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Abstract

This thesis comprises three studies in behavioural and experimental economics. The first Chapter is a methodological investigation into the effect of self-selection bias on measurement of disease prevalence. The main issue is that “random" testing is commonly used to estimate prevalence. However, as long as such testing is voluntary, field estimates suffer from selection bias. We conduct an empirical application of this insight to Covid-19 testing and prevalence. In an incentivised lab-in-field experiment we show that people feeling symptoms are up to 42 times likelier to seek testing. This leads to prevalence bias: test positivity can inflate true prevalence fivefold. We validate using external data and confirm the bias varies intertemporally, making comparisons misleading. We suggest sampling the population to bypass the bias, yielding more accurate estimates, real-time. Our results are relevant to any epidemic, besides Covid-19, when carrier status informs beliefs.

The following Chapter explores the effect of retirement on prosocial behaviour. We show that retirement leads to more altruistic behaviour, and this change is not just attributable to external factors, such as a lower need for virtue-signalling, but seems to be caused by a change in preferences. To measure the impact of retirement we use a novel combination of representative cross-sectional and longitudinal individual-level survey data from 22 European countries, and a complementary incentivised field experiment on a representative sample of individuals. The effect on volunteering is strong in the survey data, and using the field experiment we identify
a change in preferences as a probable cause. Given the ageing of the population these are policy-relevant findings. The welfare gain driven by increased prosociality, through increased volunteering and transfers, should be considered in retirement
age reforms.

The subsequent Chapter addresses the problem of identifying the worst-off members of society. We take various measures of subjective wellbeing (SWB) as indicators of the how well people are doing in life and employ Latent Class Analysis to identify those with greatest propensity to be among the worst-off in a nationally representative sample of over 215,000 people in the UK. Our results have important implications for how best to analyse data on SWB and who to target when looking to improve the lives of those with the lowest SWB.

Publication Type:	Thesis (Doctoral)
Subjects:	H Social Sciences > HB Economic Theory
Departments:	Doctoral Theses City, University of London (-2022) > School of Arts & Social Sciences School of Arts & Social Sciences School of Policy & Global Affairs > Economics School of Policy & Global Affairs > School of Policy & Global Affairs Doctoral Theses

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