Since 2022, total energy generation has increased 128 terawatt-hours for the first six months of the year (January to June), or about 6 percent. In July, regional transmission operator (RTO) Pennsylvania, New Jersey, and Maryland Interconnection (PJM) recorded some of the highest auction prices for electricity in its history. For some households, that led to a stark increase in electricity costs.

Soon after, Pennsylvania Governor Josh Shapiro sued PJM to cap their auction prices and prevent rate hikes on customers. PJM pointed to the ongoing supply-demand imbalance as the source of high auction prices.

While the increase in demand in AI is substantial, net capacity—total potential energy generation—has been increasing year over year. According to Energy Information Administration (EIA) data, about 69 gigawatts of capacity was added between 2022 and 2024, with a potential additional generation of 604 terawatt-hours per year—more than enough to cover the growth from AI.

But capacity is sometimes a loose term. It can mean what a coal plant could theoretically deliver if it was run constantly for 24-hours, or it can mean what a solar panel can deliver at peak sunlight.

Those situations are not always possible. Coal plants go offline for repairs. Solar panels can never obtain full sunlight for 24 hours at a time.

For example, China often boasts of its installed solar capacity that easily dwarfs that of its coal generation capacity. But actual generation numbers show that China still gets much more of its energy from coal. China’s massive solar capacity doesn’t equate to massive solar generation. That’s because China’s capacity factor for solar—the ratio of what it actually generates to its theoretical nameplate capacity—can be as low as ten percent. In the U.S., average capacity factor for solar hovers around 20 to 30 percent, depending on where it is installed.

That’s why PJM and other RTOs use an adjusted metric for what is considered actually available capacity, such as unforced capacity (UCAP), which is equal to:

Where EFORd is the Equivalent Demand Forced Outage Rate—the pre-calculated rate at which an energy source is unavailable.

In PJM’s reports they have two measurements of unforced capacity: UCAP, which refers to the adjusted capacity available for market auctions, and Fixed Resource Requirement (FRR)—generation capacity at a fixed price for certain utilities with PJM that choose to not use the auctions for obtaining electricity.

In PJM’s capacity auctions, generators bid to provide capacity—energy that may or may not get used at a time in the future. If the energy is not needed, the generator still gets paid. But if the energy is needed and the generator can’t provide it, they can be penalized through deficiency charges.

In 2022, Dominion Energy of Virginia chose to leave the PJM markets and switch to FRR for their customers. Dominion is also the utility that provides energy generation for Loudon County where the largest collection of data centers resides. But in 2025 they returned to the UCAP markets.

ELCC Changes

In total, PJM’s capacity wasn’t really affected by Dominion leaving the auction and returning. Instead the drop in capacity in 2025 was largely a result of Federal Energy Regulatory Commission’s (FERC) marginal Effective Load Carrying Capability (ELCC) rule in 2024.

In 2022, Winter Storm Elliott almost led to rolling blackouts across the rust belt and exposed the lack of true capacity in energy markets. As a result, FERC reinterpreted how it valued capacity in energy markets to better weigh tight or marginal situations. The final marginal ELCC rule would weigh more resilient forms of energy over others, and some generators would bow out of capacity auctions as a result.

As part of ELCC rules, each generation source gets an ELCC rating that is effectively like a capacity factor—how reliable the source is to generate energy when needed—and the new rules set the ELCC ratings for solar much lower when they were.

With the new changes, tracking solar—solar panels that track the sun’s movements—would have its ELCC rating fall from 54 percent to 14 percent. Fixed-tilt solar would go from 36 percent to 9 percent. Battery storage, which was previously given ratings around 100 percent, would lose 20 to 30 percentage points. Wind would actually have its ratings upgraded for both onshore and offshore turbines.

The lower rates pushed the accredited UCAP much lower and make bidding in the capacity auction less appealing, with somewhere on the order of 34 gigawatts disappearing from the market as a result. The remaining generators can then charge more for the dwindling supply

It is constantly the target of shareholder activism over investment decisions that not only affect the retirement benefits of thousands of state workers, but they set the direction of pension plan investment nationwide.

A recent report funded by the Retired Public Employee's Association of California (RPEA) showed the plan chronically underperforms compared to its peers, especially considering its emphasis on alternate investments like private equity with little disclosure on fees. Other times, it is embattled over its Environmental, Social, and Governance (ESG) investments, and whether those are necessary to save the planet or simply throwing money down the well.

But little gets mentioned about how much CalPERS, and potentially other pension plans, are now paying towards health benefits since the passage of Obamacare.

What was once less than $2 billion a year in healthcare claim costs and administrative expenses for CalPERS before 2013 has doubled to become $4 billion and is now over $5 billion a year.

Some of that is related to flex-funding, where the pension fund pays for capitation—a specific set of services like primary care and some fee-for-service healthcare—up to a specified amount. Private insurers handle the rest. While claims spending has gone up, so have the premiums collected by CalPERS to pay for them, and the net balance has not changed all that much.

According to the CalPERS annual financial report, the 39.3 percent growth in claims expenses in fiscal year 2014 to 2015 was largely driven by flex-funded health plan activity and the 81 percent growth in administrative costs came from Affordable Care Act (ACA) tax payments.

Flex-funding was meant to help drive down healthcare costs. Rather than leaning on insurers to keep costs down, the pension fund handles claims and can see and control what claims get approved and for how much. They get to keep any net spending that doesn’t otherwise get used rather than the insurer.

But independent of flex-funding, CalPERS is still throwing billions at private insurers—mainly Health Maintenance Organization (HMO) plans like Kaiser Permanente—and Medicare buy-ins for the other end of healthcare for its members, upwards of $10 billion a year.

In terms of net healthcare spending, between Medicare, HMOs, and flex-fund spending and premiums, CalPERS is likely spending on order of $6 billion more per year in inflation-adjusted dollars than they were in 2008.

It’s a decent chunk of change considering that the pension fund earned $61.4 billion in investment returns in fiscal year 2024-2025.

Not Just More Enrollees

Even when accounting for the growing number of CalPERS healthcare plan enrollees, the spending is growing off the charts. What was once on the order of $6,500 per enrollee on private plans is now about $9,000 in 2024 dollars.

Medicare buy-in spending per enrollee has actually declined in inflation-adjusted dollars, although those plans benefit from government subsidization of Medicare.

