The Quantitative Peace

While the IR people are away, the Americanists will play.

Editor's note: This is a guest post by Joshua N. Zingher, a doctoral candidate in the Department of Political Science at Binghamton University. The content of this post is based on Josh's paper, ‘An Analysis of the Changing Social Bases of America's Political Parties: 1952-2008’, appearing in Electoral Studies. This piece is cross-posted at the London School of Economics' USAPP Blog, which can be found here. 

 

Demographic changes mean that traditional Republican constituencies are shrinking as the Democrats’ grow.

It is difficult to discuss electoral politics in the United States without talking in terms of social groups.  Journalistic accounts of party competition often stress the important role that specific constituencies play (such as the “evangelical” or “Latino” vote) in determining the outcome of presidential elections.  A great deal of political science research has shown that these social cleavages structure party competition.  However, the political importance of these social cleavages can change, which can lead to a reshuffling of the social bases of the party coalitions.  In addition, demographic changes also have the potential to dramatically reshape the political landscape, as new groups enter the electorate and the ratio of existing groups’ changes.  I am interested in how changes in the cleavage structure and changes in the ratio of groups work in conjunction to transform electoral coalitions.  In new research, I investigated two related questions: what group memberships influence individual level voting behavior and how has the number of votes each political relevant group contributes to the party coalitions changed over time?

 

Identifying the Group Bases of Party Support 

So what groups are politically relevant?  To answer this question, I conduct a multivariate statistical analysis utilizing nearly 60 years of American National Election Survey (ANES) spanning 1952 to 2008.  I utilized group memberships as independent variables and vote choice as the dependent variable.  The battery of group memberships included variables for class, race, ethnicity, region, education, birth cohort and age. I define politically relevant group memberships as those that exert a statistically significant influence on individual level vote choice.  I ran a separate regression for each decade, which allowed me to assess whether the effect of specific group memberships have changed over time.  The substantive results of these models are displayed in Figure 1 below.

 

Figure 1 – Predicted effect of group membership on vote choice in presidential elections

 

 

The results of the analysis displayed in Figure 1 demonstrate that the following group memberships lead individuals to be consistently and statistically significantly more likely to support one party over the other: 

 

Table 1: The Core Democratic and Republican Social Groups 

Democratic Groups	Republican Groups
African Americans	Whites
Latinos	Protestants
Catholics	Weekly Church Attendees
Union Members	Southern Whites
Non-Religious	Men
College Graduates
Women Jews

 

This analysis highlights the continuity and change that characterizes the parties’ social bases.  Some groups have been stable members of a party coalition for the last 60 years.  African Americans, Catholics, union members and Jews have all been consistent members of the Democratic coalition while whites and Protestants have reliably favored the Republicans. 

Yet, other groups’ political leanings have changed considerably.  Whites in the Southern United States were once reliably Democratic voters; today they are one of the most solidly Republican constituencies.  The divide between the religiously devout and the secular has also become more politically important in recent elections.  Non-religious voters have become increasingly likely to vote for Democratic candidates while weekly church attendees have become more likely to support Republicans.  Similarly, a partisan “gender gap” emerged in the 1980s.  Men have become significantly more likely to vote for Republican candidates while women have become more likely to support Democrats.  In sum, many traditionally politically important social cleavages have endured (e.g. black/white) while other cleavages have become politically important only in recent decades (secular/religious or female/male).

           

Group Contributions and the Importance of Demographic Changes

The preceding analysis established what group memberships significantly influence vote choice, however, simply understanding what group memberships shape individual level voting behavior paints an incomplete picture of electoral change.  Winning an election is not about having core groups of loyal supporters.  Rather, parties must be able to obtain a majority of votes in order to win elections.  Thus, any process that leads to a party gaining more or less from a specific social group is potentially politically consequential.  Figures 2 and 3 show my calculations for each group’s contribution to the Democrats’ and Republicans’ party coalitions, as measured as a proportion of the party’s total votes. There have been considerable changes in the size of group contributions to the party coalitions—many of which highlight the important role that demographic shifts play in reshaping the party coalitions. 

