↳ Analysis

December 18th, 2019

↳ Analysis

Unequal and Uneven: The Geography of Higher Education Access

Mapping market concentration in the higher education industry

In much of the existing higher education literature, “college access” is understood in terms of pre-college educational attainment, social and informational networks, and financial capacity, both for tuition and living expenses. The US ranks highly on initial college access by comparison with other countries, but this access—along with all major metrics of college success, including completion rates, default rates, and debt-to-income ratios—exhibits drastic inequality along familiar lines of race, gender, class, and geography.

Along with other pernicious myths, the media stereotype of the college student often figures undergraduates traveling far from home to live in a dorm on a leafy campus. The reality is far from the case: over 50% of students enrolled in four-year public college do so close to their home. This means that the geography of higher ed institutions strongly determines the options available to a given student. While much higher education policy discourse justly attempts to improve students’ access to information on school costs, financial aid information, completion rates, or post-graduation employment statistics to inform their school choices, political attention to geographic access remains overlooked.

Previous research on the geography of higher ed has simply reported the number of institutions in a given area. But the raw number of schools is ambiguous, as it fails to account for enrollment. We wanted to complicate the picture: given the uneven distribution of higher ed institutions and institution types—public and private non-profits, as well as for-profits of all kinds—around the country, we wanted to examine what role market concentration might play in a higher education industry increasingly characterized by a wide divide between elite institutions and the landscape of what Tressie McMillan Cottom has termed "Lower Ed." Starting from the perspective that many students are not going to travel long distances to be in residence full time at a leafy campus, how many options are they realistically looking at? And what’s the relationship between concentration, disparities on the basis of race, class, and geography, institutions’ resulting market power, and college cost, debt loads, and post-graduate earnings?

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November 22nd, 2019

Development and Displacement

The effects of big development initiatives

Infrastructure lies at the heart of development. From transportation and telecommunication networks to electrical grids and water pipelines, large-scale infrastructure projects play a pivotal role in the global development landscape. (In 2015, infrastructure spending totaled $9.5 trillion or 14% of global GDP). Infrastructure development also holds political significance.

Both historically and in the present, state investment in resource generation in the Global South has been a cornerstone of national movements for economic independence. But while infrastructure development projects generate jobs and drive long-term growth, the economic gains are often unevenly distributed. The burden of development weighs heavily on individuals and communities who are forced to leave their homes to make way for these large-scale projects.

In the development literature, this phenomenon is referred to as development-induced displacement and resettlement (DIDR)—individuals and communities being forced to leave their place of residence and abandon their land due to development initiatives. Some accounts estimate that 200 million people were displaced by development projects over the last two decades of the 20th century, and the current scale of DIDR is estimated to be around 15 million people per year. People displaced by development projects fall into the broader category of Internally Displaced Persons (IDPs)—a United Nations designation for "persons or groups of persons who have been forced or obliged to flee or to leave their homes or places of habitual residence as a result of armed conflict, internal strife, and habitual violations of human rights, as well as natural or man-made disasters involving one or more of these elements, and who have not crossed an internationally recognized state border." In the case of DIDR, resettlement—if any occurs—is often inadequate, leaving migrants impoverished and disempowered. Unlike refugees that cross international borders and are under the protection of international law, internally-displaced persons remain within the jurisdiction of their own government—vulnerable to the same lack of protection that caused their displacement. Urban, transportation, and water supply projects account for the majority of displacements—between 1986 and 1993, 80 to 90 million people were involuntarily displaced by these three types of infrastructure development projects alone.

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November 7th, 2019

Collective Ownership in the Green New Deal

What rural electrification can teach us about a just transition

This year, we once again shattered the record for atmospheric carbon concentration, and witnessed a series of devastating setbacks in US climate policy—from attempts to waive state protections against pipelines to wholesale attacks on climate science. Against this discouraging backdrop, one idea has inspired hope: the “Green New Deal,” a bold vision for addressing both the climate crisis and the crushing inequalities of our economy by transitioning onto renewable energy and generating up to 10 million well paid jobs in the process. It’s an exciting notion, and it’s gaining traction—top Democratic presidential candidates have all revealed plans for climate action that engage directly with the Green New Deal. According to the Yale Project on Climate Communications, as of May 2019, the Green New Deal had the support of 96% of liberal democrats, 88% of moderate democrats, 64% of moderate republicans, and 32% of conservative republicans. In order to succeed, however, a Green New Deal must prioritize projects that are owned and controlled by frontline communities.

