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United States v. Apple : competition in America
One of the most followed antitrust cases of recent times--United States v. Apple--reveals a missed truth: what Americans most fear is competition itself. In 2012 the Department of Justice accused Apple and five book publishers of conspiring to fix e-book prices. The evidence overwhelmingly showed an unadorned price-fixing conspiracy that cost consumers hundreds of millions of dollars. Yet before, during, and after the trial millions of Americans sided with the defendants. Pundits on the left and right condemned the government for its decision to sue, decrying Amazon's market share, railing against a new high-tech economy, and rallying to defend beloved authors and publishers. For many, Amazon was the one that should have been put on trial. But why? One fact went unrecognized and unreckoned with: in practice, Americans have long been ambivalent about competition. Chris Sagers, a renowned antitrust expert, meticulously pulls apart the misunderstandings and exaggerations that industries as diverse as mom-and-pop grocers and producers of cast-iron sewer pipes have cited to justify colluding to forestall competition. In each of these cases, antitrust law, a time-honored vehicle to promote competition, is put on the defensive. Herein lies the real insight of United States v. Apple. If we desire competition as a policy, we must make peace with its sometimes rough consequences. As bruising as markets in their ordinary operation often seem, letting market forces play out has almost always benefited the consumer. United States v. Apple shows why supporting cases that protect price competition, even when doing so hurts some of us, is crucial if antitrust law is to protect and maintain markets.-- Provided by publisher
Book
Artificial Intelligence, Algorithmic Pricing, and Collusion
Increasingly, algorithms are supplanting human decision-makers in pricing goods and services. To analyze the possible consequences, we study experimentally the behavior of algorithms powered by Artificial Intelligence (Q-learning) in a workhorse oligopoly model of repeated price competition. We find that the algorithms consistently learn to charge supracompetitive prices, without communicating with one another. The high prices are sustained by collusive strategies with a finite phase of punishment followed by a gradual return to cooperation. This finding is robust to asymmetries in cost or demand, changes in the number of players, and various forms of uncertainty.
Journal Article
Forty Years of Oil Price Fluctuations: Why the Price of Oil May Still Surprise Us
It has been 40 years since the oil crisis of 1973/74. This crisis has been one of the defining economic events of the 1970s and has shaped how many economists think about oil price shocks. In recent years, a large literature on the economic determinants of oil price fluctuations has emerged. Drawing on this literature, we first provide an overview of the causes of all major oil price fluctuations between 1973 and 2014. We then discuss why oil price fluctuations remain difficult to predict, despite economists’ improved understanding of oil markets. Unexpected oil price fluctuations are commonly referred to as oil price shocks. We document that, in practice, consumers, policymakers, financial market participants, and economists may have different oil price expectations, and that, what may be surprising to some, need not be equally surprising to others.
Journal Article
MEASURING THE TRUE HARM FROM PRICE-FIXING TO BOTH DIRECT AND INDIRECT PURCHASERS
Legal actions by direct and indirect purchasers to recover damages from price-fixing, common in the United States for years, are now appearing in a number of other countries. Traditional measures of damages are flawed as measures of the true harm suffered and will often significantly understate that true harm. This paper provides measures of the degree of understatement of the true harm when traditional approaches are used and shows how the size of the error depends on the degree of competitiveness of downstream markets. The paper also provides measures of distribution of the true harm between direct and indirect purchasers.
