Using optimal matching to analyze the timing of daily life

In this paper, I reflect on the conditions required to apply optimal matching (OM) to time-use data and I propose a parameterization adapted to the analysis of the timing of daily life. OM allows time use analysts to build typologies of sequences of daily life, hence to take into account simultaneously the duration and the timing of activities. OM is a family of distance concepts originating in information and coding theory where it is known under various names among which Hamming or Levenshtein distance. Although it was imported into social sciences from biology, its success in this field is not due to any resemblance sequence transformation operations may share with gene mutations but is on the contrary the result of parameters set in accordance with biological concepts and materials. Consequently, applying OM to time use data requires a cost system able to uncover sub-rhythms from sequences of daily events. I propose to use only substitution operations and to derive their costs from the series of transition matrices describing daily collective rhythms. A Stata plug-in freely available on the Internet implements this methods and is briefly introduced. This variant of OM is applied to the question of the scheduling of work using the 1985-86 and 1998-99 French time-use surveys: twelve types of workdays are uncovered. Aggregate and individual chronograms are used to interpret and assess the quality of the typology