The Quantitative Peace

QP LIVE! Where to Find Us at MPSA 2014

It's the time of year that I affectionately call "conference season," i.e. the springtime one-two punch of the annual meetings of the International Studies Association and the Midwest Political Science Association.  This week, thousands of political scientists will descend upon Chicago hoping to learn about new research, present some research of their own, and/or connect with old friends and new colleagues.  Your trusty bloggers here at the Quantitative Peace will be among those making the trek, so, in case you are interested in seeing the kind of stuff we work on when we're not blogging, I've put together a schedule of our presentations with a brief description of each.  Check it out after the jump.

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.     

 

 

 

Bipartisanship and the Foreign Policy Bureaucracy

Editor's note: A version of this piece is cross-posted at the LSE USAPP blog, and can be found here. This post is based on my recent article, The International and Domestic Sources of Bipartisanship in U.S. Foreign Policy, in Political Research Quarterly.

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The idea that foreign policy and national security issues are somehow exempt from the partisan rancor that often characterizes domestic politics is perhaps best encapsulated by the old adage, “politics stops at the water’s edge.” In American foreign policy, nowhere is this spirit better exemplified than by President Franklin Roosevelt’s appointment of Henry L. Stimson as Secretary of War. Since World War II, Stimson and other prominent bureaucratic appointees—such as Robert Lovett and John McCloy—have come to represent this ideal. This phenomenon of bipartisan cooperation that is held to have characterized the early Cold War period has been popularly referred to as the “Cold War Consensus”, or as “Establishment” bipartisanship.

However, following the Vietnam War pronouncements of the collapse of this bipartisan consensus became commonplace. Similar claims followed the end of the Cold War, and more recently, the terrorist attacks of September 11, 2001. Indeed, such assertions have become common in discussions of American foreign policy, with the broader substantive implication being that the conduct of American foreign policy has become increasingly erratic and subject to the kind of partisan political battles that have long afflicted domestic politics. 

Some of my recent work evaluates just how closely these stylized truths match up with reality. Earlier accounts of the breakdown of bipartisanship in foreign policymaking relied largely on anecdotal accounts or individual’s assertions with little in the way of systematic evidence (see here, here, and here). Some work by political scientists has done better in approaching the concepts of bipartisanship and consensus in a more systematic way (see here, here, here, and here). Still, there are some gaps that my work tries to address. First, earlier qualitative works often focused on elite bureaucratic policymakers (like Stimson), while quantitative studies have tended to focus on congressional voting patterns or public opinion. Second, foreign policy scholars often view bipartisan cooperation through the lens of foreign threats and crises, ignoring or downplaying more basic domestic political pressures that may drive bipartisanship. My work aims to apply a more systematic form of analysis to the subject of bipartisanship in presidential appointments, while also assessing the relative importance of domestic and international factors in affecting bipartisan cooperation.

Using data on over 1,000 individual appointees to the foreign policy bureaucracy—agencies like the State Department and the Department of Defense—I identified those individual appointees who were members of the opposition party (e.g., Republican serving in a Democratic administration). Figure 1 shows the number of bipartisan appointees in a given year between 1948 and 2011. While this is only a starting point, it does not lend much support to the conventional narrative regarding the apparent collapse of bipartisanship following either Vietnam or the end of the Cold War. In fact, the early Cold War period sees some of the lowest counts observed in the data.

Figure 1 — Annual count of bipartisan appointees, 1948–2011.

However, Figure 1 does not provide us with any insights into the effects of a wider range of variables that might be influencing bipartisanship. Accordingly, I modeled the probability of observing a bipartisan appointment in a given year as a function of these different forces. Briefly stated, I find no evidence to support the idea that bipartisanship has undergone a structural shift following either Vietnam or the end of the Cold War. In fact, as is displayed in Figure 2, each party’s probability of making a bipartisan appointment has converged when comparing the pre- and post-Vietnam periods. Though it may be contrary to our expectations, this finding makes sense when we consider the domestic political environment of the early Cold War period. The McCarthy hearings, as well as the long absence of Republicans from the White House, help to explain the scarcity of bipartisan appointments during some of the early Cold War years.

 

Figure 2 — Predicted probability of a bipartisan appointment by party and time period

 

 

Similarly, I find no evidence that US conflict involvement promotes bipartisanship. On the contrary, my findings indicate conflict involvement actually has a negative effect on bipartisanship. As Figure 3 shows, the predicted probability of observing a bipartisan appointment decreases as the duration of US war involvement increases. Furthermore, these results actually match the findings of previous studies. Using different metrics, an earlier study by Peter Trubowitz and Nicole Mellow also finds a negative correlation between international crises and bipartisanship in Congressional voting. This negative relationships is likely due to the increasingly contentious and politicized nature of particularly long military engagements.

