Jonathan F. Schulz, Petra Thiemann, and Christian Thöni, “Nudging Generosity: Choice Architecture and Cognitive Factors in Charitable Giving,” USC-INET Research Paper No. 16-26, September 13, 2016.
• The subjects (n=869) are students at a Swiss university, who are asked at the end of a pen-and-paper study if they would like to donate to charity some of any winnings they make from the experiment.
• In one treatment, the students who indicate that they want to donate to charity must write in a charity name in a blank space. In the alternative treatment, the students are given a list of five well-known charities which they can choose among, along with a blank space in case they want to indicate another charity.
• The one-line summary of the results is that providing a short list of recommended charities, along with a “choose your own” option, doubles the number of donors relative to just having the “choose your own” option.
• The average donation per donor is unchanged, so the “list” treatment also doubles total contributions. Most donations in the "list" treatment go to listed charities; other charities are "crowded out" by not being listed.
• Females donate significantly more than males.
Thursday, March 8, 2018
Shakespeare on the Benefits of Information Avoidance
What sense had I of her stol'n hours of lust?
I saw't not, thought it not, it harm'd not me:
I slept the next night well, was free and merry;
I found not Cassio's kisses on her lips:
He that is robb'd, not wanting what is stol'n,
Let him not know't, and he's not robb'd at all.
(Othello, Act 3, Scene 3, Lines 2015-2020; this passage appears as the epigraph to Russell Golman, David Hagmann, and George Loewenstein, “Information Avoidance,” Journal of Economic Literature 55(1): 96-135, March 2017.)
********************************************
I had been happy, if the general camp,
Pioners and all, had tasted her sweet body,
So I had nothing known. O, now, for ever
Farewell the tranquil mind! farewell content!
(Othello, Act 3, Scene 3, Lines 2022-2025)
********************************************
How blest am I
In my just censure, in my true opinion!
Alack, for lesser knowledge! how accursed
In being so blest! There may be in the cup
A spider steep'd, and one may drink, depart,
And yet partake no venom, for his knowledge
Is not infected: but if one present
The abhorr'd ingredient to his eye, make known
How he hath drunk, he cracks his gorge, his sides,
With violent hefts. I have drunk,
and seen the spider.
(The Winter's Tale, Act 2, Scene 1, Lines 645-655)
I saw't not, thought it not, it harm'd not me:
I slept the next night well, was free and merry;
I found not Cassio's kisses on her lips:
He that is robb'd, not wanting what is stol'n,
Let him not know't, and he's not robb'd at all.
(Othello, Act 3, Scene 3, Lines 2015-2020; this passage appears as the epigraph to Russell Golman, David Hagmann, and George Loewenstein, “Information Avoidance,” Journal of Economic Literature 55(1): 96-135, March 2017.)
********************************************
I had been happy, if the general camp,
Pioners and all, had tasted her sweet body,
So I had nothing known. O, now, for ever
Farewell the tranquil mind! farewell content!
(Othello, Act 3, Scene 3, Lines 2022-2025)
********************************************
How blest am I
In my just censure, in my true opinion!
Alack, for lesser knowledge! how accursed
In being so blest! There may be in the cup
A spider steep'd, and one may drink, depart,
And yet partake no venom, for his knowledge
Is not infected: but if one present
The abhorr'd ingredient to his eye, make known
How he hath drunk, he cracks his gorge, his sides,
With violent hefts. I have drunk,
and seen the spider.
(The Winter's Tale, Act 2, Scene 1, Lines 645-655)
Friday, February 23, 2018
Zarghamee et al. (2017) on Nudging Charitable Giving
Homa S. Zarghamee, Kent D. Messer, Jacob R. Fooks, William D. Schulze, Shang Wu, and Jubo Yan, “Nudging Charitable Giving: Three Field Experiments.” Journal of Behavioral and Experimental Economics 66: 137-
149, February 2017 [abstract here].
• Study 1: Students had earned money over the duration of the semester by taking part in experiments; this study asked them if they wanted to donate some or all of their earnings to charity.
• Envelopes were provided such that students could either donate some of their earnings (opting in to charity, n=69), or, request a refund from the contribution of their money (opting out of charity, n=118).
