[I should have noted this before, but, in order to get a better view of larger visualizations, you should click on the “<>” symbols on the upper right corner of the page for flexible page width. I have finally figured out how to embed from Tableau but it makes a mess of the page.]

Here is just quick data snippet from the Pew Global Attitudes Project that measured changes on acceptance of homosexuality (link to Tableau) for selected countries, from 2007 to 2013. Here is a static dual point plot, the link in the previous sentence will take you to the interactive version:

Changing Views Dual Points Static

First of all, out of 26 countries, 17 had a better acceptance score from 2007 to 2013, 2 had no change, and 7 had a worse score in 2013 than in 2007. But just to look at these raw numbers does not tell us much. Not every country was at the same level of acceptance in 2007.

Look at the 2007 static bar chart (interactive one here):

Views 2007

So what changes in 2013? (interactive chart here)

Views 2013

France takes a tumble, for sure, but is still in the top 5. I would explain this with the fact that France legalized gay marriage this year, and the run-up to the final passing of the law revealed a rather dark and ugly underbelly of homophobia that might have been latent when there was no law in perspective, but that reared its hideous head when legislative action started. The top of the list is still occupied by the same countries with a bit of shuffling but still high acceptance scores. Interestingly enough, the US plays middle of the pack in both years, probably due to its high level of right-wing religiosity and puritanism compared to European countries. The bottom of the list stays roughly the same. The religiosity hypothesis seems confirmed by this scatterplot:

2013-Homosexuality-03 scatterplot

We see a very negative correlation: as religiosity increases, acceptance scores decline. Finally, let’s look at the differences between years (interactive chart here):

Changing Views Chart

Looking at the differences between 2007 and 2013, South Korea and France are the two shocking stories. The acceptance score for South Korea jumps 21 percentage points (even though the majority still finds homosexuality unacceptable), and France takes a 6 percentage points drop (Which I explained above). Frankly, I have no idea what the heck is happening in South Korea. Most Western countries see improvements in their already high acceptance scores. Another noticeable improvement is Kenya, with a jump of five percentage points, which gets it out of the bottom three. The bottom of the list is now occupied by quite a few Western countries. One could argue rising activism in the Global South, and backlash in the Global North.

Looking a bit more globally, the North / South differences are still striking:

Acceptance of homosexuality - global

As is the case here:

No matter which category you click on, the distribution across age groups show limited variation, across media. And printed books still lead the reading game.

Now, on other topics, though, generational comparisons might lead to significant differences.

For instance:

millennialssaliencechar

In this case, there is a clear generational pattern that holds over time (and also holds true over other dimensions of religiosity).

In other words, the fact that individuals are born within the same time period cannot be assumed to create homogeneity on a variety of attitudes and behaviors but the fact that Paula Deen and her defenders rushed to that explanation is reflective of how much we tend to assume that a given zeitgeist deterministically shapes an individual’s view, with little to no possibility of change. First of all, to be born during a given time period means something different across borders, and within borders (based on social class, race, and gender).

An assertion of consistency across a generational cohort should always be supported by evidence, and not assumed. It might exist in some respects (like religiosity), but not in others (reading habits).

Obviously, Immanuel Wallerstein belongs in the prestigious list of cranky sociologists. His conceptualization and critical analysis of the modern capitalist world-system provides a powerful framework to understand global capitalism.

Thankfully, Kevin Moore perfectly illustrated the idea of the world-system as a globally integrated entity, where natural resources are sucked out of the periphery, where labor and manufacturing power are extracted from the semi-periphery, to create goods to be consumed in the core. Periphery, semi-periphery and core are not countries, but areas, that are globally distributed. Indeed, as Saskia Sassen has long demonstrated, one can find the three types of areas in global cities, especially in the Global South.

Of course, the core is not limited to that ‘absorption’ function. And the cartoon also has a third pod or leg illustrating what comes out of the core towards the other areas: capital, garbage, and money and weapons for the useful warlord-du-jour as long as he gives access to whatever resource he controls and the core needs.

wallerstein

Wallerstein takes his rightful place in the Gallery.

[Note: for all the visualizations, you can get larger views by clicking on the images.]

I have never found culture to be a particularly difficult or painful topic to teach in introduction to sociology classes. There is though a tendency in my students to perceive the relationships between technology and culture to be a one way street: technology -> culture and behavior. One of the challenges is to explore the other way around in that relationship: culture -> technology -> behavior or culture -> behavior -> technology. And that is even before you start discussing the social structures of technological production and the social institutions involved in the process (education, government, military, etc.). And that is before you even begin to discuss the fact that technology access and use is never homogeneously distributed within and between population (e.g. the digital divide), and that layers of social stratification are crucial in that respect.

