Time Packages and Their Effect on Life Satisfaction

The expected response of individuals to policy changes usually requires that they use their resources in a different way, according to the changed relative opportunity cost of undertaking each that the policy effects. However, it has often been noted that the allocation of time to different activities does not respond smoothly, and rather appears to be influenced by a range of non economic factors that lead to opportunity costs and trade-offs being different for different individuals, depending not just on the constraints they face, but also on the activities they are already ‘specialised’ at. In this paper we use the British Household Panel Survey to examine how time packages - the allocation of weekly hours to a combination of paid and unpaid work and leisure - affect life satisfaction, and the marginal returns from additional hours spent in paid work, overtime, caring and housework. We observe that for men in general, the marginal benefits of an additional hour of paid work, or extra work (in the form of overtime or a second job) are positive, while an additional hour of caring has a negative effect on life satisfaction. For men who are leisure rich, however, the marginal benefits of an additional hour of housework are positive. Leisure rich men appear to gain satisfaction from doing housework, in a way that other men do not. The same applies to women. Women are in general less satisfied by taking on overtime or second jobs, presumably preferring to use that discretionary time at home in leisure pursuits or with children. For women doing full-time paid work, the marginal effect of an additional hour of extra work (overtime or a second job) is negative; for women already stretched by full-time paid work, extra hours are an unwelcome burden. We discuss the role that different kinds of constraints, including gender attitudes, play in determining our results and the implications for policy design.happiness, time use

Time Packages and Their Effect on Life Satisfaction

The expected response of individuals to policy changes usually requires that they use their resources in a different way, according to the changed relative opportunity cost of undertaking each that the policy effects. However, it has often been noted that the allocation of time to different activities does not respond smoothly, and rather appears to be influenced by a range of non economic factors that lead to opportunity costs and trade-offs being different for different individuals, depending not just on the constraints they face, but also on the activities they are already ‘specialised’ at. In this paper we use the British Household Panel Survey to examine how time packages - the allocation of weekly hours to a combination of paid and unpaid work and leisure - affect life satisfaction, and the marginal returns from additional hours spent in paid work, overtime, caring and housework. We observe that for men in general, the marginal benefits of an additional hour of paid work, or extra work (in the form of overtime or a second job) are positive, while an additional hour of caring has a negative effect on life satisfaction. For men who are leisure rich, however, the marginal benefits of an additional hour of housework are positive. Leisure rich men appear to gain satisfaction from doing housework, in a way that other men do not. The same applies to women. Women are in general less satisfied by taking on overtime or second jobs, presumably preferring to use that discretionary time at home in leisure pursuits or with children. For women doing full-time paid work, the marginal effect of an additional hour of extra work (overtime or a second job) is negative; for women already stretched by full-time paid work, extra hours are an unwelcome burden. We discuss the role that different kinds of constraints, including gender attitudes, play in determining our results and the implications for policy design.happiness, time use

What do I want? The effects of individual aspiration and relational capability on collaboration preferences

ESR

Machine learning for real-time aggregated prediction of hospital admission for emergency patients

Machine learning for hospital operations is under-studied. We present a prediction pipeline that uses live electronic health-records for patients in a UK teaching hospital's emergency department (ED) to generate short-term, probabilistic forecasts of emergency admissions. A set of XGBoost classifiers applied to 109,465 ED visits yielded AUROCs from 0.82 to 0.90 depending on elapsed visit-time at the point of prediction. Patient-level probabilities of admission were aggregated to forecast the number of admissions among current ED patients and, incorporating patients yet to arrive, total emergency admissions within specified time-windows. The pipeline gave a mean absolute error (MAE) of 4.0 admissions (mean percentage error of 17%) versus 6.5 (32%) for a benchmark metric. Models developed with 104,504 later visits during the Covid-19 pandemic gave AUROCs of 0.68-0.90 and MAE of 4.2 (30%) versus a 4.9 (33%) benchmark. We discuss how we surmounted challenges of designing and implementing models for real-time use, including temporal framing, data preparation, and changing operational conditions