Measuring Cost-Benefit of Social Spending Programs

Welfare, food stamp and other social spending is controversial. Conservatives generally want to reduce or completely eliminate it, while liberals generally want to increase or maintain it. The to sides tend to disagree about the cost-benefit of such spending. To try to at least partly rationalize the debate, In July of 2019, Harvard University economists, Nathaniel Hendren and Ben Sprung-Keyser devised a way to analyze the costs and benefits of social spending. They wanted to both try to find a reliable a way to do a cost-benefit analysis and then apply the analytic protocol to see how 133 different federal, state and local spending programs performed over the last 50 years.

The analytic protocol is explained in detail at this link. Click on the “How is the MVPF calculated?” button at the top of the page and then scroll down the pop-up box that explains how each cost and benefit is measured from the time benefits are paid until the recipient is 33 years old. MVPF stands for the marginal value of public funding. Each element of cost and benefit is then laid out for the spending program under scrutiny, admissions expansions at Florida International University (FIU) for the example spending program. The published paper is at this link.

The basic measure MVPF is recipient willingness to pay divided by net government cost. WTP, willingness to pay, is a measure of how much the recipient would be willing to pay for the benefits.

Cost-benefit at age 33 for FIU admissions expansion

Cost-benefit projected to age 65 for FIU admissions expansion

In their executive summary, Hendren and Sprung-Keyser comment:
1. Direct investments in the health and education of low-income children yield the highest returns, but not every policy targeting children has a high MVPF. We find that expansions of health insurance to children, investments in preschool and K-12 education, and policies increasing college attainment all yield high returns.

2. Many direct investments in low-income children’s health and education pay for themselves. MVPFs are lower for policies targeting adults. We find lower MVPFs for policies that target adults. Indeed, MVPFs for these policies are often close to 1, indicating that their benefits are approximately equal to their costs.

3. MVPFs are lower for policies targeting adults. We find lower MVPFs for policies that target adults. Indeed, MVPFs for these policies are often close to 1, indicating that their benefits are approximately equal to their costs.

4. Some policies targeting adults have high MVPFs, particularly if they have spillovers onto children We find that spending on adults can result in high MVPFs if those policies have positive spillover effects on children.

5. We find high MVPFs for policies that target children throughout the full duration of childhood. This is true for a range of policies spanning from preschool and health programs for young children to college policies for older youth. This finding directly challenges the notion that opportunities for high-return investments in children decline rapidly with age.

Medicare analysis: MVPF = 1.63

Medicaid analysis: MVPF = 10.24

If this method to analyze cost-benefit is reasonably accurate, it should help inform governments and policy makers to make better funding decisions. For people who want to increase or decrease welfare spending, they will either take this kind of analysis into account, or ignore or even reject it as flawed or lies. Presumably, most libertarians and anti-government conservatives will continue to argue that all welfare spending is illegal or unconstitutional. They will continue to make those arguments, but at least they do that in the face of data strongly suggesting there can be significant social benefit from at least some government spending programs.

1983 Medicaid expansion to children analysis: MVPF = infinite

One Way Corporations Shield Themselves from Liability

A New York Times article discusses how Amazon shields itself from liability when drivers delivering items for Amazon get into accidents. The tactic is simple. Amazon considers its delivery drivers to be independent contractors, not employees. It makes all drivers sign an agreement that states the driver will “defend, indemnify and hold harmless Amazon” for “all loss or damage to personal property or bodily harm including death.” That's all it takes. Just a few words in an agreement shifts the social risk and cost for delivering goods onto the drivers.

The story began by describing an accident where an Amazon driver accidentally rear-ended a car with a 9-month-old baby strapped into a car seat. The baby died. The truck driver said that he was running late in making his deliveries and simply didn't see the Jeep in time to avoid the crash. Although Amazon claims it has no legal liability for the human toll, it keeps a tight grip on how the drivers do their work.

So far, 60 accidents including 10 deaths have been reported. When people sue, they often do not know the drivers work for Amazon. Their delivery vehicles are unmarked and not linked to Amazon.

