Thoma and Taxes

This post is a response to Mark Thoma’s article in the Financial Times yesterday, found here.

In his article, Thoma suggests that a Pigovian tax should be imposed on the financial industry to correct for the negative externality it generates (the negative externality being too much risk).

Two questions come to mind after reading Thoma’s article.

Will this tax decrease the amount of risk in the financial industry? My gut reaction is that a tax will not change the fact that many people in the financial sector are risk-takers. Taxing the bad behavior of Wall Street will not change the composition of its workers, who will continue to take speculative risks. In fact, if the tax is too high—and it would be if banks had to pay $6-$14 trillion as Thoma suggests—volatility of financial markets could increase if firms fight fire with fire and increase their risks given the higher taxes. Historical evidence (pdf) suggests that high financial transaction taxes is associated with higher risk taking and increased volatility.

Will this tax help the people it is intended to help? Given higher taxes, financial firms might increase their rates or the fees they charge to their customers. Long-term investments (such as buying a house) usually have a high price elasticity of demand, so imposing a tax on financial firms could lead to poor outcomes for the people Thoma claims the tax should help: those in the middle-class who were hurt in the Great Recession.

small percent changes in mortgages lead to large percent changes in number of houses bought (demand elastic)
Demand for houses is elastic

The fact that people tend to wait until the “market is right” to buy a house implies that a small percentage increase in price of mortgages will lead to a large percentage decrease in the amount of homes purchased. This might be a bad thing if a place thinks people ought to buy homes instead of rent them.

I agree with Thoma that mitigating risk in the financial sector is important and that Wall Street should be held more accountable for what happened in the lead-up to the financial crisis, but an FTT is probably not the best way to achieve this goal.

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Apple’s Music Factory

Last week, Apple released their new music app, aptly named Apple Music.

The iTunes music store opened in 2003 and has sold more than 25 billion songs since then. iTunes changed the way that music was sold in 2003, but in 2015 it is mostly playing catch-up. The new Apple Music is similar to existing music streaming services such as Spotify, and its radio service iTunes released in 2013 is identical to Pandora.

It seems that there’s always a doomsayer who warns of a coming tech bubble return the economy to reality. These doomsayers claim that Snapchat is overvalued, Instagram is not worth a billion dollars, Twitter can’t make a profit, etc. Streaming services such as Pandora and Spotify rely solely on subscriptions and advertisements for revenue. iTunes also requires a subscription ($10 for a single license and $15 for a family license) and its radio service similarly plays ads.

Apple Music is different than these other music streaming services in one key respect: it has a way to generate profits. Apple makes money through selling phone and macs, and it seems that this new service is a marketing strategy to lure more people over from the Android and MS Windows market. The revenues Apple earns from its music subscription service are only needed to cover the costs of the streaming services, such as paying the artists and record labels. Other services, such as Pandora and Spotify, must find ways to generate profit without selling hardware, which is fairly difficult.

Apple’s move to subscription-based music is interesting to me for a couple reasons.

The first reason is that iTunes has maneuvered around its main problem to this point: charging a (relatively high) amount of money for files people can receive (steal) online for free. I feel guilty whenever I pirate music illegally, but I don’t like dishing out ten bucks for an album when I want to build my music collection. iTunes operates within the monopolist model–it charges much higher for its music than the marginal cost of distributing it, and by doing so, many people might purchase its music cannot afford to do so. Young people often wisen up to this fact and resort to stealing music online. Artists ought to be compensated for their work, and iTunes causes a significant amount of dead-weight loss right now through its sorta monopoly on digital music. Will pirating go out of fashion? Probably not, but Apple Music will probably lower the number of illegal downloads.