 

Figure 2 - Group contribution as a proportion of Democrats’ total votes: 1952-2008

 

 

Figure 3 - Group contribution as a Proportion of Republicans’ total votes: 1952-2008

 

The Democratic Party has been obtaining an increasing proportion of its support from groups that are growing as proportion of the electorate, such as Latinos, college graduates and the non-religious. In addition, increasing turnout among Latinos and African Americans has also led to the size of these groups’ contributions to the Democratic coalition.  The Republican Party is being forced to confront the opposite scenario—many Republican groups, whites, Protestants and weekly church attendees, are making up a continuously shrinking proportion of the electorate.  Thus far, the Republican Party has been able to offset demographic changes via high turnout among their core groups as well as through winning an increasingly large majority of the Southern white vote.

So what are the conclusions can be drawn from this analysis? Firstly, the group sources of party support are dynamic, the political relevance of certain social cleavages ebb and flow across decades, which is evidenced by the changing relationship between group membership and vote choice.  Secondly, demographic changes can be politically consequential.  The Republican Party is currently in the undesirable position of having to find new sources of electoral support in order to remain viable in future elections.  The Republican Party’s need to find new sources of electoral support has come about not as a result of the loss of electoral support among loyal groups—rather, the Republican Party must find new sources of electoral support simply because traditionally Republican constituencies are making up a shrinking proportion of the electorate.     

 

 

 

The Dark Knight Rises and the Economic Impact of Bane's Occupation of Gotham

I started working on this post a long time ago and, for whatever reason, never got around to finishing it. So please keep in mind that this was largely written shortly after the film first came out. I should also disclose at the outset that this post will contain spoilers, so if there is some sort of unbelievably powerful force that has kept you from seeing this fantastic movie, please be warned. For those still interested, there's more after the jump.

The Day of the Doctor and the Veil of Ignorance (Warning: Spoilers!)

The 50^th Anniversary episode of "Doctor Who" aired this week in theatres throughout the world. There was an interesting scene towards the end of the movie, which I would like to share. Below I give a very simplified version of the scene.

There are two main characters in the scene, a human and a shape-shifting alien refugee that took on the human’s form.

The human and the alien are located in a secret storage facility in London and it has the most dangerous alien weapons on Earth.

The alien wants to take over the earth, and she plans to use the dangerous weapons in the room to do it. The human will do anything to stop them from using the weapons.

The alien has two choices: (1) continue to plan to take over earth, or (2) back down and leave earth with no other reasonable place to go, resulting in certain death of her species.

The human has two choices: (1) she can set off a nuclear warhead that will destroy the weapons facility and all of London, or (2) she can allow the alien to take the weapons, take over the earth, and destroy all humans. She prefers the first option. Even though she expects to destroy London, she believes she can save the rest of the earth.

The two figures below outline the game. There are two Nash equilibriums, {Take Over; Set Off} and {Leave; Set Off}. Since the Alien is indifferent between these two outcomes- she has nothing to lose, as her species would die regardless of what the human did- it is hard to predict what the alien should do. In the scene, the alien chooses to try to take over earth. Then the human starts the timer to set off the bomb, and this results in the {Take Over; Set Off} outcome.

When it seems like London is doomed, a third character, the Doctor, enters the room and tries to convince the characters to come to a peaceful agreement. When neither character backs down, the Doctor comes to an ingenious solution: he puts a veil of ignorance over the human’s and alien’s minds, causing them to forget whether they are human or alien. Immediately both players agree to halt the warhead timer and begin to negotiate an agreement.

Ultimately, the Doctor forces the alien and the human to consider each other’s preferences. The Doctor’s solution is similiar to Rawls’ veil of ignorance solution. According to Rawls, if people are unaware of their own personal characteristics at the negotiating table, they are more likely to come to equitable or fair agreements. Since people under a veil of ignornance are unable to determine if they are rich and powerful, or poor and weak, they will  develop an equitable agreement over the distribution of opportunities and rights. 

