Abstract
The paper reports the results of a survey designed to elicit probability judgements for different types of events: ‘pure chance’ events, for which objective probabilities can be calculated; ‘public’ events, about which there may be some discussion in social groups and the media; and ‘personal’ events, such as those relating to crime or accidental injury. Even among respondents deemed to be ‘well-calibrated’ in the domain of pure chance events we find limited sensitivity to the ‘temporal scope’ of public and personal events—this being especially marked for personal events. We discuss possible reasons and some implications for policy-related survey work.
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Notes
A copy of the interview schedule is available on request from the authors.
Respondents were given examples of how they might respond: “You could say ‘1 in 10,’ or ‘1 in 100,’ or ‘1 in 500,’ or ‘1 in a million’; or you could give your answer as a percentage, such as ‘5%,’ or ‘less than 1%’; or whatever you think.”
Analysis of the distribution of estimates given by the four groups in response to Question 1 shows no significant differences for any of the three events.
Not having any strong prior alternative hypothesis, two-tailed test statistics are reported.
Percentages were a favoured mode of expression, although sometimes people responded in terms of fractions. However, despite a tendency to round, we note in passing that the ‘50–50’ response that is reported as a significant ‘blip’ in some earlier studies (see Bruine de Bruin et al. 2000) was not particularly prominent in our data: it was somewhat over-represented, as one might expect, but usually occurred no more frequently than two or three other ‘round’ numbers in response to any particular question.
The most recent General Election had occurred in May 2005, just a couple of months prior to the survey being conducted. Technically, a government may operate for 5 years before being required to hold another General Election, although in recent years a pattern has emerged of elections every 4 years. However, even on this latter basis it could easily be the case that Mr. Blair would be Prime Minister for at least 3 years after the date of the survey.
Inevitably, such an assignment is somewhat arbitrary; but drawing the line between two and three mistakes provides the most even split between the two sub-sets.
Strictly speaking, the correct probability of having a birthday in February is 0.077, or approximately 1/13; however, for this event, we count as correct any answer in the range 1/13 to 1/12. We also allow 2% as a correct answer to the question of the likelihood of drawing the 7 of Hearts.
These include surveys eliciting respondents’ estimates of job security, their income expectations, views about retirement benefits and investment opportunities, and students’ expectations about the returns to education.
However, even for these cases it is worth remembering that although the differences between means and distributions register as statistically significant, there may still be doubts about whether the ratios of the 3-year estimates to the 12-month estimates are as great as they ‘should’ be.
References
Bruine de Bruin, Wandi, Baruch Fischhoff, Susan Millstein, and Bonnie Halpern-Felsher. (2000). “Verbal and Numerical Expressions of Probability: It’s a Fifty–Fifty Chance,” Organizational Behavior and Human Decision Processes 81, 115–131.
Camerer, Colin. (1995). “Individual Decision Making.” In John Kagel and Alvin Roth (eds.), Handbook of Experimental Economics. Princeton: Princeton University Press.
Dominitz, Jeff, and Charles Manski. (1997). “Perceptions of Economic Insecurity: Evidence from the Survey of Economic Expectations,” Public Opinion Quarterly 61, 261–287.
Frederick, Shane, and Baruch Fischhoff. (1998). “Scope (In)sensitivity in Elicited Valuations,” Risk, Decision and Policy 3, 109–123.
Loewenstein, George. (1999). “Experimental Economics from the Vantage-Point of Behavioural Economics,” Economic Journal 109, F25–F34.
Loewenstein, George, Elke Weber, Christopher Hsee, and Ned Welch. (2001). “Risk as Feelings,” Psychological Bulletin 127(2), 267–286.
Loomes, Graham. (2006). “(How) Can We Value Health, Safety and the Environment?” Journal of Economic Psychology 27, 713–736.
Manski, Charles. (2004). “Measuring Expectations,” Econometrica 72, 1329–1376.
Slovic, Paul, Melissa Finucane, Ellen Peters, and Donald MacGregor. (2004). “Risk as Analysis and Risk as Feelings: Some Thoughts about Affect, Reason, Risk and Rationality,” Risk Analysis 24, 311–22.
Smith, V. Kerry, and Laura Osborne. (1996). “Do Contingent Valuation Estimates Pass a ‘Scope’ Test? A Meta-analysis,” Journal of Environmental Economics and Management 31(3), 287–301.
Starmer, Chris. (2000). “Developments in Non-expected Utility Theory: The Hunt for a Descriptive Theory of Choice Under Risk,” Journal of Economic Literature 38, 332–382.
Acknowledgement
This study was undertaken as part of the ‘Social Contexts and Responses to Risk’ (SCARR) network, funded by Economic and Social Research Council Grant L326 25 3054. We thank the editorial team and an anonymous referee, as well as participants at various conferences and seminars, for their constructive comments and suggestions.
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Loomes, G., Mehta, J. The sensitivity of subjective probability to time and elicitation method. J Risk Uncertainty 34, 201–216 (2007). https://doi.org/10.1007/s11166-007-9012-y
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DOI: https://doi.org/10.1007/s11166-007-9012-y