By claiming workers as independent contractors, employers including Amazon, Uber, Lyft and many trucking companies. Not only can employer off load social risk and cost to workers, the employers avoid paying Medicare and Social Security taxes, and they are exempt from paying into state workers compensation.

Cost, risk and responsibility shifting from employers to workers is not new. In 2107, a Wall Street Journal article, The End of Employees, discussed the trend by many companies that are moving to get rid of employees by contracting out as much work as possible. The WSJ quoted a Virgin Airlines executive as saying “We will outsource every job that we can that is not customer-facing.” By then, Virgin had outsourced baggage delivery, heavy maintenance, reservations, catering and other jobs to contractors to reduce both cost and risk.

In the face of this kind of a ruthless, cost-cutting employment situation, one can only wonder how it will be possible to maintain income and quality of life over time. Employers simply do not want to mess with employees any more than is absolutely necessary. With the powerful twin incentives of cost cutting and social risk avoidance, employers will probably do a whole lot more employee slashing in the years to come.

Democratic Messaging: A Failure So Far

An article in the September 6 issue of The Week quotes Joe Biden’s wife as saying: “Your candidate might be better on, I don't know, health care than Joe is, but you've got to look at who is going to win this election, and maybe you have to swallow a little bit and say ‘OK, I personally like so-and-so better,’ but your bottom line has to be that we have to beat Trump.”

That is crappy messaging, plain and simple. People's bottom line does not have to be ‘we have to beat Trump’. That is no endorsement of Biden. It is a plea for mercy.

It isn't just that or the amazing string of gaffes and false statements flowing from Biden that makes democratic messaging a failure.[1] Arguably, it is just about everything they talk about.

Democrats are unclear and incoherent about what it is they propose to do about immigration, a critically important issue. There should be clarity about it, but there isn't. For many people, democrats seem to want open borders and that is how they are constantly being portrayed by the right. That isn't the case, but that is how the messaging comes out to many people. Similarly, messaging about health care and how to pay for it appears to be an incoherent mess, regardless of what it is or might be. The situation is the same for global warming, gun control, jobs, and everything else that comes to mind at the moment.

If this high degree of democratic messaging incompetence continues and no catastrophe comes along to sweep him out of office, the president will probably be re-elected.

Footnote:
1. Some Biden supporters downplay the gaffes and false statements as something no one cares about, but that is pure nonsense. It is guaranteed that if Biden is nominated, he will be relentlessly attacked, smeared and lied about by Trump and the vast, ruthless propaganda machine that supports him. The opposition is keeping careful track of Biden and his public statements, true, false and muddled. Even his true statements will be turned into something stupid or evil if they can figure a way to do it. Dark free speech will be used.

Dark free speech: Constitutionally or otherwise protected (1) lies and deceit to distract, misinform, confuse and/or demoralize, (2) unwarranted opacity to hide inconvenient truths and facts, and corruption (lies and deceit of omission), and (3) unwarranted emotional manipulation (i) to obscure the truth and blind the mind to lies and deceit, and (ii) to provoke irrational, reason-killing emotions and feelings, including fear, hate, anger, disgust, distrust, intolerance, cynicism, pessimism and all kinds of bigotry including racism. (my label and my definition)

Two Kinds of Political Correctness

Political correctness:  the avoidance, often considered as taken to extremes, of forms of expression or action that are perceived to exclude, marginalize, or insult groups of people who are socially disadvantaged or discriminated against

A 2017 article at Quartz, a site self-described as being for ‘bold arguments and big thinkers’ commented: “Not long ago, political correctness stood for an ideal of fairness and open-mindedness. Yet today, “PC” is a widely bashed catchphrase, with politicians gaining popularity worldwide by destroying its rosy image. .... Politicians who aim to discredit the notion of PC point to its moralistic connotations. Implicitly endorsing traditional social conventions and hierarchies, they commonly portray political correctness as a norm that is imposed on society in a top-down manner. By constructing political correctness as an arbitrarily enforced, biased agenda, anti-PC politicians adopt common discursive strategies across the globe in their attempt to undermine and discredit PC.”

