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Investigating Scheduling of Work: A Two-Stage Optimal Matching Analysis of Workdays and Workweeks

Investigating Scheduling of Work: A Two-Stage Optimal Matching Analysis of Workdays and Workweeks. Man Yee Kan (CTUR, University of Oxford) Laurent Lesnard (CNRS/Sciences Po). Measuring working time. Stylised data Vs diary data

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Investigating Scheduling of Work: A Two-Stage Optimal Matching Analysis of Workdays and Workweeks

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  1. Investigating Scheduling of Work: A Two-Stage Optimal Matching Analysis of Workdays and Workweeks Man Yee Kan (CTUR, University of Oxford) Laurent Lesnard (CNRS/Sciences Po)

  2. Measuring working time • Stylised data Vs diary data • Stylised data (LFS type) - Recalled ‘usual’ ‘normal’ weekly working hours, taking less account of atypical hours/days • Diary data – more accurate, recording timing as well as duration of events

  3. Workweek grid diary data • UK Time Use Survey (ONS, 2000-01) • June 2000 to August 2001 • Couple level of data • Response rate: 45% (61% for household questionnaires, 73% for subsequent diaries) • Workweek grid + 2 day diaries + questionnaires

  4. The ONS Workweek Grid

  5. Sequence analysis • Optimal Matching Analysis (OMA) • Measure of dissimilarity between sequences • Similarity of two sequences based on the difficulty to transform one sequence into the other (matching) • Three kind of operationsallowed: • Insertion and deletion • Substitution • Costsfor each of these operations • Minimum cost to match two sequences = distance

  6. Example A : X – Y – Y – Y B : X – X – X – X – Y • One solution: A : X – X – X – X – Y – Y – Y B : X – X – X – X – Y • Another one: A : X – X – X – X – Y B : X – X – X – X – Y

  7. Example A : X – Y – Y – Y B : X – X – X – X – Y • One solution: A : X – X – X – X – Y – Y – Y B : X – X – X – X – Y • Another one: A : X – X – X – X – Y B : X – X – X – X – Y 3 insertions 2 deletions 1 insertion 2 substitutions

  8. Dynamic Hamming Distance • Only substitutionoperations are used to preserve the timing of events • Interpretation of substitution cost as the degree of proximity of two states at one point in time • Substitution costs are inversely proportional to transition frequencies Cluster analysis of the distance matrix gives the empirical typology

  9. Scheduling of work hours within days Scheduling of work days within weeks Scheduling of work within the week

  10. Scheduling of work hours within days 9 to 5 vs. 12 to 8 Same duration, different scheduling What is important here is whether work hours are contemporaneous or not Scheduling of work days within weeks Scheduling of work within the week

  11. Scheduling of work hours within days 9 to 5 vs. 12 to 8 Same duration, different scheduling What is important here is whether work hours are contemporaneous or not Scheduling of work days within weeks Working from Mon to Fri vs Sat to Wed Same number of days but totally different week What is crucial is whether work days are contemporaneous or not Scheduling of work within the week

  12. Two-stage OMA UK 2000 TUS

  13. 1st stage: typology of workdays

  14. Tempograms of the typology of workdays

  15. Simplifiedworkweeks (UK)

  16. 2nd stage: Types of workweeks UK

  17. Types of workweeks

  18. Summary statistics of the final typology of workweeks

  19. Summary • Applying OMA at two stages to identify workdays and workweeks • Costs are set according to the transitional frequencies of the events in the whole sample • 5 types of workdays and 7 types of workweeks • Standard workdays constitute just over 50% workdays • Standard workweeks constituteabout 25% workweeks. • 3 types of part-time workweeks: standard-workday part-time, part-workday part-time, and short workweek.

  20. Thank you! • For more detail, see our forthcoming paper at Journal of Royal Statistical Society Series A • Or, at the Department of Sociology Working Paper Series: http://www.sociology.ox.ac.uk/documents/working-papers/2009/swp09_04.pdf

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