Olympics 2016 Rio

It’s become a bit of a biannual tradition for me to write an about the Olympics – specifically the distribution of medals between countries.

I don’t have any sport by sport insight into how these games will look, but judging from the last couple years this should be another showdown between the US and China over Olympic supremacy. In three of the last four Olympics the United States has edged out China with China’s lone victory coming as a big win when they hosted in 2008.

Other medal count story lines to watch:

  • Can Britain build on their impressive growth, or was 2012 only about the home country bounce?
  • How will Russia do? Historically they were a powerhouse, but the breakup of the Soviet Union and the struggles of their economy let to a prolonged slump. Their economy has improved and they have seemed determined to reassert themselves on the world stage. Will that show up in their medal count? As a result of a huge doing scandal some of their athletes were banned. How much of a factor will that  be?
  • How will Brazil do? There’s been a lot of press coverage about how Rio want ready to host, but is Brazil ready to compete?
  • Will Japan see a spike add they prepare to host in 2020?

A new type of graph

[DRAFT] Introducing the Dan Chart (ok, that name won’t fly. The Dart? Darts fly!), or to name it descriptively, the aligned stacked bar chart. i have had trouble representing data where there are both a lot of categories (series), and a lot of periods (or values). The clustered column stops being useful around 5×5. The typical solution has been to use either the stacked column, or the 100% stacked column. Both of these have serious drawbacks and an easy solution.

First, observe how unintelligible the clustered column chart is:

The stacked bar char shows how the total changes over time, and tries to give you an idea of how the composition of that total changes. Even though changes in an individual category are represented in the chart, ease of interpretation depends on the order of the categories. Changes over time will be very easy to observe for the first/bottom category. You can easily see, for example that for category 1 October is slightly higher than May (0.6 vs 0.53). However, it’s takes a lot more effort to compare Oct to May for category even though the difference is bigger (0.56 to 0.39). If I asked you which category grew more from Oct to May, you’d be pretty hard pressed to answer that (without the numbers).

The 100% stacked bar chart ignores changes over time and only focuses on the change in composition. This is useful for when those changes either don’t matter, or are very small. It gives you a clearer picture for how composition changes. However, it still has the same problem that it is hard to get insights about the changes for those middle categories. It gets unwieldy around 4 or 5 categories. Consider the following example. The total data series is stationary (always sums to 1). You can clearly see that category 10 increased a lot and category 1 decreased. It’s harder to see what is happening to the interior categories, like category 5. It looks like it is staying pretty much the same. Can you notice the consistent decrease from July to Dec (goes monotonically from 8.7% to 5.6%).

These changes are hard to spot because each series is not aligned with itself. What the Dart (ok, fine. the aligned stacked bar chart) does is put white space between each series so that each series is aligned to itself. In the examples I centered aligned them, but they could just as easily chosen to align them to the bottom or the top. With this format you can easily look across the series to see how it changes over time, and make those comparisons. You also can look at a particular time period and see the composition of the total. It works with both with data that changes over time, and data that is stationary. I am not sure why we don’t see this type of graph! I’ve created a hack to make it work in MS excel (contact me if you’re interested) but hopefully this could be adapted into the program to improve chart readability!

Winter Olympics Medals Over Time – Post Olympics Update

The Winter Olympics have now closed. The host nation Russia walked away with both the most medals and the most golds. The increase was pretty dramatic. Russia only managed to pick up 3 gold medals in Vancouver but napped 13 in Sochi. Canada, while lacking the home country bounce they had in 2010, continued to be a top competitor. Norway had another good games reinforcing the fact that their meager haul in Turin 2006 was just an aberration.

A lot of pundits have called this year’s games a huge disappointment for the USA (ESPN: Team USA disappoints in Sochi)However, by historical standards the 2014 games were pretty standard. The USA pulled in the same number of gold medals as 2010, 2006 and only one down from our all time high in 2002 (which we hosted). In both the Summer and Winter Olympics consistency seems to be the name of the game for the United States as I pointed out in my first summer Olympics post. Though, as I mentioned before, the number of events and medals have been increasing so in terms of the percent of gold medals the United States is slipping.

Winter Olympic Gold Medal Counts 1988-2

The total medal counts show pretty much the same story. The United States failed to defend their total medal lead – however, hopefully this time it won’t take us 78 years to be back on top! As with the Summer Olympics it appears that the home-country bounce is more pronounced in the count of gold medals rather than number of total medals. To me this is counter-intuitive and is begging for some good statistical analysis to investigate potential systematic judging bias in favor of the home country. Perhaps if I find myself with some extra time (highly unlikely) I can look into that.

