Studying internal migrations with census microdata.

1. European Doctoral School of Demography 2009 May 20 Studying internal migrations with census microdata. Claire Kersuzan, Christophe Bergouignan Census project (IEDUB, INED U13, ODE)

2. Studying internal migrations with census microdata • Census data about internal migration • International comparisons in the field of internal migration with IECM-IPUMS microdata • Pointing out interactions between factors of mobility and type of move : • Statistical significance and interactions effects, • Sampling design and geographical specificities, • Interactions between type of move, age, educational grades, social status and family status in France, • Conclusions, limitations and other ways to work. • The particular case of interactions between fertility and mobility : • The Own Children Method, • Interactions between fertility and mobility in France, • Exploratory analysis with IECM-IPUMS databases.

3. Census data about internal migration • Place of birth • Previous residence • at the last census, • n (1, 5, 10,…..) years ago. • Year of settlement • for indivudals, • for households (the first settlement of a member of the current household). • Limitations of these data • Not a complete list of residences, • Many errors in previous residence location, • Previous residence but no other previous characteristics (social status, family status,…). • But some consistent results for cross-sectional analysis

4. International comparisons in the field of internal migration with IECM-IPUMS microdata

5. International comparisons in the field of internal migration with IECM-IPUMS microdata

6. Global statistical significance IC (95%) of Odds ratios estimates of the probability of changing home between 1990 and 1999 by age* (France, 1/20, 1999 Census sample) * Logit(Pr{Change Home=1})=a+bAge+e (class option ; ref age=16)

7. Statistical significance and interactions taken into account Odds ratios estimates of the probability of changing home between 1990 and 1999 (gender effect by age) ((1), (2), (3) or (4) models) (France, 1/20, 1999 Census sample) • Logit(Pr{Change Home=1})=a+b1Age+b2Sex+e (class option for age ; ref : age=16, sex=male) • Logit(Pr{Change Home=1})=a+b1Age+b2Sex+ b3age*sex+e (without class option ; ref : sex=male) • Logit(Pr{Change Home=1})=a+b1Age+b2Sex+b3age*sex+e (class option for age ; ref : age=16, sex=male) • Logit(Pr{Change Home=1})=a+b2Sex+e (one model for each age ; ref : sex=male)

8. Statistical significance and interactions taken into account IC (95%) Odds ratios estimates of the probability of changing home between 1990 and 1999 (gender effect by age), ((3) or (4) models) (France, 1/20, 1999 Census sample) (3) Logit(Pr{Change Home=1})=a+b1Age+b2Sex+b3age*sex+e (class option for age ; ref : age=16, sex=male) (4) Logit(Pr{Change Home=1})=a+b2Sex+e (one model for each age ; ref : sex=male)

9. Geographical specificities of age distributions Students territories Paris and Parisian suburbs Without migration since 1982 Without migration since 1982 Without migration since 1990 Without migration since 1990 1999, French Census 1999, French Census 1999, French Census Peri-urban belt Suburbs Without migration since 1982 1999, French Census Without migration since 1990

10. Survey’s sampling design and geographical specificities Odds ratios estimates of the probability to move between 1990 and 1999 (by type of move and by destination) (France, 1/20, 1999 Census sample) Logit(Pr{move=1})=a+b1Age+e (one model for each type of move and destination ; class option for age ; ref age=16)

11. Some interactions between type of move, age, and educational grades, social status, family status in France Odds ratios estimates of the probability to move to heart of big regional cities between 1990 and 1999 (by gender, geographical type of origin and educational grade) (France, 1/20, 1999 Census sample) (1) Logit(Pr{Move=1})=a+b2Sex+b3geotype of origin+e (2) Logit(Pr{Move=1})=a+b2Sex+b3geotype of origin+b4graduate+e (one model for each age ; class option for age, geotype of origin, graduate; ref : age=16, sex=male, geotype of origin=Paris and its suburbs, non graduate)

12. Some interactions between type of move, age, and educational grades, social status, family status in France Odds ratios estimates of the probability to move to rural places between 1990 and 1999 (by gender, geographical type of origin and educationnal grade) (France, 1/20, 1999 Census sample) (1) Logit(Pr{Move=1})=a+b2Sex+b3geotype of origin+e (2) Logit(Pr{Move=1})=a+b2Sex+b3geotype of origin+b4graduate+e (one model for each age ; class option for age, geotype of origin, graduate ; ref : age=16, sex=male, geotype of origin=small cities, non graduate)