The article points out that right wing politicians, including the current US president and populist right wing parties, were attacking the PC concept. The president was quoted as characterizing the attacks on PC in terms of cost: “We just can’t afford anymore to be so politically correct.” Exactly what the president referred to was, as usual, not specified.

Are there two kinds of political correctness?

Given the sophistication of conservative messaging, the attacks on PC raise the question of why the political right has chosen to attack the concept. Conservative and populist messaging usually chooses tactics for a good reason. What is the good reason? After consideration, a possible explanation comes to mind.

Some people who attack PC appear to see two kinds of PC. One is trivial and that criticism goes something like this: “Jeez. If we are too PC, it will get to the point where no one can say anything bad about anyone else. Just look at the PC run amok on college campuses. People are attacked for just expressing opinions that might offend someone else. That’s nuts.”

When pressed, those folks will usually concede that blatant expressions of bigotry, racism or hate go too far and generally ought not to be used in public.

Where the logic fails is in how to draw the line between the acceptable PC against bigotry, racism or hate and the acceptable ‘trivial’ kind of PC. In fact, the line often isn’t drawn at all. Thus, when a politician publicly utters expressions of racism, the politician and defenders either deny it is racist or they claim people are being too PC, too sensitive, too snowflake.

In essence, attacks on PC by conservatives and populists amount to a defense of speech that polarizes and divides a society. That kind of speech includes expressions of unwarranted bigotry, racism, hate, anger, intolerance, disgust and distrust. It is used to attack democracy, fact, truth, reason, personal freedoms and the rule of law, while promoting irrational demagoguery, corruption and/or authoritarianism

Why defend and use that kind of speech? Because it works. As discussed here before, non-PC speech helps dehumanize and/or distort political opposition. In turn that makes it easier to be irrational in thinking about political opposition and vilifying it.

Therefore, it may be the case that conservative and populist attacks on PC are part of a process to normalize polarizing, divisive speech. As discussed previously, experts judged the current US president to be the most polarizing president in US history (and the least great). That accords with his frequent use of non-PC speech.

What about liberals and pragmatists?

 What about attacks on PC by other political groups? To the extent other political groups attack PC, that can be for the same reasons that conservatives and populists attack it now, i.e.  to polarize, divide and emotionalize politics. Some criticism of PC by the left points to something more akin to the trivial kind of PC, e.g., “person of size” instead of “obese” or “person who lacks advantages that others have” instead of “poor person”, but some of it undoubtedly is the more virulent kind of non-PC speech that the right frequently uses. There is no reason to think that the rationale to polarize and divide would not be used by both the right and left. That said, it seems that the tactic is more common on the right than the left.

Whether the tactic works as well for liberals as it does for conservatives is an open question. It might. But given the differences in mindset and attitudes toward facts, truths and logic between the two groups, it is plausible that non-PC speech works better for conservatives than for liberals. Social science research indicates that liberals and conservatives are not alike in how they perceive the world and react to it, and that presumably extends to non-PC speech.

Does Big Data Increase Injustice and Threaten Democracy?

In Cathy O' Neil's 2016 book, Weapons of Math Destruction, the author discusses data driven decision-making beyond the financial sector,  and raises ethical objections and questions regarding algorithms that make decisions about qualitative issues such as who is best qualified for a job, school, or promotion and who is not. O'Neil makes at least 2 major claims in this book: a) Our culture primes us to think of mathematical models as objective, impartial, fact-based and thus, crucially, *trustworthy* on the whole. and b) Algorithms turn out to have irrational and, more importantly, discriminatory effects which have already affected many and have the potential to increasingly confer advantages on those already privileged while compounding the disadvantages and problems of those*tagged* as liabilities or undesirables.

