Happy Friday! Here’s what caught our attention this week:

1. Industrial Policy for Housing Policy is a new blueprint for tackling the housing crisis from Arpit Gupta and Steve Teles. One bit I particularly liked was the five-part diagnosis of why factory-built housing has failed to really take off in a sustainable way: (1) housing’s boom-and-bust cycle makes it hard for large investments into factory production to survive lean times, especially because (2) the government has not traditionally moved in to smooth fluctuations; (3) a patchwork of different regulations makes it hard for learning by doing to get as much traction, (4) it’s expensive to transport housing components away from factories, and (5) it’s hard to assemble lots of little (separate) parcels into a contiguous area that can support dense new buildings. The diagnosis suggests several possible fixes and Gupta and Teles have a bunch of other ideas about how to overcome these barriers. — Matt Clancy

2. Why America is so much better at immigration than Europe is a fascinating new piece by Kelsey Piper and Alexander Kustov (who also writes the great Popular by Design substack on immigration). Piper and Kustov point to a number of factors that have led to better outcomes and more public support for immigration in the United States (I know, right?), not least of which is America’s flexible labor market. When immigrants can get to work, they are more likely to integrate, impose less of a fiscal burden, and less likely to engage in crime. That, in turn, leads to more public support for immigration. Indeed, Piper and Kustov argue that the turn in public opinion against immigration in the USA coincides with shifts that made it harder for immigrants in the US to work! The labor market is only one part of the argument Piper and Kustov make, but it’s highly relevant to our thinking about policy work we could support in Europe. — Matt Clancy

3. We at AGF don’t work as directly on AI issues as many of our peers at Coefficient Giving, but you can’t really think about the medium to long-run state of the US and world economies without thinking seriously about AI. I found a big new paper from the Forecasting Research Institute (summary thread here) asking AI experts, economists, “superforecasters,” and the general public to predict AI’s effects on the economy by 2030 and 2050 very helpful in calibrating my own views. Perhaps the most surprising thing to me is that the four groups weren’t terribly far apart from each other. In a world of rapid AI progress, economists expect 3.5 percent GDP growth in 2050; AI experts expect 5.3 percent. This pales next to forecasts of double-digit annual economic growth caused by transformative AI that more optimistic commentators have made. For more, see Matt, Alex, and my follow-up posts on the survey. — Dylan Matthews

4. This week, there were two very different pieces on reforming how we fund science. In City Journal, Michael Gibson (former VP of Grants at the Thiel Foundation) argues that Jim O’Neill’s nomination as NSF director is a chance to shake up the agency’s approach to talent identification and grant design. His specific proposals will be familiar to anyone who follows metascience, and include partial lotteries, scout programs, open access, and indirect cost caps. Meanwhile, in PNAS, Harvey Fineberg (former president of the Gordon and Betty Moore Foundation) echoes the need to reinvigorate the U.S. scientific ecosystem, but presents a more macro vision for getting there: sustained increases in federal science funding, networked innovation clusters, state-level funding programs, and a focus on domestic and global talent. Notably, both emphasize the need for increased support of younger scientists, but otherwise their priorities diverge. — Jordan Dworkin

5. However you want to reform science funding, structural shake-ups require that there are functioning agencies to reform; to keep tabs on how things are going on that front, Grant Witness is now tracking NIH and NSF funding curves in real time. — Jordan Dworkin

6. It’s a big week for electricity price data - the Lawrence Berkeley National Laboratory updated its Retail Electricity Price Trends and Drivers with 2025 data and new analysis, and a Heatmap/MIT/CleanEcon collaboration launched an Electricity Price Hub with electricity bill and price data down to the utility level. Both illustrate how fragmented price patterns continue to resist a coherent story: the most dramatic price increases continue to be geography-specific, like outsize generation costs where PJM load growth has hit supply constraints. LBNL’s update does flag a few trends that will likely continue to shape prices going forward: investor-owned utilities requested their largest revenue increases since the 1980s in 2025 (and regulatory approval rates for such increases have gone up significantly since 2020, particularly in New England and the Southeast); and equipment prices increases for transmission and distribution inputs continue to far outpace inflation. — Willow Latham-Proenca

7. Research in action: Why permitting reform matters. Evan Soltas translated his recent LA permitted reform paper, that we featured a few weeks ago, into a localized estimate of the value of permitting reform in NYC. Each year of permitting time saved in NY is equivalent to an 8% drop in construction costs. With state SEQRA reform and local NYC streamlining, 2.5 years of promised permitting time savings could save quite a bit. Chris Elmendorf further notes the Governor & State Senate would need to prioritize SEQRA in budget negotiations with a still-recalcitrant Assembly to get clean reform over the finish line — Alex Armlovich

8. We might finally get a vaccine against Lyme disease! Maybe. Hopefully? Unfortunately, the confidence intervals in the phase three trial were really wide: after four doses, its efficacy was estimated at 73.2%, with a 95% confidence interval of 15.8% to 93.2%. (The trial’s primary endpoint was to surpass a lower bound of 20% efficacy, which it failed to do.) That huge uncertainty is probably because Lyme disease is so rare that you’d need a huge sample size to reach a precise estimate with a standard trial design. This trial had over 12,000 participants, but it seems even that wasn’t enough for more precision. But also… we actually had a Lyme vaccine in the ‘90s! And then it was withdrawn after a lack of public demand for it. (Not because people don’t care about Lyme disease, but because of fears that the vaccine caused arthritis, even though no connection was found by FDA analysis and later research…) If that sounds incredibly frustrating, that’s probably because it is. Maybe we’ll have better luck this time. — Saloni Dattani

We also wanted to share a few updates from our team and grantees:

• Our grantee Witold Więcek co-authored a perspective in JAMA on the FDA’s new Bayesian statistics guidance for clinical trials. The piece argues that Bayesian methods can improve both trial design and regulatory decisions by formally incorporating prior information from related studies.

• DC’s City Council passed single stair reform on first reading, which our grantee Greater Greater Washington has long supported. If it clears second reading and the mayor’s signature, the reform will unlock small multifamily-zoned sites where lot assembly for double-loaded stairs is difficult or impossible - particularly valuable given DC’s history of allowing creative exterior stair workarounds.

• The Institute for Progress released a new report on Department of Labor wage proposals that affect H-1B visa eligibility, analyzing how different wage ranking methodologies would impact skilled immigration flows.

• Alex Armlovich published Zoned Capacity Is Like an Artificial Organ Donor Registry, exploring how cities create the illusion of housing supply through theoretical zoned capacity that often fails to translates into actual construction.

• And if you’re looking for abundance-related job opportunities, the Abundance Network maintains a job board with openings across housing, energy, infrastructure, and innovation policy organizations.

Zoned capacity is the gap between what land use regulators allow to be built on a parcel and what’s already been built. The volume of unbuilt zoned capacity gets treated, in popular conversation, as a bank account with a fungible dollar balance that planners can exchange for housing, unit by unit without diminishing returns. The discourse engages as if the next dollar in the balance is just like the last dollar. It doesn’t work like that!

Instead, a better metaphor for zoned capacity is an artificial, regulatory oil deposit.

Unlike bank accounts, oil is not perfectly fungible. Oil deposits vary in grade and extraction difficulty. As the easy deposits deplete, producers move to harder, more expensive sites1. Just so, zoned capacity varies enormously across parcels in quality, quantity, and location. And after years of building out the best sites allowed by regulators in a city, the zoned capacity left over is not like the zoned capacity you started with: it’s the leftover sites that are more expensive or less desirable to work with. All else equal, permitting rates in a city should slowly decline and rents should face gradually increasing pressure as a city’s cheapest and easiest development sites get built out and only the more expensive, and/or less desirable, parcels remain.

With oil, the size of a deposit is set by geology. With zoned capacity, the volume that can be “mined” by developers is set by regulators - importantly, this means regulators can make more of it. Eliminating or substantially relaxing this artificial zoning cap to legalize an abundant supply of housing is the entire premise of the YIMBY movement.

However, the US Geological Survey distinguishes between contingent oil “resources”—oil that physically exists in the ground—and “proved reserves”—the amount that is economically and technically recoverable. “Proved reserves” are thus a hybrid engineering & economic concept: At current prices with current technology, how much can you actually produce? If prices spike or technology improves, proved reserves increase even though nothing physical has changed underground. Urban planning has analogues: what the oilmen call technically recoverable “resources” and economically feasible “proved reserves” for oil, urban planners call “zoned capacity” and economically buildable “soft sites” for new housing.

Zoned capacity, as a raw “resource”, is an invisible cap over every zoned plot in a city. But the usable “reserve” citywide is just a small fraction: it’s the capacity that has a realistic probability of becoming housing in any reasonable near term forecast period. It depends on the size and shape of an individual lot, the condition and value of what’s already built, interest rates, construction costs, the land-value-to-structure-value ratio, neighborhood demand expectations, and more. Change any of those and you could change the usable reserve.

Once one understands this difference between theoretical “zoned capacity” and economically feasible “soft site” estimates, one can begin to understand why California’s state-mandated zoned capacity targets have struggled for so long; why Washington, DC’s profoundly confused land use planning process has produced such a terrible first Comprehensive Plan draft; and why NYC’s “City of Yes” reform for 82,000 new homes over 15 years sounded so small compared to other reforms that purport to legalize huge numbers of units.2

Why do finance-focused and regulation-focused commentators differ on what “drives” permitting?

For any given level of zoned capacity, financial conditions and the macroeconomy dominate timeseries variation in permitting within a city and in the national headline average. “Housing is the business cycle”.3 If you’re looking at a time series of permits in a single city or nationwide, the business cycle is doing much of the timeseries work.4

But financial conditions do not explain massive and persistent cross-sectional variation across cities at any given point in time. At any macroeconomic moment—boom or bust—some cities permit vastly more housing per capita, and as a percentage of the existing housing stock, than others. That variation between cities is overwhelmingly the result of binding growth control regulations within the commutable zone of a labor market. Cities and neighborhoods with reserves that widely exceed existing built densities by at least double the existing floor area “by-right” permit far more than cities with thin or fake capacity, permitted on a discretionary project by project basis, regardless of macro conditions.5

The housing crisis is not a crisis of theoretical zoned capacity on paper. There is “enough” theoretical capacity on paper in some expensive cities like DC—but this is about as relevant as the observation that there is theoretically enough deuterium in the ocean to solve all of humanity’s energy needs. The crisis is one of usable, proved reserves of soft sites: large increments of development rights on ripe sites, in high-demand locations, where the economics of teardown and redevelopment actually pencil. As we will discuss, DC’s comprehensive plan just demonstrated in real time how the failure to understand this distinction leads directly to policy complacency. If you want housing this decade, you need proved reserves–buildable soft sites!–not theoretical resources stranded far from any realistic near term use.

Why this all matters: Thinly spread zoned capacity is mostly stranded capacity

Proponents of incremental development often note, accurately, that NYC is a “3-story city”. If one could instantly and frictionlessly turn every 3-story building into a 5-story building, the housing shortage would be over for now. The problem is it does not make economic sense to tear down most 3-story multifamily buildings only to build a 5-story building. The average successful redevelopment in NYC is 3.4 times larger in floor area than the building that came before it. Thinly spread zoned capacity—generally meaning any zoned capacity that doesn’t allow a proposed building to at least double in floor area from its existing use6—is functionally trapped. It is uncommon to tear down a 3-story multifamily building to build a 5-story building, unless the 3-story building already happens to be in end-of-life condition; adds significant floor area beyond the immediately demolished site; or constitutes a rare exception, insofar as even buildings with a 1% annual probability of redevelopment do eventually get redeveloped.7

Vincent Rollet’s groundbreaking job market paper at MIT studied every parcel-level redevelopment in New York City from 2004 to 2022. He found that, on average, new buildings in NYC are 3.4 times larger than the building they replaced. The average torn-down building had a floor area ratio (FAR) of about 1 (i.e., a two-story building covering half the lot). To achieve even a ~15% chance of redevelopment over the nearly 20 year sample period, a parcel needed permission to build at least two extra floor area ratio points’ worth of square footage on top of the existing use. In simplified terms that means unless a typical property owner could add roughly 3 to 4 stories beyond the existing typical one-or-two-story building torn down in Vincent’s dataset, there was not a significant probability of teardown and redevelopment. The near-term production effects of upzoning are almost entirely concentrated on parcels with low existing density and high allowed capacity in high-priced neighborhoods. Upzoning parcels in the cheapest areas yields almost nothing—the reward for adding floorspace is too low to cover the large fixed costs of demolition and reconstruction.

To be sure, America’s oldest big cities are full of squat, unsprinklered, highly flammable wood-framed tenements reaching the end of their design life. Under current zoning, these get gut-rehabs without adding any new homes, but under incrementalist zoning, they’d likely add a story or two once the structure deteriorates enough to merit redevelopment. The incremental development advocates consider this century-long slow growth process a feature, not a bug. Outside the nation’s largest megacities (and the small but extremely high-wage “superstar cities” like San Francisco, Boston, & Seattle) slow growth might be enough.8 But in the cities with the largest housing backlogs, a human lifetime is far too long to wait for housing abundance.

Another key thing about the incrementalist “three story city becoming a five story city” vision that doesn’t get enough attention: the sheer physical disruption of redeveloping a city’s entire landmass within a single business cycle, by a small increment on each lot, would be staggering. As former Director of Planning in DC, Harriet Tregoning, astutely observed amid circa-2013 debates over lifting DC’s height cap: Adequate housing production at modest Paris heights would entail rapid Paris-scaled demolition and physical transformation of wide swathes of DC.

Even stipulating for argument’s sake that it would be economically feasible to quickly tear down and rebuild three-story apartment buildings to grow them by 1 or 2 stories each without invoking eminent domain, it would be a disruptive and politically troubled undertaking.9 We are almost lucky that regulatory approaches intending voluntary, wide-area lowrise intensification have low redevelopment probabilities on any one parcel, because if development were feasible widely, we’d have a political maelstrom of universal construction noise and tenant displacement on every lot in every block of the city.10

To unlock feasible housing supply in the highest-demand metropolitan areas now with minimum disruption, one must lift caps on density near downtowns and near rail transit altogether, allowing the important housing typology known as “Missing Massive”.11 Along frequent bus corridors and other high-resource locations, rezone to allow at least the building code’s “high-rise” threshold—usually 75 feet in most US states and cities—at which construction costs begin to spike as cheap light wood framing is no longer allowed. Everywhere else can be zoned for the incrementalist “gentle density” that will glacially produce new housing over the next century as existing buildings wear out and need major repair or replacement. Having said all this as a necessary corrective to pure regulatory incrementalists, it is nonetheless true that, in the highest-land-value metro areas with the biggest housing production backlogs, there’s no tradeoff or tension between legalizing rapid Missing Massive near transit and downtowns and incrementalist gentle density elsewhere. In the half dozen coastal superstar metros targeted by Coefficient Giving’s housing reform portfolio to date, both strategies will be required if we are to address the accumulated scale of housing underproduction through voluntary bottom-up action alone, without resorting to the coercive state-led eminent domain and urban renewal tools of Robert Moses or Baron Haussmann.

Zoned capacity confusion is doing real damage right now

This past week, DC’s Office of Planning released its draft Future Land Use Map (FLUM) for DC 2050, the city’s first comprehensive plan rewrite in nearly 20 years. Their reasoning was laid out plainly: DC has about 325,000 households today. The current comprehensive plan allows for 445,000. They estimate the city needs capacity for 460,000 by 2050 to keep prices from rising faster than inflation.12 So the proposed plan adds capacity for just 15,000 more households—a rounding error on a quarter-century planning horizon—because they figure it’s already almost there.

This is a huge disappointment. The Office of Planning’s 445,000 figure is a theoretical resource, not a proved reserve of buildable sites…worse still, it comprises the leftover “rump” of the last comprehensive plan, whose most feasible sites have disproportionately already been built out. It counts every parcel in the District where zoning technically permits more than what currently exists—regardless of whether the existing building is a rent-stabilized apartment complex that will never be torn down, a recently renovated rowhouse whose owner has zero interest in selling, or a parcel where the gap between allowed and existing density is so small that no builder would ever bother. Even California has recently begun forcing cities to stop the policy of simply “rolling over” planned sites that were not ultimately rezoned or built from one planning cycle to the next in full knowledge that these sites will not yield any housing production without further regulatory relief. DC’s planners shamelessly rolled forward all existing planned capacity without interrogation.

Nobody at DC’s Office of Planning (OP) did—or at least nobody published—a soft site analysis asking: of that 120,000-unit gap between 325,000 existing households and 445,000 theoretical capacity, how much sits on parcels where the economics of teardown and redevelopment actually pencil?

If the Office of Planning ran that analysis, the answer would almost certainly be that a large share of their 120,000-unit theoretical surplus is vaporware–or, returning to our petroleum engineering metaphor, “contingent resources” that are not realistically deliverable anytime soon, anywhere close to current prices. That means the actual shortfall between usable reserves and the 460,000 target is not 15,000 units! And because the city is planning on the assumption that it has nearly enough capacity already, it is not poised to create the deep, transit-adjacent and high-opportunity upzonings that would actually generate the housing equivalent of feasible “proved reserves”.

The confusion over zoned capacity becomes self-reinforcing: claim adequate capacity, do nothing, watch housing costs rise, blame everything except the zoning.

The zoned capacity problem is not limited to DC. California has the same problem on a massive scale. California’s Regional Housing Needs Allocation (RHNA) housing element law advises jurisdictions to zone for 15%–30% more capacity than their housing target, as a buffer. Since the housing element law’s inception in 1969, California’s cities have met RHNA nominal zoned-capacity mandates by upzoning land that is literally underwater, designating City Hall as a place likely to be demolished for new housing to be built, and counting narrow strips of abandoned railroad tracks and polluted brownfields.13 Though DC may not actively be gaming their zoned capacity estimates, their shockingly unambitious Office of Planning proposal’s 30% zoned capacity “buffer” above the existing number of built homes is still very Californian: A buffer of that size has historically been more than sufficient to give a city a passing grade under California’s benighted RHNA framework.

California’s preemption of local land use law yields a principal-agent battle between state government principals and local government agents, which entails a hostile intergovernmental dynamic in which cities try to game their way out of state law. But even outside California’s RHNA gaming dynamics, zoned capacity does not (and cannot ever!) have any fixed ratio or simple linear relationship with realistic development potential. Yes, the relationship gets directionally worse when a city is actively gaming the numbers to break state law, but even innocent attempts to measure zoned capacity—like DC’s—can’t distinguish between thinly spread development rights on already-built land versus thickly mapped, fully usable development rights on ripe underbuilt parcels. The ratio of theoretical zoned capacity to economically buildable soft sites must inherently (all else equal) decline over time as a result of increasing marginal cost or decreasing marginal desirability of the remaining sites left over after the good ones have been used up.

Environmental Review in NYC: The Rare Case of Tallying Soft Sites, not Zoned Capacity

New York City’s Department of City Planning has a forecasting framework for environmental review of rezonings called the “Reasonable Worst Case Development Scenario.” (The name itself reflects the anti-housing disposition of the people who devised NYC’s regulations. The “worst case” they’re worried about is the scenario where the most housing gets built.)

Rather than forecasting development from some concocted flat percentage of maximum theoretical zoned capacity, DCP does a lot-level analysis of “soft sites”: parcels that have a plausible likelihood of actually being redeveloped within a 10- to 15-year forecast period. To be a soft site, a lot generally has to be at least 50% underbuilt relative to its maximum allowable floor area ratio, and at least 5,000 square feet, among other things.14

Among the identified parcels, DCP sorts them into “projected” sites (likely to develop) and “potential” sites (could develop, probably won’t). Only the projected sites count toward the density analysis that determines whether a rezoning triggers mitigation requirements. This is NYC’s most rigorous attempt to convert raw zoned capacity into a realistic forecast of how many homes will actually get built—and even this method routinely misses the mark in both directions, historically underestimating production in the strongest markets and overestimating the weakest markets.

Housing watchers may notice that NYC’s recent citywide upzoning, called City of Yes, is forecast to unlock only 82,000 new homes as a headline result. That sounds small compared to other reported actions in smaller cities like San Francisco’s “Family Zoning Plan”–coincidentally also headlined with 82,000 new homes–or Columbus, Ohio’s “Zone In” upzoning at 88,000 units. The answer is partly that, yes, NYC’s City of Yes is much weaker than a true response to the housing crisis needs to be. But the bigger answer is that San Francisco and Columbus are reporting out theoretical zoned capacity figures, not feasible soft site estimates like NYC. San Francisco’s plan is likely to produce less than 20,000 soft site units over 20 years; Columbus does not have a soft site feasibility estimate.

The upshot: In evaluating housing production outlooks for particular reform scenarios across jurisdictions, compare apples to apples. New York City does not produce a theoretical zoned capacity estimate for rezonings like City of Yes, but if it did, the number would be enormous. Alternatively, if other cities reported soft sites like NYC instead of on-paper zoned capacity, their topline numbers would be smaller too.

What is to be done?

What planners in places like DC and California both need is to forecast feasible development the way Vincent Rollet’s earlier-described paper does: parcel by parcel, accounting for existing density, structure value, lot size, neighborhood prices, and the fixed costs of demolition. Not “how much does zoning theoretically allow” but “how much is actually plausible to get built, where, and by when.”

There is a growing consensus among housing expert practitioners that RHNA-style housing targets should upgrade from zoned capacity to soft site forecasts. This is helpful not only to start focusing on feasibility instead of symbolic zoning wins, but also to avoid the fear-mongering associated with theoretical zoned capacities in the millions of units.15

The first step is for planners to start thinking in terms of proved reserves rather than contingent resources. Until they do, they will keep producing comprehensive plans that look adequate on paper and fail in practice.

Hope you’ve had a great week! Here’s what caught our attention:

1. The very best biology magazine in the world, Asimov Press, announced they’re winding down (for now). I’ve been an advisor to them since before they started, and I’m very sad to hear the news, even though I’m looking forward to what the team behind it – Niko McCarty and Xander Balwit – are doing next. At Asimov, they published some of the deepest and most thoughtful writing on biology and actually made it feel enchanting – so unlike the textbook descriptions most people encounter at school. It’s the kind of writing I’ve tried to achieve in my own work. They covered topics across the history of technology, biological curiosities, ideas for science policy reform, and visual explainers of how molecular processes work. Here are some of my favourite pieces: Origins of the lab mouse, Making the micropipette, Where’s the synthetic blood?, Before they hatch, Driving Toward Nanopores, and you know what, I’ll even include a piece of my own, Measuring the Black Death. At the end of their farewell post, they leave open the possibility that it might be revived in the future; I really hope it happens one day! — Saloni Dattani

2. How about one more from Asimov before they go: this week Alvin Djajadikerta published “Designing AI for Disruptive Science.” In it, he explores the possibility that (current approaches to) AI-for-science might enhance our ability to predict within current frameworks while simultaneously weakening our capacity to identify and shift into entirely new paradigms. He lays out histories of important paradigm shifts, discusses some interesting papers on technology-based scientific narrowing (arising from both AI and, interestingly, the internet), and closes with a call for more and better study of the mechanisms of science – perhaps using AI agents as a metascientific model organism for better understanding the effects of different institutional and incentive designs (I, for one, would immediately subscribe to a science-focused AI village-style experiment). It’s a thoughtful and thought provoking piece, and well worth the read. — Jordan Dworkin

3. I’ve been revisiting some older papers that might shed light on the benefits of faster clinical trials. Let’s start with a short 2007 paper by Frank Lichtenberg, which looked at the correlations between deaths from various diseases over time, and the share of medical prescriptions for that disease relying on newer medicines. Lichtenberg found that mortality was lower for a given disease when more of its prescriptions relied on newer medicines, and he used this to back out an estimate of the consequences for US health if those new prescriptions had not been available. For example, in 2003, he estimates there would be roughly 1.6 million fewer life-years if drugs invented since 1990 had not been available. With roughly 380 new molecular entities approved between 1990 and 2002, that works out to 4,200 life years lost, on average, per drug: i.e., if a drug invented after 1990 had not been available during 2003. Just one year later, Philipson et al. (2008) looked at the impact of the Prescription Drug User Fee Act of 1992. Philipson and coauthors argue the act sped up drug approvals by 6-7%, and generated a surplus (the gap between what consumers would have been willing to pay and the cost of producing medicine) equal to $14-31 billion. — Matt Clancy

4. In most product markets, transitory supply and demand shocks can spike prices, but as long as there’s open entry to the market allowing elastic supply, prices come back down near marginal supply costs. This is rare in American housing markets because infill supply responses are usually illegal under local growth control laws–but not, per Pew’s new analysis, in Austin, Texas. Austin enjoyed among the highest permitting rates in the country for several years; as in-migration has slowed somewhat, supply finally caught up to demand and rents are back down to pre-pandemic levels in nominal terms and down significantly in real terms and as a share of incomes. Austin’s biggest zoning reforms actually came during the boom, allowing Austin to keep permitting at pre-2019 rates in 2025 despite substantially lower real rents. That said, once rents fall to near construction costs plus a competitive gross margin, development will slow substantially as it has in Minneapolis (where median market rate rents are now at federally subsidized LIHTC levels). — Alex Armlovich

5. More progressives in the housing space are coming out against the Senate’s build-to-rent ban. The growing concern is that the ban could reduce rental housing supply when affordability remains a pressing issue. It’s a tough situation and we hope all stakeholders come to a pro-housing solution with sustainable politics. — Alex Armlovich

6. “You will be hard-pressed to find a true admirer of Excel,” writes David Oks (who I first noticed as one of the Gravel Teens and is now at a16z, truly an ideological evolution for our time). But Oks is, if not an admirer, then surely a believer that Excel and predecessors like VisiCalc and Lotus 1-2-3 are inventions of deep historical importance. The most intriguing part of his argument is the claim that the rise of finance in the 1980s, particularly leveraged buyouts, was driven in large measure by the dawn of the spreadsheet. Before, “analyzing a single company would take weeks”; now it might take a few hours, and your model of that company could be amended in seconds. I’m not fully convinced (is the spreadsheet a driver of financialization or did financialization provide a larger market that spurred better spreadsheets?) but it’s an intriguing case study of the ways in which a particular technology’s diffusion can help reshape the economy, or perhaps solidify a reshaping already occurring. — Dylan Matthews

7. The nation’s largest offshore wind farm to date, the Coastal Virginia Offshore Wind project - which clocks in at 2.6 GW - sent its first commercial power to the grid this week (it joins Rhode Island’s Revolution Wind, which sent its first electrons onto the New England grid earlier this month). In stranger wind news, Interior announced a ~$928 million settlement with TotalEnergies to surrender two Atlantic wind leases. Jake Bittle & Rebecca Egan McCarthy have a pragmatic take on the deal - the payment mostly reimburses lease fees TotalEnergies paid in 2022, and the “redirected” oil and gas investments (Rio Grande LNG in South Texas and conventional production in the Gulf of Mexico) were already committed. Neither offshore lease was under active development or even near to completing federal permitting, and the deal doesn’t preclude future re-auction. Encouragingly, permitting reform negotiations in the Senate have apparently continued undisturbed. — Willow Latham-Proenca

Meanwhile, Jordan Dworkin published Who Will Program-Manage the Program Managers? this week on this blog, exploring how science funding agencies can build capacity to support ambitious, unconventional research programs. If you’re interested in this space, ARIA (the UK’s Advanced Research and Invention Agency) is currently hiring.

