I propose a new way of thinking about how the processes of everyday life influence social change.
Daily events—driving to work, cinema visits, playing with children—are the atoms of social life. Event sequences aggregate to form societal characteristics (employment levels, rates of cultural participation, amounts of care), and accumulate into biographical characteristics, as a history of continuous job participation increases “human capital” (expected future wage). And each event may have parallel links to others’ activities at the same time—spouses’ shared leisure, customer-client interdependence.
Time, unlike money, is an appropriate unit of account for leisure and unpaid work as well as paid, so random samples of event sequences represent all the time devoted to a society’s activities. New modelling techniques, developed in biology (to classify DNA sequences), and in artificial intelligence, will be used to analyse a harmonised collection of event sequence data from more than a dozen countries across successive decades. The diary sequences show the effects of technical and public regulatory change on work-life balances, and age-, gender- and class-related inequalities. They give a more comprehensive view of socioeconomic change than emerges either from economics’ near-exclusive focus on money-related phenomena, or from conventional sociology’s often overly anecdotal approach to issues such as globalisation or inequality.
The proposal is for a hierarchically integrated set of sub-projects, building progressively from models of the determinants of individuals’ successive daily experiences, via new approaches to interpreting individual time budgets, to new systems of social accounts. Behind these lie two major methodological innovations: a new way of combining day-diary data with questionnaire evidence on activity participation rates to improve estimates of long-term time use; and substantial advances in the use of diaries to measure “instantaneous utility”.
Fields of science
Call for proposal
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Funding SchemeERC-AG - ERC Advanced Grant