Because of the high degree of trust most of us place in mathematical models (despite the madness of the 2008 recession) they remain opaque to us. They are seldom challenged, and when they are challenged only a few of those affected by them ever get a chance to "look under the hood" to see just how they work, and what they really do when calculating decisions. They operate without public scrutiny or even awareness. If we do not start auditing and monitoring social algorithms they may, O'Neil suggests, amplify the pre-existing inequalities in our society. If such a phenomenon goes unchecked and unchallenged, then what started out as accidental bias might be jealously guarded by those who control and benefit from the technology. This could result in a technocratic power elite. Already, she suggests, people who are tagged by "bad" address, medical and psychiatric background, ethnicity, gender, educational affiliations, et al., are discriminated against. A certain address or school may carry less cultural capital or be correlated with race or ethnicity (e.g. Howard vs. Yale). So in the absence of transparency, with uninformed and credulous citizens relying on what they think are fair decisions, a technocracy could emerge which would no longer be a matter of cumulative accidental feedback loops, but a planned plutocracy in which the "winners" will have convinced themselves that they worked for and deserve their blessings. So that's the broad outline. Below is an excerpt from a larger review that originally appeared in Scientific American in August of 2017.

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From Scientific American (8/16/17):

"Weapons of math destruction" [which the author refers to as WMDs]...are mathematical models or algorithms that claim to quantify important traits: teacher quality, recidivism risk, creditworthiness but have harmful outcomes and often reinforce inequality, keeping the poor poor and the rich rich. They have three things in common: opacity, scale, and damage. They are often proprietary or otherwise shielded from prying eyes, so they have the effect of being a black box. They affect large numbers of people, increasing the chances that they get it wrong for some of them. And they have a negative effect on people, perhaps by encoding racism or other biases into an algorithm or enabling predatory companies to advertise selectively to vulnerable people, or even by causing a global financial crisis.

She shares stories of people who have been deemed unworthy in some way by an algorithm. There’s the highly-regarded teacher who is fired due to a low score on a teacher assessment tool, the college student who couldn’t get a minimum wage job at a grocery store due to his answers on a personality test, the people whose credit card spending limits were lowered because they shopped at certain stores. To add insult to injury, the algorithms that judged them are completely opaque and unassailable. People often have no recourse when the algorithm makes a mistake[note: these are not actually "mistakes" but consequences of the design, which is the main point-ed].

O’Neil is an ideal person to write this book. She is an academic mathematician turned Wall Street quant turned data scientist who has been involved in Occupy Wall Street and recently started an algorithmic auditing company.She is one of the strongest voices speaking out for limiting the ways we allow algorithms to influence our lives and against the notion that an algorithm, because it is implemented by an unemotional machine, cannot perpetrate bias or injustice.

Many people think of Wall Street and hedge funds when they think of big data and algorithms making decisions. As books such as The Big Short and All the Devils Are Here grimly chronicle, subprime mortgages are a perfect example of a WMD. Most of the people buying, selling, and even rating them had no idea how risky they were, and the economy is still reeling from their effects.

O’Neil talks about financial WMDs and her experiences , but the examples in her book come from many other facets of life as well: college rankings, employment application screeners, policing and sentencing algorithms, workplace wellness programs, and the many inappropriate ways credit scores reward the rich and punish the poor. As an example of the latter, she shares the galling statistic that “in Florida, adults with clean driving records and poor credit scores paid an average of $1552 more than the same drivers with excellent credit and a drunk driving conviction.” (Emphasis hers.)

Many WMDs create feedback loops that perpetuate injustice. Recidivism models and predictive policing algorithms—programs that send officers to patrol certain locations based on crime data—are rife with the potential for harmful feedback loops. For example, a recidivism model may ask about the person’s first encounter with law enforcement. Due to racist policing practices such as stop and frisk, black people are likely to have that first encounter earlier than white people. If the model takes this measure into account, it will probably deem a black person more likely But they are harmful even beyond their potential to be racist. O’Neil writes,

A person who scores as ‘high risk’ is likely to be unemployed and to
come from a neighborhood where many of his friends and family have had run-ins with the law. Thanks in part to the resulting high score on the evaluation, he gets a longer sentence, locking him away for more years in a prison where he’s surrounded by fellow criminals—which raises the likelihood that he’ll return to prison. He is finally released into the same poor neighborhood, this time with a criminal record, which makes itthat much harder to find a job. If he commits another crime, the recidivism model can claim another success. But in fact the model itselfcontributes to a toxic cycle and helps to sustain it.

