Giving is a Question of Time: Response Times and Contributions to an Environmental Public Good

Abstract

Does it matter whether contribution decisions regarding environmental public goods are arrived at through intuition or reflection? Experimental research in behavioral economics has recently adopted dual-system theories of the mind from psychology in order to address this question. This research uses response time data in public good games to distinguish between the two distinct cognitive processes. We extend this literature towards environmental public goods by analyzing response time data from an online experiment in which over 3400 subjects from the general population faced a dichotomous choice between receiving a monetary payment or contributing to climate change mitigation efforts. Our evidence confirms a strong positive link between response times and contributions: The average response time of contributors is 40 % higher than that of non-contributors. This suggests that reflection, not intuition, is at the root of pro-environmental contributions. This result is robust to a comprehensive set of robustness checks, including a within-subjects analysis that controls for potentially unobserved confounds and recovers the relationship at the individual level.

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Fig. 1
Fig. 2

Notes

  1. 1.

    In non-incentivized settings, for example, evidence from stated preference surveys has shown that giving respondents ’additional time to think’ significantly reduces WTP estimates (Cook et al. 2012).

  2. 2.

    In a non-incentivized CVM survey, Fischer and Hanley (2007) use the time subjects take for their decision (i.e. response time data) in order to classify responses as either carefully considered or impulsive. Depending on the framing of the environmental good in question, the authors conclude that 10–20 % of stated WTPs in their study were impulsive or habitualised and hence less likely to contain accurate information on the underlying preferences. Börger (2015) similarly finds that slower survey responses to a choice situation increase choice precision.

  3. 3.

    Rand et al. (2012, (2014), for example, find that higher contributions in standard public good games are driven by intuitive decision making. In their series of studies, faster choices are associated with higher contributions and the application of time pressure shifts contributions towards the public good. Tinghög et al. (2013) and Verkoeijen and Bouwmeester (2014), however, fail to replicate the findings on the effects of time pressure, as do Duffy and Smith (2014) and Martinsson et al. (2014) when using cognitive load or priming designs in a public goods setting. In the closely related context of non-strategic distribution tasks, Piovesan and Wengström (2009) and Ubeda (2014) conclude that more generous allocations in dictator games are associated with reflection, not intuition. On the other hand, Schulz et al. (2014) find the opposite when analyzing the effects of cognitive load in a series of mini-dictator games. Similarly, Cappelen et al. (2015) find that participants sharing half of their endowment in a dictator game decide more quickly than those keeping their full endowment

  4. 4.

    In psychology response time data have been used for some time in order to distinguish between intuitive and reflective decision making (e.g. Luce 1986). For a recent overview over the use of response time data in behavioral economics see Spiliopoulos and Ortmann (2014).

  5. 5.

    Based on the categorization introduced by Charness et al. (2013).

  6. 6.

    Based on these choices, Diederich and Goeschl (2014) estimate a WTP for the voluntary contribution to emissions reductions and investigate determinants of the contribution decision while the present paper uses the RT data obtained by the same experiment to explore the underlying cognitive processes.

  7. 7.

    There were four variations in total. For example, in some conditions, a contribution decision was made public after the session. Session effects are therefore explicitly included when analyzing pooled data in Sect. 3. The main relationship between response times and contribution behavior is unaffected by the different variations.

  8. 8.

    For each of the 50 reward categories, there are between 56 and 83 observations.

  9. 9.

    Our design strived to present the experiment as indistinguishable from other YouGov polls as possible. These efforts included payment type and height (the polling company usually incentivizes panel members participating in a poll through either a piece-rate reward of approximately €1 for 20 min expected survey time or random lottery prizes, e.g. in the form of shopping vouchers), layout, and language of common questions on sociodemographics.

  10. 10.

    We tested for difference to the general population of German voters: Using two-sided t tests, we reject the hypothesis that the means of socio-demographic characteristics coincide at the 1 % level. Our subjects are slightly more likely to be male, younger, and educated than the average German of voting age. Income is self-reported, and therefore the lower average income in the sample is unsurprising. Compared to the full set of subjects who finished the experiment, we exclude observations with missing values in one or more of the variables used in Sect. 3.

  11. 11.

