2

Hurricanes and Baby Names

Recently there has been bit of buzz about a study claiming that female named hurricanes cause more fatalities, on average, than male ones. The authors suggested that the discrepancy is attributable to gender bias. Female named hurricanes do not seem as threatening to people, so presumably they take fewer precautions. From the start this seemed pretty far-fetched, and in fact a number of problems have been found with the study.

But it got me thinking about hurricane names. A more likely effect of a hurricane’s name would be to discourage parents from giving their children that name, if the hurricane is associated with death and destruction. Fortunately, there is readily available data with which to test this hypothesis. For hurricanes, I used the same data as the hurricane gender study described above (they may have had some problems with their methodology, but at least they released their data). It contains data on 92 Atlantic hurricanes that made landfall in the U.S. since 1950*. For baby names I turned to the Social Security Administration. There is a great R package called babynames that makes the yearly SSA data available in a readily accessible format for use in R. As an aside, the SSA baby names data is the source of all sorts of interesting visualizations and analyses, such as the baby name voyager and this article from fivethirtyeight.com on predicting a person’s age based on their name.

The tricky part of this analysis is deciding how to define a decrease in name usage after a hurricane. The simplest way would be to look at how many times a name was given in the year of a hurricane versus how many times that name was given the following year. For example, how many baby Kartrinas were there in 2005 versus 2006. However, this method does not take into account that most names are either decreasing or increasing in popularity as part of a longer-term trend. So you have to look at how the popularity of a name was changing before the hurricane as well. To see why, look at this plot of the number of babies named Katrina over time.

katrina

Katrina peeked in popularity in in 1980 and has been declining ever since. But from 2004-2005 the number of Katrina’s actually increased about 13%. From 2005-2006, however, it decreased dramatically – by 26%. It’s a pretty good bet that this rapid decrease was due to the hurricane.

To quantify the change in a name’s usage after a hurricane, I made the assumption that the best predictor of how a name’s popularity will change in a given year is how it changed last year. To calculate the post-hurricane change in name usage I subtracted the percent change in name usage in the year before the hurricane from the percent change after the hurricane. In the Katrina example the post hurricane change would be (-26%) – (13%) = -39%. This post-hurricane percent change value is what I use in the analysis below.

Before we get to the results, let’s take at look at the fascinating case of Carla:

carla

Hurricane Carla was an extremely intense storm that hit Texan in 1961, killing 43. The name “Carla” had been surging in popularity,  but after 1961 it started a decline in popularity from which it never recovered. It seems a pretty good bet that the hurricane had a major role in Carla’s decline. Interestingly, the first live television broadcast of a hurricane was of Carla, with a young Dan Rather himself reporting from Galveston. Could the shock of the American TV-viewing public seeing footage of the storm in their living rooms have contributed to the demise of Carla as a name?

Back to the analysis. Indeed, the hurricane baby name effect seems real. After running the numbers, I found that names associated with a landfalling hurricane were about 15 percent less common in the year after the hurricane. Out of the 93 hurricanes in the data set, 65 were associated with a decrease in the popularity of their names, and only 21 were followed by increasing name usage. (Seven hurricane names were not found in the SSA data in their landfall year).

So far this is pretty intuitive. Of course people are less likely to name their dear infant after a natural disaster. Based on this reasoning, you’d expect that the more fatalities caused by a hurricane, the greater the baby name effect. Let’s test that.

names.fatal

The effect is quite small. When we take Katrina out (a massive outlier in terms of fatalities), it’s smaller still:

name.fatal.ex.kat.rug

So the correlation between change in baby name usage and hurricane fatalities is quite weak. Finally, I had to see if the gender of the hurricane name affected this relationship. Were more deadly female-named hurricanes more or less likely than male names to affect baby name popularity? Maybe I’d even find that male baby name usage goes up with hurricane fatalities because parents associate the names with strength? I can see the Slate headline now! Alas, there is no significant difference:

names.fatal.ex.kat.bysex

By the way, there are more female names because from 1950 – 1979 all Atlantic hurricanes were given female names.

There’s an almost endless amount of interesting things to glean from the baby names data. My ultimate dream is an algorithm to determine the perfect name for your baby based on a number of criteria chosen by the expectant parents. It would really take the stress out of the naming process. One of the criteria would certainly be that the name is not on the World Meteorological Association’s list of tropical storm names!