But anyhoo, it is always interesting to trying to explore some data on technology as part of a larger discussion on other non-material aspects of culture, as they are all interrelated. This is my attempt at doing so (I’ll deal with values and norms in another post). I got all the data from Gapminder. The maps and bar charts were done in Tableau (sorry I still cannot embed my workbooks here, so, links to Tableau Public will have to do). The Gapminder screengrabs are what I did when I combined technology variables with GDP per capita (as a rough measure of economic development). Click on the screen grabs to go to the Gapminder interactive graphs. At the same time, because the technologies I an discussing below are relatively recent, the time series are not as crucial as they might be for other variables.

Anyhoo, feel free to use these visualizations as part of exercises to get students to think about data, patterns and technology.

So, one easy way to start is with personal computers.

Personal computer per 100 people (interactive world map and rankings via Tableau)

Static map:

PC per 100 map

It is not hard to see the big gap between North America / Western Europe and pretty much everybody else. This is more visible in the rankings.

Static partial rankings:

PC per 100 - Partial rankings

Personal computer per 100 people correlated with GDP per capital (bubble chart where bubble size reflects population size, via Gapminder)

Static image:

PC per person and GDP

I would think that 2006 data for PCs are already pretty old data, but that was the most recent on Gapminder, so there you have it. As you can see though, coloring the bubbles based on GDP per capita helps identify a pattern of higher income -> higher availability of technology, but with a very steep curve (therefore, a bigger gap between high PC users / high GDP per capita versus the rest). One interesting question is whether this clear pattern will persist across other related technologies.

Like cell phones:

Cell phone rates per 100 people (interactive world map and rankings via Tableau)

Static map:

Mobiles per 100 people - Map

The rates are markedly different than those for PCs (and the data a bit more recent). Anyone who travels to the Global South has to notice the omnipresence of mobile phones. It looks like a lot of countries of the Global South practically bypassed the landline stage and went straight to mobile. The rates reflect that, even though stratification patterns are still obvious.

Partial rankings – static image:

Mobiles per 100 people - Partial rankings

Cell phone rates per 100 people correlated with GDP per capita (bubble chart where bubble size reflects population size, via Gapminder)

Static image:

Cell phones oer 100 and GDP

As you can see, this is a very different bubble chart than the preceding one. No steep, highly stratified curve here. There is definitely a positive correlation between GDP per capita and mobile phones subscriptions, as reflected by the color gradient, but the curve is more regular and linear.

Internet use rates per 100 people (interactive world map and rankings via Tableau)

Static map:

Internet per 100 map

Regional patterns are rather clear on this one. Of course, the data only measure internet use, but not the quality of connections and networks.

Static partial rankings:

Internet per 100 - Partial rankings

One can clearly in which countries full access is available and achieved.

Internet user rates per 100 people correlated with GDP per capita (bubble chart where bubble size reflects population size, via Gapminder)

Static image:

Internet per 100 and GDP

Here, we get a third different pattern: steep and linear, with the same stratification.

So, we know that Internet use is pretty widespread, except for the poorest countries in Sub-Saharan Africa, but as I mentioned above, Internet use does not measure quality of connection. So, let’s look at broadband.

Broadband rates per 100 people (interactive world map and rankings via Tableau)

Static image for map:

Broadband per 100 people - Map

Static partial rankings:

Broadband per 100 - partial rankings

The rates are overall much lower, across the board (with two top outliers), so one can see that broadband penetration still has a way to go, even in the richer countries.

Broadband rates per 100 correlated with GDP per capita (bubble chart where bubble size reflects population size, via Gapminder)

Static image:

Broadband per 100 and GDP

And here is a fourth different pattern: a lot of countries at the bottom of the graph, close to 0, with a very flat pattern, a slow rise as we get towards the high GDP per capita countries, a very smooth and slow upward curve (with our two outliers in the top right corner).

So, again, even though the different technologies I have visualized here are related to each other, one can see that their access varies widely, by technology, by regions, and by levels of development. At the same time, comparative graphs also show different patterns of global penetration of these technology, from brutally stratified curves (PC), to almost perfectly linear (mobile subscriptions), to steep curve (Internet), to long left-tailed curve (broadband). The trick would be to try to find explanations for these different patterns.

And, of course, we focused only on the technologies themselves here. Not on what people do with them and the different forms of usage. That is for another post.

Following the good news from SCOTUS Wednesday, we need to recognize the egregious error they made the day before in Shelby County v. Holder (2013) which invalidated part of the Voting Rights Act (VRA) of 1965.

The strangest thing about Roberts’ majority opinion is that he acknowledges not only the success of Section 4 of the VRA (concerning the 6 southern states of Alabama, Georgia, Louisiana, Mississippi, South Carolina, Virginia and parts of North and South Carolina) by noting increased minority voter participation, minority office holders and the like, but he even cites the repeated attempts of the southern states in question to erect barriers and dilute minority access to voting in response to the Act.

And because it’s all been so extraordinary and accomplished what it set out to do (he actually writes: “There is no doubt that these improvements are in large part because of the Voting Rights Act”) that now it’s time to get rid of it.

That’s like saying, “because I plugged the leak 20 years ago and it worked, it’s now time to pull the plug and move on. It’s not 20 years ago, after all.” The logic is as tortured as it is circular.

