Enlighten Radio Podcasts:Winners and Losers -- 2 Podcasts on the week that gave most people a headache that won't go away.

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Link: http://podcasts.enlightenradio.org/2017/07/winners-and-losers-2-podcasts-on-week.html

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Is the Democratic Party’s 'Better Deal' Good Enough? [feedly]

Is the Democratic Party's 'Better Deal' Good Enough?
http://prospect.org/article/democratic-party%E2%80%99s-better-deal-good-enough

AP Photo/Cliff Owen

House Minority Leader Nancy Pelosi, accompanied by Representative Hakeem Jeffries, Senator Amy Klobuchar, and Senate Minority Leader Chuck Schumer, unveil the Democrats new agenda. 

democracy_rules.jpg

It's a good thing for Democrats that the "Better Deal" agenda that party leaders Chuck Schumer and Nancy Pelosi unveiled on Monday will not be their only campaign message as they head into the 2018 midterms.

There's nothing exactly wrong with Democrats' plan to raise wages, train workers, invest in infrastructure, and break up monopolies that hurt competition. It's just that the whole rollout, staged in the white, working class town of Berryville, Virginia, had a self-consciously scripted air about it. It's easy to see why voters at the party's base want their leaders to show more passion and grit.

That's why House Democrats' recent moves to force debates on ethics and accountability issues deserve special notice. In a new package of bills and in a series of aggressive procedural maneuvers, Democrats in the House are sounding themes that have the potential to resonate powerfully across the ideological spectrum. These themes include the message that the system is rigged in favor of moneyed interests, and that ethics abusers must be held to account.

"This sense of frustration with government and elites and insiders now runs broad and deep across the American electorate, and there's no question it was a factor in Donald trump's election," says House Democrat John Sarbanes, of Maryland. "He tapped into this sense of resentment that people have about the current political system, and the undue influence that big money and special interests have."

Sarbanes chairs a House Democracy Reform Task Force that recently announced a package of bills focused on campaign-finance fixes, tougher ethics and disclosure rules, curbs on gerrymandering, and plans to strengthen the Federal Election Commission. A key element of the "By the People" plan is a proposal to match low-dollar campaign donations with public funds. The package draws a sharp line between Democrats who have spelled out a proactive democracy agenda, says Sarbanes, and Republicans who reject such reforms. Democrat Dick Durbin, of Illinois, has also introduced small-donor legislation in the Senate.

The "Better Deal" agenda unveiled by Schumer and Pelosi this week does gesture to big money and special interest abuses. "Corporate interests, the super wealthy, are allowed to spend unlimited, undisclosed amounts of money on campaigns and lobbying so they can protect their special deals in Washington," declared Schumer in Berryville. But the plan made no mention of the need to rein in Wall Street banks—long perceived as closely allied with Schumer. The Senate Minority Leader is arguably treating Wall Street with kid gloves, despite the fact that most Americans side with Democrats on Dodd-Frank. And though Democrats are stacking all their chips on the economy, Americans care less about it than they did six months ago, and are more worried than ever about government dysfunction. The number of Americans who think economic troubles are the most important problem facing the country today has actually fallen in recent months, according to Gallup, from 30 percent in January to 21 percent in July.

In the meantime, public concern over an issue Gallup identifies as "Dissatisfaction with government/Poor leadership" is soaring, with 19 percent identifying it as the nation's biggest problem—ahead of health care (16 percent), "Immigration/Illegal aliens" (7 percent), and "Unifying the country" (also 7 percent.) The phrase "dissatisfaction with government" and "poor leadership" could be read a number of ways, but it spells frustration and anger with Washington elites no matter how you slice it.

House Democrats have set out to tap that anger not only through legislation, but through a series of procedural moves designed to force Republicans to act on the many ethics allegations dogging the Trump administration. The most aggressive of these is a bid to make full use a little-known parliamentary tool known as a resolution of inquiry. Under House rules, if such a resolution is filed in a committee—even by a member of the minority—the full committee must act on the measure within 14 days, or it must move to a vote on the House floor.