O’Neil’s book is important in part because, as she points out, an insidious aspect of WMDs is the fact that they are invisible to those of us with more power and privilege in this society. As a white person living in a relatively affluent neighborhood, I am not targeted with ads for predatory payday lenders while I browse the web or harassed by police officers who are patrolling “sketchy” neighborhoods because an algorithm sends them there. People like me need to know that these things are happening to others and learn more about how to fight them....

In the last chapter, she shares some ideas of how we can disarm WMDs and use big data for good. She proposes a Hippocratic Oath for data scientists and writes about how to regulate math models.” [At present] we are not doing what we can—but there is hope as well. The technology exists! If we develop the will, we can use big data to advance equality and justice. [O'Neil has started to do just that. She is designing algorithms to "audit" potentially harmful algorithms.]
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Quotes from the author:

-"There are ethical choices in every single algorithm we build...."

-"I saw all kinds of parallels between finance and Big Data. Both industries gobble up the same pool of talent, much of it from elite universities like MIT, Princeton and Stanford. These new hires are ravenous for success and have been focused on external metrics– like SAT scores and college admissions – their entire lives. Whether in finance or tech, the message they’ve received is that they will be rich, they they will run the world…"

-"In both of these industries, the real world, with all its messiness, sits apart. The inclination is to replace people with data trails turning them into more effective shoppers, voters, or workers to optimize some objective… More and more I worried about the separation between technical models and real people, and about the moral repercussions of that separation. If fact, I saw the same pattern emerging that I’d witnessed in finance: a false sense of security was leading to widespread use of imperfect models, self-serving definitions of success, and growing feedback loops. Those who objected were regarded as nostalgic Luddites."

-"I wondered what the analogue to the credit crisis might be in Big Data. Instead of a bust, I saw a growing dystopia, with inequality rising. The algorithms would make sure that those deemed losers would remain that way. A lucky minority would gain ever more control over the data economy, taking in outrageous fortunes and convincing themselves that they deserved it...."
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O'Neil gave a very thought-provoking 12 minute TED talk on the issues raised in her book:

Here's a link to a google API that measures the "Toxicity" levels of typed words and sentences, for those who want to see how their own word choices are scored. https://www.perspectiveapi....

Questions to consider:

Do you think that we are moving towards a secretive Technocracy in which those who control algorithms that make fateful decisions are less and less accountable and transparent? Does the author go too far in suggesting that if algorithms for important decisions in society are not challenged we may well end up with a Plutocracy perpetuated by a techno-social elite?

Suppose everybody who designed or implemented these mathematical models was a) honest and b) well-intentioned. Would that prevent the ramping up of inequalities the author discusses? Is the problem one of the ethical integrity of those who control the machines, or is it deeper (e.g. quantifying merits and qualifications mechanically is bound to produce odd results)?

Random Thoughts

“Patriotism is when love of your own people comes first; nationalism when hate of people other than your own comes first.” Charles de Gaulle

“We apply fight-or-flight reflexes not only to predators, but to data itself.” Chris Mooney, science writer

 “The universe is a pretty big place. If it’s just us, it seems like an awful waste of space.” Carl Sagan

“Books serve to show a man that those original thoughts of his aren't very new after all.” Abe Lincoln

“The human mind is programmed for survival, not for truth.” John Gray, English philosopher (b. 1948)

“When my brothers try to draw a circle to exclude me, I shall draw a bigger circle to include them.” Pauli Murray, transgender activist

US security agencies found that Russia and China listen to Trump as he talks on his unsecured iphone. Trump vilified Clinton in the 2016 campaign for her email server being unsecured and her sloppiness about security.

B&B orig: 11/11/18