Worse, he blithely claims that the south just isn’t the same old south that it was once, dismissing outright the dissent’s eloquent list of “second-generation barriers to minority voting” that have come about (and been shot down, thanks to VRA) over the past 50 years.

From Ginsburg’s dissent:

Efforts to reduce the impact of minority votes,in contrast to direct attempts to block access to the ballot, are aptly described as “second-generation barriers” to minority voting. Second-generation barriers come in various forms. One of the blockages is racial gerrymandering…Another is adoption of a system of at-large voting in lieu of district-by-district voting in a city with a sizable black minority…discriminatory annexation by incorporating majority white areas into city limits…”

And so on. Maybe it’s because I live in Georgia, but I can cite very specific examples of each of the above that have played out in just the last few years. We go through it state-wide every ten years because of the Census, and locally, whenever they damn well feel like it. Here in Athens we just went through a very ugly racial gerrymandering of our county commission districts that ended up under DOJ review and in a flood of lawsuits. Now, such remedies won’t be available for review.

The worse thing about the majority’s “reasoning” is the naive and simplistic view they have of race today in the south. There is this head-in-the-sand belief that racial discrimination is a thing of the past (even though Roberts actually says “voting discrimination still exists; no one doubts that”) and that just because blacks and Hispanics vote and hold office today, it’s all good.

If you think that undoing section 4 of the VRA is not going to be taken advantaged of at every turn going forward, you know nothing about the deep south. This is only the beginning of an intense desire which exists in many, many circles to return to the bad old days of the past and keep “them minorities” in their place (see also: the Paula Deen brouhaha and her vociferous defenders).

I always tell my students that you don’t have to drive too far outside of metro Atlanta, or much more than 10 minutes or so from Athens, and you are entering into a different world…a world where time stopped about a hundred years ago. Here’s a quick excerpt from an AJC expose on Greene County, just minutes from Athens, from 2004:

The civil rights movement was gathering force when Dr. William H. Rhodes Jr., the son of a local druggist, returned home in 1962 to open a family medical practice. The new brick office downtown included a design feature common in the South: a “colored” waiting room accessible by a separate, backdoor entrance.

Today the segregated waiting rooms remain a fixture of Rhodes’ practice — even though, he quickly points out, black patients are free to come in the front door if they choose.

Some do. But a few older patients continue to use the “colored” entrance, as they still call it, just as they did when they were young and had no choice. 

“Some of them prefer to come to the back,” Rhodes says. 

This is life today in many parts of the rural south. And to assume that Tuesday’s decision won’t make things worse is beyond comprehension. While we have definitely made great improvements in the south over the past 50 years (and how I wish the Chief Justice were right, and it was as simple as “the way to stop discrimination on the basis of race is stop discriminating on the basis of race”) , the decision in Shelby County will go down as major setback for race relations in this country.

I’ll defer to William Faulkner, who apparently the Chief Justice has never read: “In the south, the past is never dead. It’s not even past.”

Cross posted to the Power Elite

[First of all, please take a look at the Databases page, which I will be updating as I find more data and tools for data analysis.]

One of the issues that I commonly face while teaching introduction to sociology is that I have to battle the fact that undergraduate students know very little about public policy in general and how powerful it can be to generate powerful outcomes for large numbers in affected populations, sometimes for better, and sometimes for worse.

Yesterday, the Guardian got a good series of maps on this (based on the Global Maps published by Children’s Chances, out of the World Policy Center).

Let’s get started:

Mapping children's chances - Paid Leave

First note the overall North / South divide. But also note that the red countries are not the poorest ones. Some poorer countries offer greater benefits than the US. But that is for mothers, what about fathers?

Mapping children's chances - Paid Leave 2

Now, what could explain the discrepancies? This might be a good opportunity to discuss gender roles and how they are enforced / supported / challenged through public policy. One can see right away that there is much more red on this map, that is, far fewer countries offer paternal leaves. However, there is a bit of overlap: blue areas in the first map match blue areas in the second one.

There is also a good opportunity to discuss how one should read a map. As much as one’s reading is guided by the colors, one should pay attention to the legend as well, otherwise, a careless reading might lead one to think that Australia offers longer paternal leaves than maternal leaves… well, no. The legend is different between the maps: blue in the first one means 26 weeks or more, where it means 14 weeks or more in the second one. So, comparisons should not be based just by a skimming of the colors: only red means the same in both maps.

Moving on,

Mapping children's chances - work

This one is interesting because, good luck finding a pattern. And Maude knows we sociologists love patterns because nothing says social structure like a good pattern of behavior or policies and outcomes. For instance, look at the Americas and you find countries for each type of regulation (or lack thereof). So what do we do in a case like this? After all, the first map was relatively easy to analyze. Well, first of all, it is a good opportunity to recognize that Western countries do not have a monopoly on children- and family-friendly policies and that the US tends to do pretty badly on those compared to countries at the same economic level but it is not the only one (Australia). On the other hand, other less wealthy countries have strict standards (check out the grey countries). But overall, what this tells us is “dig deeper”.