Data and code available on github.

* The authors of the hurricane fatalities study did not include Katrina in their data set. I added it in with data from Wikipedia.

 

2

Graphics for Fitness Motivation using Plot.ly

This post is intended to illustrate the cool things you can do with plot.ly’s API for R. Plot.ly is a web-based tool for making interactive graphs. It uses the D3.js visualization library, and lets you create very attractive plots that can be easily shared or embedded in a web page. With the R API you can manipulate data in R and then send it over to plot.ly to create an interactive graph. There’s also a function that let’s you create a plot in R using ggplot2, and then shoot the result directly over to plot.ly (summarized nicely here).

I have great little free app on my iPhone called Pedometer++ that keeps track of how many steps I take each day. I exported the data, plotted up a time series with ggplot2, and used the API to make the graph in plot.ly. It worked quite nicely. The only hiccup was that plot.ly did not recognize the local regression curve, so I had to add that separately.

You can see from the plot that I’m not consistently meeting my 10,000 step goal. In fact, I averaged 7,002 steps over this period. That still comes out to a total of 1,470,463 steps. From October through February my step count was trending slightly downward, but since then it’s picked up. Maybe that had something to do with the cold winter. Hopefully as the weather (and my motivation) improves, I’ll hit my goal.

steps_taken_per_day_october_2014_-_november_2014

Click to see the interactive version

Any here’s a bonus box plot showing steps taken by day of the week (also using the R API):

steps_per_day2c_october_2013_-_may_2014

Click to see the interactive version

If there are any pedometer users out there who are interested, let me know and I can post the code.

1

Updated Global Mercury Pollution Viz and Graphics

One of the first posts on this blog was about using Tableau to visualize data on global emissions of mercury.  I’ve gotten suggestions from a few people and given the graphic a bit of a face lift. Click on the image to see the interactive viz:

Dashboard_1 (3)

Click for interactive graphic

I also used the same dataset to make some static graphics using ggplot2 and the ggthemes package. I’d love any input on how to improve the the look and feel of both these and the Tableau viz. I’ve always found picking good colors very challenging, so thoughts on the palettes are especially welcome.

hg.emissions.bysec

The 8 industry sectors with the highest global mercury emissions. Data for 2010 from the 2013 UNEP Global Mercury Assessment.

hg.emissions.bycty_fewm

Countries with the highest mercury emissions. Data for 2010 from the 2013 UNEP Global Mercury Assessment.

4

Visualizations about Data Visualization

It’s no secret that interest in data visualization has been growing in recent years. Don’t believe me? Let me show you a graph:

google trends

From Google Trends

Sure, humans have been presenting information graphically for hundreds, if not thousands, of years, with increasing sophistication.  We still study the work of people like John Snow, William Playfair and Florence Nightingale for their innovations in graphical presentation. Today, however, with the increasing availability of large, rich, and easily accessible datasets, and the proliferation of software tools for creating graphics, we are seeing an explosion in the amount of data visualizations. This is a great development. I obviously think so, since I jumped on the bandwagon.

The recent ubiquity of the data visualization brings with it a new subgenre, the meta-visualization. Visualizations about visualizations. Some of these describe what data visualization is, or should be. Some present information about common types or characteristics of visualizations. Still others poke fun at cliches, poor practices, and the very pervasiveness of visualization as a medium for communicating information. Let’s take a look at some examples.

First, here’s the Infographic of Infographics:

Then there’s this periodic table of types of visualizations:

periodic viz

 

Robert Kosara is not amused. For an nice take on the actual periodic table (the one with the elements), have a look at this.

Continuing with the periodic table theme, here is a periodic table of period tables. This is very meta.

The Periodic Table of Periodic Tables

But does this periodic table of periodic tables contain itself? (It does.) And, more importantly, should a periodic table of all periodic tables that do not contain themselves contain itself.

Some graphics attempt to illustrate what characteristics a good data visualization should have. Like this 4-set Venn diagram, for example:

Or like this Venn-like diagram, which I’m not quite sure how to read:

Now if you really want to turn it up to 11, or more accurately, up to seven, you could employ this epic 7-set Venn diagram:

7venn

Click on this. You won’t regret it.

Another category of meta-visualizations contains humorous or satirical ones. These are not literally visualizations of other visualizations, but they are about visualization as a medium. These are funny, self-aware takes on the cliches and excesses in the field. Pie charts that skewer the graphical form of the pie chart itself are practically a sub-subgenre in themselves:

pie-i-have-eaten-chart

Really, nobody seems to have any love for the pie chart.