• The opt-out version increases contributions by some 25%.
• Study 2 (n at most 59) looks at whether some cheap talk and a vote prior to deciding to contribute to charity – and potentially to keep donating for 10 months – can induce more giving than without the chitchat and vote. The cheap talk does the (cheap) trick, inducing higher donations by some 47%.
• Study 3 (n=70) involves one treatment where the charity mentions HIV, whereas the other treatment does not. The treatment makes no difference to donations, but changes to positive and negative affect during the experiment do influence giving. A lower positive affect decreases giving, but an increase in negative affect increases giving.
• Study 1: Students had earned money over the duration of the semester by taking part in experiments; this study asked them if they wanted to donate some or all of their earnings to charity.
• Envelopes were provided such that students could either donate some of their earnings (opting in to charity, n=69), or, request a refund from the contribution of their money (opting out of charity, n=118).
• The opt-out version increases contributions by some 25%.
• Study 2 (n at most 59) looks at whether some cheap talk and a vote prior to deciding to contribute to charity – and potentially to keep donating for 10 months – can induce more giving than without the chitchat and vote. The cheap talk does the (cheap) trick, inducing higher donations by some 47%.
• Study 3 (n=70) involves one treatment where the charity mentions HIV, whereas the other treatment does not. The treatment makes no difference to donations, but changes to positive and negative affect during the experiment do influence giving. A lower positive affect decreases giving, but an increase in negative affect increases giving.
Bosman, Hennig‐Schmidt, and van Winden (2016) on Power-to-Take Games
Ronald Bosman, Heike Hennig‐Schmidt, and Frans van Winden, “Emotion at Stake: The Role of Stake Size and Emotions in a Power-To-Take Game Experiment in China with a Comparison to Europe.” CESifo Working Paper Series No. 5858, April 19, 2016.
• In the two-player power-to-take game, Player A indicates what percentage of Player B’s monetary endowment Player A will claim.
• Player B learns of A’s claim, and then can choose to destroy some or all of her own endowment. Whatever is left after the destruction, Player A receives the chosen percentage of it, while Player B retains the remainder.
• Power-to-take is sort of a generalized version of the ultimatum game, and in particular, it allows Player B to have intermediate responses, in between accepting Player A’s suggestion or destroying the entire “endowment.”
• Three conditions: China Low (stakes), n=36; China High, n=36; and, EU, n=40. The results for the two China treatments are similar.
• Take rates in China average more than 50%; while most people do not destroy any of their endowment, the average amount of destruction is considerable, more than 20%. Higher take rates lead to more destruction.
• Higher take rates strengthen negative emotions in Players B, and it is possibly worse with higher stakes.
• Destruction decisions seem to be mostly driven by emotions.
• The results, including emotional responses, seem to be similar in China and Europe.
• In the two-player power-to-take game, Player A indicates what percentage of Player B’s monetary endowment Player A will claim.
• Player B learns of A’s claim, and then can choose to destroy some or all of her own endowment. Whatever is left after the destruction, Player A receives the chosen percentage of it, while Player B retains the remainder.
• Power-to-take is sort of a generalized version of the ultimatum game, and in particular, it allows Player B to have intermediate responses, in between accepting Player A’s suggestion or destroying the entire “endowment.”
• Three conditions: China Low (stakes), n=36; China High, n=36; and, EU, n=40. The results for the two China treatments are similar.
• Take rates in China average more than 50%; while most people do not destroy any of their endowment, the average amount of destruction is considerable, more than 20%. Higher take rates lead to more destruction.
• Higher take rates strengthen negative emotions in Players B, and it is possibly worse with higher stakes.
• Destruction decisions seem to be mostly driven by emotions.
• The results, including emotional responses, seem to be similar in China and Europe.
Sunday, January 28, 2018
Koessler, Torgler, Feld, and Frey (2016) on Promising to Pay Your Taxes
Ann-Kathrin Koessler, Benno Torgler, Lars P. Feld, and Bruno S. Frey,
“Commitment to Pay Taxes: A Field Experiment on the Importance of
Promise.” Tax and Transfer Policy Institute, Australian National University,
Working Paper 10/2016, November 29, 2016 [pdf here].