Journal Article
A Dynamic and Multidimensional Framework to Reveal and Interpret Marginal Values in Cascade Reservoir Scheduling Under Competing Demands
Global climate change and growing water demand exacerbate the imbalances in reservoir resource allocation, necessitating advanced frameworks that move beyond static valuation methods. Traditional valuation methods, constrained by static or homogenized assumptions, fail to capture the spatiotemporal heterogeneity and dynamic trade‐offs inherent in cascade reservoir operations. To address this gap, this study develops a novel marginal value framework based on shadow pricing to assess the marginal values of multidimensional resources—water, storage capacity, and turbine capacity—in weekly hydropower scheduling. The key methodological contribution is overcoming the duality gap challenge in mixed‐integer linear programming (MILP) models by proposing two practical methods: Method‐I fixes integer variables to derive dual multipliers via linear programming, while Method‐II computes shadow prices through perturbation analysis. Validated on 26 cascaded reservoirs in Yunnan, China, both methods yield consistent results, with Method‐I demonstrating superior computational efficiency. Key findings reveal that: (a) irrigation water values in dry seasons and upstream regions exceed wet seasons and downstream by 1.11–7.34 times; (b) Nuozhadu Reservoir's storage capacity shadow price peaks in week 44, signaling flood control‐power generation trade‐offs; (c) Wunonglong and Dachaoshan exhibit the highest marginal turbine capacity values for spillage reduction; and (d) reserve capacity costs surge by 32%–45% in weeks 36–37. This work bridges the fields of resource economics and hydraulic engineering, providing actionable insights for dynamic water pricing, infrastructure investment prioritization, and seasonal ancillary service markets.
Journal Article
Optimizing Building Energy Systems Using Generalized Disjunctive Programming: Improving Model Precision and Decision‐Making Efficiency
This paper examines the optimization of building energy systems (BESs) through the lens of generalized disjunctive programming (GDP), a methodology that streamlines the modeling of intricate logical structures and discrete decisions. The study concentrates on three pivotal aspects of BES optimization: the imposition of minimum part‐load constraints, the selection of discrete equipment sizes and pricing, and the integration of subsidy policies. These elements are of critical importance due to the operational limitations of real‐world technologies, the necessity for precise equipment investment strategies, and the significant influence of policy incentives on system design. The results of comprehensive case studies demonstrate the significant impact of these constraints on decision‐making and system performance. Specifically, part‐load constraints result in a shift in the operational priorities of equipment, with an increased reliance on energy storage systems, particularly during periods of low demand. Additionally, pricing models have a significant impact on equipment selection, while subsidy integration has the effect of reducing overall costs and encouraging the adoption of energy‐efficient technologies, such as heat pumps. However, the adoption of GDP introduces computational challenges, particularly due to the complex disjunctive constraints applied across multiple time steps, which require careful consideration in BES optimization.
Journal Article
Price Rigidity: Microeconomic Evidence and Macroeconomic Implications
We review recent evidence on price rigidity from the macroeconomics literature and discuss how this evidence is used to inform macroeconomic modeling. Sluggish price adjustment is a leading explanation for the large effects of demand shocks on output and, in particular, the effects of monetary policy on output. A recent influx of data on individual prices has greatly deepened macroeconomists’ understanding of individual price dynamics. However, the analysis of these new data raises a host of new empirical issues that have not traditionally been confronted by parsimonious macroeconomic models of price setting. Simple statistics such as the frequency of price change may be misleading guides to the flexibility of the aggregate price level in a setting in which temporary sales, product churning, cross-sectional heterogeneity, and large idiosyncratic price movements play an important role. We discuss empirical evidence on these and other important features of micro price adjustment and ask how they affect the sluggishness of aggregate price adjustment and the economy’s response to demand shocks.
Journal Article
From Value Pricing to Pricing Value
This book will reveal how to understand, implement and master the pricing value methodology.Understanding and applying the concept of \"pricing value\" is critical for today's accountants-especially for self-employed accountants, CPAs, and small firms who primarily work with small business clients.This book will reveal how to understand, implement and master the pricing value methodology. It will explain the solid academic research behind this approach, discuss how to avoid common pitfalls, and demonstrate step-by-step how to implement the methodology in a practical and persuasive way.
eBook
China's Economy Is Facing A New Threat, in Economist Video
China’s economy is struggling with involution,where companies slash prices to gain market share, causing profits to collapse and wages to stagnate. Oversupply in high-tech sectors like EVs and solar panels has worsened the problem, and while boosting demand is the best solution, Xi Jinping’s focus on manufacturing makes a fix challenging.
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