Not only does the Doctor force the alien and the human to take into account each other's prefereces, the Doctor also forces the characters to consider a third option: sharing the earth. It is possible that neither the human nor the alien believed they could credibly commit to sharing the earth, and this is why they did not consider it prior to the Doctor’s arrival. However, since the Doctor is forcing them to negotiate, they may be more likely to consider the option.

The two figures below outline the new game. The strategic form game takes into account that neither player knows their own preferences, so there is 50% chance of receiving the human’s payoff and a 50% chance of receiving the alien’s payoff. In addition, I think when both players are individually choosing their optimal strategies, they choose the set of strategies that best work out for both players, instead of just for themselves.

The Nash equilibriums of this new game are {Leave; Set Off} and {Divide Pie; Accept Proposal}. Note that neither of these solutions includes setting off the nuclear warhead, so the Doctor successfully changes the outcome of the game. In addition, since {Divide Pie; Accept Proposal} is a better outcome than {Leave; Set Off} (an expected payoff of 0 for both players is better than an expected payoff of -15), I think both players would end up choosing to {Divide Pie; Accept Proposal} as their final agreement.

The main issue I have with the Doctor’s solution is that there is no suggestion of an enforcement mechanism. Both the human and alien could come to an equitable agreement, but they might intend to renege once they find out who they really are. In the very least, after the agreement is reached, both parties would have to be locked out of the room before the veil is lifted. Otherwise, the alien could try to take over again and the human could set off the nuclear warhead. With respect to sticking to the agreement in the long term, both parties would have to come up with some way to force each other to adhere to the terms.

Educational Attainment and Race in Alabama

A part of my responsibilities as a post-doc here at Alabama entails helping faculty with seeking out external funding for projects. One of these projects has involved developing a program to promote educational outreach throughout the state. In the course of this work I've been digging through some data on educational attainment rates in Alabama. In terms of learning about the political and economic characteristics of my new state, this has been an informative exercise. I also think it's helpful in illustrating the benefits of digging through your data, and helps to shed some light on the benefits of considering the distribution of your data more carefully, and so I thought I'd write a quick post on the subject. For the purposes of this post I'll be drawing on data collected from the American Community Survey's 2006–2010 five year population estimates. 

The mean percentage of Alabama's population holding a bachelor's degree or higher for this time period is 21.7%.  Compare this figure with that of my home state—New York's overall rate is 32.1%. This is a fairly large disparity, but further disparities emerge when we begin to disaggregate the data by race. The graph below shows the mean value of this same variable across the black and white populations of both states. 

From this we can see that the total values mask some important differences between groups within each state. In both cases the college attainment rate of the white population is slightly higher than the overall mean value. Alternatively, the attainment for the black population is significantly lower than the overall mean value in each state. Even New York, which on its face appears to have a significantly higher overall college attainment rate, is not much different from Alabama when we compare black college attainment rates with Alabama's values. 

Even when disaggregating the data in this way, we often still lean on the mean value pretty heavily. The distribution of the data around the mean can be incredibly informative as well, but we don't often pay as much attention to this as we should. Below I present a quick illustrative example of why this can matter.

Looking at Alabama's data again, I have drawn on county-level college attainment data for both groups.

Looking back at the first graph we can see that the statewide mean white college attainment rate was about 24%, and the statewide black attainment rate is roughly 14%. Looking at the county-level distributions can help us to get a better grasp on educational attainment rates throughout the state. In the case of white attainment, we can see that the distribution is slightly skewed towards the low end, but the lowest value in the distribution is around 8–9%. Alternatively, the distribution of black college attainment rates at the county level is more heavily skewed, and the lowest value in the distribution is considerably lower than for the white attainment rates, as shown in the preceding graphs where the X-axes are held constant to help make the figures more comparable. In fact, .30% is the lowest black attainment rate recorded in the data. 

