1. Immigrant mortality in Israel Jon Anson Dept.of Social Work Ben Gurion University of the Negev 84105 Beer Sheva, Israel. BSPS, September 2004, Leicester Session 5: Health and Ethnicity Analysis made possible by a grant from the Israel Science Foundation Data provided by Central Bureau of Statistics, Israel

2. Population Pyramid, Israel, Census 1995

3. Distribution of immigrants to Israel, by period and origin, 1948 – 1995 114,28125,40536,717196,464Avg / Year 685,683558,907587,472687,624482,857 Total 1.010.011.134.58.5 Asia 4.910.047.613.70.8 Africa 90.855.235.147.578.2 Europe 3.124.55.50.71.6 America 0.10.30.83.510.9 Unknown 1990-19951968-19891952-19671948-1951Pre-State Period

4. Population groups, Israeli population at census, 1995 9.2130,0443,262,61142.5546,294 Total 3.11,027101,75747.53.1 FSU As. 14.06,600472,36647.514.8 FSU Eur 12.12,465203,59747.56.3 W. Eur. 28.57,239253,79967.58.2 E. Eur. 20.51,33564,99862.52.1 Balkans 4.4910723,80942.50.7 Ethiopia 11.63,503300,88852.59.3 N. Africa 13.22,860216,27857.56.7 Asia 2.042,4151,181,20637.535.4 Israel Jews Born In: 5.622,493443,91137.513.4 Arab CDRDeaths Exposure Med AgePercentOrigin Group Population 25 and above; Age in 5 year categories; Exposure 4/11/95 – 31/12/2001

5. Survivorship Analysis, Control Variables, Ages 25+ Exp(coefficient) Control Entered 344 (4)2146 (3)24 (1)70231 (11)LR Gain (df) 0.884Householder 1.249Single 1.236Widowed 1.242Divorced Marital Status (base: married) 0.6890.687University 0.8080.806High School Certificate (base: none) 0.5180.522 Working 1.0541.0991.126Immigrant 1.0871.0901.102 Age 0.5610.6110.683 Sex N = 564,294, all coefficients significant at p < 0.05

6. Relative Risks: Population Groups, Ages 25 and above, by Control Variables introduced

7. Survivorship Analysis, Control Variables, Ages 15 -34 Exp(coefficient) Control Entered 81 (4)85 (3)7 (1)256 (11)LR Gain (df) 0.917Householder 1.824 *** Single 1.066Widowed 3.242 *** Divorced Marital Status (base: married) 0.588 *** 0.608 *** University 0.740 *** High School Certificate (base: none) 0.620 *** 0.592 *** Working 1.525 ** 1.486 ** 1.505 ** Immigrant 1.063 *** 1.029 *** 1.017 *** Age 0.372 *** 0.347 *** 0.369 *** Sex N = 314,867, ** p < 0.01, *** p < 0.001

8. Relative Risks: Population Groups, Ages 15 -- 34, by Control Variables introduced

9. Survivorship Analysis, Control Variables, Ages 35 -59 Exp(coefficient) Control Entered 153 (4)786 (3)11 (1)2764 (11)LR Gain (df) 1.108Householder 1.966 *** Single 1.326 *** Widowed 1.566 *** Divorced Marital Status (base: married) 0.651 *** 0.655 *** University 0.790 *** 0.787 *** High School Certificate (base: none) 0.440 *** 0.426 *** Working 1.0121.0371.246 *** Immigrant 1.098 *** 1.097 *** 1.109 *** Age 0.445 *** 0.461 *** 0.604 *** 0.605 *** Sex N = 273,496, *** p < 0.001

10. Relative Risks: Population Groups, Ages 35 -- 59, by Control Variables introduced

11. Survivorship Analysis, Control Variables, Ages 60-64 Exp(coefficient) Control Entered 198 (4)1350 (3)12 (1)15160 (11)LR Gain (df) 0.871 *** Householder 0.982Single 1.191 *** Widowed 1.106 ** Divorced Marital Status (base: married) 0.698 *** 0.695 *** University 0.811 *** 0.808 *** High School Certificate (base: none) 0.544 *** 0.546 *** Working 1.0521.094 *** 1.098 *** Immigrant 1.089 *** 1.093 *** 1.100 *** 1.099 *** Age 0.592 *** 0.643 *** 0.702 *** Sex N = 131,880, ** p < 0.01, *** p < 0.001

12. Relative Risks: Population Groups, Ages 60 and up, by Control Variables introduced

13. Explanations Non-selective migration? Society of migrants? Origins of migrants? Quality of data?

14. Conclusions Need theory, not just empirical generalisations Theory needs to look at migrant – resident relations Be wary of data – socially generated