Prompted by something that I read on a Twitter post, I’ve decided to embark on a 30-day challenge of my own–creating 30 different visualizations of data. The types of visualization will vary–maps, charts, graphs, etc., and I will not be completing the challenge on sequential days.
This challenge will give me a chance to put “on paper” some ideas and concepts that I’ve been thinking about for some time, all of which are broadly related to the topic of politics. So, stay tuned.
We noted today in lecture that Polity IV gives countries like the United States very high scores on the ”democraticness” variable, even during periods when a majority of the adult population–African-Americans, and women–were legally not allowed to vote. While Switzerland (1971) was the last European democracy to grant universal suffrage for women, Portugal was the last European country to do so (1976)–Portugal was run by a military dictatorship during in the early years of the 1970s.
In this era of social media abuse and bullying, it’s interesting to learn about some of the abuse hurled at the Suffragettes:
Since we all talk about political abuse on Twitter, this is really interesting on the abuse directed at the suffragettes by their opponents. Here’s a postcard sent to Emmeline Pankhurst. More here: https://t.co/j65EzWjdB3pic.twitter.com/yReqKQEBjr
In a recent working paper by Hanson and Sigman, of the Maxwell School of Citizenship and Public Affairs at Syracuse University, the authors explore the concept(s) of state capacity. The paper title–Leviathan’s Latent Dimensions: Measuring State Capacity for Comparative Political Research, complies with my tongue-in-cheek rule about the names of social scientific papers. Hanson and Sigman use statistical methods (specifically, latent variable analysis) to tease out the important dimensions of state capacity. Using a series of indexes created by a variety of scholars, organizations, and think tanks, the authors conclude that there are three distinct dimensions of state capacity, which they label i) extractive, ii) coercive, and iii) administrative state capacity.
Here is an excerpt:
The meaning of state capacity varies considerably across political science research. Further complications arise from an abundance of terms that refer to closely related attributes of states: state strength or power, state fragility or failure, infrastructural power, institutional capacity, political capacity, quality of government or governance, and the rule of law. In practice, even when there is clear distinction at the conceptual level, data limitations frequently lead researchers to use the same
empirical measures for differing concepts.
For both theoretical and practical reasons we argue that a minimalist approach to capture the essence of the concept is the most effective way to define and measure state capacity for use in a wide range of research. As a starting point, we define state capacity broadly as the ability of state institutions to effectively implement official goals (Sikkink, 1991). This definition avoids normative conceptions about what the state ought to do or how it ought to do it. Instead, we adhere to the notion that capable states may regulate economic and social life in different ways, and may achieve these goals through varying relationships with social groups…
…We thus concentrate on three dimensions of state capacity that are minimally necessary to carry out the functions of contemporary states: extractive capacity, coercive capacity, and administrative capacity. These three dimensions, described in more detail below,accord with what Skocpol identifies as providing the “general underpinnings of state capacities” (1985: 16): plentiful resources, administrative-military control of a territory, and loyal and skilled officials.
Here is a chart that measures a slew of countries on the extractive capacity dimension in
Those of you in my IS210 class may find the Polity IV data to be of use when writing your paper. Click on the image below to take you to the website, where (if you scroll down to the bottom) you can see the regime scores (between -10 and +10) for each country over many years. See the example at the bottom of this post.
Here’s an exampe of the history of movements in regime for El Salvador from 1946 until 2010. How many changes in regime does El Salvador seem to have experienced in the post-WWII period? What happened in the early 1980s?
The Failed State Index is created and updated by the Fund for Peace. For the most recent year (2013), the Index finds the same cast of “failed” characters as previous years. There is some movement, the “top” 10 has not changed much over the last few years.
Notice the columns in the image above. Each of these columns is a different indicator of “state-failedness”. If you go to the link above, you can hover over each of the thumbnails to find out what each indicator measures. For, example, the column with what looks like a 3-member family is the score for “Mounting Demographic Pressures”, etc. What is most interesting about the individual indicator scores is how similar they are for each state. In other words, if you know Country X’s score on Mounting Demographic Pressures, you would be able to predict the scores of the other 11 indicators with high accuracy. How high? We’ll just run a simple regression analysis, which we’ll do in IS240 later this semester.