 

Figure 3 — Predicted probability of a bipartisan appointment as a function of war duration.

 

Lastly, I find that domestic political conditions play an important part in promoting bipartisanship. I argue that bipartisan appointments can serve to compensate presidents for lower levels of partisan support in Congress and aid in the construction of moderate coalitions. However, this dynamic only works when the parties are close and there are moderate legislators in Congress with whom the president and his appointees can work. But as the parties in Congress have become more polarized, moderate legislators have become increasingly scarce. Figure 4 illustrates this dynamic. At low levels of polarization, a decrease in the number of seats that the president’s party controls in Congress leads to an increase in the probability of seeing a bipartisan appointment. As polarization increases the size of this effect becomes smaller, eventually becoming negative.

 

Figure 4 — Marginal effect of a decrease in presidential support in Congress on the probability of a bipartisan appointment

Note: Gray box denotes area of statistical insignificance.

 

Overall, these findings cast doubt on some of the conventional wisdoms surrounding American foreign policy, pointing instead to the importance of the domestic political environment in promoting bipartisanship. Accordingly, my research suggests that we should expect to see less bipartisan cooperation through presidential appointments moving forward. The fact that polarization unfolds over several years at a time also suggests that this trend is unlikely to reverse itself any time soon. President Obama’s retention of Robert Gates, and his appointment of Chuck Hagel as Secretary of Defense, would seem to provide some evidence to the contrary. However, while these appointments are surely important, they are not necessarily indicative of broader trends.

 

The Military and Competitive Sports

I missed my last post, but thankfully I'm on top of this week's edition. 

Kyleanne Hunter and Oliver Kaplan have a piece at Political Violence @ a Glance discussing some of the military roots of the biathlon. I've never known much about the sport's history, but it certainly makes sense that its origins are so closely tied to the kinds of security needs that Hunter and Kaplan cite. This was particularly interesting to me as both my wife and I grew up close to Lake Placid, New York. Lake Placid hosted the winter Olympics twice—once in 1932 and again in 1980—and continues to be a destination for tourists and athletes alike. Additionally, the 10th Mountain Division is headquartered about three hours from there at Fort Drum, and they have an armory located in Saranac Lake, New York where I grew up. Two members of the US biathlon team are also from Saranac Lake: Annelies Cook and Tim Burke. 

I went to high school with both Annelies and Tim, and it's great to see them doing so well. Hunter and Kaplan's discussion of the biathlon's history also makes me appreciate the sport and where I grew up a little bit more, and it's neat to think about the sport in terms of how it intersects with my own personal historical/research interests. The idea that specific military assignments can evolve into cooperative/competitive sporting events is really interesting, and I'm sure there's been much more written about it than I'm aware of (which is little). Naturally this makes me wonder what other sports have evolved out of military activities. Archery comes to mind, but I suspect that has competitive origins that are less exclusively military. There are a number of other shooting-related events during the summer games (see here), but again, many of these events are not necessarily exclusively military in origin either. Some of the historical information the link provides, and the descriptions of some of the events, suggest more of a hunting-related background. And many of these events appear to have been phased out, or have only been held once or twice.

It also makes me wonder as to what kind of selection effects might be at work in terms of how these activities evolve into competitive sports. Presumably you need activities that are a mixture of some highly specialized skill, but also something that's not overly secretive. After all, you don't want other states knowing that you have individuals highly trained in a particular task if that will compromise their work in some way. You also need an activity where multiple countries have need of the same type of training or force. As Hunter and Kaplan discuss, multiple countries were deploying similar sorts of forces to patrol remote, mountainous border regions, thereby facilitating the development of some sort of competitive event based on these shared activities. So not every state needs to have a given force, but at least a few do in order for it to be competitive. 

So what are some contemporary examples of the sorts of military activities that could evolve into internationally competitive sports? And how has technological innovation changed the landscape, so to speak, in terms of the pool of potential activities that could evolve in this way? My mind immediately went to some underwater sports, thinking of some combination of navigating an underwater obstacle course, and then performing some task requiring a demonstration of dexterity. This list of underwater sports suggests there are some sports that at least partly resemble these activities. Underwater navigation and underwater target shooting, specifically. This article also indicates that no underwater sports have been a part of the Olympic games. This appears to be due to the International Olympic Committee selecting which games it will grant approval to. Still, these seem like activities that require a high degree of specialization, but are also pursued by enough countries to facilitate competition. 

