These studies compare the effects of different ways or presenting / describing beneficiaries.

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Keywords: numbers, representing beneficiaries

 

Source:Which statistical formats facilitate what decisions? The perception and influence of different statistical information formats’, by G. L. Brase, Journal of Behavioral Decision Making (2002) vol.15.5, pp.381-401

 

Conclusions: In this experiment

1. When reading about relatively small groups/proportions, figures expressed in numerical amounts, such as 2.8 million (which is 1% of the U.S. population) were seen as representing more significant issues, and had more influence on decisions, in comparison to the same figures expressed as percentages or fractions (e.g. 1%, .01, 1/100).

2. However, when reading about relatively large groups/proportions, figures expressed in numerical amount such as 277 million (which is 99% of the US population) were seen as representing less significant issues, and had less influence on decisions, in comparison to the same figures expressed as percentages or fractions (e.g. 99%, .99,99/100).

This study therefore suggests that when talking about small groups/proportions, using figures such as x hundred / thousand / million is more persuasive for more people, but when talking about large groups/proportions, using fractions or percentages is more persuasive for more people.

 

Key caveats: Tested under laboratory conditions, with hypothetical decisions.

 

URL/DOI: 10.1002/bdm.421

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Keywords: numbers, representing beneficiaries

 

Source: ‘A Field Study of How Different Numerical Information Formats Influence Charity Support’, by G. L. Brase, Journal of Nonprofit & Public Sector Marketing (2008) vol. 20.1, pp.1-14

 

Conclusions: In tests using charity mailshots, where a relatively small group/proportion was in focus, statistics expressed as numerical amounts, such as x million in the UK/EU/world, were more effective in motivating responses than statistics expressed as fractions or percentages (e.g. one in 18 or 6%), though the difference was smaller than that obtained in similar tests in laboratory conditions (e.g. Brase 2002).

 

Key caveats: The percentages used in this test were very small, i.e. ‘0.38% of persons’, ‘one in every 261 persons’. ‘0.38 of persons’ is likely to cause cognitive dissonance, as it evokes a fraction of a person, which is hard to process/imagine. Larger percentages, but still relatively small (e.g. between 1% and 20%) may yield different results.

 

URL/DOI: 10.1080/10495140802165337

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Keywords: beneficiary identifiability

 

Source: ‘Helping a victim or helping the victim: Altruism and identifiability', by D. A. Small and G. Loewenstein, Journal of Risk and Uncertainty (2003) vol. 26.1, pp.5-16

 

Conclusions: The results of this experiment provide some evidence to support the ‘identifiable victim effect’: in this study, use of an identified beneficiary (already chosen by the charity to be the recipient of any donations) prompted more donations, and elicited a greater total amount donated, than an appeal in which the beneficiary had not yet been selected by the charity.

 

Key caveats: The experiment took place in the USA.

 

URL/DOI: 10.1023/A:1022299422219

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