But it is all well and good to have a paid leave, but how paid is it really to be effective and allowing parents to not have to work during the leave time? The Guardian does not have that information but you can find it at the original site.

So, for mothers:

Mapping children's chances - How Paid  Mothers

Note that the values are maximum values, not necessarily what people are receiving but it is still amazing to see most of the world in the dark green category although one wonders how many women in the poorest countries, especially those who live in rural areas, actually receive any benefit at all considering we are talking wage replacement.

What about fathers? We already know they are less likely to get a leave in the first place:

Mapping children's chances - How Paid  Fathers

No surprises here.

What is missing here, though, is the impact these policies might have (or not) on other social indicators. If we assume that these policies should generate positive outcomes for children, we would need to correlate them with other variables, such as infant mortality, educational achievements, life expectancy, etc. One would also need to know the effectiveness in implementing these policies and have benefits distributed across the population especially on countries in the Global South.

So, as much as I like visualizations, these are a bit short on content. They are a starting point and raise a lot of interesting issues and questions but provide few answers.

Same-Sex Marriage Gets Court’s Imprimatur:

As the Supreme Court issued its last-day-of-court rulings on Wednesday, nullifying the federal law that defined marriage as a union between a man and a woman and effectively permitting same-sex marriage in California, what was also clear was just how rapidly much of the country had moved beyond the court. Rulings that just three years ago would have loomed as polarizing and even stunning instead served to underscore and ratify vast political changes that have taken place across much of the country.

The 5-to-4 decision overturning the Defense of Marriage Act, written by Justice Anthony M. Kennedy, was sweeping and hardly technical, an affirmation of same-sex marriage written in broad constitutional terms that produced cheers and even some surprise among same-sex marriage supporters standing in front of the Supreme Court. And though the court declined to hear the California case on procedural grounds, the effect was to let stand a lower-court decision that threw out a voter-initiative banning gay marriage in this state.

What that means is that as of now, 30 percent of the nation’s population live in states that allow same-sex marriage.

They had a chance to run the table on the issue, but declined, ultimately, for fear of issuing another Roe v. Wade and forcing the other 37 states which “aren’t there yet” into accepting same-sex marriage. But while the California case was definitely a punt (involving the usual dodge of “standing”), the Windsor case was an unequivocal signal that same-sex marriage is heading your way soon.

From Prof. Larry Tribe at SCOTUSblog:

The pair of decisions taken together left the most contentious questions about same-sex marriage for the political process to continue grappling with – postponing to another day, when the generational wave that is moving this question to an inevitable conclusion has proceeded still further, the Court’s next encounter with the questions of equal human liberty and dignity that lie at its constitutional core. Both decisions, handed down by very different 5-4 majorities, seem to me worthy of celebration.

Kennedy wrote:

  • “Same-sex couples should have the right to marry and so live with pride in themselves and their union and in a status of equality with all other married persons”
  • and argued the original Defense of Marriage Act of ’96 “humiliates and brings financial harm to the tens of thousands of children now being raised by same sex couples”
  • and “the law degrades and demeans…disparages and injures…stigmatizes without consent” gay and lesbian couples.

Again, not a lot of ambiguity about where it’s going, and it’s hard to see (as Scalia et al wrote examples of in their dissent) how the other 37 state laws and state constitutional amendments will survive scrutiny. Kennedy’s opinion is both eloquent and foreboding: your days of discriminating against people based on sexual orientation are coming to an end.

For the knuckle-draggers like Scalia, who trotted out the same tired, worn, cliches from Lawrence v. Texas (decided the same day ten years ago, June 26, 2003) about “polygamy” and “bestiality” and “incest” and yada yada end of the world predictions, it was indeed a pathetic display. From Tribe again:

But Justice Scalia – in a portion of his dissent that Chief Justice Roberts conspicuously declined to join – couldn’t resist the temptation to use the occasion to insult the Court’s majority, and Justice Kennedy in particular, in essentially ad hominem (and ad feminem) terms. I write this comment principally to highlight the extraordinary character of this particularly vitriolic and internally inconsistent dissent.

He even insulted one of his fellow dissenters (Alito) for his arguments. It was sort of classic Scalia: foot-stomping, in-your-face, bluster with bitterness…and dead wrong, as usual.

I’m not sure U.S. v. Windsor (2013) will join the pantheon of cases like Brown v. Board of Education or Roe v. Wade that we’ll be talking about fifty years from now. But to say it’s not a historic day for civil rights in the gay and lesbian communities would be a misstatement.

Change comes both rapidly and incrementally, and for those who prefer chipping away at the walls of intolerance and discrimination rather than simply blowing the walls up, it was a very good day.

Cross-posted to The Power-Elite

Ok, so yesterday, I went over a possible exercise I had in mind for a module on data analysis for my introduction class. Today, I thought I might as well share the databases that I have started collecting for the purpose of designing more such exercises on different topics. So, here is a partial list that I will constantly update as I find good stuff on the Intertoobz.