Looking at the distribution of the data in addition to the mean values for these different groups can provide some additional insights into the substantive issues we're interested in. In this case, it's already apparent from considering the mean values that there are disparities in the college attainment rates of the black and white segments of the state's population. However, these values may actually underestimate the magnitude of those disparities. Though the distribution for white attainment rates is still skewed, it's floor is considerably higher than that of black attainment rates. In fact, by my count there were 14 counties in which the recorded black attainment rate was lower than the lowest recorded white attainment rate. The maximum for each group is roughly the same, however. Areas around Tuscaloosa, Birmingham, and Huntsville have fairly high college attainment rates. But these areas are highly unrepresentative when considering the state more broadly. In fact, although the mean statewide black attainment rate is 14%, the majority of counties fall well below this figure. And while most counties also fall below the mean on white attainment rates, there are, comparatively, more counties falling above the mean. All of this suggests that the mean statewide black college attainment figures are more heavily influenced by outliers in the data than are white attainment rates.  

Lastly, looking at these plots reveals another important feature of the distributions—the variance. The variance in white college attainment rates is 66.2. The variance in black college attainment rates is nearly half that, at only 37.5. This tells us that not only are black college attainment rates lower than white attainment rates, but they are more uniformly so. To put it differently, there is greater dispersion in white college attainment rates, while black college attainment rates are more tightly clustered around lower values. This is made apparent by looking at the two preceding graphs. Though both are skewed towards the lower end of the X-axis, white college attainment figures are less tightly clustered here, as I mentioned above.

This is an issue that I'll come back to in the future, but for now it seemed like this was a useful example for illustrating the importance of familiarizing yourself with the data that you're working with, and how relying less on simple mean values can reveal interesting features of the data you're working with. Bear Braumoeller has an interesting Political Analysis piece about examining the variance, which is worth checking out. More substantively, I also think that this exercise helps to more fully draw out the disparities that may exist between social groups. Relying on simple indicators of central tendency can be useful in some contexts when we want to quickly convey bits of information, but these figures can also be misleading. At the very least, they don't always tell us the full story.

 

Teaching Introductory Graduate Methods: Some Ideas About Understanding Sampling Distributions

Everyone has their own way of teaching methods, but in this entry I thought I’d share a brief exercise that I thought worked well. This code was developed when I was the teaching assistant for a second-semester methods class focusing on regression. Specifically, this was a section of the class from the first few weeks that dealt with two things: first, showing/repeating the idea of a sampling distribution, and second, showing some introductory code on loops and graphics.

The code uses the ggplot2 package for R. This software, and this package, have (in my experience) a somewhat steep learning curve so starting slow is important, even in the second semester. Equally, the concept of a sampling distribution is something that takes a while to click with most people who don’t have a statistical background. So what I’m trying to do with this code is show some basic graphics, and also introduce sampling distributions by ‘bringing one to you on a plate’.

            The first part of the code simulates ten rolls of a (pseudo) six-sided die:

cenlt<-vector(length=10)

for (i in 1:10)
{

            x<-runif(10,1,6)

            cenlt[i]<-mean(x)

}

cenlt

Here we create a vector, called cenlt (named for the central limit theorem), of length ten. We don’t give R any instructions about what to put in this vector, so if we inspect it (typing “cenlt”) it will display ‘FALSE’ ten times. We leave this empty vector to one side. Then we create a new imaginary conveyor belt, telling R to sit beside the conveyor belt and perform the same operation every time the belt rolls past, which it will do ten individual times. 

            The operation R will perform is to create ‘x’, which is a set of ten numbers, each randomly sampled from a uniform distribution between 1 and 6. We then take the mean of this set, and store it as a new element of cenlt. When we’ve done this ten times, then, cenlt will be full. We then display cenlt. We can actually display it a little bit more attractively if we graph it: 

library(ggplot2)

p1<-ggplot()

p1<-p1+geom_histogram(aes(x=cenlt),binwidth=0.2,
linetype=1, alpha=.3, color="black", fill="red")

p1<-p1+ ylab("Count") +
xlab("Sample Mean") + xlim(c(0,6)) + ylim(c(0,6))

p1<-p1+opts(title="Distribution of Sample
Means")

p1<-p1+geom_segment(aes(x = 3.5, y =0, xend =
3.5, yend =6))

p1

            The first line creates a new object called p1, which we set to be a graph object. Then we take it, and add another layer. The second line tells it to fill the graph with the shapes necessary to make a histogram: aesthetically, this means an x-axis defined by the values of cenlt. We ask for the shapes to have a solid black outline (linetype = 1, color = “black”) and to be semi-transparent and red (alpha = 0.3, fill = “red”). We then add a line showing the expected mean (geom_segment) as well as other details. 

