↳ Analysis

January 23rd, 2020

↳ Analysis

What Would a UBI Fund?

Lessons from the 1970s experiments in guaranteed income

One of the questions at the heart of contemporary debates over the merits of UBI is ‘what would it fund?’ In other words, what type of activities would it encourage? There are of course the widely debunked quibbles about guaranteed income encouraging anti-social behaviors, but there’s also a feminist critique of basic income proposals.

The feminist case against a UBI centers around the fear that, in contrast to more robust funding for social programs such as subsidized child care or parental leave, UBI would disproportionately encourage women to leave the labor force to provide care work in the home— reinscribing the gendered division of labor against which women have long struggled. In this view, UBI is undesirable—expected to fund the isolation of women in the domestic sphere, and preventing them from wielding influence over the real machinations of society.

⤷ Full Article

January 17th, 2020

UBI & the City

A new working paper models the effects of a basic income in New York City

Skeptics of guaranteed income tend to worry about the policy’s inflationary effects; absent rent regulation, for instance, one might expect housing costs to rise in proportion to the increase in disposable income generated by the policy. A new JFI-supported working paper presents the first attempt to model a UBI’s general equilibrium effects at the city level. In “Universal Basic Income and the City,” Khalil Esmkhani, Jack Favilukis and Stijn Van Nieuwerburgh explore the effects of a guaranteed income policy implemented at the city-level in New York City. They find that, when financed through a progressive income tax, a UBI increases general welfare and, perhaps most surprisingly, does not lead to housing market inflation. Their research sheds new light on the possible inflationary effects of basic income policies. It also suggests that the method used to finance a UBI has significant implications for the policy’s outcomes and characteristics. Though the results are tentative and the authors plan to expand their analysis to examine different scenarios and to perform sensitivity checks, their efforts already represent a major advance in the study cash transfer policy. In what follows, I present an overview of the macroeconomic literature on basic income before turning back to the model, its findings, and the plan for future work.

⤷ Full Article

January 16th, 2020

Macro Modeling in the Age of Inequality

On incorporating distributional concerns into macroeconomic models

Recent years have seen the revival of academic conversation around rising wealth inequality and its distributional consequences. But while applied, microeconomics-oriented fields like public and labor economics have long engaged with questions around inequality, macroeconomics has historically paid less attention to these questions, particularly as they relate to business cycles. Instead, it has focused more on the relationships between aggregate macroeconomic outcomes—such as unemployment, income, and consumption—and how they fluctuate during booms and recessions. As a result, research on rising income and wealth inequality in the United States tends to overlook the macroeconomic consequences of these developments, as well as the long-term macroeconomic trends which have contributed to their rise.

In order to assess what rising inequality means for our society, and what policies we should enact to mitigate its effects, we must understand its relationship to the economy as a whole. What macroeconomic forces have contributed to rising inequality, and how might elevated levels of inequality be shaping our economy? We need macroeconomic research to fully understand how income and wealth inequality have evolved in the United States. Particularly, we need a range of macroeconomic models, each of which can capture meaningful differences in household income or wealth but emphasizes different, potentially relevant features of the economy.

⤷ Full Article

December 18th, 2019

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?

⤷ Full Article

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.”

⤷ Full Article

January 24th, 2019

Why Rational People Polarize

U.S. politics is beset by increasing polarization. Ideological clustering is common; partisan antipathy is increasing; extremity is becoming the norm (Dimock et al. 2014). This poses a serious collective problem. Why is it happening? There are two common strands of explanation.

The first is psychological: people exhibit a number of “reasoning biases” that predictably lead them to strengthen their initial opinions on a given subject matter (Kahneman et al. 1982; Fine 2005). They tend to interpret conflicting evidence as supporting their opinions (Lord et al. 1979); to seek out arguments that confirm their prior beliefs (Nickerson 1998); to become more confident of the opinions shared by their subgroups (Myers and Lamm 1976); and so on.

The second strand of explanation is sociological: the modern information age has made it easier for people to fall into informational traps. They are now able to use social media to curate their interlocutors and wind up in “echo chambers” (Sunstein 2017; Nguyen 2018); to customize their web browsers to construct a “Daily Me” (Sunstein 2009, 2017); to uncritically consume exciting (but often fake) news that supports their views (Vosoughi et al. 2018; Lazer et al. 2018; Robson 2018); and so on.

So we have two strands of explanation for the rise of American polarization. We need both. The psychological strand on its own is not enough: in its reliance on fully general reasoning tendencies, it cannot explain what has changed, leading to the recent rise of polarization. But neither is the sociological strand enough: informational traps are only dangerous for those susceptible to them. Imagine a group of people who were completely impartial in searching for new information, in weighing conflicting studies, in assessing the opinions of their peers, etc. The modern internet wouldn’t force them to end up in echo chambers or filter bubbles—in fact, with its unlimited access to information, it would free them to form opinions based on ever more diverse and impartial bodies of evidence. We should not expect impartial reasoners to polarize, even when placed in the modern information age.

⤷ Full Article

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.

⤷ Full Article

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.

⤷ Full Article

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.

⤷ Full Article

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.”

⤷ Full Article