"There are any number of things—the president's failure to divest, the violation of the Emoluments Clause, the Russian intervention in the election, the possible obstruction of justice, the firing of James Comey—all of which richly deserve congressional investigation and elucidation so the public knows what's going on and so that improper behavior is deterred," says House Democrat Jerrold Nadler, of New York.

Nadler pioneered the resolution of inquiry tactic in February when, buoyed by 838,000 petition signatures, he forced a GOP vote on his resolution asking the Justice Department to turn over information on Trump's business conflicts and his contacts with Russia. As expected, Republicans rejected Nadler's resolution.

This week, House Democrats filed four additional resolutions of inquiry in the House Judiciary, Financial Services, Homeland Security, and Transportation and Infrastructure Committees, all dealing in one way or another with Trump-related ethics matters, including Comey's firing as FBI head, and the lease on the Trump International Hotel. GOP no votes were inevitable, but the resolutions forced Republicans to go on the record against ethics and accountability, says Nadler.

"When they vote against the resolution of inquiry, they are, in effect, voting for a cover-up," says Nadler, noting that the House Judiciary panel is the only committee of jurisdiction that is not investigating the widening Russia probe. This is significant because only the House has the power to impeach a president, and it would be the Judiciary Committee's job to draft impeachment articles should lawmakers ever move in that direction.

Democrats will continue to refine their message in the months ahead, and it remains to be seen whether ethics, accountability, and special interest money ever move closer to the top of their agenda. But party leaders overlook such issues and possible reforms at their peril. "Unless you un-rig the game," says David Donnelly, president and CEO of the campaign reform group EveryVoice, "you are not going to get the kinds of comprehensive and far-reaching proposals that the Democrats suggested this week."

 -- via my feedly newsfeed

What We Talk About When We Talk About the Gig Economy [feedly]

What We Talk About When We Talk About the Gig Economy
http://prospect.org/article/what-we-talk-about-when-we-talk-about-gig-economy

(AP Photo/Seth Wenig)

Capital & Main is an award-winning publication that reports from California on economic, political and social issues. The American Prospect is co-publishing this piece.

"I have a nostalgia for the golden age of Craigslist," says Morgan Jones between sips at a coffee shop on Oakland's Telegraph Avenue. Jones, who is tall and thin with a bushy goatee, lets out a faint sigh. For the 51-year-old, the golden age occurred from the late 1990s to the mid-2000s, when someone like him—smart, but without a college degree—could land a decent job through the listings website. "I would show up to interviews willing to work hard, and I'd get hired," he tells me.

Jones returned to his native Bay Area in 1999, after a decade in the Midwest. He found work at an architectural journal, which led to a communications position at a design firm and a salary in the mid-60Ks. Then came the financial crisis of 2008. The company laid off most of its communication staff; Jones was let go in 2010, two months after he learned that his partner was pregnant. He collected unemployment for the first time in his life, and used the downtime to paint their house and focus on his baby daughter. Jones had always worked, and he figured he'd get a new job soon enough. But the recession dragged on, and companies weren't building like they had before. Months went by without an offer. It also dawned on him that he was quite a bit older than the other job applicants.

"I'm figuring out that the market has changed, that the elbows have gotten sharper, and I'm starting to wonder where I have failed," he tells me. He was hired for two short-term jobs, but other than that, spent most of his time sending resumes and waiting to hear back. Late in 2015, while returning from a job interview, he saw an ad for a courier service in Oakland. It didn't pay much above the minimum wage, but Jones was desperate, so he applied to be a driver. He didn't mind the work, calling it "disorganized but fun," though he was laid off several months later, after the company lost a contract.

That's when he saw a posting on Craigslist for Rev, an online transcription and caption-writing service company. It seemed ideal. Jones could work from home, choose assignments that interested him—like a documentary about a mining disaster in Canada—and set his own schedule. Best of all, as an independent contractor, he wouldn't have a boss. Jones, who can type 60 words a minute, figured he'd make around $25 an hour.