    Given these relatively small average RTs it seems unlikely that our observed effects are driven by subjects who leave the decision screen in order to search the internet for additional information on the public good.

  12. 12.

    We show below that part of this moderation can be attributed to those subjects who contribute in the first decision and do not change their behavior in the second decision.

  13. 13.

    A CDF C(t) of the action c is said to stochastically dominate a CDF D(t) of the action d if \(D(t)\ge C(t)\forall t\).

  14. 14.

    One additional second spent on the information screen increases RT by an average of 0.04 %.

  15. 15.

    As a further robustness check instead of controlling for the time spent on the information screen within the regression, we use the total time spent on both information and decision screen as a dependent variable. We still find a significant difference between contributors and defectors. One interpretation is that subjects who are more oriented towards pro-social goals spent more time acquiring information on how their decision could affect others. Fiedler et al. (2013) provide evidence along these lines.

  16. 16.

    This estimate is the sum of coefficients from contribution decisions 1 and 2 minus the coefficient of the interaction term.

  17. 17.

    For a more detailed description of each method, refer to Diederich and Goeschl (2013) and the notes of Table 4.

  18. 18.

    Note, that roughly three quarters of those who switch, change their behavior from being a non-contributor to being a contributor. This would be expected if defection truly followed from a (potentially error prone) first impulse.

  19. 19.

    Potential candidates for unobserved time-varying factors could be boredom or fatigue by the subjects. Their role can be considered minor in light of the fact that the median subject completed the experiment within 6 min.

  20. 20.

    The first stage regression F statistic returns \(F = 28.60\). This indicates that the instruments are not weak.

  21. 21.

    This result is especially pronounces if no fifty-fifty split is possible. For deviating results, see Schulz et al. (2014) and Cappelen et al. (2015).

References

  1. Abdellaoui M, Baillon A, Placido L, Wakker PP (2011) The rich domain of uncertainty: source functions and their experimental implementation. Am Econ Rev 101(2):695–723

    Article  Google Scholar 

  2. Baltussen G, Post GT, Van Den Assem MJ, Wakker PP (2012) Random incentive systems in a dynamic choice experiment. Exp Econ 15(3):418–443

    Article  Google Scholar 

  3. Beshears J, Choi JJ, Laibson D, Madrian BC (2008) How are preferences revealed? J Public Econ 92(89):1787–1794

    Article  Google Scholar 

  4. Börger T (2015) Are fast responses more random? Testing the effect of response time on scale in an online choice experiment. Environ Resour Econ. doi:10.1007/s10640-015-9905-1

  5. Buhrmester M, Kwang T, Gosling SD (2011) Amazon’s mechanical turk a new source of inexpensive, yet high-quality, data? Perspect Psychol Sci 6(1):3–5

    Article  Google Scholar 

  6. Cappelen AW, Nielsen UH, Tungodden B, Tyran J-R, Wengström E (2015) Fairness is intuitive. Exp Econ. doi:10.1007/s10683-015-9463-y

  7. Carlsson F (2010) Design of stated preference surveys: is there more to learn from behavioral economics? Environ Resour Econ 46(2):167–177

    Article  Google Scholar 

  8. Charness G, Gneezy U, Kuhn MA (2013) Experimental methods: extra-laboratory experiments-extending the reach of experimental economics. J Econ Behav Organ 91:93–100

    Article  Google Scholar 

  9. Chetty R (2015) Behavioral economics and public policy: a pragmatic perspective. Am Econ Rev 105(5):1–33

    Article  Google Scholar 

  10. Cook J, Jeuland M, Maskery B, Whittington D (2012) Giving stated preference respondents time to thin: results from four countries. Environ Resour Econ 51(4):473–496

    Article  Google Scholar 

  11. Corgnet B, Espín AM, Hernán-González R (2015) The cognitive basis of social behavior: cognitive reflection overrides antisocial but not always prosocial motives. Front Behav Neurosci 9:287

  12. Croson R, Treich N (2014) Behavioral environmental economics: promises and challenges. Environ Resour Econ 58(3):335–351

    Article  Google Scholar 

  13. Diederich J, Goeschl T (2014) Willingness to pay for voluntary climate action and its determinants: field-experimental evidence. Environ Resour Econ 57(3):405–429

