524523) 0 (-3.236715) 0.0017 (-0.920188) 0.36 (-0.717893) 0.4749 0.000057 0.0000746 -0.00021 -0.000498 RURALI (1.310302) 0.1933 (1.387024) 0.1687 (-1.266496) 0.2087 (-1.81358) 0.0734 -12.58891 -0.184379 4.701129 4.841957 UBENI (-3.891385) 0.0002 (-0.054345) 0.9568 (1.513526) 0.1337 (1.684481) 0.0959 -1.834959 -1.311431 0.338476 -0.571864 UBENJ (-7.337288) 0 (-4.423312) 0 (0.322152) 0.7481 (-0.545876) 0.5867 -0.191004 -0.147228 -0.013165 -0.013573 UMPI (-6.662038) 0 (-4.240946) 0.0001 (-0.969186) 0.3351 (-1.019977) 0.3108 0.029993 0.07304 0.005026 0.014188 UMPJ (0.889723) 0.3759 (1.776839) 0.0788 (0.180615) 0.8571 (0.535166) 0.594 17.86308 10.50801 -41.42536 -18.02349 C (6.894575) 0 (3.561826) 0.0006 (-0.112252) 0.9109 (-0.286729) 0.7751 1.0019 0.803423
AR(1) n/a n/a n/a n/a
(70.82968) 0 (7.269532) 0 0.200857
AR(2) n/a n/a n/a n/a n/a n/a
(1.800148) 0.0756
R-squared 0.849849 0.767035 0.979198 0.98028
Adjusted R-squared 0.829084 0.737607 0.976125 0.976872
S.E. of regression 0.660853 0.818821 0.246136 0.242941
Sum squared resid 41.05233 63.6945 5.331294 4.780641
Log likelihood -101.012 -124.7316 5.788594 7.77103
Durbin-Watson stat 0.839247 0.43358 2.252734 1.777996
Mean dependent var 3.270825 3.270825 3.349972 3.420199
S.D. dependent var 1.598502 1.598502 1.592944 1.597467
Akaike info criterion 2.129853 2.550585 0.161008 0.150604
Schwarz criterion 2.477536 2.873434 0.521298 0.551283
F-statistic 40.92597 26.06548 318.6394 287.6132
Prob(F-statistic) 0 0 0 0
Notes:
Case 1- Including in regression all variables Case 2 - Excluding the variable Inci from the model
Table 4. Regression results II
1 2 3
Dependent variable log
(Imfloij) Coefficient Prob. Coefficient Prob. Coefficient Prob.
17.10664 -12.9374 -8.607438 CHILDI (0.55927) 0.5773 (-1.02424) 0.3085 (-0.664486) 0.5083 -0.566492 0.066545 0.081376 ESTATEI (-2.849887) 0.0054 (0.717025) 0.4753 (0.842314) 0.4021 -2.817974 -0.795246 -1.919293 GINII (-0.856293) 0.394 (-0.424658) 0.6721 (-1.040941) 0.301 5.290816 0.457331 2.046113 GINIJ (1.649391) 0.1024 (0.273742) 0.7849 (1.155937) 0.2511 2.783823 -0.144111 -0.703415 INCI (5.652458) 0 (-0.163174) 0.8708 (-0.801538) 0.4252 0.778549 0.056004 0.088563 INCJ (5.30902) 0 (0.193076) 0.8473 (0.303327) 0.7624 -11.89107 -2.308169 -1.632501 JOBI (-5.277115) 0 (-1.549477) 0.1249 (-1.120212) 0.2659 -5.321439 -1.671319 -0.832017 JOBJ (-4.667519) 0 (-0.584276) 0.5605 (-0.299857) 0.7651 -0.0000213 -0.000213 -0.000578 RURALI (-0.516048) 0.607 (-1.274965) 0.2057 (-2.134317) 0.0358
UBENI n/a n/a n/a n/a n/a n/a
-1.789744 0.596704 -0.191763 UBENJ (-6.683956) 0 (0.563783) 0.5743 (-0.187453) 0.8518 -0.217665 -0.013691 -0.013487 UMPI (-7.294319) 0 (-0.993687) 0.3231 (-1.006581) 0.3171 0.068658 -0.004646 0.003042 UMPJ (1.988487) 0.0496 (-0.166209) 0.8684 (0.119512) 0.9052 15.22465 -8.289251 -9.005636 C (5.680247) 0 (-0.268657) 0.7888 (-0.496039) 0.6212 1.007237 0.766502
AR(1) n/a n/a
(72.25701) 0 (7.124985) 0 0.243022
AR(2) n/a n/a n/a n/a
(2.237279) 0.028
R-squared 0.825661 0.978627 0.97973
Adjusted R-squared 0.803639 0.97547 0.976227
S.E. of regression 0.708339 0.249487 0.246305
Sum squared resid 47.66563 5.477467 4.913977
Log likelihood -109.0777 4.409103 6.450602
Durbin-Watson stat 0.578322 2.273731 1.740308 Mean dependent var 3.270825 3.349972 3.420199 S.D. dependent var 1.598502 1.592944 1.597467 Akaike info criterion 2.260697 0.188057 0.178112
Schwarz criterion 2.583546 0.548347 0.578792
F-statistic 37.4929 309.9554 279.6521
Prob(F-statistic) 0 0 0
Notes:
Case 1 - Excluding the variable Ubeni from the model Case 2 - Including AR(1)
Table 5. New CORRELATION MATRIX
ESTATE CHILD GINII GINIJ JOBI JOBJ RURAL UBENI UBENJ UMPI UMPJ INCI INCJ
Table 6. Regression using lag variables
Dependent variable
log(Imflowij) Coeff Prob. Variable Coeff Prob.
