Regulators of the railroad industry are tasked with protecting shippers from excessive rates for shipments in which the railroad is market dominant, defined as an absence of effective competition from intramodal and intermodal competition. This task requires shipment costs at an extremely disaggregate level. The current regulatory accounting approach of allocating costs is heavily criticized and cost functions in the academic literature are generally highly aggregated. In this paper, I develop a method to measure costs and markups that retains their disaggregate properties. I then use these results to explore market dominance, wherein the markup and the presence of competition determine whether shippers may be eligible to contest the reasonableness of the rate. I find that a movement from rail monopoly to duopoly is associated with an average 6.8% decline in rail markups. The results suggest nearby ports decrease the impact of rail competition on rail markups. This approach can be operationalized by regulators and market participants to assess the reasonableness of a rate and to streamline and expedite market dominance inquiry.
The supply of housing for short-term rental (STR) has grown dramatically with the emergence of platforms such as Airbnb. This trend has led to contradictory concerns about increasing housing prices and negative externalities. We provide evidence that in some areas, STRs can decrease housing prices. Using a parsimonious model of housing occupancy with externalities, we show the marginal effect of STRs on housing prices depends on the net impact of STRs on local amenities. Using zip-code-level data from Los Angeles County, California, we show heterogeneity in the marginal effects of Airbnb listings on housing prices across localities. We then examine the consequences of a 2015 law restricting STRs within the City of Santa Monica in the coastal region of Los Angeles County. In that City, we estimate a negative relationship between the prevalence of STRs and housing prices. Using a differences-in-differences approach, we show that the 2015 law increased housing prices – which can be rationalized by our theory. Finally, we provide evidence for a potential mechanism: “party-related’’ nuisance calls to the Santa Monica Police Department decreased after the policy was enacted.
In this paper, we use Bayesian techniques to develop nowcasts for the quantity of waterborne traffic in the United States in total and for the four primary commodities. These waterborne traffic levels are released with a considerable time lag, but yet are of current interest. Nowcasts (i.e. predictions of the waterborne traffic levels to be released based on other variables that are available) have been constructed using an array of different variables and techniques. However, the large number of potential predictor variables and changes in the distribution of traffic levels leads to both model and estimation uncertainty, which has likely hampered the accuracy of these existing nowcasts. We use Bayesian Model Averaging (BMA) to create nowcasts, which confronts model and estimation uncertainty directly via the averaging of models with different sets of predictors. We also use rolling window techniques to account for possible changes in the nowcasting relationship over time. Based on a variety of evaluation metrics, we find that BMA substantially improves nowcast accuracy.