George MacKerron

Are you happy while you work

Recent work in psychology and economics has investigated ways in which individuals experience their lives. This literature includes influences on individuals’ momentary happiness. We contribute to this literature using a new data source, Mappiness (www.mappiness.org.uk), which permits individuals to record their wellbeing via a smartphone. The data contain more than a million observations on tens of thousands of individuals in the UK, collected since August 2010. We explore the links between individuals’ wellbeing measured momentarily at random points in time and their experiences of paid work. We explore variation in wellbeing within-individual over time having accounted for fixed unobservable differences across people. We quantify the effects of working on individuals’ affect relative to other activities they perform. We consider the effects of working on two aspects of affect: happiness and relaxation. We find paid work is ranked lower than any of the other 39 activities individuals engage in, with the exception of being sick in bed. Although controlling for other factors, including person fixed effects, reduces the size of the association its rank position remains the same and the effect is still equivalent to a 7-8% reduction in happiness relative to circumstances in which one is not working. Paid work has a similar though slightly larger negative impact on being relaxed. However, precisely how unhappy or anxious one is while working depends on the circumstances. Wellbeing at work varies significantly with where you work (at home, at work, elsewhere); whether you are combining work with other activities; whether you are alone or with others; and the time of day or night you are working.

Implementation, implementation, implementation: old and new options for putting surveys and experiments online

The Internet offers enormous possibilities for surveys and experimental data collection, including randomised treatments, customisation, and interactivity. These capabilities are well suited to the implementation of choice modelling experiments. However, the implementation of web surveys is not a simple task, and the existing options open to researchers are commonly unsatisfactory in a number of ways. The result is that few Internet surveys and experiments are able to exploit the unique capabilities of the web. This paper suggests a new approach, illustrated with a working prototype: an open-source, domain-specific language (DSL) designed for specifying web surveys and experiments, which is called websperiment. The paper first looks at the existing approaches, highlighting their strengths and weaknesses. It then outlines the concepts underlying websperiment, and this DSLs nature and scope, with simple code examples. Finally, it shows how the DSL can be used to concisely specify a highly dynamic choice modelling survey.