    Article  Google Scholar 

  14. Diederich J, Goeschl T (2013) To give or not to give: The price of contributing and the provision of public goods. NBER working paper series 19332

  15. Dreber A, Fudenberg D, Levine DK, Rand DG (2004) Altruism and self-control. Working paper: SSRN 2477454

  16. Duffy S, Smith J (2014) Cognitive load in the multi-player prisoner’s dilemma game: are there brains in games? J Behav Exp Econ 51:47–56

    Article  Google Scholar 

  17. Ein-Gar D, Levontin L (2013) Giving from a distance: putting the charitable organization at the center of the donation appeal. J Consum Psychol 23(2):197–211

    Article  Google Scholar 

  18. Evans AM, Dillon KD, Rand DG (2015) Fast but not intuitive, slow but not reflective: decision conflict drives reaction times in social dilemmas. J Exp Psychol Gen 144(5):951

    Article  Google Scholar 

  19. Evans JSBT (2003) In two minds: dual-process accounts of reasoning. Trends Cogn Sci 7(10):454–459

    Article  Google Scholar 

  20. Evans JSBT (2008) Dual-processing accounts of reasoning, judgment, and social cognition. Annu Rev Psychol 59:255–278

    Article  Google Scholar 

  21. Fiedler S, Glöckner A, Nicklisch A, Dickert S (2013) Social value orientation and information search in social dilemmas: an eye-tracking analysis. Organ Behav Hum Decis Process 120(2):272–284

    Article  Google Scholar 

  22. Fischer A, Hanley N (2007) Analysing decision behaviour in stated preference surveys: a consumer psychological approach. Ecol Econ 61(2):303–314

    Article  Google Scholar 

  23. Frör O (2008) Bounded rationality in contingent valuation: empirical evidence using cognitive psychology. Ecol Econ 68(12):570–581

    Article  Google Scholar 

  24. Fudenberg D, Levine DK (2006) A dual-self model of impulse control. Am Econ Rev 96(5):1449–1476

    Article  Google Scholar 

  25. Gangadharan L, Nemes V (2009) Experimental analysis of risk and uncertainty in provisioning private and public goods. Econ Inq 47(1):146–164

    Article  Google Scholar 

  26. Gilboa I (2009) Theory of decision under uncertainty, vol 1. Cambridge University Press, Cambridge

    Book  Google Scholar 

  27. Grether DM, Plott CR (1979) Economic theory of choice and the preference reversal phenomenon. Am Econ Rev 69(4):623–638

    Google Scholar 

  28. Hanley N, Shogren JF (2005) Is cost-benefit analysis anomaly-proof? Environ Resour Econ 32(1):13–24

    Article  Google Scholar 

  29. Harrison GW, List JA (2004) Field experiments. J Econ Lit 42(4):1009–1055

    Article  Google Scholar 

  30. Kahneman D (2003) Maps of bounded rationality: psychology for behavioral economics. Am Econ Rev 93(5):1449–1475

    Article  Google Scholar 

  31. Kahneman D (2011) Thinking, fast and slow. Farrar, Straus and Giroux, New York

    Google Scholar 

  32. Kahneman D, Thaler RH (2006) Anomalies: utility maximization and experienced utility. J Econ Perspect 20(1):221–234

    Article  Google Scholar 

  33. Kahneman D, Knetsch JL, Thaler RH (1990) Experimental tests of the endowment effect and the coase theorem. J Polit Econ 98(6):1325–1348

    Article  Google Scholar 

  34. Kessler JB, Meier S (2014) Learning from (failed) replications: cognitive load manipulations and charitable giving. J Econ Behav Organ 102:10–13

    Article  Google Scholar 

  35. Kocher M, Myrseth K, Martinsson P, Wollbrant C (2012) Strong, bold, and kind: Self-control and cooperation in social dilemmas. Working paper

  36. Krajbich I, Oud B, Fehr E (2014) Benefits of neuroeconomic modeling: new policy interventions and predictors of preference. Am Econ Rev 104(5):501–506

    Article  Google Scholar 

  37. Krajbich I, Bartling B, Hare T, Fehr E (2015) Rethinking fast and slow based on a critique of reaction-time reverse inference. Nat Commun 6. doi:10.1038/ncomms8455

