Phenomenal World

March 22nd, 2019

Phenomenal World

The Emerging Monopsony Consensus

Early on in The Wealth of Nations, Adam Smith asked who had the edge in negotiations between bosses and wage laborers. His answer: the bosses. In the case of a stalemate, landlords and manufacturers “could generally live a year or two” on their accumulated wealth, while among workers, “few could subsist a month, and scarce any a year, without employment.” Thus, concluded Smith in 1776, “masters must generally have the advantage.”

As economic thought progressed over subsequent centuries, however, Smith’s view of labor markets gave way to the reassuring image of perfect competition. In recent years, a model more in line with Smith’s intuitions has grown to challenge the neoclassical ideal. Under the banner of monopsony, economists have built up an impressive catalog of empirical work that offers a more plausible baseline model for labor markets.

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March 19th, 2019

Ideology in AP Economics

When the media talks about ideological indoctrination in education, it is usually assumed to refer to liberal arts professors pushing their liberal agenda. Less discussed is the very different strain of ideology found in economics. The normative import is harder to spot here, as economics presents itself as a science: it provides an empirical study of the economy, just as mechanical engineering provides an empirical study of certain physical structures. When economists offer advice on matters of policy, it’s taken to be normatively neutral expert testimony, on a par with the advice of engineers on bridge construction. However, tools from the philosophy of explanation, in particular the work of Alan Garfinkel, show how explanations that appear purely empirical can in fact carry significant normative assumptions.1 With this, we will uncover the ideology embedded in economics.

More specifically, we’ll look at the ideology embedded in the foundations of traditional economics—as found in a typical introductory micro-economics class. Economics as a whole is diverse and sprawling, such that no single ideology could possibly be attributed to the entire discipline, and many specialized fields avoid many of the criticisms I make here. Despite this, if there are ideological assumptions in standard introductory course, this is of great significance.

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March 1st, 2019

The Case for an Unconditional Safety Net

Imagine a system where everyone had a right to basic material safety, and could say “no” to abuse and exploitation. Sounds utopian? I argue that it would be quite feasible to get there, and that it would make eminent economic, moral, and political sense.

In my paper, I discuss four sets of arguments why it would make economic, moral, and political sense to transition from the current system of subsidizing low wage work to a system providing an unconditional safety net.

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February 4th, 2019

Cash and Income Studies: A Literature Review of Theory and Evidence

What happens when you give people cash? How do they use the money, and how does it change their lives? Every cash study on this list is different: the studies vary in intervention type, research design, location, size, disbursement amount, and effects measured. The interventions listed here include basic income and proxies--earned income tax credits, negative income tax credits, conditional cash transfers, and unconditional cash transfers. The variety present here prevents us from being able to make broad claims about the effects of universal basic income. But because of its variety, this review provides a sense of the scope of research in the field, capturing what kinds of research designs have been used, and what effects have been estimated, measured, and reported. The review also allows us to draw some revealing distinctions across experimental designs.

If you’re interested in creating a UBI policy, there are roughly three levels of effects (after ODI) that you can examine.

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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.

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December 14th, 2018

Cash Transfer, Knowledge Transfer

An interview with Johannes Haushofer on the state of the evidence for cash transfers

We’re pleased to introduce a new interview series for the Phenomenal World. We will be speaking with an array of academics and policymakers on the most ambitious yet tractable new ideas in the social sciences.

Johannes Haushofer is assistant professor of Psychology and Public Affairs at Princeton University. His work includes development economics, behavioral economics, psychology, and neurobiology. We spoke to him primarily about a neopolicy idea on which he has unique expertise: unconditional cash transfers (UCTs). Along with Jeremy Shapiro, he has led research on GiveDirectly’s UCT program in Kenya, and his work on short-term and long-term effects there has both provided the field with new evidence and set a course for deeper questions. Now Johannes is starting to work on a UBI pilot in a major US city, across the developing-nation/developed-nation divide.

We spoke to Johannes broadly about (1) where he sees the state of the evidence, (2) what conclusions can be drawn from developing nations to developed, and (3) his larger vision for the march of evidence regarding policies like this.

We’re grateful that Johannes took the time to speak with us and to inaugurate this interview series. Interviewing him was Michael Stynes, who leads JFI, as well as Sidhya Balakrishnan and Lauren Burns-Coady of JFI. This interview has been condensed and edited.

MS: Johannes, first of all, thank you very much for speaking with us. Your work is incredibly important to the entire basic income/cash transfer research community and I’m happy that Lauren, Sidhya, and I will have the opportunity to talk through some of your work. We are particularly interested in the point in which a pilot intervention becomes viable policy. Here specifically I want to try to understand what the state of the evidence is in favor of unconditional cash transfers in the developing world, and how your research there might extend to the developed world. Can we start with an overview of the work you’ve done so far, particularly on cash transfers in Kenya?

JH: The main completed study that I’ve done so far is a randomized controlled trial [RCT] on GiveDirectly’s UCT program in Kenya, that we finished maybe five years ago, and published two years ago. In that study, we delivered transfers that are on average $700, which is about two years of per capita consumption, to poor families in western Kenya. And we found pretty sizeable effects on outcomes like consumption, asset holdings, psychological well-being, and income. That piece of evidence is part of a larger body of evidence, which is that cash transfers do a bunch of good things. So they increase consumption, and other welfare outcomes we care about. There are lots of studies that make us think that. And there aren’t a lot of the negative effects that people were original worried about—temptation goods, conflicts, violence, and so on. We’re just now working on a paper that shows pretty large decreases in domestic violence, as a result of cash transfers.