Or, you know how there are like a million maps on the internet showing which state or country is the most this, or the most like that? Well that’s the set up for this brilliant satirical tweet:

And on the topic of maps, here’s a gem from xkcd:

Its fully because it’s true!

Finally, we venture into silliness with one of my all-time favorites, All You Need to Know about Lady Gaga’s Hit “Bad Romance” in One Chart:

To sum up, here is a word cloud visualization of this post:

viz word cloud

0

Getting to Know the Worldwide Governance Indicators

A while ago I wrote a post suggesting that Ukraine’s propensity for revolution might have something to do with its high level of government corruption in combination with its relatively well-developed civil society. As evidence for this, I showed that Ukraine (together with Kyrgyzstan and Moldova, two countries that have also recently experienced political unrest) was an outlier among post-Soviet states with respect to the relationship between corruption perceptions and authoritarianism. This finding was interesting, but by no means robust enough to warrant broad generalizations about corruption and democracy and revolution.

Since then, a few others chimed in with some ideas. Ben Jones suggested looking at corruption and authoritarianism in countries that experienced revolutions over time. Cavendish McCay looked at corruption and authoritarianism data from the same sources but over the entire globe, and produced a very cool visualization. He also pointed me to the World Bank’s Worldwide Governance Indicators, which contains measures of democracy, corruption, and political stability. Perhaps it would be possible to test my hypothesis empirically using these data. This could be done for individual regions or for the whole world, and could also have a temporal component (the indicators have been published since 1996).

In order to determine if such an analysis is feasible, I decided to take a closer look at the dataset (which is free and downloadable from the website). The Worldwide Governance Indicators (WGI) project is an ambitious one. The authors compile data from 31 different sources (such as think tanks, NGOs, private firms) and produce annual scores for every country for six indicators of the quality of governance. The indicators are:

  • Voice and Accountability
  • Political Stability and Absence of Violence
  • Government Effectiveness
  • Regulatory Quality
  • Rule of Law
  • Control of Corruption

First off, we can look at the data on a map. Fortunately the WGI website has a series of nice Tableau interactive graphics, including maps:

Screen Shot 2014-04-27 at 2.17.49 PM

Looking at the indicators geographically is helpful. But to evaluate whether they can be used to test the hypothesis, I want to see how each indicator is correlated with all the others. For this, we’ll turn to R. Here is a correlation matrix of the six indicators as calculated for 2012. Positive correlations are reflected as positive values. The closer the the number to one, the stronger the correlation. wgi.corrplot As you can see, all the indicators are positively correlated to each other, some very strongly. This is not surprising. We would expect well-governed countries to get high marks for rule of law, regulatory quality, control of corruption, etc. One interesting observation here is that Control of Corruption actually has the lowest correlations of all the indicators. A scatter plot matrix is a good way to look at the data in more detail:
wbi.splom.plot

The idea for this variation on the scatter plot matrix comes from Winston Chang’s R Graphics Cookbook. Its structure is similar to the correlation matrix in that all of the indicators are plotted against each other. The lower panels show scatter plots with LOESS regression lines for each indicator pair. This plot has some extra bells and whistles thrown in – histograms of the distribution of each in indicator in the diagonal panels and correlation coefficients (just like the correlation matrix) in the upper panels. The scatter plots show the strong to moderate correlations that we already saw in the correlation matrix, but allow us to make out some curious features of the data, like the non-linear relationship between Voice and Accountability and many of the other indicators.

The indicator values are in units of a standard normal distribution. A value of zero is the mean, while a value of one is one standard deviation higher than the mean. Given the distributions,  the indicator values range from about -2.5 to 2.5.  Positive values represent better governance, negative represent worse. Because each indicator is measured on the same scale, we can simply sum all six to determine the overall “best governed” country. The top six are:

Country     sum
FINLAND     11.19
SWEDEN      10.94
NEW ZEALAND 10.83
NORWAY      10.67
DENMARK     10.59
SWITZERLAND 10.57

And the bottom six:

SOMALIA              -13.65
CONGO, DEM. REP.     -9.76
SUDAN                -9.74
SYRIAN ARAB REPUBLIC -9.53
AFGHANISTAN          -9.48
KOREA, DEM. REP.     -9.35

