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Dissemination and interpretation of time use data

Dissemination and interpretation of time use data. Social and Housing Statistics Section United Nations Statistics Division International Workshop on Social Statistics, Bejing,22-24 November 2010. Dissemination and interpretation of time use data.

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Dissemination and interpretation of time use data

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  1. Dissemination and interpretation of time use data Social and Housing Statistics Section United Nations Statistics Division International Workshop on Social Statistics, Bejing,22-24 November 2010

  2. 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

  3. 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

  4. Coding and processing time use data • Modes of dissemination • Issues in dissemination of time use data • Examples of processing and interpreting time use data Some key lay-outs from a study carried out based on last French time use survey

  5. 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

  6. 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

  7. 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

  8. Examples of processing and interpreting • Descriptive statistics • Chronograms • Econometrics tools • Optimal matching

  9. Examples of processing and interpreting • Descriptive statistics • Chronograms • Econometrics tools • Optimal matching

  10. 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

  11. Descriptive statistics

  12. Examples of processing and interpreting • Descriptive statistics • Chronograms • Econometrics tools • Optimal matching

  13. 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

  14. Chronograms

  15. Examples of processing and interpreting • Descriptive statistics • Chronograms • Econometrics tools • Optimal matching

  16. 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

  17. 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

  18. Econometric tools • 2nd equation (D): fact of practicing or not a specific activity • 1st equation (Yi): duration of practicing this activity • Instrument variable

  19. Econometric tools

  20. Examples of processing and interpreting • Descriptive statistics • Chronograms • Econometrics tools • Optimal matching

  21. 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

  22. 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

  23. 2nd stage • Classification of the sequences: the statistician has to choose the most relevant number of groups to describe the heterogeneity of the population.

  24. Graphics

  25. 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

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