We propose a dynamic pricing model in a multiproduct oligopoly setting, in which consumers exhibit inertia in their choices and firms face costly price adjustments. The primitives of the model, including firms’ discount factor, are estimated from scanner data. Our context is the UK butter and margarine industry. We use the model to evaluate the effects of frictions on price dynamics, profits and consumer welfare. First, we show that manufacturers’ discount rates vary between 0.92 and 0.99, suggesting that pricing decisions – even for simple consumer goods – have an important intertemporal component. Second, in line with evidence from the macro literature, we find that price adjustment costs are substantial and represent between 24-34% of manufacturers’ net margins. Third, our model predicts that the removal of these costs reduces persistence in prices, increases firms’ profits but has little effect on consumer surplus. Fourth, we show that when price adjustments are costly the effects of consumer inertia on prices are much more pronounced than in the standard model where firms can adjust prices freely. This implies that if price adjustment costs are not factored in researchers may underestimate the effects of consumer inertia on prices.
We propose an empirical model of nonsequential consumer search model where both consumers and firms are heterogeneous. Consumers differ in search costs. Firms have private costs of production. We characterize pure strategy Bayesian Nash equilibria of the game that generate continuous price distributions in a system of equations. We provide conditions under which the model can be nonparametrically identified in closed-form from data on market shares and prices. In particular, we identify the probability density function (pdf) of firm’s costs using a similar strategy to Guerre, Perrigne and Vuong (2000)’s as firms decisions can be cast as a procurement auction problem. Due to a particular feature of the equilibrium, the traditional kernel estimator for the cost pdf cannot reach the optimal convergence rate uniformly over its entire support. It can come arbitrarily close to it over an expanding support after a suitable transformation. Our results apply to both models with vertically differentiated or non-differentiated products
We formulate a structural model of search with lender and borrower heterogeneity to estimate the value of information provided to UK households by mortgage brokers. Using administrative data on loans originating in 2016 and 2017, we document the existence of a substantial degree of unexplained price dispersion, and observe that while mortgages obtained from brokers are cheaper, borrowers who use intermediaries pay more once commissions are factored in. Assuming that it is borrowers with high search costs who use brokers, we nonparametrically estimate the distributions of search and banks’ costs of providing the loan. Our results show that search costs vary by demographic groups, and that broker presence exerts negative pressure on lenders’ market power. Compared to a world where broker advice is not available, we estimate that their presence reduces average monthly mortgage costs by 21% and welfare losses arising from search frictions by 70% – although the results differ by borrower and loan characteristics. We also find that regulation in support of market centralization halves lenders’ markups and lowers monthly costs of an average mortgage by 4.4%.
We combine multiple data sources to construct an extensive dataset on London property prices, rents, residential amenities, travel times and wages. These data are then used to estimate a structural location choice model in the spirit of Ahlfeldt, Redding, Sturm, and Wolf (2015) to explain residential sorting patterns and the impact of planning decisions and infrastructure investments on house prices, rents, and location choices. To control for endogenous amenities we leverage techniques developed in the IO literature on demand estimation, such as BLP instruments.
The paper introduces a dynamic oligopoly model in which firms optimally choose investment in durability of their products to study whether manufacturers engage in planned obsolescence, that is sell products with socially suboptimal level of durability. We modify the empirical framework of Goettler and Gordon (2011) to allow for endogenous durability which is excluded from consumers’ flow utility but affects the probability of repurchase. To infer durability levels, we use a novel, detailed dataset which records failure rates of different models of HDDs to estimate brand- and capacity-specific survival functions. We supplement this with data on HDD sales and prices to estimate the demand side of the model.
The National Infrastructure Commission (NIC) commissioned a team of academics and researchers at the IFS and UCL to create a software tool that estimates how land values respond to changes in land purpose or infrastructure improvements.
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