            This code was then followed by a discussion of what each element of ‘cenlt’ represented. Each was a mean. So the mean of cenlt was a ‘mean of means’, and so cenlt has its own distribution: the distribution of means of ‘x’, and if we consider each x to be a sample, then seeing cenlt as the sampling distribution is relatively straightforward. The code can then be tweaked to make ‘x’ bigger, or to run ‘x’ more times, and discussing each of these options helps elucidate the idea of a sampling distribution. Changing the distribution away from uniform can also be useful. We also got some good discussions out of manipulating the basic parameters of the graph (the axis labels, the colors and binwidth of the histogram), and then later by switching the geoms to different options. All in all, this was a useful exercise that led to some good discussions, and helped introduce the ideas of sampling distributions, loops, and statistical graphics.

 

History vs. Political Science? Temporally Constrained Studies and Generalizability.

So this is my first post back from a prolonged break. As I mentioned in a previous—albeit brief—entry, I’ve had a busy but enjoyable summer. I got married, defended my dissertation at the beginning of July, and my wife and I have since relocated to Tuscaloosa, Alabama where I’ve accepted a position as a post-doc. We’re both pretty excited about the move and we’re really enjoying ourselves. Having grown up in the Adirondacks in upstate New York, summers that extend beyond a two month window have a certain appeal. And now that we’re getting settled in down here, I’m slowly getting back into a routine for work. I’ve been sitting on some papers for a while now that I’m finally turning my attention to, and it’s my experience with one of these papers that motivated me to write this particular post.

I’ve been thinking recently about how we view the place that history holds in our discipline. Now, I suspect there is an initial reaction that many people (particularly quantitative political scientists) are likely to have to the word “history,” and I’m guessing that is probably one of derision. It’s not that political scientists necessarily dislike history or historians, but this reaction is conceivable given that these disciplines are marked by some important epistemological differences, and many quantitative political scientists are often taught from the outset to avoid relying on single cases when drawing more generalizable lessons and conclusions (with good reason). When we think of historians, it is often in the context of someone who provides an excruciating level of detail about one particular event, and then tries to explain its origins and broader implications/lessons. My issue is not in fact with this particular point—as social scientists, we want to be able to assess just how generalizable the relationships that we’re looking at really are. We are typically interested in exploring systematic relationships, trends, and patterns that hold over broader periods of time and apply to a wide range of actors than are many historical studies. So to be clear from the outset, I am not advocating that quantitative political science should attempt to emulate the professional approach or methods that many historians employ.

My issue, really, stems more from the assertion by many quantitative political scientists that a study that is time bound in some way is “history”. This does not apply to all political scientists, but it’s been my experience that reviewers will often look at the temporal range of a study and dismiss its if it is explicitly bound to a particular period of time that does not overlap with the present (allowing a few years of slack on just what constitutes the “present”, that is). Indeed, some seem to be under the impression that it’s not even political science if the gap between the time period covered by a study and the present is sufficiently large. For example, let’s say I’m interested in understanding the domestic determinants of pre-hegemonic US foreign policy behavior. In this particular instance, there are theoretical reasons to suppose that the behavior of the US could be different in this time period as compared to later periods. Let’s further suppose that I have theoretical expectations regarding the relationship between X and Y, and I write a paper on this relationship for the period between 1900 and 1945. I would not at all be surprised to receive reviews rejecting the paper at least in part because the reviewer(s) questioned the study’s broader relevance, given that it’s bound to a 46-year time period.  

