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Full paper: tinyurl.com/k35hne5h This is joint with Ignacia Mercadal. #EconSky

tinyurl.com

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Because utilities pass their energy costs through to bundled rates almost directly, retail prices went up! Deregulation made electricity cheaper to produce, but that did not make it cheaper to buy. Lower costs need not translate into lower prices when markets are not perfectly competitive!

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That gap widened over time. Early rate caps and long-term contracts held wholesale prices down during the transition, but once they expired, wholesale prices increased. In a perfectly competitive market, falling costs would be expected to push prices down. Instead, we observe the opposite.

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When utilities sold off their plants, they could no longer supply power to themselves at cost. Instead, they had to purchase it on the wholesale market, where prices sit above marginal cost. So even as the cost of making electricity fell, the price utilities paid for it rose.

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We assembled data following electricity from the power plant to the household, and compared states that deregulated with similar states that did not.

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In our study (now accepted at JPE Micro), we looked more comprehensively at wholesale markets. We found that generation did become cheaper, but utilities ended up paying more for energy. As a result, consumers didn’t see lower prices.

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Alex MacKay @amackay.bsky.social · 27/04/2026

Does deregulation reduce prices? In the US electricity sector, studies had shown that deregulated markets led to more efficient production than the traditional model. But there was not much evidence about the impact to consumers.

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Predictable: by the industry investors work in, local economic conditions, and the recent performance of investors’ employers. Heterogeneity in beliefs is costly for investors, reducing risk adjusted returns by about 0.7 percent per year. Investors may be insulated by limited plan menus.

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Alex MacKay @amackay.bsky.social · 27/04/2026

We extend demand estimation techniques to portfolio choice using a new identification strategy. This allows us to recover varying expectations across investors for different assets. Our estimates indicate that investors vary widely in their expectations, and that expectations are predictable.

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Alex MacKay @amackay.bsky.social · 27/04/2026

Some people are optimistic about stock returns, some are pessimistic. And, when presented with a similar menu of options, investors will make very different choices. In this paper, we try to understand why, using the actual decisions that investors make in their retirement plans.

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Alex MacKay @amackay.bsky.social · 07/04/2026

Congratulations to my colleague @jmcolmer.bsky.social and co-authors for being recognized with an AEJ Best Paper award for their paper "Do Carbon Offsets Offset Carbon?" in AEJ: Applied! Paper is here: www.aeaweb.org/articles?id=...

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Reposted by Alex MacKay

Alex MacKay @amackay.bsky.social · 28/07/2025

New NBER WP on algorithmic "coercion." Unlike collusion, coercion can be achieved by a single firm's pricing algorithm. It can be worse for consumers than collusion. @nber.org

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Alex MacKay @amackay.bsky.social · 10/12/2025

Several observations related to pricing, current technology, and consumer welfare, in this New York Times article.

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Reposted by Alex MacKay

Ben Casselman @bencasselman.bsky.social · 09/12/2025

Same product. Same store. Same time. But on Instacart, different customers may see different prices. My story on a fascinating new experiment from @groundwork.bsky.social & @consumerreports.org and how the idea of a single price is breaking down in the digital age: www.nytimes.com/2025/12/09/b...

nytimes.com

Same Product, Same Store, but on Instacart, Prices Might Differ

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Reposted by Alex MacKay

NBER @nber.org · 21/08/2025

New evidence from a field experiment finds that Amazon brands generate positive benefits to consumers, despite having similar substitutes and receiving priority in search rankings, from Chiara Farronato, Andrey Fradkin, and @amackay.bsky.social www.nber.org/papers/w34135

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The paper, joint with Chiara Farronato and @andreyfradkin.bsky.social , is now available as an NBER working paper. @nber.org link: nber.org/papers/w34135 @ssrn.bsky.social link: ssrn.com/abstract=538... #EconSky

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Our findings suggest that policymakers and regulators should exercise caution when the assessing the role of vertically integrated products and services on platforms. Even corrections for self-preferencing can reduce consumer welfare, as we show in the paper.

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A big reason why is that tastes vary, and some consumers have a preference for Amazon products. Amazon brands add about 5% to consumer surplus in the short run. This effect varies a lot across categories. We found the largest benefits in acid reducers and batteries.

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We found: 1. Consumers select similar products when Amazon brands are not available 2. No evidence for changes in search behavior 3. No evidence for shifts to other retailers Despite these findings, we still found that consumers valued Amazon brands!