A month later, having written captions for the Rachael Ray show and various MTV programs, he calculated his earnings. They came to roughly $5 an hour. On the best assignments, the rate could rise to $7—still 40 percent less than Oakland's minimum wage. He was his own boss, but the work he was doing, he said, felt "close to volunteerism." With such low wages, it was no longer possible to avoid the fact that, as he settled deeper into middle age, he was "falling out of the middle class."

 

JONES, OF COURSE, isn't alone. During the last several years, stories about the rapid growth of the gig economy have saturated the media, often focusing on behemoths like Uber. These contractor jobs are different than those of a traditional employee, providing no guarantees of a minimum wage or benefits like unemployment insurance or workers' compensation. But if we zoom out from Jones, how much do we actually know about this workforce? Are their ranks growing? Is our country undergoing a revolution in how we work?

To try to find out, researchers at the University of California, Berkeley Labor Center published a report last month called "What Do We Know About Gig Work in California?" They sifted through government statistics and recent industry studies, and came away with many more questions than answers.

"For a group of workers that is seen as the archetype of the 21st century economy," they write, "what we don't know about independent contractors far exceeds what we do know."

Consider that most basic of questions, the size of the gig workforce. Estimates of the number of independent contractors in the United States range widely, from 600,000 to 55 million. Part of the challenge has to do with differing definitions. Some datasets focus on those who earn the majority of their money as freelancers. Other surveys consider a gig worker to be someone who has earned any amount of money as an independent contractor—for example, a schoolteacher who also sold $300 worth of jewelry on Etsy last year.

A related question is whether the ranks of full-time independent contractors have grown, or whether more people are simply supplementing their regular jobs with small gigs. (Another option is that both are true.) The data here isn't conclusive, though there are indications that—despite numerous articles to the contrary—the number of full-time gig workers has remained relatively steady in recent years. Last year, according to the Census Bureau, these independent contractors made up 6.3 percent of the workforce. So one hypothesis is that any rise in gig workers is due to traditional employees taking on extra work.

"There's an important question here of whether gig work is the way people are making up for low wages at their W-2 jobs," says Sarah Thomason, a co-author of the study. "In other words, is gig work becoming the new safety net? And what does that say about our public programs meant to play that role?"

The report, while national in scope, pays special attention to California, the fount of much of the excitement—and anxiety—about the gig economy. In California, the study finds, independent contractors are more likely to be like Jones: white, male, 50 years or older, and with some college under their belt. (Jones attended but didn't graduate from Antioch College.) An estimated one in five, like Jones, don't have health insurance, twice the rate of traditional employees. Data on income, due to underreporting, is notoriously unreliable; the report puts typical wages anywhere from $24,000 to $66,667.

That California's gig workers skew older might be surprising to those who associate the industry with millennials. But perhaps the most counterintuitive finding in the report is that on-demand platform jobs—think Uber and TaskRabbit—represent just 0.5 percent of the workforce. That number is a bit higher in select California cities like San Francisco, which tops the list at 1.2 percent. Yet even in the Bay Area, the growth of such jobs has been slowing dramatically.

"We've reached the point of diminishing returns in running the gig economy discussion through the lens of Uber," says Annette Bernhardt, a co-author of the report who directs the Labor Center's Low Wage Work Program. "Policymakers need to understand that this is a much more diverse workforce."

 

FROM THE BEGINNING, one of the challenges to understanding the gig economy has been to see past its triumphalism. "The sharing economy isn't an app, it's a mindset based on trust and cooperation," wrote Sara Horowitz, the founder of the Freelancers Union. Way back in 2001, Daniel Pink penned Free Agent Nation: The Future of Working for Yourself. "Free agents are the marketing consultant down the street, the home-based 'mompreneur,' the footloose technology contractor," he proclaimed. The book promised that we were about to enter an "exhilarating new world." On the back cover, in capitalized red font, Pink listed three characteristics of the coming employment revolution: FULFILLMENT, FREEDOM, and TIME.