International Organizations

Other Global Data

US Data

There are others, I’m sure. And I’ll add them as I find them.

Also, some of these databases contain tools to create customized visualizations or to manipulate the data, like the Human Development Report.

Otherwise, there are a couple of tools I’m seriously considering:

  • GSS (via Survey Documentation and Analysis, Berkeley… thanks to Jay Livingstone for mentioning this to me)
  • Statwing (I’m still on the fence on this one because even though it looks like a neat statistical software that is actually affordable, the visualization part lacks options to customize and you might end up having to download things back to Excel, which I’d rather avoid. I wish the software did the whole “upload / visualize / analyze / publish” thing. But I need to work more on this)
  • Health Data Interactive

America’s Broken Bootstraps:

Today, the dominant distinction defining socioeconomic class is between those with and without college degrees. Graduates earn 70 percent more than those with only high school diplomas. In 1980, the difference was just 30 percent.

Soon the crucial distinction will be between those with meaningful college degrees and those with worthless ones. Many colleges are becoming less demanding as they become more expensive: They rake in money — much of it from government-subsidized tuition grants — by taking in many marginally qualified students who are motivated only to acquire a credential and who learn little.

Lindsey reported that in 1961, full-time college students reported studying 25 hours a week on average; by 2003, average studying time had fallen to 13 hours. Half of today’s students take no courses requiring more than 20 pages of writing in a semester. Given the role of practice in developing expertise, “the conclusion that college students are learning less than they used to seems unavoidable.” Small wonder those with college degrees occupying jobs that do not require a high school diploma include 1.4 million retail salespeople and cashiers, half a million waiters, bartenders and janitors, and many more.

“Most American kids,” Lindsey concluded, “are now raised in an environment that is arguably less favorable for developing human capital than that in which their parents were raised.” America’s limited-government project is at risk because the nation’s foundational faith in individualism cannot survive unless upward mobility is a fact.

I know, right? George Freaking Will wrote this? He also drops gems like ““assortative mating” — likes marrying likes — concentrates class advantages, further expanding inequality” and “class distinctions in vocabularies are already large among toddlers,”and even “people raised in the upper middle class are far more likely to stay there than move down, while people raised in the working class are far more likely to stay there than move up.”

Uh, welcome to Sociology 1101, George. We call it stratification and inequality, and it’s something every undergraduate learns, no matter how much eye-rolling or head shaking the facts may prompt.

The family as vessel of inequality, perpetuating inequality from one generation to the next? Check. Endogamous marriages (much more pleasurable sounding than “assortative mating”) wherein society arranges relationships based on like social class or race/ethnicity? Check. Inequality in both public and higher education, which perpetuates it in the larger society? Check.

It’s always nice when someone who denies the importance (if not existence of) the social sciences suddenly has a revelation and comes over to join the socialist/communist/evil doer pointy heads in stating the obvious. But you have a long way to go, George. Your much beloved Supreme Court conservative majority is busy upending affirmative action in higher education (hint: necessary because of inequality) and gutting sections of the Voting Rights Act (also necessary because of inequality).

But for now, welcome, comrade.

Cross-posted to the Power Elite

As much as I can, I only integrate a data analysis component to my introduction to sociology classes. I am not trying to do anything really complicated but I want my students to get a very basic taste of what it means to think with data. For a long time, I had the perfect tool at hand in the form of Microcase Workbooks. There were several of them (a couple for introduction, one for marriages and families, one for social research). MicroCase is a bare bone version of more commons statist8ical software in the social sciences. It is a small program (does not take too much space on your hard drive) that runs on Windows only. However, it uses the GSS, American Community Survey, the World Value Survey and gives students the opportunity to select their own variables, construct their own tables / maps / pie charts / scatterplots / time lines. Students have always found it easy to use and actually fun. Well, that is over as the publisher decided to no longer update the software or the databases. Since then, I have been looking for alternatives. And, of course, publishers’ reps have been more than eager to try to sell me on their latest tools… which are all inadequate for my own purpose. And sending intro students into SPSS is out of the question… heck, I don’t want to go into SPSS.

So, what is a SocProf supposed to do? Well, there are now tons of data and databases that are publicly available. Why not create my own exercises? It will be cheaper to my students and my exercises can be exactly the way I want them. There are also now a lot of visualizing tools, either directly provided by the same organizations that make the data available (like the UN development report or Gapminder). I don’t get dependent upon the good will of a corporate publisher to keep on updating a product that is going to be costly to students. Win-win. On the losing side, it is going to be time-consuming to build up these exercises. I just spent an hour cleaning up data from the CDC on suicide in the US. And it the visualization tools are not available, I can always use Tableau.

So, for instance, indeed, I started simple with some data on suicide in the US. The CDC was the organization with the most data on that. Starting with this:

Suicide Map 1

The first problem with this map is that it is not interactive and the level of detail (by county) makes it a bit busy even if you can clearly regional patterns. These regional patterns actually make for an interesting puzzle for my students to solve. That can be a starting point but it is hard to create rankings, for instance.