• A natural field experiment (n≈2000) is conducted in Switzerland (in 2013); the subjects, Swiss taxpayers, do not know that they are taking part in an experiment.
• The experiment concerns whether it is possible to encourage timely tax payments by having taxpayers voluntarily promise to remit their taxes on time. One potentially confounding factor, however, is that a new “dunning” policy for late taxpayers is enacted concurrently with the experiment.
• There are two “promise” treatments. In both cases, subjects are told that if they fill in and return a postcard promising to pay their taxes on time, and then do pay their taxes on time, they will be entered into a lottery. The differences between these two treatments is that in one case the lottery prize is cash – 1000 Swiss francs – and in the other, the prize is a wellness spa trip for two, worth approximately 1000 Swiss francs.
• Both promise treatments have parallel treatments that provide the same lottery to punctual payers, but that do not require or provide the option for the non-binding promise. A control treatment with no lottery or promise completes the collection.
• Almost one-third of the subjects who are given an opportunity to promise to pay on time make the promise. The willingness to promise is a pretty strong signal both of whether you have paid on time in the past, and of whether you will pay on time this year.
• Those who were in the spa lottery and who made the promise saw a significant jump in their compliance rates. But the lotteries with promise opportunities do not seem to do any better overall than the lotteries without the promise opportunities.
• My takeaway, perhaps not as optimistic as that of the authors, is that the “promise” intervention is pretty weak tea.
• A natural field experiment (n≈2000) is conducted in Switzerland (in 2013); the subjects, Swiss taxpayers, do not know that they are taking part in an experiment.
• The experiment concerns whether it is possible to encourage timely tax payments by having taxpayers voluntarily promise to remit their taxes on time. One potentially confounding factor, however, is that a new “dunning” policy for late taxpayers is enacted concurrently with the experiment.
• There are two “promise” treatments. In both cases, subjects are told that if they fill in and return a postcard promising to pay their taxes on time, and then do pay their taxes on time, they will be entered into a lottery. The differences between these two treatments is that in one case the lottery prize is cash – 1000 Swiss francs – and in the other, the prize is a wellness spa trip for two, worth approximately 1000 Swiss francs.
• Both promise treatments have parallel treatments that provide the same lottery to punctual payers, but that do not require or provide the option for the non-binding promise. A control treatment with no lottery or promise completes the collection.
• Almost one-third of the subjects who are given an opportunity to promise to pay on time make the promise. The willingness to promise is a pretty strong signal both of whether you have paid on time in the past, and of whether you will pay on time this year.
• Those who were in the spa lottery and who made the promise saw a significant jump in their compliance rates. But the lotteries with promise opportunities do not seem to do any better overall than the lotteries without the promise opportunities.
• My takeaway, perhaps not as optimistic as that of the authors, is that the “promise” intervention is pretty weak tea.
Friday, January 5, 2018
Horton and Zeckhauser (2016) on Peer Effects in Production
John J. Horton and Richard J. Zeckhauser, “The Causes of Peer Effects in Production: Evidence from a Series of Field Experiments.” NBER Working Paper No. 22386, July 2016 [ungated pdf version here].
• A worker’s productivity is influenced by the productivity of her co-workers. Why?
• Do low-productivity workers fear punishment, because their slackerdom imposes more work on others, or because others simply view the provision of low effort as unfair? Do workers have a preference to not be unproductive? Does the performance of other employees signal to a worker the employer’s expectations?
• The field experiments involve hiring workers online (MTurk) to label images; the workers do not know that they are taking part in an experiment.
• Among the findings are that peers will punish slackers even when the slackers do not impose any harm on the other workers; and, that equity concerns motivate such punishment, through a suspicion of low effort from slackers. Workers therefore want either to avoid being slackers, or, perhaps, to avoid being perceived as slackers.
• Workers (the subjects in the experiments) label images and evaluate the labelling performance of other workers. Evaluations involve a recommendation as to whether the peer worker should be paid, and also, a suggestion of how a 9-cent bonus should be split between the evaluator and the evaluated. The recommended bonus splits are implemented. A recommendation that a worker not be paid, though not implemented, is taken to be a type of punishment of that worker.