Another future possibility would be sort sort of aerial drone competition. Competitors might be judged on their ability to take off, perform certain maneuvers, perhaps "paint" a moving target for a given period of time, and then land. Though there are more advanced military variants, less sophisticated and less expensive drone variants are already being experimented with for a variety of purposes (see Amazon's recent talk of drone-based delivery). Though this would certainly emphasize physical performance less than other athletic activities, I'm not sure it's all that different from other activities like shooting, much of which emphasizes hand-eye coordination. This is also technology that more countries are likely to pursue for security/military purposes as time goes on.

If anyone can think of other military activities that might translate into internationally competitive sports, please share in the comments. 

The Conversation on Political Science & Public Engagement

As I'm sure most of you are aware, Nicholas Kristof believes that American academics (especially political scientists) don't do enough to engage with the public.  Political scientists have responded:

• Steve Saideman discusses how Kristof's complaints are oddly out of date, the importance of academic journals, and his role in the ISA blogging dustup;
• Erik Voeten points out that we're doing a lot of relevant research and more outreach than ever before; 
• Will Moore reminds us that professors are already getting their ideas out to the public by teaching them to the next generation and sheds some light on the reasoning behind the ISA blogging proposal that contributed to Kristof's complaints;
• Seth Masket discusses the degree to which Kristof misunderstands what political scientists do and the many ways that political science is increasingly entering the public conversation;
• Corey Robin uses a few examples to show how strong academics that are off of the tenure track are a missed opportunity for the Kristofs of the world;
• and Erica Chenoweth shows that academic research can be important to a much broader audience than the U.S. government leaders that appear to be Kristof's focus.

Overall, I think it is good that we regularly have these moments to reflect on how we engage with the public. In recent years, we have made great strides in our ability to get the substance of our research out to a larger audience, and I see no evidence that we are slowing down. Still, it never hurts to take some time to think about how we might do more of this and do it better. 

Of course, the conversation is ongoing, and many more are likely to contribute.  Feel free to add links in the comments!

Mapping Torture Allegations Using ITT

In conjunction with a project on state capacity and repression, I've recently been exploring the specific allegation data from Courtenay Conrad and Will Moore's Ill-Treatment & Torture (ITT) Data Collection Project.  ITT's specific allegation data are interestingly different from existing human rights data sets like the one (full disclosure) I co-direct, the CIRI Human Rights Data Project.  Whereas CIRI provides a yearly country score based on a standards-based ordinal coding of the level of government respect for its citizens' right to be free from torture and ill-treatment, the ITT data provide information on all allegations of government torture or ill-treatment made by Amesty International.  As such, the unit of observation for the ITT data is the individual Amnesty International torture allegation. Further, for each allegation, the ITT specific allegation data contain information about the type of abuse, the number of victims, the agents responsible for carrying out the abusive act(s), and the victim type, as well as many other topics.  For more information, the codebook can be found HERE, and a forthcoming Journal of Peace Research article introducing the data, written by Courtenay Conrad, Jillienne Haglund, and Will Moore, can be found HERE.

Figure 1. Total AI Torture Allegations, 1995 - 2005

As is my wont, I decided to get to know the data by making a few maps.  In doing so, I stumbled on some potentially interesting patterns.  First, the map above shows the total number of torture allegations that Amnesty International made against each state over the time period from 1995 to 2005, i.e. the time period covered by the ITT data.  As one might suspect given the research on Amnesty International's information efforts (e.g. Ron, Ramos, and Rodgers 2005; Hendrix and Wong 2013), the number of allegations does not neatly match up with the level of torture in each state, as AI's strategic considerations lead it to focus its attention more on some states rather than others. This problem can be partly remedied by taking into account the approximate size of the affected population in each allegation, which is provided by ITT. However, as Conrad, Haglund, and Moore point out, due to the strategic bias in Amnesty's reporting as well as the impossibility of observing all torture events, even this method is likely to result in a biased undercount of the number of torture events that actually took place in a given country over this time period. There are several ways of attempting to deal with coverage bias in such measures; indeed, Patrick Brandt and John Beieler each recently posted on ways to deal with similar issues in GDELT. For the purposes of this blog post, the maps below will simply display the various attributes of torture allegations as a proportion of the total allegations made against the state. With that in mind, you may want to remember to refer to the map above regularly if some states seem to perform particularly poorly in one area or another, as it could be the result of having been the target of relatively few allegations overall (for instance, see Mongolia).

