# The Phenomenal World

## Phantom Perspective

### A new report from Fordham CLIP sheds light on the market for student list data from higher education institutions

From the paper authored by N. CAMERON RUSSELL, JOEL R. REIDENBERG, ELIZABETH MARTIN, and THOMAS NORTON of the FORDHAM CENTER ON LAW AND INFORMATION POLICY:

“Student lists are commercially available for purchase on the basis of ethnicity, affluence, religion, lifestyle, awkwardness, and even a perceived or predicted need for family planning services.

This information is being collected, marketed, and sold about individuals because they are students."

Drawing from publicly-available sources, public records requests from educational institutions, and marketing materials sent to high school students gathered over several years, the study paints an unsettling portrait of the murky market for student list data, and makes recommendations for regulatory response:

1. The commercial marketplace for student information should not be a subterranean market. Parents, students, and the general public should be able to reasonably know (i) the identities of student data brokers, (ii) what lists and selects they are selling, and (iii) where the data for student lists and selects derives. A model like the Fair Credit Reporting Act (FCRA) should apply to compilation, sale, and use of student data once outside of schools and FERPA protections. If data brokers are selling information on students based on stereotypes, this should be transparent and subject to parental and public scrutiny.
2. Brokers of student data should be required to follow reasonable procedures to assure maximum possible accuracy of student data. Parents and emancipated students should be able to gain access to their student data and correct inaccuracies. Student data brokers should be obligated to notify purchasers and other downstream users when previously-transferred data is proven inaccurate and these data recipients should be required to correct the inaccuracy.
3. Parents and emancipated students should be able to opt out of uses of student data for commercial purposes unrelated to education or military recruitment.
4. When surveys are administered to students through schools, data practices should be transparent, students and families should be informed as to any commercial purposes of surveys before they are administered, and there should be compliance with other obligations under the Protection of Pupil Rights Amendment (PPRA)."

## PAVEMENT, NURSING, MISSILES

### Algorithm Tips, a compilation of "potentially newsworthy algorithms" for journalists and researchers

DANIEL TRIELLI, JENNIFER STARK, and NICK DIAKOPOLOUS and Northwestern’s Computational Journalism Lab created this searchable, non-comprehensive list of algorithms in use at the federal, state, and local levels. The “Methodology” page explains the data-scraping process, then the criteria for inclusion:

“We formulated questions to evaluate the potential newsworthiness of each algorithm:

Can this algorithm have a negative impact if used inappropriately?
Can this algorithm raise controversy if adopted?
Is the application of this algorithm surprising?
Does this algorithm privilege or harm a specific subset of people?
Does the algorithm have the potential of affecting a large population or section of the economy?

If the answers for any of these questions were 'yes', the algorithm could be included on the list."

Link. The list includes a huge range of applications, from a Forest Service algorithmic ranking of invasive plants, to an intelligence project meant to discover “significant societal events” from public data—and pavement, nursing, and missiles too.

• Nick Diakopolous also wrote a guide for journalists on investigating algorithms: “Auditing algorithms is not for the faint of heart. Information deficits limit an auditor’s ability to sometimes even know where to start, what to ask for, how to interpret results, and how to explain the patterns they’re seeing in an algorithm’s behavior. There is also the challenge of knowing and defining what’s expected of an algorithm, and how those expectations may vary across contexts.” Link.
• The guide is a chapter from the upcoming Data Journalism Handbook. One of the partner organizations behind the guide has a website of advice and stories from the data-reporting trenches, such as this on trying to figure out prescription drug deaths: “The FDA literally found three different ways to spell ASCII. This was a sign of future surprises.”

## ARTIFICIAL INFERENCE

### Causal reasoning and machine learning

In a recent paper titled "The Seven Pillars of Causal Reasoning with Reflections on Machine Learning", JUDEA PEARL, professor of computer science at UCLA and author of Causality, writes:

“Current machine learning systems operate, almost exclusively, in a statistical or model-free mode, which entails severe theoretical limits on their power and performance. Such systems cannot reason about interventions and retrospection and, therefore, cannot serve as the basis for strong AI. To achieve human level intelligence, learning machines need the guidance of a model of reality, similar to the ones used in causal inference tasks. To demonstrate the essential role of such models, I will present a summary of seven tasks which are beyond reach of current machine learning systems and which have been accomplished using the tools of causal modeling."

The tasks include work on counterfactuals, and new approaches to handling incomplete data. Link to the paper. A vivid expression of the issue: "Unlike the rules of geometry, mechanics, optics or probabilities, the rules of cause and effect have been denied the benefits of mathematical analysis. To appreciate the extent of this denial, readers would be stunned to know that only a few decades ago scientists were unable to write down a mathematical equation for the obvious fact that 'mud does not cause rain.' Even today, only the top echelon of the scientific community can write such an equation and formally distinguish 'mud causes rain' from 'rain causes mud.'”

