1. I n f o r m a t i o n e n www.statistik.at Wir bewegen
Data Imputation and Estimation for the Austrian Register Based Census Test
I n f o r m a t i o n e n
Reinhard Fiedler
Peter Schodl
April 23rd, 2008
© STATISTIK AUSTRIA

2. Welcome
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3. Introduction Background Information Estimation procedures Time Plan
Pros and Cons
Registers used for RBCT (Register Based Census Test)
Estimation procedures
Record Linkage
Estimation
Hot-deck technique
Clustering
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4. Background Information
Time Plan
2001: last conventional census
: reference date for RBCT
April 2008: first report RBCT
2010: first register-based census
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5. Background Information
Pros and cons
Pros:
economic efficiency
faster
more often
unburden respondents
privacy
Cons:
incomplete data
inconsistent data
timeliness
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6. Background Information
Registers used for RBCT
8 basis registers, e.g.
Central Population Register (CPR)
Central Social Security Register (CSSR)
Register of Educational Attainment
7 comparison registers for cross-checks, e.g.
Register of Family Allowance
Register of Social welfare
Linkage by unique keys
Branch-specific identification number (bPK)
(a specific personal code)
Social Security Number (RBCT)
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7. Background Information
Missing data
Low missing rates
Covered by more than one data source
Sex (<1% missing)
Date of birth (<1% missing)
Medium to high missing rates
Marital status (11% missing)
Graduates (7% missing)
Not included in any register
Occupation (100% missing)
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8. Estimation Strategy Record Linkage For all registers Estimation
Marital status (high missing rate)
Occupation (not contained in any register)
Graduates (immigrants since last census)
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9. Record Linkage Problem: imperfect linkage of registers
Wrong or missing keys
Attributes used:
Date of birth
Address
Nationality
Sex
Standardization of notations
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10. Record Linkage Example: Current school enrolment
By record linkage, people in school-age without current school enrolment are reduced by 40%
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11. Estimation Occupation and graduates Graduates Occupation
Source: RBCT itself
people with graduation
people with missing graduation
Occupation
Source: Labour Force Survey
Quarterly sample survey
About people with occupation in survey
People with missing occupation (all working persons)
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12. Estimation Basic idea:
Same procedure for estimation of occupation and graduates
Estimation on person-level
Target-distribution
Building of groups, to transfer the distribution of the source to the corresponding group of the target
Groups are formed by attributes with significant influence on the target-variable
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13. Hot-deck technique Example:
1000 People from 30 to 34 years living in Tyrol form one deck
Labour Force Survey
200 with occupation A
300 with occupation B
500 with occupation C
Weighting scheme gets applied to all people within the deck in the RBCT
20% probability for occupation A
30% probability for occupation B
50% probability for occupation C
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14. Which attributes have influence?
Graduates
Age
Status in employment
Sex
Nationality
Urban / rural environment
Occupation
Age
Status in employment
Sex
Nationality
Region
NACE of employment
Level of educational achievement
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15. Clustering Groups must not be too small No donor for many persons
Wrong distribution
Example:
Source:
Tyrol, male, 87 years, German nationality: 10 Persons
5 occupation A (50% A)
5 occupation B (50% B)
Tyrol, female, 87 years, German nationality: 1 Person
1 occupation B (100% B)
Target:
Tyrol, male, 87 years, German nationality: Persons  500 A, 500 B
Tyrol, female, 87 years, German nationality: Persons  1000B
Tyrol, male, 87 years, German nationality: occupation A
1500 occupation B
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16. Clustering  Optimal groups by cluster analysis
Groups must not be too big
Correct distribution only on highest level, incorrect distribution on lower levels
Example:
2 Groups: Male / Female.
Distribution for males and for females are transferred to the target
distribution of source and target is the same for males and females.
But: distribution for regions, age,… can be incorrect!
 Optimal groups by cluster analysis
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17. Clustering Occupation First clustering:
Variables with many values (age, nationality, region,…)
Second clustering:
Since the groups after first clustering are too small, the groups are clustered again
nationality
age
First aggregation
Second aggregation
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18. Results
Graduates:
No second clustering, 457 groups with about Persons in each group
Never more than 2.4% deviation on highest level to the Labour force survey 2006
Occupation:
65 groups after second clustering with about 500 Persons in each group
Never more than 1.7% deviation to the traditional census on highest level
Never more than 3.2% deviation on medium levels
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