Winter Olympic Total Medal Counts 1988-2

Unlike host country Russia, Germany continued to struggle to regain their passed Winter Olympics glory. They took home the fewest gold medals since 1972 (combining East and West) and fewest total medals since 1968! (See the German/Russian dominance in my original Winter Olympics post)

Probably the most surprising country was the Netherlands. Because historically they were not a big player, I did not even include them in my charts. At one time they led the total medal count and finished with 24 medals and 8 golds. Previously the Netherlands’ highest haul had been 11 medals and 5 golds  (1998) and in 2010 they took home a total of 8 medals.

Winter Olympics Medals Over Time

My Summer Olympics post two years ago was fairly popular, so now that I have a brief respite from graduate school deadlines I put together a couple of charts showing the Winter Olympic Medals over time.

Winter Olympic Gold Medal Counts 1988

There are significantly fewer events, and thus medals in the Winter Olympics – currently 302 to 98. That makes the Gold medal counts a bit more noisy (small sample size), so I’m also including a chart total medal counts (unweighted).

Winter Olympic Total Medal Counts 1988

Both charts show Canada and the United States rising. One thing that these charts are missing is the fact that the total number of events has been climbing significantly as well – from 46 in 1988 to 86 in 2010 and this year’s games features 98 events.

Number of Winter Events 2

Another fact that is not captured by the narrow window of years featured above is how dominant Germany and the Soviet Union were beginning in the early 1970s. From 1972 to 1998 they combined took home 40.5% of the gold medals. The last time the United States led the gold medal counts for the Winter Olympics was 1952 – which makes the lead of this story (The United States has a case of Olympics medal envy) seem a bit odd and ill-informed. The 2010 Winter games was the first time that the United States had led the total medal count since 1932 – a games which were hosted by the United States and only featured 10 other countries. Even then, we only beat out Norway by 2 medals.

Winter Olympic Gold Medal Counts 1952

So congratulations to Canada on becoming a Winter Olympics power house – because let’s be honest, you don’t have all that much else going for you. But that success and growth has been alongside the United States and not at her expense.

Is Congress About to Accidentally End the Shutdown

The House just unanimously passed a bill allowing federal workers to get back pay. The Senate and President are in support so this should soon be law. This measure was not controversial. Even Congress realizes that they, not the Federal workers, are the reason these people are not working and Congress, not the workers, is the problem. While they are getting paid without working (government efficiency?) that workers should miss car payments, house payments, and be unable to pay other crucial expenses while Congress figures stuff out is a hard position to defend.

However, in doing so, they may have un-wittingly removed the legal justification for the shutdown itself. Modern government shutdowns date back to the Carter administration’s interpretation of the, at the time obscure antideficiency act (for an overview of the history see here or here). In short, government cannot enter into a contract without funding – so workers cannot work with the expectation that Congress will fund them (even though  they probably will). Now with the passage of a bill to guarantee backpay (here), workers would no longer be working with a hope that Congress might or expectation that Congress will fund, but instead a legislative guarantee.

A lawyer may need to take a closer look, but from this layman’s eyes it is time for Federal workers to go back to work, and they don’t need a dysfunctional Congress to agree first. Perhaps in this case two wrongs of Congressional dysfunction have made a right?

The Deficit: It’s the Economy, Stupid!

Government-money 46

America’s approaching debt crisis is the topic de jour, both in the news room and in the living room. We have the fiscal cliff, the battle of the debt ceiling (parts I, II and soon to be III), the sequester, fiscal commissions, etc. We are shown scary charts, and told (truthfully) that the federal government is running another trillion dollar deficit and borrows 46 cents of every $1 it spends.

Federal Deficit

However, what we do not see is a careful examination of what is driving these deficits, and what that means for our future and our policy choices. In this, my first series of post, I will drive to do just that with simple charts from freely available data sources.

There are three things that go into the debt to GDP ratio. First, there is the GDP. A fall in GDP will drive up the ratio even if debt does not change. Second, there are the two parts to the debt: revenues, and expenditures. While the economy goes through booms and bust, over the long haul GDP tends to grow at a pretty steady rate. Economist and statistician have a handy, standardized way of removing cyclical components from GDP. It is called potential GDP. A simple view of it is just to say it is what would GDP be if everyone was employed. As you can see below, it correlates pretty closely with the inverse of the unemployment rate:

Unemp and GDP Gap

For reference, here is the long term view of GDP gap, which is just 1 – Potential GDP / GDP:
GDP Gap
It is clear that something happened in the fall 2007. Something very bad! We will return to that. Here is another view that again, shows that something bad happened to GDP in 2007, and we have not recovered:
Nominal GDP
It is only with the background of what was happening in the economy that we can start to look at the deficit. Here are two simple charts of federal government expenditures and revenue:
Federal Government Expenditures

Federal Government Revenue
We see a small spike in expenditures during the recession, but they then go flat. The revenue side is more dynamic. Federal government revenues drop precipitously (17.4% from peak to trough). In fact, in purely nominal terms (not adjusting for inflation or population) they did not pass the pre-recession peak set in Q2 2005 until Q1 2012 – nearly 5 years!