21.10658 93.58653 CHILDI(-1) (0.777507) 0.4389 CHILDI(-2) 2.885275 0.0053 -0.491576 -1.643563 ESTATEI(-1) (-2.610646) 0.0106 ESTATEI(-4) -5.448996 0 -6.588749 -9.093392 GINII(-1) (-1.9698) 0.052 GINII(-3) -2.484122 0.0156 7.865101 7.566624 GINIJ(-1) (2.687429) 0.0086 GINIJ(-2) 2.609914 0.0113 4.171331 6.143499 INCI(-1) (8.013471) 0 INCI(-2) 8.867933 0 0.741798 0.68575 INCJ(-1) (5.525612) 0 INCJ(-2) 4.763216 0 -13.14257 -24.90572 JOBI(-1) (-6.022346) 0 JOBI(-2) -6.434804 0 -5.488768 -5.136372 JOBJ(-1) (-5.172032) 0 JOBJ(-2) -4.280814 0.0001 0.0000991 7.88E-05 RURALI(-1) (2.342762) 0.0214 RURALI(-5) 1.690825 0.0957 -15.64984 -34.06378 UBENI(-1) (-5.017427) 0 UBENI(-2) -6.427659 0 -1.990345 -2.139682 UBENJ(-1) (-8.020767) 0 UBENJ(-2) -8.100476 0 -0.184202 -0.121035 UMPI(-1) (-6.515515) 0 UMPI(-2) -4.726123 0 (0.028107) -0.037472 UMPJ(-1) 0.857947 0.3933 UMPJ(-4) -1.091199 0.2793 16.30954 25.82087 C (6.542276) 0 C 7.235848 0 R-squared 0.866131 R-squared 0.907969
Adjusted R-squared 0.846355 Adjusted R-squared 0.889275 S.E. of regression 0.624395 S.E. of regression 0.526358 Sum squared resid 34.30851 Sum squared resid 17.7314 Log likelihood -89.16318 Log likelihood -52.90372 Durbin-Watson stat 0.737267 Durbin-Watson stat 0.922974 Mean dependent var 3.349972 Mean dependent var 3.650445 S.D. dependent var 1.592944 S.D. dependent var 1.581828 Akaike info criterion 2.022807 Akaike info criterion 1.71548 Schwarz criterion 2.383098 Schwarz criterion 2.138479
F-statistic 43.79691 F-statistic 48.57065
Figure 1. Distribution of IMflowij
Figure 2. Distribution of log(IMflowij)
Fig 3. Residual test – Normality test AR(1)
Fig 4. Residual test – Normality test AR(1) and AR(2)
0 4 8 12 16 20 -0.25 -0.00 0.25 0.50 0.75 1.00 1.25
Series: Standardized Residuals Sample 1992 2007 Observations 96 Mean 1.76e-09 Median -0.044385 Maximum 1.207547 Minimum -0.351600 Std. Dev. 0.223328 Skewness 2.457787 Kurtosis 11.84415 Jarque-Bera 409.5271 Probability 0.000000 0 4 8 12 16 20 24 28 -0.5 -0.0 0.5 1.0
Table 7 Regression using country differences
Dependent variablelog(Imflowij) Coeff Prob. INCJ-INCI 0.003555 0.023501 0.9813 GINIJ-GINII 1.287663 0.381502 0.7036 JOBJ-JOBI 0.176359 0.142267 0.8872 UBENJ-UBENI -0.458822 -1.354149 0.1788 UMPJ-UMPI 0.021645 1.035595 0.3029 -0.90834 CHILDI -0.02596 0.9793 0.724029 ESTATEI 4.544273 0 0.000261 RURALI 7.169666 0 -0.134047 C -0.299022 0.7655 R-squared 0.621448 Adjusted R-squared 0.590858 S.E. of regression 1.02247 Sum squared resid 103.499 Log likelihood -150.9466 Durbin-Watson stat 0.11561 Mean dependent var 3.270825 S.D. dependent var 1.598502 Akaike info criterion 2.961975 Schwarz criterion 3.185486 F-statistic 20.31533 Prob(F-statistic) 0
Table 8. Structural break
Chow Breakpoint Test: 1999F-statistic 39.20870 Probability 0.000000 Log likelihood ratio 231.6359 Probability 0.000000 Chow Breakpoint Test: 2004
F-statistic 0.650093 Probability 0.814681 Log likelihood ratio 11.63671 Probability 0.635452
Table 9. Stability tests – Ramsey.