I think these sorts of reactions expose some problems and some important assumptions that we often make as political scientists. First, not every study necessarily needs to neatly map onto the present time period—or any other arbitrarily chosen time period. There is nothing inherently unscientific about the notion that certain relationships or phenomena can only be found in a particular temporal context. I’m fairly certain people have not abandoned the study of dinosaurs simply because we no longer see T-Rex roaming around the countryside. And our field is rife with examples of research, the temporal context of which is fundamentally limited in some way. Scholars of American politics provide perhaps the clearest example of an entire sub-field that cannot be held to apply to a period extending back beyond 1789. The sub-field of international relations is similarly dependent in many ways upon the existence of the modern nation-state, which we typically trace back to 1648, and some of the most widely used only go back to 1815. Do we consider our endeavors in these areas “history” because our studies are bound to these time periods?

Also on this point, there seems to be a double standard when we consider the broader implications of our research. Taking my example from earlier, reviewers will commonly ask how the study of US foreign policy between 1900 and 1945 is relevant for today, but rarely do we consider how a study of US foreign policy from 1945–2013 informs our understanding of the 1900–1945 period. It strikes me that this is (1) perpetually moving the goalpost, and (2) that it may be the wrong goalpost. This may seem like an odd point, but what it “relevant” by current standards is something that obviously changes on a day-to-day basis, and it’s a standard that says nothing about the quality of a paper as a piece of social science, or whether or not a paper helps us to understand a particular question about a given set of relationships. Really, this only represents our own innate temporal biases, but it says nothing about how scientific a piece of research is.  If the goal is truly generalizable theoretical and empirical knowledge, in a temporal sense, then this kind of consideration should apply just as much as thinking about how a study informs our understanding of the present. Conducting a study with the purpose of expanding our knowledge of systematic relationships between societal actors is not synonymous with expanding our knowledge of systematic relationships between societal actors for the purposes of informing our understanding of the present.

And it’s not as though we don’t attempt to deal with “unique” time periods and cases in our current research. However, it’s often the case that the manner in which we deal with these cases is fairly crude. For example, we might include a dummy variable in a model to control for a time period (or characteristics of a time period) that we believe to be unique in some way. Bipolarity during the Cold War, for example. Similarly, we might include a dummy variable in our model to control for states that we believe to be unique—depending on our topic, it might be a state like Israel, Egypt, the US, or maybe a group of states like the “Great Powers”. Sometimes we might also include an interaction term to account for how the effect of one variable might be conditional upon another. 

But these approaches are not always appropriate methods for dealing with the questions that we want to answer. Dummying out a particular time period is only going to tell us whether or not a given time period or group has a higher or lower intercept than the alternative time period or group. This approach is also often atheoretical. For example, we might have a belief that a time period is somehow different, but cannot fully articulate why or how. Similarly, interaction terms with a variable capturing a particular time period, for example, are implicitly suggesting that a given relationship is time-bound in some way. However, these approaches don’t allow us to examine whether or not the remaining variables in our models also have different effects in the context of a particular time period. For example, maybe we’re interested in whether or not both regime type and economic interests have a different effect on conflict propensity during the Cold War as compared to after. This, then, would suggest that maybe splitting our sample into two time periods is the more appropriate means of addressing our question. In fact, the notion that we would dummy out a particular time period because we suspect that it’s “different” in some way, but don’t exactly know how, is exactly the reason why we would want to conduct a study that is temporally bound in the first place.

This points to another issue. I think these biases are somewhat rooted in, and reinforced by, our relatively limited access to “good” data. Almost every journal article contains passages wherein the authors attempt to assert the broader relevance of their work. In the case of international relations, it is also quite common for these articles to then proceed to test their arguments using data that is only available for the post-World War II period. Sometimes, these tests will use data for a single country—often the US. Yet the arguments the authors make are often asserted as applicable to a broader set of countries than just the US, and rarely do such papers even address their own temporal limitations. We implicitly accept the generalizability of papers in which the tests of broader theoretical arguments rely on data from an incredibly narrow and often unrepresentative set of states, but push back when a study openly acknowledges its more narrow temporal confines. Why should we automatically assume that such studies inform our understanding of international relations and state behavior in the 1800s?

