Link: http://www.npr.org/blogs/money/2014/01/24/265396928/when-a-65-cab-ride-costs-192
Summary: This podcast sheds light on supply and demand as well as elasticity using the well know car service Uber.
Original Air Date: January 24, 2014
Length: 4 minutes 9 seconds Note there is a longer version that addresses issues of fairness, rationing by time v. money, different profit maximizing strategies by firms
Discussion Prompt (1) for short or long version: Think about yourself as a consumer of Uber/Lyft services– how elastic is your demand for uber? How do you know? What factors do you think make your demand for an Uber ride more or less elastic? You can think about specific times, or you can think about comparing yourself to other people whose elasticity of demand might be different.
Discussion Prompt (2) for short or long version: This podcast focuses on the topic of ‘surge pricing’: how does this relate to price elasticity of supply? Is the supply of Uber drivers price elastic or inelastic? What factors might impact that?
Discussion Prompt (1) for long version: Planet Money asks this question: If this is how markets generally work (ex. Stock market, copper market), why is what Uber’s doing considered so strange? This podcast is from 2014 (useful to us because it explains Uber in detail because it was new). In the time that has passed, do you think people have come around to this ‘economic way of thinking’ about surge pricing? Why, why not?
Discussion Prompt (2) for long version: The podcast compares the pricing strategy of Home Depot with ‘ice salt/melt,’ where they don’t change the price but they do run out, to the strategy of Uber where they raise the price rather than ‘run out’. Economist Richard Thaler notes that these choices represent different profit maximizing strategies by firms focusing on long-run vs. short-run strategies. What does he mean here? How do these actions represent different profit-maximizing strategies by these firms? Do you think one is ‘more fair’?
Written Prompt: Read this related article: Cohen, P., Hahn, R., Hall, J., Levitt, S., & Metcalfe, R. (2016). Using big data to estimate consumer surplus: The case of uber (No. w22627). National Bureau of Economic Research. Part of what makes this article innovative, is that it provided a ‘real-life’ consumer surplus estimate drawn from actual consumer data. Why do you think that it might have been hard to determine consumer surplus in real life before Uber (and similar apps/services)? Can you make any other links between this article and the podcast?
audioecon 
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