Ramsey RESET Test: 1 fittedSejas et al (2006) Gravity model Newtonian gravity equation
Immigrants stocks from home to host destination in 2000 Distance, Language, Population, Schooling, GDP, Unemployment, Inflation, GINI, Trade, Welfare state.
Analyzing the immigration into 13 European countries from 139 source countries. Most of the parameters have the expected signs. Unemployment is significant at 10% level. Population and distance are highly statistically significant. Both “push” and “pull” factors are important explanatory factors.
Hatton and Williamson (2008) Two-factor Herckscher – Ohlin model Computable general equilibrium (CGE) Instrumental variables regression (OLS).
Net migration rate into the state Rate of growth of manufacturing employment
Share of labor force in manufacturing Proportion of population urban Share of population aged 15-24 years Real earnings
Analyzing the immigration on balanced panel of region / years
GDP per capita in selected countries and inequality level have a positive sign, that’s leads to the fact that increasing in immigration is correlated to an increase in inequality. An increase in GDP will lead to a decrease in immigration
Weyerbrock (1995) Computable General Equilibrium Analyses
Immigration, Unemployment, Wages. She concludes that labor migration into the EU does not cause the dramatic consequences that EU citizens fear. The changes in unemployment or decreasing wages and income per capita are small compared with huge migration flows
Barry (2002) Three stages model
Immigration flows, Foreign direct investments Whether the initial population benefits from the increased immigration, however, is uncertain. The emergence of Central and Eastern European countries as competitors for FDI produces the opposite effect.
Volger, Rotte (2000) OLS
GNP per capita, GDP per capita, GDP growth, Trade, Population
The results leads to the hypothesis that even with persistently converging 25 living standards, development progress in the Third World will result in an increase of migration to the industrialized countries in the short and medium run. The flows from Africa respond more strongly to economic influences than those from Asia.
Fertig et al (2000) GMM
Growth rates in home and host country, Unemployment rates in home and host country Stock of migrants
Results show that the variables are positive related to the immigration flows.
Zoubanov (2003) GLS
Incomes, Economic growth, Stocks of immigrants.
Income factor is highly important for migration in general thus supporting the view of migration as a form of investment in human capital
Mayda (2007) OLS / Panel data analysis
Per worker GDP, Distance between countries Language, Share of younger population
First, the emigration rate is positively related to the destination country’s (log) per worker GDP. Doubling the great circle distance between the source and host country decreases the number of emigrants by 41 per 100,000 individuals in the origin country.
Parikh et al (2002) Pooled OLS
Wages, population, unemployment rent, hospitals bed per inhabitant, cost of living, expenditures
The impact of skilled workers’ wage differences on migration is different to that of unskilled workers’ wage differences. Immigrants are discourage by the high unemployment in destination regions. Decreasing of wages will conduct to an increase in migration between regions.
Murphy et al (2006) OLS/Panel Data
Earnings differentials, Unemployment rates Housing evolution
The decrease in average earnings and prices of houses will conduct to more immigration.
Pose et al (2004) OLS/Panel Data GDP, Education, Job Satisfaction The job satisfaction has a positive effect to immigration. OLS/Panel Data
Schepf et al (2007)
Buch et al (2006) OLS/Panel Data GDP, Immigration, FDI. The results suggest an agglomeration that is driven by complementarily between inward FDI and high-skilled immigrants.
Lach (2007) OLS/Panel Data Changes in prices, Immigration The results are showing that peoples are tend to migrate when the inflation is bigger.
Okkerse (2008) Different methods Literature review on the empirical evidence on labour market effects of immigration.