This is understandable. Particularly in the field of international relations, the availability of data is exponentially greater in the post-World War II period than before. Commonly used indicators like GDP and trade either don’t exist, are often missing, or are highly inaccurate for earlier time periods. Accordingly, many of our studies focus on this 50–60 year time period—not because it somehow matters more, or because we are interested only in this particular time period, but because this is the period for which we have access to relatively abundant data sources. But even in the post-World War II time period some of the data we use can still be of questionable reliability. Accordingly, when we see an article that focuses on a much earlier time period it sticks out like a sore thumb, and reviewers will often proceed to subject that paper to a different standard than other papers—a standard that really has nothing to do with the execution of the paper or the soundness of its argument.

This knee-jerk reaction against studies that are temporally bound in some way can also have deleterious consequences for our ability to understand the world. Finding that a particular relationship between two variables holds only for a given time period can reveal new and interesting questions. For example, we have evidence that Republicans and Democrats have switched their positions on military spending over the course of the Cold War. If we were looking for a relationship between Republicans and higher military spending over the entirety of this time period, we might erroneously conclude that there is no relationship. Alternatively, the finding that this relationship is temporally bound in some way raises new questions: Why did they switch? What caused the switch? Etc.

If our goal is to continuously develop and refine our understanding of how the world works then we must think carefully about the standards we set. If that standard is that we must only examine relationships that hold for centuries at a time, then we are imposing some very serious limitations on ourselves as researchers. These kinds of “big” systematic relationships are clearly important, but the march of scientific progress is not marked exclusively in these terms. 

Military Capabilities

Phil Arena has a very cool post up wherein he explores a potential alternative to the ubiquitous CINC score for measuring military capabilities. This strikes me as something that is long overdue.  And there are a couple of things that I like about the measure just from taking a quick glance at it. First, given that China just launched its first aircraft carrier, which is actually a refurbished Soviet carrier if I recall correctly, I think the notion that China has already surpassed the US in terms of military capabilities is a big pill to swallow. Second, just glancing at his figures for European powers during the late 19th century, Phil's measure looks more noisy, which in truth is kind of what I'd actually expect to see. The CINC measure makes there appear to be a lot more stability than I would expect to come along with a period where many of these European powers are vying for access to colonial possessions, foreign markets, etc. Furthermore, there were definite differences in terms of how each of these countries allocated their military resources. England certainly had the dominant navy during the late 1800s, but countries like France, Germany, and Russia had advantages in terms of their ability to field ground forces that the British just didn't possess.  

As Phil mentions, the measure may need some further development, but I still think this is really neat. 

On the Utility of IR Research

At the Duck of Minerva, Josh Busby has a post on the gap between political science research/IR and the policymaking community. I don't have a whole lot to say about the specific content of Busby's post, aside from the fact that I think this is an interesting an important debate to have. (Also, please note that the links in the quotations were provided in Busby's original post).

Math's PR Problem OR Changing our Educational System

I was listening to the BBC's Global News Podast this morning and found an interesting segment on the utility of mathematical modeling. Professor John Adam's book (which can be found here) looks at how one can model various aspects of urban life—pollution, traffic congestion, number of dentists offices, etc. I've not read the book, but Adam's message in his interview was something that I can sympathize with. In particular, Adam talks about the degree to which the educational process divorces math from any kind of applied usage. Learning math in this way, he goes on, is partially responsible for why more children/people are not as interested in mathematics as they might otherwise be. 

Sure, there are the ubiquitous "if a train leaves city A at rate X, and a train leaves city B at rate Z, when will their paths cross," kind of examples. But seriously, is that the best we can do? Now, I recognize that I'm far from being the strongest, or the quickest, when it comes to picking up a lot of the methods that I've been taught over the past few years. I, like many people I suppose, was not the biggest fan of math class in high school. Some of this was laziness on my part, but some of it was also the framework in which the subject matter was taught. Even in college it was far from clear that any kind of math beyond balancing a checkbook would play as big a part in my life as it currently does. I did well enough in these classes, but it was not until graduate school that I really had to seriously engage math and statistics.  