For Jones, it has often felt like the opposite. He recently worked at Instacart for several months, loading up shopping carts for well-heeled customers at a Whole Foods in San Francisco. You've been to a grocery store. Now imagine spending eight hours of your day walking the aisles and picking out products for people you'll never meet: Fulfillment isn't the first word that comes to mind. For another gig, Jones delivered packages for an Amazon subcontractor, using a vehicle that was monitored in real time by supervisors. So much for freedom. And time? At $5 an hour at the transcription job, he'd need to work 100 hours a week to clear $2,000 a month. That's not even enough to rent a typical one-bedroom apartment in Oakland.

Jones is relatively fortunate. His partner is an arts instructor with a steady income, and she purchased their modest house long before the Oakland housing market boomed. But his search for stable work continues. He still has an active account with Rev, the transcription service company, and each day wakes up to scan a number of job sites. Recently, he applied for a position as a gas station attendant at Chevron.

"It's not the kind of job I would have looked at before," he says. "But now, having done my share of jobs in the sharing economy, being a gas station cashier looks very attractive."

 -- via my feedly newsfeed

Another study confirms mountaintop removal’s impacts on Appalachian water quality [feedly]

Another study confirms mountaintop removal's impacts on Appalachian water quality
http://blogs.wvgazettemail.com/coaltattoo/2017/07/14/another-study-confirms-mountaintop-removals-impacts-on-appalachian-water-quality/

Earlier this week, former West Virginia University researcher Michael Hendryx was explaining the findings of his many studies of mountaintop removal's public health impacts to a National Academy of Sciences panel examining the issue … but this week also saw the publication of yet another report that details the environmental impacts of large-scale strip mining.

The latest study, published in the peer-reviewed journal Environmental Science and Technology, reports that mountaintop removal mining causes many streams and rivers in Appalachia to run consistently saltier for up to 80 percent of the year. The scientists, from the University of Wyoming and Duke University, examined water quality in four watersheds that flow into the Mud River basin, the site of extensive mountaintop removal over several decades.

Fabian Nippgen, assistant professor of ecosystem science and management at the University of Wyoming, explained:

Over time, alkaline salts and other contaminants from the coal residue and crushed rocks in these valley fills leach into nearby streams and rivers, degrading water quality and causing dramatic increases in salinity that are harmful to downstream ecosystems.

These significant alterations are likely to lead to saltier and more perennial streamflows throughout Appalachia, where at least 7 percent of the land has already been disturbed by mountaintop-removal mining. It's not just the mountains that are being changed.

 -- via my feedly newsfeed

AI leads to reward function engineering [feedly]

AI leads to reward function engineering
http://www.digitopoly.org/2017/07/26/ai-leads-to-reward-function-engineering/

[co-authored with Ajay Agrawal and Avi Goldfarb; originally published on HBR.org on 26th July 2017]

With the recent explosion in AI, there has been the understandable concern about its potential impact on human work. Plenty of people have tried to predict which industries and jobs will be most affected, and which skills will be most in demand. (Should you learn to code? Or will AI replace coders too?)

Rather than trying to predict specifics, we suggest an alternative approach. Economic theory suggests that AI will substantially raise the value of human judgment. People who display good judgment will become more valuable, not less. But to understand what good judgment entails and why it will become more valuable, we have to be precise about what we mean.

What AI does and why it's useful

Recent advances in AI are best thought of as a drop in the cost of prediction. By prediction, we don't just mean the future—prediction is about using data that you have to generate data that you don't have, often by translating large amounts of data into small, manageable amounts. For example, using images divided into parts to detect whether or not the image contains a human face is a classic prediction problem. Economic theory tells us that as the cost of machine prediction falls, machines will do more and more prediction.

Prediction is useful because it helps improve decisions. But it isn't the only input into decision-making; the other key input is judgment. Consider the example of a credit card network deciding whether or not to approve each attempted transaction. They want to allow legitimate transactions and decline fraud. They use AI to predict whether each attempted transaction is fraudulent. If such predictions were perfect, the network's decision process is easy. Decline if and only if fraud exists.