Figure 2. Type of Torture as a Proportion of Total AI Torture Allegations, 1995 - 2005

(Click the image to enlarge)

The series of maps displayed in Figure 2 show the proportion of total AI torture allegations made up by the four types of abuse distinguished in ITT: scarring torture, stealth torture, ill-treatment, and unstated torture.  It should be noted that abuses are not mutually exclusive; that is, a single AI allegation may contain information on several types of abuse.  As such, the proportions for any individual state will often not sum to 1.  Overall, it appears that stealth torture, i.e. torture that is more difficult to detect after the fact because it leaves no scars on the body, is, on average, less common than the other types of torture and ill-treatment.  Indeed, the average state received only about 22 allegations of stealth torture from 1995-2005, whereas the average state was accused of ill-treatment approximately 48 times, scarring torture about 56 times, and unstated torture around 39 times.

Matching up with work done by Darius Rejali, highly developed states, and particularly highly developed democracies, appear to be more likely to engage in higher proportions of stealth torture than states at lower levels of development. The United States, Canada, Norway, Sweden, Finland, Belgium, Chile, South Africa, and South Korea have all been the targets of fairly high proportions of stealth torture allegations. That said, stealth torture also appears to be relatively more common among Asian states, particularly China, Kazakhstan, Uzbekistan, Myanmar (Burma), Laos, and Vietnam. On the other hand, scarring torture makes up a much lower proportion of allegations in these states. Instead, the proportion of allegations made up by scarring torture is much higher in many Sub-Saharan African states, like Angola, Mozambique, Central African Republic, Niger and Ghana, as well as in Eastern European states, such as Bulgaria, Romania, Hungary, and Slovakia. Finally, it seems we know the least about the type of torture used in North African and Middle Eastern states, with unstated torture accounting for a large proportion of allegations in Algeria, Libya, Egypt, Saudi Arabia, Syria, and Iran.

Figure 3. Agency of Control as a Proportion of Total AI Torture Allegations, 1995 - 2005

(Click the image to enlarge)

The ITT data also provide information on the agency of control, i.e. the "domestic institution and/or agent(s) ... responsible for a given allegation of torture" (Conrad and Moore 2011: 12).  Figure 3 maps the various agencies of control as a proportion of each state's allegations.  As with the various types of torture and ill-treatment, agencies of control are not mutually exclusive; a single torture allegation may name several government agencies as responsible for engaging in abuse.  

Across Figure 3, a few interesting patterns stand out.  While Canada, Australia, and European states are the targets of a relatively moderate number of torture allegations overall, it would appear that the majority of those allegations target torture and ill-treatment carried out by the police. On the other hand, prison abuse appears to account for a relatively high proportion of allegations in the United States, China, and Brazil; unsurprisingly, these states rank 1st, 2nd, and 4th, respectively, in total prison populations. The map of immigration abuse shows that these allegations primarily apply to highly developed democracies, like the U.S., Germany, Italy, Australia, South Korea, and Japan, possibly due to the high demand for admittance into those countries. Abuse by intelligence agents appears to account for a relatively high proportion of allegations in Middle Eastern and North African states, like Egypt, Jordan, Israel, Yemen, and the United Arab Emirates. Intelligence abuse also accounts for a high proportion of allegations in South Korea and Mongolia, although those states were subject to relatively few allegations overall. Finally, the states for which we see high proportions of military and paramilitary abuse align fairly well with the states highlighted in Figure 4 below, which shows the states in which armed conflicts were fought between 1995 and 2005 according to the PRIO Conflict Site data developed by Johann Hallberg.

Figure 4.  Locations of Armed Conflict, 1995 - 2005

(Conflict Sites in Red)

Of course, all of the patterns that I see in these maps could disappear upon controlling for other factors, and there are surely many patterns that I have missed. Primarily, I wanted to draw attention to the wealth of information present in the ITT data. I have only focused on the number of allegations, the type of abuse, and the agency of control; the ITT also contains information about the identities and numbers of victims, whether the abuse occurred within the state or abroad, whether the abuse was investigated, and many other factors. Overall, these data should open the door to many studies of phenomena that we previously could not investigate crossnationally, and I hope we see more projects like the ITT in the future.