While $9,000 for annual insurance might seem cheap considering that family plans can run into the tens of thousands of dollars, that is an average across all enrollees, individuals and families. Plus, large pension funds should pay lower rates on average as they leverage their large populations for discount rates.

Phrases like “Mike’s Nature trick” were references to ongoing issues in years after 1961 where tree ring data no longer correlated with atmospheric temperature data—otherwise known as the “divergence.”

“I’ve just completed Mike’s Nature trick of adding in the real temps to each series for the last 20 years (ie from 1981 onwards) and from 1961 for Keith’s to hide the decline.”

Critics pounced on the language which seemed to implicate some kind of coordinated fraud or unethical massaging of the data.

But scrutiny of the research following Climategate, including an investigation by the National Science Foundation (NSF), would eventually absolve the researchers of any accusations of manipulation.

And the divergence issue that was ostensibly being manipulated was largely irrelevant. While divergence is a well-established issue for some tree ring data, not all tree ring data sets have the problem, and it is assumed to be a relic of local conditions for specific tree populations, like droughts and other various theories.

North Cairngorms’ Tree Ring Example

For other tree ring data sets, like the one from North Cairngorms, Scotland (NCAIRN), tree ring data tracks quite well with historic temperature measurements like that of the Central England Temperature (CET) set, which goes back to 1659 AD. Divergence is not an issue there.

The decennial average of atmospheric temperature from CET correlates significantly with the decennial average of temperature anomalies based on the Scottish tree ring widths (Pearson: .8064).

That aligns with the research from climatologist Keith Briffa—who was on the Climategate email exchange—showing the sensitivity of tree growth to temperature. His data showed even higher correlations of over .90 at times depending on the tree ring dataset.

But what was left unsaid is that, if tree ring data is a valid estimate of temperature, then the current day warm period is not that different from prior warm periods, like the Medieval Warm Period around 1300 AD—only off by less than one degree celsius.

It would also be similar to the other warm period in the late 1500s that doesn’t have a unique name.

Tree Ring Data Vs. Mann’s Hockey Stick Graph

What might be more controversial is that while tree ring data tracks well with historic temperatures, Michael Mann used his own model that incorporated not just tree ring data, but ice core data, corals, lake sediments, historical documents, and other records to develop his “hockey stick” chart showing a much more significant and anomalous uptick in recent temperature compared to prior centuries. In that model, prior warm periods, like the one in the 1500s, don’t appear at all.

While tree ring data is relatively well established as a reliable source of temperature data, minus divergences, ice core data has its own issues when used on more recent time frames.

Cuccinelli vs. Mann’s Source Code

As impactful as Mann’s research is on climate research at-large, his methods for incorporating numerous temperature sources are unknown.

In 2010, then-Virginia attorney general Ken Cuccinelli demanded a wide range of documents from Mann because of the implication of fraud from the Climategate emails.

Cuccinelli used a Virginia anti-fraud statute to demand all communication used by Mann to apply for taxpayer funded grants at the University of Virginia as well as all source code and algorithms used to generate the research.

Eventually the Virginia Supreme Court ruled against Cuccinelli that the state had no right to Mann’s files and the algorithm was never disclosed.

There is little data on the population of mentally ill in prison, and the data that is there is inconsistent in its definition of mental illness. The larger majority of psychiatric institutions were elderly and many were women—two populations not commonly associated with crime and prison. Local jails could certainly have shouldered the burden of the untreated mentally ill, but there was little systematic evidence to measure and confirm it.

Instead, much more data points to deinstitutionalization affecting the sharp growth in homelessness that began in the 1980s. While data there is also spotty—there were few surveys of the homeless population back then and even fewer of mental health issues amongst the homeless—there are plenty of anecdotal descriptions showing the connection.

A 1985 Government Accountability Office (GAO) paper noted that they had no accurate estimate of how large the homeless population potentially was at the time—either between “250,000 and 350,000 by the Department of Housing and Urban Development (HUD) to a high of 2 to 3 million by the Community for Creative Non-Violence, a Washington, D.C.-based advocacy group”—or how fast it was growing—somewhere between 10 and 38 percent per year.

But the consensus was that it was growing relatively fast and was visibly noticeable in major cities. The top two major causes were growing unemployment in the late 1970s and early 1980s and deinstitutionalization.

The paper also admits gauging the mental health of the homeless population is difficult, but the two are commonly connected, noting a National Institute of Mental Health (NIMH) survey showing 50 percent of the homeless having severe mental illness as well as high percentages of drug and alcohol abuse. Other common estimates put it at one-third of the homeless having mental health issues.

But deinstitutionalization started in the 1950s and was effectively complete by the mid-1970s following the Medicaid Institution for Mental Diseases (IMD) exclusion in 1965. The IMD exclusion prevented Medicaid from funding inpatient mental health and substance use treatment in residential facilities or psychiatric hospitals that have more than 16 beds, specifically for adults aged 21 through 64.

There were still a few mental institutions being emptied into the 1980s, but before the 1980s homeless crisis. And again, the majority were elderly or female; Not a common population for the homeless or imprisoned with mental health problems, many of whom were younger men with schizophrenia or substance abuse problems.

Instead, it was the economic recessions of the late 1970s and early 1980s that, combined with deinstitutionalization, drove homelessness in the early 1980s. Major psychiatric institutions were no longer there to help shoulder the burden of those suffering from mental health issues as a result of joblessness, homelessness, or any other personal crises.

A 1984 American Psychiatric Association (APA) study showed that “a substantial number of men who in years past would have been long-term residents of state mental hospitals but now have no place to live.” The few community-based care facilities that was ostensibly supposed to replace major psychiatric institutions was ill prepared to handle them, and state hospitals tightened their admittance policies.

Potentially the best description of what happened comes from the book Over the Edge: The Growth of Homelessness in the 1980s. During the 1981 to 1982 recession, soup kitchens and emergency shelters saw the large influx of homeless. Based on estimates from Housing and Urban Development (HUD) in 1984 and one from Burt and Cohen in 1987, homelessness doubled in the span of three years.

Previously, homelessness was largely confined to those who couldn’t afford a roof over their heads and lived in subsidized housing. But the 1980s saw the growth of actual street living. The causes were often a complex portrait of personal strife, financial struggles, mental illness, and drug and alcohol abuse.

The economic downturn with less affordable housing and fewer options for psychiatric treatment or psychiatric stays meant that those on the edge of homelessness and mental health crises were pushed over the edge and wound up on the street.

