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2. Age and intention to Migrate Respondents in this study are of different ages. So, as shown in figure 4 (in the appendix), 26% of respondents’ ages are between 25 and 30 years old, 49% between 31 and 45 years old, and 25% are above 46 years old. It is expected that young people have more intention to migrate for many reasons, including health, less responsibilities, and higher motivation to make a good career even outside home countries. In order to test this assumption, a logistic regression is performed with age as an independent variable and intention to migrate as a dependent variable (a dummy variable). The independent variable “age” is the average age for each age category. Results The results are shown in the following table. Table II.2.3.2: Logit regression Output (Age VS Intention to migrate) Regression Output Coefficient df P-Value Age -.043 1 .071 Constant 2.854 1 .004 The results show that there is a significant causal relationship between age and intention to migrate (P-Value < 0.1). So, age is negatively related to intention to migrate (coefficient = -0.43). These results support our initial hypothesis and suggest that the higher the age of the medical doctor, the less is his intention to migrate. 3. Workplace and salaries Respondents to this questionnaires work in public as well as in private hospitals. The questionnaire data indicate that 26% of respondents work in private hospitals (or have their own cabinet) and 74% work in public hospitals (as shown in figure 5 in the appendix). The Data show also that 42% of respondents are general practitioners while 58% are specialists (as shown in figure 6 in the appendix). One of the questions in the survey is about the rating of current salaries of respondents (very good, good, or poor). The records, as shown in figure 7 (in the appendix), indicate that 62% of respondents rate their current salaries as poor, 27% as good, and 4% as very good. The remaining 7% did not answer to this question. However, the rating of current ! *%#!
salaries differs according to the workplace and the specialty of medical doctors. Figure 8 (in the appendix) shows that for medical doctors practicing in private, 48% rate their salaries as good, 29% as poor, and 23% as very good. For medical doctors practicing in the public sector, 76% rate their salaries as poor, and 24 % rate it as good. Hence, one might conclude that medical professionals working in the public sector are less satisfied with their salaries than those working in the private sector. The data suggest also that the current salary rating differs according to the specialty of the medical professional. Figure 9 (in the appendix) indicates that for specialists, 66% rate their salaries as poor, 22% rate it as good, and 6% as very good (the remaining 6% did answer to the questions). For general practitioners, 56% rate it as poor, 33% rate it as good, and 2% as very good (the remaining 9% did not respond to the question). The results shown in figure 9 suggest that the specialty of the doctor is not significantly related to the rating of salaries. 4. Workplace and intention to migrate In the previous section, it was suggested that medical doctors practicing in public hospitals are less satisfied with their salaries than those working in the private sector. Based on this finding, one might assume that medical professionals working in the public sector might have higher intention to migrate than those working in private clinics. In order to test the relationship between workplace (private or public sector) and intention to migrate (yes or no), a Chi-square test is performed with the two categorical variables workplace (public or private) and intention to migrate (yes or no). Results The results are indicated in Table II.2.3.3 below: Table II.2.3.3: Chi-square test output for Workplace VS Intention to Migrate Chi-Square Tests Pearson Chi- Square Pearson Chisquare Value Degrees of freedom Pvalue Confidence interval 2.489 a 1 .115 95% As shown, the p-value for the chi-square test is greater than 0.1. This suggests that there is no significant relationship between the workplace and intention to migrate. In other ! *%$!
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F E M I S E R E S E A R C H P R O G
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What Can Be Learnt from the New Eco
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AMGA: American Medical Group Associ
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USA: United State of America WB: Wo
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medical doctors are then free to en
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elate to quality research and educa
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This may concern also EU countries
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discussed both in theory and practi
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EU are then described and discussed
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specialties. This study shows that
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with this trade. Special arrangemen
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into the developed countries; where
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them as follow: Cross-border delive
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In this regard, the EU is suggestin
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Given the importance of health care
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Serbia. Low wages, poor working con
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• How does tertiary education wit
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Driouchi & Kadiri (2010) are promis
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annual living costs per student. Th
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Lithuania 4.12 4.15 4.01 4.03 3.97
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Egypt 67.57 68.86 70.17 71.52 72.89
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observed externally from developing
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Relative Wages : MIN MENA / By comp
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No future, Heavy workload, to save
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one-year qualification apprenticesh
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Other European Countries Austria 4
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one medical university and studies
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includes a three-year vocational tr
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has 18 medical universities and stu
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général du Gouvernement to gradua
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of Medical Science, Bachelor of Med
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Egypt Iran 29463 31445 32134 25990
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major policies and strategies relat
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The aim of this sub-part is to prov
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• What is the role of mobility of
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Commonwealth of Independent states
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Dodani and LaPorte (2005) suggest t
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In regards to the mobility of Europ
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The general conclusions of these th