• All of the subjects also evaluate the work of either a (specific) high effort or a low effort worker, based on work from a previous experiment. A description of the four treatments in the field experiment follows:
Baseline: Workers are shown either a very good labelling job, or a minimal effort version. They then choose to take the task (if indeed they want to), and start labelling.
Punish: After seeing a sample of excellent work, this treatment proceeds like Baseline, except that then some good or bad work requires peer evaluation.
Peers: After evaluation, subjects then are given a second labelling task. Will evaluating a good or a bad job (done by someone else) alter the worker’s second-round performance?
Explicit: This is like Peers, except that workers are told to produce only two labels per image. But when they evaluate others, some of the subjects see an image with the requisite two labels, while the others see an image with 11 labels (produced by some Stakhanovite). Note that this excessive production explicitly contravenes the injunction to provide only two labels.
• And the results...
Baseline: If you are shown a high-quality sample, you are less likely to take the job, but if you take it, you increase your effort.
Punish: Peer evaluation leads to lessened punishment for good as opposed to bad work; workers who themselves produce good work punish more.
Peers: If you evaluate a strong worker, your subsequent work is stronger, and the effect is more pronounced if you yourself are a high productivity worker.
Explicit: Workers shown against-the-rules overproduction do not punish it, and many workers themselves switch to excessive output.
• A worker’s productivity is influenced by the productivity of her co-workers. Why?
• Do low-productivity workers fear punishment, because their slackerdom imposes more work on others, or because others simply view the provision of low effort as unfair? Do workers have a preference to not be unproductive? Does the performance of other employees signal to a worker the employer’s expectations?
• The field experiments involve hiring workers online (MTurk) to label images; the workers do not know that they are taking part in an experiment.
• Among the findings are that peers will punish slackers even when the slackers do not impose any harm on the other workers; and, that equity concerns motivate such punishment, through a suspicion of low effort from slackers. Workers therefore want either to avoid being slackers, or, perhaps, to avoid being perceived as slackers.
• Workers (the subjects in the experiments) label images and evaluate the labelling performance of other workers. Evaluations involve a recommendation as to whether the peer worker should be paid, and also, a suggestion of how a 9-cent bonus should be split between the evaluator and the evaluated. The recommended bonus splits are implemented. A recommendation that a worker not be paid, though not implemented, is taken to be a type of punishment of that worker.
• All of the subjects also evaluate the work of either a (specific) high effort or a low effort worker, based on work from a previous experiment. A description of the four treatments in the field experiment follows:
Baseline: Workers are shown either a very good labelling job, or a minimal effort version. They then choose to take the task (if indeed they want to), and start labelling.
Punish: After seeing a sample of excellent work, this treatment proceeds like Baseline, except that then some good or bad work requires peer evaluation.
Peers: After evaluation, subjects then are given a second labelling task. Will evaluating a good or a bad job (done by someone else) alter the worker’s second-round performance?
Explicit: This is like Peers, except that workers are told to produce only two labels per image. But when they evaluate others, some of the subjects see an image with the requisite two labels, while the others see an image with 11 labels (produced by some Stakhanovite). Note that this excessive production explicitly contravenes the injunction to provide only two labels.
• And the results...
Baseline: If you are shown a high-quality sample, you are less likely to take the job, but if you take it, you increase your effort.
Punish: Peer evaluation leads to lessened punishment for good as opposed to bad work; workers who themselves produce good work punish more.
Peers: If you evaluate a strong worker, your subsequent work is stronger, and the effect is more pronounced if you yourself are a high productivity worker.
Explicit: Workers shown against-the-rules overproduction do not punish it, and many workers themselves switch to excessive output.
Tuesday, January 2, 2018
Hermann and Musshoff (2016) on Measuring Time Preferences
Daniel Hermann and Oliver Musshoff, “Measuring Time Preferences:
Comparing Methods and Evaluating the Magnitude Effect.” Journal of
Behavioral and Experimental Economics 65: 16-26, December 2016.
• Two different approaches to measuring discount rates in the past have revealed similar rates for US students – but what about for entrepreneurs, and for German students?