A 2019 Stanford paper showed little discernible effect:

We find that the voter ID law did deter those without identification from voting. And we find this effect is small enough that studies of aggregate turnout would be unable to detect it.

A 2015 Government Accountability Office (GAO) report found some decline in a few states, but it wasn’t much—at most 3.2 percentage points:

GAO found that turnout among eligible and registered voters declined more in Kansas and Tennessee than it declined in comparison states—by an estimated 1.9 to 2.2 percentage points more in Kansas and 2.2 to 3.2 percentage points more in Tennessee

A meta-analysis by the Massachusetts Institute of Technology (MIT) found that the effect of voter ID laws on turnout as “unclear.” A University of California at Davis paper found only “modest turnout effects and only minor differences across politically relevant groups.”

None of those conclusions align with the ongoing and contentious political debate over voter identification. Depending on which side you are on, either millions are illegally voting or millions of legitimate voters will be disenfranchised by the requirement of presenting an ID.

Somehow a change with negligible effects has been the heart of numerous lawsuits from groups like the American Civil Liberties Union (ACLU) and Supreme Court decisions at both the state and national level, and it currently surrounds the debate on the SAVE America Act.

Following a recent speech by President Trump on voting integrity, the Department of Homeland Security released a memo showing that “400,000 deceased registrants and over 28,000 non-citizens who illegally registered to vote”—two examples of populations that would likely be prevented from voting with an ID restriction.

A number of states passed voter ID laws around the same time in 2011, and many of them saw a drop in eligible voter population (VEP) turnout—the number of ballots cast over the number of eligible citizens in that state—for the 2012 general election based on data from the Statistics of the Congressional Election and the University of Florida’s Election Lab.

But so did states like California , Maryland, and Illinois, with nothing close to a voter ID law on the books. Essentially turnout was low across the board in 2012, and having a voter ID law didn’t make much of a difference on top of that. Midterm elections saw a similar trend although it’s complicated by infrequent gubernatorial elections that can skew turnout.

Regressing to the Mean

If that seems odd, also consider that for many of the same states that passed voter ID laws, their turnout rates suddenly fell in line with the national average in a way that seems statistically unlikely.

In prior years, turnout percentage could vary wildly from state to state and election to election. In 1980, it could be in the low 40s for some southern states like South Carolina and Georgia, or it could be over 71 percent in Minnesota. Between the 1988 election and 1992 election, Alaska’s turnout jumped 10 percentage points.

But many states are now almost in exact alignment with the national average, particularly since 2008. States like Alaska, Georgia, Idaho, Illinois, Louisiana, Rhode Island, South Dakota, and Wyoming are now repeatedly within less than 1 percentage point of the national average.

It seems reasonable that voter turnout might become more consistent from state to state over time, but a sudden transformation in one year to being in sync with a national average with such precision appears incredibly anomalous.

While the states that closely follow the national average happen to have some version of voter ID, some states without voter ID aligned pretty closely as well, like Pennsylvania and Washington, D.C. For D.C., the 2016 turnout rate was within .1 percentage point of the national average. Compare that to 2004 where it was off by over 6 percentage points from the national average.

California’s Counterexample

The trend is not in every state as many are like California, where turnout has not regressed to the mean.

California also provides more evidence that other factors may be skewing turnout more than voter ID laws since the Golden State has had a significantly lower rates than the national average in recent years—by 4 to 5 percentage points, except for 2020—despite having no voter ID laws on the books.

Diminishing Variation

Over the course of a few decades, the standard deviation of turnout rate for those select states has collapsed. Not only are they more in line with the national average, but they are also not varying from election to election anymore.

Ascribing the trend to voter ID seems fraught because the trend doesn’t make much sense. Why a select number of states would have their turnout rates stop varying while others stayed the same is an oddity.

In February, The Washington Post conceded the beepocalypse—the largescale collapse of honeybee populations related to colony collapse disorder akin to “bee AIDS”—was all hype.

This lines up with what Investigative Economics published in April of last year that showed no real collapse in honeybee populations except for those following the decline in subsidies for honey production.

Readers familiar with our story on the climate warming properties of water vapor, otherwise known as humidity, would be interested to read this thorough detail of the E.P.A.’s active avoidance of regulating water vapor as a pollutant by The Honest Broker.

Essentially, regulating humidity like carbon dioxide would also be economically destructive, and the agency seems intent on completely skirting the issue.

The Washington Post has a recent story about how the Potomac Interceptor had known issues for years with little response from D.C. Water before it broke in one of the largest sewage leaks in U.S. history.

That also tracks with our story on the ongoing bacteria issues of the Anacostia river despite billions being spent. Various reports point to leaking pipes similar to those surrounding the Potomac Interceptor being the source of bacteria on that river as well.

The gist is that, while D.C. Water is eager to spend billions on major construction projects where there is plenty of bond money is available, they seem less than eager to deal with longstanding maintenance needs.

The recent Investigative Economics story on San Francisco car break-ins quoted district attorney Jenkins saying that, of the 11,000 car break-ins during the first half of the year, few of them were prosecuted.

The story is largely about prosecution numbers being unrelated to a supposed loophole in the law, but the 11,000 total car break-ins number quoted by Jenkins deserves skepticism too.

For there to be 11,000 car break-ins in half a year, that works out to be over 60 a day (11,000 / (365/2) = 60.2).

San Francisco certainly had an issue with car theft, but 60 break-ins per day is hard to fathom.

(custom video courtesy of ozavry)

Crime in the U.S. grew concurrently as mental institutions were closing down. More patients with schizophrenia were prescribed thorazine rather than committed to an insane asylum as the drug epidemic took over modern cities. Many of them wound up in jail as prisons became de facto mental institutions that were ill equipped to deal with the flood of convicts with psychosis.

But various critics have pointed out a glaring hole in that logic: the vast majority of the institutionalized population were elderly, half of them were female. Those are two populations rarely associated with inner city crime. Mental institutions were more often used as retirement homes for those suffering dementia rather than schizophrenia.

While deinstitutionalization changed sentencing standards, courts sentencing defendants to a mental health asylum through diversion or pleading insanity has always been relatively rare. For those that are, inpatient mental hospitals still exist.

When accounting for age and gender, the inverse correlation between prison rates and mental institution rates is not as glaring. Between 1960 and 1980, the number of men aged 18 to 35—the majority demographic of those in prison—grew by about 130,000 based on Census data. That same demographic cohort for mental institutions declined only by 13,000 for the same period.