Our grantee Greater Greater Washington published their take on DC's draft Future Land Use Map. The plan would add only 15,000 housing units by 2050 and confuses theoretical "zoned capacity" with economically feasible development sites. By carrying over sites from the previous Future Land Use Map, DC's Office of Planning is banking on lower-quality development opportunities left after the best sites have already been built. The silver lining: the draft was bad enough to draw mass public protest and condemnations from both leading mayoral candidates.

The Irish government is seeking volunteers for their Clinical Trials Advisory Council. If you have relevant expertise and interest in shaping European regulatory frameworks, consider applying.

This is a relatively normal week for an ARPA1 program manager. But few people who walk into this role have received any formal preparation for it.

A few weeks ago in our “What we’re reading this week”, we highlighted initiatives from two grantees — Speculative Technologies’ Brains Accelerator, and Renaissance Philanthropy’s Big if True Science (BiTS) Accelerator — that are trying to change that. These programs identify ambitious scientists with ideas for coordinated research programs,2 work with them over a few months to refine their scientific vision and planned approach for program implementation, and then help them find a role with (or funding from) federal agencies, philanthropies, or research incubators that are seeking talent.

So, essentially, programs to improve the quality of program managers’ management.

This seems like a pretty meta thing to fund, even for a program that specializes in metascience. But we can decompose the theory of these efforts’ value into four more tangible claims:

1. There are efficiency gains to be had in how we manage and allocate scientific resources.

2. Coordinated research programs are a high-leverage context for intervening in R&D resource allocation.

3. Graduates of training and accelerator programs are likely to make higher quality management and resource allocation decisions than they3 would have otherwise.

4. Graduates of these programs are reasonably likely to actually end up in charge of substantial, high-leverage R&D resources.

Are there efficiency gains to be had in scientific resource allocation?

If you’re tempted to just say “yes” and move along, stick with me. It’s true that there’s been a lot of conversation about the inefficiencies of our modern scientific ecosystem: a lack of risk-taking, homogeneity in the structure of scientific institutions, biases in what gets published, etc. These dynamics suggest that Things Could Be Better^TM. But they don’t necessarily suggest that changes in management and resource allocation, within existing scientific frameworks and institutions, could make things better.

Let’s look at two papers that provide some evidence that this is likely to be the case.

The first is Azoulay, Graff Zivin, and Manso (2011), which compares scientists funded by the Howard Hughes Medical Institute (HHMI) — which provides long-term, unrestricted support to individual researchers — to comparable NIH-funded scientists. They find that HHMI-funded scientists produce considerably more high-impact work, suggesting the mechanism of resource allocation that is used by funders can meaningfully affect the amount and quality of scientific output, even without changing the total quantity of resources.

The second is a new paper by Bertolotti, Myers, and Tham (2025), which takes a more holistic view, ambitiously attempting to measure the overall misallocation of resources as a function of productivity across the U.S. scientific landscape. They develop a survey-based method for estimating individual researchers’ productivity by using hypothetical salary-for-{resources/time} tradeoffs, and find that the distribution of scientific productivity is extremely skewed: the 90% percentile researcher is roughly 30 times more productive than the 10th percentile. Importantly, they show that current resource allocations don’t track this distribution well. Their counterfactuals suggest that more efficient allocation of existing resources could produce the same increase in scientific output as raising the federal science budget by billions of dollars.

It’s incredibly difficult, however, to predict the impact and potential outcomes of resource allocation from observables. That means that the judgement, taste, and expertise of people making allocation decisions is critical, and any mechanism that helps improve the quality of those decisions can have very large returns.

Are coordinated research programs uniquely high-leverage?

In terms of leverage for improving R&D resource allocation, ARPA-style agencies (and similar coordinated research programs) are a unique opportunity.

Azoulay, Fuchs, Goldstein, and Kearney (2019) lay out the core features of the ARPA model, highlighting in particular the importance of the empowered program manager (a.k.a. program director). PMs identify technology directions, create programs, select performers, assemble and reshape research teams, and actively manage portfolios. As they put it, “hiring talented program staff with a penchant for exploration is pivotal to the success of ARPA‑like programs.” Goldstein and Kearney (2020) provide a view into what this active management looks like in practice. Using confidential ARPA-E program data, they show that program managers routinely modify the terms of projects, reallocate resources, and make go/no-go calls throughout a program’s lifecycle.

This reliance on individual, skilled decision-makers makes these agencies high-tractability targets for improving decision quality. At NIH, allocation decisions are distributed across thousands of reviewers and study sections. At DARPA, roughly 100 program managers control $3-4 billion per year, meaning that improving one PM’s judgement potentially affects tens of millions of dollars in R&D spending. And because that spending is largely discretionary rather than process-driven, training and improved judgement can more easily flow to decisions and outcomes.

So coordinated research programs are great places to intervene in terms of tractability. What about in terms of the impact of the scientific outcomes? Anecdotally, they’re great on this as well! If you’re one of the loyal innovation policy stans following this blog, you can probably rattle off a few of DARPA’s historical wins (the internet! autonomous vehicles! GPS!). But you can do something similar for NSF (also the internet! MRI! gravitational waves! AI!), so how can we actually tell how per-dollar outcomes compare?

Quantitatively, it’s difficult. Getting apples-to-apples comparisons across institutional designs is not straightforward, and much of the excellent research on the topic of ARPAs is historical and qualitative rather than empirical.

Fortunately, in 2018, Anna Goldstein and Venkatesh Narayanamurti published a study comparing patenting and publication outcomes from ARPA-E grantees to those from the DOE’s Office of Energy Efficiency and Renewable Energy (EERE) and Office of Science (OS), which fund “applied research, development, demonstration and deployment activities” and “basic research programs”, respectively. Across a range of outcomes, they find that ARPA-E grants:

• lead to more technological output per dollar than EERE or OS (e.g. number of patents, at least one cited patent);

• lead to more scientific output than EERE, but are roughly on par with OS (e.g. number of publications, at least one highly cited publication);

• and are substantially more likely to produce both technological and scientific output than EERE or OS (e.g. at least one patent and at least one publication).4

A back-of-the-envelope synthesis of the paper’s results across comparison office, performer type, and outcome suggests that ARPA-E generated roughly 3x as much observable paper-and-patent output per dollar as traditional DOE grantmaking.5 Importantly, this includes a selection effect: at the time, ARPA-E’s budget was significantly smaller than either comparator agency, and as a result it could be more selective. Depending on how you frame the counterfactual, this could be a feature or a bug of the comparison, but in any case, a conservative adjustment comparing only within the same budget window of top-performing grants still finds a 1.3x output advantage for ARPA-E.6 With the necessary caveats about papers and patents, these findings are noteworthy in that they suggest that the model itself (even controlling for performer type and selection) is meaningful.

Does training actually help?

Here the evidence is more sparse. We don’t have a great sense of how effective these types of programs will be at improving eventual resource allocation and use.7 But we do have some broader evidence on management training that is consistently positive, and some spillover benefits from programs like these that suggest additional value above and beyond generic training.

The first place to look is the broader management training literature. A classic paper here is Bloom et al. (2013), which randomly provided management consulting to Indian textile firms, and found that it raised productivity by 17% in the first year, with effects persisting and in some cases growing over time. Giorcelli (2019) finds lasting benefits of management training on firm performance in a very different setting (Italian firms sending managers to train with US companies under the Marshall Plan’s Productivity Program), and Giorcelli (2024) finds that a WWII-era MBA-style program had significant benefits for individual performance and career outcomes. Meta-analyses (e.g. Busso, Park, and Irazoque, 2023) find that management training has productivity benefits in the 5-10% range.

A somewhat tangential literature on venture capital and private equity funds looks at the returns to experience rather than training. For example, the “first-fund” penalty in PE/VC seems to be on the order of 10% with respect to performance (Kaplan and Schoar, 2005), and more experienced VCs are better able to identify and invest in unproven talent (Gompers, Kovner, Lerner, and Scharfstein, 2006). To the extent that training can compress some of the learning curve, giving first-time PMs exposure to the tacit knowledge, strategies, and networks that typically only accumulate through experience, these benefits may apply.

The link to our context is admittedly tenuous (textile firms, postwar Italian manufacturers, and VC funds are not ARPA programs), but this literature is helpful for calibrating reasonable effect sizes; if we observed a similar 5-10% improvement in our context, it would be as if graduates’ $50m ARPA programs got an extra $3+ million-worth of scientific outcomes for essentially free.8

And importantly, accelerators don’t just train, they also select and place. PM roles are incredibly unique within the scientific ecosystem, are often difficult to hire for, and are high risk if the wrong person is selected. As a result, the identification and matchmaking that these types of programs provide can be a high-leverage activity in itself. This may be an even more important factor than management training if the literature is to be believed; Burks, Cowgill, Hoffman, and Housman (2015) study referral hiring, and find that referred workers in high-tech roles produce 19% more citation-weighted patents than non-referred workers.

While the state of the evidence on this question is currently middling, there seems to be growing interest in it. The Scientific Labs Management Project, for example, is applying the World Management Survey methodology to scientific labs, aiming to systematically measure the quality of organizational practices as a first step toward identifying where management training could help most. Results aren’t out yet, but the project’s existence signals recognition of scientific management as an under-studied bottleneck. Until then, we think the circumstantial case is relatively strong, and would be excited to see more rigorous research and evaluation.

Will graduates be put in charge?

Early signs point to yes! From the first BRAINS cohort of 16 fellows in 2024, several have already taken steps towards research leadership roles: two are now running Focused Research Organizations (FROs), another is leading a program at a philanthropic foundation, and others have entered hiring pipelines at various ARPA-style agencies. In aggregate, Speculative Technologies reports that first-cohort fellows have collectively secured more than $70 million in committed funding from philanthropists and governments. The program has since expanded to a 2025 AI-focused cohort and a 2026 cohort.

On the BiTS side, the program has scaled rapidly since its late 2024 announcement. Renaissance Philanthropy has now launched several cohorts in direct partnership with government innovation agencies: a UK cohort in partnership with ARIA,9 an EU cohort supported by SPRIND, and a Japan cohort as part of the Cabinet Office’s International Research Program. Several BiTS fellows are now in, or in the pipeline for, PM roles at ARIA and SPRIN-D, and others are in the process of setting up coordinated research programs outside of government.

The long-term vision for these efforts is even more ambitious than the impacts discussed above; ideally, these programs would raise the ambition of the scientific ecosystem, bringing more philanthropic resources into frontier science and inspiring the creation of new ARPA-style initiatives. Ben Reinhardt, CEO of Speculative Technologies, put it well, saying, “Over time, we’re hoping to do for ARPA programs, FROs, and other coordinated research programs what YC and other accelerators did for startups. Starting and working at startups went from an obscure pursuit… to a normalized (if still high-variance) path for people all over the world.” We don’t know yet whether that vision will be realized, but the early trajectory is exciting.

Happy spring! Here’s what caught our attention this week:

1. Ryan Hill and Carolyn Stein have a new working paper on how AlphaFold has affected protein structure research in the five years since its release. They ask three primary questions:

   1. Did experimental (i.e., non-computational) structure determination decline?

   2. Did basic research shift toward proteins that previously had no structural information?

   3. Did early-stage drug development start targeting those newly solved proteins?

   Pre-register your guesses for each, before reading item #3 for a discussion of what Hill and Stein found. — Jordan Dworkin

2. While you’re thinking about that, let’s stay with science. Does MAGA Actually Want American Science to Win? is the latest from Ari Shulman, writing in The New Atlantis. Shulman argues that MAGA’s critique of the failings of status quo American science are broadly on point (and part of a tradition that predates President Trump), but that the policy conclusions it draws from that critique will not make America stronger. Dramatic cuts to research budgets are not enough (I would add that restrictions to skilled immigration don’t help either). The positive vision for science that Shulman grapples most with is a revitalization of scientific ideals championed by NIH Director Jay Bhattacharya, where there are no taboos, no appeals to authority, and free debate carries us to truth. My take? The deep challenge of organizing science is that only specialized knowledge can assess the quality of scientific work.1 That means peer approval is a fundamental part of the scientific process, and from there it’s not far from appeals to authority and deferring to experts. It’s tough! But that’s not to say the status quo was optimal and things can’t be better. Indeed, I’m a fan of some of the metascience innovation we’ve seen under this administration, from the NSF tech labs proposal to the streamlining of research bureaucracy. — Matt Clancy

3. Got your guesses about Alphafold ready? Here’s what Hill and Stein found.

   1. Did experimental (i.e., non-computational) structure determination decline? The rate of experimental structure determination is unchanged, and the relevant papers are still being published in top journals; AlphaFold predictions are complementing rather than replacing experimental research (though Hill & Stein note that this lack of substitution isn’t necessarily efficient, or permanent).

   2. Did basic research shift toward proteins that previously had no structural information? Basic research on previously unsolved proteins is up 15-35% relative to proteins with known structures, with initial shifts taking place in protein function research and more recent (and larger) shifts among expression, disease, and interaction studies.

   3. Did early-stage drug development start targeting those newly solved proteins?There is no corresponding increase in early-stage drug R&D on newly solved proteins vs. previously solved ones (though the authors note that private pharma work would not be captured here).

   Overall, it seems that structure was a meaningful bottleneck for basic science, but not the binding one for drug discovery. — Jordan Dworkin

4. Back in 2023, we asked Michael Wiebe to take a look at a 2021 article by Enrico Moretti titled “The Effect of High-Tech Clusters on the Productivity of Top Inventors.” We were interested because one of the inputs to our grantmaking decisions is a quantitative estimate of the social impact of a grant, and this paper was important for our estimate of how housing affects innovation. Unfortunately Wiebe found a lot of problems with the paper, prompting us to substantially downgrade the weight we put on the innovation effects of new housing. Other (non-innovation) benefits of housing now form a much larger part our estimate of its impact (though we’ll revise estimates again as new evidence emerges). Wiebe has now written up his conclusions on his substack, and a comment on the paper will appear in the journal in which it was originally published. — Matt Clancy

5. Brian Potter examines the elusive cost savings of the prefabricated home, and as someone now living through the third modular construction hype cycle of my career, the piece is a useful corrective. There’s a reason we keep coming back to off-site construction: HUD-code manufactured housing does deliver real savings in the US; the Sears “kit home” panelized model was a legacy success in the US; and contemporary international examples (especially Germany and Sweden) show factory-built housing can work at scale. But again and again, modular construction in the US has failed to sustainably deliver on its cost promises, with high-profile bankruptcies in the UK telling a similar story. The benefits tend to be schedule, predictability, and quality control rather than price. As we politely take part in the latest wave of enthusiasm, it’s worth being clear-eyed that this has been tried before, and is not a substitute for growth control reform. — Alex Armlovich

6. Separately, Jerusalem Demsas writes about how urban divestment is (still, as Pete Saunders reminds us) a much larger problem in the US than urban “gentrification”, but compositional & mimetic drivers of un-representative stylized facts crowd out the underlying statistical & material reality of neighborhood conditions in most American cities. — Alex Armlovich

7. It’s official: the Trump administration is suspending the Jones Act, which bans non-US flagged vessels from traveling between US ports, for sixty days in an attempt to keep gas prices low amid the Strait of Hormuz crisis. I doubt the suspension will do much toward that specific purpose, but it’s as good a time as any to think about the extreme costs that law imposes on the US economy. Colin Grabow, the premier Jones Act foe at the Cato Institute, highlights some oil/gas specific costs, like the fact that California gets fuel from the Bahamas as opposed to Gulf Coast US states because of a lack of tankers that can ship oil directly from Texas and Louisiana. — Dylan Matthews

8. One of the big challenges in permitting reform is that it’s really hard to forecast how changing one piece of the massive US legal/regulatory permitting system will play out. At a more micro-level, it’s also a headache for developers to try to figure out what environmental requirements their project actually triggers. Specifically, when contemplating a new project, developers do not know if the Clean Water Act applies until the Army Corps of Engineers completes an assessment. In a new working paper, Greenhill, Walker, and Shapiro train a deep learning model to evaluate whether Clean Water Act provisions apply to a given parcel, using Army Corps of Engineers determinations tunder several recent rulemakings. They find their model is 65 times better at identifying regulated sites than the leading geophysical approach. This helps in analyzing past regulatory shifts (the authors find Sackett deregulated roughly a third of all previously regulated waters), helps in generating high-quality projections of proposed regulations before they’re implemented, and could potentially help developers better predict if they’re actually subject to CWA restrictions. — Willow Latham-Proenca

Happy Friday the 13th (again!)! Here’s what we’ve been reading:

1. Last week, Abundance NY put out The Abundance Agenda, a roadmap to, well, Abundance for New Yorkers. I’m a fan of just how concrete the document is, both in terms of the big picture and the small. On the big picture, the agenda lays out some very specific ten-year goals: reduce capital project cost overruns and schedule delays by half, build 500,000 new homes, double average bus speeds city-wide. But it also zooms in to the small details that add up to big change: challenge-based procurement, pre-approved template plans for common building types, pre-emption of local authority to de-facto ban solar projects, and so on. This Abundance Agenda is tailored to New York - it has recommendations related to scaffolding and trash pickup - but it would be great to see more local orgs put together wide ranging roadmaps to abundance, tailored to their own local needs. — Matt Clancy

2. The Senate passed the bipartisan 21st Century ROAD to Housing Act 89–10 on Thursday — a landmark pro-supply package that streamlines housing regulation, creates new building incentives, and modernizes federal programs. A late addition requiring institutional investors with 350+ single-family homes to sell after seven years threatened to shut down build-to-rent construction entirely--but then, shortly after the bill passed, Senate staff revealed an industrial policy exemption for innovative HUD Code construction methodologies, including newly chassis-free CrossMods unlocked by the bill’s other reforms. If you like your single family BTR, you can keep it--as a stable pipeline of demand for innovative new factory built homes. To be sure, there are costs and risks to aggressive industrial policy & “picking winners” in new technology. — Alex Armlovich

3. Meanwhile, closer to home, Governor Hochul’s EXPRESS NY initiative is accepting submissions through April 3 from anyone who can identify a state regulation or practice that adds unnecessary burden — with a focus on housing, infrastructure, and small business. If you know a bad rule flying under the radar in New York, this is a real channel to flag it. — Alex Armlovich

4. Federal permitting negotiations have re-started in the Senate, following the first steps of an apparent thaw in solar permitting. Alex Trembath of the Breakthrough Institute has a timely piece in response, discussing the increasing partisanship of energy policy, reinforcing the importance of passing meaningful reform this Congress, and arguing that the current need for bipartisan compromise is an opportunity, not a drawback. — Willow Latham-Proenca

5. In a previous post, we noted that Congress largely preserved science budgets for FY26, rejecting large proposed cuts. Unfortunately appropriations are only step one. A few weeks ago Nature reported that OMB has been withholding authorization for agencies to spend their congressionally approved funding. As of the beginning of this month, NIH had not received approval to spend research money from the budget bill signed February 3, and was operating on leftover funds from last fall’s stopgap. As a result, they’ve issued only ~800 new and competing awards so far in FY26, versus the ~3000 that would be typical. NSF only got its funding authorized in late February, and is making grants at an even more glacial pace than NIH. — Jordan Dworkin

6. On a happier note: This week the Astera Institute announced Radial, a new division committing up to $500M over the next decade to experimenting with how life sciences research is organized, funded, and shared. Early projects include open datasets for drug discovery, new infrastructure for protein dynamics data, and a publishing platform prototype. Alongside the launch, they are running an essay competition asking working scientists to identify systemic bottlenecks in their own research and propose ways to test solutions. The judging panel includes both owner-of-the-blog Matt Clancy and friend-of-the-blog Jacob Trefethen, and we encourage you to submit your best/most ambitious ideas. — Jordan Dworkin

7. This year has seen AI agents break through in a serious way among social scientists I respect. So it was only a matter of time until social scientists designed an AI benchmark to see how good agents really are. Meysam Alizadeh, Mohsen Mosleh, Fabrizio Gilardi, and Joshua Tucker’s SocSci-Repro-Bench tested if Claude Code and Codex could reproduce 54 distinct papers. The most striking result to me is how different the models’ capabilities were: Claude could fully reproduce 78 percent of papers accurately, while Codex only got 35.8 percent. What’s more, the study found evidence the results were not memorized by the models; they were doing the work from scratch. Funny enough, OpenAI’s success rate here is similar to that of GPT 4/4o back when the Institute for Replication tested those models in 2024 (though it’s not clear to me if the replication tasks in the two papers are similarly challenging). — Dylan Matthews

8. Adam Kroetsch has a new post arguing that if we want to make trials more efficient, we need to pay attention to the boring stuff, in other words, the unglamorous work of clinical operations like setting up trials, recruiting patients, and collecting data. — Saloni Dattani

9. I’ve also been following the BioNTech announcement that co-founders Ugur Sahin and Özlem Türeci will leave the company by year-end to start a new biotech focused on next-generation mRNA therapeutics (their third startup). The stock dropped over 20%, partly because no successors were named, and partly because BioNTech is on the cusp of multiple late-stage cancer therapy readouts. — Saloni Dattani

Finally, some updates from our team and grantees:

We’re celebrating new cohorts for two research accelerator programs we support: Speculative Technologies announced the 2026 Brains Fellows, helping scientists plan, identify funding for, and execute ambitious technical research from electronic noses to bacteriophage therapies. Renaissance Philanthropy launched the Big if True Science (BiTS) Americas cohort, training scientists to design and lead large-scale research initiatives across plasma chemistry, biosecurity, developmental neuroscience, and more.

Progress Ireland published a piece by Fergus McCullough on how Brussels can help with Galway’s housing problems, proposing that the EU use funding conditionality to reward jurisdictions that actually increase housing supply, drawing on models like Canada’s Housing Accelerator Fund and the US ROAD to Housing Act.

We also published a directory of Living Literature Reviews this week: a collection of continuously updated research syntheses by expert authors on a range of topics. If you know of a review we’ve missed, let us know - it’s a living directory!

The number of academic papers doubles every 12 years. This wealth of new knowledge is exciting, but the pace of growth makes keeping up with the latest developments increasingly difficult, even for specialists. Living literature reviews are one response to this problem: continuously updated collections of accessible articles that synthesize academic research on a specific topic, written by a single expert who maintains a consistent voice and perspective over time.

We fund several living literature reviews through the Abundance and Growth Fund, and we’re aware of others doing similar work independently. This post is our attempt to collect them all in one place and to create a directory you can browse to find out if someone is writing a living literature review on a topic you care about.

We're not the only ones who think living literature reviews are valuable. VoxDev maintains a library of living literature reviews focused on development economics, covering topics from agricultural technology to taxation. Their model is a bit different from ours (team-authored, updated annually rather than continuously), but the goal is the same: making research accessible and keeping it current. In health and clinical research, the Living Evidence Atlas tracks hundreds of living evidence efforts worldwide.

We plan to keep this post updated over time. If you know of a living literature review we’ve missed, please let us know at abundanceandgrowth@coefficientgiving.org.

