provide information Longitudinal Weights for the Production of Transitions and Flow Estimates Katrin Baumgartner Angelika Meraner Alexander Kowarik Statistics Austria Rome 16 May th Workshop on Labour Force Survey Methodology

2 | 12 April 2015 Introduction Longitudinal Dimension of the Austrian LFS Bias Analysis Imputation Weighting Summary

3 | 12 April 2015 Longitudinal Dimension of the Austrian LFS Is not in focus so far Depends on the survey design Only available for a subset of LFS sample Every quarter: exchange of 1/5 of the survey Household stays 5 consecutive quarters in the survey

4 | 12 April 2015 Longitudinal Dimension of the Austrian LFS Cross sectional point of view: quarter consists of 5 waves (rotation numbers) Longitudinal point of view: a rotation number appears in 5 quarters Q-Q-Changes: approximately 4/5 of the LFS sample can be used Y-Y-Changes: approximately 1/5 of the LFS sample can be used

5 | 12 April 2015 Longitudinal Analysis Can be based on different subsamples: One removes all incomplete cases for the flow analysis, i.e. flows are based on the subsample of persons who are successfully surveyed in both quarters, q(t) and q(t+1) (immobile persons). All persons who are successfully surveyed in one quarter, q(t) or q(t+1) and do not regularly rotate in or out, are used (mobile + immobile persons). Potentially missing information of the second q(t+1) and first quarter q(t) respectively is imputed.

6 | 12 April 2015 Bias Analysis Are there any differences between mobile and immobile persons in their sociodemographic structure? Linking to administrative data from Central Population Register Information of mobile persons is only available for one quarter: first q(t) or second q(t+1).

7 | 12 April 2015 Bias Analysis (Q1 2012) Demographic Characteristics in % MobileImmobileTotal Age Sex Male Female Nationality AT Non-AT Total (in 1 000)1,75921,76623,525 S.: Microcensus-LFS 2012 Q – Unweighted sample population in Q and Q (aged 15 to 64). – Persons with administrative identifier (bPK).

8 | 12 April 2015 Bias Analysis (Q1 2012) Demographic Characteristics in % MobileOutfluxInflux Age Sex Male Female Nationality AT N-AT Total (in 1000) 1,759 1, S.: Microcensus-LFS 2012 Q – Unweighted sample population in Q and Q (aged 15 to 64). - Persons with administrative identifier (bPK).

9 | 12 April 2015 Bias Analysis (Q1 2012) S.: Microcensus-LFS 2012 Q – Unweighted sample population in Q and Q (aged 15 to 64). Register labour status for mobile and immobile persons with administrative identifier (bPK).

10 | 12 April 2015 Bias Analysis – Flows (Q Q1 2012) S.: Microcensus-LFS 2012– Unweighted sample population in Q and Q (aged 15 to 64). Register labour status for mobile and immobile persons with administrative identifier (bPK).

11 | 12 April 2015 Imputation Proportions of imputed missing values for longitudinal data sets: Longitudinal Data Imputed missing values in % Q & Q Q Q Q & Q Q Q Q & Q Q Q Q & Q Q Q Q & Q Q Q

12 | 12 April 2015 Imputation Random hot deck imputation of important labour market characteristics Selection of domain variables (max. 7) Based on bias analysis Gender, age, nationality Administrative labour status (if available) of both quarters/years ILO labour status of `complementary´ quarter/year (if administrative labour status not available) Longitudinal conceptCurrently imputed variable pertaining to `complementary´ quarter/year Multinomial logit model (forward selection) 7 th (last) domain variable for cases with no administrative labour status

13 | 12 April 2015 Weighting: Longitudinal Weights

14 | 12 April 2015 Weighting: Longitudinal Weights Two versions of weights: 1. Reducing the bias: not calibrating against the ILO labour market status 2. Providing consistency between stocks and flows: additionally calibrating against the ILO labour market status (LMS adj) Key figures for ILO labour market status stem from published quarterly results of the microcensus -> projected data

15 | 12 April 2015 Weights 1: Reducing the bias Base weights calibrated against marginal totals for q(t) and q(t+1) consecutively 1.Population by NUTS-2 region, sex and age 2.Population by NUTS-2 region and nationality 3.Weights corresponding to people born, deceased, immigrated or emigrated in q(t) are calibrated against the natural population change and the migration statistics

16 | 12 April 2015 Weights 2: Consistency of stocks and flows Base weights calibrated against marginal totals for q(t) and q(t+1) consecutively 1.Population by NUTS-2 region, sex and age 2.Population by NUTS-2 region and nationality 3.Population by nationality, sex, age and ILO labour status 4.Weights corresponding to people born, deceased, immigrated or emigrated in q(t) are calibrated against the natural population change and the migration statistics

17 | 12 April 2015 Comparison Comparison of weighting options 1 and 2 for cross-sectional data Q of population living in private households aged without persons doing their military or civilian service according to ILO labour status.

18 | 12 April 2015 Comparison Comparison of weighting options 1 and 2 for flows corresponding to longitudinal data comprising Q and Q4 2012, i.e. the population living in private households at both time points, aged and not doing their military or civilian service according to ILO labour status.

19 | 12 April 2015 Summary Obvious differences between immobile and mobile persons for demographic characteristics and administrative labour status Random hot deck imputation of missing data  Longitudinal concept incorporated  Use of administrative labour status Weighting option 1 preferred  Bias reducing  Not adjusted to ILO Labour Market Status -> not providing consistency between stocks and flows

20 | 12 April 2015 Please address queries to: Katrin Baumgartner Angelika Meraner Alexander Kowarik Contact information: Guglgasse 13, 1110 Vienna phone: +43 (1) Thank you very much for your attention