From 1992-2007 government revenues grow about 1.38% per quarter and expenditures grew at 1.18%. If numbers held over the six quarters of the recession we would expect expenditures to grow by 7.3% and revenue to grow by 8.5%. Instead revenue fell by 16.4% (25% below the pre-recession trend!), and expenditures grew by 18.9% (11.6% off of trend).

The point of these charts and data points is to demonstrate that the budget deficit is mainly driven by the very severe recession, from which we still have not fully recovered. But one final chart to drive home this point. Revenues actually vary pretty consistently with drops in unemployment, and along with that potential GDP. When controlling for the GDP gap, the deficit we are facing is not an outlier . What is abnormal is the size, and duration of this drop in employment and GDP. This is what we should be discussing! (And, in future post, hopefully I will).
GDP Gap and Gov Revenue

APPENDIX: Data Sources

All the data was obtained using Federal Reserve Bank of St. Louis’s excellent excel plug-in: http://research.stlouisfed.org/fred-addin/install_windows.html. Here are the exact series and calculations for each chart. In parentheses are the FRED series IDs. Corrections and concerns appreciated!
1: Federal Deficit. This is simply [Federal government total expenditures] (W019RCQ027SBEA) – [Federal Government Current Receipts] (FGRECPT). Both come from the U.S. Department of Commerce: Bureau of Economic Analysis
2a: Unemployment. [Civilian Unemployment Rate] (unrate) From the U.S. Department of Labor: Bureau of Labor Statistics
2b: GDP Gap. 1 – ( [Nominal Gross Domestic Product] (GDP) ) / ( [Nominal Potential Gross Domestic Product] (NGDPPOT) ). Both from the U.S. Department of Commerce: Bureau of Economic Analysis.
3: GDP Gap. 1 – ( [Nominal Gross Domestic Product] (GDP) ) / ( [Nominal Potential Gross Domestic Product] (NGDPPOT) ). Both from the U.S. Department of Commerce: Bureau of Economic Analysis.
4: Nominal GDP. [Nominal Gross Domestic Product] (GDP). From the U.S. Department of Commerce: Bureau of Economic Analysis.
5: Federal Government Expenditures.[Federal government total expenditures] (W019RCQ027SBEA) From the U.S. Department of Commerce: Bureau of Economic Analysis
6: Federal Government Revenues. [Federal Government Current Receipts] (FGRECPT). From the U.S. Department of Commerce: Bureau of Economic Analysis
7a: Government Revenues / Potential GDP. ( [Federal Government Current Receipts] (FGRECPT) ) / ( [Nominal Potential Gross Domestic Product] (NGDPPOT) ). Both from the U.S. Department of Commerce: Bureau of Economic Analysis
7b: GDP Gap. 1 – ( [Nominal Gross Domestic Product] (GDP) ) / ( [Nominal Potential Gross Domestic Product] (NGDPPOT) ). Both from the U.S. Department of Commerce: Bureau of Economic Analysis.

Econ 101 and the Cost of Cell Phone Plans

My one time colleague (Evan Lawrence-Hurt) posted the following:

In Colombia I was able to purchase an iPhone SIM card for 7 pesos (~$5), got great reception and data that lasted a week with no contract and I could refill it whenever and wherever I wanted. Here in the U.S. I pay Verizon >$100 per month for the same service on a 2 year contract. Discuss.

Since I’m not teaching this semester, I’ve really missed the fun, illustrative graphs that can get at the heart of a question. First, the set of disclaimers. I have no special expertise in the complicated field of cell phones. This is a gross simplification, and probably wrong in a bunch of ways. I did not spend the time to actually look at data points such as regulations, market concentration, comparative consumer buying power, etc.

In the cell phone market, the biggest cost are no doubt fixed. The fixed cost of laying down physical communication lines, building towers, buying/leasing wireless spectrum cost a lot more than the marginal, per-user/megabyte cost, which in small quantities is near zero. Also, irrespective of the regulation regime, it is very difficult to have a very competitive market. Cellular providers are close to what is called a natural monopoly. This means that once fixed cost are covered, the price that is charged is determined mainly by two things: the composition of the demand curve and the ability of the telecom companies to price discriminate.