Whose power lines? Our power lines!

Efforts to electrify the rural South during the New Deal present a useful case study for understanding the impact of ownership models on policy success. Up until the mid-1930s, 9 out of 10 Southern households had no access to electricity, and local economies remained largely agricultural. Southern communities were characterized by low literacy rates and a weak relationship to the cash nexus, distancing them from the federal government both culturally and materially. They were also economically destitute—a series of droughts throughout the 20s led to the proliferation of foreclosures and tenant farming. With the initial purpose of promoting employment in the area, the Roosevelt administration launched the Rural Electrification Administration in 1935. The Rural Electrification Act of 1936 sought to extend electrical distribution, first by establishing low-interest loans to fund private utility companies. The utility companies turned them down: private shareholders had little reason to invest in sparsely populated and impoverished counties, whose residents could not be assured to pay for services; private investors lacked the incentive to fund electrification for the communities who needed it most.

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October 17th, 2019

Disparate Causes, pt. II

On the hunt for the correct counterfactual

An accurate understanding of the nature of race in our society is a prerequisite for an adequate normative theory of discrimination. If, as part one of this post suggests, limiting discrimination to only direct effects of race misunderstands the nature of living as a raced subject in a raced society, then perhaps the extension of the scope of discrimination to also include indirect effects of race would better approximate the social constructivist view of race.

Recent approaches to causal and counterfactual fairness seek fair decision procedures “achieved by correcting the variables that are descendants of the protected attribute along unfair pathways.”1 The method, thus, cancels out certain effects that are downstream of race in the diagram, thereby retaining only those path-specific effects of race that are considered fair. Despite the expanded scope of what counts as a discriminatory effect, the logic of the Path-Specific Effects method follows that of the original Pearlian causal counterfactual model of discrimination: race, as a sensitive attribute, is toggled white or black atop a causal diagram, and its effect cascades down various paths leading to the outcome variable. But, this time, the causal fairness technician does more than measure and limit the direct effect of race on the final outcome; she now also measures effects of race that are mediated by other attributes, keeping only those effects carried along paths deemed “fair.”

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October 11th, 2019

Disparate Causes, pt. I

The shortcomings of causal and counterfactual thinking about racial discrimination

Legal claims of disparate impact discrimination go something like this: A company uses some system (e.g., hiring test, performance review, risk assessment tool) in a way that impacts people. Somebody sues, arguing that it has a disproportionate adverse effect on racial minorities, showing initial evidence of disparate impact. The company, in turn, defends itself by arguing that the disparate impact is justified: their system sorts people by characteristics that—though incidentally correlated with race—are relevant to its legitimate business purposes. Now, the person who brought the discrimination claim is tasked with coming up with an alternative—that is, a system with less disparate impact and still fulfills the company’s legitimate business interest. If the plaintiff finds such an alternative, it must be adopted. If they don’t, the courts have to, in theory, decide how to tradeoff between disparate impact and legitimate business purpose.

Much of the research in algorithmic fairness, a discipline concerned with the various discriminatory, unfair, and unjust impacts of algorithmic systems, has taken cues from this legal approach—hence, the deluge of parity-based “fairness” metrics mirroring disparate impact that have received encyclopedic treatment by computer scientists, statisticians, and the like in the past few years. Armed with intuitions closely linked with disparate impact litigation, scholars further formalized the tradeoffs between something like justice and something like business purpose—concepts that crystallized in the literature under the banners of “fairness” and “efficiency.”

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September 12th, 2019

Money Parables

Three competing theories of money

In the past year, Modern Monetary Theory (MMT) has shifted the policy debate in a way that few heterodox schools of economic thought have in recent memory. MMT’s central notion—that nations with their own strong currencies face no inherent financial constraints—has made its way into politics and, notably, the world of finance. The last few months have brought MMT explainers from financial media outlets including Reuters, CNBC, Bloomberg, Barron’s, and Business Insider, as well as from investment analysts at Wall Street firms including Goldman Sachs, Bank of America, Fitch, Standard Chartered and Citigroup.

Popularizing the shorthand notion that “deficits don’t matter” has been an achievement for those promulgating MMT. Yet one largely unappreciated change brought about by the MMT debates involves a somewhat subtler point: a shift in the implicit story we tell about money.