  38. Lee J (2008) The effect of the background risk in a simple chance improving decision model. J Risk Uncertain 36(1):19–41

    Article  Google Scholar 

  39. Loewenstein G, O’Donoghue T (2007) Animal spirits: affective and deliberative processes in economic behavior. SSRN working paper 539843

  40. Loewenstein G, Small DA (2007) The scarecrow and the tin man: the vicissitudes of human sympathy and caring. Rev Gen Psychol 11(2):112

    Article  Google Scholar 

  41. Loewenstein G, Rick S, Cohen JD (2008) Neuroeconomics. Annu Rev Psychol 59:647–672

    Article  Google Scholar 

  42. Löschel A, Sturm B, Vogt C (2013) The demand for climate protection—empirical evidence from Germany. Econ Lett 118(3):415–418

    Article  Google Scholar 

  43. Luce RD (1986) Response times. Oxford University Press, Oxford

    Google Scholar 

  44. Martinsson P, Myrseth KOR, Wollbrant C (2014) Social dilemmas: when self-control benefits cooperation. J Econ Psychol 45:213–236

    Article  Google Scholar 

  45. Menzel S (2013) Are emotions to blame? The impact of non-analytical information processing on decision-making and implications for fostering sustainability. Ecol Econ 96:71–78

    Article  Google Scholar 

  46. Nielsen UH, Tyran J-R, Wengström E (2014) Second thoughts on free riding. Econ Lett 122(2):136–139

    Article  Google Scholar 

  47. Nordhaus WD (1993) Reflections on the economics of climate change. J Econ Perspect 7(4):11–25

    Article  Google Scholar 

  48. Paolacci G, Chandler J, Ipeirotis PG (2010) Running experiments on amazon mechanical turk. Judgm Decis Mak 5(5):411–419

    Google Scholar 

  49. Piovesan M, Wengström E (2009) Fast or fair? A study of response times. Econ Lett 105(2):193–196

    Article  Google Scholar 

  50. Rand DG, Greene JD, Nowak MA (2012) Spontaneous giving and calculated greed. Nature 489(7416):427–430

    Article  Google Scholar 

  51. Rand DG, Peysakhovich A, Kraft-Todd GT, Newman GE, Wurzbacher O, Nowak MA, Greene JD (2014) Social heuristics shape intuitive cooperation. Nat Commun 5. doi:10.1038/ncomms4677

  52. Recalde MP, Riedl A, Vesterlund L (2014) Error prone inference from response time: The case of intuitive generosity. CESifo Working paper series, 4987

  53. Rubinstein A (2007) Instinctive and cognitive reasoning: a study of response times. Econ J 117(523):1243–1259

    Article  Google Scholar 

  54. Rubinstein A (2013) Response time and decision making: an experimental study. Judgm Decis Mak 8(5):540–551

    Google Scholar 

  55. Schulz JF, Fischbacher U, Thöni C, Utikal V (2014) Affect and fairness: dictator games under cognitive load. J Econ Psychol 41:77–88

    Article  Google Scholar 

  56. Small DA, Loewenstein G (2003) Helping a victim or helping the victim: altruism and identifiability. J Risk Uncertain 26(1):5–16

    Article  Google Scholar 

  57. Small DA, Loewenstein G, Slovic P (2007) Sympathy and callousness: the impact of deliberative thought on donations to identifiable and statistical victims. Organ Behav Hum Decis Process 102(2):143–153

    Article  Google Scholar 

  58. Smith A (2013) Estimating the causal effect of beliefs on contributions in repeated public good games. Exp Econ 16(3):414–425

  59. Spiliopoulos L, Ortmann A (2014) The BCD of response time analysis in experimental economics. SSRN 2401325

  60. Starmer C, Sugden R (1991) Does the random-lottery incentive system elicit true preferences? An experimental investigation. Am Econ Rev 81(4):971–978

    Google Scholar 

  61. Suter RS, Hertwig R (2011) Time and moral judgment. Cognition 119(3):454–458

    Article  Google Scholar 

  62. Tinghög G, Andersson D, Bonn C, Böttiger H, Josephson C, Lundgren G, Västfjäll D, Kirchler M, Johannesson M (2013) Intuition and cooperation reconsidered. Nature 498(7452):E1–E2