For now, though, I was curious as to how closely each indicator was correlated with the total score. Rather than run regression analyses, I chose (for now) to simply plot the associations. [To be fair, one would want to plot each indicator not against the total but against the total less that indicator, since each indicator comprises a portion (1/12, I suppose) of the total score. In the end, the general results are similar,if not exactly the same.]
So, what does this look like? See the image below (the R code is provided below, for those of you in IS240 who would like to replicate this.)
Here are two questions that you should ponder:
If you didn’t have the resources and had to choose only one indicator as a measure of “failed-stateness”, which indicator would you choose? Which would you definitely not choose?
Would you go to the trouble and expense of collecting all of these indicators? Why or why not?
install.packages("gdata") #This package must be installed to import .xls file
library(gdata) #If you find error message--"required package missing", it means that you must install the dependent package as well, using the same procedure.
fsi.df<-read.xls("http://ffp.statesindex.org/library/cfsis1301-fsi-spreadsheet178-public-06a.xls") #importing the data into R, and creating a data frame named fsi.df
pstack.1<-stack(fsi.df[4:15]) #Stacking the indicator variables in a single variable
pstack.df<-data.frame(fsi.df,pstack.1) #setting up the data correctly
names(pstack.df)<-c("Total","Score","Indicator") #Changing names of Variables for presentation
install.packages("lattice") #to be able to create lattice plots
library(lattice) #to load the lattice package
xyplot(pstack.df$Total~pstack.df$Score|pstack.df$Indicator, groups=pstack.df$Indicator, layout=c(4,3),xlab="FSI Individual Indicator Score", ylab="FSI Index Total")
That’s quite a comprehensive title to this post, isn’t it? A more serious social scientist would have prefaced the title with some cryptic phrase ending with a colon, and then added the information-possessing title. So, why don’t I do that. What about “Nibbling on Figs in an Octopus’ Garden: Explanation, Statistics, GDP, Democracy, and the Social Progress Index?” That sounds social ‘sciencey’ enough, I think.
Now, to get to the point of this post: one of the most important research topics in international studies is human welfare, or well-being. Before we can compare human welfare cross-nationally, we have to begin with a definition (which will guide the data-collecting process). What is human welfare? There is obviously some global consensus as to what that means, but there are differences of opinion as to how exactly human welfare should be measured. (In IS210, we’ll examine these issues right after the reading break.) For much of the last seven decades or so, social scientists have used economic data (particularly Gross Domestic Product (GDP) per capita as a measure of a country’s overall level of human welfare. But GDP measures have been supplemented by other factors over the years with the view that they leave out important components of human welfare. The UN’s Human Development Index is a noteworthy example. A more recent contribution to this endeavour is the Social Progress Index (SPI) produced by the Social Progress Imperative.
How much better, though, are these measures than GDP alone? Wait until my next post for answer. But, in the meantime, we’ll look at how “different” the HDI and the SPI are. First, what are the components of the HDI?
“The Human Development Index (HDI) measures the average achievements in a country in three basic dimensions of human development: a long and healthy life, access to knowledge and a decent standard of living.”
So, you can see that it goes beyond simple GDP, but don’t you have the sense that many of the indicators–such as a long and healthy life–are associated with GDP? And there’s the problem of endogeneity–what causes what?
The SPI is a recent attempt to look at human welfare even more comprehensively, Here is a screenshot showing the various components of that index:
We can see that there are some components–personal rights, equity and inclusion, access to basic knowledge, etc.,–that are absent from the HDI. Is this a better measure of human well-being than the HDI, or GDP alone? What do you think?
During our second lecture in Research Methods, when asked to provide an example of a relational statement, one student offered the following:
Playing violent video games leads to more violent inter-personal behaviour by these game-playing individuals.
That’s a great example, and we used this in class for a discussion of how we could go about testing whether this statement is true. We then surmised that watching violence on television may have similar effects, though watching is more passive than “playing”, so there may not be as great an effect.
If television viewing can cause changes in our behaviour that are not socially productive, can it also lead viewers to change their behaviour in a positive manner? There’s evidence to suggest that this may be true. In a recent study,
there is evidence to suggest that watching MTV’s 16 and Pregnant show is associated with lower rates of teen pregnancy. What do you think about the research study?