I would say that the state of the evidence is: cash transfers do pretty good things. The 1.0 question for cash transfers is, “Is it better to get cash than to get nothing?” I think the answer to that is yes, it’s better to get cash. This isn’t surprising to many people, but it is surprising to some people who thought the poor are bad at handling money, and were going to blow it and make their lives worse.

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November 9th, 2018

Banking with Imprecision

​In 1596, Spanish troops under the leadership of the Duke of Medina-Sidonia set fire to their own ships in the waters near Cadiz. The sinking of these thirty-two vessels was a tactical necessity: a joint Anglo-Dutch navy had annihilated the slapdash defenses of the city, driving the Spanish ships off to nearby Puerto Real. The Spanish had preferred to see their ships sunk rather than captured by the enemy. Cadiz itself was occupied and sacked, and its most prominent civilians were held for ransom. War, as the Spanish were acutely aware, was very costly. Later that very year, Philip II, King of Spain, would declare bankruptcy. 1

Though he was one of the most powerful monarchs of the era, it is difficult to sympathize with the sheer magnitude of the work with which King Philip II of Spain had to contend. Not only did he have to protect his Iberian possessions, but he also had to prosecute a war against the recalcitrant Dutch in the Low Countries, outmaneuver the Protestants in France, and maintain a bulwark against the Turks in the Mediterranean. 2

In their book, Lending to the Borrower from Hell, Drelichman and Voth have done a remarkable job of illuminating Spanish finance in the 16th century.Notably, the fiscal machinery underpinning imperial operations was managed mostly by a tight-knit cartel of Genoese bankers. Sovereign lending, astonishingly, allowed for a plethora of state actions in a time before instant communication. The foundations of empire rested on a relatively simple model: control certain streams of income and then borrow against them. The institutional origins of our modern sovereign lending come from this tradition. Dealing with uncertainty is an inherent part of this model – now as it was then. What is of use to modern scholars is how the same problem was conceived of and partly surmounted by our institutional forebears.

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October 18th, 2018

Machine Ethics, Part One: An Introduction and a Case Study

The past few years have made abundantly clear that the artificially intelligent systems that organizations increasingly rely on to make important decisions can exhibit morally problematic behavior if not properly designed. Facebook, for instance, uses artificial intelligence to screen targeted advertisements for violations of applicable laws or its community standards. While offloading the sales process to automated systems allows Facebook to cut costs dramatically, design flaws in these systems have facilitated the spread of political misinformation, malware, hate speech, and discriminatory housing and employment ads. How can the designers of artificially intelligent systems ensure that they behave in ways that are morally acceptable--ways that show appropriate respect for the rights and interests of the humans they interact with?

The nascent field of machine ethics seeks to answer this question by conducting interdisciplinary research at the intersection of ethics and artificial intelligence. This series of posts will provide a gentle introduction to this new field, beginning with an illustrative case study taken from research I conducted last year at the Center for Artificial Intelligence in Society (CAIS). CAIS is a joint effort between the Suzanne Dworak-Peck School of Social Work and the Viterbi School of Engineering at the University of Southern California, and is devoted to “conducting research in Artificial Intelligence to help solve the most difficult social problems facing our world.” This makes the center’s efforts part of a broader movement in applied artificial intelligence commonly known as “AI for Social Good,” the goal of which is to address pressing and hitherto intractable social problems through the application of cutting-edge techniques from the field of artificial intelligence.

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October 10th, 2018

Who cares about stopping rules?

Can you bias a coin?

Challenge: Take a coin out of your pocket. Unless you own some exotic currency, your coin is fair: it's equally likely to land heads as tails when flipped. Your challenge is to modify the coin somehow—by sticking putty on one side, say, or bending it—so that the coin becomes biased, one way or the other. Try it!

How should you check whether you managed to bias your coin? Well, it will surely involve flipping it repeatedly and observing the outcome, a sequence of h's and t's. That much is obvious. But what's not obvious is where to go from there. For one thing, any outcome whatsoever is consistent both with the coin's being fair and with its being biased. (After all, it's possible, even if not probable, for a fair coin to land heads every time you flip it, or a biased coin to land heads just as often as tails.) So no outcome is decisive. Worse than that, on the assumption that the coin is fair any two sequences of h's and t's (of the same length) are equally likely. So how could one sequence tell against the coin's being fair and another not?

We face problems like these whenever we need to evaluate a probabilistic hypothesis. Since probabilistic hypotheses come up everywhere—from polling to genetics, from climate change to drug testing, from sports analytics to statistical mechanics—the problems are pressing.

Enter significance testing, an extremely popular method of evaluating probabilistic hypotheses. Scientific journals are littered with reports of significance tests; almost any introductory statistics course will teach the method. It's so popular that the jargon of significance testing—null hypothesis, $p$-value, statistical significance—has entered common parlance.

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October 2nd, 2018

The "Next Big Thing" is a Room

If you don’t look up, Dynamicland seems like a normal room on the second floor of an ordinary building in downtown Oakland. There are tables and chairs, couches and carpets, scattered office supplies, and pictures taped up on the walls. It’s a homey space that feels more like a lower school classroom than a coworking environment. But Dynamicland is not a normal room. Dynamicland was designed to be anything but normal.

Led by the famous interface designer Bret Victor, Dynamicland is the offshoot of HARC (Human Advancement Research Community), most recently part of YCombinator Research. Dynamicland seems like the unlikeliest vision for the future of computers anyone could have expected.

Let’s take a look. Grab one of the scattered pieces of paper in the space. Any will do as long as it has those big colorful dots in the corners. Don’t pay too much attention to those dots. You may recognize the writing on the paper as computer code. It’s a strange juxtaposition: virtual computer code on physical paper. But there it is, in your hands. Go ahead and put the paper down on one of the tables. Any surface will do.

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