    Article  Google Scholar 

  63. Tversky A, Kahneman D (1981) The framing of decisions and the psychology of choice. Science 211(4481):453–458

    Article  Google Scholar 

  64. Ubeda P (2014) The consistency of fairness rules: an experimental study. J Econ Psychol 41:88–100

    Article  Google Scholar 

  65. Verkoeijen PP, Bouwmeester S (2014) Does intuition cause cooperation? PLoS One 9(5). doi:10.1371/journal.pone.0096654

  66. Zaki J, Mitchell JP (2013) Intuitive prosociality. Curr Dir Psychol Sci 22(6):466–470

    Article  Google Scholar 

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Acknowledgments

The authors gratefully acknowledge financial support by the German Science Foundation (DFG) under Grant GO1604/1 and the German Ministry for Education and Research under grant OIUV1012. Furthermore, we would like to thank the audiences at IMEBESS Oxford, RGS Bochum, HSC New York, RES Manchester, and ZEW Behavioral Environmental Economics Workshop Mannheim for their valuable comments.

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Correspondence to Johannes Lohse.

Appendix: Instructions

Appendix: Instructions

Original in German available from the authors on request. Translated instructions for the relevant screens (screen-shots below) and treatments. Further information regarding the procedures available in Diederich and Goeschl (2013).

Introduction Screen

Dear participants,

We would like to invite you to participate in two lotteries and to answer some questions about CO\(_2\)-emissions and climate change. Your participation will take approximately 10 min. In the lotteries, you have the chance to win points worth up to a three-digit amount in Euros. As usual, all your information will be treated confidentially.

Information Screen

In the lotteries, you may choose between the following two prizes:

  • A cash prize in points

    or

  • the reduction of carbon (CO\(_2\)) emissions by 1 ton

How will the reduction of the CO\(_2\) emissions take place? We will make use of a reliable opportunity provided by the EU emissions trading system: We will purchase and delete an EU emissions allowance for you. Emissions allowances are needed by power plants and other large installations within the EU in order to be allowed to emit CO\(_2\). Since there is only a fixed overall amount of allowances in place, deleted ones are no longer available to facilitate emissions. Emissions in Germany and other EU countries decrease by exactly one ton through one deleted allowance. Because of the way in which CO\(_2\) mixes in the air, it does not matter for the effect on the climate where CO\(_2\) emissions are reduced. What counts is only total emissions worldwide. In the lotteries, 100 winners will be randomly selected out of about 5.000 participants. The following two lotteries may differ in the prizes offered as well as in the payoff procedures.

Decision Screen

Order of prizes randomized.

In this lottery, you have the choice between the two prizes listed below:

If you choose the cash amount and win, then the corresponding amount of points will be transferred to your points account within the next few days. All winners will receive a short notification email.

For every winner who chooses the emissions reduction one additional allowance will be deleted. Winners will receive a short notification email containing a hyperlink to Heidelberg University web pages where they can reliably verify the deletion.

Please choose now, which prize you prefer if drawn as winner:

  • (Radiobutton) The reduction of CO\(_2\) emissions by one ton through the deletion of one EU emissions allowance

  • (Radiobutton) “Random Cash Price” Euro in bonus points.

Follow Up Questions Used

  • Do you think that you will personally benefit from positive effects of reduced CO\(_2\) emissions (for example from the mitigation of climate change)?

  • Do you think that future generations in Germany (for instance your children and grand-children) will benefit if climate change mitigating CO\(_2\) emissions reductions are undertaken in the present time?

Screenshots of German Version

See Figs. 3 and 4.

Fig. 3
figure3

Information screen

Fig. 4
figure4

Decision screen

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Lohse, J., Goeschl, T. & Diederich, J.H. Giving is a Question of Time: Response Times and Contributions to an Environmental Public Good. Environ Resource Econ 67, 455–477 (2017). https://doi.org/10.1007/s10640-016-0029-z

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Keywords

  • Public goods
  • Cooperation
  • Dual-system theories
  • Response times
  • Climate change
  • Online experiment

JEL Classification

  • C93
  • H41
  • D03