# ↳ Analysis

## Experiments for Policy Choice

Randomized experiments have become part of the standard toolkit for policy evaluation, and are usually designed to give precise estimates of causal effects. But, in practice, their actual goal is to pick good policies. These two goals are not the same.

Is this the best way to go about things? Can we maybe make better policy choices, with smaller experimental budgets, by doing things a little differently? This is the question that Anja Sautmann and I address in our new work on “Adaptive experiments for policy choice.” If we wish to pick good policies, we should run experiments adaptively, shifting toward better policies over time. This gives us the highest chance to pick the best policy after the experiment has concluded.

## 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.

## 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.

## 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.

## 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.

## 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.

## The Politics of Machine Learning, pt. I

Terminology like "machine learning," "artificial intelligence," "deep learning," and "neural nets" is pervasive: business, universities, intelligence agencies, and political parties are all anxious to maintain an edge over the use of these technologies. Statisticians might be forgiven for thinking that this hype simply reflects the success of the marketing speak of Silicon Valley entrepreneurs vying for venture capital. All these fancy new terms are just describing something statisticians have been doing for at least two centuries.

But recent years have indeed seen impressive new achievements for various prediction problems, which are finding applications in ever more consequential aspects of society: advertising, incarceration, insurance, and war are all increasingly defined by the capacity for statistical prediction. And there is crucial a thread that ties these widely disparate applications of machine learning together: the use of data on individuals to treat different individuals differently. In this two part post, Max Kasy surveys the politics of the machine learning landscape.

## 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.

## 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.

## 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.