That is a sizable difference even if those suffering from severe mental illness in public are significantly more likely to commit crimes and be repeat offenders; potentially a contributing factor but difficult to describe as a main cause of prison overpopulation.

What Defines “Severe”

Additionally, the accusation that prisons were suddenly filled with the severely mentally ill is suspect as the data on mental illness in prisons is spotty at best. Much of it is self-reported on sample populations.

While self-reported data can be useful, it has major biases and definitional issues. What is considered severe is sometimes in the eye of the surveyor and the prisoner being surveyed, and the rates vary wildly from report to report.

On top of that, there are the common difficulties of statistics on psychological conditions. Clinical definitions of conditions like schizophrenia and major depression that make up severe mental illness have consistently changed over time, so even clinical surveys can be vague in historical context. While prisoners are rife with mental health issues, the number that are severe and pre-existing to detainment are hard to pin down.

A 2010 paper from researchers for the Treatment Advocacy Center and Sheriff’s Association estimated the percentage of those in jail or prison with a severe mental illness was 1.5 percent in 1930 but now stands at around 16 percent based on BOP reports. Combined with the growth in the total prison population, that would imply an explosion in serious mental illness.

Bureau of Prisons (BOP) data shows a prison population in 1933 at 189,433. With 1.5 percent mentally ill, that would be on the order of 2,850 in total.

For 2016, a 16 percent rate with around 2.2 million incarcerated would mean on the order of 352,000 mentally ill prisoners in total, far beyond what might have come from deinstitutionalization. It would put the growth rate since 1933 at a seemingly implausible 1,220 percent.

Despite coming from a large survey published by the Bureau of Justice Statistics (BJS) in 1999, the data that the 16 percent mentally ill prisoner rate comes from is based on self-reported data of whether a prisoner had a mental condition or an overnight stay in a mental hospital.

Another BJS self-reported survey of inmates in 2005 put the number of mentally ill prisoners between 45 to 64 percent of state, federal, and local jail populations. One BJS report in 2016 estimated 13 percent suffered from recent severe psychological distress.

Yet a more exhaustive, non-self-reported survey with a clinical definition of severe mental illness by the Government Accountability Office (GAO) in 2018 put the estimate at a much lower 4.2 percent of those in federal custody.

Between the 1930s and 1990s there were few exhaustive surveys, self-reported or otherwise. Most data comes from academic research sample surveys of a single prison population, like this study of inmates in Cook County, Chicago from 1983, which estimated a 6.36 rate of any severe disorder. One from 1976 from a county jail put the estimate at 22 to 23 percent, significantly higher than more recent numbers.

Without a consistent definition of “severe mental illness,” there’s no good determiner of how deinstitutionalization affected prisons.

According to current San Francisco district attorney Brooke Jenkins, the vast majority of those incidents had not been prosecuted.

“My prosecutors could go into court today even having an eyewitness to an auto burglary, but without having an individual who owns or possesses the car say they locked the doors, could be required to dismiss the case,” Jenkins said.

The city’s roughly 11,000 break-ins in the first half of this year resulted in just 86 felony auto burglary cases, about half of which actually ended in a conviction, Jenkins said. Many of those incidents happened in popular tourist destinations like the Marina District, Fisherman’s Wharf and near Civic Center.

A bill by state representative Scott Weiner would close that loophole (SB 905), something that he had been trying to pass for a few years, and it eventually passed in 2024 as section §465 and was effective starting in 2025.

While car break-ins were a huge concern for San Francisco a few years ago, almost none of the rest of that is true.

Based on numbers from DataSF, most auto burglaries have been, and continue to be, prosecuted under the section of the law that contains the loophole about requiring car doors to be locked—section §459. Over 80 percent were prosecuted in 2021 under that law, and 88 percent were prosecuted in 2025 when the new law passed.

If anything, there’s been a sharp decline in auto burglary arrests since 2019, years before the loophole was closed potentially following a sharp decline in incidence.

A San Francisco Chronicle story noted the sharp decline back in 2024 due to increased police enforcement, although that may not be totally accurate as arrests have been in continuous decline since 2020.

Similar Conviction Rate to All Burglaries

While the number of cases and arrests have plummeted, conviction rates under the old law remain about the same.

If the loophole somehow affected prosecution success—rather than whether charges were brought as Jenkins described—then conviction rates for that statute would ostensibly be low.

But conviction rates under section §459 are almost exactly in line with conviction rates for all types of burglary and robbery that aren’t automobile-related—varying between 40 and 60 percent.

Conviction rates solely refers to total convictions and does not include other potential outcomes in favor of prosecution, such as diversions or pleading guilty to a separate case. But even when considering those other conclusions, auto burglaries have had similar outcomes to other burglaries, and they are regularly prosecuted successfully.

The new law (§465) could certainly improve upon those numbers, although the data currently only shows seven cases to be prosecuted under the new law. As it was only passed in 2025, it’s still early and there is little data available on its effects.

Ongoing Prosecutorial Issues

Besides auto burglaries, the San Francisco district attorney’s office appears to have significant issues with conviction rates for all crimes as well as an increasing number of fugitives.

Robberies and burglaries used to have conviction rates almost twice as high as they currently do. The overall conviction rate used to be around 30 percent rather than current rates around 10 percent.

The number of cases left on hold because the defendant absconded and is now considered a fugitive is now around 12 percent a year when it used to be less than one percent. Then there’s a separate issue of Jenkins dropping narcotics cases.

On May 16th, Cleveland, Ohio voted to stop the $1.6 billion Slavic Village datacenter project from moving forward. The Box Elder datacenter campus in Utah has seen intense backlash, some of it over the confusion about how many acres of land it would encompass.

Criticism of the projects has largely hinged on environmental issues related to the large amounts of water used for cooling, electricity consumption, costs to ratepayers, carbon and heat generation, and their use for A.I.

The scale of the outrage targeted at datacenters appears out of place. While they use large amounts of water for cooling, so do many other industries—factories, farms, golf courses—that generate far less tax revenue for the areas they are located in.

In response, the pro-energy group American Energy Institute (AEI) published a report showing the foreign funding of nonprofits advocating against datacenter development. According to a report from Americans for Public Trust, environmental groups like 350.org and Greenpeace receive substantial sums from foreign investors like the Oak Foundation—headquartered in Switzerland and run by a British citizen Alan Parker—as well as the Swiss billionaire Hansjörg Wyss through the Wyss Foundation and Berger Action Fund, the British hedge fund manager Chris Hohn, the UK-based Quadrature Climate foundation, and the Denmark-based KR Foundation.