However, even the best AIs make mistakes, and that is unlikely to change anytime soon. The people who have run the credit card network know from experience that there is a trade-off between detecting every case of fraud and inconveniencing the user. (Have you ever had a card declined when you tried to use it while traveling?) And since convenience is the whole credit card business, that trade-off is not something to ignore.

This means that to decide whether to approve a transaction, the credit card network has to know the cost of mistakes. How bad would it be to decline a legitimate transaction? How bad would it be to allow a fraudulent transaction?

Someone at the credit card network needs to assess how the entire organization is affected when a legitimate transaction is denied. They need to trade that off against the effects of allowing a transaction that is fraudulent. And that trade-off may be different for high net worth individuals than for casual card users. No AI can make that call. Humans need to do so. This decision is what we call judgment.

What judgment entails

Judgment is the process of determining what the reward to a particular action is in a particular environment.

Judgment is how we work out the benefits and costs of different decisions in different situations.

Credit card fraud is an easy decision to explain in this regard. Judgment involves determining how much money is lost in a fraudulent transaction, how unhappy a legitimate customer will be when a transaction is declined, as well as the reward for doing the right thing and allowing good transactions and declining bad ones. In many other situations, the trade-offs are more complex, and the payoffs are not straightforward. Humans learn the payoffs to different outcomes by experience, making choices and observing their mistakes.

Getting the payoffs right is hard. It requires an understanding of what your organization cares about most, what it benefits from, and what could go wrong.

In many cases, especially in the near term, humans will be required to exercise this sort of judgment. They'll specialize in weighing the costs and benefits of different decisions, and then that judgment will be combined with machine-generated predictions to make decisions.

But couldn't AI calculate costs and benefits itself? In the credit card example, couldn't AI use customer data to consider the trade-off and optimize for profit? Yes, but someone would have had to program the AI as to what the appropriate profit measure is. This highlights a particular form of human judgment that we believe will become both more common and more valuable.

Setting the right rewards

Like people, AIs can also learn from experience. One important technique in AI is reinforcement learning whereby a computer is trained to take actions that maximize a certain reward function. For instance, DeepMind's AlphaGo was trained this way to maximize its chances of winning the game of Go. Games are often easy to apply this method of learning because the reward can be easily described and programmed – shutting out a human from the loop.

But games can be cheated. As Wired reports, when AI researchers trained an AI to play the boat racing game, CoastRunners, the AI figured out how to maximize its score by going around in circles rather than completing the course as was intended. One might consider this ingenuity of a type, but when it comes to applications beyond games this sort of ingenuity can lead to perverse outcomes.

The key point from the CoastRunners example is that in most applications, the goal given to the AI differs from the true and difficult-to-measure objective of the organization. As long as that is the case, humans will play a central role in judgment, and therefore in organizational decision-making.

In fact, even if an organization is enabling AI to make certain decisions, getting the payoffs right for the organization as a whole requires an understanding of how the machines make those decisions. What types of prediction mistakes are likely? How might a machine learn the wrong message?

Enter Reward Function Engineering. As AIs serve up better and cheaper predictions, there is a need to think clearly and work out how to best use those predictions.

Reward Function Engineering is the job of determining the rewards to various actions, given the predictions made by the AI.  

Being great at it requires having an understanding of the needs of the organization and the capabilities of the machine. (And it is not the same as putting a human in the loop to help train the AI.)

Sometimes Reward Function Engineering involves programming the rewards in advance of the predictions so that actions can be automated. Self-driving vehicles are an example of such hard-coded rewards. Once the prediction is made, the action is instant. But as the CoastRunners example illustrates, getting the reward right isn't trivial. Reward Function Engineering has to consider the possibility that the AI will over-optimize on one metric of success, and in doing so act in a way that's inconsistent with the organization's broader goals.