A second option is to use the CDC interactive tool through WISQARS. So, basically, it looks like this:

Suicide CDC Interactive 1

As you can see above, you have a series of menus, drop down and radio buttons. You can filter things out. I kept the entire US but I selected “suicide” for intent of injury. And I kept the largest spread (2000 – 2006). I kept all the demographic subset at default. And I  got this as a result:

Suicide CDC Interactive 2

Several problems, with this: (1) on the right hand side, it says “Hover over a state with your mouse to see its name and rate”… that does not work. I tried different browsers including *gasp* Explorer, and no dice. (2) The export data function creates a csv file that takes a lot of cleaning up if you want to do the most simple statistical operations and visualizations. Which is what I ultimately ended up doing in Tableau Public (sorry, the embed still does not work).

The map, though, shows the same pattern as the county one above.

Third option, if you really want an interactive map, and still from the CDC, there is another interactive tool that is a bit trickier to manipulate but does the job: Health Data Interactive:

Suicide CDC Interactive 3

Again, you get to set your options and get an interactive map (with some missing data and only 44 states reporting, which is kinda annoying).

Beyond maps, though, the CDC has some good data visualizations but again, the raw data are harder to track down. For instance, you can get a broad overview over time:

Suicide Overall

Again, you can set up some interesting questions regarding the shifts in age categories with the highest suicide rates, when the shift happened and why. But you can drill down even further and consider race and ethnicity:

Suicide Race Ethnicity

Why whites and American Indian / Alaskan Native / Pacific Islanders (from my little Tableau thing, we already know that Alaska has a high rate)?

Ok, let’s add sex into the mix:

Suicide Race Ethnicity Sex

Across the board, men are way more likely to commit suicide than women. Adding sex does not alter the racial / ethnic patterns. So, should we pity white men after all?’

Finally, let’s add age. Let’s start with the 10-24 age category:

Suicide Age 10-24

One can only ask, what is going on with young American Indian / Alaskan Native / Pacific Islanders? Whites are no longer strikingly higher than other racial and ethnic category, for that age category.

But once you move up the age ladder, into the 25 – 64 category:

Suicide Age 25-64

Then, whites pop up again in the higher rates.

Ok, how about 65 and older:

Suicide Age 65 over

See what happens with American Indian / Alaskan Native / Pacific Islanders? And Whites?

Ok, how about some trends?

Suicide gender trend

Note the uptick with the recession. Otherwise, a familiar gender pattern.

Let’s separate men and women and compare by age categories, first, for men:

Suicide trend males age

The interesting trend here is the progressive joining of the 25-64 (up) and the 65+ (down).

Now, women:

Suicide trend females age

Now, we already know that women are much less likely to commit suicide than men. And this visualization has an extra age category but one can see that the relative increase is greater for women than men. This is especially the case in the 45-54 category.

And now, for the fun of a different visualization, let’s add yet another variable: the means of suicide:

Suicide mechanisms

I am normally not a big fan of stacked bars, but in this case, I think it works. You can clearly see that men are more likely to use a firearm in all age categories whereas suffocation and poisoning are more used by women. One could explore access and cultural factors in the decision to use one mechanisms or another to kill oneself.

This gender aspect is more visible if one filters out other variables:

Suicide mechanisms gender

So, as you can see, there is a lot to explore and a lot of sociological puzzles to be solved, just by using some very basic data, with limited variables, and just by using publicly available data visualizations.

I’ll continue to share these things as I build them.

For those of us interested in sociology, globalization, global stratification, and data analysis, the annual Human Development Report is a must-read and a highly expected source of data. This year’s edition is no exception. You can check out the highlights in the short video below:

There are some extra goodies, though, for the data analysts of all tripes. The report’s website has a great amount of visualizations and tools for people to explore the data on their own, based on their own interest. There is something for everyone and you can drill down to your heart’s content, using a variety of data visualizations or tables. That is what I did and the results are below.

Human Development Index 2013 from SocProf on Vimeo.

This is where the real good stuff is:

HDR visualizations

Click on the image to be taken to the actual page and you can start from there. It is a great exploration / teaching / learning tool.

Being a total nerd, I am currently going over the United Nations 2013 Human Development Report. As always, the report goes over the types of policies that improve the Human Development Index of a country. But take a look at this excerpt from page 88, that compares different educational scenarios over time for South Korea and India (the red emphases are mine and click on the image for a larger view):

Differential educational prospects 2

Now, granted, there are other major differences between South Korea and India. However, it is not exactly news to assert that better educated women provide benefits to society as a whole and that therefore, educational equality by gender is a pre-condition to higher development and major social change. Religious fundamentalists like the Taliban understand the dynamic very well, which is why they get all hung up about educated girls and are willing to use extreme violence to prevent even the primary education of girls.

[This is a repost from a review I posted when this book came out, but it seems like the topic of unpaid internship is making a comeback on the Internet, so, revisiting this might be useful.]

