Innovations on methods and survey process for the 2011 Italian population census

Innovations on methods and survey process for the 2011 Innovations on methods and survey process for the 2011 Italian population censusItalian population census

European Conference on Quality in Official Statistics 8-11 July, 2008

Giancarlo Carbonetti, Marco Fortini, Fabrizio Solari

European Conference on

Quality in Official Statistics Italian Census and municipal population registersItalian Census and municipal population registers

Italian census can be defined register improved• Conventional methodology• Check against population register, followed by follow up

Population registers enlist people dwelling in Municipalities• Are managed locally• Record vital and internal/external migration events• Are used in updating population between two censuses

New census strategy aims in improving• Integration between census and population registers• Field organization and timeliness


Quality in Official Statistics Size as the most critical factor for data qualitySize as the most critical factor for data quality

TYPE SIZE (inhabitants) Municipalities % Population (01.01.07)

A > 50,000 and chief towns 165 35,8

B 20,000 – 49,999 339 17,0

C1 5,000 – 19,999 1,859 29,6

C2 < 5,000 5,738 17,6

Organizational impact strongly dependent on the size of the municipality demographic size as one of the most important risk factors in the past census round

As a result, it will be considered a modularity for the innovation scheme which depend on the municipalities population size


Quality in Official Statistics General Scheme for largest municipalities (A and B)General Scheme for largest municipalities (A and B)

Source 3) Addresses Register (called RNC)

Units included in LAC• mail out of partially prefilled questionnaires• multi-mode response (mail back, web, Cati)• Enumerators field work:

• late respondents collection

• check for over-coverage

Units not included in LAC• option 1: complete search of missing units by enumerators on the basis of LIFA and RNC• option 2: survey based on capture recapture and adjustment of census counts

Source 1) Municipal Population Register (called LAC)Source 2) Auxiliary Sources Information Lists (called LIFA)


Quality in Official Statistics Use of samples of households for long form enumerationUse of samples of households for long form enumeration

Mail out of census forms requires lighter questionnaires so as to • Increase the spontaneous response rate • Reduce as much as possible the response time delay

Less enumerators will be necessary to retrieve information

But using only short form would decrease census informative power Census variables are partitioned into two mutually exclusive

subsets• demographic variables, such as gender, date of birth and nationality• socio-economic variables, such as educational level, occupational

status and commuting Random sampling from population registers

• Sample of households provided with long forms• Remaining households provided with short forms


Quality in Official Statistics Sampling design and estimation domainsSampling design and estimation domains

Domains: national, regional, provincial, municipal and sub-municipal Sub-municipal areas define a partition of municipal urban territory, each of

about 15,000 inhabitants For 504 Italian municipalities with 20,000 inhabitants or more (or Chief

Towns), at least an urban area can be built

Sampling from population registers Municipalities greater than 20,000 inhabitants

• urban areas: long forms sent to a sample of households and short forms sent to remaining households

• rural areas: long form sent to all the households residents Municipalities between 5,000 and 19,999 inhabitants

• municipal level: long forms sent to a sample of households and short forms sent to remaining households (not yet decided)

In municipalities smaller than 4,999 inhabitants long forms are sent to the whole residents.


Quality in Official Statistics ExperimentsExperiments

Goal: evaluating the expected efficiency of sampling estimates Carried out on 2001 census data 40 municipalities with at least 10,000 inhabitants (15 ≥ 100,000) Chosen not randomly

• across the country• different populations size

10% of Italian households considered in analyses Various sampling rates considered (10%, 20%, 33%) Multivariate tables of short and long form variables together Calibrated estimates of 90 absolute and relative frequencies Sub-municipal (among 5,000 and 15,000 people), to regional domains Montecarlo techniques simulating sampling distribution of estimates Comparisons carried out by means of Coefficient of Variation (CV)


Quality in Official Statistics Main resultsMain results

Coefficient of Variation (average and maximum expected values) for the estimates referred to census sub-municipal areas

cv%_average cv%_max cv%_average cv%_max cv%_average cv%_max

<10 143.3 191.8 101.4 123.7 66.5 95.810├30 75.9 85.1 48.4 54.6 33.8 38.530├50 51.8 57.1 31.8 37.1 23.4 25.650├100 38.6 41.3 22.3 28.4 17.4 19.1100├250 25.4 28.5 15.7 19.6 11.4 12.8250├500 16.1 18.3 10.4 12.5 7.5 8.1500├1,000 11.8 12.8 7.5 8.2 5.3 5.91,000├2,500 7.5 8.9 4.7 5.9 3.3 3.92,500├5,000 4.9 5.4 3.0 3.6 2.0 2.55,000├10,000 3.2 3.8 2.0 2.5 1.3 1.9

Classes of absolute frequency T

sampling ratio = 10% sampling ratio = 20% sampling ratio = 33%


Quality in Official Statistics Other resultsOther results

CV improvement when increasing sampling rate from 10%• to 20%: (33-38%)• to 33%: (53-58%)

Average CV improvement when size of sub-municipal areas increases from less than 10,000 inhabitants to• 10,000-12,000: (14-20%)• More that 12,000: (22-28%)

10% 20% 33%< 10% 29.80 51.07 65.42

10% - 50% 54.23 40.29 29.52> 50% 15.97 8.64 5.05

Classes of cv%Sampling ratio

Percentage distribution of estimated absolute frequencies Areas with at least 12,000 people


