Category: Economics

The Davis-Bacon Act requires that workers on federally funded construction projects be paid at least the “prevailing wage” for their trade in the local area.

Mike Schmidt, Director of the CHIPS Program Office, has an excellent piece on how Davis-Bacon impacted the CHIPS program. My initial understanding was that it simply required paying construction workers more—an unnecessary transfer from taxpayers to a politically favored group, but not one that would impede efficiency. I was wrong.

Start with the complexity. Davis-Bacon’s prevailing wage isn’t a simple minimum wage: plumbers are not electricians are not fitters, and the required rate varies by locale. The Department of Labor maintains a list of more than 130,000 (!) wage rates to implement it.

That’s complicated enough. But it gets worse. Some firms building fabs used their own employees rather than contractors—and Davis-Bacon applies regardless but it covers only the portion of time an employee spends on “construction” work:

[A]pplying Davis-Bacon to company employees rather than contractors proved to be a big hurdle. Davis-Bacon required tracking every hour each employee spent on covered construction activities — by trade classification, with a different prevailing wage applying to each — and paying a wage differential for that portion of their work as distinct from fab operations work or non-Davis-Bacon construction work. The company also relied heavily on profit-sharing (where a portion of employees’ pay was tied to the firm’s profits) and Davis-Bacon’s guaranteed wage floor was difficult to reconcile with a pay structure that was inherently variable. Moreover, Davis-Bacon has a statutory requirement to pay wages weekly, meaning the company would need to change its payroll systems for a portion of the pay for a portion of its workforce.

Thus, DB required that two salaried employee with equal salaries and profit-sharing plans be paid differentially depending on whether one of them did “construction” work. This created internal strife.

Davis-Bacon was passed in 1931, when a carpenter was a carpenter. How does it apply to building a semiconductor factory?

The construction tasks involved in building and modernizing semiconductor fabs don’t always map cleanly onto DOL’s Davis-Bacon classifications, so applicants must go through a construction plan line-by-line to determine which rate applies to which activity. In traditional Davis-Bacon contexts this is less burdensome because contractors know the system and have processes in place. But semiconductor construction was a novel application, and all of our applicants — and most of their contractors — were navigating Davis-Bacon for the first time.

For large recipients, the administrative cost of this work was real but manageable relative to project scale: they could hire consultants, procure software systems, and build internal compliance capacity….

Perhaps the biggest fiasco involved timing. The government wanted firms to move quickly and encouraged them to break ground before the Act’s rules were finalized. But when Davis-Bacon was added to the Act it required that the firms pay the prevailing wage *retroactively*:

The financial and operational implications of retroactive application were significant. A leading-edge project might have 10,000–12,000 construction workers on site at peak, with a rotating workforce totaling perhaps 30,000 individuals over the project’s life. Working through 300-plus subcontractors across multiple tiers, retroactive application could require identifying wages paid to 20,000 workers who had already cycled off the project, determining what each worker should have been paid under Davis-Bacon, and paying the difference — resulting in hundreds of millions of dollars in additional cost.

The retroactive pay exposes the law’s true nature. Firms and workers had already struck voluntary agreements; the work was done, the wages paid. No one can pretend this has anything to do with incentives. Workers received a pure windfall (“DB Christmas!”) for one reason only: “construction workers” are a politically favored class. Janitors and scientists got nothing extra.

Moreover, a large fraction of the cost wasn’t the higher wages at all—it was compliance. Firms likely spent as much reworking payroll systems and hunting down thousands of former workers in this Byzantine classification system as they spent on the wage premiums themselves. Every dollar transferred to workers may have cost firms—and ultimately taxpayers—two dollars or more. A very leaky bucket indeed.

If the Trump administration is serious about cutting regulatory costs and reviving industrial competitiveness, Davis-Bacon is an obvious target. It delivers little to workers, plenty to lawyers and consultants, and a bill to taxpayers for both. Rescind it.

The Guardian has an interesting article on prediction markets. There are the usual worries about betting on death, as if insurance markets don’t already exist and about insider trading, which public markets have long dealt with. But there is also interesting material on who decides what happened when resolving bets about events made in language (as opposed to more objectively verified numbers).

On Monday, anonymous user “Harshad” asked in a Discord channel if there was “any chance” that he could still win his bet about whether US forces would enter Iran by the end of April. His money was on “no”.

But Polymarket appeared to be resolving the market to “yes”, after the US conducted an operation to rescue a crew member shot down on a mission over Isfahan over the weekend.

…At the moment, when there is a dispute, markets on Polymarket are settled by an anonymous group of people who hold a crypto token called UMA.

It’s an unusual way to decide what has happened. Some longtime users suggest it opens the platform to corruption. Different individuals hold different amounts of UMA, and therefore have different voting power.

It isn’t known who the largest UMA holders are, or what might affect how they vote. It is entirely possible that the people who finally settle a bet on UMA have large amounts of money staked on it.

There was also this bit about Prediction Hunt (I am an advisor) which is focused on cross-market arbitrage opportunities:

“I love to gamble,” said Joseph Francia.

Now in his early 30s, Francia counted cards in casinos while studying economics at Berkeley, and spent weekends in Reno, Nevada, playing blackjack. He’s not a thrill-seeking “Yolo” (you only live once) gambler, he said: he likes to bet when he has an edge on the house.

At university, he and a friend decided to collect data from a number of offshore sportsbooks, and start placing arbitrage bets: playing on the discrepancies in odds given by different betting sites.

“If the odds on the Lakers are really good on one site, and the odds on the Pacers are really good on another site, you could bet on basically both teams on different sportsbooks and make guaranteed profit,” he said.

That project was a student lark in 2017. But in 2025, he remembered it when he was suddenly laid off from his full-time job, just as prediction markets were taking off.

“I’m a spiritual, religious person,” he said. “The more secular people would say, this opportunity is coincidence. But in my head, I was like, this is a sign of something to some extent. Let me lean into this.”

So Francia started Prediction Hunt, a Discord channel and online community where thousands of people gather to trade tips and ideas for how to make money – and bet smart – on Polymarket. The Guardian spent roughly three weeks in this Discord channel.

There are alerts to track “fade” bets, where you try to follow the smart money: profitable wallets were betting “yes” on the Iranian regime falling by 30 April, for example, while unprofitable wallets were betting “no”.

There are alerts to track potential insiders, so you can copy their bets: one of these appears to have an inside line on interest rate decisions by the US Federal Reserve.

Getting these details right will be important but overall I am pleased that the news now regularly reports prediction market data when reporting stories–this is disciplining news from noise, something I predicted long ago in Entrepreneurial Economics.