Investigating scheduling of work a two stage optimal matching analysis of workdays and workweeks
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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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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

  • 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


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


The ONS Workweek Grid


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


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


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


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


Scheduling of work hours within days

Scheduling of work days within weeks

Scheduling of work within the week


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


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


Two-stage OMA

UK 2000 TUS


1st stage: typology of workdays


Tempograms of the typology of workdays


Simplifiedworkweeks (UK)


2nd stage: Types of workweeks UK


Types of workweeks


Summary statistics of the final typology of workweeks


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.


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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