At other times, such hard-coding of the rewards is too difficult. There may so be many possible predictions that it is too costly for anyone to judge all the possible payoffs in advance. Instead, some human needs to wait for the prediction to arrive, and then assess the payoff. This is closer to how most decision-making works today, whether or not it includes machine-generated predictions. Most of us already do some Reward Function Engineering, but for humans — not machines. Parents teach their children values. Mentors teach new workers how the system operates. Managers give objectives to their staff, and then tweak them to get better performance. Every day, we make decisions and judge the rewards. But when we do this for humans, prediction and judgment are grouped together, and the distinct role of Reward Function Engineering has not needed to be explicitly separate.

As machines get better at prediction, the distinct value of Reward Function Engineering will increase as the application of human judgment becomes central.

Overall, will machine prediction decrease or increase the amount of work available for humans in decision-making?  It is too early to tell.  On the one hand, machine prediction will substitute for human prediction in decision-making.  On the other hand, machine prediction is a complement to human judgment. And cheaper prediction will generate more demand for decision-making, so there will be more opportunities to exercise human judgment.  So, although it is too early to speculate on the overall impact on jobs, there is little doubt that we will soon be witness to a great flourishing of demand for human judgment in the form of Reward Function Engineering.

 -- via my feedly newsfeed

Senate Health Bill (BCRA) Would Hit West Virginia Hardest [feedly]

Senate Health Bill (BCRA) Would Hit West Virginia Hardest
http://www.wvpolicy.org/senate-health-bill-bcra-would-hit-west-virginia-hardest/

While it is unclear what version of the legislation the U.S. Senate will plan to take up on Tuesday (7/25) when they vote to proceed to repeal and replace the Affordable Care Act (ACA), the revised version of the Better Care Reconciliation Act (BCRA) would be particularly harmful to West Virginians.

A updated report from the national Center on Budget and Policy Priorities shows that West Virginia would be among the hardest hit states in the nation. Not only would the number of uninsured West Virginians grow by nearly 300% – the largest increase in the nation (See Map) – but it would reduce federal Medicaid/CHIP spending by half or $1.8 billion by 2022. Last-ditch efforts by Senate leadership to offer more money to Medicaid expansion states won't fix this bill either. Below is a quick summary of BCRA's impact on West Virginia and here's a one-page fact sheet.

West Virginia Would Sustain Huge Coverage Losses

• 211,000 West Virginians would lose coverage by 2022 if BCRA is passed.
• The BCRA would increase West Virginia's non-elderly uninsured rate from 5% to over 19%, a 299% increase, more than any other state.
• 1 out of 7 non-elderly West Virginians who would have coverage under the ACA would lose it because of the BCRA

West Virginia's Medicaid and CHIP Programs Would Cut in Half

• The BCRA would cut West Virginia's CHIP program by 47% by 2022 (compared to 26 percent nationally)
• The number of people enrolled in Medicaid would fall by more than half by 2022, or 263,000 people.

 BCRA Would Drastically Increase West Virginia's Costs to Maintain Medicaid Expansion

• The state's cost to maintain expansion would rise by 50% by 2021, 100% by 2022, and 150% by 2023.

 BCRA Would Make Access to Substance Use Disorder Treatment Less Available

• West Virginia has the highest drug overdose death rate in 2015.
• The share of West Virginians with substance use or mental health disorders who were hospitalized but uninsured fell from 23 percent in 2013 to 5 percent in 2014.
• Rolling back expansion would roll back coverage for the 33% of West Virginia expansion enrollees who used mental health or substance use disorder services in 2014.

 -- via my feedly newsfeed

Dylan Matthews’ critique of my UBI view is mistaken, but here’s a better one [feedly]

Dylan Matthews' critique of my UBI view is mistaken, but here's a better one
http://jaredbernsteinblog.com/dylans-matthews-critique-of-my-ubi-view-is-mistaken-but-heres-a-better-one/

In pushing back on an argument against Universal Basic Income (UBI) plans, Dylan Matthews engages in a curious non-sequitur. Let me point out where he's wrong but offer him a more salient contradiction in my position.

Matthews, in defending the idea that "every American gets a basic stipend from the government," quotes me as follows: "If we instead choose to use our resources on people who don't need them, we won't be able to build on the progress we've made."