In February, Nebraska attorney general Mike Hilgers requested the Department of Justice investigate a collection of foreign entities funding U.S. environmental groups under the Foreign Agent Registration Act (FARA), some of which like 350.org are actively advocating against datacenters.

But besides 350.org and Greenpeace, many of the environmental groups funded by foreign entities detailed in the AEI report don’t appear to be active in anti-datacenter advocacy.

Interestingly enough another large funder of groups advocating against datacenters is the Schmidt Family Foundation. The 501(c)3 group created by Google ex-CEO Eric Schmidt heavily funds the nonprofit Food and Water Watch at the center of the activism and organizer of the letter to Congress.

The irony of course is that Alphabet, Google’s parent company, is heavily invested, if not one of the largest American investors in datacenters. Through the Schmidt Family Foundation it appears that they might be funding their own opposition.

Based on 2024 filings, the foundation holds almost $1 billion in Alphabet stock, and it gave at least $3.75 million to the signatories of the 2025 anti-datacenter letter including $1.2 million to the letter’s coordinator, Food and Water Watch along with others like Columbia Riverkeeper, FracTracker Alliance, and Physicians for Social Responsibility. In total, the Schmidt Family Foundation handed out $195 million that year to a larger number of nonprofit groups that could also be advocating against datacenters in other ways.

For example, the Google datacenter in Dalles, Oregon is at the center of a battle over water usage. The Google datacenter there currently uses in the range of 500 million gallons a year, and the city is pushing to fund improvements to the nearby Crow Creek Dam. Resident water bills could potentially double as a result.

But while Google is a large source of the town’s water consumption, the city would likely need to make the improvements no matter what, and Google already paid for a new water storage system and will likely pay millions for further upgrades including the Crow Creek Dam, as well as expenditures for ongoing water consumption.

Schmidt’s funding of FractTracker Alliance and Frack Action—two of a number of anti-natural gas fracturing nonprofits that signed the letter—is also interesting as Google is invested in numerous datacenter projects connected to natural gas plants sourcing their energy from fracking.

In 2026, Google announced the Goodnight project in conjunction with the Texas’ Crusoe energy for a datacenter in Armstrong County. Crusoe sources much of their energy from methane flaring and hydrologic fracturing, originally as a way to fund bitcoin mining. Google is also connected to other datacenter projects with attached natural gas plants in Illinois and Nebraska.

A previous Investigative Economics story detailed how Google funded the opposition to a nuclear powered datacenter in Georgia despite Google itself involved in nuclear-powered datacenters in other parts of the country.

But the pound was overvalued, and, despite its best efforts, the Bank of England couldn’t prop up its value any longer. Currency speculators swooped in to bet against the pound, with George Soros’ Quantum Fund alone betting an incredible £10 billion on its collapse by borrowing and selling 61 percent of the pounds in circulation at the time (£16.4 billion). That bet would effectively earn him £1 billion in profit—possibly one of the largest single day windfalls in investment history. Britain’s attempt to join the EMS was abandoned and within a month sterling would lose 20 percent of its value.

Campaign to Drive Sterling’s Value

In their efforts to prop up the currency, the Bank of England borrowed at least £5 billion European Currency Units (ECU)—a basket currency that was based on a collection of various European currencies before the Euro—to buy up pounds and keep its value high as well. Soros was quoted saying that the British finance minister was willing to borrow $15 billion to defend sterling. Interest rates would spike to an incredible 27 percent for the day.

Likely due to that debt, total liabilities for the bank would double within a week, topping £10 billion at a time. While some interviews have described the attempts to prop up sterling as completely ineffective, the value against the dollar would rise by 5 percent in the week leading up to Black Wednesday.

But the fervent effort by the bank to prop up the value of sterling was only temporary, and the U.K. would announce its exit from the ERM as the pound went into freefall.

Yet somehow borrowing billions and buying billions of pounds that would lose 20 percent of their value in a matter of days didn’t make much of an impact on the bank’s bottom line. A loss of £1 billion to currency speculators would seemingly stand out in total liabilities. Other anecdotes have put the price at £3.3 billion. But within a month total liabilities were back where they were pre-crisis. It was as if the bank borrowed billions only to immediately pay it right back with no major loss.

Large holdings of currency—whether it be in pounds, ECU, or another currency that the U.K. might have traded for—would ostensibly show up in the total reserves of the bank, but there is no visible sign of billions in debt or reserves left on their books when it was all done.

Devaluation Was Expected

While sometimes Soros’ bet is described as investing genius, the collapse of the pound was not that big of a surprise. Italy and Spain also devalued their currency around the same time when they realized they couldn’t keep up their currency’s value and the ERM was not in their best interests.

For years prior, Britain struggled with the burden of high exchange rates, which made their exports more expensive to the rest of the world. Additionally, high interest rates burdened ordinary borrowers, from local businesses to those trying to buy a house. The idea that Britain would continue to keep their currency value high by raising interest rates even higher just to be a part of Europe comes across as economic suicide.

Some of their strategy was based on the hopes that Germany’s Bundesbank would lower their interest rates, which would in turn cause the pound to be more valuable. But in the days prior to Black Wednesday Germany announced the increase to the country’s interest rates, and it was a measly .25 percentage points for the Lombard rate and .50 percentage points for the discount rate. With that announcement, the collapse of the ERM agreement was all but inevitable.

The recent change was ostensibly an indication of Jenkins’ tough on crime approach to the office: she was still filing charges at a similar rate yet winning more cases.

While all of that was true at the time the article was published, her office has seen a steep drop in the number of narcotics cases charged during her tenure since then. What was a 73 percent prosecution rate for narcotics crimes is now down to 50 percent. In 2023, 408 cases—25 percent of narcotics cases that year—were discharged, meaning charges were dropped before being filed. While that has since gone down, it’s an astounding amount.

Rather than prosecuting new cases, the district attorneys’ office is now more likely to move-to-revoke (MTR) cases—which means revoking a prior probation and moving forward on the prior charges that the probation was set under—or referring charges to other agencies.

The change is likely related to how the police—mainly San Francisco Police Department (SFPD) but also other entities like the county sheriff’s office and Bay Area Rapid Transit (BART)—were making 983 more narcotics arrests a year in 2025 compared to 2022.