 

*NOTE: All maps in this post were made using Weidmann, Kuse, and Gleditsch's CShapes GIS Vector Dataset, Version 0.4-2, in ArcMap 10.0.

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.

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.

 

How Do You Review?

I’ve been sending articles out for review for a few years now, and I’ve also had the opportunity to review several papers for journals. Having been through each side of the ordeal, I’ve begun to form some opinions on the peer review process as a whole. This is something other political scientists have written extensively about. For example, Michael Allen recently addressed the issue on this blog, and Nathan Jensen recently wrote an interesting piece about one of his paper’s journeys through the political science peer review process.  However, some of my recent experiences have prompted me to think about the process from the vantage point of the reviewer. Most of what I can recall reading on the subject tends to focus on the process as a whole, or discusses the process from the point of view of the submitter, rather than the reviewer. So I thought I’d reflect on my experiences and what practices I’ve tried to incorporate into my reviews.

1. Take reviews seriously: This might seem like an obvious point, and it's one Jensen makes in his post, but for anyone who has been through the process you know you can’t always take this for granted. We’ve all gotten terrible reviews in some way shape or form—some of them are condescending in their tone, some of them seem to address topics or ideas that are completely irrelevant to the paper in question (even after much reflection), some of them are incredibly short, and some of them are just wrong. And these points just concern content. Some reviewers just don’t respond to review requests from journals, and others don’t seem to get around to the review until several months after the paper was submitted.

For the most part, all of these things are well within the power of the reviewer to control. I say “for the most part” because sometimes comments or suggestions made in a review may be wrong, but I’d like to believe reviewers are not dishing out incorrect information on purpose. And maybe sometimes people just aren’t communicating their thoughts well, thereby leaving the reader with no idea as to how those thoughts apply to their paper. Also, sometimes life does get in the way, causing unexpected delays. But, quite often, you as a reviewer can control these sorts of factors.

The review process can and does put a burden on journal editors and reviewers—there’s no question about that. As Jensen notes in his post, senior scholars in particular can be swamped with review requests. But reviewing is probably one area in which scholars can have the greatest impact on the professional fortunes of other scholars. And I don’t mean to imply that reviewers should fail to reject a paper just to provide a boost to someone else’s career. Not every paper deserves to be accepted or given a revise and resubmit, but editors rely to varying degrees on the advice given to them by reviewers. We also know that there are plenty of hard working people out there who face rejection after rejection, many of which are for really silly reasons. If you’re going to reject a paper, the author deserves to know why. A flippant one-line response is not helpful to anyone.

I typically try to get my reviews back within a month or two, if possible. I'm still pretty early on in my career, at a point where not many people know much of my work, so it's probably pretty easy for me to meet this goal as compared to more senior scholars. On the one occasion that I've known in advance that I wasn't able to get reviews back in a timely manner, I’ve communicated this to the editor. It’s easy to blow this responsibility off—I mean, this isn’t your work, right? You are taking time away from your own busy schedule to review someone else’s work. Still, more likely than not you are asking others to do the same for you. 

2. Be clear and detailed: Along the lines of the previous point, a lack of detail in a review doesn’t help anyone. I once received reviews on a paper that ran the gamut from a single sentence saying to publish the article as is, to a rather lengthy, and in no uncertain terms, reject. As nice as it was to see that someone enjoyed your paper so much that they suggested accepting it, I’m not sure that such a brief reply helps. Truthfully, some of the more lengthy and critical reviews provided me with ideas for changes that I really do believe made the paper a better one.  

More often than not, papers are going to be rejected, and reviewers should know this. Even if you like a paper so much that you want to see it accepted as is, or with minor revisions, there will usually be at least one other reviewer on a given paper, and it is quite possible that this other reviewer will have a very different take. No paper is perfect, so it can be helpful to know why a reviewer believes that the strengths of a paper outweigh any weaknesses. Especially when other reviewers are less supportive. It’s also helpful to be able to compare reviews to see if there are any common themes/complaints that are popping up across readers. Given the high rate of rejection, I think it’s important to remember that it’s not really just about the one or two reviews that the author receives on a given round of submission. Setting aside for a moment the fact that one person can get the same paper to review over the course of that paper’s journey through the review process, if reviewers are clearly raising the same concerns through multiple rounds of reviews that might be a pretty good signal that something needs to be changed. Sometimes these sorts of things are easy to spot from one set of reviews, and at other times they’re not, but the authors can’t tell if the reviewer doesn’t provide the detail. Given the high probability of rejection, if you, as a reviewer, think that a paper's strengths outweigh its weaknesses, a failure to highlight those very features could result in them being erased in future rounds of reviews.