Current directory:

New Things Under the Sun by Matt Clancy. Social science research on science and innovation. How new ideas are generated, who generates them, and what helps or hinders the process. Topics: Science & Innovation, Economic Growth

The Patentist by Gaétan de Rassenfosse. Accessible, evidence-based essays synthesizing academic research on how patents shape innovation, competition, licensing, and litigation. Topic: Science & Innovation

AI Accountability Review by Nick Diakopoulos. Translating research to bridge the gap between knowledge and practice for AI policymakers, practitioners, and researchers. Topic: AI & Technology

Scaling in Human Societies by Michael Goff. How and why size matters, scaling patterns across institutions, cities, and systems. Topics: Economic Growth, Interdisciplinary Research

Bridging Boundaries by Rachel George. How institutions shape collaboration across fields, what barriers get in the way, and how emerging technologies like AI may help bridge disciplinary divides. Topics: Interdisciplinary Research, Science & Innovation

Lauren Policy: Migration Literature Review by Lauren Gilbert. Research on migration, progress, innovation, growth, and development. Topics: Labor & Immigration, Economic Growth

The Care Gap by Nalini Gulati and Vikas Dimble. Applying a gender lens to healthcare demand and supply in low- and middle-income countries, drawing on research across development economics, public policy, and behavioral science. Topic: Global Health & Development

Existential Crunch by Florian Jehn. An interdisciplinary look at societal collapse: what factors make it more likely, and what societies can do to be more resilient. Topic: Climate & Resilience

The Price of Power by Nikhil Kalyanpur. Synthesizing what we know about when money does and doesn’t mean power, across democracies and autocracies, in peace and war, from oligarchs to civil society. Topic: Governance & Political Economy

Monitoring Gene Drives by Felix Moronta Barrios. Research on gene drive technology and its implications. Topic: Biotechnology

Reskilled by James Ransom. From the aftermath of war to the AI frontier: how do you get people back into work when their jobs disappear? Topics: Labor & Immigration, Economic Growth

Building Abundance by Michael Wiebe. Research on housing and infrastructure policy. What gets built, what doesn’t, and why. Topics: Housing & Land Use, Infrastructure

Related projects

Here are a few related projects worth knowing about:

Generative AI for Economic Research by Anton Korinek. A Journal of Economic Literature article on how generative AI can assist economists, with regularly published supplemental updates tracking the latest capabilities as AI systems evolve. Topics: AI & Technology, Science & Innovation

Ghosts of Electricity by Alex Imas. Individual research syntheses on AI adoption and its economic effects, including posts on who uses AI and how and the impact of AI on productivity. Topics: AI & Technology, Economic Growth

Under Review by Seth Werfel & Devon Magliozzi. Research-driven policy analysis that synthesizes peer-reviewed evidence to evaluate whether popular policy ideas hold up. Posts are treated as living documents, updated as new evidence emerges. Topics: Housing & Land Use, Governance & Political Economy

Specialized Research Bibliographies by Ben Trettel. A curated directory of expert-maintained research bibliographies across fields. Less detailed than full literature reviews, but broader in coverage and a useful complement.

What are we missing?

This post is itself a living document. If you write or follow a living literature review that belongs here we’d like to hear about it. We’re agnostic about topic areas and interested in any regularly updated, publicly accessible research synthesis written by someone with deep expertise in the field. Drop us a line at abundanceandgrowth@coefficientgiving.org.

1. If policymakers in the US can agree on anything, it’s that they want more semiconductor plants (“fabs”) built in the US. The bipartisan CHIPS & Science Act promised hundreds of billions of dollars in federal subsidies toward this purpose, and President Biden even signed legislation exempting the factories from federal environmental reviews. Problem solved, right? Lol, of course not. Micron, a US-based chip company, is building a fab near Syracuse, NY, costing $100 billion and employing some 9,000 workers in an area that’s lost a lot of manufacturing jobs. Naturally, for this they are getting sued by a foundation/union-funded group, Jobs to Move America, for not doing sufficient review under New York’s environmental review statute. The best summary comes from the trade pub SemiAnalysis, which notes, “The project has already taken an absurd 1200 days between announcement and groundbreaking. Competitors overseas who began at the same time now have built and working fabs.” — Dylan Matthews

2. Last month we posted a Kiesling/Macey paper, which argued that perverse incentives for incumbents help keep energy supply constrained. A paper published last week by Dasom Ham, Owen Kay, and Catherine Hausman dovetails nicely with that post, providing more quantitative evidence for how those incentives are created. The authors find that eliminating some of the current constraints to moving energy would have reduced electricity generation costs in the continental US by roughly $3 - 5 billion in 2023 and $6 - 7 billion in 2022, when natural gas prices spiked. Better connections between energy supply (particularly in the renewables-rich interior of the country) and demand centers (primarily on the coast) would also help equalize prices, trimming revenues for coastal incumbent generators currently benefiting from their locational “advantage.” — Willow Latham-Proenca

3. When novel technologies get shoehorned into ill-fitting permitting systems, the results are rarely enviable. In a recent piece, Samuel Roland tours the byzantine landscape of state-level geothermal regulation. While some states consider geothermal a “surface estate” - where the landowner holds the rights - others allocate it as a mineral right to a separate - sometimes difficult to locate - entity. Attempts to map geothermal permitting onto existing technologies like oil & gas drilling have also led to permitting processes that don’t match actual risks for each project phase, and a dizzying patchwork of agency jurisdictions. To top it off, unclear application of western water law can create enough legal risk to kill project financing. The piece proposes several practical fixes, including a voluntary model code, statutory safe harbors for non-consumptive water systems, and risk-tiered permitting. — Willow Latham-Proenca

4. Around 23% of the terrestrial territory of the Netherlands is a protected area; to secure a permit to build near these areas, environmental review about nitrogen impacts are particularly important. The country used to use a system called the Programma Aanpak Stikstof to determine when the nitrogen impact of new construction was compliant with regulations, but a 2019 court ruling determined this program was invalid. Thus began the Dutch Nitrogen Crisis, which has constrained building in the country since. Inferring the impact of this policy change is hard because construction in the following years was probably impacted by covid-19; but a new paper makes some headway by comparing building rates across sites nearer and farther away from the protected areas. It finds the policy change reduced construction by 2.7 permits per quarter, against a mean of 41.4 permits per quarter. This paper is interesting in other ways too - see item #5 for more. — Matt Clancy

5. Another interesting thing about the paper discussed in item #4, about Dutch nitrogen regulation and residential building, is that it was written completely by AI! It’s part of the Autonomous Policy Evaluation Project (Project APE), an initiative from the Social Catalyst Lab at the University of Zurich. They’ve used AI to generate 241 papers so far, each evaluating the various impacts of some policy with real code and data pulled from official sources, that you can download from github to audit. The Dutch nitrogen paper has some serious issues - many of the figures and citations have been improperly processed and don’t show up correctly in the paper. But AI continues to improve. There’s been a lively debate playing out online about how the ability of AI to churn out papers could break peer review and the journal system; but I found project APE a good reminder of the potential upside: for us, transparent quantitative analysis on every policy for which data is available. — Matt Clancy

6. The main thing Alex has been reading this week is the text of the Senate Banking Committee housing reform bill, for which he is deep in the details. It’s encouraging to see bipartisan movement on federal housing policy, though there’s a lot to get right as it moves forward. And Jordan is on holiday. We’re excited to hear about his beach reads next week! — Nisha Austin

Here are a few other updates from our grantees and team:

• Ben Schifman discusses the NEPA litigation “doom loop” - and its potential fixes - in a recent long-form piece at IFP.

• Ruxandra Teslo argues that AI alone won’t speed up clinical trials. The real bottlenecks are regulatory and operational, not technical, and solving them requires institutional reform.

• Finally, Saloni wrote a new post on the history of clinical trial reforms and what we should do next. She discussed how practices like randomization, preregistration, and results reporting were once seen as radical, and why greater data transparency is the natural next step.

It’s easy to see that proposal as an ambitious reform, and in some ways it is. But the history of medicine shows us that clinical trials have already undergone a series of transformations that once seemed equally bold. Over time, the way we test treatments has become more rigorous and standardized. Improvements in transparency are a continuation of this trend.

In this post, I’ll give a brief history of clinical trial reforms and why transparency is the natural next step.

The rise of randomized controlled trials

One of the most consequential rigor-enhancing changes in clinical trials was the rise of the randomized controlled trial. In 1962, the United States passed the Kefauver-Harris Amendments, which required that new drugs demonstrate efficacy through adequate and well-controlled investigations before receiving approval. In practice, this pushed regulators and manufacturers toward randomized controlled trials (RCTs). This was seen as a dramatic shift.

At the time, clinicians had to rely on scattered uncontrolled studies, controlled studies without randomization, and case reports of claims of miracle treatments. They’d see some patients improving and others worsening, with no clear way to estimate the average effect or detect harms that only become visible at scale.

The requirement for controlled trials might have raised costs and overturned established practice. But it addressed a real problem: if you want to understand the effectiveness and safety of a drug using observational, uncontrolled data, you’d face many challenges:

• Regression to the mean: Many conditions bring people to the doctor when symptoms are at their worst, and some improvement would have happened anyway. Someone might seek treatment for severe back pain after a particularly bad week, only for the pain to ease as the flare-up subsides. The same pattern appears in mental health: a person may begin therapy during an especially intense depressive episode, then improve as the episode naturally wanes. If improvement follows treatment, it’s tempting to credit the intervention, even when part of the change reflects a return toward a person’s usual baseline.

• External events: Outcomes can also shift because the world around patients changes. For example, asthma admissions may spike during a period of high air pollution and fall when air quality improves, a heatwave can increase dehydration and kidney stress. Even public health campaigns, changes in food availability, or a stressful economic downturn can influence sleep, diet, and cardiovascular risk. If a treatment is introduced during such periods, its apparent effects may partly reflect broader changes.

• Selection into treatment: People who receive a treatment often differ in systematic ways from those who do not. Patients who opt for a new preventive medication might be more health conscious, adherent to medical advice, or able to afford regular care. Conversely, a specialist clinic may see the sickest patients, or certain drugs may only be prescribed at severe stages of a disease, making treatment look less effective than it truly is.

• Blinding and concealment address a different source of bias: the expectations and behaviours of the people involved in a trial. Even after randomization, participants or researchers may figure out who received the active treatment because the drug looks, tastes, or smells different from the placebo, or because it produces noticeable side effects. When this happens, it can partly reverse the benefits of randomization: if participants or researchers can identify who received the active treatment, differences between groups may reflect expectations and behavioural changes rather than the drug alone, meaning the estimated effect could be larger or smaller1 than the true effect of the treatment itself. Concealment is a first step, preventing researchers from knowing which treatment someone will be assigned to before allocation. Blinding goes further, keeping both participants and researchers unaware of group assignments throughout the trial.2

Without careful design, these differences can wrongly appear to be treatment effects. But adjusting for these factors is difficult, and the flexibility can also open the door to questionable research practices: analysing data in selective ways, choosing favourable subgroups, or stopping analysis when results look promising.

Randomization helps because it creates groups that, at the outset, have roughly equal propensities for the outcomes being studied. Darren Dahly has a clear explanation of this logic. And like my friend Julia Rohrer, I think of randomisation as the closest thing epidemiology has to magic.

In my view, there’s no question that some treatments did have good evidence for them even before the rise of randomized controlled trials. In particular, researchers could notice when treatments were followed by large, otherwise unexplainable changes – like the elimination of a disease following vaccination, or reversals of diseases that usually progressed quickly or fatally, as was sometimes the case after treatment with drugs like antibiotics and insulin.

But in most cases, it would have been hard to identify effective drugs, especially if their benefits were more modest, or if their benefits took a long time to become visible.

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It’s useful at this point to break down randomized controlled trials into their components: an experiment, with randomization, a controlled group, and often the practice of ‘blinding’ as well. Each of these components has had many precursors.

Take controlled groups as an example, which trace back – at least as far as I knew – to 1747, when James Lind compared treatments for scurvy aboard a Royal Navy ship and found citrus fruit dramatically effective. [After publishing this, Erin Braid pointed out that there were far earlier experiments with controlled groups, including in Ancient Greece; James Lind’s experiment is commonly named as the first one or one of the first ones.] I also found it interesting to learn that some 19th and 20th century controlled studies assigned patients to treatment in an alternate sequence, as they arrived to see a doctor, which is somewhat close to randomisation, but doesn’t involve blinding the researcher or participant to which treatment they received.

Blinding has an even more interesting history. It traces back to at least 1784, when King Louis XVI commissioned scientists including Benjamin Franklin and Antoine Lavoisier to investigate the claims of Franz Mesmer, who said he could cure illness through “animal magnetism” (later named ‘mesmerism’ after him). Franklin and Lavoisier designed an experiment in which they physically blindfolded subjects while performing the magnetic treatment; but crucially, they sometimes withheld the treatment without telling the subjects.

When they reported sensations regardless of whether they actually received the treatment, the experiment showed the effects were driven by their expectations rather than magnetism. It helped popularise the idea of blinding, in which patients are prevented from knowing which treatment they receive – although it was often the case that blinding was used to ‘debunk’ findings, rather than being open-minded to the results. Louis XVI’s interest, for example, was not purely scientific: mesmerism had become a fashionable and lucrative craze in Paris, and he likely worried about public disorder and the erosion of medical authority, giving the monarchy strong incentives to discredit it.

Combining these practices into randomised controlled trials, however, is relatively recent, at least historically speaking. The first widely recognised modern RCT was the UK Medical Research Council’s 1948 streptomycin trial for pulmonary tuberculosis.3 Patients were randomly assigned to receive the antibiotic streptomycin (a new breakthrough at the time) plus bed rest or bed rest alone. The trial showed clear improvements in survival and lung disease according to radiology scans in the treated group, but they also found early evidence of antibiotic resistance emerging.

As the twentieth century progressed, RCTs became more common. A very interesting example I’ve read of recently was the US National Institutes of Health’s Coronary Drug Project, conducted in the late 1960s and early 1970s, before statins were introduced.

In the decades leading up to it, researchers were experimenting with a surprisingly eclectic set of ways to lower cholesterol. If high cholesterol drove coronary heart disease, then lowering it ought to prevent heart attacks, so investigators tried hormones, vitamins, industrial resins, and metabolic drugs. Doctors claimed various drugs might be effective:

• Thyroid hormone (notably dextro-thyroxine)

• Oestrogens

• Niacin (vitamin B3)

• Bile-acid sequestrants such as cholestyramine

• Fibrates

The Coronary Drug Project trial allowed for a proper, large-scale testing of these drugs in a randomized controlled trial to see whether they actually reduced heart attacks and mortality. The results were surprising – niacin and cholestyramine showed meaningful benefits, but some other treatments, including oestrogen therapy and dextrothyroxine, actually slightly increased mortality – and changed clinical practice.

By the 1970s, randomized controlled trials were widely accepted as a gold standard for evaluating therapies. And after the 1962 reforms in the United States, along with similar regulatory shifts elsewhere, controlled evidence became the norm for drug approval.

Preregistration

Randomization solved the design problem, but new challenges emerged in how results were analyzed. By the late twentieth century, the environment surrounding drug regulation had changed profoundly. To bring a medicine to market, companies were now expected to demonstrate both safety and effectiveness, usually through at least two well-controlled clinical trials, and typically using randomized controlled designs.

This was a major improvement over earlier eras, but it also raised the stakes dramatically. When development costs run into the hundreds of millions and a successful drug may earn billions annually, a failed trial can mean the loss of an enormous commercial opportunity.

The 1980s and 1990s saw a wave of breakthrough therapies reach patients. Statins transformed the prevention of cardiovascular disease. Angiotensin-converting enzyme (ACE) inhibitors improved survival in heart failure. Antiretroviral drugs began to turn HIV from a fatal diagnosis into a manageable condition.

But not every promising therapy proved effective, and even successful drugs often looked more impressive in early studies than they did later in routine practice. Understanding why requires a brief detour into how trial results are judged.

In most trials, success is judged using a statistical test that produces a p value, roughly the probability of seeing a result at least this extreme assuming the treatment has no real effect. By convention, results are called “statistically significant” if this probability falls below 0.05.

At the margins, this threshold creates a temptation. When a trial narrowly misses its endpoint, researchers may search for a significant result elsewhere: in a secondary outcome, a composite measure, or a subgroup analysis that, by chance, clears the bar. Given the enormous financial investment involved in developing a drug, there are strong incentives to tweak analyses at the margins, for instance, by changing which outcome is analyzed after seeing the data, a practice called outcome switching.

This flexibility undermines the validity of results. While randomisation improved how trials are designed, it did not fully address how results are analysed and reported; and flexibility after seeing the data creates opportunities for finding results that are favorable but unreliable.

To reduce the scope for this type of post hoc reinterpretation, regulators and journals began requiring trial protocols to be registered in advance. Starting in the late 1990s and expanding in the early 2000s, investigators were asked to document their study design and prespecified outcomes before data collection was complete. In the United States, these requirements broadened over time and were formalized through federal law and FDA regulations, with trial protocols and outcomes posted on ClinicalTrials.gov. Registration also became a prerequisite for publication in major journals, reinforcing the norm across the research community.

Analyses of trials funded by the National Heart, Lung, and Blood Institute found that before prespecification became standard, published trials were more likely to report positive results. After registration became mandatory, the pattern changed: many trials found no effect, and some detected harm. It became harder to redefine what success meant.

Even so, the value of prespecifying analyses is not about eliminating judgement but in improving transparency. It allows readers to see what researchers initially set out to measure, what changed, and why. There are countless examples from history of how both successes and failures in science have contributed to knowledge; science advances by testing predictions, learning from the results, and refining theories over time.

Trial reporting

By the late twentieth century, clinical trials had become the gatekeeper for drug approval. Regulators increasingly required convincing evidence of both safety and effectiveness, usually from multiple well-controlled studies, before a medicine could reach the market. This shift improved the reliability of medical evidence. But even as trial design grew more rigorous, a problem remained: what happened to the results once a study ended.

The results of many clinical trials were never published. Studies showing clear benefits were far more likely to appear in medical journals, while trials finding no effect or suggesting harm often remained unpublished. The problem became increasingly visible in the 2000s, as systematic reviewers began documenting “missing” trials.

Analyses of antidepressant trials comparing journal publications with the full set of studies submitted to regulators found many studies went unpublished, and were disproportionately those with negative or null results (see here for a more recent analysis, which finds improvement). The published literature suggested overwhelming effectiveness, while the complete dataset showed more modest benefits. When meta-analyses relied only on published studies, average effect sizes were inflated, meaning clinicians and patients were often seeing an overly optimistic picture of drug effectiveness.

There are probably many reasons for this: Journals prefer novel and positive findings, and often require multiple submissions and long delays before acceptance, companies have little commercial incentive to highlight disappointing results, academic researchers face career pressures that reward publishing and success, and negative results seem less actionable. The path of least resistance is to simply move on, hopefully to something more promising.

But the implications are not merely academic. Incomplete reporting makes it harder to learn from failures and adjust drug development plans, to estimate the true average benefit of treatments, to detect rare harms, and to make informed decisions about patient care.

Since the 2000s, governments and regulators began introducing legal requirements for trial registration and results reporting. The 2007 FDA Amendments Act in the United States required many trials to post summary results in a public registry. In Europe, transparency rules evolved into the EU Clinical Trials Regulation and a centralized database designed to make trial information publicly accessible. These policies aimed to ensure that results entered the public record regardless of whether they were favourable.

Compliance did not improve overnight and enforcement was initially limited, but pressure emerged from multiple directions. The AllTrials campaign pushed for the registration and reporting of all trials, the Good Pharma Scorecard tracked company transparency commitments, and the FDAAA TrialsTracker made reporting performance publicly visible. In the United Kingdom, parliamentary scrutiny and reviews by the National Audit Office and the House of Commons Public Accounts Committee criticized failures to report trial results, increasing political and public pressure.

This combination of regulation, monitoring, and public accountability gradually shifted behaviour. Reporting rates rose substantially in the United States after reporting requirements took effect. Recent research by Cunningham et al. finds that academically funded trials initially had very low compliance rates, but rose rapidly. Industry funded trials began at higher compliance rates, and also increased. The researchers also found that this shifted the behaviour of other firms, which became more likely to wait for trial results before advancing similar drug candidates. As with earlier reforms, mandatory reporting was initially framed as a bureaucratic burden, but it’s increasingly considered basic infrastructure for trustworthy medicine.

Today, roughly 75–80% of clinical trials that meet the FDA requirement report their results within a year, with higher rates of reporting among industry-funded trials than academic trials.

Conclusion

This blogpost is about clinical trial reform, but it’s worth stepping back to ask why we care about trials at all. Clinical trials are not an end in themselves; they exist to generate reliable information about whether treatments are safe and effective. The reforms described above, randomization, preregistration, mandatory reporting, addressed ways that information was being distorted or lost. Data sharing is the natural continuation of that arc.

In my previous post, I argued that sharing individual patient data could unlock a range of benefits that go beyond what published results alone can offer: pooling data across trials allows meta-analyses to detect effects on rare outcomes and confirm or rule out rare harms; it helps make sense of inconsistent results by letting researchers explore whether differences across trials reflect chance, patient populations, or study design; it makes it easier to ask new questions of old data, including when drugs developed for one condition show unexpected benefits for another; it enables better head-to-head comparisons between treatments, and can help tailor recommendations to patients’ characteristics; and it reduces redundancy: when trial results are visible, researchers can learn from failures and avoid repeating them.

So why isn’t this already standard practice? For industry-funded trials, the commercial disincentive is straightforward: sharing data helps competitors at least as much as it helps you. A firm that shares its patient data is effectively subsidising rivals’ research programs. But if all firms shared, the field as a whole would benefit, and so would each firm within it, gaining access to a much larger pool of evidence than any single company could generate alone. The case for coordination, whether through regulation or shared norms, follows from that.

For academic trials, the commercial disincentive largely disappears, and most academic research is publicly funded, which creates its own obligation to make results available. But it’s still uncommon to share data, probably as a result of practical frictions, career incentives that reward further publications foremost, and the absence of strong mandates. The relative neglect of data sharing in academic trials is harder to justify.

The case for sharing individual patient data from trials is strong enough that I think it warrants the same kind of coordinated push that succeeded for trial registration and results reporting. The answer could be better incentives, data infrastructure, and in some cases mandates from funders and regulators to make sharing practical and beneficial rather than burdensome.

Scientists often say that the best research doesn’t just answer a question, it opens up new ones. That’s how I think about data sharing as well. Without individual patient data, we’re left mainly with headlines, with limited ways to verify or explore the data to answer those questions, or build upon accumulated knowledge.

I’d like to thank Matt Clancy, Dylan Matthews, Nisha Austin, Ruben Arslan, Jamie Cummins, and Adam Kroetsch for feedback that improved this post.

1. “It is simply remarkable how robust public support for skilled immigration is.” Alexander Kustov takes a look at research on the US and abroad, and points out that despite all the rhetoric, support for skilled immigration remains very high (including on the right). Kustov argues this is because people intuitively grasp the argument that bringing in skilled workers benefits the receiving country. If it’s so obvious though, why do we have so many restrictions on skilled immigration? There are a lot of reasons, but a factor in our current climate is that the vocal minority opposed to skilled immigration (as well as immigration in general) is concentrated on the right, which gives that consituency disproportionate influence when the right is in power. Meanwhile, the left has tended to focus more on the humanitarian case for immigration, leaving the pragmatic argument for skilled immigration more neglected. — Matt Clancy

2. One exception to the generalized support for skilled immigration, however, is support for a specific visa, the H-1B, among US tech workers (see the previous link for some discussion). A recent analysis by George Borjas argued that H-1B workers were paid, on average, 16% less than comparable natives, which provided one argument for the new $100,000 fee for H-1B visas. However, a new analysis from the Economic Innovation Group argues this analysis did not actually compare like-for-like jobs: most importantly, Borjas’ sample of H-1B workers spans 2020-2023, while his sample of native workers wages comes from 2023. Even if H-1B and native-born workers were paid the same in each year, wage growth (via economic growth and inflation) would mean a sample of wages from 2023 would be higher than a sample from 2020-2023. When EIG compares only 2023 wages of H-1B workers to native born workers in that year, there is still a wage gap, but it falls to 7.5%; this falls even further when EIG makes some additional reasonable tweaks. Finally, they show that the wage gap varies considerably across workers: young workers with newly minted PhDs (for example) tend to be paid more than native born counterparts. — Matt Clancy

3. How much do permitting burdens raise the price of housing? This is a big question in our housing work, and it can be hard to measure the exact contribution of zoning applications, historical preservation review, and other permitting processes to our housing shortage. Princeton’s Evan Soltas and MIT’s Jonathan Gruber found a clever way to get at the number: compare land in Los Angeles that’s pre-approved for development to land that isn’t pre-approved, and see how much permit approval raises the price of land. The answer: it raises the price by 50 percent! — Dylan Matthews

4. Tokyo is the world’s largest city, with 38 million people sharing one agglomerated labor market. Despite high land prices, Tokyo’s structure rents are remarkably affordable by global megacity standards; they allow high densities and build far more new homes on a per-capita basis than peer megacities in the English-speaking world, inspiring NYC Mayor Mamdani and many other YIMBYs on the left and right. How do policy elements like land use regulation, moderate rent regulation with vacancy decontrol, and publicly subsidized construction work in Japan? The US-Japan Foundation offers Lessons from Japan: Improving US Housing Outcomes. — Alex Armlovich

5. Sometimes a pro-housing reform’s most articulate communicator is that reform’s biggest opponent. In a remarkable video, NYC Councilmember Vicky Paladino describes how New York’s pro-housing ballot measures have ended the “member deference” housing vetoes that are typically inevitable in city councils with single member districts. (Though member deference is impossible in at-large city council charters, and at-large charters can be combined with Ranked Choice Voting to satisfy the Voting Rights Act free of district-based residential segregation, NYC voters just achieved similar results through an override board with citywide officials.) — Alex Armlovich

6. On the trend of surprising public opinions, solar is remarkably popular among Trump voters, according to two MAGA-aligned polls released this month. The first, a survey run by Trump’s head campaign pollster, found 51% of GOP+ voters support utility-scale solar, jumping to 70% when panels are American-made. A second, this one from Kellyanne Conway’s firm, found 75% of Trump voters across five red / swing states agree that solar should be used to strengthen US energy supply. Both are helpful illustrations not just of solar’s bipartisan appeal, but also how central affordability and reliability are to the story. In a depressing counterpoint, Ysabelle Kempe shows that the broader trend of GOP solar support may actually be heading down – underscoring the importance of getting common-sense reforms over the line before solar becomes another culture war casualty. — Willow Latham-Proenca

7. There was a good back-and-forth on NIH funding in the Good Science Project last week. Aishwarya Khanduja and Stuart Buck first made the case that science funders would benefit from adopting some VC-style practices, then former NIGMS director Jeremy Berg responded. The most interesting disagreement — whether grantees are functionally locked into their original grant scope, or have flexibility to adjust relatively nimbly — highlighted how useful these exchanges can be. Berg clarified that, in practice, researchers can and do follow the science where it leads without bureaucratic pre-approval. Pair that with the common observation that researchers often apply for projects they’ve effectively already completed so they can use the funds for the next thing, and R01s start to look more like “person-based funding” than the metascience community gives them credit for. Of course, that’s not to say expansion of actual person-based funding is not worthwhile! But we have limited bandwidth to push for large-scale reforms; exchanges like this help us figure out where the system is actually stuck vs where it’s perhaps working better than some think. — Jordan Dworkin

8. STAT reports on a growing number of state-level science funding proposals. Massachusetts’ governor has proposed a $400 million DRIVE initiative, a coalition of medical schools and researchers is pushing New York to create a $6 billion Empire Biomedical Research Institute, and Texas voters already approved $3 billion for dementia research modeled on CPRIT (which has disbursed roughly $4 billion in cancer research grants since 2009). Jeff Alexander and Ivy Estabrooke frame the efforts in a recent Science editorial: state involvement in science funding can make institutions more resilient and provide an outlet for experimentation with funding design, but they are no replacement for federal support. — Jordan Dworkin

9. The rise and fall of DOGE was an interesting state capacity story to me on a number of dimensions: DOGE cuts threatened to undermine state capacity in a number of ways; it was pitched as a way to bring in outside tech expertise to boost state capacity; it was an attempt to show that at least a small arm of the state could build the capacity to bring the whole budget to heel. If it was the latter, though, it totally failed, and that meant that prominent tax economist Alan Cole made himself $128,000 by betting his entire life savings that DOGE would fail to reduce federal spending. Congratulations, Alan! — Dylan Matthews

Meanwhile, some updates from our team and grantees:

• For the last few months, our team has been collaborating with the Alfred P. Sloan Foundation to stand up the Pop-Up Journal Initiative, a new publication model chronicling and spurring progress on a single important policy question over five years. The first journal will be on the “Griliches Question”, which asks what societal return-on-investment we can expect from R&D. We’re pleased to announce that the National Bureau of Economic Research will serve as the host organization for this first pop-up journal. The project will be co-led by Tim Simcoe and Craig Garthwaite, and over the course of five years will include an annual research conference, the commissioning of original papers, an up-to-date synthesis of the state of knowledge, and annual tracking of how expert views on this question are evolving in response to new evidence. As part of this effort, we are also collaborating with the Sloan Foundation to support research that will advance our understanding of the returns to R&D investment. If you are interested, you can find more details in the Request for Letters of Inquiry.

