Gaechter, Simon
(2017).
Combining 'real effort' with induced effort costs: The ball-catching task.
[Data Collection]. Colchester, Essex:
UK Data Archive.
10.5255/UKDA-SN-852809
This network project brings together economists, psychologists, computer and complexity scientists from three leading centres for behavioural social science at Nottingham, Warwick and UEA. This group will lead a research programme with two broad objectives: to develop and test cross-disciplinary models of human behaviour and behaviour change; to draw out their implications for the formulation and evaluation of public policy.
Foundational research will focus on three inter-related themes: understanding individual behaviour and behaviour change; understanding social and interactive behaviour; rethinking the foundations of policy analysis.
The project will explore implications of the basic science for policy via a series of applied projects connecting naturally with the three themes. These will include: the determinants of consumer credit behaviour; the formation of social values; strategies for evaluation of policies affecting health and safety.
The research will integrate theoretical perspectives from multiple disciplines and utilise a wide range of complementary methodologies including: theoretical modeling of individuals, groups and complex systems; conceptual analysis; lab and field experiments; analysis of large data sets.
The Network will promote high quality cross-disciplinary research and serve as a policy forum for understanding behaviour and behaviour change.
Data description (abstract)
We introduce the “ball-catching task”, a novel computerized task, which combines a tangible action (“catching balls”) with induced material cost of effort. The central feature of the ball-catching task is that it allows researchers to manipulate the cost of effort function as well as the production function, which permits quantitative predictions on effort provision. In an experiment with piece-rate incentives we find that the comparative static and the point predictions on effort provision are remarkably accurate. We also present experimental findings from three classic experiments, namely, team production, gift exchange and tournament, using the task. All of the results are closely in line with the stylized facts from experiments using purely induced values. We conclude that the ball-catching task combines the advantages of real effort tasks with the use of induced values, which is useful for theory-testing purposes as well as for applications.
Data creators: |
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Contributors: |
Name |
Affiliation |
ORCID (as URL) |
Huang Lingbo |
University of Nottingham |
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Sefton Martin |
University of Nottingham |
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Sponsors: |
Economic and Social Research Council
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Grant reference: |
ES/K002201/1
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Topic classification: |
Economics
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Keywords: |
Real effort task, Piece-rate theory, Team Incentives, Gift Exchange, Tournaments, Online real effort experiments
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Project title: |
Network for Integrated Behavioural Science
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Grant holders: |
Chris Starmer, Daniel John Zizzo, Nick Chater, Gordon Brown, Anders Poulsen, Martin Sefton, Neil Stewart, Uwe Aickelin, John Gathergood, Robert Sugden, Graham Loomes, Enrique Fatas, Simon Gaechter, Theodore Turocy, Shaun Hargreaves-Heap, Abigail Barr, Robert MacKay, Robin Cubitt, Daniel Read
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Project dates: |
From | To |
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31 December 2012 | 30 September 2017 |
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Date published: |
24 Nov 2017 17:02
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Last modified: |
24 Nov 2017 17:02
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Collection period: |
Date from: | Date to: |
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31 December 2012 | 30 September 2017 |
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Country: |
United Kingdom |
Data collection method: |
Experimental data. The lab version of the ball-catching task is a computerized task programmed in z-Tree, and requires subjects to catch falling balls by moving a tray on their computer screens.
Study 1 examined performance on the ball-catching task under piece-rate incentives. Each subject worked on the same ball-catching task for 36 periods. Each period lasted 60 s.4 In each period one combination of prize-per-catch (either 10 or 20 tokens) and cost-per-click (0, 5 or 10 tokens) was used, giving six treatments that are varied within subjects.
For study 2, we use the ball-catching task in three classic interactive experiments that have been used to study cooperation, reciprocity, and competition. We ran five sessions, each with 32 subjects, for a total of 160 subjects. In each session two unrelated treatments were conducted, each involving ten repetitions of a task.
For study 3, as a test of the versatility of the ball-catching task, we introduce an online version. This online version is programmed in PHP and has been designed to resemble the lab version as closely as possible.17 The purpose of this section is to show the potential (and limitations) of using the ball-catching task in online experiments, which increasingly appear to be a valuable complement to experiments in the physical laboratory. We ran the same experiment as in Study 1 on Amazon Mechanical Turk (MTurk; see the supplementary materials for instructions).18 In total, we recruited 95 subjects from MTurk and 74 of them finished the task.
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Observation unit: |
Individual |
Kind of data: |
Numeric |
Type of data: |
Experimental data
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Resource language: |
English |
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Rights owners: |
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Contact: |
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Notes on access: |
The Data Collection is available to any user without the requirement for registration for download/access.
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Publisher: |
UK Data Archive
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Last modified: |
24 Nov 2017 17:02
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