From adam-wola:

Take the annual income of the wealthiest 20 percent, divide it by the annual income of the poorest 20 percent, and you get a number. The larger a country’s number is, the more economically unequal the country is.

Here is Latin America. The United States (wealthiest 20% earns 16 times more than the poorest 20%) is now in the middle of the pack. In 1980, the U.S. number was 10.5.

The source is the UN Economic Commission for Latin America and the Caribbean annual Statistical Yearbook, which was released January 10. All numbers are for 2011, except Bolivia (2009), El Salvador (2010), Guatemala (2006), Honduras (2010), and Nicaragua (2009). The U.S. figure comes from Census Bureau data cited by Congressional Research Service [PDF].

BTW. You can easily do this for any country in the world. Here’s the World Bank data for income share held by lowest 20% and income share held by highest 20%. Find any country you’re interested, and calculate the ratio.

I was compiling some data for some class exercises and lecture presentations for next semester, and wanted to make a small observation. This stems from the difficulty I find in explaining to my students why states (or “governments”—although there’s obviously an important conceptual distinction between state and government) are important. Many of my students have a fairly strong anti-government bias, which is reinforced by their biases against taxes.

Whether strong states (or “central governments”) are a good thing or not is a question for moral or political philosophy. Certainly, strong states can be authoritarian (although, ironically, many authoritarian regimes actually have weak states). 

Similarly, whether taxes are useful or not is also a question for moral or political philosophy. And certainly many countries (including our own) spend tax dollars wastefully and/or on things we’d rather they didn’t (e.g. pacifists still pay taxes that go to military spending).

But the question of whether high taxes erode, weaken, or otherwise undermine democracy is an empirical question. That is, we can test it with existing data. To compare taxes across the world, I used tax burden as percent of GDP (this is better than using tax rates, since it looks at the share of taxes as the share of the total economy). I used data from the conservative Heritage Foundation. I wanted to see if there was any relationship between taxes and democracy. I used the 2011 Democracy Index measure developed by The Economist (the higher the score, the higher the quality of democracy). 

Turns out, there is a relationship between taxes and democracy. But it’s not what some of you might expect. The figure below plots 161 countries on both dimensions; the red line is the statistically estimated relationship (or “trendline”) between the two variables. The quality of democracy increases as tax burden as percent of GDP increases. The relationship is fairly strong (Pearson’s r = 0.6553; p < 0.000). 

There are outliers, obviously. But for the most part, countries w/ high democracy scores have high tax burdens. In contrast, countries w/ the low democracy scores tend to have low tax burdens.

How low? The ten countries with the lowest tax burdens as percent of GDP are:

I thought I’d infuse some (very simple) empiricism into the evolving gun debate. Using data readily available on Wikipedia on gun-related deaths across countries and gun ownership across countries, I simply plotted a simple linear regression using Excel.

(The whole process was very simple. If you ever want to try something like this, just save any Wikipedia entry that has a table of data, then open it w/ Excel. It took me about two minutes to match up the countries, then another minute to make the chart above. So there’s no excuse for not using empirical data!)

Because (gun-related homicide death rates) data was lacking for many countries, it ended up matching up only 73 countries. I trimmed the sample to limit it to OECD member & observer countries. This is what’s often used as a “peer set” for the United States (countries that we should be on par with on various levels). The result was a dataset of 50 countries. That’s not a massive dataset, but in comparative politics, that’s not too bad (after all, there are only about 200 countries in the world, so this is about a quarter of the total “universe” — plus the sample is made up of mostly “comparable” countries).   

Here’s how you read this simple bivariate regression: The red line is a simple regression estimate (you can do bivariate regressions in Excel by choosing the “trendline” option). The little equation (also an option in Excel) gives you the estimation. It’s basically the old y=mx+b equation you learned in basic middle school algebra. The “slope” is the estimated relationship between the two variables. Notice that the slope goes up: more guns, more gun-related homicides deaths. The R² (R-squared) number is an estimation of how much of the total variation is explained by the model (the equation). The R² value is not too high, but still manages to explain about 25% of the variation in the data.

The red dot is the United States, which has the third highest gun-related homicide deaths in the 50-country sample. The other two are Mexico & South Africa. Notice, however, that even though (individually) Mexico & South Africa have both high gun-related homicide death rates and few guns per capita, the overall relationship among the 50 countries is still clear: more guns, more gun-related homicides deaths.

In particular, it’s useful to note that all the advanced industrial democracies (that is, all the “first world” wealthy countries) are clustered together: few guns, few gun-related homicides deaths. Why does that matter? Because unless you believe that American criminals are somehow much more resourceful than criminals in Japan, Germany, or Australia, the claim that “if guns were more restricted only criminals would have guns” is patently ridiculous. Despite strong gun restrictions in Japan, Germany, and Australia, criminals there don’t seem to get their hands on enough guns to commit as many murders.

ADDENDUM: A correction using logarithmic scales and regression through the origin is posted here.

CORRECTION: I originally misstated the main variable. I actually didn’t use gun-related deaths, but rather the more specific gun-reated homicides (I had originally used the first, but then corrected myself, but forgot to change the labels on the graph). Using the specific gun-related homicides removes all accidental gun-related deaths & gun-related suicides.

States & Taxes

This week in POL 102, we’re discussing the state. As part of that discussion, students are looking at the 2012 Failed States Index.

One of the things I try to impress on my students is that the old adage “the government that governs least, governs best” is not necessarily true (ask a Somali refugee). The top ten “failed” states include a number of countries with little or no effective governance. One way to think about the positive attributes of states is to simply look at the relationship between state “strength” (operationalized by the Failed States Index) and “size of government” (operationalized as tax burden as a % of GDP, with data from the Heritage Foundation). The above graph shows that relationship.

The graph shows that states with smaller tax revenues tend to have higher Failed States Index scores. This simple statistical model explains about 36.5% of variation in the data (R²=0.36501), so the relationship is not particularly strong. The data reflects a representative sample of 62 countries. The orange dot represents the US.

Official statistics: Don’t lie to me, Argentina | The Economist

An interesting lesson in why statistics matter. When you play loose with evidence, you risk losing credibility. In the end, your greatest asset is credibility. Once it’s gone, it’s tough to get back. That’s true for countries, corporations, and people.

This is what I’m teaching today in INST 316. Yes, I know that Golosov’s (2010) equation is better.