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Optimal Matching and Social Sciences

Listed author(s):
  • Laurent Lesnard

    (Crest)

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    This working paper is a reflection on the conditions required to use optimalmatching (OM) in social sciences. Despite its striking success in biology, optimalmatching was not invented to solve biological questions but computer science ones:OM is a family of distance concepts originating in information and coding theorywere it is known under various names among which Hamming, and Levenshteindistance. As a consequence, the success of this method in biology has nothing to dowith the alleged similarity of the way it operates with biological processes but withchoices of parameters in accordance with the kind of materials and questionsbiologists are facing. As materials and questions differ in social sciences, it is notpossible to import OM directly from biology. The very basic fact that sequences ofsocial events are not made of biological matter but of events and time is crucial forthe adaptation of OM: insertion and deletion operations warp time and are to beavoided if information regarding the social regulation of the timing of event is to befully recovered. A formulation of substitution costs taking advantage of the socialstructuration of time is proposed for sequences sharing the same calendar: dynamicsubstitution costs can be derived from the series of transition matrices describingsocial sub-rhythms. An application to the question of the scheduling of work isproposed: using data from the 1985-86 and 1998-99 French time-use surveys,twelve types of workdays are uncovered. Their interpretability and quality,assessed visually through aggregate and individual tempograms, and box plots,seem satisfactory.

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    File URL: http://www.crest.fr/images/doctravail/2006-01.pdf
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    Paper provided by Centre de Recherche en Economie et Statistique in its series Working Papers with number 2006-01.

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    Length: 27
    Date of creation: 2006
    Handle: RePEc:crs:wpaper:2006-01
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    1. Wil Dijkstra & Toon Taris, 1995. "Measuring the Agreement between Sequences," Sociological Methods & Research, , vol. 24(2), pages 214-231, November.
    2. Daniel S. Hamermesh, 2002. "Timing, togetherness and time windfalls," Journal of Population Economics, Springer;European Society for Population Economics, vol. 15(4), pages 601-623.
    3. Joel H. Levine, 2000. "But What Have You Done for Us Lately?," Sociological Methods & Research, , vol. 29(1), pages 34-40, August.
    4. Andrew Abbott, 2000. "Reply to Levine and Wu," Sociological Methods & Research, , vol. 29(1), pages 65-76, August.
    5. Glenn Milligan, 1980. "An examination of the effect of six types of error perturbation on fifteen clustering algorithms," Psychometrika, Springer;The Psychometric Society, vol. 45(3), pages 325-342, September.
    6. Gershuny, Jonathan, 2000. "Changing Times: Work and Leisure in Postindustrial Society," OUP Catalogue, Oxford University Press, number 9780198287872.
    7. Andrew Abbott, 1998. "The Causal Devolution," Sociological Methods & Research, , vol. 27(2), pages 148-181, November.
    8. Laurent Lesnard, 2004. "Schedules as sequences: a new method to analyze the use of time based on collective rhythm with an application to the work arrangements of French dual-earner couples," electronic International Journal of Time Use Research, Research Institute on Professions (Forschungsinstitut Freie Berufe (FFB)) and The International Association for Time Use Research (IATUR), vol. 1(1), pages 60-84, August.
    9. Glenn Milligan, 1981. "A monte carlo study of thirty internal criterion measures for cluster analysis," Psychometrika, Springer;The Psychometric Society, vol. 46(2), pages 187-199, June.
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