The second reason I find Apple Music interesting is that direction music is moving seems to be the opposite from videogames, another industry with very low marginal costs to distribute its goods. Both music and videogames have moved fully into the digital marketplace. However, the most popular videogames right now (League of Legends, DOTA 2, and Hearthstone) are all free-to-play, and make money from selling characters or cosmetic items that impatient players can buy with real-money instead of the in-game currency. Blizzard, the developer of Hearthstone, recently released “character portraits” for Hearthstone, which means that it is charging $10 for a purely cosmetic detail (players protested pretty loudly at this announcement). I am very confused why people can justify buying a character portrait for $10 but balk at the idea of buying a Kanye album for the same price. The free-to-play model seems to have taken over the videogame industry, while music is moving towards a subscription model. I wonder if this trend will continue.

Anyway, Apple Music is really cool. If you haven’t updated your iPhone to the latest OS I would recommend doing so and signing up for the free 3 month trial. If you’re looking for new music, check out Vince Staples’ new album: Summertime ’06. It’s a really good album.

Dismal but Strong: Why Economics is a Hard Science

I originally wrote this for a class I took a couple weeks ago. Here’s an interesting blog post by Noah Smith he published a couple days after I wrote this piece that connects well with this post.


I entered college as a biology major. Unfortunately, I was bored to tears in a biology class I took during my first semester. During the same time, I was in an introduction to macroeconomics class. While I fell asleep listening to biologists explain their worm experiments, I was fascinated by economics and looked forward to every class. After that semester, I switched majors from biology to economics. When I told my parents I had made this switch, I could not help but notice their disappointment. Both of them had majored in engineering, and my brother studied physics; when I switched majors I became the non-science black sheep. However, after taking many economics classes since coming to college, I now see it as a science on the same level as physics, chemistry, or mathematics.

Providing an exact definition of science is difficult, but for the most part, science is the discipline of organizing ideas and theories about the universe using observations and repeatable tests. This can be as simple as mixing water with oil to make claims about their densities, or as complex as smashing atoms together in an electromagnetic particle accelerator to make claims about the nature of atomic particles. Both experiments organize ideas (albeit polar opposites in complexity) through observations, and both can be tested again (given a large enough budget) in a laboratory.

Given that science is broad and encompasses many fields, there are distinctions used to categorize it. One way to divide scientific fields is to group these fields into “hard sciences” and “soft sciences.” Math, physics, and chemistry are typically most often thought of as “hard sciences,” because they use a high degree of objectivity and rely heavily on the scientific method of testing hypotheses through observations and mathematical models. Psychology, political science, and sociology are generally regarded as “soft sciences,” because these fields make claims that may be difficult to replicate in a laboratory and rely less on empirical data. There is no definitive way to rate different fields based on “hardness,” but a 2000 paper published in Social Studies of Science suggests that the “harder” sciences use more graphs in their publications. That is, “hard” sciences are mostly grounded in data and observations.

Where does economics fit into the picture? Is it a “hard” science, such as physics or chemistry, or a “soft” science like sociology or psychology? Economics is the study of how people choose to allocate limited resources. The study of an individual’s choice of allocating his resources is referred to as microeconomics, while the study of how nations and larger systems choose to allocate their resources is referred to as macroeconomics. Because of its reliance on mathematical models, and the credible claims it can make about the world, economics should be considered more “hard” than it is “soft.”

One way economics is more of a “hard” science is that it analyzes data from the world’s economy to produce ideas and theories about the economy. Just as an oceanologist studies the movement of ocean currents or a chemist studies chemical equilibrium during an experiment, economists use real data to study movements and equilibria in an economy. Once economists analyze the economic data available to them, they construct mathematical models to explain a certain phenomenon. Indeed, formulating these mathematical models requires many years of education, and to be able to understand them requires the same degree of education. Though the complexity of high-level economics does not add to its “hardness,” the construction of these models does. “Hard” sciences requires the use of empirical observations to explain systems, and economists are doing just that by organizing ideas about the economy through observed economic data.