• Our grantee Sightline Institute has been advancing a “funded inclusionary zoning1” model that captures the social benefits of income-mixing without suppressing new housing production. A bill inspired by their approach just passed the Oregon Senate.

• Finally, Dylan Matthews published What Gilded Age America and 1960s police can teach us about state capacity exploring how governments can build the capability to deliver on ambitious goals.

To judge from recent news and research, nothing much. State governments can’t build high-speed rail: just look at California’s project, which due to a mix of political chicanery and permitting problems has yet to open nearly two decades after announcement. The federal government can’t build ships for the navy: China’s shipbuilding capacity is 232 times that of the US, and the ships the US does construct are routinely overbudget and years late. The feds and states can’t get people benefits on time; in the wake of the pandemic, the unemployment insurance system went from paying out over 97 percent of claims within three weeks to barely half. States and localities have gotten worse at building highways, with per-mile costs of highway construction more than tripling between the 1960s and 1980s.

These problems, and many more besides, are what writers and analysts mean when they talk about the US problem with “state capacity,” or the ability of the government to actually execute on its policy goals. The US used to have a lot of state capacity. The US used to be able to get a nuclear power plant online within four years of construction starting; to build a 17-station line on the DC Metro within eight years of groundbreaking; to erect the tallest building in the world in barely over a year. The question is how to get back to that.

There isn’t a lot of high-quality evidence on specific policies that enhance or erode state capacity, especially in high-income countries like the US. (If you’re an economist or political scientist interested in producing more high-quality evidence, please email me.) But we do have a few studies I’ve found particularly helpful in thinking about these questions.

Most of the relevant work here examines workforce reforms: attempts to create civil service programs or otherwise premise hiring, firing, and promotions on merit rather than political savvy. This isn’t terribly surprising. Civil service reforms offer a way for researchers to pinpoint a major change to the way government works, and see its effects. But I’d love to see more work on state capacity enhancements that took different forms as well.

Everyone’s favorite case study: the Pendleton Act

Here in the US, by far the biggest change in the way government employees are managed, hired, and fired occurred in 1883, with the passage of Pendleton Civil Service Reform Act. Netflix has popularized the history here a bit with the show Death by Lightning but to recap: prior to the Pendleton Act, most federal positions were allocated per the spoils system, largely to loyalists of the party in power, rather than on the basis of experience or competence. Attitudes about this system were a major cause of intra-party friction among Republicans.

Then a disgruntled office-seeker who failed to get a spoils position murdered President James Garfield. The new president, Chester A. Arthur, was historically a “Stalwart” (that is, a Republican supportive of the spoils system) but had a change of heart in the wake of the assassination and signed the Democrat-authored bill creating a merit-based civil service.

There are a two big questions to ask about this change, from a state capacity perspective:

1. Did it change much? Did the Pendleton Act have teeth and actually transform the composition of the federal bureaucracy?

2. Did it improve the government’s efficiency or performance?

The answer to 1 seems to be an emphatic “yes.” In a 2024 paper, UC Davis’ Diana Moreira and Santiago Pérez looked at the effect of the Act on customs collectors, who in the tariff-heavy Gilded Age were among the most important (and most easily corruptible) bureaucrats in the federal government. The Act, they found, professionalized the workforce considerably.

New customs hires who had to undergo exams as part of the new civil service system “were 8 percentage points less likely to report working in an unskilled occupation prior to joining the Customs Service and 7 percentage points more likely to report working in a professional one. Moreover, targeted employees were also 4 percentage points more likely to report being literate in the census.” After ten years, nearly half of customs collectors had been hired through such an exam system. The implication is that the Act made the federal workforce, at least in this area, markedly more qualified.

In a separate paper also released in 2024, UC Berkeley’s Abhay Aneja and Guo Xu examined the Pendleton Act’s effect on another common federal job: postal carriers. They note that eight years after the Pendleton Act took effect, applying at first only to large post offices with 50 or more employees, “almost 20 percent of the average reformed post office was still comprised of patronage appointees.” Aneja and Xu emphasize the continuity, but the same data indicates that over 80 percent of employees had been hired by merit-based processes. The Act seemed to actually change who was working in post offices.

Where the research diverges is on question 2: did this improve government efficiency and performance? Aneja and Xu found it did: “On average, reformed cities experience a reduction in delivery errors by 22 percent.” The Postal Service got better at its core function — getting mail to the right person — and if you buy Aneja and Xu’s differences-in-differences design1, the effect was caused by the Pendleton Act. The Act also, they found, increased productivity: the volume of mail delivered per carrier grew by 8 to 14 percent in cities adopting reformed hiring.

But Moreira and Pérez didn’t see these kinds of improvements in customs collection. They found no evidence that the reform reduced the expenses of customs offices, or their number of employees. They also found no positive effect on revenue collected by the customs offices, and no improvement in “revenue per employee” (not surprising, given that the number of employees and the total revenue didn’t change).

Both papers touch on the seeming discrepancy between their findings. Moreira and Pérez emphasize that in the Customs Service, low-paid employees were exempted from the reforms, providing a way for the spoils system to persist within customs offices. That might explain the lack of improvements they found. Aneja and Xu argue that the value of the act for postal workers came from insulating them from political influence, and the risk of losing their job with a change in administration. The reforms to customs, with the carve-out for lower-paid employees, provided less insulation, and produced less benefit, in this reading.

Customs collection and postal delivery are, of course, only two elements of government performance, albeit elements that lend themselves easily to measurement. The positive reading is that these papers show that civil service reforms meaningfully increase state capacity, but that they only do so when they’re reasonably comprehensive (as they were for post offices). A more nuanced reading is that these reforms had heterogeneous effects: they might improve government performance at some things and not matter for others. I lean toward the latter view, but am open to persuasion.

Zooming out from the 1880s

The Pendleton Act is a nice case study: it affected the whole federal government, and it was far enough in the past that we can get reasonably long-run data on its importance and effects. But most of the history of states and state capacity took place outside of the 1880s, and it’s possible that these other context periods also hold valuable information. (I’m still keeping the focus on the US specifically for now, though international evidence on civil service reforms could also be relevant to the US case.)

We could look at lower-level bureaucracies in the US. A 2014 paper from the University of Houston’s Gergely Ujhelyi, who is responsible for an astonishing share of the economics literature on civil service issues, examines the introduction of civil service reforms at the state level. Ujhelyi’s results are fairly ambiguous, normatively speaking. States that introduce merit systems for hiring spend less at the state level, and instead transfer more funds to the city level. This looks a lot like evasion of the reform rather than real compliance: politicians move money to the part of government where they can still control hiring. Beyond just shifting spending, the reforms lead to less spending on big infrastructure projects like roads. “The policy implication is that reforming multiple levels of government simultaneously could be more desirable than a gradual approach that focuses on specific levels,” he concludes.

In a 2019 working paper, the Hertie School’s Arianna Ornaghi looked at civil service reforms in the US specifically affecting police departments between 1960 and 1980. Adoption of merit systems led, she finds, to an overall crime rate reduction of 45 percent (a pretty staggering effect size).2 Both the scale of the effect and the specifics surprised me: there’s no effect on violent crime rates, and the entire effect is driven by reductions in property crime. Despite this, there was no effect on clearance rates for property crime (which are abysmal - police almost never figure out who stole from you) but a meaningful improvement in violent crime clearances.

Ornaghi interprets the findings as driven by the reforms’ ability to limit politicians’ pressure on police. The effects were bigger in cities where police chiefs were also protected, which is suggestive that the potential for political interference warped incentives and worsened police performance. This isn’t necessarily a story about outright corruption, where politicians are protecting specific criminals, so much as an indication that police chiefs who want to please political bosses act in ways that hamper police effectiveness. Maybe a good analogy is research finding that independent central banks outperform politically controlled ones.

Most of the high-quality studies I found concerned these kinds of merit reforms. But of course that’s not the only way to examine changes in state capacity. One promising genre of study examines how outcomes vary based on how effective individual bureaucrats are. Francesco Decarolis, Leonardo Giuffrida, Elisabetta Iossa, Vincenzo Mollisi, and Giancarlo Spagnolo have a nice 2020 paper using unexpected deaths in the US federal government to measure the importance of individual bureaucratic competence independent of other factors.

They use a rich dataset, the Federal Employee Viewpoints Survey, which asks a huge sample of federal workers a slew of questions, including about the overall competence of their immediate working division. Competence measured this way drops unexpectedly when younger managers die suddenly — and because those deaths are more or less random, they let the researchers see what a sudden shock to competence does to the office’s performance. (These deaths might seem too rare for an analysis like this to work, but the team found 440 such deaths in the years they analyzed. The federal government is very large!)

Decarolis et al conclude that a one standard deviation increase in bureaucratic competence causes a 23 percent reduction in number of days of delays in executing a procurement contract, a 29 percent reduction in cost overruns, and a 52 percent reduction in the number of renegotiated contracts. If every federal office handling procurement were as competent as, say, NASA’s John Glenn Research Center near Cleveland (which the authors highlight as especially competent), then the government would save $2.6 billion and 841,000 days of delay each year.

Cost overruns are a nice dependent variable; they provide a convenient way to quantify just how much better state capacity is saving taxpayers. Yale’s Zachary Liscow, Berkeley’s Cailin Slattery, and Columbia’s William Nober looked at the effect of government engineer quality on costs in state-level infrastructure projects. They find that these engineers pay for themselves many times over. Replacing an engineer at the 25th percentile of their quality measure with one at the 75th percentile leads to 14 percent lower costs per mile, they find — a savings representing more than three times the average salary for engineers.

We need better data, and better metrics

Put together, this literature makes a pretty compelling case that state capacity, or at least higher levels of competence in the public workforce, matters quite a bit for the quality of public services in a number of domains. In some of the literature, the mechanism for those competence improvements is clear (like passing civil service reform when the background context is a spoils system). In others it’s less obvious. We don’t have any simple way to make all highway engineers good.

Beyond that limitation, I’m struck by the sheer number of different outcome metrics being considered: cost per mile of highway construction; cost overruns for federal procurement; mail delivery accuracy; violent and property crime; revenue collection at ports. These are all context-specific and bespoke.

That’s fine so far as it goes, but limits our ability to evaluate broader reforms. There’s an active debate in the public management world over whether moving to “at-will” employment at the state level has had good outcomes (see Judge Glock and Renu Mukherjee for the “pro” case and Don Moynihan for the “con”). A big part of the debate is that we don’t have great outcome metrics. There are self-reports from employees and HR directors, which tell us something but are at best heuristics for the stuff we actually care about: lower crime, more services getting to people, lower-cost contracts.

It’s hard to improve at something unless you measure it — and right now, we’re not really measuring state capacity. Something I’d be excited to see are more efforts to develop broad metrics for state capacity that take into account performance across a number of these areas. Imagine something like Transparency International’s Corruption Perceptions Index which, like that measure, is a weighted average of several different factors: how good police are at solving crimes, how fast government benefits go out the door, how quickly major infrastructure projects are built, how much roads and subways cost per mile, etc. That would take a lot of doing, but could be an important cornerstone for future state capacity pushes.

Today’s post is about everything else under the broader abundance umbrella: infrastructure and transportation spending; health and medical abundance; immigration and assimilation; and state capacity, to name a few big subcategories.

Job market papers matter a lot in econ. First and foremost they’re how departments do hiring for junior faculty, but for those of us outside of academia they are a strong signal of where economics is heading as a field. They’re also a category that shows off some of the coolest methodological tricks developed in recent years. Newly minted PhDs want their papers to pop so employers notice them, which means that job market papers use new tools and often tackle more interesting and policy-relevant topics than other kinds of papers.

Overall we identified a staggering 51 (!) job market papers unrelated to innovation, housing, and energy, but nonetheless interesting to us. You can read that full list here. Here are eight of them, though, that struck us as particularly important or novel.

Competition and the Cost of U.S. Infrastructure

Lindsey Currier

Can limited competition in procurement auctions explain the high, and rising, price of road infrastructure in the U.S.? I assemble a new dataset covering the near-universe of state highway auctions between 2002 and 2024. I first document thin competition: one- or two-bidder auctions account for a third of awards, and this share has risen over the past decade. Using spatial variation in inter-state bidder locations, I then estimate the average causal effect of competition on prices; an additional bidder reduces prices by ten percent. To decompose bids in the data into markups and production costs, I develop a semi-parametric structural auction model that incorporates bidders’ uncertainty over the number of competitors they face. I show that price increases over the past decade are primarily attributable to increasing markups, not increasing production costs. Limited competition, in turn, is consistent with patterns generated by fixed costs of entry, but not broad construction-sector fixed costs. Embedding the markup estimates in an entry model, I estimate large auction and market entry costs, consistent with an important role for procurement complexity and regulatory barriers.

Link

Road to Green? The Effect of Highway Expansion on Industrial Emissions Intensity

Siyu Zhang

This paper examines whether highway expansion, beyond improving connectivity and lowering trade costs, can also reduce the emissions intensity of industrial production. I develop a general equilibrium model in which emissions are an endogenous by-product of firms’ output, and show that highways can lower aggregate emissions intensity by fostering tougher competition and improving resource allocation, especially where trade costs were initially high. The sign of the effect depends on the relative elasticities of wages and productivity with respect to trade costs. Empirically, I use detailed firm-level data from China and a network-based instrument variable to show that highway expansion reduces markup dispersion and lowers sulfur dioxide (SO2) emissions and chemical oxygen demand (COD) per unit of output at the province level, consistent with improved resource allocation. Using geospatial highway data, I further document reductions in emissions intensity at the county level. These results highlight the potential for transportation infrastructure to jointly enhance economic efficiency and environmental sustainability, a co-benefit thus far overlooked.

Link

Paving the Way for Higher Costs? The Impact of Steel Tariffs on Highway Procurement

Ashwin Nair

I study the impact of the 2018 U.S. steel tariffs on bidding in highway procurement auctions. The requirement to use domestically produced steel did not insulate procurement spending from the effects of tariffs in import-reliant coastal states. I find that bids on steel inputs saw a significant increase in these states but were unaffected in the Midwest, where most domestic steel is produced. Using a structural model of bidding and entry, I show that bidders in California faced higher costs but also earned higher markups as fewer bidders participated in steel-intensive projects. I estimate that California incurred an additional $100 million (a 6.8% increase) to construct highway projects, with one-third of the increase driven by the decline in competition. These effects are absent in Michigan.

Link

Moving Opportunity Closer: How Public Transit Transforms Firm Composition and Employment

Akhila Kovvuri

Transportation infrastructure can improve workers’ access to existing economic opportunities, but it can also reshape economic opportunity itself by influencing where and what kinds of firms locate. This paper studies how public transit infrastructure influences firm location, composition, and employment at the neighborhood level. We construct novel data tracking over one million establishment entries and employ both difference-in-differences and market access specifications, exploiting the phased expansion of the Delhi Metro Rail in India. Transit access increases firm entry near stations, with larger, established retail and service firms locating first and inducing subsequent entry of other firms. These patterns create new economic hubs in peripheral areas, increasing employment per capita, especially for women in a context of low baseline female labor force participation. Counterfactual decompositions using a quantitative spatial model with estimated gender-specific commute elasticities reveal that compositional shifts toward larger establishments and consumer-facing industries that ex-ante employ more women account for the majority of this differential employment effect. Understanding how infrastructure reshapes the demand side of the labor market is thus critical for predicting and enhancing its distributional impacts.

Link

The Impact of Medical Innovation on Health and Disability

Jinyeong Son

Despite substantial growth in medical technology, there is limited causal evidence on the impacts of many medical innovations on health and disability. This paper investigates the impact of a major surgical innovation: the move from conventional open surgery to minimally invasive surgery. Using an instrumental variables research design along with administrative data on injured workers undergoing orthopedic surgery, we quantify the impact of minimally invasive surgery (compared to analogous open surgery) on subsequent health care use, return to work, long-term disability, and social insurance payments. The findings suggest minimally invasive surgery reduces health care spending in the two years following surgery by 30%—through both reduced complexity of the surgery itself and large reductions in subsequent health care use. Analysis by type of service suggests minimally invasive surgery reduces subsequent office visits, opioid use, and revision surgeries. Moreover, we document that minimally invasive surgery also improves broader measures of patient health and disability—speeding return to work (by 37 days), reducing the severity of permanent disabilities (by 30%), and reducing associated social insurance costs (by 28%). We conclude by documenting trends in the adoption of minimally invasive surgeries and exploring the policy implications of our findings.

Link

Can Drug Pricing Transparency Reduce Drug Costs?

Nicola Maria Fiore

High drug prices and opaque middle-man contracting have driven calls for transparency. I examine whether mandates to disclose rebate flows lower prescription drug costs. I find no meaningful reduction in premiums, deductibles, or out-of-pocket payments across different market structures and incentive conditions. Instead, I find that costs can rise in markets not directly regulated—consistent with intermediaries shifting costs across jurisdictions. Contrary to regulators’ expectations, transparency alone may not reduce drug spending—and may even redirect it—when contracts span multiple markets.

Link

The Social Consequences of Technological Change: Evidence from U.S. Electrification and Immigrant Labor

Sara Benetti

This paper examines how technological change in production processes affects social cohesion in ethnically diverse societies. I study the early expansion of the electric grid in the United States between 1900 and 1940, when electrification transformed manufacturing and large-scale immigration reshaped the labor force. Using newly digitized maps of the U.S. high-voltage transmission network linked to full-count census data, I exploit the staggered rollout of electrification across counties to estimate its causal effects on the integration of immigrant and native workers. Electrified industries became more diverse and less segregated along ethnic lines. These effects extend beyond the workplace. Electrification is associated with lower residential segregation among manufacturing workers and a partial attenuation of the negative relationship between immigrant presence and local public service provision. Overall, I find that, in this context, technological change reshaped the social fabric by promoting integration both at work and within local communities.

Link

Economic Ideas and Policy Implementation: Evidence from Malthusian Training in British Indian Bureaucracy

Eric Robertson

Public officials often fail to implement government policy as directed, yet the role of economic ideas in shaping these implementation choices is poorly understood. This paper provides causal evidence that exposure to economic ideas can durably influence bureaucrat behavior. I study British colonial bureaucrats in India, exploiting a natural experiment created by the abrupt death of Thomas Malthus in 1834, replacing his economics instruction at a bureaucrat training college for that of a contemporary critic, Richard Jones. Whereas Malthus regarded economic distress as a natural mechanism for restoring equilibrium by reducing population growth, Jones disagreed with this view. Linking rainfall shocks to district-level fiscal responses, I show that officials trained by Malthus delivered less relief during droughts, providing 0.10-0.25 SD less aid across all major measures compared with officials taught by Jones. The results reveal that exposure to abstract economic ideas can shape real-world policy implementation for decades.

Link

Happy February 20! Here’s what caught our attention this week:

1. We don’t usually talk about permitting reform as a life-or-death issue. But evidence from a new paper suggests that maybe we should, at least where wildfire management is concerned. Min Zhang and coauthors find that wildfire smoke — specifically, the PM2.5 particulate matter in it — contributed to roughly 24,000 deaths per year in the contiguous US between 2006 and 2020. Concerningly, the authors find no evidence of a “safe” threshold — indicating that chronic exposure is risky even at low levels. Combined with growing evidence that proactive fuel management significantly reduces the severity of subsequent wildfires (Davis et al. 2024 found 62–72% reductions in severity for treatments involving prescribed burns), this makes an even stronger case for streamlining the regulatory requirements that slow down forest management. — Willow Latham-Proenca

2. Continuing that theme, the weaponization of environmental review as a tactic to delay or block new building has tended to cast the conservation movement as an antagonist to the Abundance Movement. But the same permitting and decision-processes which take so long and introduce so much uncertainty into the building of new infrastructure projects also hamper the goals of the conservation movement. As noted above, the burden of complying with regulation delays controlled burns, which leave forests vulnerable to fire. Shawn Regan, writing for The Ecomodernist, catalogs a number of additional ways that the conservation movement and the abundance and progress studies movements are working towards the same goals. — Matt Clancy

3. This week I’m reading a father-son team with competing explanations of why Europe lags behind the US in generating innovative companies. Luis Garicano (and coauthor Per Strömberg) look specifically at Sweden, which has been unusually successful at generating “unicorns” (tech companies worth over $1 billion) given its small population. They credit this to the existence of an active angel investor community in the country, which in turn they credit to a tax provision that allows startup founders to avoid capital gains tax on shares they sell — but only if they in turn invest the money in other startups. Pieter Garicano, a very smart commentator on European economic issues and Luis’s son, has a long essay in Works in Progress that places the blame instead on Europe’s restrictive labor laws, which make it time-consuming, expensive, and sometimes outright impossible to fire workers; this then makes it a bad idea to hire lots of workers on projects that might fail, which hampers innovation. The solution, he argues, isn’t to abandon workers but to embrace Denmark’s “flexicurity” model pairing rules allowing easy firing with a generous safety net to catch workers who are let go. — Dylan Matthews

4. A new NBER working paper introduces GABRIEL, an open-source library that wraps GPT for extracting, rating, ranking, and categorizing qualitative information from text data. Of note for innovation policy: they apply the framework to study technology diffusion, filtering 18 million Wikipedia articles down to ~25,000 industrial-age technologies and extracting characteristics for each. They find that lags between invention and adoption have shortened from ~50 years in the 1800s to ~5 years today (with interesting heterogeneity, e.g. military and transportation technology diffuse faster than average, while agricultural, medical, and energy technology diffuse slower). The pace of technology adoption is an important input for innovation policy decision-making; CBO recently conducted a preliminary analysis of the budget impacts of R&D investment, within which they modeled a 15-year ramp from outlays to peak productivity effects. If the invention-to-adoption gap is truly compressing as fast as the authors find, R&D may pay off in shorter windows than typically assumed. — Jordan Dworkin

5. It’s been a big week for clinical trials. The FDA refused to review Moderna’s flu vaccine trial despite previously indicating their trial design was acceptable. Then, they reversed course. It’s unclear what prompted the reversal (likely political backlash), but as Ruxandra Teslo argues in a new blog post, the episode highlights how regulatory uncertainty can be worse than strict standards, making it harder to plan for and meet standards. On a more positive note, Adam Kroetsch laid out thoughts on the FDA’s new Bayesian statistics guidance that I found particularly helpful. He explains that the new guidance has been a long time coming and should help translate subjective judgment into a more transparent, quantitative framework. He also argues that it is especially valuable as the FDA has shifted from screening large numbers of low-quality trials and requiring two statistically significant trials per drug, to evaluating fewer, larger trials that can incorporate additional sources of evidence. — Saloni Dattani

6. Ed Mendoza of the Metropolitan Abundance Project has a new feature leveraging research by the Pew Charitable Trusts on how new housing construction moderates older and more affordable “Class C” housing rents even more than other, newer, “Class A” housing prices. Although organizations like the National Low Income Housing Coalition rightly remind supply-side YIMBYs that families earning less than 60% of metropolitan Area Median Income in the US will usually need income transfers to afford safe and dignified housing that meets contemporary middle class norms, YIMBYism in high-rent regions is still a powerful antipoverty tool that helps people of all incomes. — Alex Armlovich

7. Transportation policy is the flip side of housing policy. In fact, in many urban economics models, the two are inseparable: house prices are determined by time to commute to the city centre (pay more for a shorter commute). More broadly, a lot of the value of our housing policy work is premised on helping people access the benefits of agglomeration economies; better transportation within a city achieves the same goal. In Transportation for the Abundant Society, Gregory Shill and Jonathan Levine argue that transportation policy should focus less on mobility and more on creating an abundance of accessibility to valuable places. They go on to then lay out an agenda for achieving transportation abundance, focusing on major legal and institutional barriers. — Matt Clancy

For more on why we’re sharing these, take a look at our first post on job market papers.

