dissemination and interpretation of time use data social and housing statistics section united...
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Dissemination and interpretation of time use data
Social and Housing Statistics SectionUnited Nations Statistics Division
Time Use Statistics workshop for Arabic speaking countries, Amman,25-28 April 2011
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
Dissemination and interpretation of 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
• 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
• Timing diagrams
• Econometrics tools
• Optimal matching
Examples of processing and interpreting
• Descriptive statistics
• Timing diagrams
• 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
• Timing diagrams
• Econometrics tools
• Optimal matching
Timing diagrams
People might be interested in having a dynamic perspective
For that, the statistician can set up timing diagrams
Timing diagrams represent the proportion of people practicing an activity for each hour around the clock
Timing diagrams
Examples of processing and interpreting
• Descriptive statistics
• Timing diagrams
• 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
• 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
• Timing diagrams
• 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 general 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