There is a clear contrast between the process used in economics and the methods used in “soft” sciences. “Soft” sciences use theories about human actions to make claims about society. While “soft” sciences might use experiments to arrive to their conclusions, the scientists who conduct these studies are ultimately influenced by how they think humans are influenced by their nature—a vague notion that cannot be objectively measured. Economics throws away these complications by assuming all humans act rationally and all markets compete perfectly. These are strong assumptions to make, but they remove subjectivity from the equation which contributes to economics’ “hardness.”

A common criticism of economics is that the claims economists make are difficult to reproduce in the real world. Economists do not rule the world or any single nation, so they lack a laboratory to perform their tests. This lack of despotism leads some to argue that economists cannot test their models in a fair or accurate way—that economic theory is simply theory that is only important to academia. Nobel Prize winning economist Robert Lucas rebuts this notion in his 1988 Commencement Address at the University of Chicago. Lucas compares economists to story tellers who tell stories about smaller economies and then apply those stories to larger economies. In his speech, Lucas tells a story about a carnival and ticket sales to point out the connection between changes in the money supply and economic depressions in the United States. Through his story, Lucas’s speech suggests that when it is applied, economic theory can make strong claims about the world’s economy. Just as physicists must use a large, manmade hadron collider to tell stories about how atoms work, economists must often rely on smaller economies to tell stories about larger scale economic systems.

Another major tenet of the “hard” sciences is that they contain a higher degree of accuracy and objectivity than the “soft” sciences. Taking this into consideration, economics better resembles a “hard” science because the claims economists make are widely accepted by the outside world. For example, economists such as Nobel Prize recipient Paul Krugman are regularly featured in the op-ed section of the New York Times, and economists often report for finance and business publications, such as Noah Smith of Bloomberg View. The United States Federal Government is another employer of economists, which hires hundreds of them to research and recommend monetary and fiscal policies the government should support. Similarly, Wall Street hires thousands of economists, who are highly prized for their mathematical expertise and ability to study complex systems to make market predictions. Clearly, economists’ knowledge is highly valued in both the public and private sector. This value comes from economists’ ability to make scientific-like claims about the global economy.

Another criticism economics regularly receives is its inability to predict economic crises. Indeed, seven years after the 2008 financial crisis, many non-economists resent those in the field who failed to see the bumpy road ahead. In a blog post from April, economist Noah Smith sums up these grumbles against macroeconomics as: failing to predict the crash, acting too confidently before the crash, failing to focus enough on finance, and being too complacent in their models. However, just as it is unreasonable to blame a seismologist for not anticipating an earthquake, it is unreasonable to blame an economist for not anticipating a financial crisis that is totally out of his control. As Bob Lucas points out in a 2009 article he wrote for The Economist:


One thing we are not going to have, now or ever, is a set of models that forecasts sudden falls in the value of financial assets, like the declines that followed the failure of Lehman Brothers in September. This is nothing new. It has been known for more than 40 years and is one of the main implications of Eugene Fama’s “efficient-market hypothesis” (EMH), which states that the price of a financial asset reflects all relevant, generally available information. If an economist had a formula that could reliably forecast crises a week in advance, say, then that formula would become part of generally available information and prices would fall a week earlier.


This section from Lucas defends economists from criticism that they should—but always fail to—predict financial crises.

Ironically, the financial crisis of 2008 helps define economics as a “hard” science. Just as the fields of physics or chemistry change as new, unforeseen developments arise, economic theory constantly changes as a result of observed data from the economy. Using the lessons of the 2008 financial crisis, economists have developed new theories and models, and have gained new knowledge that will help guide their future prescriptions for the economy.

Last February, I went to my economics professor’s office hours to ask him a question about an assigned problem set. When I was in his office, I noticed his mathematics undergraduate degree on the wall. When I asked him why he decided to pursue economics and not math, he told me that while in math there were infinite problems to solve, in economics, there are more defined questions to study and answer through using economic analysis. By using mathematical models and observing data to make credible inferences on the economy, economics better resembles the “hard” sciences than it does the “soft” sciences. As the field of economics continues to develop and grow in the 21st century, I know my parents will be happy I chose to study economics.