First we present only categories and titles, so you can quickly skim what’s here. After the title index, we present titles plus abstracts.

Titles Index

Titles are presented in random order under each category. There might be additional authors on these papers - we’ve listed the associated job market candidate only.

State Capacity

1. Startups and the State - Raman Singh Chhina

2. Education Policy and the Quality of Public Servants - Juan Martín Pal

3. Economic Ideas and Policy Implementation: Evidence from Malthusian Training in British Indian Bureaucracy - Eric Robertson

Infrastructure

1. Competition and the Cost of U.S. Infrastructure - Lindsey Currier

2. Strategic Transportation Investment and Coordinative Policies: Evidence from the U.S. Highway Network - Yuyang Jiang

3. Travel Mode Choice and Distributional Impacts of Congestion Charge Policy in New York City - Yikuan Ji

4. The Trade-Offs of Curbside Parking: Evidence from Demand-Based Pricing - Tuyetanh L. Tran

5. Road to Green? The Effect of Highway Expansion on Industrial Emissions Intensity - Siyu Zhang

6. Infrastructure Investment, Self-Employment, and Structural Change in the US Labor Market - R. Benjamin Rodriguez

7. Fiscal Capacity, Railway Federalism, and German Railway Development 1835-1885 - Paul Lowood

8. Driving Inclusion: The Effect of Improved Transportation for People with Disabilities - Melissa Gentry

9. Equilibrium Commuting Costs: The Role of Private and Public Transit - Jordan Mosqueda Juárez

10. Climate Trade Costs: Extreme Weather, Transportation, and Supply Chains - Hubert Massoni

11. Load it Up: Network Disruptions, Economies of Scale, and Variety Loss in Freight Trucking - Gustavo Nino

12. On the Move: How Changes in Public Transit Accessibility Affect Household Food Security - Elena Krasovskaia

13. Public Transit, Residential Sorting and Labor Supply: Evidence and Theory from Lahore’s Bus Rapid Transit System - Bisma Haseeb Khan

14. Paving the Way for Higher Costs? The Impact of Steel Tariffs on Highway Procurement - Ashwin Nair

15. Beyond the Flypaper Effect: Crowding-In from Federal Investment in Public Transit - Arseniy Braslavskiy

16. Moving Opportunity Closer: How Public Transit Transforms Firm Composition and Employment - Akhila Kovvuri

Healthcare and Clinical Trials

1. The Economics of Choosing Traditional Medicine: Theory and Evidence from India - Zincy Wei

2. Hospitals, Health, and Growth: The Local Impacts of Public Investment in Health Infrastructure - Spencer McCloy

3. Hospital Ownership and Quality of Care - Radhika Ramakrishnan

4. Can Drug Pricing Transparency Reduce Drug Costs? - Nicola Maria Fiore

5. The Propagation of FDA Regulatory Enforcement Through Drug Supply Chains - Karna Malaviya

6. The Impact of Medical Innovation on Health and Disability - Jinyeong Son

7. Do Pharmacies Matter? - Joseph Battles

8. Restructuring Public Delivery Care - Guilherme Amorim

9. Market Size and the Returns to Surgeon Volume: Evidence from Joint Replacements - Dante Domenella

Economic Dynamism

1. Labor Market Dynamics after Cost-of-Living Shocks - Yannick Reichlin

2. Trapped or Transferred: Worker Mobility and Labor Market Power in the Energy Transition - Minwoo Hyun

3. For Better or For Worse: The Added Worker Effect in the 21st Century U.S - Luwen Mai

4. Competitive Occupational Licensure: Doctors Versus Chiropractors - John Fallon

5. Does Government Procurement Promote Small Business Growth? - Jiaming Soh

6. Employer Competition and Certification - Hershdeep Chopra

7. Labor Force Growth, Firm Dynamics, and Declining Labor Mobility - Hector Cardozo

8. A Taste For Luxury - Josemaria Larrain

High-Skilled Immigration

1. International Undergraduate Student Inflows and College Pricing Strategies - Sheng Qu

2. The Social Consequences of Technological Change: Evidence from U.S. Electrification and Immigrant Labor - Sara Benetti

3. Immigration, job sorting, and health: Evidence from 1920s US immigration policy - Patrick Szurkowski

4. Elasticity of Taxable Income and Social and Cultural Norms: Evidence from Immigrants in Canada - Kuot D. Manyang

5. Quality and Location Choice of Immigrant Doctors - Jason Chen

6. Creating Opportunity: The Impact of Immigration on Native Entrepreneurship - Gabriel Chaves Bosch

7. Immigration Policies and Human Capital: The Impact on Undocumented College Attendance - David Titus

General Abundance

1. Information Bias and Selection of Female Professors - Stephanie El Khoury

2. Competition, Signaling, and Status Externalities in Ph.D. Admissions - Siqi Li

3. Toxic Tradeoffs: Impact of Environmental Regulations on Workplace Safety in Mining - Sanjukta Mitra

4. Sticky Intra-household Resource Allocation in the Face of Technological Change: Evidence from a Framed Field Experiment in Mozambique - Rachel Jones

5. Environmental Regulation with Irreversible Investments: Evidence from High Plains Aquifer Depletion - Nathaniel Hickok

6. The Hidden Curriculum - Michael G. Cuna

7. Does Greater Policy Intensity Improve Policy Effectiveness? Evidence from Seoul, South Korea - Hayeon Jeong

8. Beyond argumentation: AI-powered Socratic dialogue and political moderation in public deliberation - José Ramón Enríquez

Titles, Abstracts, and Links to Papers

Startups and the State

Raman Singh Chhina

Can bureaucrats in developing countries identify and selectively promote high-growth startups? How much additional gain does selective targeting provide over uniform startup subsidies? I develop a quantitative endogenous growth model of selective targeting in which the entry and growth of different startup types responds heterogeneously to the government’s ability to filter and select startups. To estimate this selection ability and quantify the resulting gains, I build a novel dataset of startup selections and rejections from an online startup registry and bureaucratic board meeting minutes, as well as novel patent application data and hand-collected income statements, in the context of the Startup India Program—one of the largest such policies, launched in 2016. I find substantial variation in selection ability across components of the program: startup labeling selects below average-quality firms and distorts exit decisions, whereas provisions of R&D benefits and tax-holiday approvals by a bureaucratic board successfully identify innovative, high-growth startups. The latter double the benefits-to-cost ratio relative to uniform subsidies. I also derive implications for optimal program design by evaluating counterfactual policies that vary the duration and composition of subsidies.

Link

Education Policy and the Quality of Public Servants

Juan Martín Pal

This paper studies the design of higher education policies targeted at improving the recruitment of public servants. I leverage the introduction of a policy in Chile that aimed to increase teacher quality by combining financial incentives and admission standards. Exploiting a sharp assignment rule for financial incentives eligibility, I estimate that, at the threshold, enrollment of high performing students at teacher colleges increased by 42%. For low-income students, two thirds of the increase is due to switching away from non-enrollment. The policy led to a 0.25SD increase in the scores at the college entrance exam, which translated into a 0.11SD increase in Teacher Value Added (TVA). I embed the reduced-form results into a demand and supply model of higher education that incorporates a novel method for solving discrete-continuous games in large markets. Alternative combinations of incentives and admission standards lead to increases of up to 6.6% in the test scores of students enrolled at teacher colleges, and up to 20% in TVA. Targeting the policy to low-income students yields further gains in TVA at no additional cost. To achieve similar gains, the expected wages of graduates from teacher colleges would need to increase by approximately 35%, highlighting the cost-effectiveness of the policy.

Link

Economic Ideas and Policy Implementation: Evidence from Malthusian Training in British Indian Bureaucracy

Eric Robertson

Public officials often fail to implement government policy as directed, yet the role of economic ideas in shaping these implementation choices is poorly understood. This paper provides causal evidence that exposure to economic ideas can durably influence bureaucrat behavior. I study British colonial bureaucrats in India, exploiting a natural experiment created by the abrupt death of Thomas Malthus in 1834, replacing his economics instruction at a bureaucrat training college for that of a contemporary critic, Richard Jones. Whereas Malthus regarded economic distress as a natural mechanism for restoring equilibrium by reducing population growth, Jones disagreed with this view. Linking rainfall shocks to district-level fiscal responses, I show that officials trained by Malthus delivered less relief during droughts, providing 0.10-0.25 SD less aid across all major measures compared with officials taught by Jones. The results reveal that exposure to abstract economic ideas can shape real-world policy implementation for decades.

Link

Competition and the Cost of U.S. Infrastructure

Lindsey Currier

Can limited competition in procurement auctions explain the high, and rising, price of road infrastructure in the U.S.? I assemble a new dataset covering the near-universe of state highway auctions between 2002 and 2024. I first document thin competition: one- or two-bidder auctions account for a third of awards, and this share has risen over the past decade. Using spatial variation in inter-state bidder locations, I then estimate the average causal effect of competition on prices; an additional bidder reduces prices by ten percent. To decompose bids in the data into markups and production costs, I develop a semi-parametric structural auction model that incorporates bidders’ uncertainty over the number of competitors they face. I show that price increases over the past decade are primarily attributable to increasing markups, not increasing production costs. Limited competition, in turn, is consistent with patterns generated by fixed costs of entry, but not broad construction-sector fixed costs. Embedding the markup estimates in an entry model, I estimate large auction and market entry costs, consistent with an important role for procurement complexity and regulatory barriers.

Link

Strategic Transportation Investment and Coordinative Policies: Evidence from the U.S. Highway Network

Yuyang Jiang

Transportation networks are often constructed by multiple jurisdictions. When each decision-maker fails to internalize the full surplus generated by their investment, the resulting network can be inefficient. How large are the resulting inefficiencies, and to what extent can coordinative policies improve outcomes? This paper builds a framework to evaluate the welfare implications of non-cooperative transportation investments, accounting for goods trade, commuting and fiscal budget, and the efficacy of subsidies as a coordinative policy. In this framework, regional governments choose investments in their portion of the network to maximize constituents’ utility, taking as given investments set by other regions and subsidy rates set by a central government. Applying the model to U.S. highways, the observed network is underinvested by 15% relative to the national optimum and has incurred a welfare loss equivalent to 30% of the current level of investment. Across space, underinvestment patterns reflect a terms-of-trade externality, a fiscal externality, and spillovers of logistics technology due to through traffic. Finally, counterfactual exercises indicate that raising federal subsidy rates can improve efficiency, but excessively generous subsidies may backfire.

Link

Travel Mode Choice and Distributional Impacts of Congestion Charge Policy in New York City

Yikuan Ji

This paper evaluates the distributional and welfare impacts of New York City’s recently implemented congestion pricing program, the first in the United States. Using the 2022 Citywide Mobility Survey, I estimate a discrete choice model of travel mode with heterogeneous responses to cost and time, embedded in a congestion feedback framework. The analysis distinguishes between two channels: a price-side effect, capturing the direct burden of the toll on travelers, and a revenue-side effect, reflecting how toll revenues are recycled. The results show that the current toll ($9 peak /$2.25 off-peak) reduces private vehicle trips involving the Manhattan Core by about 19,000 per weekday, with most shifts to subways and walking. Accounting for congestion feedback raises driving speeds and substantially increases welfare relative to a fixed-traffic analysis. The toll alone lowers consumer surplus by $0.32 per person-day, with larger relative burdens on lower-income travelers, while a uniform rebate raises net welfare by $2.24 and makes the policy mildly progressive. Distributional analysis shows that equity outcomes hinge on these two effects: the price-side burden follows income patterns, while the revenue-side outcome depends on policy design. Transit fare subsidies generate smaller average gains while reinforcing mode shifts. The findings highlight that the equity and efficiency of congestion pricing depend jointly on traveler behavior and revenue design, with lessons for other U.S. cities planning similar policies.

Link

The Trade-Offs of Curbside Parking: Evidence from Demand-Based Pricing

Tuyetanh L. Tran

City governments face a trade-off in managing curb space: providing parking to facilitate access to consumption amenities and generate revenue, versus allocating it to alternative land uses. In this paper, we quantify the welfare implications of curbside parking and evaluate alternative policies for managing curb space through parking instruments. We develop a structural model of drivers’ joint destination and parking decisions: drivers choose which destination to visit under imperfect information about parking availability, then decide where to park near the chosen destination. We estimate the model using high-frequency data on metered parking transactions and GPS data on visits to points of interest in San Francisco, one of the few cities that have implemented demand-based pricing for curbside parking. We find that, while drivers value curbside parking, the present discounted value of parking revenue and driver surplus generally falls short of local assessed land values, which proxy for the economic value of land uses. Compared to a revenue-maximizing uniform pricing scheme, San Francisco’s demand-based pricing generates about 30% more revenue while reducing cruising trips by nearly 70%. Our counterfactuals show that reducing parking supply by roughly 6% and lowering the status quo demand-based prices by $1.25 citywide preserves parking welfare, with only a modest revenue loss, while freeing curb space for other uses.

Link

Road to Green? The Effect of Highway Expansion on Industrial Emissions Intensity

Siyu Zhang

This paper examines whether highway expansion, beyond improving connectivity and lowering trade costs, can also reduce the emissions intensity of industrial production. I develop a general equilibrium model in which emissions are an endogenous by-product of firms’ output, and show that highways can lower aggregate emissions intensity by fostering tougher competition and improving resource allocation, especially where trade costs were initially high. The sign of the effect depends on the relative elasticities of wages and productivity with respect to trade costs. Empirically, I use detailed firm-level data from China and a network-based instrument variable to show that highway expansion reduces markup dispersion and lowers sulfur dioxide (SO2) emissions and chemical oxygen demand (COD) per unit of output at the province level, consistent with improved resource allocation. Using geospatial highway data, I further document reductions in emissions intensity at the county level. These results highlight the potential for transportation infrastructure to jointly enhance economic efficiency and environmental sustainability, a co-benefit thus far overlooked.

Link

Infrastructure Investment, Self-Employment, and Structural Change in the US Labor Market

R. Benjamin Rodriguez

This paper studies the effects of infrastructure investment on the labor market. Between 1920 and 1950, the US government began constructing its first interstate highways, the Numbered Highway System. Regions gaining access to the Numbered Highway System experienced significant population growth alongside a shift from self-employment to salaried jobs. To identify the causal effect of this highway network on local labor markets, I use a novel instrumental variable approach exploiting a hypothetical set of highways proposed by the US Army for national defense. I further interpret these causal effects through a spatial equilibrium model in which highway construction induces households to select out of self-employment as local agglomeration—manifested in higher land costs and denser markets—increases the fixed cost of production near highways. I find that investment in the Numbered Highway System can account for one fifth of the decline in the aggregate self-employment rate over this period, underscoring how infrastructure investment fosters regional integration and structural change.

Link

Fiscal Capacity, Railway Federalism, and German Railway Development 1835-1885

Paul Lowood

This paper analyzes the relationship between fiscal capacity and railroad development in Germany. German states understood the benefits of railroads but faced budget constraints when supporting network construction. Using newly constructed fiscal capacity and railroad ownership datasets, I estimate the effect of state revenue growth on the decision to grant concessions to private companies or expand public firms. I find that increases in government revenues led to a significant switch away from public construction towards a concession based system without changing the overall rate of construction. I hypothesize that this is because revenue shocks were not large enough to fund new public railroad projects, but could subsidize significant private investment. This may have allowed states to meet capital demands for consistent levels of construction while issuing less debt.

Link

Driving Inclusion: The Effect of Improved Transportation for People with Disabilities

Melissa Gentry

People with disabilities face substantial barriers to economic and social participation. I explore the extent to which these barriers are overcome by the availability of reliable and flexible transportation, which may serve as “reliability insurance” in case other modes of transit fail. Leveraging the roll-out of Uber, I use a stacked difference-in-differences approach to show that the availability of reliable and flexible transportation leads to improvements in social and economic participation through increased marriage rates and labor force participation, and reduced reliance on public assistance. The reduction in public assistance outweighs expected rideshare costs, lending support to the recent push towards public-private partnerships in the transportation space.

Link

Equilibrium Commuting Costs: The Role of Private and Public Transit

Jordan Mosqueda Juárez

Developing cities rely on a mix of private minibuses and public transit, with many commutes being multimodal. This paper investigates how private providers’ decisions shape commuting costs considering complementarities with the public network, and the welfare and spatial consequences of policies that directly shift prices such as fare regulation and subsidies. I develop a quantitative spatial model in which commuters choose multimodal routes and private providers shape commuting costs through entry, pricing, and frequencies, affecting congestion and network-wide costs. The model is disciplined with newly collected geographic and service data covering the near-universe of transit lines in the Mexico City metropolitan area. To identify key substitution and congestion elasticities, I exploit road-link-level speed changes induced by an exogenous subway-line collapse. Counterfactual analyses suggest that price-based policies can generate welfare gains comparable to infrastructure expansions. The mechanisms underscore that the endogenous response of the private sector and network-wide cost interactions are central to understand the effects of transit interventions.

Link

Climate Trade Costs: Extreme Weather, Transportation, and Supply Chains

Hubert Massoni

Transportation infrastructure is vulnerable to extreme weather events. Vulnerability is prominent at maritime ports, where tropical cyclones frequently halt operations and force firms to adapt to transportation disruptions. I quantify these responses by linking high-frequency maritime shipment data to tropical cyclone tracks. Exposure to tropical cyclones temporarily disrupts port activities (≈1–2 weeks), prompting firms to adjust route choices along transportation networks (rerouting), even after ports resume operations (≈2–6 months). To evaluate the general equilibrium implications of these weather disruptions, I develop a quantitative model of spatial production networks with endogenous routing. Structural estimation reveals that maritime transportation costs decrease with port capacity (scale), but increase with port traffic (congestion) and cyclone risk. Investigating future climate hazards to the transportation network, I find that rerouting is a key adaptation mechanism that prevents global welfare losses. To translate evidence into policy, I derive modelbased sufficient statistics for evaluating and targeting future port investments in light of climate change. Allocation rules that ignore weather risk and firms’ adaptive responses systematically misallocate investment.

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Load it Up: Network Disruptions, Economies of Scale, and Variety Loss in Freight Trucking

Gustavo Nino

Sudden disruptions in supply chains often raise fears of sharp shipping price spikes and economic losses, yet policy responses are difficult to establish when the mechanisms of freight firms’ reactions to these shocks are not well understood. This study examines how sudden increases in marginal costs, triggered by landslides on roads, affect ground freight transportation prices and market structure. I use a comprehensive dataset of more than 3.5 million truck movements, over one million detailed route calculations, and geolocated records of landslides on roads that increased the travel times across Colombia in 2019. The findings indicate that disruptions lead to a notable decline in the number of trucks operating (-21%) and a considerable rise in the price per unit of Kg transported (7.5%). Economies of scale are critical to understand the market response, as evidenced by an increase in the average load per truck and a redistribution of market share in favor of larger vehicles. Price increases are driven primarily by a shift from “economy” services offered by small-scale trucking to more expensive services mainly provided by large-scale trucking, rather than by opportunistic price manipulation. A structural consumer welfare model indicates that 88 percent of the resulting consumer welfare loss comes from reduced variety (fewer low price options) and 12 percent from price increases linked to the cost shock. Findings suggest that, in the short run, policies that keep small trucks operating when disruptions occur are very cost effective and might outperform those designed to reinforce infrastructure.

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On the Move: How Changes in Public Transit Accessibility Affect Household Food Security

Elena Krasovskaia

Food insecurity is an increasing issue in the United States. Transportation barriers are an indirect yet potentially formidable challenge to access affordable and nutritious food. This paper investigates the impact of public transit (PT) accessibility on household food security in the US. While extensive research exists on food security and PT separately, the intersection of these topics remains underexplored. Using data from the Panel Study of Income Dynamics, supplemented with several datasets providing transit accessibility measures, I examine how changes in transit accessibility influence household food security outcomes. This study leverages a quasi-experimental design, focusing on “movers” - households that relocate between census tracts with different levels of PT accessibility to obtain a within-household estimate of the effect of PT accessibility on food security. I find that during the period from 1999 to 2003, a one std. dev. increase in PT accessibility was associated with 2-3 percentage points decrease in the probability of food insecurity among Black and poor households, as well as households without a car, and households that used PT in the past. However, during a more recent period from 2015 to 2019, I find no significant relationship between PT access and food security, suggesting that the US food and transportation systems changed substantially between the early 2000s and the late 2010s.

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Public Transit, Residential Sorting and Labor Supply: Evidence and Theory from Lahore’s Bus Rapid Transit System

Bisma Haseeb Khan

Public transit can transform how people live and work, yet its distributional effects remain unclear, particularly in developing cities where most households rely on low-quality transit. This paper studies the establishment of the Lahore Bus Rapid Transit (BRT) system to examine how mass transit reshapes residential sorting and household labor supply. Using a novel geo-spatial dataset, and exploiting the staggered roll-out of the planned BRT lines, I show that younger, nuclear, non-college-educated households relocate closer to BRT corridors, with greater labor force participation of men in these households. Women’s labor market participation, however, remains largely unchanged—consistent with tied-mover dynamics. To interpret these patterns, I build a spatial model that incorporates gender specific constraints, age-based mobility, and endogenous amenities and provides a framework for evaluating the distributional welfare consequences of transit infrastructure in developing-country contexts.

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Paving the Way for Higher Costs? The Impact of Steel Tariffs on Highway Procurement

Ashwin Nair

I study the impact of the 2018 U.S. steel tariffs on bidding in highway procurement auctions. The requirement to use domestically produced steel did not insulate procurement spending from the effects of tariffs in import-reliant coastal states. I find that bids on steel inputs saw a significant increase in these states but were unaffected in the Midwest, where most domestic steel is produced. Using a structural model of bidding and entry, I show that bidders in California faced higher costs but also earned higher markups as fewer bidders participated in steel-intensive projects. I estimate that California incurred an additional $100 million (a 6.8% increase) to construct highway projects, with one-third of the increase driven by the decline in competition. These effects are absent in Michigan.

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Beyond the Flypaper Effect: Crowding-In from Federal Investment in Public Transit

Arseniy Braslavskiy

I examine how targeted federal grants affect state and local spending on public transit. The analysis uses comprehensive U.S. expenditure data from 2000–2019 and exploits an exogenous shock from the 2009 American Recovery and Reinvestment Act (ARRA). ARRA funds were apportioned to Urbanized Areas through preexisting formula programs, independent of potential changes in transit investment. Using ARRA apportionments as an instrument, I find that each $1 of exogenous federal grants generates a $0.20 annual increase in capital transit spending from all sources. This average effect reflects two distinct phases between 2009–2019: an initial rise in federally funded expenditures with no displacement of state or local spending (the flypaper effect), followed by substantial crowding-in of state funding to the same localities. I develop a conceptual model of local officials’ investment decisions that distinguishes between guaranteed and flexible funding from each source, the latter requiring costly negotiation. In this framework, the initial flypaper effect arises from the stickiness of guaranteed funds, while the subsequent state crowding-in results from increased negotiation for flexible funding. This negotiation can be driven by two mechanisms: (i) higher returns to additional investment or (ii) lower costs of negotiation. Empirical evidence on the nature of additional spending rejects the first mechanism, while crossstate variation in institutional settings supports the second. Taken together, these results suggest that federal grants empowered local officials to secure additional flexible state funding by reducing the cost of negotiation, leading to a disproportionate and persistent increase in total public transit spending.

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Moving Opportunity Closer: How Public Transit Transforms Firm Composition and Employment

Akhila Kovvuri

Transportation infrastructure can improve workers’ access to existing economic opportunities, but it can also reshape economic opportunity itself by influencing where and what kinds of firms locate. This paper studies how public transit infrastructure influences firm location, composition, and employment at the neighborhood level. We construct novel data tracking over one million establishment entries and employ both difference-in-differences and market access specifications, exploiting the phased expansion of the Delhi Metro Rail in India. Transit access increases firm entry near stations, with larger, established retail and service firms locating first and inducing subsequent entry of other firms. These patterns create new economic hubs in peripheral areas, increasing employment per capita, especially for women in a context of low baseline female labor force participation. Counterfactual decompositions using a quantitative spatial model with estimated gender-specific commute elasticities reveal that compositional shifts toward larger establishments and consumer-facing industries that ex-ante employ more women account for the majority of this differential employment effect. Understanding how infrastructure reshapes the demand side of the labor market is thus critical for predicting and enhancing its distributional impacts.

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The Economics of Choosing Traditional Medicine:Theory and Evidence from India

Zincy Wei

Why do patients reject clinically superior treatments, even when they are accessible and affordable? This phenomenon is particularly acute in India, where half of urban households rely on traditional practitioners for chronic conditions such as hypertension, despite modern medicine being more effective and cheaper. I conduct a randomized controlled trial with 1,819 adults at high risk of hypertension who primarily use traditional medicine. I randomly vary only the institutional sources of clinical evidence about modern medicine’s efficacy. I find three main results: (i) the messenger matters more than the message—attributing evidence to India’s traditional medicine authority (AYUSH) increases modern medicine spending and take-up rates, while attributing to international medical journals has modest, insignificant effects; (ii) patients can simultaneously adopt superior treatments while experiencing smaller decreases in trust in traditional institutions when information comes from trusted sources; and (iii) effects fade within two months without reinforcement, revealing that sustained behavior change requires repeated engagement rather than one-time messaging. The results demonstrate that institutional credibility is a first-order determinant of healthcare decision-making, with implications for any domain where multiple authorities compete for legitimacy.