13. Some interactions between type of move, age, and educational grades, social status, family status in France Odds ratios estimates of the probability to move to Paris and its suburbs between 1990 and 1999 (by gender, geographical type of origin, educationnal grade and social status) (France, 1/20, 1999 Census sample) (1) Logit(Pr{Move=1})=a+b2Sex+b3geotype of origin+e (2) Logit(Pr{Move=1})=a+b2Sex+b3geotype of origin+b4graduate+b5social status+e (one model for each age ; class option for age, geotype of origin, graduate, social status; ref : age=16, sex=male, geotype of origin=Rural places, non graduate, social status=employee)

14. Some interactions between type of move, age and educational grades, social status, family status in France Odds ratios estimates of the probability to move to periurban belts of big regional cities between 1990 and 1999 (by gender and geographical type of origin geographical type of origin, educationnal grade, social status and family status) (France, 1/20, 1999 Census sample) (1) Logit(Pr{Move=1})=a+b2Sex+b3geotype of origin+e (2) Logit(Pr{Move=1})=a+b2Sex+b3geotype of origin+b4graduate+b5social status+b6family status+e (one model for each age ; class option for age, geotype of origin, graduate, social status, family status; ref : age=16, sex=male, geotype of origin=Paris and its suburbs, non graduate, social status=employee, family status=living alone)

15. Conclusions, limitations and other ways to work • Moves almost properly studied with an usual process of census microdata • Moves needing other methods to be completely understood • Another way to work : the Own Children method

16. Moves almost properly studied with an usual process of census microdata • Students moves (but high non response rate for previous residence so some problems of imputation), • Retirement moves. Moves needing other methods to be completely understood • Professionnal moves (specially those of skilled workers), • Moves linked with family process.

17. Another way to process census microdata : the Own Children method

18. Data sources • The 1999 French census sample available from Quetelet Network. • Microdata from European countries (Spain, France, Portugal, Greece, Hungary, Romania, Belarus, Austria, Italia). • Availability : IECM-IPUMS database. • Individual Microdata, not just aggregated summary data. • Data in which each individual record is identifiable within the household and in the family.

19. Variables availability • Several data sources to study the links between fertility and internal migration at a detailed geographical level : • civil register (all developed countries) • population register (some developed countries) • Census Microdata

20. Variables availability

21. How to use Census Microdata? • The Own Children Method reliability • The Own Children Method limitations

22. The Own Children Method reliability • Ways of measurement: • Census data are not only an instantaneous population photography : present children are births of the past. • Method links all the children with the fertile woman of the household family to which these children belong.

23. Reliability test (1)

24. Reliability test (2)

25. The Own Children Method limitations • Factors that could affect total fertility rates calculated from Own Children Method: • Childhood mortality • Children not living with their biological mother : • Living only with their father • Living with another woman (new wife of their father, complex households)

26. The Own Children Method limitations • Difficulty to define the link between children and mother in complex households: • Good reliability of the harmonized mother pointer (called Momloc). • But, best reliability with the use of variables on family. • Difference between the two ways of measurement : children living with another woman than their biological mother.

27. The Own Children Method limitations • Duration of period of reliable estimation: • For all births : • The frequency and timing of leaving home patterns of young people • Statistical practices of the countries concerning the exact moment when considering independence of young people

28. The Own Children Method limitations • Duration of period of reliable estimation: • For births by order: • Difficulty to estimate annual birth rates by birthorder from Own Children method (overestimation of first birth rates and underestimation of those of higher orders) • Period of reliable estimation depending on the frequency and timing of leaving home patterns of young people. Spain / France

29. Annual birth rates by birth order calculated from two methods (traditional/Own Children Method) and two data source (Census microdata/Survey data or population register)

30. Some results for France, 1999 • Fertility and type of move • The impact of the housing statute.

31. Total fertility rates according to the type of move between 1990 and 1999

32. Total fertility rates according to some places of departure and arrival (use of urban zoning classification, ZAU) From towns center From suburbs or peri-urban belts

33. Total fertility rates according to the type of move and the year of the moving into the dwelling Same commune, different house Same house in 1990 and 1999 Same region, different department Same department, different commune Different region

34. Total fertility rates according to the housing status and the year of moving into the dwelling (for women who moved in the same department between 1990 and 1999) Owners of their dwelling Private housing tenants Social housing tenants

35. IPUMS and European comparisons • Exploratory analysis for some European countries. • European comparability limitations from IPUMS database.

36. Exploratory analysis for some countries