dissemination and interpretation of time use data
Download
Skip this Video
Download Presentation
Dissemination and interpretation of time use data

Loading in 2 Seconds...

play fullscreen
1 / 25

Dissemination and interpretation of time use data - PowerPoint PPT Presentation


  • 114 Views
  • Uploaded on

Dissemination and interpretation of time use data. Social and Housing Statistics Section United Nations Statistics Division Time Use Statistics workshop for Arabic speaking countries, Amman,25-28 April 2011. Dissemination and interpretation of time use data.

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Dissemination and interpretation of time use data' - dexter


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
dissemination and interpretation of time use data

Dissemination and interpretation of time use data

Social and Housing Statistics Section

United Nations Statistics Division

Time Use Statistics workshop for Arabic speaking countries, Amman,25-28 April 2011

dissemination and interpretation of time use data2
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 data3
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 data4
Dissemination and interpretation of 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

modes of dissemination
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

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
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 interpreting8
Examples of processing and interpreting
  • Descriptive statistics
  • Timing diagrams
  • Econometrics tools
  • Optimal matching
examples of processing and interpreting9
Examples of processing and interpreting
  • Descriptive statistics
  • Timing diagrams
  • Econometrics tools
  • Optimal matching
descriptive statistics
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
examples of processing and interpreting12
Examples of processing and interpreting
  • Descriptive statistics
  • Timing diagrams
  • Econometrics tools
  • Optimal matching
timing diagrams
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
examples of processing and interpreting15
Examples of processing and interpreting
  • Descriptive statistics
  • Timing diagrams
  • Econometrics tools
  • Optimal matching
econometric tools
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 tools17
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 tools18
Econometric tools
  • 2nd equation (D): fact of practicing or not a specific activity
  • 1st equation (Yi): duration of practicing this activity
  • Instrument variable
examples of processing and interpreting20
Examples of processing and interpreting
  • Descriptive statistics
  • Timing diagrams
  • Econometrics tools
  • Optimal matching
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
1 st stage
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
2 nd stage
2nd stage
  • Classification of the sequences: the statistician has to choose the most relevant number of groups to describe the heterogeneity of the population.
conclusion
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
ad