The rise of MMT poses a challenge to the mainstream commodity money story. That parable, familiar to anyone who has taken high school economics or read Adam Smith, involves an inefficient barter system that gives way to the more convenient use of some token that represents value, typically a precious metal. If government plays a role in this story, it is only to regulate money after the marketplace births it.

The MMT parable—known in the literature as chartalism—reverses the commodity money view. For chartalists, money arises through an act of law, namely the levying of a tax which requires citizens to go out and get that which pays taxes; the state comes first and markets are subsequent. As Abba Lerner puts it, money is “a creature of the state.”

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August 23rd, 2019

Is it impossible to be fair?

Statistical prediction is increasingly pervasive in our lives. Can it be fair?

The Allegheny Family Screening Tool is a computer program that predicts whether a child will later have to be placed into foster care. It's been used in Allegheny County, Pennsylvania, since August 2016. When a child is referred to the county as at risk of abuse or neglect, the program analyzes administrative records and then outputs a score from 1 to 20, where a higher score represents a higher risk that the child will later have to be placed into foster care. Child welfare workers use the score to help them decide whether to investigate a case further.

Travel search engines like Kayak or Google Flights predict whether a flight will go up or down in price. Farecast, which launched in 2004 and was acquired by Microsoft a few years later, was the first to offer such a service. When you look up a flight, these search engines analyze price records and then predict whether the flight's price will go up or down over some time interval, perhaps along with a measure of confidence in the prediction. People use the predictions to help them decide when to buy a ticket.

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August 1st, 2019

Decentralize What?

Can you fix political problems with new web infrastructures?

The internet's early proliferation was steeped in cyber-utopian ideals. The circumvention of censorship and gatekeeping, digital public squares, direct democracy, revitalized civic engagement, the “global village”—these were all anticipated characteristics of the internet age, premised on the notion that digital communication would provide the necessary conditions for the world to change. In a dramatic reversal, we now associate the internet era with eroding privacy, widespread surveillance, state censorship, asymmetries of influence, and monopolies of attention—exacerbations of the exact problems it portended to fix.

Such problems are frequently understood as being problems of centralization—both infrastructural and political. If mass surveillance and censorship are problems of combined infrastructural and political centralization, then decentralization looks like a natural remedy. In the context of the internet, decentralization generally refers to peer-to-peer (p2p) technologies. In this post, I consider whether infrastructural decentralization is an effective way to counter existing regimes of political centralization. The cyber-utopian dream failed to account for the exogenous pressures that would shape the internet—the rosy narrative of infrastructural decentralization seems to be making a similar misstep.

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July 18th, 2019

Student Debt & Racial Wealth Inequality

How student debt cancellation affects the racial wealth gap

The effect of cancelling student debt on various measures of individual and group-level inequality has been a matter of controversy, especially given presidential candidates’ recent and high-profile proposals to eliminate outstanding student debt. In this work, I attempt to shed light on the policy counterfactual by analyzing the Survey of Consumer Finances for 2016, the most recent nationally-representative dataset that gives a picture of the demographics of student debt.

When we test the effects of cancelling student debt on the racial wealth gap, we conclude that across all samples, across all quantiles, the racial wealth gap narrows when student debt is cancelled, and it narrows more the more student debt is cancelled.

With respect to the two presidential candidates’ plans, this means that the Sanders plan, completely eliminating outstanding student debt, reduces racial wealth inequality more than does the Warren plan, which only forgives $50,000 of debt, and phases that out for high earners. But the difference between the two plans as measured by the reduction in the racial wealth gap is not large. It would be fair to say that the Warren plan achieves the vast majority of the racial wealth equity gains that the Sanders plan achieves, while leaving the student debt held by the highest-income borrowers intact.

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July 3rd, 2019

The Politics of Machine Learning, pt. II

The uses of algorithms discussed in the first part of this article vary widely: from hiring decisions to bail assignment, to political campaigns and military intelligence.

Across all these applications of machine learning methods, there is a common thread: Data on individuals is used to treat different individuals differently. In the past, broadly speaking, such commercial and government activities used to target everyone in a given population more or less similarly—the same advertisements, the same prices, the same political slogans. More and more now, everyone gets personalized advertisements, personalized prices, and personalized political messages. New inequalities are created and new fragmentations of discourse are introduced.

Is that a problem? Well, it depends. I will discuss two types of concerns. The first type, relevant in particular to news and political messaging, is that the differentiation of messages is by itself a source of problems.

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