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Hospitals, Health, and Growth: The Local Impacts of Public Investment in Health Infrastructure

Spencer McCloy

This study examines how large-scale public investment in the health care capital stock shapes local health and economic outcomes. I exploit the Hill-Burton Act of 1946–the largest federal program to expand and modernize United States medical facilities–as a quasi-experimental setting. Using newly digitized archival data, I show that Hill-Burton causally increased both the capacity and quality of local hospitals, leading to higher patient utilization and greater physician staffing. Expanded access to care improved mortality rates among the general and infant populations. This investment generated broader spillovers, fostering growth in non-medical employment, patenting, and academic publications. These results demonstrate that public investment in health care capital can improve population health and promote local economic development, highlighting the policy relevance of efforts to improve healthcare infrastructure.

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Hospital Ownership and Quality of Care

Radhika Ramakrishnan

Differences between nonprofit and for-profit hospitals within the private US hospital market have been a matter of longstanding theoretical and empirical interest in economics and are the subject of much policy debate. Despite this, few prior works take a causal approach to examining the difference in quality of care between these hospital types. I apply an instrumental variables strategy based on ambulance preferences for individual hospitals (Doyle et al. 2015) to mitigate patient selection into hospital types. I find that for-profit hospitals offer slightly worse care (5.7% higher readmissions) and cause patients to experience higher costs (6.7%). These effects are not attenuated over time. Higher costs are likely driven in part by higher treatment intensity in the form of more frequent inpatient admissions and longer stays.

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Can Drug Pricing Transparency Reduce Drug Costs?

Nicola Maria Fiore

High drug prices and opaque middle-man contracting have driven calls for transparency. I examine whether mandates to disclose rebate flows lower prescription drug costs. I find no meaningful reduction in premiums, deductibles, or out-of-pocket payments across different market structures and incentive conditions. Instead, I find that costs can rise in markets not directly regulated—consistent with intermediaries shifting costs across jurisdictions. Contrary to regulators’ expectations, transparency alone may not reduce drug spending—and may even redirect it—when contracts span multiple markets.

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The Propagation of FDA Regulatory Enforcement Through Drug Supply Chains

Karna Malaviya

The Food and Drug Administration (FDA) upholds the safety of US drug supply, but persistent shortages and concentration in manufacturing have raised concerns that the agency’s regulatory enforcement actions may disrupt access to essential medicines. I study this trade-off between access and safety by estimating the causal effects of FDA enforcement at upstream drug manufacturing facilities on downstream hospitals and the patients they serve. Linking Medicare claims to newly assembled data on the origin facilities of sterile injectable opioid drugs, I find that enforcement causes substantial production disruptions, reducing volume from the implicated facility by one-third. Yet, responses by other elements of the supply chain provide resilience: hospitals draw on multi-sourced procurement networks to rapidly substitute to alternative suppliers, such that more-exposed hospitals experience no detectable declines in drug supply relative to less-exposed hospitals. Even in high-risk conditions under which supply does decline—among rural hospitals and when enforcement occurs during critical shortages—the disruption does not compromise patient health or quality of care. These findings suggest that, despite concerns about fragility in this setting, FDA enforcement in concentrated supply chains shields patients from drugs produced at non-compliant facilities while maintaining access and quality of care.

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The Impact of Medical Innovation on Health and Disability

Jinyeong Son

This paper investigates the impact of one of the most important surgical innovations in recent decades: the move from traditional open surgery to minimally invasive surgery. Using an instrumental variables strategy along with administrative data on injured workers undergoing orthopedic surgery, we quantify the impact of minimally invasive surgery (compared to analogous open surgery) on subsequent health care use, return to work, long-term disability, and social insurance payments. The findings suggest minimally invasive surgery reduces health care spending in the two years following surgery by 30%—through both reduced complexity of the surgery itself and large reductions in subsequent health care use. Analysis by type of service suggests minimally invasive surgery reduces subsequent office visits, opioid use, and revision surgeries. Moreover, we document that minimally invasive surgery also improves broader measures of patient health and disability—speeding return to work (by 37 days), reducing the severity of permanent disabilities (by 30%), and reducing associated social insurance costs (by 28%). We conclude by documenting trends in the adoption of minimally invasive surgeries and exploring the policy implications of our findings in light of these trends.

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Do Pharmacies Matter?

Joseph Battles

Despite potential health benefits of prescription drugs, many patients do not take them as prescribed. While prior work has emphasized the role of prices and insurers in patients’ drug consumption decisions, this paper provides novel evidence on a non-price driver of drug consumption: pharmacies. Using a staggered event study design, I find that pharmacy closures cause Medicare patients’ drug use to decline in the short-run—especially among low-income patients—but increase in the long run. To explain the long-run increase in drug consumption following a pharmacy closure, I model three potential mechanisms driving the reduced-form effect: temporary disruption/switching costs, permanent changes in patient costs (e.g., copays or travel distance), and permanent shifts to higher- or lower-dispensing pharmacies. To quantify the relative impacts of these mechanisms, I estimate a two-way-fixed-effects model in the style of Abowd, Kramarz, and Margolis (1999) of pharmacies’ effects on low-income patients’ drug use. Combining the pharmacy effects from the AKM model with my reduced-form closure analyses, I find that the long-run increase in drug consumption following a pharmacy closure is explained by patients switching from lower-dispensing pharmacies (which are disproportionately likely to close) to higherdispensing pharmacies. More generally, the variation in pharmacy fixed effects is about half that of prescriber fixed effects, indicating that pharmacies matter for drug consumption.

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Restructuring Public Delivery Care

Guilherme Amorim

While health systems around the world increasingly consolidate services into fewer hospitals, potentially improving care quality by reallocating patients to better-resourced facilities, such changes can also increase travel distances and hinder access to care. This paper studies the impacts of public hospital closures on delivery care in Brazil, a middle-income country with persistently high maternal and infant mortality and exceptionally high C-section rates. Using administrative birth and hospitalization records from 2007 to 2019 and a difference-in-differences design, we show that closures shift deliveries toward larger, better-equipped hospitals and reduce C-section rates, but yield no meaningful improvements in maternal or newborn health. Instead, we observe slight declines in birthweights and increases in preterm births, along with substantial heterogeneity in impacts across local demographics. Moreover, we find no evidence of improved treatment efficiency: post-closure C-section decisions are not better targeted to clinically recommended cases and appear largely driven by hospital-level practices, as shown using a “movers” design. Using a structural model of hospital demand, we show that closures reduce patient welfare and provide cost-utility estimates for alternative staffing policies that could mitigate their negative effects.

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Market Size and the Returns to Surgeon Volume: Evidence from Joint Replacements

Dante Domenella

Why are people in larger geographic markets more productive? This paper investigates one potential mechanism in the health care sector–the relationship between a surgeon’s procedural volume and patient health outcomes–that may generate benefits to market size. I study this topic in the context of hip and knee replacements, two of the most frequently performed medical procedures in the U.S. Using differential distance as an instrument, I find that surgeon volume significantly affects patient health outcomes. I then attribute 22% of the benefits of market size to this mechanism. This result highlights an externality, as a patient’s choice of surgeon affects other patients’ health outcomes through surgeon quality. Incorporating this externality into a demand model, I evaluate the welfare consequences of a first-best policy and three feasible policies: a minimum volume standard, transportation subsidies, and a policy that moves surgeons to shortage areas. Failing to incorporate the externality substantially understates the welfare effects of the feasible policies. Among the feasible policies, the minimum volume standard generates the largest welfare increase, yet it only achieves 7% of the gain from the first-best policy.

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Labor Market Dynamics after Cost-of-Living Shocks

Yannick Reichlin†

This paper provides causal microevidence on how idiosyncratic cost-of-living shocks transmit into labor market outcomes. Using the case of energy price changes, I combine 20 years of German employer-employee data with a representative household expenditure survey and rely on regional differences in energy expenditure patterns to identify the pass-through of energy cost shocks into earnings. A one-standard-deviation shock raises annual earnings growth by 0.6 percentage points, offsetting approximately 40% of local cost increases within a year and nearly fully compensating workers over five years. Job-to-job mobility is a key mechanism: higher exposure to energy price changes both increases the likelihood of switching and makes switches more profitable. Through the lens of an equilibrium model of the labor market, I clarify that while labor market responses substantially mitigate the distributional impact of relative price changes, they impose additional non-pecuniary costs on workers.

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Trapped or Transferred: Worker Mobility and Labor Market Power in the Energy Transition

Minwoo Hyun

Using matched employer-employee data covering 1.35 million US workers separated from the fossil fuel extraction industry between 1999 and 2019, I estimate how local fossil fuel labor demand shocks affect employment and earnings. Employment probabilities fall markedly after exposure, and earnings decline gradually over the first seven years with only partial recovery by ten years since exposure to the shocks. Workers who remain in the fossil fuel sector, disproportionately men in sector-specific roles, experience nearly twice the earnings losses of those who switch sectors, possibly due to limited occupational mobility. Among non-switchers, losses are larger in labor markets with high employer concentration, indicating that scarce outside options translate into lower reemployment wages and weaker bargaining positions. Geographic movers fare worse than stayers, reflecting negative selection (younger, lower-earning) and relocation to metropolitan areas where fossil fuel or low-skilled service sectors remain highly concentrated, leaving monopsony power intact.

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For Better or For Worse: The Added Worker Effect in the 21st Century U.S

Luwen Mai

The average Added Worker Effect (AWE) in the United States appears small. This paper shows that such muted estimates mask substantial heterogeneity across the life cycle and household structures. Using newly linked restricted administrative and census data from 2000–2022, I exploit mass-layoff events as exogenous shocks to identify involuntary job loss and its impact on spousal labor supply. Event study and matched difference-indifference estimates reveal that younger spouses with children increase employment and earnings in response to displacement, consistent with a financially motivated AWE. In contrast, childless young female spouses often reduce labor supply, particularly when both partners work in the same industry, reflecting a ”trailing spouse” dynamic. Among older couples, job displacement accelerates joint labor market exit, consistent with coordinated retirement. These findings highlight the role of age, parental status, and joint career dynamics in shaping household labor supply responses to job loss, and help reconcile why aggregate AWE estimates in high-income countries are small despite strong responses among population subgroups.

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Competitive Occupational Licensure: Doctors Versus Chiropractors

John Fallon

This paper provides the first analysis of competitive occupational licensure, where substitute professions maintain separate licensing boards that set entry requirements strategically. I develop and structurally estimate a model where professional organizations choose licensing stringency to maximize industry profits while accounting for competitive responses, as workers with heterogeneous abilities select occupations based on expected returns and consumers observe only average quality within each profession. Testing this theory using historical competition between medical doctors (MDs) and chiropractors (DCs) from 1907-1960, I exploit digitized American Medical Association records and state-by-year variation in chiropractic board adoption. Medical boards responded strategically by increasing college requirements by 10 percentage points, mandating internships (10+ percentage points), and reducing pass rates by 5 percentage points. These regulatory changes generated substantial economic effects: doctors experienced 26% higher home values while their numbers declined by 17-40 practitioners per 100,000 population, and chiropractors saw 44% higher home values with increased market presence of 2-12 practitioners per 100,000. Structural estimation reveals that observed equilibria closely approximate profit-maximizing sequential competition rather than welfare-maximizing behavior.

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Does Government Procurement Promote Small Business Growth?

Jiaming Soh

In the United States, policymakers increasingly rely on federal procurement to support small-business growth, yet it remains unclear whether government contracts generate only temporary demand boosts or lead to persistent firm growth. This paper creates a novel dataset that links federal contracts and construction bidding records to firm-level outcomes from restricted U.S. Census data and proprietary credit scores to study the impact of government procurement on small business growth in the U.S. The analysis combines matched differencein-differences and winner-versus-loser designs to establish several findings. First, procurement contracts lead to sustained increases in small business revenue, employment, and earnings. These effects persist for at least five years beyond the contract period and are not primarily driven by repeat contracting or long-duration contracts. Winning procurement contracts also leads to a short-run crowding out of small businesses’ private sales, which later recover and exceed their pre-contract levels. Second, using micro-level credit scores and labor share data, the study finds that financially constrained and capital-intensive firms experience stronger post-contract growth. A dynamic firm investment model with financial frictions, irreversible investment, and procurement-induced productivity dynamics explains these results through relaxed borrowing constraints that enable irreversible investment and productivity gains from procurement. Counterfactual simulations indicate that the observed persistence is primarily driven by productivity gains rather than short-lived easing of financing constraints

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Employer Competition and Certification

Hershdeep Chopra

This paper develops a theory of employer competition over hiring standards in labor markets where employers rely on third-party certification to screen applicants. The certifier sells tests to an applicant, who possesses imperfect private information about his ability and seeks to persuade employers to offer him employment. An employer hires if conditional on the test outcome her posterior belief about the applicant’s ability meets her standard. Employers compete in their selectivity or hiring standards to attract the applicant. The certifier faces both a screening problem, due to applicants’ private information, and an information design problem, as tests must be informative enough to persuade employers. The screening frictions faced by the certifier affect the equilibrium determination of hiring standards and the certification mechanism. I show that this inefficiency in test allocation leads to two distortions relative to a benchmark where the certifier only faces an information design problem. First, there is an increase in the probability of unemployment. The certifier’s optimal selling mechanism leads to exclusion of some applicant types. Thus, there is an excessive restriction on labor supply relative to the benchmark. Second, competition among employers intensifies. The inefficient test allocation steers the applicant supply towards less selective employers, resulting in equilibrium constriction of standards. An excessive amount of the (restricted) labor supply joins the less selective employer, incentivizing the more selective employer to lower its standards.

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Labor Force Growth, Firm Dynamics, and Declining Labor Mobility

Hector Cardozo

Labor mobility in the United States has fallen steadily since the 1980s. I examine the role of slower labor force growth in this long-run decline, specifically for employer-to-employer (EE) transitions. I develop a general equilibrium model of firm dynamics with on-the-job search along a wage-indexed ladder. In the model, job creation at higher rungs triggers EE moves and costless replacement hiring that cascade down the ladder. When labor force growth slows, firm entry declines, the firm distribution shifts toward slower-growing incumbents, job creation—especially in high-wage positions—falls, workers’ search incentives weaken, and EE declines. Using the calibrated model, I find that the observed slowdown in U.S. labor force growth since the 1980s accounts for more than 60% of the decline in EE, with meaningful implications for wage growth. A decomposition attributes the bulk of the decline (79%) to lower job creation, with smaller contributions from reallocation away from top rungs and an endogenous fall in search effort. Counterfactual exercises on immigration, aging, and female participation link labor supply policies to job creation, mobility, and wages. Halting immigration since the 1980s would have lowered EE transitions by about 20% and further slowed wage growth.

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A Taste For Luxury

Josemaria Larrain

This paper examines the role of heterogeneous taste for luxury goods in occupational sorting, and thus in the intergenerational correlation of income and consumption. I show that individuals with a higher weight on luxury consumption are more willing to accept riskier and back-loaded occupations. Using the expanded consumption data from the PSID, I document that there is substantial variation in luxury expenditure shares and that these shares are correlated between childhood and adulthood. I show these facts arise from heterogeneous taste for luxury goods and childhood consumption of luxury goods shaping adulthood preferences. I then develop a two-generation model to explore how childhood luxury consumption and its shaping of adulthood taste for luxury can affect occupational choice, and thus reinforce the intergenerational correlation of income. I find that 12.2% of the intergenerational correlation of income can be explained by this mechanism.

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International Undergraduate Student Inflows and College Pricing Strategies

Sheng Qu

This paper examines how growth in international undergraduate enrollment affects both sticker-price and net-price tuition at U.S. PhD-granting institutions. Leveraging the relaxation of U.S. visa policy and the appreciation of the Chinese yuan as natural experiments that drove a rise in Chinese undergraduate enrollment beginning in 2005, I use institution-level panel data from 2000 to 2019 and employ difference-indifferences and instrumental variable approaches to identify the causal effects of rising international undergraduate enrollment on tuition outcomes. I find that increases in international undergraduate enrollment raise out-of-state sticker-price tuition at public PhD-granting universities but reduce it at private PhD-granting institutions. Private PhD-granting institutions with greater exposure to international undergraduate enrollment growth also experience reductions in average net-price tuition, while public PhD-granting institutions show no significant change. These divergent responses highlight differing institutional priorities: private universities appear to prioritize school quality and student subsidization, while public institutions emphasize in-state access and budget stability. The findings suggest that domestic students at private universities benefit more from international undergraduate student growth than their counterparts at public institutions.

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The Social Consequences of Technological Change: Evidence from U.S. Electrification and Immigrant Labor

Sara Benetti

This paper examines how technological change in production processes affects social cohesion in ethnically diverse societies. I study the early expansion of the electric grid in the United States between 1900 and 1940, when electrification transformed manufacturing and large-scale immigration reshaped the labor force. Using newly digitized maps of the U.S. high-voltage transmission network linked to full-count census data, I exploit the staggered rollout of electrification across counties to estimate its causal effects on the integration of immigrant and native workers. Electrified industries became more diverse and less segregated along ethnic lines. These effects extend beyond the workplace. Electrification is associated with lower residential segregation among manufacturing workers and a partial attenuation of the negative relationship between immigrant presence and local public service provision. Overall, I find that, in this context, technological change reshaped the social fabric by promoting integration both at work and within local communities.

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Immigration, job sorting, and health: Evidence from 1920s US immigration policy

Patrick Szurkowski

This project examines how an exogenous decline in immigration flows induces sorting across the distribution of health conditions in the local labor market adjustment process and documents significant changes in the community health indicators. In the 1920s, a set of immigration laws in the United States imposed quotas based on national origin and restricted immigrant flows. These policies cut immigration flows from constrained countries to around 150,000 individuals a year. The effects of this policy change are identified in a difference-in-difference framework utilizing the LIFE-M, historical labor survey, and full count census data. Results indicate that individuals in areas facing larger declines in immigration are observed transitioning into occupations and industries with worse associated health measures. Increasing native-born employment in high health cost (HHC) positions offset lost immigrant labor leading to no change in HHC employment share. Further, policy exposure was associated with declining average lifespan and increasing mortality rates among counties’ working age US-born population.

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Elasticity of Taxable Income and Social and Cultural Norms: Evidence from Immigrants in Canada

Kuot D. Manyang

Comprehending how cultural and social norms shape taxpayer responses to changes in tax policy is vital to promoting tax compliance and morale. Exploiting exogenous variation in the tax rate from Canadian reforms and detailed administrative data, I estimate elasticity of taxable income (ETI) and find that immigrants have a larger ETI (0.094) than non-immigrants (0.078). Then, I estimate the ETI for each country of origin and examine how it varies with those countries’ cultural and social norms. Immigrants from countries with cultural norms such as individualism, uncertainty avoidance, trust in others and government, and religiosity have lower elasticities. In contrast, those from countries with cultural norms such as high power distance, masculinity, long-term orientation, and political corruption are highly sensitive to tax changes. These findings show that cultural and social norms shape tax behaviour and suggest that promoting trust in government may enhance tax compliance.

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Quality and Location Choice of Immigrant Doctors

Jason Chen

Doctor shortages are a widespread and growing concern in the healthcare systems of many developed countries, including in the United States. Allowing for immigration of working doctors is a commonly proposed policy to expand doctor supply. In the US, however, licensing requirements that impede immigrants with medical training from working as doctors are commonly justified on the grounds of ensuring their quality. I study the quality of domestically trained and immigrant doctors in the US, focusing on a setting with strong identification and measurement – Medicare patients and hospital emergency rooms. I find quality premiums associated with care provided by immigrant doctors, both within a given hospital and across the entire distribution of emergency room doctors. Notably, I do not find such quality premiums for US citizen medical students educated abroad. I also find immigrant doctors are significantly more likely to work in designated health professional shortage areas. Estimates from a structural matching model of the doctor labor market reveal that neither mobility preferences nor vertical sorting can fully explain this geographic pattern, suggesting immigrants have a greater preference towards working in these areas. These results show the important role of immigrant doctors in providing quality healthcare in the areas of greatest need in the US.

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Creating Opportunity: The Impact of Immigration on Native Entrepreneurship

Gabriel Chaves Bosch

This paper examines the impact of immigration on native entrepreneurship using rich social security data and a unique immigration episode in Spain. Using variation across local labour markets and employing a shift-share instrumental variable for identification, I find that immigration has a positive effect on native entrepreneurship. The effect is primarily driven by wage workers who transition into entrepreneurship following the immigration episode. To rationalise these findings, I propose and calibrate a model of occupational choice with immigration. The model shows the empirical results are consistent with a profit-driven channel: an immigration-induced labour supply expansion lowers immigrant wages but has a limited impact on native wages. As a result, immigration lowers labour costs, enabling the creation of businesses that would not otherwise be profitable.

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Immigration Policies and Human Capital: The Impact on Undocumented College Attendance

David Titus

I estimate the impact of Universal E-Verify laws on the college attendance of undocumented Hispanics in the United States. To do so, I implement a series of event studies that account for staggered adoption over time, and I use a random forest algorithm as my primary approach to predict undocumented status. My results indicate that Universal E-Verify laws lower the college attendance of undocumented Hispanics ages 18-24 by about 3.7 percentage points. This is a substantial effect: only 15.7 percent of undocumented Hispanics ages 18-24 in treated states were enrolled in college following the passage of the laws. This effect is robust to using logical imputation on non-citizen Hispanics to proxy for undocumented immigrants, using a logit model instead of random forest, testing for migration spillover effects on bordering states, and considering potentially confounding impacts of other state-level policies. I develop a theoretical model that explains the mechanisms through which Universal E-Verify affects college education, and I test this model’s implications. I find suggestive evidence that the effect is driven by a negative labor market shock on undocumented adults ages 25-54, which likely leads to worse schooling for their children and renders college less attainable. These findings indicate that employment restrictions targeting working-age undocumented adults hinder the human capital development of undocumented youth.

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Information Bias and Selection of Female Professors

Stephanie El Khoury

Individuals often rely on crowd-generated ratings to form beliefs and guide decisions, yet these signals often embed bias. In higher education, students frequently consult online teaching evaluations when selecting instructors. Using survey evidence from one of the largest U.S. public universities along with a randomized control trial, I document that female professors receive ratings that are 5.5% lower than otherwise comparable male professors. Students place substantial weight on higher ratings but remain unaware of the bias embedded in them, resulting in distorted beliefs and enrollment choices. To assess whether such distortions can be corrected, I implement an informational intervention that shifts student beliefs. The intervention raises students’ valuation of female instructors by 10% relative to course cost, attenuates biased rating behavior among active raters, and increases female-taught chosen courses by 18% in the semester following the treatment. To interpret these findings, I develop and estimate a Bayesian updating model of belief formation and professor selection. The model shows that students who rely on professor review websites disproportionately internalize biased signals, generating systematic undervaluation of female faculty consistent with statistical discrimination. Finally, I quantify the consequences of this bias by documenting a utility loss equivalent to a 5% increase in textbook costs over a six-course semester due to suboptimal enrollment decisions.

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Competition, Signaling, and Status Externalities in Ph.D. Admissions

Siqi Li

Rising competition in imperfect markets pushes agents to invest in costly signals that differentiate themselves. Such investments can mitigate unraveling and improve matching efficiency, but also generate rat races that reallocate resources towards relative standing. I develop an empirical framework to quantify how competition affects signal adoption in matching markets and its welfare consequences, applying it to the role of pre-Ph.D. experiences—master’s and predoctoral programs—in Ph.D. admissions. These experiences help programs screen applicants and provide research training. Yet when capacity is limited and grade inflation reduces informativeness, students pursue additional research experience to stand out. Using LinkedIn data on Economics and Business Ph.D.s, I find that pre-Ph.D. experience improves admission outcomes, with 54% of the gain attributable to signaling and 46% to training. While signaling restores about half of the matching efficiency lost under pooling, its opportunity costs exceed benefits, yielding a 15% net welfare loss. Benefits are concentrated among economics majors from top colleges; other groups are worse off. Grade inflation explains roughly one-quarter of the rise in pre-Ph.D. experience.

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Toxic Tradeoffs: Impact of Environmental Regulations on Workplace Safety in Mining

Sanjukta Mitra

Environmental regulations reduce ambient pollution exposure, which may benefit workers at regulated firms; on the other hand, new compliance costs may crowd out safety investments at firms, increasing the risk of worker injuries. This paper estimates the short-run net effects of the 1990s Clean Air Act (CAA) PM10 standards on workplace injuries in the mining sector by employing Difference-in-Differences, using a panel linking Mine Safety and Health Administration mine-year injury records to novel sub-county PM10 nonattainment boundaries for 1983–1997. I find that serious nonattainment designation increased workplace injuries by 3.7 per 100 full-time workers, and severe injuries by 0.972 per 100 full-time workers, imposing an economic cost on workers of roughly $0.20 billion (1990 dollars) per year. These estimates persist across specifications and are driven by reduced safety compliance, increased work hours, and overexertion among retained staff. The findings reveal thorny distributional tradeoffs: health benefits of the 1990 CAA Amendments are large but diffuse, while safety costs are small and concentrated among vulnerable, less-experienced workers.

