Inferring Anticompetitive Price Effects From Difference-in-Difference Analysis: A Caveat

Ulrick, Shawn W.; Sacher, Seth B.

doi:10.1093/joclec/nhv032

Cited by 5 publications

(4 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The final part of the robustness check applies for the complementary analysis of the DiD model proposed by Ulrick and Sacher (2015) to make sure the estimated DiD effect is not spurious. These authors argue that a positive and significant estimator of the interaction term in the DiD model is not a sufficient condition for concluding a causal effect.…”

Section: Sensitivity Analysis Of the Outcome Distributionmentioning

confidence: 99%

“…The author proposed a complementary analysis to check the proportion of the sample in the treatment group in the outcome distribution. To implement the approach proposed by Ulrick and Sacher (2015), Table VII presents the proportion of treatment group in each quartile of the distribution for ADR, AOR and RevPAR, respectively.…”

Section: Sensitivity Analysis Of the Outcome Distributionmentioning

confidence: 99%

See 1 more Smart Citation

Peer-to-peer-travel: is Airbnb a friend or foe to hotels?

Mhlanga

2019

IJCTHR

View full text Add to dashboard Cite

Purpose The sharing economy has caught great attention from researchers and policymakers. However, due to the dearth of available data, not much empirical evidence has been provided. This paper aims to empirically assess the impacts of Airbnb on hotel performances in South Africa. Design/methodology/approach Using South Africa as a case study, the study measures the impacts of Airbnb on hotel performances on three key metrics, namely, room prices, occupancy and Revenue per available room (RevPAR). A difference-in-difference model is estimated using a population-based data set of 809 hotels from 2016 to 2018. Findings The results reveal that despite Airbnb significantly and negatively impacting on hotel occupancies it has a non-significant effect on hotel prices and RevPAR. Although from the theoretical perspective a disruptive innovation business model such as Airbnb can possibly have a negligible effect on hotel performances because it may attract a different group of customers and create a new market, the empirical findings of this study fail to support this theoretical hypothesis. Consequently, the findings diverge with newly developed knowledge in other markets and point to nuanced and contextual complementary effects. Research limitations/implications Although some interesting findings are revealed into his study, some caveats remain. For instance, the study relied on data from hotels not from Airbnb. If the data of Airbnb can become available, it would be interesting to further examine whether the aggregated RevPAR of Airbnb can compensate for the aggregated loss of hotel RevPAR. This type of analysis could provide a broader evaluation scope regarding the overall effect of Airbnb on hotel performances. Moreover, if a longer time series data set of hotels in the post-Airbnb time period could become available, it would be interesting to further investigate the time-varying dynamic effects of Airbnb on hotel performances. Practical implications While hotels have launched a campaign to portray Airbnb as being commercial operators looking to compete illegally with hotels for the same segment of customers, this study shows that the rhetoric has been exaggerated. Airbnb, and more broadly, vacation rentals do not represent a war with hotels. They represent an answer to a different need. Indeed, the study reveals that Airbnb’s offer is a mere supplement to the market contrary to media rhetoric that it is meant to substitute hotels. The study has several implications for practitioners. First, these results are important because they serve as evidence against news articles that claim Airbnb is driving hotels out of business. They also show that if current trends continue, employees in the hotel industry in South Africa do not need to be concerned about losing their jobs because of Airbnb’s emergence. It is also important information for investors who may be concerned that Airbnb is hurting the hotel industry’s bottom line. Second, as the share of Airbnb listings on the accommodation market varies dramatically between cities, it is likely that eventual regulations/restrictions should be introduced in the provincial levels, while most of the cities continue benefiting from the increasing number of Airbnb visitors. Originality/value To the best of the author’s knowledge, this study is the first in South Africa to provide empirical evidence that Airbnb is significantly changing consumption patterns in the hotel industry, as opposed to generating purely incremental economic activity.

show abstract

Section: Sensitivity Analysis Of the Outcome Distributionmentioning

confidence: 99%

Section: Sensitivity Analysis Of the Outcome Distributionmentioning

confidence: 99%

Peer-to-peer-travel: is Airbnb a friend or foe to hotels?

Mhlanga

2019

IJCTHR

View full text Add to dashboard Cite

show abstract

“…An analogy is the growth in infants from 6 to 12 months of age: Half of the babies will experience above average growth. Because half of price changes are above average, merely finding an above average price change cannot by itself signify an anticompetitive price increase ( see Ulrick and Sacher 2015). The prime issue is judging how atypical Keystone's price increases are among the observed changes in the control groups.…”

Section: Merger Effectsmentioning

confidence: 99%

“…1515 See Ulrick and Sacher (2015), p. 1004. See also the discussion in Section 3.2 infra , where we discuss how physician contracts specify pricing by a myriad of mechanisms. …”

mentioning

confidence: 99%

Price Effects of a Merger: Evidence From a Physicians' Market

Koch

Ulrick

2020

Economic Inquiry

Self Cite

View full text Add to dashboard Cite

The relationship between physician concentration and prices is of interest to policy makers, public health officials, and insurers. We examine how a merger of orthopaedic physicians in Berks County, Pennsylvania, affected prices. We use claims‐level data from three private payors in the region to compare the price changes of the merged practices to those in nearby areas plausibly not affected by the merger. The merged practices increased prices to two of the three payors by 10–20%, while prices in the control geographies remained largely unchanged, on average, suggesting prices increased due to postmerger increases in market power. (JEL I13, K21, L4)

show abstract