Political scientist James Fearon has an interesting blog post on the political science blog, The Monkey Cage. In it, he asks, and then gives an answer to, the question “How do states act after they get nuclear weapons?” The issue, Fearon notes, is gaining increasing attention in the United States, given the alleged quest by Iran to develop nuclear weapons. The issue also resonates in Canada, with Stephen Harper recently affirming his fear of the Iranian regime acquiring nukes. From this CBC interview with Peter Mansbridge, Harper responds in the affirmative to Mansbridge’s characterization of an interview Harper had given a couple of weeks earlier on the issue of Iran’s quest for nuclear weapons:
…in your view, they [Iran’s regime] want nuclear weapons, and they would not be shy about using them.[see the exchange below]
In opposition to views like Harper’s are the views of what Fearon calls “proliferation optimists” such as the well-known realist Kenneth Waltz, who claims that contrary to our repeated expectations about the behaviour of post-nuclear states, the opposite has turned out to be true much more often than not. What does Fearon find empirically? First, he sets up what it is, specifically, that he is measuring:
The following graph shows, for each of the nine states that acquired nuclear capability at some time between 1945 and 2001, their yearly rate of militarized disputes in years when they didn’t have nukes, and the rate for years when they did.
Here is a graph of Fearon’s finding with his summary below:
China, France, India, Israel, Pakistan, and the UK all saw declines in their total militarized dispute involvement in the years after they got nuclear weapons. A number of these are big declines. USSR/Russia and South Africa have higher rates in their nuclear versus non-nuclear periods, though it should be kept in mind that for the USSR we only have four years in the sample with no nukes, just as the Cold War is starting.
We talked a little bit in class today about the link between ethnic (cultural) diversity and public spending. The empirical record seems to find that the more ethnic diversity in a polity, the less public spending–health, education, etc.–there is. A recent article in the American Political Science Review (Habyarimana et al. 2007) addresses the theoretical mechanisms that may underlie this empirical association:
A large and growing literature links high levels of ethnic diversity to low levels of public goods provision. Yet although the empirical connection between ethnic heterogeneity and the underprovision of public goods is widely accepted, there is little consensus on the specific mechanisms through which this relationship operates. We identify three families of mechanisms that link diversity to public goods provision—what we term “preferences,” “technology,” and “strategy selection” mechanisms—and run a series of experimental games that permit us to compare the explanatory power of distinct mechanisms within each of these three families. Results from games conducted with a random sample of 300 subjects from a slum neighborhood of Kampala, Uganda, suggest that successful public goods provision in homogenous ethnic communities can be attributed to a strategy selection mechanism: in similar settings, co-ethnics play cooperative equilibria, whereas non-co-ethnics do not. In addition, we find evidence for a technology mechanism: co-ethnics are more closely linked on social networks and thus plausibly better able to support cooperation through the threat of social sanction. We find no evidence for prominent preference mechanisms that emphasize the commonality of tastes within ethnic groups or a greater degree of altruism toward co-ethnics, and only weak evidence for technology mechanisms that focus on the impact of shared ethnicity on the productivity of teams. (my emphasis)
Thus, what the experimenters found was that (at least in their experiment) co-ethnics were more likely to co-operate in a strategic setting than non-co-ethnics. An additional important factor is the ability of the threat of social sanction to be stronger within a homogenous social group, presumably due to more closely linked social networks. (“I’ll tell your mother on you!” as a threat has more of a potential enforcement effect if you think the person making the threat may actually know your mother. And the likelihood of that person knowing your mother increases, other things being equal, if s/he shares the same ethnicity as your mother.
As I noted in POLI 1140 today, your blog assignment for this week is to write a post related to anything in Chapters 1 or 2 of the Mingst and Arreguin-Toft textbook. You have until midnight, Friday January 20 to publish your post. Here is an example of what I would consider to be a good post–format, content, and length.
On p. 3 of Chapter 1 of the text (in the Thinking Theoretically section), the authors write:
In brief, realism posits that states exist in an anarchic international system. Each state bases its policies on an interpretation of national interest defined in terms of power.
While there are many types of power–economic, political, prestige, etc.,–the most important source of power and the one which states generally seek to increase as much as possible, is military power. Because of anarchy, realists believe that states are constantly concerned about their security. States that feel more insecure seek to increase their power, thereby increasing the sizes of their military, all else being equal. It would be interesting to find out which states spend a lot on their military, and which states spend less. Fortunately, Globalsecurity.org has compiled the data for us. In their most recent summary of global military expenditures (from 2011), we find some interesting data. I have copied the top 20 (in terms of absolute dollars spent) in the table below. For a list of all countries, click on the link above.