I got a bit carried away examining the correlations between the governance indicators, but in a subsequent post I hope to look closer at the democracy – corruption – stability hypothesis. I’m still not quite sure what statistical tests to use and how to apply them, and I’d welcome any ideas. Data and code are posted on Github (github.com/caluchko/wgi)

 

1

Another Way to Look at Mercury in Seafood

In the previous post, I used Tableau Public to create a visualization of the Seafood Hg Database. That graphic showed the mean mercury content and number of samples by seafood category. But there are several other dimensions in the database, including the year of the study and the particular species of seafood sampled. I couldn’t resist playing around with the data a little more, this time using the lattice package in R.

The plot below shows the mean mercury concentration (y-axis) in studies of the 12 seafood categories with the highest median mercury concentration. The x axis shows the date of the study. I’ve also plotted a trend line for each panel. This is a nice way to visualize the data, but I wouldn’t read too much into this plot. For one thing, many of the seafood categories contain multiple species, some of which are higher than others in mercury. Also, this plot does not account for the geographical region where the fish were sampled.

fish.hg.latticeplot
We can tease a little more from the dataset by looking at the individual species within a seafood category. Here is a plot of the six tuna species with the greatest number of studies. The larger species, like bluefin, seem have higher mercury contents than the smaller ones, like skipjack. One curious feature of the dataset is also visible here: there were very few studies of mercury in seafood in the 1980s.
fish.hg.tunaplot

3

How Much Mercury is in Your Favorite Seafood?

I’ve written before about mercury emissions, mercury as a commodity, and mercury use in artisanal mining. But the reason we pay so much attention to mercury is because of its human health impacts, and these are primarily caused by eating contaminated seafood.

Different types of seafood have different amounts of mercury. Because mercury is bioaccumulative, organisms that are higher on the food chain tend to have greater mercury concentrations. Of course, the particular environment where the organism lives also plays a big part.

Scientists have been interested in the mercury content of seafood for decades. Recently, a group of researchers undertook the herculean task of aggregating data from almost 300 studies. The result is the Seafood Hg Database (and an accompanying paper). The database contains the mean mercury concentrations measured in each study for one or more of 62 seafood categories. Overall, the database represents over 62,000 individual measurements from around the world.

It’s a great dataset to play around with and experiment with visualizations. In the graphic below, I plot mercury concentrations for a subset of common seafood types. Each circle represents the mean concentration measured in one study, and the size of the circle is proportional to the number of samples in that study. I’ve overlaid box plots for each seafood category that show the median of all the means, as well as first and third quartiles (whiskers go to 1.5x the IQR).

I think this is much more instructive than simply plotting the grand mean (average of all the study averages) for each seafood category. For one thing, you lose a lot of information on how much mercury concentration varies within a category. Take tilefish, for example. This is one of the species that EPA and FDA advise pregnant women not to eat. But there are relatively few studies of tilefish, and the mean mercury concentrations they measured vary by an order of magnitude.

Click on the image below to bring up the full interactive Tableau Public visualization:

Hg in seafood

Click on the image to see full version

3

Satellite Image Time Lapse of Artisanal Mining in Peru

My last post was about gold and mercury prices, and how we might measure their relationship. We would expect a relationship between prices of these metals because mercury is used in artisanal and small scale gold mining (ASGM). We may or may not see a signal in mercury prices related to ASGM, but we most definitely see the effects of ASGM on the landscape on a massive scale. Using the Landsat Annual Timelapse tool in Google Earth Engine, I created this animation showing the explosive growth of ASGM and associated deforestation near Huaypetue in the Madre de Dios region of Peru. Click on the image below to view the animation.

asgm landsat anim

You can see that beginning in the late 1990s, large areas around rivers turn from green (rain forest), to brown (cleared areas for mining). The trend seems to accelerate in the last 10-15 years. You can explore the region as it appears today in Google Maps:

And because it’s fun to play with Google maps, here is a striking oblique image of the region.

Zooming in a bit closer, seen from a plane flown by the Carnegie Airborne Observatory, the impacts of mining come into even sharper view:

The scenes on the ground look every bit as desolate as you would expect from the satellite and airborne imagery:

If you are looking for more information on artisanal mining in Madre de Dios, this article in Nature is a good place to start. The Guardian has also been covering this region. This piece focuses on mercury use in mining and its toxic impacts.

