Estimating Supply Functions for Wine Attributes: A Two-Stage Hedonic Approach

A vast body of literature exists on estimating hedonic price functions, which relate the price of wine to its attributes. Some existing literature has employed producer-specific variables such as quantity sold and producer reputation in hedonic functions to potentially capture supply influences on prices. This practice is inconsistent with the original Rosen (1974) hedonic theoretic foundation. To overcome this deficiency, we extend the literature by using the Rosen two-stage approach, employing data from multi-markets for similar wines to estimate inverse supply functions. The application to Australian produced wines sold in different countries demonstrates the importance of a wine's quality and age as attributes in inverse supply functions. Results imply the additional costs of producing better quality and older wines are increased as both quality and age are increased. Estimates also suggest that lower marginal costs for attributes are associated with a smaller producer size and older more established producers. (JEL Classifications: C21, Q11)

Core Pricing in Combinatorial Exchanges with Financially Constrained Buyers: Computational Hardness and Algorithmic Solutions

The computation of market equilibria is a fundamental and practically relevant problem. Although we know the computational complexity and the types of price functions necessary for combinatorial exchanges with quasilinear preferences, the respective literature does not consider financially constrained buyers. We show that computing market outcomes that respect budget constraints but are core stable is a problem in the second level of the polynomial hierarchy. Problems in this complexity class are rare, but ignoring budget constraints can lead to significant efficiency losses and instability. We introduce mixed integer bilevel linear programs (MIBLP) to compute core-stable market outcomes and provide effective column and constraint generation algorithms to solve these problems. Although full core stability quickly becomes intractable, we show that realistic problem sizes can actually be solved if the designer limits attention to deviations of small coalitions. This n-coalition stability is a practical approach to tame the computational complexity of the general problem and at the same time provides a reasonable level of stability for markets in the field where buyers have budget constraints.

Learning in Markets: Greed Leads to Chaos but Following the Price is Right

We study learning dynamics in distributed production economies such as blockchain mining, peer-to-peer file sharing and crowdsourcing. These economies can be modelled as multi-product Cournot competitions or all-pay auctions (Tullock contests) when individual firms have market power, or as Fisher markets with quasi-linear utilities when every firm has negligible influence on market outcomes. In the former case, we provide a formal proof that Gradient Ascent (GA) can be Li-Yorke chaotic for a step size as small as Θ(1/n), where n is the number of firms. In stark contrast, for the Fisher market case, we derive a Proportional Response (PR) protocol that converges to market equilibrium. The positive results on the convergence of the PR dynamics are obtained in full generality, in the sense that they hold for Fisher markets with any quasi-linear utility functions. Conversely, the chaos results for the GA dynamics are established even in the simplest possible setting of two firms and one good, and they hold for a wide range of price functions with different demand elasticities. Our findings suggest that by considering multi-agent interactions from a market rather than a game-theoretic perspective, we can formally derive natural learning protocols which are stable and converge to effective outcomes rather than being chaotic.

Regional determinants of Hungarian wine prices: The role of geographical indications, objective quality and individual reputation

Analysing the determinants of wine prices has always been a field of interest in the wine economics literature. By estimating hedonic price functions, however, most papers generally remain at the country level with regions generally neglected or treated as simple dummy variables. The aim of this paper is to analyse the determinants of wine prices at the regional level by using Latent Variable Path Modelling with Partial Least Squares and Principal Component Analysis on the example of Hungarian wines. This approach is able to capture the regional specialties of wine production and provides a better insight into price determination. Results suggest that intrinsic values play a major but ambiguous role in determining regional wine prices, especially in the case of sugar content. It also becomes apparent that specific Geographical Indications (GIs) play a crucial role in price determination, instead of GI use per se. Moreover, individual brands also have an important role, as Tier1 and Tier2 wineries tend to sell their wines at higher prices and in smaller batch sizes.

Wine price determinants. Is there a homogeneous international standard?

This article presents an international comparison of the main determinants of wine prices in specialist online wine shops. Hedonic price functions were estimated for 9624 wines spread among four datasets from France, Italy, Germany and Australia. To explain price variation data was collected on wine classification, closure type, wine origin, medals or awards, vintage, alcohol content, color, and grape variety. Results from quantile regression models show that the wine vintage is a common price driver in all markets and quantiles. A quite similar effect was found for alcohol content. In terms of color, the implicit prices for red and white wines are also structurally different between countries, particularly in origin, blend, closure, awards and age. Thus, the markets should be assumed as heterogeneous, and the extrapolation of the results from one market to another may lead to erroneous management decisions.