Quality in Official Statistics Tackle with small domains and rare populationsTackle with small domains and rare populations

So as increasing estimates efficiency, standard small area estimators considered in the Eurarea Project are being studied

Both unit level and area level model based estimators have been tested

Early results show good performance when estimating relative frequencies up to 1% (among 50 and 150 cases for sub-municipal level, depending on their population size)

Reduction of MSE is in between 40% and 80% with respect to calibrated estimators

https://www.statistcs.gov.uk/eurarea


Quality in Official Statistics Undercounting evaluation and integration of missing unitsUndercounting evaluation and integration of missing units

Population register have to be amended also by undercount• e.g. those units actually dwelling on the field that are not enlisted into

the correspondent population register About 235,000 was the number of people enrolled in municipal

population registers after enumeration during 2001 census • People already residing elsewhere in Italy • People coming from abroad (foreigners are underestimated)

Two options are being studied so to overcome the registers undercount• Complete search of missing units on the field

labour intensive

• Capture recapture model based on a sample survey depending on various statistical hypotheses


Quality in Official Statistics Option 1 - Complete search of missing units on the fieldOption 1 - Complete search of missing units on the field

Use of alternative lists of names and addresses (LIFA) List of street numbers (RNC) supplemented with information

referred to the related housing units1. whole number of dwellings (without housing units reserved for

business activities)2. number of dwellings used as usual residence by households enlisted

into the population register (LAC) LIFA is used to contact the households directly at their address RNC is used to search for new households only at street number

where (1) – (2) > 0 RNC not available for municipalities under 20,000 inhabitants

• complete search supported by the list of the households which already sent back a short/long form


Quality in Official Statistics Option 1 - Critical aspects of complete searchOption 1 - Critical aspects of complete search

It works well only if vacant housing units are few or very clustered Can be used as a method of optimisation when lack of resources

prevent the complete investigation of the field• In this case an estimation procedure has however to be considered

Example: Florence - 2001 Census data;

• 72% of buildings are fully occupied by resident households.• 90% of remaining buildings contain only up to 3 vacant housing units• these buildings are made by 8.5 housing units on average, against a

general average of 5.6 housing units per building Higher risk of people and households duplication is expected

• Demanding use of record linkage techniques


Quality in Official Statistics Option 2 - Capture recapture estimation based on sample surveyOption 2 - Capture recapture estimation based on sample survey

Estimation through a sample survey of the population living in each municipality being not enlisted into the related population register• Not all the municipalities are included in the sample• Simplified field activities and better control of costs

First capture: population register corrected for over-coverage (A) Second capture: complete enumeration inside a sample of areas True population count P in a given area can be estimated by

• tover rate of not eligible people enclosed into the register

• rate of residents non enlisted into the register among those enumerated during the sample survey

underover ttAP ˆ11ˆ

undert̂


Quality in Official Statistics Option 2- A simplified scheme of the survey in a municipalityOption 2- A simplified scheme of the survey in a municipality

Some interesting features Both the tasks can be carried out at the same time Operational independence between the two procedures is assured Differently than in the usual Capture Recapture approach,

households are contacted only once

Check for over-coverage in LAC

Check for under-coverage in LAC

Municipality areas


Quality in Official Statistics Option 2 - Sampling schemeOption 2 - Sampling scheme

Quite large sample (300,000 – 1,000,000 people)

Direct dual system estimates until regional level Model assisted estimation for municipal and sub-municipal

estimates

Primary sampling units Secondary sampling unitsAll municipalities ≥ 50,000 inhabitants

Sample of street numbers (RNC)

Sample of municipalities 20,000-50,000 inhabitants

Sample of street numbers (RNC)

Sample of municipalities with less than 20,000 inhabitants

Sample of enumeration areas


Quality in Official Statistics Option 2 - Some early results on small area estimationOption 2 - Some early results on small area estimation

Evaluated through 2001 census post enumeration survey data 180,000 people and 70,000 households in 98 Italian municipalities Unit level small area estimation following Wolter (1986)

pi1+ estimated by means of a logistic model on the PES data Jacknife estimation of the variance is under study Next step: using an area level approach Example: Florence

Di

iiD pxx 11 ˆˆ


Quality in Official Statistics Option 2 - Example: Coverage evaluation for FlorenceOption 2 - Example: Coverage evaluation for Florence


Quality in Official Statistics Option 2 - Critical aspects of capture recapture approachOption 2 - Critical aspects of capture recapture approach

Dependencies on statistical hypothesis • Independence between captures, homogeneity

Influence of record linkage errors activities on • Timeliness• Accuracy• Identification of duplicates

Quality evaluation of estimates carried out by the method Enumeration of housing units and present population Municipalities population register will not be directly amended for

under-coverage• Over-coverage: complete correction• Under-coverage: estimate number of people to be included


Quality in Official Statistics Concluding remarksConcluding remarks

2011 Census as a step toward a register supported census Statistical policy and technical issues Many different methodological issues related each other

• Sampling• Modelling• Imputation• Record linkage

Find the trade off among• Innovations• Results comparability• Stakeholder needs

April 2009 – Pilot survey

Innovations on methods and survey process for the 2011 Italian population census

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