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Optimal Matching for Time Use Data: Workshop Insights

Learn about disseminating and interpreting time use data with a focus on the Stiglitz Commission's recommendations and examples of processing techniques. Get insights into coding, processing methods, and applications such as descriptive statistics and econometric tools. Explore optimal matching for analyzing daily schedules.

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Optimal Matching for Time Use Data: Workshop Insights

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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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