Equilibrium computation in resource allocation games

We study the equilibrium computation problem for two classical resource allocation games: atomic splittable congestion games and multimarket Cournot oligopolies. For atomic splittable congestion games with singleton strategies and player-specific affine cost functions, we devise the first polynomial time algorithm computing a pure Nash equilibrium. Our algorithm is combinatorial and computes the exact equilibrium assuming rational input. The idea is to compute an equilibrium for an associated integrally-splittable singleton congestion game in which the players can only split their demands in integral multiples of a common packet size. While integral games have been considered in the literature before, no polynomial time algorithm computing an equilibrium was known. Also for this class, we devise the first polynomial time algorithm and use it as a building block for our main algorithm. We then develop a polynomial time computable transformation mapping a multimarket Cournot competition game with firm-specific affine price functions and quadratic costs to an associated atomic splittable congestion game as described above. The transformation preserves equilibria in either game and, thus, leads – via our first algorithm – to a polynomial time algorithm computing Cournot equilibria. Finally, our analysis for integrally-splittable games implies new bounds on the difference between real and integral Cournot equilibria. The bounds can be seen as a generalization of the recent bounds for single market oligopolies obtained by Todd (Math Op Res 41(3):1125–1134 2016, 10.1287/moor.2015.0771).

Dealer

Data-driven machine learning has become ubiquitous. A marketplace for machine learning models connects data owners and model buyers, and can dramatically facilitate data-driven machine learning applications. In this paper, we take a formal data marketplace perspective and propose the first en<u> D </u>-to-end mod <u>e</u> l m <u>a</u> rketp <u>l</u> ace with diff <u>e</u> rential p <u>r</u> ivacy ( Dealer ) towards answering the following questions: How to formulate data owners' compensation functions and model buyers' price functions? How can the broker determine prices for a set of models to maximize the revenue with arbitrage-free guarantee, and train a set of models with maximum Shapley coverage given a manufacturing budget to remain competitive ? For the former, we propose compensation function for each data owner based on Shapley value and privacy sensitivity, and price function for each model buyer based on Shapley coverage sensitivity and noise sensitivity. Both privacy sensitivity and noise sensitivity are measured by the level of differential privacy. For the latter, we formulate two optimization problems for model pricing and model training, and propose efficient dynamic programming algorithms. Experiment results on the real chess dataset and synthetic datasets justify the design of Dealer and verify the efficiency and effectiveness of the proposed algorithms.

An innovative methodological approach for the analysis of the effects of urban interventions on property prices [Un approccio metodologico innovativo per l’analisi degli effetti degli interventi di trasformazione urbana sui valori immobiliari]

The evaluation of the effects that an urban interventiongenerates on the area in which it is realized, and moregenerally on the city-system, plays a central role in thedefinition of its effectiveness, as it measures the effectsthat may derive from its implementation in terms of im-proving the quality level of the natural and built envi-ronment. The present research intends to propose andtest a methodological and operational approach to eva-luate, with quantitative indicators, the effects that anurban redevelopment initiative can have on propertyprices. In particular, the aim of the work concerns thedevelopment of a procedural protocol articulated intoclear and replicable phases, in order to support the ana-lysis of the urban transformation effects. The protocolis applied to four redevelopment projects currently inprogress, located in four municipal trade areas in thecity of Bari (Southern Italy). For each urban area, a sam-ple of two hundred residential properties sold in the pe-riod 2017-2019 has been collected. The implementationof a genetic algorithm has allowed the definition of theeconometric price function, able of identifying the setof variables - intrinsic and extrinsic – which, in each ofthe four intervention areas, contributes to the formationof property prices in the “ante-project” situation. Theanalysis of the effects of urban redevelopment in termsof variation of the urban quality has been carried out.Through an exogenous approach based on the inter-view of experts and community individuals, the valuesof the factors related to the urban quality following theimplementation of the projects have been determinedagain (“post-project” situation). By inserting the new va-lues into the econometric price functions alreadyfound, the market values of the post-project situationhave been estimated. The comparison between ex anteand ex post market values shows the increase in pro-perty prices following the redevelopment initiative andallows to quantify it, confirming the full consistencywith the expected empirical trends.

Price Premiums and Discounts for Australian Sparkling Wines

This paper assesses the price premiums and discounts for nine sparkling wine types or names commonly employed in Australia. Hedonic wine price functions are estimated for 10 years of wine releases to identify the specific price impact of different sparkling wine types or names, after controlling for other wine price determining factors. Results identify that important price premiums occur with the use of blanc de blancs and other less common sparkling reds. An important price discount is estimated for prosecco wines. Sparkling wine type interactions with climatic regional conditions and cellaring potential also point to some interesting results. The estimates may have potentially important implications for the strategic use of wine types and names by producers and for consumers in identifying good valued sparkling wines.

Dynamic Models of Pollution Penalties and Rewards with Time Delays

In cases of nonpoint pollution sources, the regulator can observe the total emission but unable to distinguish between the firms. The regulator then selects an environmental standard. If the total emission level is higher than the standard, then the firms are uniformly punished, and if lower, then uniformly awarded. This environmental regulation is added to n-firm dynamic oligopolies, and the asymptotical behavior of the corresponding dynamic systems is examined. Two particular models are considered with linear and hyperbolic price functions. Without delays, the equilibrium is always (locally) asymptotically stable. It is shown how the stability can be lost if time delays are introduced in the output quantities of the competitors as well as in the firms’ own output levels. Complete stability analysis is presented for the resulting one- and two-delay models including the derivations of stability thresholds, stability switching curves, and directions of the stability switches.