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

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.

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

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

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

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)

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)

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)

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

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)

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)

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)

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)

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)

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

Moves needing other methods to be completely understood 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.

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

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. 18

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 Why the use of Census Microdata? How to use Census Microdata? Some results for France, 1999 IPUMS and European comparisons 19

Variables availability   Data sources Civil Register Population Register Census Microdata Variables Fertility Yes. Indirectly. Mobility Only place of birth. All movements are recorded. Only some movements are recorded. Periods of recording vary across countries and over time. Individual characteristics Usually occupational status and date of birth. Usually occupational status, gender, date of birth. Dwelling characteristics No.

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

Ways of measurement: The Own Children Method reliability 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. Why the us of Census Microdata? How to use Census Microdata? Some results for France, 1999 IPUMS and European comparisons 22

Reliability test (1)

Reliability test (2)

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) Why the use of Census Microdata? How to use Census Microdata? Some results for France, 1999 IPUMS and European comparisons 25

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. Why the use of Census Microdata? How to use Census Microdata? Some results for France, 1999 IPUMS and European comparisons 26

Duration of period of reliable estimation: 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 Why the use of Census Microdata? How to use Census Microdata? Some results for France, 1999 IPUMS and European comparisons 27

Duration of period of reliable estimation: The Own Children Method limitations Duration of period of reliable estimation: For births by order: Difficulty to estimate annual birth rates by birth order 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 Why the use of Census Microdata? How to use Census Microdata? Some results for France, 1999 IPUMS and european comparisons 28

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)

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

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

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

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

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

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

Exploratory analysis for some countries