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Sticky Intra-household Resource Allocation in the Face of Technological Change: Evidence from a Framed Field Experiment in Mozambique

Rachel Jones

Although new agricultural technologies are widely expected to enhance the productivity of rural households, evidence on how households reallocate resources to capitalize on these innovations is limited. To explore how households respond to a profitable, but risky new technology, I conduct a framed field experiment played with maize growing couples that simulates the exchange of labor and income in Mozambican households. When presented with a familiar local maize variety that is relatively insensitive to the wife’s labor input, households allocate labor to husbands’ maize production fairly efficiently. But when faced with an improved maize variety whose higher returns would induce cooperative households to increase maize labor, couples forego 17% of expected cooperative income. Analysis of husbands’ income sharing rules reveals that wives stood to gain just 7% in expected income had they increased their labor as predicted, and in exchange for a 68% increase in their probability of coming away from the game with no income. These results suggest that stickiness in intra-household income sharing may dampen the reallocation of productive resources in households that adopt profitable new technologies. Novel empirical evidence on real life intra-household income sharing from sample communities corroborates key findings from the game.

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Environmental Regulation with Irreversible Investments: Evidence from High Plains Aquifer Depletion

Nathaniel Hickok

Many of the world’s major aquifers are being rapidly depleted from agricultural irrigation, generating dynamic common-pool externalities by raising future extraction costs. Entry restrictions are commonly used to limit depletion because well drilling is easily monitored, but they are second-best compared to Pigouvian taxes that directly target the intensive margin of water use. When policies cannot be tailored to heterogeneous users, however, the relative effectiveness and political feasibility of entry fees and water-use taxes become theoretically ambiguous, depending crucially on the correlation between water users’ productivity and externalities. To study this question, we develop a dynamic model of farmers’ joint well-drilling and water-use decisions, integrated with a physically realistic model of groundwater flows, and estimate it using field-level data on aquifer levels, water use, and crop production in the Kansas High Plains Aquifer from 1959 to 2022. We find that field-level productivity and water-use externalities are strongly positively correlated due to the spatial concentration of high-productivity fields, leading uniform taxes to outperform entry fees in terms of aggregate welfare. Nevertheless, entry fees are preferred by most users because the optimal uniform tax exceeds the marginal social cost of water use for all but the most productive fields. However, driven by irreversible well investments that lock in depletion from high-externality early entrants, the effectiveness and popularity of entry fees decline rapidly over time. These findings highlight how heterogeneity and irreversibility jointly shape the efficiency and political feasibility of environmental regulation.

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The Hidden Curriculum

Michael G. Cuna

Despite dramatically expanded access to selective U.S. colleges, first-generation students persistently trail continuing-generation peers in GPA, internship attainment, and early-career outcomes. We identify a key mechanism: the hidden curriculum– unwritten strategies like cold-emailing alumni or strategically engaging faculty– essential for success yet unknown and costly to discover without guidance. Leveraging survey and administrative data from 100,000+ undergraduates across 20 public universities, we document stark disparities: first-generation students invest 14-26% less in these high-return hidden actions while over-investing in formal tasks. Standard explanations–income, ability, or preferences–do not fully explain these gaps. Through a field experiment at UC Berkeley, we isolate causal channels by randomizing information on action availability (awareness) versus returns (beliefs): awareness treatments close the 30% baseline gap almost entirely. Finally, we develop an AI college advisor to expose underlying search frictions in an online experiment; first generation students allocate just 11% (versus 16%) of queries to hidden topics and follow up about 48% less on hidden curriculum nudges. However, an “active” AI that increases awareness, narrows these search gaps and follow up behaviors. By formalizing the hidden curriculum as dual informational frictions, we demonstrate that overcoming these invisible barriers requires more than equal access.

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Does Greater Policy Intensity Improve Policy Effectiveness? Evidence from Seoul, South Korea

Hayeon Jeong

Many policies in practice become gradually more intensive over time in the hope of becoming more effective. While stricter policies may enhance compliance, they may also encourage loophole exploiting behaviors that offset intended policy benefits. If the increase in loophole exploitation outweighs the increase in compliance, overall policy effectiveness can actually decrease when a policy is intensified. I exploit the gradual intensification of Seoul’s vehicle control policy—an increasingly common policy instrument shaping pollution exposures in cities globally. In my theoretical framework, I model individual decisions to comply, exploit loopholes, or violate the policy outright, and assess how these responses vary with policy stringency. Empirical results support the theoretical prediction that higher policy intensity can induce greater loophole exploitation. I find loophole exploiting behaviors—driving outside regulated hours (before 6 a.m. or after 9 p.m.)—increased with policy tightening. Across multiple tightening events, the most stringent phase led to a 19% rise in loophole exploitation, the next to 13%, and the least stringent to 7%. I find a deterioration in air quality (PM10, PM2.5) during non-crackdown hours due to loophole exploitation. The resulting net health costs underscore the potential unintended consequences of intensified regulation. I emphasize the need to consider both the health benefits from policy compliance and the health costs from loophole exploitation to accurately estimate the true health impact of policy intensification.

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Beyond argumentation: AI-powered Socratic dialogue and political moderation in public deliberation

José Ramón Enríquez

I examine whether AI-guided reasoning reduces issue polarization in two preregistered experiments conducted on deliberation.io, a purpose-built platform for deliberation research, with 5,000 participants across contentious U.S. policy issues. Relative to reflective writing, AI-emotional regulation, and AI-grammar correction, AI-Socratic dialogue—which prompts users to articulate supporting arguments—significantly moderates extreme positions on mental health-based gun regulation. A second experiment comparing AI-Socratic dialogue to AI-grammar correction across abortion, handgun regulation, and voter ID requirements finds that extreme participants moderate their positions and exhibit behavioral changes, including increased cross-partisan donations and reduced endorsement of extreme comments. Moderation effects are largest for abortion, where baseline cross-partisan agreement was highest, and concentrate among participants with low initial confidence, high susceptibility to elite cues, and limited cross-party interactions. Text analysis of conversational transcripts reveals that moderation operates through increased consideration of alternative perspectives rather than improved argument quality, challenging standard deliberative theory. One-month follow-up displays mixed persistence and minimal spillovers to untreated issues, consistent with issue-specific effects. Perceived agency remains equivalent across conditions, suggesting preserved autonomy. Overall, results indicate that AI conversational agents can facilitate compromise through previously underexplored mechanisms, offering promising scalable applications.

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1. Why does it feel like things are always getting worse? That’s a deep question, but in the short term, part of the problem might be the internet: people who answer the University of Michigan Consumer Sentiment survey online tend to rate the economy as worse than otherwise similar people who respond by phone. Accordingly, UMich’s switch to online surveys in 2024 means that their consumer sentiment index might be about 9 percentage points lower today than it would be if we still answered surveys by phone. — Matt Clancy

2. One of AGF’s goals for 2026 is to learn more about the landscape of abundance and growth politics in the European Union, and to think about how we can support European policymakers and activists who want to build more housing, produce more energy, and generally grow faster. In that vein, I quite liked this anonymously-authored piece by European Commission staffers, arguing that members of the effective altruism community should actively seek employment at the Commission and in other EU institutions. The US policy landscape is characterized by pretty weak, understaffed offices for legislators and executive officials, plus a huge number of advocacy groups and think tanks. In Europe, that’s all much more centralized in governments and the EU themselves, meaning actually working at places like the Commission can be a very powerful route to impact. And if you’re an EU resident interested in these questions, please email me! — Dylan Matthews

3. What could the EU learn from housing incentive design ideas in the US? Progress Ireland notes growing US interest in flexible infrastructure funding grants awarded on a per-housing-unit basis. US policymakers have had to overcome Census data challenges on actual annual housing production to develop per-unit funding titration in bipartisan proposals like the Build Now Act; if the EU has the right data infrastructure, then tying EU infrastructure funds for high-opportunity metros to housing production in those metros could help both the politics and the practices of pro-growth reform. — Alex Armlovich

4. The EPA just repealed the endangerment finding – the 2009 determination that greenhouse gases endanger public health and welfare, and the regulatory foundation for US climate policy under the Clean Air Act. Philip Rossetti at R Street has a helpful take on the legal battles likely to follow, and why sound regulatory design beats legal whipsawing. Timely: a working paper out last month (bite-sized summary here) finds that this type of climate policy uncertainty reverberates through the economy, depressing firms’ investment and R&D, reducing output, and raising prices. — Willow Latham-Proenca

   

5. This week I’m working my way through Asterisk Magazine’s newest issue on science, which has many excellent pieces relevant to folks working on innovation policy. Click for Jolie Gan’s exploration of the increased importance of legibility and communication infrastructure as labs start looking beyond federal funding; stay for Abhishaike Mahajan’s conversation with Clara Collier about the role of AI across preclinical, clinical, and postclinical biology; and keep refreshing the tab for Karthik Tadepalli’s forthcoming history of ITRI, the Taiwanese government R&D lab that spun out TSMC. — Jordan Dworkin

6. Childcare is a domain where technological progress doesn’t help reduce costs that much, because labor costs are such a big part of the story. If you want no more than 4 kids per adult, you’re always going to have to pay at least 25% of someone’s wages. But that doesn’t mean we shouldn’t try to lower costs where we can. Searchlight Institute points to one seemingly easy fix: more states should let daycares operate above the ground floor. — Matt Clancy, Former Daycare Worker

Some other exciting updates from our grantees:

• Open NY stood alongside Governor Hochul and Mayor Adams to support modernizing environmental review.

• UK Research and Innovation opened their second round of metascience research grants.

• And Renaissance Philanthropy just launched Pilot City, connecting cities with local academic expertise through matchmaking events.

On the clinical trials front, several of our grantees teamed up to launch Clinical Trials Abundance, a new joint blog on making trials more efficient. Saloni Dattani is leading the effort and also published The Case for sharing clinical trial data this week.

The drug was clearly effective in reducing cholesterol levels, but in 1980, Sankyo abandoned its clinical trials after studies in dogs appeared to show intestinal tumors. The details of these findings were never formally reported; at the time, there were only rumors about what had led the company to shut down its trials.

Meanwhile, Merck was testing a nearly identical compound3, lovastatin, and heard about the decision. They expected it would be a multimillion dollar drug, and so took the sudden halt seriously: what if lovastatin would cause the same side effects?

Merck paused its own trials, and asked Sankyo for further details, but the latter declined to share them.4 So Merck conducted further toxicology studies of their own to understand whether the risks were real, which eventually showed the changes were benign and could be reversed. Three years passed before Merck resumed their clinical trials. When they were completed, they showed a large reduction in blood cholesterol reduction and few side effects. Lovastatin (now known as ‘Mevacor’) became the first statin approved in 1987.5

Decades later, after millions of people have been treated and monitored, the evidence shows no increased risk of cancers from statins. They have likely saved millions of lives by reducing the risks of heart attacks and strokes by roughly 20%, and annual mortality rates by 10%.

I’ve been wondering, what if Sankyo had shared its data publicly?

It’s possible that researchers might have analyzed the data further and reached the conclusions sooner. Merck could have avoided redundant studies, and both companies might have reached the finish line, speeding up the arrival of a drug class that would go on to save millions of lives.

Another possibility is that Merck might have instead funded trials for another drug candidate – one that was less similar in structure to Sankyo’s drug, as some within Merck suggested doing. Alternatively, if Merck had never heard about Sankyo’s halted trials at all, they might simply have proceeded, and still correctly found no evidence of harm in their own clinical studies in human patients.

The counterfactual from the story is ambiguous, but it shows that pharmaceutical companies respond to data from other firms. It also highlights a tension: transparency, or even partial transparency, can affect experimentation. When researchers or firms learn early that a similar approach has failed, they may shift toward safer, more predictable projects, at the cost of unexpected successes.

At the same time, access to the underlying data would have allowed other scientists to scrutinize the findings directly, rather than rely on rumors. That would have allowed for better decisions about what precisely to investigate further – helping understand which doses it occurred at and what types of cellular changes were seen, for example – and make more informed decisions about their own research and development plans.

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Medicine is an area where transparency has been considered valuable for a long time, and genuine progress has been made in improving it, as I’ll describe in a future post. The consequences extend well beyond the research phase; they can shape decisions about which drugs regulators approve, which treatments insurers cover, and which treatments physicians prescribe.

The stakes are unusually high. In the case of drugs like statins, such decisions touch tens of millions of patients each year6, with hundreds of millions – sometimes billions – of dollars resting on the results of trials.

Transparency at various stages of the process can shape decisions on whether to continue research into drugs, test them in clinical trials, approve them, and how to price them.

If transparency matters this much, what kind of data should be available in practice?

It can seem like an ambitious reform to ask for data at the level of individual patients so the findings can be verified and potentially re-analyzed, because research can involve sensitive personal information, and preparing datasets and documentation for public sharing can be time consuming.

But most highly-cited clinical trials say they will share data, and many say this will be anonymized data from individual patients. Moreover, a few recent efforts have tried to address this problem, including Vivli.org and ClinicalStudyDataRequest.com, with data platforms where clinical trial researchers can deposit their datasets securely.7

In practice, however, data from clinical trials remains largely inaccessible and enforcement by journals is weak.

We think there are probably many benefits of transparency in clinical trials, beyond verifying data. They include:

• Better meta-analysis. When more trial results are available, researchers can perform meta-analyses that combine evidence across studies. This improves precision and allows for stronger conclusions than single studies can support, for example by helping to identify effects on rare outcomes, such as the effects of flu vaccines in reducing mortality rates, or rare but serious harms.

  • A recent meta-analysis of 19 clinical trials on statins, for example, found no increase in the risks of most side effects listed on the drugs’ labels (such as brain fog, diarrhea, pain, vision impairment, and 58 other outcomes that had showed no difference in risk between the drug and placebo). Only a few side effects were validated, such as muscle weakness, rhabdomyolysis in rare cases, and a slight increase in diabetes. With these conclusions, the researchers suggested that drug labels should be updated.

• Understanding inconsistent results. Clinical trials studying similar treatments sometimes reach different conclusions, which leaves uncertainty about whether the differences reflect chance, patient populations, study design, or other factors. By pooling data across studies, researchers can explore these sources of variation more systematically. They could, for example, try to identify when apparent contradictions stem from targeting different biological mechanisms.

• Further exploration. Transparency makes it easier to ask new questions of old data. Researchers may want to explore hypotheses that weren’t part of the original study, or revisit results in light of new evidence. When prior trial data are available, many of these questions can be answered without launching entirely new trials. This might include, for instance, when drugs initially developed for one condition show unexpected benefits for another.

• Better clinical decision-making. Doctors often have to choose between many drugs for the same condition, without having sufficient data for head-to-head comparisons. Individual trials can rarely answer questions like “Which drug performs best for these patients?” But with more data, techniques like network meta-analyses can help indirectly compare multiple treatments against each other, or derive better conclusions on how to tailor decisions to patients’ characteristics.

• Learning how to run trials better. Pooling data across many trials could also help answer questions about studies’ operational characteristics: How does remote monitoring compare with on-site testing? Which trial sites reliably deliver high-quality data? Which eligibility criteria slow down recruitment? Which practices reduce the chances of patients dropping out of studies? These questions could help design trials more efficiently, even though individual research groups rarely run enough trials to study these questions on their own.

• Reducing redundancy and wasted effort. Finally, transparency can help avoid repeating failures. Researchers can learn from what succeeded or failed to refine their hypotheses and improve their chances of developing effective drugs in the future. More recent evidence suggests that when reporting the headline results of trials became mandatory, pharmaceutical companies actively monitored competitors’ results and adjusted their own research plans accordingly, avoiding similar studies.

And the value of AI in performing many of these analyses is also constrained by access to the underlying data; the benefits depend on the availability and quality of data available to study.

Taken together, these benefits point to a broader function of transparency in medical research: it allows knowledge to accumulate more efficiently.

By making individual patient data available, studies can move beyond fixed headline results and become part of a cumulative evidence base, giving researchers and clinicians a fuller picture of data that they can use to answer broader questions.

This flow of information can influence the pace of basic research, shape drug development, guide clinical decisions, and ultimately, affect the health of millions of people.

1. David Card, Jesse Rothstein, and Moises Yi had a cool study published last year to try and get a reliable estimate of the wage benefits you get from living in a superstar city. You might think you can tease this out by just looking at wages in one city and comparing them to wages for the same kind of work in another. But this doesn’t work so well if the workers differ across cities - if more productive workers migrate to superstar cities, then that means the wage comparison is conflating the wage premium associated with the worker and the wage premium associated with the place. So Card and company instead look at how a worker’s wage changes when they move from city to city (even that makes it sound simpler than it really is!). They find coastal cities like New York, the Bay Area, and Washington, DC, pay a wage premium of 10-18% relative to the average city, though those gains are mostly offset by increases in the cost of living (especially housing). — Matt Clancy

2. This year’s remarkably bipartisan burst of federal housing policy legislation is back in action after the earlier breakdown of House-Senate talks: The House has put their package on the calendar this week, and the Senate is exploring floor time this month. This fresh NYT coverage provides a useful, highly simplified introduction to the two packages if you’re tuning in for the first time. — Alex Armlovich

3. Why is it so hard to build enough electricity supply to meet surging demand? A new draft paper from Joshua Macey and Lynne Kiesling argues that the current regulatory system creates perverse incentives for incumbents to keep supply constrained. Utilities that own both transmission and generation have reason to slow-walk competitors in interconnection queues or hold up new transmission. Incumbents control planning processes and cost allocation, deciding where new lines get built and who pays. And rate-of-return regulation rewards capital spending rather than system performance. The fix, they argue, is to separate out the parts of the system that are still true natural monopolies and reintroduce competition in the parts that aren’t - reducing opportunities to “ration by process rather than price.” — Willow Latham-Proenca

4. Here at AGF we care a lot about ensuring basic scientific research gets funded and conducted well, and this piece in Works in Progress by Aria Schrecker is a nice illustration of why that kind of work can matter in achieving a prosperous, healthy future for humans. Schrecker examines the characteristics of species that can outlive humans: lobsters, quahog clams, “the immortal jellyfish,” Greenland sharks, Aldabra tortoises, bowhead whales, and naked mole rats. That’s an astonishingly diverse list, including vertebrates and invertebrates; aquatic and land animals; mammals, fish, and reptiles. But Schrecker argues that some of the things they have in common, like slow metabolisms, could hold useful lessons for human longevity. — Dylan Matthews

5. It’s admin burden week in the AGF Innovation Policy vertical. Let’s start with the good: last week the NIH announced that they will no longer consider “Basic Experimental Studies Involving Humans (BESH)” to be clinical trials. Since 2014, these studies – broadly, interventional experiments designed to produce fundamental knowledge about biology or behavior, rather than directly inform health or treatment – had been subject to the same policies and requirements as full clinical trials. From a neuroscientist: “The fact that collecting EEG data from healthy young adults while they did a simple visual memory task was considered a ‘clinical trial’ made a lot of our research a lot more difficult. I’m very happy to see this is no longer the case.” — Jordan Dworkin

6. Now for the ugly: a few days ago the Council on Government Relations released a new report on research regulations; they find that 66% of new or revised requirements since 1991 have been issued in the last 10 years, with 2025 showing the biggest year-on-year increase since their measurement began. Over on Twitter there’s some interesting discussion about how these numbers should be interpreted (some of the new regulations replace existing ones, some might be intended to reduce burden rather than enhance it), but the top-line interpretation seems to hold: the administrative and compliance burden on researchers is increasing rapidly. — Jordan Dworkin

   

7. AI makes it easier to do good science, but it also makes it easy to churn out junk papers. Late last month, arXiv, under strain from a rapid increase in submissions, changed its policy to block new university-affiliated authors from submitting their work to the site, unless they were personally endorsed by existing arXiv authors. For some thinking about how this all shakes out, Oliver Hanney (managing editor at VoxDev) has an interesting post on the future of scientific communication. Rising in importance for Hanney are curation, academic influencers, direct contact, living documents and more. This resonated with me and a lot of the thinking behind our program to support living literature reviews (not too late to submit an application; we’re reviewing for the next batch in the next few weeks!). — Matt Clancy

We also wanted to celebrate some exciting announcements from a couple of our grantees this week. Adam Kroetsch launched a new research project, the Clinical Trials Efficiency Project, where he’ll be thinking about systemic reform and collective action around clinical trials. Another grantee, Ruxandra Teslo, will be focusing on Clinical Trial Abundance work as a Fellow at Renaissance Philanthropy. We’re excited to support multiple grantees who are fully focused on this important work!

We use job market papers to track where the next generation of researchers is pushing the frontier on energy economics - particularly when they’re addressing timely, real-world questions like the role of energy in productivity, how firms respond to environmental regulations, and how local incentives affect generation buildout.

We found 19 papers covering everything from transmission to distributed solar to the local externalities of wind generation (twice!). We’ve posted the full list of those papers here. Below are five papers (titles and abstract) that I’m especially excited to dig into.

The Role of Energy Efficiency in Productivity: Evidence from Canada

Anil Gogebakan

This paper quantifies how misallocation of energy, alongside capital and labor, across provinces and sectors reduces productivity. Using Canadian provincial input–output data (2014–2020) within a Hsieh–Klenow framework, I decompose productivity losses into interprovincial (within-sector) and intersectoral (within-province) components and estimate each input’s contribution separately. Unlike most studies focused on the manufacturing sector, this is the first comprehensive analysis of energy misallocation covering the entire economy. Results suggest misallocation lowers aggregate productivity by 5–8%, with most of the gap driven by within-sector distortions. Energy, though only around 8% of input costs, accounts for up to 1.5% of the gap—comparable to capital and exceeding labor—highlighting its outsized role. The findings identify interprovincial barriers and energy market distortions as key areas for narrowing productivity gaps and guiding climate policy. Reallocating energy could significantly improve productivity while reducing emissions, delivering a ‘double dividend.’

Link

Transmission Congestion is Knocking the Wind Out of Renewable Investments

Abigail Boatwright

Transmission congestion represents a critical bottleneck for the efficient deployment of renewable energy necessary to meet rising electricity demand and decarbonization targets. This paper provides the first causal quantification of how constraints within an electricity market region alter the scale and spatial allocation of energy investments. Crucially, congestion modifies incentives, creating downside risk that deters renewables while generating profitable rents that sustain otherwise uneconomic fossil fuel projects. I estimate a developer entry and location choice model to distinguish how congestion drives both early site screening and later project attrition. Empirical results using data from Texas confirm that high congestion losses strongly discourage applications and push renewables from optimal resource-rich sites. Counterfactual simulations illustrate substantial welfare gains: relieving the ten most constrained lines boosts planned renewable output by 1.6 million MWh and corrects spatial misallocation, with 8% of the increase coming from projects moving to more productive sites. Simultaneously, the targeted congestion relief deters over 1,500 MW of planned fossil fuel investment, avoiding an estimated $608 million annually in CO2 damages. These findings demonstrate that current U.S. regulatory practice, which omits the value of generation investment effects in transmission cost-benefit analysis, significantly undervalues strategic transmission planning, risking suboptimal infrastructure deployment.

Link

Building with Externalities: Local Governments and Wind Farms

Zane Kashner

Does local government regulation of new infrastructure with local externalities result in efficiency? Although local governments’ choices can internalize local costs, political or contracting frictions may cause actual outcomes to deviate from the idealized benchmark of Coase (1960). I study this problem in the context of wind farms. I develop a model of interaction between wind developers and local governments where wind farms are built only if they are both profitable and allowed by local governments, who weigh local costs against payments from developers. I estimate that the average household’s cost of living three miles from a wind farm is around 7.5% of its home’s value. I find that built wind farms trade off more than $7 of engineering profit for each $1 of cost to households. This arises in part because local governments must be paid roughly $3 for every $1 of externality to approve projects. Moreover, I find that state regulations limiting payments to local governments further depress wind-farm construction. I compare the performance of alternative developer-government contracting rules in reaching the United States’ net-zero carbon goals. I find that requiring wind developers to pay local governments 20% of nearby homes’ value raises social welfare by about $220 billion relative to when developers cannot pay local governments.

Link

WIMBY: Wind In My Back Yard

Jacob Ebersole

Wind energy projects generate global environmental benefits that greatly exceed local property value losses. Yet county governments often reject proposed projects. To assess the electoral incentives of permit-issuing county officials, I link spatial variation in local costs and benefits to precinct-level election results in Illinois. Using a difference-in-differences design, I find that incumbent county officials lose vote share in precincts that incur property value losses following project approvals, but gain votes in precincts that benefit from higher school district property tax revenues.

Link

The Role of Incumbent Firms and Regulation in America’s Natural Gas Energy Transition

Michael R. Karas

This paper examines how incumbent firms’ adoption of new energy technologies is shaped by the regulatory environment, focusing on the transition from manufactured to natural gas in the United States during the first half of the twentieth century. Using detailed, newly digitized panel data on municipality-level gas utility services, I exploit variation in pipeline proximity for municipalities along the pipeline’s path and regulatory changes introduced by the Natural Gas Act of 1938 to investigate incumbent utility firms’ decisions to switch from manufactured to natural gas. I find that incumbent firms delayed adopting natural gas during the initial unregulated period but accelerated adoption after the implementation of federal regulation. When considering the factors that predict early adoption by incumbents, ownership by a holding company is associated with switching before federal regulation, while higher switching costs are associated with switching after regulation. These results are consistent with federal regulation helping reduce the gap between regulated retail prices charged by gas utilities and previously unregulated wholesale pipeline prices, allowing incumbents to recoup switching costs, illustrating that coordination through regulation or holding company ownership can reduce uncertainty and expedite technological transitions.