Pearl also has a new book out, co-authored by DANA MCKENZIE, in which he argues for the importance of determining cause and effect in the machine learning context. From an interview in Quanta magazine about his work and the new book:

"As much as I look into what’s being done with deep learning, I see they’re all stuck there on the level of associations. Curve fitting. That sounds like sacrilege, to say that all the impressive achievements of deep learning amount to just fitting a curve to data. If we want machines to reason about interventions ('What if we ban cigarettes?') and introspection ('What if I had finished high school?'), we must invoke causal models. Associations are not enough—and this is a mathematical fact, not opinion.

We have to equip machines with a model of the environment. If a machine does not have a model of reality, you cannot expect the machine to behave intelligently in that reality. The first step, one that will take place in maybe 10 years, is that conceptual models of reality will be programmed by humans."

## SHOCK-LEVEL-ZERO

### Jobs guarantees vs. basic income

In a characteristically lengthy and thorough post, SCOTT ALEXANDER of SLATE STAR CODEX argues for a basic income over a jobs guarantee, in dialogue with a post by SIMON SARRIS.

Here's how Alexander addresses the claim that “studies of UBI haven’t been very good, so we can’t know if it works”:

“If we can’t 100% believe the results of small studies – and I agree that we can’t – our two options are to give up and never do anything that hasn’t already been done, or to occasionally take the leap towards larger studies. I think basic income is promising enough that we need to pursue the second. Sarris has already suggested he won’t trust anything that’s less than permanent and widespread, so let’s do an experiment that’s permanent and widespread.”

Link to the full piece on Slate Star.

For another angle on the same question, MARTIN RAVALLION recently published a paper at the CENTER FOR GLOBAL DEVELOPMENT looking at employment guarantees and income guarantees primarily in India:

“The paper has pointed to evidence for India suggesting that the country’s Employment Guarantee Schemes have been less cost effective in reducing current poverty through the earnings gains to workers than one would expect from even untargeted transfers, as in a UBI. This calculation could switch in favor of workfare schemes if they can produce assets of value (directly or indirectly) to poor people, though the evidence is mixed on this aspect of the schemes so far in India.”

Ravallion takes a nuanced view of arguments for the right to work and the right to income, as well as the constraints of implementation, and concludes, "The key point is that, in some settings, less effort at fine targeting may well prove to be more cost-effective in assuring economic freedom from material deprivation." Full study available here. ht Sidhya

## EACH POINT ON THE CHAIN

### Arguments for Value-Added Tax in the US, and using VAT to fund basic income

#### VAT

The Wall Street Journal lays out the basics: “Unlike a traditional sales tax, a VAT is a levy on consumption that taxes the value added to a product or service by businesses at each point in the chain of production.”

VATs are ubiquitous—except in the United States. According to a 2013 Hamilton Project report, “In recent years, the VAT has raised about 20 percent of the world’s tax revenue (Keen and Lockwood 2007). This experience suggests that the VAT can raise substantial revenue, is administrable, and is minimally harmful to economic growth.”  The TPC notes that “every economically advanced nation except the United States” has a VAT. Countries adopted VATs over time: the EU first unified all its VATs in the 1970s, China adopted a VAT in 1984, Canada in 1991, and so on. Now the US is the only country in the OECD without one.

#### Why is there no VAT in the US?

"Back in 1988, Harvard economist Larry Summers [...] explained that the reason the U.S. doesn't have a VAT is because liberals think it's regressive and conservatives think it's a money machine. We'll get a VAT, he said, when they reverse their positions." (Forbes.)

A VAT could certainly be a revenue-raising powerhouse. According to the CBO, a 5% VAT could raise 2.7 trillion dollars in 2017-2026 with a broad base, or 1.8 trillion with a narrow base—the most massive of all the options for revenue in their 2016 report.

And as for the regressive concerns, VAT proposals usually suggest adjusting other taxes or credits commensurately. A 2010 Tax Policy report considers a VAT in the context of lowering payroll or corporate taxes, and the Hamilton Project suggests adding tax credits or straightforward cash to low-income households.

VATs are appealing beyond their ability to raise a lot of money. They’re also easier to administer and document than other tax forms. A 2014 study by Dina Pomeranz examines the way the VAT is documented in Chile, and finds that "forms of taxation such as the VAT, which leave a stronger paper trail and thereby generate more information for the tax authority, provide an advantage for tax collection over other forms of taxation, such as a retail sales tax." Beyond that, Michael Graetz argues in the Wall Street Journal, "shifting taxes from production to consumption would stimulate jobs and investments and induce companies to base headquarters here rather than abroad." The Tax Foundation has advocated for a VAT to replace the Corporate Income Tax for similar reasons.

## Lay of the Land

### A new paper on the labor effects of cash transfers

SARAH BAIRD, DAVID MCKENZIE, and BERK OZLER of the WORLD BANK review a variety of cash transfer studies, both governmental and non-governmental, in low- and middle-income countries. Cash transfers aren’t shown to have the negative effects on work that some fear:

"The basic economic model of labor supply has a very clear prediction of what should be expected when an adult receives an unexpected cash windfall: they should work less and earn less. This intuition underlies concerns that many types of cash transfers, ranging from government benefits to migrant remittances, will undermine work ethics and make recipients lazy.

