Dissemination and interpretation
of time use data
Social and Housing Statistics Section
United Nations Statistics Division
International Workshop on Social Statistics, Bejing,2224 November 2010
Dissemination and interpretation of time use data
 Stiglitz commission on the Measurement of
Economic Performance and Social progress
 Aim 1: Identify the limits of GDP as an indicator
of economic performance and social progress
 Aim 2: Consider additional information required
for the production of a more relevant picture
Dissemination and interpretation of time use data

The 2008 report recommends to take into consideration unpaid
activities and more precisely “household production”

Revival of interest for Time use surveys beyond the traditional
concern about labor-leisure tradeoff

Time use survey for use in public policy to deal with a large
range of social issues (quality of life, gender, work…)

Dissemination and interpretations stages are crucial because
they are not regular surveys
Coding and processing time use data
1) Modes of dissemination
2) Issues in dissemination of time use data
3) Examples of processing and interpreting
time use data
Some key lay-outs from a study carried
out based on last French time use survey
Modes of dissemination
Up to the statistical office to assess the
suitability of the differing modes of
dissemination
•
•
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Microdata
Macrodata
Metadata
Suitable combinations of formats and media
which meet the differing capabilities of
users
Ex: Eurostat
Disclosure control
Disclosure control =measures taken to protect
statistical data in such a way as not to
violate confidentiality requirements as
prescribed or legislated
•
Suppression of cells values on the basis of
a “sensitivity”criterion
•
Table redesign
•
Perturbing data through the addition of
noise
Examples of processing and interpreting

Introduce a study carried out with
some other former colleagues of
INSEE

Bringing out how poor people use
their time in France: context of
“Inactivity Trap”

Not an exhaustive overview of
what can be done but examples
of different ways of exploiting
time use data
Examples of processing and interpreting
• Descriptive statistics
• Chronograms
• Econometrics tools
• Optimal matching
Examples of processing and interpreting
• Descriptive statistics
• Chronograms
• Econometrics tools
• Optimal matching
Descriptive statistics
At the first stage, the statistician can lay out
descriptive statistics:
• On the fact of practicing or not one or some
activities
• On the duration of practicing one or some
activities
Descriptive statistics
Examples of processing and interpreting
• Descriptive statistics
• Chronograms
• Econometrics tools
• Optimal matching
Chronograms
 People might be interested in having a dynamic
perspective
 For that, the statistician can set up
chronograms
 Chronograms represent the proportion of
people practicing an activity for each hour
around the clock
Chronograms
Examples of processing and interpreting
• Descriptive statistics
• Chronograms
• Econometrics tools
• Optimal matching
Econometric tools
 Descriptive statistics are not sufficient if you want to
work “all else equal”
 Given the complexity of time use survey sampling, it
is sometimes required to investigate more
complicated modeling. The sampling and the social
inquiries often induce biases
Econometric tools
 In our study, regression of duration of practicing an
activity on the poverty status by OLS. However the
estimations are biased
 Time dedicated to an activity available providing that
the respondent did practice it on the sampled day
 Actually, the duration of practicing an activity is a
censored variable
 Tobit model
Econometric tools
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•
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2nd equation (D): fact of practicing or not a specific
activity
1st equation (Yi): duration of practicing this activity
Instrument variable
Econometric tools
Examples of processing and interpreting
• Descriptive statistics
• Chronograms
• Econometrics tools
• Optimal matching
Optimal matching
•
Comparing sequences of activities between all the
respondents
•
Coming up with homogeneous groups which share
similarities in their use of time and representing their
“typical” daily schedule
•
2 stages
1st stage
•
Computes a distance between every two sequences.
•
All the possibilities to convert a sequence to the other
via three operations: suppression, substitution or
insertion
•
Each operation is associated with a cost
•
Ends up selecting the minimum global cost as the
distance
2nd stage
• Classification of the sequences: the
statistician has to choose the most
relevant number of groups to
describe the heterogeneity of the
population.
Graphics
Conclusion
 Crucial topic: should be considered as much
as collecting and coding stages
 TUS are a rich and vast source of data
 But underexploited in general
 While they are costly