2

Is Artisanal Gold Mining Driving the Price of Mercury?

This is the second in a multiple part series on mercury. In the last post, we explored global mercury prices and production over the last century. In this post, my aim is to answer the following questions: Is is possible to resolve a signal in the price of mercury that is attributable to its use in gold mining? Could the price of mercury be used as a predictor of the amount of gold produced using mercury?

First, some background.  Mercury has a very interesting property in that it forms amalgams with other metals.  A silver dental filling is an amalgam of mercury and silver. If you add mercury to ore or sediment containing gold, the mercury will suck up some of the gold into an amalgam. Then you can heat the amalgam to evaporate the mercury, leaving you with just gold.

This method was used for centuries to recover gold and silver. Today, large-scale industrial mines use other methods that are more efficient and do not release persistent, toxic, and bio-accumulative mercury into the environment. However, mercury is still widely used in artisanal and small-scale gold mining (ASGM). In fact, mercury use in this sector is probably increasing, and is now believed to be the largest source of mercury pollution in the world. The recent spike in gold prices is often cited as a cause of increased ASGM and associated mercury use.

Because ASGM activity is decentralized, often illegal, and commonly occurs in hard to reach parts of developing countries, it is very difficult to estimate the magnitude and trends of mercury use. But we do have data from the USGS on the prices of gold and mercury. In the last post we looked at the time series for mercury prices since 1900. Here, we are only going to look at the period from 1980-2011. (The modern ASGM period really started around 1980.) The chart below shows the inflation-indexed prices of mercury and gold. I’ve normalized them to an index where the 1980 price equals one so that I can show both series on one plot.hg.auMercury and gold prices appear to be closely correlated. The high correlation coefficient (0.89) confirms what we see in the plot. The series only diverge significantly after 2009, and we’ll look at that period more closely at the end of the post.

But the close correlation of mercury and gold prices is not enough to conclude there is a causal relationship. Perhaps there is a lurking variable that is correlated with the prices of both metals. Mercury and gold are certainly not substitutes for each other. No one buys mercury when they are worried about inflation, for example. But maybe mercury and gold prices are both are correlated to overall commodity prices. To find out I plotted an index of metals prices from the IMF (also normalized to one and corrected for inflation) together with the metals prices:hg.au.inThe correlation looks close, and indeed the the correlation coefficients of  the metals price index with the prices of  gold and mercury are both about 0.8. This is not quite as close as the correlation of gold and mercury prices to each other, but it’s too close to conclude that either time series is all that different from the overall trend in commodity metal prices.

Now is a good time to point out that mercury has other uses besides to gold mining, such as in certain products (like thermometers) and industrial processes (like making chlorine). Demand from these other uses is going to affect the price. Of course, the supply of mercury will also have an affect on price. In attempting to see a signal in the price of mercury caused by gold mining, the implicit assumption is that other factors affecting the price of mercury (the supply and demand) remain relatively constant with respect to each other over the time period. This is not a terrible assumption. In general both non-ASGM demand for mercury and mercury supply have been decreasing over the last 30 years. But the assumption does introduce some real uncertainly into the analysis. It is difficult to correct for because we don’t have good data on mercury use by sector over the time period.

There’s one more problem. Recall that the hypothesis is that mercury use in ASGM affects the price of mercury. We were using the price of gold as a proxy for mercury use in ASGM. That sounds like a reasonable assumption. High gold prices should mean more gold being extracted, and greater demand for mercury to extract the gold.  But what really determines mercury use is the amount of gold produced, not the price. And we actually have data on global gold production. It tells a different story:au.qIf anything, global gold production is negatively correlated with gold price over the last ~30 years! I don’t know why this is. One possible explanation has to do with the lag time of starting a mining operation. Perhaps the record high gold prices of the late 1970s and early 1980s caused a wave of exploration and new mines. Once those mines were developed, they could produce gold economically even at low prices. Perhaps technology improved so that it was cheaper to find and develop gold deposits.