Welcome to the brave new world of work, where you work more and get paid nothing! Travailler plus pour ne rien gagner (maybe that should be Sarkozy’s slogan for his reelection campaign!). This is the reality experienced by more and more people in the US, and thoroughly explored by Ross Perlin in Intern Nation: How To Earn Nothing and Learn Little in the Brave New Economy.

The premise of the book is that internships have exploded in numbers as they have become an almost mandatory of someone’s education in order to gain legitimate entry on the labor market. But Perlin considers them to be “a form of mass exploitation hidden in plain sight” (xiv), with roughly 9.5 million college students, roughly 75% will participate in at least one internship before graduation. He argues that a significant share of those are unethical if not illegal.

In other words, interns are becoming the fastest-growing category of American workers, the largely unpaid ones.

The simple fact of non-payment, for Perlin, also points to the fact that internships have become a site of reproduction of privilege as only those of financially comfortable background can hope for the glamorous internships in Congress, in Hollywood or television and journalism that truly open doors for permanent (and paid) jobs, guaranteeing that the upper-classes will remain the major cultural producers in the mass media. In that sense, internships contribute to both exploitation and reproduction of inequalities in opportunities.

Finally, Perlin argues that internships devalue labor, especially for young people and at entry-level positions at the same time that interns may displace workers.

The book itself is full of a variety of examples in a diversity of settings. The first chapter is dedicated to the Disney internships whose promotion is so present at so many college campuses, as Disney runs one of the largest internship program, with 7,000 to 8,000 interns every year:

“In its scale and daring, the Disney Program is unusual, if not unique – a “total institution” in the spirit of Erving Goffman. Although technically legal, the program has grown up over thirty years with support from all sides with almost zero scrutiny to become an eerie model, a microcosm of an internship explosion gone haywire. An infinitesimally small number of College Program “graduates” are ultimately offered full-time positions at Disney. A harvest of minimum-wage labor masquerades as an academic exercise, with the nodding approval of collegiate functionaries. A temporary, inexperienced workforce gradually replaces well-trained, decently compensated full-timers, flouting unions and hurting the local economy. The word “internship” has many meanings, but at Disney World it signifies cheap, flexible labor for one of the world’s largest and best-known companies – magical, educational burger-flipping in the Happiest Place on Earth.” (3-4)

Needless to say, Perlin is merciless in his investigation of the world of internships, and Disney is not the only entity getting a drubbing, but is presented as somewhat representative of the trend: “a summer job with a thin veneer of education, virtually unleavened by substantive academic content.” (8).

Perlin identifies two major post-War trends that contributed to the internship explosion:

1. The rise of the “new” economy, post-industrialism, service jobs and networked capitalism along with its cohort of contingent labor. This casualization of the workforce is a well-known trait of the post-fordist regime based on flexibility and exploitation and the rise of the ubiquitous “independent contractor”, a catch-all category.

2. The rise of the field of Human Resources and the “Human capital” approach to education.

What this boils down to is what Bauman and Beck have described as individualization in the post-modern era. Students now have to see themselves as having to cultivate individually their own human capital and internships do just that. The student is his/her own entrepreneur, an entrepreneur of one’s self, one’s own independent contractor.

This is also part of the trend of vocationalism in education, that is, seeing education as job training rather than, well, education.

Perlin also notes that internships have also risen on the ashes of traditional apprenticeships that have a medieval connotation and have long been associated with industry and the trades. There are still a few apprenticeships in the US, they are usually paid, with benefits and unionization. There is still an Office of Apprenticeship as part of the government but it seems to be a well-kept secret and the trades are not the hot career when one dreams of working for Google.

I was also surprised to learn that a great deal of internships might actually be illegal (not that anyone is watching). The Fair Labor Standards Act is still the law of the land and, based on a US Supreme Court decision and explained by the Wage and Hour Division of the Department of Labor, one category of people is exempt from the FLSA provisions: trainees. And since the USSC has never ruled on interns, they are considered trainees, therefore exempt. Except that there are six condition that must ALL be met for trainees to be exempt, as listed by Perlin:

  1. The training, even though it includes actual operation of the facilities of the employer, is similar to that which would be given in a vocational school.
  2. The training is for the benefit of the trainee.
  3. The trainees do not displace regular employees, but work under close observation.
  4. The employer that provides the training derives no immediate advantage from the activities of the trainees and on occasion the employer’s operations may actually be impeded.
  5. The trainees are not necessarily entitled to a job at the completion of the training period; and
  6. The employer and the trainee understand that the trainees are not entitled to wage for the time spent in training.

All six criteria have to be met for a position to be considered exempt. If one of these provisions is not met, then, it’s a job and it falls under the provision of the FLSA. How many internships actually meet all six criteria? Who knows. So, employers just looking for cheap labors should not get interns or their internships are illegal. But again, who’s checking? Although Perlin does mention that the Obama administration did increase the number of DOL inspectors.

More than that, because they are not considered workers, unpaid interns receive none of the protection against discrimination or harassment that regular employees get (however inadequate) and they have no legal recourse. On the other hand, corporations receive $124 million annual contribution in the form of free labor.