I’ve gotten to the point where I offer a brief statement at the beginning of the review in which I explicitly state whether or not I’m recommending acceptance, rejection, etc. I also try to offer a brief summation of what factors I’m considering in making this recommendation. I also try as hard as possible to not waver in my judgment—if you have “mixed feelings” or you “could be convinced” that a paper could be worthy of publication, don’t try to pass the buck to the editor and the other reviewers. Just recommend an R&R and move on. Nothing says you have to accept the second or third round of revisions, but given how competitive the review process is, this kind of waffling is effectively the same thing as rejecting the paper outright, and it's probably not all that helpful to the author.

Beyond the introduction, I also try to clearly identify each individual point that I want to address. And yes, this often involves an enumerated list and/or bullet points. To some extent I think this is just a matter of style. However, it can be difficult in a meandering review to discern the exact points that the reviewer wants you to address, and as much as it is the author’s responsibility to take their revisions seriously, it is also the reviewer’s responsibility to make clear those points that need addressing. Furthermore, we hold ourselves to a pretty high standard when it comes to organizing our thoughts and arguments in the context of an academic paper, so why shouldn’t it bethe same in reviews? It is, after all, all part of the same, broader, knowledge-generating process.

3. Offer constructive criticism: Again, this seems like an obvious one, but you can’t take it for granted. We spend an awful lot of time in college and graduate school learning/practicing how to pick apart other people’s arguments. So while pointing out the flaws in someone’s article is certainly an important part of the review process, I also think it’s equally important to be able to point out its strengths.

For the most part, my experience on this point leads me to believe that most reviewers are pretty good about this. However, I have received reviews that are highly critical, with little in the way of constructive commentary. "Complainy" might be the most appropriate made-up adjective to describe these reviews. If you as a reviewer think that there are some deficiencies with an argument or method, you need to spell that out for the author. If such deficiencies were clear to all, then they probably wouldn’t be in the paper to begin with. Further, I think the degree to which any flaw is damning to the overall article is contingent upon the availability of feasible alternatives. If you don’t like a measure that the author uses, don’t just complain and then move on. At the very least, ask the author to clarify whether or not superior measures are actually available. If you are aware of one that you believe is superior, then please share it and explain why you believe it to be better suited to the analysis at hand. Again, this is a collaborative venture, and part of the review process is being able to access the knowledge of other scholars.

I would also say that personally, as a junior scholar, I really do appreciate a good review when I get one. Some people have clearly taken the time to think about my work, and I appreciate it. As I said before, I’ve been fortunate enough to get some great feedback that I have subsequently incorporated into my work, and I do think the work is better for it. Criticism accompanied by constructive feedback and advice generally seems to be more helpful than merely pointing out the faults of a particular project.

4. Be transparent when it comes to your own limitations: As I stated above, sometimes reviewer comments are just wrong. Sometimes this is an honest mistake, and the reviewer is trying to offer sincere advice or criticism, but just gets it wrong. But, as an author this can be incredibly annoying—particularly if some factually incorrect statement could have been a major contributing factor to your paper’s rejection. If I’m reviewing a paper and I’m unclear about a measure, statistic, or method, I typically try to be entirely transparent when it comes to my own ignorance on the matter. Now I'm pretty confident in the substantive and methodological training that I received in graduate school, but this doesn’t mean I’ve mastered every analytical approach or estimation technique. Nor does it mean that I can’t learn even more about those topics and methods that I have been exposed to. Indeed, I’ve learned a lot over the past seven years, but as this SMBC comic so nicely conveys, sometimes expanding your knowledge base can raise new questions. So, when you think you don’t know, or you know you don’t know, just say, “I don’t know.” Feel free to even pose a question on the issue to the author in your review. After all, there is a certain irony in the idea that certain people, having devoted themselves to an industry that (ostensibly) is based on the discovery and accumulation of knowledge, would be so reluctant to utter those three words.

I'm sure senior scholars may have a different take on some of these issues. As I said, I'm still very early on in my career, so I'm not facing the same kinds of pressures from as many sources as they are. I don't have to deal with dissertation committees or a never ending stream of review requests. Still, I don't mind reviews all that much. They can provide me with some insights into what people in my general area of expertise are working on, and they often lead me to read works that I've not previously noticed or thought much about. But perhaps my views will change given time.