Methodological Support to National Urban Audit Co-ordinators Urban Audit II - Lot 2 Methodological Support to National Urban Audit Co-ordinators State of Progress November 2003

Objective Lot 2: Provide required and necessary Methodological Expertise in Urban Statistics and Estimation Methods to National Urban Audit Co-ordinators

Expert Team Well-known Urban Statisticians and Scientific Experts: Asta MANNINEN – Helsinki Urban Facts Klaus TRUTZEL – Statistical Bureau Nuremberg Denise PUMAIN – University Paris I Risto LEHTONEN – University Jväskylä

Tasks carried out Organise Definition and Methods Workshop 7-8 April 2003 in Paris Develop Glossary on Definitions Meanwhile 7th version diffused last week Ad-hoc Support On-going: numerous questions bilaterally discussed Use of newsgroups: not really a success (in total 4 requests !) Estimation methods proposed and applied (e.g. AT, DE)

Tasks to be carried out Continue Ad-hoc Support (Glossary, advice on estimation methods) Draft Urban Audit Methodological Handbook Description of Spatial Units Sampling Design for participating cities Definitions of Variables and Indicators Description of Estimation Methods applied Description of Quality Control procedures applied INPUT REQUIRED FROM YOU !!!

Clarification on some variables DE3008V Lone pensioner household: Age Persons that retired from work and will not work anymore Not person that receive unemployment benefit (they might work again) and that never worked (e.g. mentally handicapped person receiving pension) SA2013-14V Nb of deaths due to heart diseases and respiratory illness: Classification Apply International Classification of Diseases and health problems of the WHO

Clarification on some variables (cont.) SA2023-24V Doctors and dentists: persons who have an official accreditation to practice First Access: focus on doctors working in practice, not in hospitals SA3005V Murders and violent deaths Exclude suicides (not a crime)

Clarification on some variables (cont.) EC1001V … Economically Active Population: Age all resident persons in employment and unemployed (and looking for work) above 15 and under 65, in accordance with Labour Force Survey (LFS). Persons in employment includes employers, self-employed (own account workers), employees and unpaid family workers LFS: Working age: above 15; Unemployed persons: age from 15 – 74 EC1142V Total Economically Active population 15-24: also for Sub-City-Districts Correction EC1034V: Full/Part-Time employment: INCLUDE self-employed (not exclude !)

Clarification on some variables (cont.) EC2002V Total resident population of area relating to reported GDP: refers to the total population (all ages, working or not) resident in the area to which the reported GDP is related. This area may be different from the area of the UA2 spatial unit (City or LUZ). It enables to calculate GDP per capita for the area.

Clarification on some variables (cont.) EC2015V Total employment of area relating to reported GDP: refers to the total employment (jobs) in the area, so including residents of the area, in-commuters from outside the area And excluding out-commuters from the area. This enables to calculate regional GDP per employed within the area.

Clarification on some variables (cont.) CI2 Local Administration: Objective of these Variables is to provide an idea of the scope of influence of the municipal government. Therefore, privatised enterprises that need to report the City Council (directly or indirectly) should be included if they are owned >50% by the local authority.

Clarification on some variables (cont.) EN2 Air quality and Noise: Harmonised European database on Air Quality by EMEP (European Monitoring and Evaluation Program of Air Pollution): model-derived data for grid-cells of 50Km size http://www.emep.int/index.html EN2007V the Lday indicator and for EN2008V the Lnight indicator, both defined in EU directive 2002/49, are to be used.

Clarification on some variables (cont.) EN3003V Water consumption: please provide in “m3” CR2: Airports: include all airports with regular commercial flights

Examples of support procedures Action to be taken for “B” variables Case Studies: Estimation of Unemployment for Austria Estimation of „ILO“ Employment and Unemployment in Germany Risto LEHTONEN Klaus TRUTZEL

(1) Updating the current list of "B" variables. “B" variables for Core City, LUZ and SCD: Please examine whether the list is up to date or not, and inform on the current state. For example, if there are already changes from "B" to "A", please mark these changes accordingly. In addition, if there are already identified changes from a "C" classified variable to "B", please add all such variables in the list. If the list is completely up to date, please notify this also.

