Stigma and risky behaviors among male clients of sex workers in the UK in 2001

This paper builds on existing theoretical work on sex markets (Della Giusta, Di Tommaso, and Strøm, 2009a). Using data from the British Sexual Attitudes Survey, we aim to replicate the analysis of the demand for paid sex previously conducted for the US (Della Giusta, Di Tommaso, Shima and Strøm, 2009b). We want to test formally the effect of attitudes, risky behaviors and personal characteristics on the demand for paid sex. Findings from empirical studies of clients suggest that personal characteristics (personal and family background, self-perception, perceptions of women, sexual preferences etc), economic factors (education, income, work) as well as attitudes towards risk (both health hazard and risk of being caught where sex work is illegal), and attitude towards relationships and sex are all likely to affect demand. Previous theoretical work has argued that stigma plays a fundamental role in determining both demand and risk, and that in particular due to the presence of stigma the demand for sex and for paid sex are not, as has been argued elsewhere, perfect substitutes. We use data from the British Sexual Attitudes Survey of 2001 to test these hypotheses. We find a positive effect of education (proxy for income), negative effects of professional status (proxies for stigma associated with buying sex), positive and significant effects of all risky behavior variables and no significant effects of variables which measure the relative degree of conservatism in morals. We conclude with some policy implications.

Detection and attribution of human influence on regional precipitation

Understanding how human influence on climate is affecting precipitation around the world is immensely important for defining mitigation policies, and for adaptation planning. Yet despite increasing evidence for the influence of climate change on global patterns of precipitation, and expectations that significant changes in regional precipitation should have already occurred as a result of human influence on climate, compelling evidence of anthropogenic fingerprints on regional precipitation is obscured by observational and modelling uncertainties and is likely to remain so using current methods for years to come. This is in spite of substantial ongoing improvements in models, new reanalyses and a satellite record that spans over thirty years. If we are to quantify how human-induced climate change is affecting the regional water cycle, we need to consider novel ways of identifying the effects of natural and anthropogenic influences on precipitation that take full advantage of our physical expectations.