• This article consist of a web-based experiment with German farmers (n=111) and students (n=178); farmers are standing in for entrepreneurs, as farmers must make significant investment decisions that only yield results in the long-term.
• The experiments are conducted with both 100 and 300 euro benchmark amounts; the idea is to test for the “magnitude effect,” in which revealed discount rates fall as monetary amounts rise.
• Further, some previous estimates of discount rates might be skewed by the assumption of risk neutrality.
• The Coller and Williams (CW) task: You can receive €100 in three weeks. Or, you can receive more than €100 in twelve weeks. How much more than €100 do you need before you are willing to wait the extra nine weeks? This experiment is repeated for amounts 3 times as high.
• The Holt and Laury (HL) task: Lottery A offers prizes of either €180 or €144, while lottery B offers the outcomes €346.50 or €9. The higher prize has the same probability of occurring in both lotteries. How high does the probability of the higher prize have to be to get you to choose Lottery B?
• The p task, from Laury et al. (2012): Lottery A pays out zero half the time and €100 half the time, with the prize collected in three weeks. Lottery B pays out zero or €100 too, but doesn’t pay out until 12 weeks from now. How much greater than .5 does the probability of winning €100 have to be to get you to choose Lottery B, and thus wait the additional nine weeks? As with CW, this experiment is replicated with payouts three times as high.
• For farmers, the estimated average discount rate from CW is 12.9% for €100 tests, and 8.8% for €300 tests. For the p task, rates are significantly higher, at 30.6% for the €100 test, and 28.6% for the €300 test.
• In the joint estimation, student discount rates are similar to the farmers’. For the p-test on students, while this method still led to higher discount rates (significantly so for the €300 version) compared to the joint estimation, the increase was not nearly as a great as it was for farmers.
• For both students and farmers, raising the stakes to €300 lowers discount rates significantly in the joint estimation – the decline is greater for students. In the p-test approach, the fall in discount rates associated with higher stakes is not significant. This non-result suggests that for risky alternatives, the magnitude effect might not exist.
• Two different approaches to measuring discount rates in the past have revealed similar rates for US students – but what about for entrepreneurs, and for German students?
• This article consist of a web-based experiment with German farmers (n=111) and students (n=178); farmers are standing in for entrepreneurs, as farmers must make significant investment decisions that only yield results in the long-term.
• The experiments are conducted with both 100 and 300 euro benchmark amounts; the idea is to test for the “magnitude effect,” in which revealed discount rates fall as monetary amounts rise.
• Further, some previous estimates of discount rates might be skewed by the assumption of risk neutrality.
• The Coller and Williams (CW) task: You can receive €100 in three weeks. Or, you can receive more than €100 in twelve weeks. How much more than €100 do you need before you are willing to wait the extra nine weeks? This experiment is repeated for amounts 3 times as high.
• The Holt and Laury (HL) task: Lottery A offers prizes of either €180 or €144, while lottery B offers the outcomes €346.50 or €9. The higher prize has the same probability of occurring in both lotteries. How high does the probability of the higher prize have to be to get you to choose Lottery B?
• The p task, from Laury et al. (2012): Lottery A pays out zero half the time and €100 half the time, with the prize collected in three weeks. Lottery B pays out zero or €100 too, but doesn’t pay out until 12 weeks from now. How much greater than .5 does the probability of winning €100 have to be to get you to choose Lottery B, and thus wait the additional nine weeks? As with CW, this experiment is replicated with payouts three times as high.
• For farmers, the estimated average discount rate from CW is 12.9% for €100 tests, and 8.8% for €300 tests. For the p task, rates are significantly higher, at 30.6% for the €100 test, and 28.6% for the €300 test.
• In the joint estimation, student discount rates are similar to the farmers’. For the p-test on students, while this method still led to higher discount rates (significantly so for the €300 version) compared to the joint estimation, the increase was not nearly as a great as it was for farmers.
• For both students and farmers, raising the stakes to €300 lowers discount rates significantly in the joint estimation – the decline is greater for students. In the p-test approach, the fall in discount rates associated with higher stakes is not significant. This non-result suggests that for risky alternatives, the magnitude effect might not exist.
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