He then argues, based on a paper by Wiederspan et al, that UBI's aren't as expensive as I suggest. The problem, however, is the paper he cites isn't about UBI. It's about a means-tested, non-universal program that phases out at (in Matthews' favored version) twice the poverty threshold, which excludes the majority of American families (68 percent; i.e., 32 percent are below twice poverty).

By shifting from UBI to this targeted approach, Matthews is implicitly agreeing with me. One of my major objections to UBI ideas that don't cost a lot more than the current system of taxes and transfers, like the one proposed by Charles Murray, is that by consolidating all our social welfare spending and then essentially giving those resources to everyone as a per-capita UBI, poverty will go up. The anti-poverty impact of the current system will be diluted by spreading the same money over millions more people.

In fact, Wiederstan et al clearly recognize this cost constraint, suggesting that if the phase-out threshold is set too high, "the program will be exorbitantly expensive."

These authors, and Matthews, are thus arguing for something other than a UBI. By dropping the U, they raise a different, albeit still interesting, question. They're no longer asking if we should have what Matthews describes as a "basic stipend from the government" for every American. They're instead suggesting that the poor and near poor might be better served if instead of the existing collection of anti-poverty programs, we consolidated them all and just gave them the cash.

This obviates the concern raised in my quote above: turning anti-poverty programs into a cash grant to poor and near-poor people doesn't squander resources on those who don't need them. In fact, as Matthews points out, the cost of the Wiederstan et al version he prefers (~$220 billion) amounts to about what we spend on non-health, anti-poverty programs.

This idea, however, raises other concerns. While I like the consumer sovereignty (I very much like that part), simplicity, and scope of the idea, I worry that a sole, stand-alone, anti-poverty fund would be a lot more open to attack than the many existing programs that meet specific needs. My strong sense is that it's easier for conservatives to reduce cash support than in-kind support. EG, I'll bet one thing that tanked the Republican health care replacement plans was taking away poor and moderate-income people's health coverage to pay for tax cuts for the rich.

This, however, is really a political call, and I could be wrong. The idea Matthews touts here, though not a UBI, is an NIT—a negative income tax—which oldsters like myself remember being introduced by conservative icon Milton Friedman and embraced by President Nixon, of all people.

Getting back to actual UBIs, however, I do think there's a coherent critique of my beef about the U in UBI. If I were debating me on this point, I'd hit me with this: "So, I guess you're also against Social Security, which is very much a universal cash income program."

Of course, I'm not (against Soc Sec), but I can maybe wiggle out by pointing out that while Social Security has UBI characteristics, it's targeted at elderly retirees (I'm just talking about the retirement program, not the disability one, which is quite different in this context). In this sense, it addresses a market failure of sorts, which is the risk of poverty in retirement. In fact, Social Security reduces elderly poverty from about 40 to about 10 percent!

But while its benefit structure (pay-ins relative to pay-outs) is progressive, why does it have to be universal? The answer has always been: to stave off political attack. And there's something to that. But I also like the intergenerational contract inherent in the program. Today's retirees bequeathed today's economy to today's workforce. In return, we're going to shave some of the productivity gains off the top for those past their working years. It's kind of a "circle of life" thing; cue the music, Disney!

But UBI'ers might say I'm being too narrow in where I see market failures. They often argue, against the evidence, I'd say, that the UBI is necessary as technological unemployment is or will be rampant. So, if my criterion for universality is a pervasive market failure, like aging out of your working years, then the lack of jobs certainly qualifies. (FTR, I have argued for a guaranteed jobs program.)

But it may be the case that this whole sort of thinking—we intervene where the market fails—is not shared by UBIers and may demarcate older from younger people in this space. My approach is pretty "neo-classical" (though to be clear, I see market failures around many more corners than the median economist of my generation) and I think many younger people, to their credit, are looking at economics' track record in recent years and concluding that we need a whole new paradigm.

In other words, there are many interesting currents swirling around right now. But as we try to make sense of them, we should, for clarity's sake, be disciplined in our analysis and not conflate UBIs with NITs.

 -- via my feedly newsfeed