Improving Conviction Rate

Prosecution rates in San Francisco hit a historic low in 2013. While that was under George Gasçon who took the reins in 2011, the trend started under the previous district attorney Kamala Harris who dropped plenty of cases, particularly those related to fraud, theft, and counterfeiting. Under Gasçon is when the trend would reverse itself.

While Gascon took on more cases, he failed to convict on many of them, instead preferring diversions. While Jenkins declines to prosecute many narcotics cases, conviction rates have significantly improved under her tenure.

Essentially, the complete collapse of the DA’s conviction rate largely under Gasçon in favor of diversions has flipped. At a time felony conviction rates barely made it above 55 percent but have since risen above 70 percent.

But maybe the biggest unexplained shift is the large jump in the disabled population. Starting in 2020, an additional 1 million people over 16 would be identified as disabled per year. There are now 6 million more people disabled than there were pre-pandemic. What was once 9 percent of the population would now be almost 11 percent.

Potentially the large swing could be some remnant of those affected by COVID-19 or appealing to COVID-19 related benefits, but detailed survey data from the Census shows distinct growth across practically all types of disabilities including hearing, cognitive, and vision difficulties since 2019 to 2020.

Much of that is likely a remnant of the elderly, whose population is rising with generational trends. The majority of population growth in disability happened in those over 64. The pandemic might have simply encouraged those to seek diagnosis during a time when not much else was happening. As a percentage of the population—the prevalence—it actually went down for those over 64.

Instead, one of the largest growing prevalent categories would be those with a cognitive difficulty between the ages of 18 and 34 based on U.S. Census data.

There are now 3 million more people reporting to have a cognitive disability since 2016, with half of those being between 18 and 34—going from 4 percent of the population to 6 percent.

Anxiety, Autism, and ADHD

While the change surrounding the COVID-19 pandemic is unique, the growing prevalence in mental health disorders is nothing new.

Prior stories from Investigative Economics noted the sharp upswing in reported anxiety disorders, particularly in white women in various states like Minnesota.

Based on Social Security data, fewer workers are filing for disability claims for themselves, but significantly more are filing on behalf of their children, particularly for mental health conditions.

Autism Growing On Top of Definitional Change

There has already been an acknowledged growth in certain mental disorders due to revised definitions from the updated DSM V manual, like that of autism and ADHD. But those came out in 2013.

And while rates of autism and ADHD had been rising following those definitional changes, the post-pandemic changes made their prevalence grow even faster, and not by all age groups at once.

Based on sample data from the National Survey of Children’s Health (NSCH), there would be a sharp upswing first in those aged 3 to 5 years in 2020. Then by those 6 to 11 years. And then a few years later kids 12 to 17 years old would show higher rates of autism beginning in 2022 to 2023. Rates for ADHD diagnosis would be similar.

• Senate Report on BCCI

• House Banking Report on BCCI

• The U.K.’s Sandstorm Report on financial audits of BCCI (via Internet Archive), Wikileaks Version

• Declassified C.I.A. Files on BCCI

• False Profits: The Inside Story of BCCI, The World’s Most Corrupt Financial Empire (book)

Miscellaneous Articles on BCCI (plenty more not included here):

• New York Times

• Foreign Policy

• Washington Post

In 2024, Netflix released the four part docuseries American Conspiracy: The Octopus Murders about the mysterious 1991 death of journalist Danny Casolaro and his investigation into the Inslaw/PROMIS affair—a spiraling conspiracy theory that connected every major scandal of the 1980s to a federal software contract dispute.

Even before the Netflix series, the Inslaw/PROMIS affair had been a subject of much theorizing, including books, articles, an off-broadway stage play, and an episode of the TV show Unsolved Mysteries.

The short version is that PROMIS (Prosecutors Management Information System) was the name of software written in COBOL created by the software company Inslaw in the 1980s for federal prosecutors to keep track of cases winding through the legal system. Eventually, Inslaw fell into bankruptcy, leading to a protracted legal battle between Inslaw and the Department of Justice (DOJ).

During the bankruptcy dispute, Inslaw would accuse the DOJ of an extensive conspiracy that connected the software to everything under the sun—the Iran-Contra scandal, BCCI scandal, the October surprise, drug cartels, Israeli intelligence, etc—which is why it was dubbed “the Octopus.”

One version described the U.S. government of providing a copy of the software to Israel, who then augmented it with spyware and backdoor access and then sold it to intelligence firms across the world.

While there was plenty of speculation about what Casolaro could have unearthed during his investigation via a source connected to Lyndon Larouche, the Inslaw/PROMIS affair might be one of the most researched subjects in U.S. government history with at least five separate federal investigations—including ones from the DOJ, CIA, House Judiciary Committee, and the Senate Select Committee on Intelligence (SSC)—all of it leading to dead ends. Much of the investigations asked seemingly insignificant questions about whether government agencies used the software or shared it with others.

Compare that to the investigation into the Bank of Credit and Commerce International (BCCI), which originally got little attention or scrutiny but would eventually be described by a Senate investigation as an “international financial crime on a massive and global scale.”

Democrats in Congress at the time successfully demanded an independent counsel be appointed to investigate the affair, even though similar attempts at investigating illegal military funding to Iraq around the same time went nowhere.

The DOJ investigation ordered by then-attorney general William Barr would entail interviewing dozens of people related to Inslaw and the DOJ team handling the contract. None of them corroborated Inslaw’s accusations that an employee of the DOJ intentionally targeted the company and aimed to bring them down, steal their software, or add in spyware.

According to the Bua report—named after the DOJ counsel investigating it—Inslaw’s finances fell apart as the grants that originally funded PROMIS via the Law Enforcement Assistance Administration (LEAA)—a subagency of the DOJ—ended.

Inslaw was originally created as a nonprofit only to later switch to for-profit status, similar to the transformation done by the OpenAI Foundation. As it was funded by government grants, PROMIS was actually in the public domain, and Inslaw had no exclusive rights to it.

Prior to losing its funding, Inslaw received what were called “advanced payments,” wherein they were paid even before their invoices were processed—a unique situation for most contractors. Without government grants, advanced payments, or a unique claim to PROMIS’ intellectual property, Inslaw was unable to privately fund additional work.

Another story of the time detailed how Inslaw was way over their heads for other software projects: unable to complete contracts and far over budget.

