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Environmental conditions and environmental behavior in reliance to household income

Statistical Matching of Micro Census Environment and EU-SILC

• A. Wegscheider-Pichler

Unit Analysis Brussels 12th March 2015

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Starting Point (Surveys)

Micro Census “Environmental conditions & behaviour”:  Data on quality of life, noise exposure, mobility… Micro Census “Labour Force Survey (LFS)”:  Socio-demographic variables like age, gender… + subsequently income of employed from administrative data!

No income variable Income

EU-SILC “European Community Statistics on Income & Living Conditions” Environmental conditions and behavior - is income a decisive factor?

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Project at Statistics Austria

Connecting the Micro Census environmental data with income variables from EU-SILC through „Statistical Matching“, using socio-demographic matching variables New information on environmental living conditions and environmental behaviour in dependence of income New insights into matching methodologies

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Matching Process

• Identifying relevant socio-demographic matching variables and

harmonizing them in the data sets

• Selection of a donor of minimal distance from EU-SILC for each

micro census data case

• In case of several donors with the same distance one was

randomly chosen

• Each EU-SILC data case was admitted only once as donor
• 5 alternatives with different assumptions (weight, selected

variable, household perspective) were calculated, best chosen

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• Matching variables were weighted differently for the distance

function

• “Income of employed” was used for matching process
• “Income of employed” was considered from the household

perspective, i.e. a total net income from all employed household members was aggregated

Chosen alternative (Detail)

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How to check validity?

• Micro Census LFS adds subsequently the variable “monthly income
• f employed” from registers
• Household-income can also be calculated by regression

Possibilities to check the validity of the matched data:

• 1. Income of employed gained from data matching compared to

LFS-income of employed

• 2. Total disposable household income gained from data matching

compared to LFS-income of employed

• 3. Total disposable household income gained from data matching

compared to Regression Household income (Micro Census)

• 4. Compare results with “expected outcomes” according to literature

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Data check results

** Statistically significant at a level of 0.000 (Pearson).

• 1. Data-Matching with use of the subsequently added variable

“income of employed” improves on the individual level the correlation between EU-SILC “income of employed” & Micro Census “income of employed” from 0,63** to 0,97**

• 2. The household perspective increases the correlation between the

EU-SILC “total household income” & Micro Census “income of employed” from 0,33** to 0,64**

• 3. Correlation (household perspective) between EU-SILC “total

household income” & Micro Census “total household income by regression” gives a result of 0,72**

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Results - verifying the matching (Example)

S: Micro Census Environment; EU-SILC (2011)

Quality of life by household income (terciles)

45.6 36.9 44.9 56.1 50.7 56.4 52.5 42.4 3.1 5.7 2.1 1.3 0.6 1.0 0.4 0.2 10 20 30 40 50 60 70 80 90 100 Total Low house- hold income Medium house- hold income High house- hold income P ercentage S hare Bad Not so good Good Very good

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Further descriptive outcomes

• Households with low income report higher disturbance by noise

than households with high income

• Households with high income report more often to by organic

food

• Public transport is less attractive for households with high

income

• Households with low income use less often private transport (by

car) as households with medium or high income

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Findings for Statistical Matching

Results of Data matching depend strongly on the selection and harmonization of the socio-demographic variables used for matching Variable "income of employed" significantly improved the matching process (for employed income is a substantial part of total household income)

• → Data matching just on the basis of socio-demographic

variables such as gender or age means to manage with correspondingly lower correlations than those in this study