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We also used structural modeling to quantify the benefits to consumers of Amazon products and simulate the potential price effects of having low-cost Amazon brands.

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Using a custom browser extension we designed, we hid Amazon brands from a random subset of shoppers. We then looked at: - What products were selected in their absence - If consumer search behavior changed - If consumers went to other retail websites more often

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Using a field experiment, we studied how consumers valued Amazon brands and the extent of self-preferencing by Amazon. We found that Amazon brands bring benefits to consumers, even though they receive a modest prioritization in search rankings.

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Markups and Cost Pass-through Along the Supply Chain

There is more in the paper, including how dynamics in bargaining translate to dynamic in pass-through. @nberpubs link here: nber.org/papers/w34110 Also available on SSRN: dx.doi.org/10.2139/ssrn... Joint with Santiago Alvarez-Blaser, Alberto Cavallo, and Paolo Mengano #econsky

nber.org

Founded in 1920, the NBER is a private, non-profit, non-partisan organization dedicated to conducting economic research and to disseminating research findings among academics, public policy makers, an...

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The model helps explain the observed patterns. It allows us to interpret manufacturer-retailer profit shifts as changes in bargaining leverage. We evaluate the drivers of this leverage. The manufacturer gets a higher split with (e.g.) lower costs and greater market penetration.

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Given the heterogeneity in product-level markups and the stability over time, we model pricing as occurring in two stages: 1. The manufacturer proposes downstream retail prices. 2. The manufacturer and retailers bargain over the wholesale price (and/or lump sum transfers).

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We document supply chain markups in the US, the UK, Canada, and Mexico. We find several similarities, though the split of markups between manufacturers and retailers varies across countries.

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But: manufacturer and retailer markups move up and down quite a bit. Retail markups fell at the end of 2020 and increased starting in 2022. Manufacturer markups did the opposite. The movements offset each other, leading to stable total markups.

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In the US, total product markups are around 0.67 and relatively flat from July 2018 through June 2023. Retail prices increase starting in 2022, coinciding with general inflation. But there is no increase in total markups, i.e., no evidence for "greedflation."

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NBER @nber.org · 31/07/2025

With unique data from a large global manufacturer, we were able to study how product markups vary along the supply chain and over time. One thing that surprised us: though manufacturer and retail markups bounce up and down, total (manufacturer + retail) markups are stable.

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Reposted by Alex MacKay

Pricing algorithms that provide a speed advantage and commitment can be worse for consumers than full collusion, from Zach Y. Brown and Alexander MacKay www.nber.org/papers/w34070

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Alex MacKay @amackay.bsky.social · 28/07/2025

New NBER WP on algorithmic "coercion." Unlike collusion, coercion can be achieved by a single firm's pricing algorithm. It can be worse for consumers than collusion. @nber.org

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Alex MacKay @amackay.bsky.social · 24/07/2025

Rivals' prices, and the ability to monitor them, play a special role in pricing algorithms. Zach Brown and I discuss the research and the implications in the latest CPI Antitrust Chronicle on Surveillance Pricing. Read the article here: tinyurl.com/2pj93aat

tinyurl.com

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Link here: alexandermackay.org/files/Algori... This is joint work with Zach Brown. Comments welcome. I'll wrap up this thread with a question: What are some examples where you think pricing speed matters?

alexandermackay.org

This has implications for the pricing technology that platforms make available to sellers on the platform. Platforms that prioritize profits might allow some sellers faster pricing algorithms, as this softens competition without explicit coordination.

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Coercion can happen even with naive rivals that use simple learning rules. An algorithm that is linear in the rival’s price can lead the rival to set prices well above the competitive level - even when the rival isn’t aware of the algorithm.

In a few ways, coercive equilibrium is more robust than traditional collusion. It doesn't require all firms to be forward-looking or understand dynamic strategies. This suggests a broad scope for high-speed algorithms to strategically raise prices in practice.

The intuition: faster pricing provides the algorithmic firm with a “stick” to punish rivals, while commitment across periods enables the algorithmic firm to lead with a high price: the “carrot.”

The coercive equilibrium can be worse for consumers than the collusive or multi-product monopoly outcome. This is because the algorithmic firm can push a rival’s price above the joint profit maximizing level.

In our model, this firm uses a pricing algorithm that enables faster pricing and multi-period commitment. This yields a "coercive equilibrium" where the algorithmic firm maximizes profits subject to its rival's incentive compatibility constraint.

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