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Specific factors Impact of HIV/AIDS
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Portugal X X Romania X X X Russian
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Germany Italy Spain Lithuania Slova
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Italy (2%) and Russia (4) in 2008 (
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theory assumes perfect transferabil
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The data available suggest potentia
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This chapter deals with the migrati
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networks and on the major channels
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assumed here that emigration decisi
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Increases (respectively decreases)
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Figure II.1.2: The shape of H as a
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The optimal emigration rate that ca
Page 103 and 104: Figure II.1.4: Effects of the Level
Page 105 and 106: empirical investigation focuses on
Page 107 and 108: Table II.1.3.1: Country annual tren
Page 109 and 110: Table II.1.3.2: Country annual tren
Page 111 and 112: ! ! C/D>)! 01::! 0103! 5881::7! E#%
Page 113 and 114: "#$%&'!()*)+!Eurostat Database Abou
Page 115 and 116: Using these parameter estimates, co
Page 117 and 118: different websites for different co
Page 119 and 120: the theoretical values of the relat
Page 121 and 122: p-value 0.33 0.03 t test 0.99 2.25
Page 123 and 124: The following set of graphs are the
Page 125 and 126: The countries with higher number of
Page 127 and 128: Turkey (m*=0.41, m**=0.77) Yemen (m
Page 129 and 130: periods but should be concerned abo
Page 131 and 132: health report “Comptes Nationaux
Page 133 and 134: In accordance with the health insur
Page 135 and 136: 95 adults >15 years old per 100000
Page 137 and 138: To achieve progress in the health s
Page 139 and 140: The external problems comprise (1)
Page 141 and 142: first four hospital universities ex
Page 143 and 144: monitoring trends and economic fact
Page 145 and 146: Other support staff 6300 2.11 2004
Page 147 and 148: areas, the case of doctors who reti
Page 149 and 150: areas of Morocco. It is computed ye
Page 151 and 152: political instability, economic dep
Page 153: 1. Gender VS Intention to Migrate A
Page 157 and 158: Results The results are given below
Page 159 and 160: These results suggest that the numb
Page 161 and 162: Finally, the questionnaire records
Page 163 and 164: The past decades have witnessed an
Page 165 and 166: changed the notion of space, where
Page 167 and 168: implications on health, Huynen et a
Page 169 and 170: Figure III.1.3: Social stratificati
Page 171 and 172: Lee et al., (2007) primarily focus
Page 173 and 174: However, Collins (203) does recogni
Page 175 and 176: America. In Africa, where there are
Page 177 and 178: physicians from Liberia practice in
Page 179 and 180: Figure III.1.7: Migrant care worker
Page 181 and 182: espectively. An interesting particu
Page 183 and 184: Table III.1.5: Supply of foreign (-
Page 185 and 186: Table III.1.6: Health care wages in
Page 187 and 188: Figure II.1.11: Factors affecting h
Page 189 and 190: Figure III.1.12: Factors contributi
Page 191 and 192: Table III.1.10: International Quali
Page 193 and 194: health as integral to wellbeing thr
Page 195 and 196: III.2.1 Need for A cooperative Fram
Page 197 and 198: graduate spends of certain number o
Page 199 and 200: III.2.2 Migration of Medical Doctor
Page 201 and 202: The convexity condition is simply a
Page 203 and 204: • A payoff vector (ai)i S is said
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III.2.2.2 Discussion and Public Pol
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Europe. Second, in the North Africa
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In the short run, the country of or
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great incentive for medical student
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system, and therefore to the rest o
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MENA countries could be moving to p
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of their medical school. The other
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Beine, M., Docquier, F. & Rapoport,
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German Medical Association.(2011).
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World Bank Database, 2007. World Ba
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http://gse.buffalo.edu/org/inthighe
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Berland, Y. (2002). Demographie des
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Gericke, C.A. (2006). Finance healt
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Salehi-Isfahani, D., Tunali, I., As
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Beine, M., Docquier, F. & Rapoport,
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Docquier, F., Lohest, O. & Marfouk,
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Labdelaoui, H. (2010). Les dimensio
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Stark, O. (1991), The Migration of
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WHO - World Health Organization (20
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Linacre, S. (2007). “Migration: p
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Challenging Inequities in Health: F
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Mullan, F. (2007). Doctors and Socc
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Vujicic M, Zurn P, Diallo K, Dal Po
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Collins, T. (2003). Globalization,
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Kana, M.A., (2009). From Brain Drai
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Sagoe, K. (2004). Using the Diaspor
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Sub-part II.1: Appendix I Appendice
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means that So if , then: > which is
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A, B,C>0 and , , Then Demo 5: Sign
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t test 3.21788138 1.86856175 3.1713
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Lithuania Years m Relative wage H/H
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Romania Years m Relative wage H/H0
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MENA Countries Risk Neutral Model C
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Tunisia Turkey 1993 0.1781 2.50 1.9
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Syria Tunisia Turkey Yemen Stand.de
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Morocco Syria Tunisia t-stat (theor
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Risk aversion model summary table C
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Table II-2-1.2: Projections of the
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Observations 5 Coefficients Erreur-
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Figure 4: Age distribution within r
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Figure 11: Migration conditions dis
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! "(&!
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! "((!
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! ")+!
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! ")"!
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