So, if we 1) assume that the market is similar to a simple monopoly (and ignore price discrimination), 2) realize that because American’s have more disposable income they have a higher willingness to pay, we can draw the simple monopoly set up for each country:

I apologize for the crudeness of these charts and this analysis, but I think it actually gets pretty far. We  start with the two demand curves. The higher disposable income in the US is reflected by the higher demand curve (willing to pay more at any quantity). We then put in the marginal cost curves, which for simplicity I’ve kept low, constant and the same across the two countries. The marginal revenue curve is the extra revenue the monopolist sees by lowering the price and selling another plan. Because the monopolists are not price discriminating in this example, it is a simple downward sloping curve.

A profit maximizing monopolist will sell at the point where MR=MC. While the quantity that this happens at is not very different across the countries (looks to be near 40% in this totally made up example), due to the different in the demand curves the price that reaches the profit maximizing quantity is actually very different.

So what this very simple analysis tells us, is ignoring any differences in regulations, market structure, population density, etc, a lot of the variation in prices could potentially be explained simply by the differences in consumer disposable income across countries.

The Free-Market Case for a Strong Social Safety Net

My time constraints mean this needs to be brief, but I think one of the strongest arguments for a strong social safety net comes directly from free-market economics. Let me explain.

In last night’s snoozer of a presidential foreign policy both candidates argued that they would be better able to project American jobs from foreign competition. Obama in particular pointed to tire tariffs on China as a major success. But these tariffs are an economic disaster that the American consumer is paying for (see http://www.washingtonpost.com/blogs/ezra-klein/wp/2012/10/23/how-obamas-tire-tariffs-have-hurt-consumers/). A very conservative estimate says that each job has cost consumers nearly $100,000 a year.

There is no unified lobby for the American consumer. When we all take small hits, it gets ignored.

Instead of pursuing really bad economic trade policy that cost the American consumer loads of money to save a couple of jobs, we would be much better off pursuing free trade, helping the world specialize and grow. By lowering the cost of job loss, a strong social safety net can free us up to pursue good policies at a cheaper end cost to consumers. There is a true pareto improvement to be had here.

Sadly, this is a case of the all-to-often true: when the political parties agree, they usually are both wrong. George W pursued similar policies in the steal industry, with will documented massive cost to the American consumer and economy.

Broaden the Base and Lower the Rate

The Romney/Ryan tax plan calls for lower tax rates. They argue that the plan is going to be at least close to revenue neutral due to some unspecified base broadening. I’ll leave that as stated.

My question is, if this is such a good idea, why not pursue the same sort of base broadening plan with Social Security/Medicare. Combined FICA taxes are 15.3% when you include both employer and employee contributions (and ignore the temporary payroll tax decrease). In 2010, this generated $781.1B in revenue (2011 report). The big catch with these taxes are they are regressive. Dollar earned beyond $108,000 are not taxed. If we broadened the base (taxed those earnings) we could substantially lower the rate.

A quick calculation: if we just stuck to wages we could lower the rate to 9.83% (based on BEA compensation data). If we widened it to include non-wage income (i.e. capital gains) the rate could be dropped all the way down to 6.02% (based on BEA personal income data).

It would seem that this proposal would appeal to the Romney/Ryan idea of broadening the base and lowering the tax rate, while appealing to the Democratic idea of soaking the rich. Why then is it not even being discussed?

Note: The reason it historically has not been discussed is SS/Medicare was not supposed to be a “entitlement”. It was a designed as pay in / pay out system, where benefits were not means-tested and contributions were not regressive. I think since we’ve been calling them entitlement programs, and thinking about them as such for quite some time we should make the move to structure them that way.

UPDATE (9/26): My post was a bit hasty. I corrected Medicaid to Medicare. However, I realized that Medicare is not actually subject to income restrictions (only SS is), and the revenue number I quoted was only for Social Security anyway. Here is the redone, back of the envelope calculation, with some additional  notes:

Excluding Medicare, the current tax rate is 12.4% (combined employee and employer). The rates with the broader base are the same at 9.8% and 6.0% – and the tax reductions for each would be 2.6% or 6.4%. For the median worker ($50,054 p31), these two plans represent increases of $3,200 and $1,275 respectively. To put this in perspective, between 1967 and today (45 years) median income rose about $8,000.

Gas is Still Too Cheap

Yesterday, for the first time, I spent more than $50 to fill my tank of gas. I do not have a particularly big car (it’s a Subaru Legacy), but a combination of a very empty tank and more expensive gas than usual (was $0.13 cheaper this morning, burn!) caused my to set the record.

The president and Detroit recently agreed to raise the fuel economy standards. Eduardo Porter has a great piece in the New York Times demonstrating why this is a poor strategy. If we want less of something (gas usage) we need to make that more expensive (tax it).

in Britain, where gas and diesel are taxed at $3.95 a gallon, the American automaker Ford sells a compact Fiesta model that will go nearly 72 miles on a gallon. In the United States, where gas taxes average 49 cents, Ford’s Fiestas will carry you only 33 miles on a gallon of gas.