Partitioned Pricing and Consumer Welfare
[DIW Discussion Paper 1888]
In online commerce, obfuscation strategies by sellers are hypothesized to mislead consumers to their detriment and to the profit of sellers. One such obfuscation strategy is partitioned pricing in which the price is split into a base price and add-on fees. While empirical evidence suggests that partitioned pricing affects consumer decisions through salience effects, its consumer welfare consequences are largely unexplored. Therefore, I provide a quantification of the welfare impact of the behavioral response to partitioned pricing. To do so, I derive a discrete choice model that jointly allows for differences in the reaction to marginal changes in add-on fees and the base price as well as a discontinuous effect of a zero fee. The model is based on a framework on limited attention and I estimate it using web scraped data of posted price transactions on eBay Germany. My results suggest under-reaction to marginal changes in the shipping fee, consistent with previous results in the literature. However, I also document a discontinuous positive effect of free shipping on consumer demand, which is novel to the literature. The combined impact of these effects on consumer welfare is less than six percent of consumer surplus. The welfare impact is attenuated because the maximum shipping fee on eBay is capped and the free shipping effect partly counteracts the under-reaction to shipping fees in expectation.
Airbnb and Rental Markets: Evidence from Berlin
(joint with Tomaso Duso, Claus Michelsen, and Maximilian Schäfer; was circulated as "Airbnb and Rents: Evidence from Berlin")
[Bristol Discussion Paper 21/746,
CEPR DP16150,
CESifo Working Paper No. 9089]
[Older version: DIW Discussion Paper 1890]
Non-academic publication (German): DIW Wochenbericht 7 / 2021
Press coverage (German):
taz,
heise online,
Spiegel,
n-tv
Podcast (German):
Man lernt nie aus
We exploit two policy interventions in Berlin, Germany, to causally identify the impact of Airbnb on rental markets. While the first intervention significantly reduced the number of high-availability Airbnb listings bookable for most of the year, the second intervention led to the exit of mostly occasional, low-availability listings. We find that the reduction in Airbnb supply has a much larger impact on rents and long-term rental supply for the first reform. This is consistent with more professional Airbnb hosts substituting back to the long-term rental market. Accordingly, we estimate that one additional nearby high-availability Airbnb listing crowds out 0.6 long-term rentals and, consequently, increases the asked square-meter rent by 1.8 percent on average. This marginal effect tends to be smaller in districts with a higher Airbnb density. However, these district experienced a larger slowdown in rent increases following the reform due to larger reductions in Airbnb supply.
Airbnb, Hotels, and Localized Competition
(joint with Maximilian Schäfer)
[DIW Discussion Paper 1889]
The rise of online platforms has disrupted numerous traditional industries. A prime example is the short-term accommodation platform Airbnb and how it affects the hotel industry. On the one hand, consumers can profit from Airbnb due to an increased number of choices and lower prices. On the other hand, critics of the platform argue that it allows professional hosts to operate de facto hotels while being subject to much laxer regulation. Understanding the nature of competition between Airbnb and hotels as well as quantifying consumer welfare gains from Airbnb is important to inform the debate on necessary platform regulation. In this paper, we analyze competition between hotels and Airbnb listings as well as the effect of Airbnb on consumer welfare. For this purpose, we use granular daily-level data from Paris for the year 2017. We estimate a nested logit model of demand that allows for consumer segmentation along accommodation types and the different districts within the city. We extend prior research by accounting for the localized nature of competition within districts of the city. Our results suggest that demand is segmented by district as well as accommodation type. Based on the parameter estimates, we calibrate a supply-side model to assess how Airbnb affects hotel revenues and consumer welfare. Our simulations imply that Airbnb increases average consumer surplus by 4.3 million euro per night and reduces average hotel revenues by 1.8 million euro. Furthermore, we find that 28 percent of Airbnb travelers would choose hotels if Airbnb did not exist.
Value for Money and Selection:
How Pricing Affects Airbnb Ratings
(joint with Christoph Carnehl, Maximilian Schäfer, and
André Stenzel)
We investigate the impact of prices on seller ratings. In a stylized model, we illustrate two opposing channels through which pricing affects overall ratings and rating subcategories. First, higher prices reduce the perceived value for money which worsens ratings. Second, higher prices increase the taste-based valuation of the average traveler which improves ratings.
Using data from Airbnb, we document a negative relationship between prices and ratings for most rating subcategories indicating that the value-for-money effect dominates the selection effect.
In line with our model, we find that hosts of low-rating listings exert more effort than those of high-rating listings.
Finally, an empirical assessment of the dynamics in the market suggests that taking the effect of prices on future ratings into account pays off: entrants who set low entry prices obtain better ratings and higher revenues in the medium run. A median entry discount of 8.5 percentage points increases medium-run monthly revenues by approximately 50 euros.