This leads to one more complicating factor. Most gold is produced by large scale mines (which do not use mercury). Common estimates suggest that only about 12-20% of gold is produced by rough artisanal miners. Another implicit assumption in this analysis has been that the fraction of gold produced by ASGM has remained constant over time. But this may not be the case. Small-scale miners are likely to be able to take advantage of high gold prices more quickly than the majors, where exploration, permitting, and construction can mean many years before a mine becomes operational. Small-scale miners can often start mining almost immediately. This would mean than gold and mercury prices would be more closely correlated than one would expect when looking at global gold production. On the other hand, work by the Artisanal Gold Council has shown ASGM prevalence is “sticky” with respect to gold prices. That is, once they start mining, artisanal miners are likely to continue their operation even after the price of gold drops. 

Finally, let’s reexamine the period from 2009-2011, when the price of mercury rises much more rapidly that the price of gold. I don’t think there’s an obvious explanation for this. Perhaps mercury use in ASGM really takes off in this period. Another wrinkle is the establishment of bans on mercury export in the EU (took effect in 2011) and the U.S. (took effect in 2013). Maybe buyers were trying to purchase European and U.S. mercury ahead of the ban, driving up the price. We could look at export data to find out.

As you can see, this is an extremely complicated issue. Without better data, it is not possible to resolve a signal in mercury prices that can be attributed to gold prices or gold production. Even though this exercise did not yield a clear result, I think it is important to document the effort. In data analysis (and science in general), the lack of a clear conclusion is in itself  an important piece of information.

In the next mercury installment we’ll travel to Ukraine and Kyrgyzstan to learn how the elusive metal is wrested from the earth and what sorts of environmental, economic, and social impacts this mining brings.

3

One Chart that Explains Why Ukraine was Vulnerable to Revolution

After months of protests, Ukraine slipped into violence last week as government forces attacked protesters in Kyiv. Then, in a frantic 48 hours, President Viktor Yanukovych’s government collapsed, rival politician Yulia Timoshenko was released from prison, and Yakukovych fled into hiding.  It was a stunning victory for the “maidanovtsi”, those protesting on Kyiv’s Maidan and those supporting the protesters around the county and the world.

I’m reading Bruce Bueno de Mesquita’s The Predictioneer’s Game, which is about analyzing incentives to make political forecasts. This book got me thinking about Ukraine. Why did Yanukovych fall? Sure, he was corrupt, but so are many leaders in the region.

What happened in Ukraine was very complex. But it seems to me that at a basic level, the obvious corruption of the Yakukovych government,  combined with Ukraine’s relatively open and democratic society, led to an unstable situation.

To test this intuition, I looked at data from The Economist’s Democracy Index and Transparency International’s Corruption Perception Index. This plot shows where the former Soviet republics fit on the corruption – authoritarianism plane (click on the image for interactive version):

demo cor2

It is instructive to divide this plot into quadrants. The lower left quadrant shows those countries that are both very corrupt and authoritarian. These governments have survived very high levels of corruption in part because they resort to anti-democratic means of staying in power, such as restricting citizens’ political and civil rights.

The upper right quadrant contains nations with lower levels of corruption and authoritarianism. Chief among these are the Baltic states, which have enjoyed a high degree of stability. Georgia, although it experienced a revolution in 2003, has been more politically stable in recent years.

The lower right quadrant is a null set. We just don’t see countries that are very authoritarian but not very corrupt in this region. An example of a non-Eurasian country that sits in this quadrant would be the United Arab Emirates.

And then there’s the upper left quadrant: states that are less authoritarian but have high levels of corruption. Countries occupying this space have experienced lots of political instability. Kyrgyzstan has had two revolutions in the last decade: the Tulip Revolution of 2005, and the more violent second Kyrgyz revolution in 2010. Moldova suffered widespread unrest in 2009 (the so-called Twitter Revolution), although recent trends point to a more democratic and pro-European direction. And Ukraine had the Orange Revolution in 2004 before the political order was upended again last week as a result of Euromaidan.

Of course, there are many other factors that determine how likely a government is to fall. Economic growth and inequality surely play a part, as do the personalities and governing styles of individual leaders. Yakukovych, for example, was indecisive and incompetent, and many of his allies quickly abandoned him.

So what are the lessons here? Well, if you are going to blatantly siphon money away from your constituents while ignoring many of their basic needs, you better rule with an iron fist. If not, they are going to rise up and throw you out. Or better yet, don’t run a corrupt regime in the first place.

The events in Ukraine illustrate how a relatively democratic society, with a strong civil society and a (mostly) free press can be an important check on corruption in government. Although far from being “fully democratic” in the eyes of international indices, Ukraine was democratic and open enough for Euromaidan to take root and ultimately succeed.