Perlin is also severe in his critique with regards to what he considers the complicity of colleges and universities in the explosion of exploitative internships. Schools endorse internships without a second thought. Sometimes, they make money off of deal with employers or non-profit organizations. And they provide the academic cover in the form of academic credit for sometimes questionable internships. Often, academic credit is supposed to replace the pay that anyone would normally receive for the same work that interns do. So, not only do students pay for credit, but they don’t get any pay for the internship. They pay to work for free.

“In certain cases, paying college tuition to work for free can be justified – particularly if the school plays a central role in securing the internship and makes it a serious, substantive academic experience. Providing credit certainly can cost the school in terms of supervision time and administrative work, although the costs are unlikely to match those of a classroom experience. And in the most miserable, increasingly common scenario, employers use the credits in an attempt to legitimize illegal internships while universities charge for them and provide little in return, and interns are simply stuck running after them, paying thousands of dollars for the privilege of working for free.” (86)

Instead, of course, colleges and universities actively promote internships  just like they have online education as a low-cost (for them) option to get money from students. The worst offenders, in my view, have the (often for-profit) colleges and universities who offer their credits to highly expensive private internship-abroad organizations (both shall remain nameless, as in, no free publicity, but their practices are truly disgusting) who charge thousands of dollars for unpaid internships outside of the US, but there are also all the non-profit organizations, largely staffed by interns in the name of “service-learning” or the start-ups that wouldn’t even get off the ground if they didn’t use free labor. How many NGOs or such companies would not function without free labor? Or maybe they would need to revise their activities or business plans or pay interns minimum wage.

The other issue that is central, in my view, and that Perlin discusses at length, is this: what about the students who have mandatory internships in their curriculum but cannot afford unpaid work? Or whose parents cannot support them? Well, they get left behind in the race to pad one’s résumé with prestigious internships. In other words, the ability to engage in unpaid internships is yet another privilege that the already-privileged enjoy, at the expense of other students. While privileged students might spend the summer on Capitol Hill, interning for a Congressperson for free (even though there is a big bogus element to these internships, as Perlin shows), others actually have to work to pay for next year’s tuition.

And in addition to the experience and the lengthening of one’s CV, these privileged students get to network and accumulate social capital, something that their less privileged counterparts do not get to do. And finding prestigious internships in the first place is a matter of social connections. For instance, the donor to an NGO can pretty much impose to have a child or relative or friend as intern. Access matters a lot, when it comes to internships.

“Many internships, especially the small but influential sliver of unpaid and glamorous ones, are the preserve of  the upper-middle class and the super rich. These internships provide the already privileged with a significant head start that pays professional and financial dividends over time, as boosters never tire of repeating. The rich get richer or stay rich, in other words, thanks in part to prized internships, while the poor get poorer because they’re barred from the world of white-collar work, where high salaries are increasingly concentrated. For the well-to-do and wealthy families seeking to guarantee their offspring’s future prosperity, internships are a powerful investment vehicle, and an instrument of self-preservation in the same category as private tutoring, exclusive schools, and trust funds. Meanwhile, a vast group of low- and middle-income families stretch their finances thin to afford thankless unpaid positions, which are less and less likely to lead to real work, and a forgotten majority can’t afford to play the game at all.” (162)

And did I mention that women are more likely to get unpaid internships than men?

And you wonder why there is an ideological continuity between politics, news and think tanks and other organizations. It is a Village and they’ve interned there before.

Part of the issue is that there is a high demand for internships (as a result of becoming an academic / graduation requirement), so much so there are now internship auctions where employers auction an internship and potential interns bid on it, and it goes to the highest bidder but not the most qualified candidate.

Of course, other countries are getting on the action as well, exploiting interns. Remember Foxconn, the company that makes your iPad and other Apple goodies, that became famous because its working conditions were so awesome that workers kept killing themselves? So much so that they now have to sign contracts promising not to commit suicide? Yup, that Foxconn… Check this out:

“Foxconn seems to have become the world’s biggest abusers of internships. According to a detailed report recently compiled by university researchers in mainland China, Hong Kong, and Taiwan, the company uses interns extensively in at least five of its major plants, compensating them at the lowest possible pay grade (under $200 per month) and often forcing them against the law to work nights and overtime. In order to avoid paying for the medical and social welfare owed to regular employees, Foxconn has in some cases reportedly filled more than half of its assembly line jobs with interns – usually with the cooperation of hundreds of schools that stand to receive a fee in return.” (196)

Welcome to the new world of labor casualization, precarization and flexibility. These global workers now have their very own patron saint: San Precario

Also, San Precario is transgender. The five icons represent income, housing, health, communication and transport. That is, there is, hopefully, a rising movement against precarization, that includes interns, as part of the global civil society.

Perlin himself offers a series of recommendations to make internships more meaningful and more fair, based on the six criteria above. But most of all, his book is a wake-up call to a major trend that has gone largely unrecognized and unexamined, and one can see why. It is an important book for anyone interested in labor issues and the future of work.