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.