In Inslaw’s eyes, the DOJ stole PROMIS and wanted to give it to one of their employees as a gift for their work as part of the Iran-Contra affair. But the DOJ employee, who was also an ex-Inslaw employee harboring a longstanding grudge, had little to do with the project and no known connections to the Iran-Contra affair. More often, government workers are accused of having a positive connection with contracting firms that enable them to receive government contracts.

A large source of the accusations that the government added spyware to PROMIS came from Michael Riconosciuto, a computer programmer that admitted to hacking PROMIS. Riconosciuto would be arrested by the Drug Enforcement Agency (DEA) for methamphetamine production shortly after his accusations.

Few of Riconosciuto’s accusations could be verified. The location where he professed to have edited the software was a largely empty native American reservation with no sign of a computer lab or computers in general.

The investigation did confirm one of Riconosciuto’s accusations: that the reservation was the site of a company working with the defense contractor Wackenhut to bid on the development of night vision goggles for the military. While the project to bid on night vision goggles existed, there was no evidence of a lab to develop night vision goggles on the reservation either.

Potentially the reservation was simply used for its status as a reservation as Alaskan or native companies benefit from 8(a) minority and small business preference in government contracting, like that of Wackenhut’s work with the Alaskan company Alutiiq.

An Israeli-Canadian businessman who previously worked for Israeli intelligence, Ari Ben-Menashe, gave testimony detailing how PROMIS was being sold to governments across the globe, but when interviewed under the Bua investigation he detailed how it was a different version of PROMIS developed by the NSA for signals intelligence, which Israel added backdoors to allowing it access to the systems of banks around the world. In total, the investigation found no basis for his testimony:

After a thorough investigation, the Task Force described Ben-Menashe’s testimony variously as totally lacking in credibility,” “fabricated,” “demonstrably false from beginning to end, “riddled with inconsistencies and factual misstatements,” and “a total fabrication.”

Eventually it was revealed that the DOJ gave a copy of Inslaw’s PROMIS to Dr. Ben Orr, a senior assistant state attorney with the Israeli Justice Ministry who was on an exchange program at the DOJ. When interviewed, Orr detailed that the justice ministry never installed the software as it was foreign-produced and instead contracted with an Israeli company to produce their own version called 2000.

Multiple PROMISs

Much of the confusion surrounding PROMIS was that there were multiple software packages called PROMIS made by different companies. Besides the one developed by Inslaw, another one, Project Management Information System, used by the CIA was developed by Strategic Software Planning Corporation of Cambridge, Massachusetts.

Accusations that the Canadian government was using PROMIS were based on confusion with the Strategic Software Planning Corporation’s software of the same name.

Then there was another software package used by the NSA called Product Related On-line Management Information System for signals intelligence.

Another package unrelated to government work, the Problem-Oriented Medical Information System, also existed at the time.

Origin of Wild-Eyed Claims

While little evidence pointed to a grand conspiracy surrounding the software, there was some legitimate reason that it might be investigated. The original court filing on behalf of Inslaw accusing the DOJ of a grandiose scheme was made by Elliot Richardson—a prominent D.C. attorney who would go on to win the Presidential Medal of Freedom in 1998.

Despite little evidence that the DOJ was trying to sabotage their business, one bankruptcy judge did side with Inslaw, finding that the DOJ intentionally harmed their business. That finding was eventually thrown out on appeal.

Many of the accusations seemed like they were plausible and might have parallels in other cases: compromised software and sensitive data, corporate sabotage and abuse of the DOJ.

But many of the original accusations made no sense. They involved coordinated illegal activity by the federal government—”trickery, fraud, and deceit” in the original affidavit—solely to obtain basic case management software and avoid payment on a relatively minor contract.

The accusations that the government would augment software with spyware and sell it to drug cartels implied that drug cartels would want or need legal case management software.

While Inslaw accused the DOJ of trying to steal their software, Inslaw actively avoided providing the government with a copy of the software they paid for, and instead encouraged them to use a newer, shared version of the software from a remote computer—something unique at the time in the mid 1980s.

All software has the ability to be compromised with spyware and backdoors, but the original version of PROMIS would have little ability to be hacked in a time when most computers had no connection to the internet or even other computers, whereas the newer, remote version that Inslaw wanted DOJ to use would not reside on government property and likely have more security concerns.

That doesn’t jibe with numerous anecdotal descriptions of tent cities appearing in cities following the financial crisis in 2008 that ascribe the growth in homelessness to housing affordability and other economic issues. Academic papers were already being written in 2017 about the rise in “tent wards”—large-scale semi-regulated homeless encampments—to deal with the flood of the unhoused showing up in West coast cities.

More so, much of the decline in homelessness that supposedly occurred during that period happened in Florida. The sunshine state went from almost 58,000 homeless— the majority of which were unsheltered, not in a temporary living situation—to 21,000 in less than ten years with no substantive explanation.

That decline accounted for 40 percent of the national decline over that period. Essentially, Florida, which had a substantial homeless issue for years, suddenly resolved most of the issue with little explanation of how.

Based on a report from the Florida Housing Coalition, it’s because the state’s economy turned around and the government started funding housing initiatives:

First, the economy has improved since 2010. Since homelessness is primarily due to a mismatch between income and housing costs, increasing income and employment opportunities result in decreased homelessness.

Second, the Florida Legislature has increased appropriations to preserve and create affordable housing, including housing for those with special needs and very low incomes. Again, because homelessness results from housing costs that are out-of-reach to those with lower incomes, increasing the stock of affordable housing reduces homelessness.

Third, in recent years Florida communities have embraced evidence-based best practices such as Housing First, collaborative case management, and rapid rehousing. Nationwide, these models have proven successful in decreasing homelessness.

None of those make much sense. To the first point, homelessness had been high prior to the economic collapse when unemployment was relatively low. Total non-farm employment in Florida fell by about a million following the financial crisis.

To the second, major funding for housing support disappeared as homelessness declined. They were still collecting hundreds of millions, but they weren’t appropriating any of it to housing.

The state collects hundreds of millions a year for its housing trust fund based on the 1992 Sadowski Housing Act, which funds programs like the State Housing Initiatives Partnership (SHIP) and the State Apartment Incentive Loan (SAIL) through the documentary stamp tax—a tax on real estate document filings.

But starting in 2009 and through 2014, much of that money got swept into the state’s general fund to plug other budgetary holes. In 2012 and 2014, 100 percent of the housing trust fund was used to plug the budget and none of it got spent on housing—right smack dab in the middle of Florida’s supposed rehousing initiative.

