Aceppting Late Bids?

 

Spirit Airlines’ bankruptcy has produced an unusual auction: Google agreed to pay $10 million for a trove of Spirit’s internal corporate data, including roughly 100 million emails and 500 million Microsoft Teams messages, beating a $7.5 million bid from AI firm Mercor. But after the auction closed, AI startup Micro1 offered $12.5 million, a sizable bump from Google’s bid. The bankruptcy court must now decide whether to consider the late offer. At first glance, the answer seems obvious. Spirit’s creditors want as much money as possible, so why not accept the higher bid?

Because changing the rules after an auction can affect bidding before the auction. A well-designed auction gives bidders confidence that deadlines and procedures are credible. If firms expect a losing bidder to get another chance after seeing the winning price, they have an incentive to hold back during the formal auction and wait to top the winner afterward. That can reduce competition and lower expected revenue in future auctions. Bidders on potential auctions for additional Spirit assets may adjust their bids downward. Conversely, rigidly rejecting a substantially higher late offer leaves money on the table today. There is an auction design tension because maximizing the highest observed bid is not necessarily the same as maximizing the seller’s expected revenue. Sometimes committing not to accept a better offer later can induce bidders to make better offers now.

WaPo: Social Security and Medicare going bankrupt

 

How AI Might Affect Demand

Artificial intelligence is often portrayed as a technology that will lower costs by replacing skilled workers, but ophthalmology provides an interesting counterexample. AI can now perform one relatively routine task, screening patients with diabetes for diabetic retinopathy, without requiring an ophthalmologist to examine every patient. In the ACCESS randomized trial, offering an autonomous AI eye exam during a diabetes visit increased screening completion from just 22% with conventional referral to 100%. Among patients receiving an abnormal AI result, 64% subsequently visited an eye-care provider. AI therefore substitutes for some ophthalmologist labor while dramatically increasing the number of patients who make it through the first stage of the eye-care supply chain. (Nature)

AI has lowered the cost of one link in the screening → referral → treatment supply chain. That cost reduction need not appear as lower overall expenses. Instead, it can appear as improved quality that increases demand: screening becomes easier, more patients are screened, and ophthalmologists can concentrate on patients who actually need their expertise. A 2026 Johns Hopkins study of 3,745 adults with diabetes provides evidence of this downstream effect, finding that AI screening in primary-care offices increased presentation to specialist eye care among an at-risk population. A recent meta-analysis of AI diabetic-retinopathy screening likewise concludes that it is generally cost-effective, particularly when it expands screening to underserved populations. (Nature)

The interesting result is that a cost-reducing technology can increase rather than decrease demand for expensive downstream services. Cheaper screening means more screening; more screening discovers more disease; and more detected disease generates referrals and treatment. Total spending on eye care could therefore rise even as the cost of producing a given level of eye health falls. The productivity gain results more in demand augmented by higher quality (shifting demand out) rather than lower price (moving along the demand curve). This may be a useful lesson for thinking about AI throughout the economy. When innovation improves one stage of a supply chain, its benefits need not appear primarily as lower prices or fewer workers. They may instead appear as better matching, greater use of complementary services, and higher-quality final output, in this case, healthier eyes.

Regulatory Limits on Personalized Pricing

The FTC has proposed a new rule governing personalized pricing, the increasingly feasible practice of using information about an individual customer to determine the price they are offered. Importantly, the FTC does not have authority to ban personalized pricing. Instead, sellers would have to disclose when personal information was used to determine a customer's price. The economics is familiar: by estimating each customer's willingness to pay, firms can practice direct price discrimination, charging higher prices to customers willing to pay more and lower prices to those who otherwise might not buy. This allows firms to capture more consumer surplus, but it can also increase sales by bringing low-willingness-to-pay customers into the market.

In some ways, personalized pricing takes us back to the nineteenth century. Before price tags became common, once a customer had selected some items at the general store, the clerk and the patron would start haggling. Different customers could therefore pay different prices for the same item, depending partly on the merchant's assessment of their willingness to pay. The spread of department stores and mass retailing replaced this individualized bargaining with posted, take-it-or-leave-it prices. Digital technology may now be reversing that evolution. Instead of a nineteenth-century shopkeeper sizing up the customer across the counter, an algorithm can use browsing histories, locations, previous purchases, and other data to more precisely size up willingness-to-pay. The FTC's proposal would still allow this twenty-first-century version of an old pricing strategy but would require firms to tell customers when it is happening.

How Low Should Seasonal Discounts Be?

Retailers often mark down seasonal merchandise to clear inventory, but how big should the discount be? An Impact Analytics article describes a clothing retailer that initially discounts winter coats by 20%, observes the resulting sales, and then increases the discount to 35% when inventory remains too high. This is a simple pricing decision in which lowering price sacrifices margin on units that would have sold anyway but generates additional sales. Keep cutting price as long as the marginal benefit from the additional sales exceeds the marginal cost from the lower price.

The pricing calculation can must compare benefits to costs. Selling another coat frees shelf and warehouse space, releases capital tied up in inventory, and avoids being stuck with merchandise that will be worth even less when the season ends. On the other hand, deeper markdowns may damage the brand or teach customers to wait for discounts. Include all the consequences of changing price, including possibly hidden changes in costs. The profit-maximizing markdown isn't the one that clears the shelves; it is the one where the benefit of cutting the price a little further no longer exceeds the cost.

Compensating differentials outlawed in Birmingham

 From MarginalRevolution:

In 2010 an employment tribunal ruled that Birmingham City Council had discriminated against thousands (6,000) of female workers — cooks, cleaners, care assistants, caretakers — who were denied bonuses paid to the mostly male binmen, gardeners, and gravediggers (400). Why were the binmen given bonuses? Well, refuse collection is filthy, heavy, outdoor work and not many people want to be gravediggers.

Birmingham went bankrupt when they had to pay the bonuses to the 6,000 women and they went bankrupt.  Then they eliminated the bonuses paid to the men, who then went on strike.  Then:

The council hired an outside contractor to take over its rubbish collection and it now pays roughly triple its pre-strike outsourcing bill..