(2) Estimation methods for "B" variables For each “B” variable, please include the following information: (a) Variable and Code (b) Current state: Already completed / To be started (c) Description of estimation method already used or planned to be used - Used estimation methods - Planned estimation methods - Estimation problems: No problems / List of problems encountered or expected for this variable  (d) Description of data sources already used or planned to be used - Used data sources - Planned data sources - Data problems: No problems / List of problems encountered or expected for this variable 

A practical method for the estimation of unemployment figures for Austria Problem: Microcensus distributions not calibrated against corresponding Census distributions Solution: Application of calibration methods (under selected model assumptions for the available unit-level data = micro census in this case) against selected marginal distributions from an auxiliary data source (Census data in this case) Marginal frequency distributions are the f. e. distribution of economically active people by sex and age group. Standard statistical software used (e.g. SAS macro CLAN) Model assumptions would consider relationship of ratios of rates based on ILO concepts and the corresponding national concepts, in different population subgroups

A practical method for the estimation of unemployment figures for Austria (cont.) In our case only aggregate data are available causing a restriction, for example, appropriate standard errors of estimates cannot be properly calculated. An ad-hoc solution proposed as follows: Calibration and a simple model assumption. Data sources: Graz/NUTS3/ILO/Microcensus Graz/NUTS3/National/Census Graz/City/National/Census. Variables: Sex-age group breakdown for the number of employed/unemployed/economically active people in 15-64 years old population.

A practical method for the estimation of unemployment figures for Austria (cont.) Tasks (1): Calibration of Microcensus data for the number of employed and unemployed by using Census data Assumption: figures for economically active population (the number of employed plus the number of unemployed) in the Census data are close to the "truth", both for Graz-NUTS3 and Graz-City. So, these figures are used to calibrate the Microcensus figures of the number of econ. active people with the following breakdown into population subgroups:   1. 15-64 years old economically active males 2. 15-64 years old economically active females 3. 15-24 years old economically active males 4. 15-24 years old economically active females 5. 55-64 years old economically active males 6. 55-64 years old economically active females.

A practical method for the estimation of unemployment figures for Austria (cont.) Just calculate the ratio of the corresponding figures in the Microcensus data and Census data for the first subgroup. Apply this ratio for the Microcensus figures to obtain updated figures for the number of ILO employed and the number of ILO unemployed. This procedure of course reproduces the same number of econ. active people for Microcensus data as it is in the Census data, in each population subgroup 1-6. Completing this task, results in more reliable figures for the number of employed and the number of unemployed. The corresponding ILO UE rates will of course remain the same as the original ones.

A practical method for the estimation of unemployment figures for Austria (cont.) Tasks (2): Approximating ILO employment and unemployment figures for Graz City Problem: small number of sample elements in Microcensus in certain subgroups of the population. Direct estimation of the ILO employment and unemployment is unreliable for such subgroups. Using census figures would solve the problem if these were harmonised, but national concepts are underlying. Solution: Model assumption: ratio of the ILO unemployment rate (UE-ILO) and the national unemployment rate (UE-National) is the same in Graz City as it is at Graz-NUTS3 level. Now, data are available for the following unemployment rates, for each subgroup in (1): UE-National-Graz-NUTS3 UE-ILO-Graz-NUTS3 UE-National-Graz-City and what is required is the rate UE-ILO-Graz-City.

A practical method for the estimation of unemployment figures for Austria (cont.) Under these model assumption on the equality of the ratios, this rate can be easily calculated and used as an adjustment factor in calculating updated figures for the number of unemployed in Graz-City (and the number of employed, because it is assumed that the Census figures for the number of economically active people is reliable for Graz-City). Because of the limited sample size in Graz-NUTS3 in Microcensus, a single adjustment factor calculated for the whole 15-64 years old population could be used for all the subgroups in question in the Graz-City data. Completing both tasks, ILO based approximate figures can be obtained for Graz-NUTS3 and GRAZ-City, being comparable with the figures from other cities in Austria. 

Estimation of „ILO“ Employment and Unemployment Data for the German Cities and LUZ Data sources: - registers of the Federal Labour Bureau (BA) and - Microcensus (= national 1% sample survey) Task: - adjust detailed statistics from the registers of employed and unemployed to ILO-defined employment and unemployment figures using the Microcensus - after adding civil servants (official statistics) and self-employed (estimate, incl. unpaid family workers)

Estimation of ILO Employment and Unemployment: Estimation Procedure BA statistics for all NUTS3 units within the German States + Civil Servants + Self-employed + unpaid family workers adjusted to Microcensus results for the German States in all components of the data set for the population at the place of residence after numerous additional estimates for data, which were only available at the national level

Estimation of ILO Employment and Unemployment: Estimation Problems BA (administrative) register shows a different „truth“ from the Microcensus as a statistical survey, even if definitions are the same (e.g. „unemployed“ in MC <-> „retired“ in BA register) Microcensus does not show „employed“ above 64 years of age Microcensus results may have sampling errors and systematic bias of unknown size, but had to be accepted as target values because of definitions consistent with ILO.

Thank you for your attention ! Any questions ? Thank you for your attention ! risto.lehtonen@maths.jyu.fi KUM.Trutzel@t-online.de pumain@parisgeo.cnrs.fr asta.manninen@.hel.fi willibald.croi@landsis.lu