Other metrics, like the count of sheltered homeless barely budged during this period as well, showing no indication that the homeless were being moved into temporary housing, like homeless shelters.

Previously, when the housing programs were receiving and appropriating millions—over $600 million in 2006 alone—it had little affect on homelessness as the point-in-time estimates kept climbing into 2010.

The increase in incarceration may have caused a decrease in crime, but the magnitude is highly uncertain and the results of most studies suggest it was unlikely to have been large.

Crime rates exploded in the 1960s, a lot of it related to the proliferation of heroin and guns against a background of civil strife, some related to deinstitutionalization—the shift away from hospitals and asylums for treatment of mental health and towards independent treatment and prescription drugs.

Within ten years, murder and assault rates doubled. Robbery rates tripled. Drugs were considered the major culprit. In 1970 Congress passed the Comprehensive Drug Abuse Prevention and Control Act, which created the illegal drug scheduling system, to limit access to pharmaceutical drugs that can be abused.

Once it was revealed that 10 to 15 percent of servicemen in Vietnam were now heroin addicts, president Nixon declared a “war on drugs” in 1971. In 1973, the Drug Enforcement Agency was established. Other measures like the Rockefeller drug laws in New York would have a similar effect at the state level. The percentage of state and federal inmates received annually for drug law violations would go from 2.5 percent in 1940 to 9.3 percent by 1980. That would add to the overall incarceration rate, which would grow four-fold between 1970 and 2000.

Yet crime rates stayed elevated into the 1990s as various American cities battled for the title of global murder capitol. It was only in the early 1990s that crime rates would drop significantly, first in New York City, but then suddenly across the country in 1994, particularly for homicide.

Various inconclusive theories have attempted to explain the sudden drop, from lead exposure to abortion laws, cocaine markets, three strikes laws, and the elimination of discretionary parole.

But while the drop in crime in the 1990s was sudden and seemingly inexplicable, the overall crime rate had been slowing down going back to the mid-1970s following the war on drugs and start of mass incarceration based on data from the Federal Bureau of Investigation’s Uniform Crime Reporting program and prisoner data from the Bureau of Justice Statistics.

Crime rates might not have declined significantly right away, but the upward trajectory stopped and the overall the trend was downward beginning in the mid-1970s, particularly for homicide and robbery.

Crime Rates Stopped Growing

Crime didn’t immediately come to a halt, but the sharp growth in crime plateaud—whether by way of incapacitation, intimidation, or simply by coincidence—a few years after more people were being thrown in prison en masse.

Prior to 1975, the total crime rate increased annually by about 1 per 10,000, doubling over the course of ten years. Ten years later, by 1985, the prison population had doubled and the crime rate stopped growing altogether.

Homicide Rates

After growing year over year through the 1960s, the homicide rate dropped in 1976—the first time in 14 years and only a few years after prison rates started growing—trending downwards after that.

If the murder rate kept going as it had been in the 1960s, by 1994 it would have been twice as high as it was—not a weak effect.

Decennial Trend

Because crime rates are somewhat volatile—they can vary from year-to-year—it’s better to look at crime trends over the course of a decade. And there it’s easily visible how the average annual crime rate over the course of a decade initially started declining in the 1970s.

Prison Rate Eventually Followed Crime Rate

While the 1970s is considered the beginning of mass incarceration, when considering the ratio of prisoners to crimes, it was at a relative nadir. That is, what might be considered large-scale incarceration existed prior to the 1960s. When crime grew in the 1960s, the incarceration rate just didn’t keep up.

With increased imprisonment, the ratio of prisoners to total crimes in the 1990s was effectively the same as what it was in the early 1960s before crime rates exploded.

If that doesn’t seem right, you’re not alone. The Richmond Federal Reserve noted how that doesn’t seem to comport with reality in a November 2025 post showing a considerably high vacancies-to-unemployed ratio (V/U rate) of .98. And only a few years prior it was twice that.

Is something fishy going on with job openings? With 7.2 million job openings as of August 2025, vacancies seem high, especially relative to the five-year pre-pandemic average of 6.4 million. Vacancies also seem high relative to unemployed workers.

Another post from the Saint Louis Federal Reserve asked a similar question. If the labor market is so tight, why aren’t wages higher? If workers are in such high demand, then they could demand more money.

All of this is relatively new. Prior to the financial collapse, the V/U rate barely ever got above .7. But beginning in 2010, post-financial collapse, the economy has been continually creating millions of jobs that are not getting filled.

Not only are they not getting filled, they might be sitting around unfilled for years. Even as the economy adds jobs that get filled, the number of job openings rarely goes down even as more and more employees are hired.

Ghost Jobs Aplenty

But there’s good reason for skepticism as many of the openings are likely ghost jobs—fake postings intended for some other purpose besides actually filling a role. Sometimes it’s meant to collect resumes for a job that doesn’t exist just yet. Sometimes it’s done to create a façade of growth for a flailing company.

Various estimates have put the number of ghost jobs between 20 to 40 percent of all jobs or at least 2 million, which is the minimum number of job openings in Bureau of Labor Statistics (BLS) data that never seem to go away. Besides misleading applicants, ghost job openings skew the labor market, making it appear that the economy is more robust and flush with jobs than it is, that wages should be higher, and that there isn’t enough skilled labor to fill the gap.

But there’s good reason to believe that the number of ghost job openings could be much higher, or at least that the Job Openings and Labor Turnover Survey (JOLTS) that the number is based on, is significantly flawed.

That’s because the number of added job openings is unrealistically high. Since 2010, the economy has almost continually added more employment—an average of 2 million jobs added each year—yet the number of job openings keeps going up. What was 2.8 million job openings in 2009 is now regularly over 8 million.

Assuming that every added job—e.g. net hires, or total hires minus total separations—had a job opening, that means that for every job filled there is a new job opening to replace it plus a few more to account for the increasing number of openings.

That works out to a seemingly impossible 193 million net job openings added since 2000—an average of 7.7 million net job openings added per year. Considering that there are currently 156 million jobs in the economy, it would mean more than one job opening added for every job in the economy.

More Reasonable Pre-Crisis

Job opening numbers were not as specious prior to 2010. Before then, the potential number of added or removed job openings varied between plus or minus 5 million a year. That’s because the number of job openings actually declined at various points when people got hired.

If the current number of job openings was similar to what it was back then—around 5 million rather than 10 million—then the V/U rate would also be similar to what it was —about .5. Not a considerably tight labor market.