Link

The Impact of Solar Panel Installation on Electricity Consumption and Production: A Firm’s Perspective

Natalia D’Agosti

Since 2010, the Uruguayan government has fostered the installation of solar panels among firms to promote the production of small-scale renewable electricity. Under this policy, firms that have installed solar panels are allowed to feed any surplus electricity into the grid. Using firm-level electricity consumption and grid injection for all the firms that installed a microgenerator from April 2011 to September 2022, we study the economic and environmental consequences of this policy. First, we find that installing a solar panel reduces the amount of electricity extracted from the grid. Second, we find that it increases the electricity injected into the grid. Third, we find that it reduces CO2 emissions only marginally. Fourth, we provide evidence of a rebound effect, which ranges from 20% to 26%. Lastly, we propose an alternative policy that allows firms to store their excess electricity in batteries rather than immediately injecting it into the grid. This policy would further reduce CO2 emissions by 2.7%, incentivizing the injection of electricity at night, when fossil-fuel-based facilities meet the demand at the margin. This result highlights the importance of integrating storage solutions into renewable energy policy design.

Link

For more on why we’re sharing these, take a look at our first post on job market papers.

First we present only titles, so you can quickly skim what’s here. After the titles, we present titles plus abstracts.

Titles Index

Titles are presented in random order. There might be additional authors on these papers - we’ve listed the associated job market candidate only.

1. Partisan Climate Action, Utility Interests, and Policy Choice in the U.S. Power Sector by Witson Peña

2. Building with Externalities: Local Governments and Wind Farms by Zane Kashner

3. Clean Energy Subsidies and Capital Misallocation by Yuqi Zhang

4. Pollution Prevention or Green Innovation? Unraveling Firms’ Environmental Choices by Sumaya Falak Memon

5. Innovation Path Choices in China’s Electric Vehicle Battery Industry by Qian Wang

6. Pairing Batteries with Renewables: How Ownership Shapes Operational Incentives and Market Outcomes by Pietro Visaggio

7. Powering the Future: The Long-Term Human Capital Effects of Rural Electrification by Pan Chen

8. The Impact of Solar Panel Installation on Electricity Consumption and Production: A Firm’s Perspective by Natalia D’Agosti

9. The Role of Incumbent Firms and Regulation in America’s Natural Gas Energy Transition by Michael R. Karas

10. Technology Complementarities and Subsidy Policy: Evidence from Electric Vehicle and Solar Panel Adoption by Maria Garcia-Osipenko

11. Power to the people: The local economic effects of renewable energy communities in the UK by Gökhan Dilek

12. Capital Replacement and the Demand for Clean Technology by Felix Samy Soliman

13. Investment Responses to Environmental Policies: Carbon Taxes, Subsidies, and Public Investment by Ellie Cothren

14. Lone Star Grid: The Impact of Texas Electricity Interconnection by Connor Neff

15. Precautionary Electrification by Audrey Azerot

16. Transmission Congestion is Knocking the Wind Out of Renewable Investments by Abigail Boatwright

17. WIMBY: Wind In My Back Yard by Jacob Ebersole

18. When to go Green? Firm dynamics & Clean Technology Adoption by Bas Gorrens

19. The Role of Energy Efficiency in Productivity: Evidence from Canada by Anil Gogebakan

Titles, Abstracts, and Links to Papers

Partisan Climate Action, Utility Interests, and Policy Choice in the U.S. Power Sector

Witson Peña

This paper investigates how U.S. gubernatorial partisanship and electric utility interests jointly shape the adoption and stringency of three widely used electricity-sector climate policies: greenhouse gas cap-and-trade, emissions standards, and renewable portfolio standards. Using panel data for 48 states over 29 years, this study applies difference-indifferences and regression discontinuity designs that exploit within-state partisan alternation and quasi-random variation from close gubernatorial elections. The results indicate that Democratic governorships associate with higher probabilities of policy adoption and greater stringency than Republican ones. However, these partisan effects attenuate in states with fossil-intensive utility capacity and strengthen in renewable-rich states, particularly for discretionary and mandatory renewable portfolio standards. This work extends the empirical political economy literature by comparing instrument choice and stringency across three major electricity-sector climate policies and by evaluating how utility sector composition and reelection incentives moderate or amplify partisan influence. The findings highlight that electricity-sector decarbonization strategies need to account for both environmental externalities and the local political-economic conditions that shape feasible policy options.

Link

Building with Externalities: Local Governments and Wind Farms

Zane Kashner

Does local government regulation of new infrastructure with local externalities result in efficiency? Although local governments’ choices can internalize local costs, political or contracting frictions may cause actual outcomes to deviate from the idealized benchmark of Coase (1960). I study this problem in the context of wind farms. I develop a model of interaction between wind developers and local governments where wind farms are built only if they are both profitable and allowed by local governments, who weigh local costs against payments from developers. I estimate that the average household’s cost of living three miles from a wind farm is around 7.5% of its home’s value. I find that built wind farms trade off more than $7 of engineering profit for each $1 of cost to households. This arises in part because local governments must be paid roughly $3 for every $1 of externality to approve projects. Moreover, I find that state regulations limiting payments to local governments further depress wind-farm construction. I compare the performance of alternative developer-government contracting rules in reaching the United States’ net-zero carbon goals. I find that requiring wind developers to pay local governments 20% of nearby homes’ value raises social welfare by about $220 billion relative to when developers cannot pay local governments.

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Clean Energy Subsidies and Capital Misallocation

Yuqi Zhang

Clean-energy subsidies can seek both aggregate decarbonization and local economic development. I study such a policy in the context of the Inflation Reduction Act’s energy communities (EC) bonus, a place-based tax credit for renewable energy projects in fossil-fuel–dependent areas. I build a quantitative multi-region model in which profit-maximizing developers choose where to build renewables, electricity is traded across localized grids, and final-good producers use electricity as an input. Calibrated to U.S. data, the model shows the EC bonus successfully attracts investment in targeted regions but crowds out a meaningful fraction of investment from nearby non-targeted areas through lower electricity prices. The policy delivers modest local gains in wages and output in targeted areas that spill over to neighboring regions. Holding the fiscal cost fixed, a hypothetical subsidy that does not vary across space produces more additional renewable energy generation and greater fossil-fuel displacement than the EC bonus.

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Pollution Prevention or Green Innovation? Unraveling Firms’ Environmental Choices

Sumaya Falak Memon

We study how firms internalize environmental constraints through two distinct channels: (i) pollution prevention; operational changes that reduce pollution at its source, and (ii) longer-term innovation captured by green patents. Linking data from S&P Compustat, the EPA TRI, and supplemental sources from 2001–2020, we find that, relative to firms that take no action and those that rely on source reduction only, firms that patent are capital-intensive, hold more cash, and operate in less-concentrated industries, while source-reduction firms are emission-intensive, and more exposed to regulatory scrutiny. Using Bartik-style and peer patenting instrumental variables to address endogeneity, we find meaningful substitution between the two actions. Relative to baseline means, ten additional green patents reduce source-reduction activity by about 10%, lower emissions by 37%, raise markups by 0.1–0.3%, and reduce the probability of a new enforcement case by roughly 3 pp. Source reduction, by contrast, delivers gains primarily on the environmental side, with ten additional measures lowering emissions by about 17% of the mean.

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Innovation Path Choices in China’s Electric Vehicle Battery Industry

Qian Wang

Technologies that are equally green can yield very different gains in social welfare. I examine whether market-driven innovation in green industries follows the socially optimal technological path when firms face competing options with distinct cost structures, and explore policies that can correct potential inefficiencies in path choices. I estimate a dynamic structural model of Chinese electric vehicle (EV) battery suppliers choosing how much to innovate along two competing paths: Lithium-Ion–Ferro–Phosphate (LFP) and Nickel–Cobalt–Manganese (NCM). The analysis shows that a social planner would undertake about four times as much innovation as the market in LFP, which involves higher innovation sunk costs but delivers lower marginal costs in production. By contrast, the market innovates about twice as much as the social planner in NCM, which has the opposite cost structure. I attribute this divergence primarily to vertical separation between battery suppliers and EV makers, competition between EVs, and limited demand for EVs in early years, rather than to innovation spillovers or environmental benefits that firms fail to internalize. Finally, I demonstrate how R&D subsidies could help correct the path choice inefficiency and shift innovation toward the socially preferred path.

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Pairing Batteries with Renewables: How Ownership Shapes Operational Incentives and Market Outcomes

Pietro Visaggio

This paper examines how battery storage ownership structure affects wholesale electricity market outcomes by shaping operational incentives. Using a dynamic dispatch model calibrated to Texas data, I show how transmission congestion creates conditions in which batteries operated jointly with a renewable plant are used strategically to increase the value of renewable production. The strength of this incentive depends on supply elasticity and the timing of renewable production. Co-owned batteries earn roughly 76 percent higher profits than standalone batteries in markets where strategic incentives arise. Despite this strategic behavior, co-owned and standalone batteries produce similar effects on consumer surplus, renewable curtailment, and carbon emissions. While market conditions do not generate enough profits for battery investment to be viable—regardless of ownership—the positive effects on consumer surplus and carbon emissions make batteries desirable from consumers’ perspective. Under a uniform subsidy policy, co-ownership’s higher profitability makes more batteries viable at moderate subsidy rates.

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Powering the Future: The Long-Term Human Capital Effects of Rural Electrification

Pan Chen

This paper examines how rural electrification during middle childhood affected longterm human capital in 1990s China. Unlike most studies that focus on grid connection, my paper emphasizes electricity affordability. I develop a simple model of human capital investment in which electrification is an adult-labor-biased technical change in agriculture. Because children in middle childhood are poor substitutes for adult laborers in agricultural work, the productivity shock has little impact on their opportunity cost of schooling. The model therefore predicts a strong income effect and a negligible substitution effect, leading to higher schooling for children. I test this empirically using a cohort difference-in-differences design, leveraging variation in electricity price reductions across counties. I find that lower electricity prices in middle childhood significantly increase educational attainment and later adult cognitive scores. Increased agricultural productivity is identified as one mechanism, consistent with the model. This paper also highlights why older children are not significantly affected. China’s late-1990s experience offers insights for rural electrification efforts in many developing countries today

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The Impact of Solar Panel Installation on ElectricityConsumption and Production: A Firm’s Perspective

Natalia D’Agosti

Since 2010, the Uruguayan government has fostered the installation of solar panels among firms to promote the production of small-scale renewable electricity. Under this policy, firms that have installed solar panels are allowed to feed any surplus electricity into the grid. Using firm-level electricity consumption and grid injection for all the firms that installed a microgenerator from April 2011 to September 2022, we study the economic and environmental consequences of this policy. First, we find that installing a solar panel reduces the amount of electricity extracted from the grid. Second, we find that it increases the electricity injected into the grid. Third, we find that it reduces CO2 emissions only marginally. Fourth, we provide evidence of a rebound effect, which ranges from 20% to 26%. Lastly, we propose an alternative policy that allows firms to store their excess electricity in batteries rather than immediately injecting it into the grid. This policy would further reduce CO2 emissions by 2.7%, incentivizing the injection of electricity at night, when fossil-fuel-based facilities meet the demand at the margin. This result highlights the importance of integrating storage solutions into renewable energy policy design.

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The Role of Incumbent Firms and Regulation in America’s Natural Gas Energy Transition

Michael R. Karas

This paper examines how incumbent firms’ adoption of new energy technologies is shaped by the regulatory environment, focusing on the transition from manufactured to natural gas in the United States during the first half of the twentieth century. Using detailed, newly digitized panel data on municipality-level gas utility services, I exploit variation in pipeline proximity for municipalities along the pipeline’s path and regulatory changes introduced by the Natural Gas Act of 1938 to investigate incumbent utility firms’ decisions to switch from manufactured to natural gas. I find that incumbent firms delayed adopting natural gas during the initial unregulated period but accelerated adoption after the implementation of federal regulation. When considering the factors that predict early adoption by incumbents, ownership by a holding company is associated with switching before federal regulation, while higher switching costs are associated with switching after regulation. These results are consistent with federal regulation helping reduce the gap between regulated retail prices charged by gas utilities and previously unregulated wholesale pipeline prices, allowing incumbents to recoup switching costs, illustrating that coordination through regulation or holding company ownership can reduce uncertainty and expedite technological transitions.

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Technology Complementarities and Subsidy Policy: Evidence from Electric Vehicle and Solar Panel Adoption

Maria Garcia-Osipenko

Government policies target air pollution and climate change by incentivizing adoption of electric vehicles (EVs) and/or residential solar panels (PVs). Knowledge of whether these goods are complements or substitutes can be used to design policies that target environmental externalities more efficiently. I use California household-level data to estimate a structural multi-product demand model. I find that consumers view PVs and EVs as complements, with the degree of complementarity varying with vehicle size and income. Counterfactual experiments reveal that complementarity significantly increases bundled EV-PV purchases. This complementarity can be leveraged to design policies that achieve emission targets at lower cost.

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Power to the people: The local economic effects of renewable energy communities in the UK

Gökhan Dilek

Local opposition to renewable energy plants (Not In My Backyard) contrasts with citizen-led

renewable energy communities (RECs), where residents actively invest in and manage renewable projects. This contrast raises an important question: Do community renewable plants generate greater local economic benefits than commercial ones? By applying a heterogeneity-robust difference-in-difference estimator, our findings suggest that they do, while benefit-sharing practices also play a key role in shaping local outcomes. These results have important implications for the distributional dimensions of the energy transition and the broader green growth agenda.

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Capital Replacement and the Demand for Clean Technology

Felix Samy Soliman

I study why clean technologies are adopted slowly and how this slow adoption undermines clean innovation. Using an event study around large energy price swings, I provide evidence that industries with short-lived assets see greater increases in energy efficiency and green patenting, consistent with lock-in among users of long-lived assets. To assess policy implications for the green transition, I embed the feedback between irreversible investment and energy saving innovation in an integrated assessment model. Slow adoption delays the pass-through of clean innovation to energy demand relative to benchmark models. The sluggish uptake of innovation justifies higher carbon taxes if the social cost of carbon rises with cumulative emissions. These higher taxes reduce investment, thereby reducing R&D incentives and further limiting the power of green innovation in facilitating emission reductions in the short to medium run. Replacement subsidies can partly substitute for carbon taxes. Uniform subsidies improve fuel efficiency but raise emissions via scale effects. Redirecting these subsidies toward electrification is a more effective second-best when the electricity mix is sufficiently clean.

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Investment Responses to Environmental Policies: Carbon Taxes, Subsidies, and Public Investment

Ellie Cothren

Using a climate-economy model featuring fossil fuel and renewable energy infrastructure owned by both the public and private sector, I compute the economy’s optimal transition path to a new long-run equilibrium following a variety of policy shocks. Policy instruments include a $45 per ton carbon tax, a 30% subsidy on renewable energy consumption, and a $1 billion public investment in renewable infrastructure over ten years. While economic welfare is consistent across all policy scenarios analyzed, the responses of private energy investments vary. Private fossil fuel investments only decrease when a carbon tax is implemented, and decrease the most when all three policy instruments are used simultaneously. Private renewable investments increase nearly 4 times as much under a renewable subsidy than under a carbon tax. When public investment policy is implemented, it crowds out some private renewable investment, but this crowding-out effect disappears if another policy is implemented alongside the public investment. These findings highlight the complementary roles different environmental policy tools can play in the energy sector’s transition to renewable energy.

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Lone Star Grid: The Impact of Texas Electricity Interconnection

Connor Neff

Using a novel least average cost dispatch (LACD) algorithm, this paper evaluates the economic and environmental costs of Texas maintaining an isolated electricity grid. We build a structural model to characterize the supply of electricity and simulate counterfactual integration scenarios. We find that Texas’s largest population zones connected with neighboring states to the East results in reductions of generation costs of $100M annually. We also show that accounting for fixed costs in the dispatch model allocates generation to units with lower average fixed costs than under least marginal cost dispatch. This change in allocation along the margin results in large differences in emissions impacts. We find that some interconnection scenarios decrease the social cost of emissions by up to $360M annually, while others result in higher emissions. In a case study for one proposed interconnection, we show that generation and revenues shift to the Texas zone. We also show that reductions in costs of maintaining reliability are about as much as generation cost reductions.

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Precautionary Electrification

Audrey Azerot

This paper argues that households engage in precautionary electrification, i.e. they adopt electricity instead of gas to insure themselves against volatile gas prices. The analysis is motivated by two facts: (1) natural gas prices in the United States have been fluctuating more than electricity prices over the past two decades; and (2) lower-income households are more likely to rely on electricity for heating than higher-income households. Using state-level data from 1999 to 2023, I show that greater gas price volatility leads to higher electrification, particularly among low-income households. I calibrate a structural model of household energy choice with non-homothetic preferences and costly fuel switching which matches the empirical relationship between income, fuel choice, and energy expenditure. In the model, higher gas volatility increases electrification. However, it also decreases welfare, with poorer households suffering the most. Finally, I compare two insurance schemes (flat transfer and proportional subsidy) and show that precautionary electrification creates a policy trade-off between short-term insurance and long-term electrification.

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Transmission Congestion is Knocking the Wind Out of Renewable Investments

Abigail Boatwright

Transmission congestion represents a critical bottleneck for the efficient deployment of renewable energy necessary to meet rising electricity demand and decarbonization targets. This paper provides the first causal quantification of how constraints within an electricity market region alter the scale and spatial allocation of energy investments. Crucially, congestion modifies incentives, creating downside risk that deters renewables while generating profitable rents that sustain otherwise uneconomic fossil fuel projects. I estimate a developer entry and location choice model to distinguish how congestion drives both early site screening and later project attrition. Empirical results using data from Texas confirm that high congestion losses strongly discourage applications and push renewables from optimal resource-rich sites. Counterfactual simulations illustrate substantial welfare gains: relieving the ten most constrained lines boosts planned renewable output by 1.6 million MWh and corrects spatial misallocation, with 8% of the increase coming from projects moving to more productive sites. Simultaneously, the targeted congestion relief deters over 1,500 MW of planned fossil fuel investment, avoiding an estimated $608 million annually in CO2 damages. These findings demonstrate that current U.S. regulatory practice, which omits the value of generation investment effects in transmission cost-benefit analysis, significantly undervalues strategic transmission planning, risking suboptimal infrastructure deployment.

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WIMBY: Wind In My Back Yard

Jacob Ebersole

Wind energy projects generate global environmental benefits that greatly exceed local property value losses. Yet county governments often reject proposed projects. To assess the electoral incentives of permit-issuing county officials, I link spatial variation in local costs and benefits to precinct-level election results in Illinois. Using a difference-in-differences design, I find that incumbent county officials lose vote share in precincts that incur property value losses following project approvals, but gain votes in precincts that benefit from higher school district property tax revenues.

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When to go Green? Firm dynamics & Clean Technology Adoption

Bas Gorrens

Carbon pricing is a central policy instrument for reducing emissions, but governments face a trade-off: faster decarbonization can raise output losses and carbon leakage, while gradual implementation slows emission reductions. This paper studies how EU carbon policies have shaped firms’ adoption of abatement technologies and identifies the optimal trajectory to reach the EU’s 2050 net zero target, particularly in a unilateral context. I develop a dynamic heterogeneous-firm model in which forward-looking manufacturing firms choose when to adopt discrete abatement technologies under a gradually tightening carbon price. I estimate it using panel data on EU ETS firms from 2005-2019. The model rationalizes the low carbon prices of the 2010s as a consequence of gradual policy and firm anticipation. Emission reductions arise mainly from large, productive, and initially polluting firms. Anticipation of future tightening mitigates half of the short-run output losses in 2025 and two-thirds by 2050, keeping overall output losses below 2%. A moderately faster tightening could cut cumulative emissions by 15% at an additional cost of only 0.11% of output. Finally, because firms anticipate future policy changes, unilateral and global carbon pricing yield nearly identical effects on domestic output and carbon leakage.

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The Role of Energy Efficiency in Productivity: Evidence from Canada

Anil Gogebakan

This paper quantifies how misallocation of energy, alongside capital and labor, across provinces and sectors reduces productivity. Using Canadian provincial input–output data (2014–2020) within a Hsieh–Klenow framework, I decompose productivity losses into interprovincial (within-sector) and intersectoral (within-province) components and estimate each input’s contribution separately. Unlike most studies focused on the manufacturing sector, this is the first comprehensive analysis of energy misallocation covering the entire economy. Results suggest misallocation lowers aggregate productivity by 5–8%, with most of the gap driven by within-sector distortions. Energy, though only around 8% of input costs, accounts for up to 1.5% of the gap—comparable to capital and exceeding labor—highlighting its outsized role. The findings identify interprovincial barriers and energy market distortions as key areas for narrowing productivity gaps and guiding climate policy. Reallocating energy could significantly improve productivity while reducing emissions, delivering a ‘double dividend.’

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1. Critical comment on ‘Does Rent Control Turn Tenants Into NIMBYs?’ - How does rent regulation affect incumbent resident outlooks on housing growth? Two common intuitions have opposite signs: Maybe rent regulation makes renters more like change-averse homeowners who are partially insulated from the negative consequences of housing underproduction. Alternatively, rent regulation’s partial insulation from housing market forces could make some tenants more risk-tolerant by alleviating fears of amenity-driven hyperlocal displacement attributed to new housing, retail, and job growth. A recent paper on rent regulation in Berlin made headlines in finding some evidence in favor of the pro-housing hypothesis; this blog critiques that finding. The critique may be a bit harsh–one author of the Berlin paper himself already cautioned in The Atlantic “the confidence intervals are really large”--but this informal peer review digs in on precisely why the literature still can’t yet be sure what the net effect of rent regulation is on attitudes toward inclusive growth. — Alex Armlovich

2. Assessing the Cost of Impact Fees on Affordable Housing: An Analysis of Low-Income Housing Tax Credit Projects in California - Housing policy watchers may be familiar with the RAND report on how Californian multifamily development costs are more than twice as high as Texas, including the finding that Californian development fees can be between 10x and 40x higher than Texas. This new Terner report zooms in on how California’s uniquely toxic local housing fee regimes affect federally subsidized low income housing, with $300M/year in local impact fees consuming a troublingly high share of the value of California’s $550M/year allocation of new federal LIHTC subsidy awards. — Alex Armlovich

3. A geographical analysis of electricity prices published this week points out the fragmented nature of the affordability crisis. 2019-2025 price increases varied widely by state, with increases in California and the broader Northeast driving much of the national pattern. While prices tend to lag and there’s some indication that increases in other regions are catching up, it’s interesting to note the variance in explanations – at least to date – for what’s usually framed as a national trend. — Willow Latham-Proenca

   

4. Much of NEPA’s power to slow down project timelines comes from the lawsuits it enables. Litigation doesn’t just slow down projects directly - it also incentivizes agencies to spend time “bulletproofing” their environmental documents before projects can even start, and the threat of lawsuits makes all projects riskier, keeping some potential developers out of the market altogether. In a recent piece, Samuel Roland offers a fascinating historical deep-dive into how NEPA – which never explicitly provides for private lawsuits – turned into a litigation machine. — Willow Latham-Proenca

5. Institutional Review Boards (IRBs) are meant to protect research participants, but today their fragmented standards often create needless delays. In a new piece for the Institute for Progress, Ruxandra Teslo argues that this bureaucratic inertia isn’t inevitable. She makes the case for IRB reform by guaranteeing federally funded researchers the right to choose any compliant external IRB, creating a public registry of accredited boards, and establishing anonymous reporting to strengthen oversight. The result, she argues, would be faster, more consistent ethical review, that would protect participants while reducing administrative drag. — Saloni Dattani

6. Most discussions of health care in the US focus on the consumer or demand side: how do we lower drug prices for patients? How do we help the 8 percent of Americans lacking health insurance? How do we make it easier for people with insurance to get care? Those are all important questions, but Ashish Jha, dean of Brown’s School of Public Health and former Covid-19 czar under Biden, makes a compelling case that they’ve overshadowed equally important questions about supply: are too many hospitals and practices merging? Are we letting enough hospitals be built, and physicians be licensed? Are insurance payments incentivizing waste? Jha is starting a series of essays on these questions, and while the intro is light on details, I’ll be checking back in regularly. — Dylan Matthews

7. When drugs lose patent protection, the incentive for large-scale clinical trials of new indications dries up. A recent working paper estimates that hundreds of new uses for existing drugs have gone undeveloped as a result. Writing for the Good Science Project, Nicholas Reville proposes an NIH High-Leverage Trials program to fill this gap. The idea is to create infrastructure for running publicly funded Phase III trials of off-patent drugs and supplements. He points to some interesting precedent, including the 2002 Best Pharmaceuticals for Children Act, which empowered NIH to identify and study high-potential off-patent drugs; and he identifies a few dozen candidate trials and some strategies the program could use to run them more efficiently. — Jordan Dworkin

8. Last week’s science appropriations news was encouraging, but a Nature feature this week documents the damage done over the last year: almost 8,000 grants were terminated or frozen (2,600 of which have not been reinstated); science agencies lost roughly 20% of their staff; and although spending caught up to expectations, the number of new grants issued by NIH and NSF fell by almost a quarter. And even with strong appropriations, some threats remain. A separate Nature piece reports that 13 NIH advisory councils are on track to have no voting members by the end of the year. If that happens, new awards at those institutes could halt. — Jordan Dworkin

9. Finally, let’s zoom out to a big question: is progress slower than it could be, because too many people don’t believe progress is possible? I’ve wondered about this for awhile; in 2024 I wrote about the decline in positive, future-oriented writing around the world, which seems at least somewhat correlated with a slowing pace of economic growth. But which way does causality flow? A new paper about an experiment in raising ambitions about the future is interesting, when seen in this light. In a remote part of Ethiopia, economists showed a random subset of the population some documentaries about people like them working hard to achieve more material prosperity (control groups saw entertainment programs or just got a survey). Five years later, the authors find surprisingly large positive impacts on work levels, schooling for kids, and more, for the group that saw the aspiration-raising documentaries. I wouldn’t extrapolate too much from any single study, but it joins a host of other papers that show how ideas around what it is possible to do with your life matter. – Matt Clancy

To close, over at The Good Science Project, our own Jordan Dworkin and Stuart Buck debated Jordan’s recent white paper on how to realize a high return on investment when funding replications.