Field | Before | After |
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Field Trial Status | Before in_development | After completed |
Field Abstract | Before This study makes use of the data on the response of output to various behavioral conditions in a real-effort experiment, which was gathered as a part of the project registered as "Response of Output to Varying Incentive Structures on Amazon Turk". The behavioral treatments include examinations of the response to incentives, altruistic motives, loss aversion, and gift exchange, among others. A group of forecasters, including experts in economics, psychology, and decision-making, will be asked to predict the output resulting from each of the conditions. We then compare these forecasts to the actual results to examine the relevance of expertise to forecasting experimental results. | After This study makes use of the data on the response of output to various behavioral conditions in a real-effort experiment, which was gathered as a part of the project registered as "Response of Output to Varying Incentive Structures on Amazon Turk" (AEARCTR-0000714). The behavioral treatments include examinations of the response to incentives, altruistic motives, loss aversion, and gift exchange, among others. A group of forecasters, including experts in economics, psychology, and decision-making, will be asked to predict the output resulting from each of the conditions. We then compare these forecasts to the actual results to examine the relevance of expertise to forecasting experimental results. |
Field Trial Start Date | Before July 09, 2015 | After May 14, 2015 |
Field Last Published | Before July 09, 2015 11:43 PM | After April 07, 2017 10:14 AM |
Field Study Withdrawn | Before | After No |
Field Intervention Completion Date | Before | After December 31, 2015 |
Field Data Collection Complete | Before | After Yes |
Field Final Sample Size: Number of Clusters (Unit of Randomization) | Before | After N/A |
Field Was attrition correlated with treatment status? | Before | After No |
Field Final Sample Size: Total Number of Observations | Before | After The final sample includes 9,861 subjects. |
Field Final Sample Size (or Number of Clusters) by Treatment Arms | Before | After Approximately 550 subjects for each treatment arm (with 18 treatment arms) |
Field Is there a restricted access data set available on request? | Before | After No |
Field Program Files | Before | After No |
Field Data Collection Completion Date | Before | After September 30, 2015 |
Field Is data available for public use? | Before | After No |
Field Intervention Start Date | Before July 09, 2015 | After May 14, 2015 |
Field Intervention End Date | Before December 31, 2015 | After July 09, 2015 |
Field | Before | After |
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Field External Link URL | Before | After https://www.povertyactionlab.org/node/22136 |
Field External Link Description | Before | After J-PAL evaluation summary |
Field | Before | After |
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Field Paper Abstract | Before | After How much do different monetary and non-monetary motivators induce costly effort? Does the effectiveness line up with the expectations of researchers and with results in the literature? We conduct a large-scale real-effort experiment with 18 treatment arms. We examine the effect of (i) standard incentives; (ii) behavioral factors like social preferences and reference dependence; and (iii) non-monetary inducements from psychology. We find that (i) monetary incentives work largely as expected, including a very low piece rate treat- ment which does not crowd out effort; (ii) the evidence is partly consistent with standard behavioral models, including warm glow, though we do not find evidence of probability weighting; (iii) the psychological motivators are effective, but less so than incentives. We then compare the results to forecasts by 208 academic experts. On average, the experts an- ticipate several key features, like the effectiveness of psychological motivators. A sizeable share of experts, however, expects crowd-out, probability weighting, and pure altruism, counterfactually. As a further comparison, we present a meta-analysis of similar treat- ments in the literature. Overall, predictions based on the literature are correlated with, but underperform, the expert forecasts. |
Field Paper Citation | Before | After "What Motivates Effort? Evidence and Expert Forecasts." This version: March 15, 2017. |
Field Paper URL | Before | After https://eml.berkeley.edu/~sdellavi/wp/BehavioralForecastsMar17withOnlApp.pdf |
Field | Before | After |
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Field Paper Abstract | Before | After Academic experts frequently recommend policies and treatments. But how well do they anticipate the impact of different treatments? And how do their predictions compare to the predictions of non-experts? We analyze how 208 experts forecast the results of 15 treatments involving monetary and non-monetary motivators in a real-effort task. We compare these forecasts to those made by PhD students and non-experts: undergraduates, MBAs, and an online sample. We document seven main results. First, the average forecast of experts predicts quite well the experimental results. Second, there is a strong wisdom-of- crowds effect: the average forecast outperforms 96 percent of individual forecasts. Third, correlates of expertise–citations, academic rank, field, and contextual experience—do not improve forecasting accuracy. Fourth, experts as a group do better than non-experts, but not if accuracy is defined as rank ordering treatments. Fifth, measures of effort, confidence, and revealed ability are predictive of forecast accuracy to some extent, especially for non- experts. Sixth, using these measures we identify ‘superforecasters’ among the non-experts who outperform the experts out of sample. Seventh, we document that these results on forecasting accuracy surprise the forecasters themselves. We present a simple model that organizes several of these results and we stress the implications for the collection of forecasts of future experimental results. |
Field Paper Citation | Before | After "Predicting Experimental Results: Who Knows What?" This version: August 16, 2016. |
Field Paper URL | Before | After https://eml.berkeley.edu/~sdellavi/wp/expertsJul16.pdf |