As it turns out, I've really enjoyed the quantitative side of my training here at Binghamton, and I'm far more willing to work at developing my understanding and skills now than I was when I was 16. Had I known in high school that I this stuff could be used to study things like war, human rights, etc., I might have cared a bit more. These are things that I've always been interested in. And this is perfectly rational behavior, right? If the expected utility of learning this material is zero, or even negative, then why expend the effort? Where is the payoff? If this is the case, then a major part of the reason that the expected utility of learning this material is so low is because the people that should be able to provide you with the relevant information to update your expectations simply do not do a very good job of that. In fact, I'd say that I didn't really get that at all until grad school. And being able to make that initial connection between math and statistics, and the substantive topics that I care about, has subsequently led me to develop a greater appreciation for the math itself that underlies the models I often use. 

I don't have any answers for how the educational system could be modified to address this issue. I also realize that you would have to find a way to incorporate applied mathematics into most subject fields, as not everyone has the particular set of interests that would make more math in history or government class a sufficient solution to the problem. Still, there must be a better way. With folks like Neil deGrasse Tyson going around trying to promote greater scientific literacy, and the recent attacks on NSF funding for political science, it seems like there is a space for us to modify how we educate our younglings and how science, math, history, etc., can be better integrated in this process. Maybe if people developed a better understanding of science as a process, or math as something with real world and substantive applications, rather than something that existed strictly from 1pm to 1:45pm during 8th period, people would be better able to develop a more robust appreciation for how mathematics and science fit into, and matter for, our daily lives. This is fairly obvious when phrased like that, but the fact is that it's not really what we do currently, is it?

Am I missing something?

Also—and this is a total aside—Brian Rathbun at the Duck of Minerva has a discussion of how Iron Maiden material can be used to teach political science. This is perhaps a good example of what I was talking about above, so perhaps this is not a total aside, but what we have here is essentially "applied awesomeness."

Poker is a game of skill

The New York Times (this may cost you one of your monthly alloted articles) reports on a new article by Levitt and Miles that argues that poker is a game of skill and not a game of chance.  The policy-relevant implications for the study deals with legalization - several prohibitions against poker are codified with terminology that prohibits gambling and randomized lotteries while allowing for skilled based gaming. The authors track a set of known players from the 2010 World Series of Poker (WSOP).  The study indicates that "good" poker players do get returns on their tournament buyins (seen as an investment) while the unknown players are more likely to lose money during the event.  The data includes 57 tournaments that run during the course of the WSOP (there are several side tournaments besides the main event).

Naturally, for those who are concerned about the endogeneity between the tracked players and their 2010 performance, the players are picked based on pre-2010 WSOP data - they were either ranked highly previous to the tournament (in poker magazines or online) or did well at the 2009 WSOP.  Naturally, if poker was entirely based around randomization, your previous performance ought to have no influence on your next tournament. For those of us who are poker players, the results should not be too surprising; however, having evidence of skilled players doing well should be reassuring.  Perhaps, also, it will allow us to hear less bad beat stories (stories about opposing players getting lucky against the story teller).

As an aside, footnote 10 indicates who not to be at the WSOP: "The biggest loser in the 2010 WSOP paid in $252,000 in entry fees, but earned only $24,000 in prize money."

Abstract:

In determining the legality of online poker – a multibillion dollar industry – courts have relied heavily on the issue of whether or not poker is a game of skill. Using newly available data, we analyze that question by examining the performance in the 2010 World Series of Poker of a group of poker players identified as being highly skilled prior to the start of the events. Those players identified a priori as being highly skilled achieved an average return on investment of over 30 percent, compared to a -15 percent for all other players. This large gap in returns is strong evidence in support of the idea that
poker is a game of skill.

The NBER paper can be found here.