Overall, cash transfers that are made without an explicit employment focus (such as conditional and unconditional cash transfers and remittances) tend to result in little to no change in adult labor. The main exceptions are transfers to the elderly and some refugees, who reduce work. In contrast, transfers made for job search assistance or business start-up tend to increase adult labor supply and earnings, with the likely main channels being the alleviation of liquidity and risk constraints."

Link to the working paper. Table 2—which covers the channels through which cash impacts labor, is especially worth a read—as many studies on cash transfers don’t go into this level of detail.

• A study on a large-scale unconditional cash transfer in Iran: "With the exception of youth, who have weak ties to the labor market, we find no evidence that cash transfers reduced labor supply, while service sector workers appear to have increased their hours of work, perhaps because some used transfers to expand their business." Link.
• Continuing the analysis of Hauschofer and Schapiro’s controversial results from a cash study transfer in Kenya, Josh Rosenberg at GiveDirectly has, at the end of his overview, some thoughtful questions for continuing research: "Is our cost-effectiveness model using a reasonable framework for estimating recipients’ standard of living over time?… GiveDirectly provides large, one-time transfers whereas many government cash transfers provide smaller ongoing support to poor families. How should we apply new literature on other kinds of cash programs to our estimates of the effects of GiveDirectly?" Link.

## POSTAL OPTION

### Renewed interest in an old model

Last week we linked to the widely publicized news that SENATOR KIRSTEN GILLIBRAND would be pushing legislation to reintroduce government-run commercial banking through the United States Postal Service.

In a 2014 article for the HARVARD LAW REVIEW, law professor and postal banking advocate MEHRSA BARADARAN describes the context that makes postal banking an appealing solution:

“Credit unions, S&Ls, and Morris Banks were alternatives to mainstream banks, but they were all supported and subsidized by the federal government through targeted regulation and deposit insurance protection.

Banking forms homogenized in the 1970s and 1980s, leaving little room for variation in institutional or regulatory design. Eventually, each of these institutions drifted from their initial mission of serving the poor and began to look more like commercial banks, even competing with them for ever-shrinking profit margins.

The result now is essentially two forms of banks: regulated mainstream banks that seek maximum profit for their shareholders by serving the needs of the wealthy and middle class, and unregulated fringe banks that seek maximum profits for their shareholders by serving the banking and credit needs of the poor. What is missing from the American banking landscape for the first time in almost a century is a government-sponsored bank whose main purpose is to meet the needs of the poor."

## ONTARIO FOR ALL

### Canada calculates expanding Ontario's guaranteed income to the entire nation

Canada’s Parliamentary Budget Office looks at the cost of expanding the Ontario pilot nationwide. Full report here. ht Lauren

ANDREW COYNE of the NATIONAL POST summarizes the findings (all figures are in Canadian dollars):

“The results, speculative as they are, are intriguing. The PBO puts the cost of a nationwide rollout of the Ontario program, guaranteeing every adult of working age a minimum of 16,989 CAD annually (24,027 CAD for couples), less 50 per cent of earned income—there’d also be a supplement of up to 6,000 CAD for those with a disability—at 76.0 billion CAD.
“Even that number, eye-watering as it is (the entire federal budget, for reference, is 312 billion CAD), is a long way from the 500 billion CAD estimates bandied about in some quarters.

## METARESEARCH AND DEVELOPMENT

### Changes in R & D funding and allocation

In a new report on workforce training and technological competitiveness, a task force led by former Commerce Secretary Penny Pritzker describes recent trends in research and development investment. Despite the fact that “total U.S. R&D funding reached an all-time high of nearly \$500 billion in 2015, nearly three percent of U.S. gross domestic product,” the balance in funding has shifted dramatically to the private sector: “federal funding for R&D, which goes overwhelmingly to basic scientific research, has declined steadily and is now at the lowest level since the early 1950s.” One section of the report contains this striking chart:

Link to the full report. ht Will

• A deeper dive into the report's sourcing leads to a fascinating repository of data from the American Association for the Advancement of Science on the U.S. government's investments in research since the 1950s. Alongside the shift from majority federal to majority private R&D funding, the proportion of investments across different academic disciplines has also changed significantly. One table shows that the share of federal R&D funding for environmental science, engineering, and math/computer science has grown the most, from a combined 43.2% in 1970 to 54.8% in 2017. Meanwhile, funding for social science research has decreased the most. In 1970, the social sciences received 4.3% of the government's R&D funding; but in 2017, that share had fallen to 1.8%. Much more data on public sector R&D investments is available from the AAAS here.
• A March 2017 article in Science explains some of these shifts.
• A section of a 1995 report commissioned by the U.S. Senate Committee on Appropriations charts and contextualizes the explosion of federal research and development funding in the immediate aftermath of the Second World War.
• A study from the Brookings Institution finds that federal funding for research and development accounts for up to 2.8 percent of GDP in some of the largest metropolitan areas in America. The authors have fifty ideas for how municipalities can capture more of the economic impact generated by that R&D.
• Michael comments: "With the diminishing share (4.3% to 1.8% of total government research) of halved expenditures—and business not naturally inclined to conduct this kind of research (except in, as one would expect, instances of direct business application like surge pricing and Uber)—social science research appears to no longer have a natural home."