Graphic Sociology

New York Times 100 Notable Books - Authors' Academic Affiliations

What works

Using the New York Time’s list of 100 Notable books of 2011 that ran over the weekend as part of their Holiday Gift Guide, I created the graph above. As an almost-academic, I am interested in the scope of academic work and found it interesting that less than half of the notable books were written by people with academic affiliations. Michael Burawoy and Craig Calhoun have both called for new roles for scholarship and the university, emphasizing that an academy unhitched from the public sphere is not a viable model and might very well be considered irresponsible, given the scale and scope of social, scientific, and technological challenges facing the globe right now and for the foreseeable future.

So what does it mean that non-academics are writing more of the notable books than are academics?

I cannot answer that question definitively, but I can offer three possible avenues for exploration. First, it could be that academics are irresponsible or lazy and that they have either failed to write well or to address relevant topics. They are off publishing pedantic articles in academic journals that nobody reads to fill out their CVs. This scenario is grave. There is an element of truth to it.

An alternative explanation would be that, in part because this is a *gift* suggestion list, these books are not necessarily the most important, but they are the most well written. If that is the case, then the fact that so many non-academic voices make the list indicate that writing itself is an art, one that is spread much more judiciously across the American populous than are academic positions. It also suggests that thinking clearly and writing well are going on in all sorts of places, not just the ivory tower. This is encouraging. There is an element of truth to it.

A third version of this story begins where the second one left off and suggests that, in fact, if academic books do appear on holiday gift lists of notable books, those academics are shirking their duties as academics. Any book with broad public appeal probably is NOT doing much to advance a field. It’s probably just regurgitating existing research in a kind of “Research Thought X for dummies” kind of way. [Many of the people who adhere to this line of thinking have deep and abiding negative thoughts about Malcolm Gladwell.] The view from this perspective argues that asking academics to be responsible to public audiences is akin to asking people to text and drive. It’s dangerous. It takes one’s eye off the critically important field of action and reorients it, likely towards one’s own navel. The primary activity – analytical research and publishing – will suffer, perhaps taking down innocent bystanders along the way. This is a fairly rigid understanding of the best practice for academic research. There is an element of truth to it.

I invite debate on the points I mentioned and those that I have overlooked in the comments.

What needs work

This graphic is not as elegant as I would like. There are far too many words.

I am fascinated with the nitty gritty details of the schools at which those with academic appointments are working. Including the names of so many schools made the endnotes lengthy. I am of two minds on that. Like I said, I enjoy knowing the details, especially when it comes to fleshing out a category like “Elite.” It’s important to know just how eliteness has been defined. In this case, I used US News and World Report. With respect to most of the schools – Princeton, Harvard, Yale, Oxford, Cambridge, Columbia – I think there is widespread agreement that these schools are at the top of the academic heap and have been for a while. Some might quibble about Pomona and Williams.

The point I was trying to illustrate was that those in academia who have books on the notables list could be seen to be public intellectuals or at least they are doing better at making their work accessible to the public than their colleagues who never make it to such lists. It is especially important that the professors in elite institutions make their work accessible because, unlike their colleagues at public schools or less exclusive private schools, the metaphor about the ivory tower as a mechanism of separation is apt. Very few of us have access to elite institutions. Some have argued that those in academia have some responsibility for making their work accessible to broader publics.

References

Burawoy, Michael. (2005 [2004]) http://ccfi.educ.ubc.ca/Courses_Reading_Materials/ccfi502/Burawoy.pdf [Presidential Keynote Address at the Annual Meeting of the American Sociological Association] American Sociological Review Vol. 70.

Calhoun, Craig. (2006) “Social Science for Public Knowledge”>The University and the Public GoodThesis Eleven Vol. 84(7).

New York Times, Sunday Magazine. (November 2011) “100 Notable Books of 2011″ [Holiday Gift Guide]

Time and Newsweek Circulation Figures | Graphic by Laura Norén

Newsweek and Time Circulation Figures | Graphic by Yolanda Cuomo

Which one works?

These two graphics portray some of the same information – household income, median age, audience and circulation – though the first one does not break down information between genders. Though it probably goes without saying, I like the one I designed best. The second one has some tantalizing shapes – I applaud the visual appeal – but it does nothing to aid people’s eyes as they try to compare relative sizes between the salient categories. I also happen to think it is easier to understand the complexity of the difference between audience and circulation with the textual explanation provided in the first one. I find the white-font-on-dark-background of the Time and Newsweek labels hard to read (it’s also a known graphic design no-no, especially with a small font size like this. It is easier for the human eye to grok the contrast with dark text on a light background than with light text on a dark background).

From a sociological perspective, comparing the readership of Time and Newsweek not only to each other but also to national averages provides a much deeper sense of context. The second graphic was built from the first though I never had a chance to meet with any of the writing or design team to understand why the national averages were removed.

There are other elements I dislike in the second one. I dislike, for instance, the need to repeat certain elements of text over and over again: “readers per copy” and “Total adult population” and even the “Time” and “Newsweek” headings. One of my closest friends and colleagues spends a lot of his time writing code. The best lesson I have learned from him is that where elements or actions have to be repeated over and over, there is inefficiency in the system. A better design is possible.

I would love to hear from my readers on this comparison. Am I suffering from too much ego investment in the graphic I made? Is the second graphic an improvement on the first? If so, how?

References

Norén, Laura. (2010) “Appendix: Data and Methods” in first draft of Dill, Nandi and Telesca, Jen Imagining Emergencies. [Information graphic].

Cuomo, Yolanda. (2011) “Readership Data Time and Newsweek 2008″ in final draft of Dill, Nandi and Telesca, Jen Imagining Emergencies. [Information graphic].

Figure 5. Average Salaries in New York City | Report 12, Office of the New York State Comptroller, Thomas DiNapoli

What works

This may not be the worldest most attractive graphic, but it makes its point: financial workers have much, much higher annual income than the rest of us and the gap is growing over time. The text of the New York State Comptroller’s report said the same thing in words.

Wages (including bonuses) paid to securities industry employees who work in New York City grew by 13.7 percent in 2010, to $58.4 billion. Nonetheless, wages remained below the record paid in 2007 ($73.9 billion), reflecting job losses. In 2010, the securities industry accounted for 23.5 percent of all wages paid in the private sector even though it accounted for only 5.3 percent of all private sector jobs. In 2007, the industry accounted for 28.2 percent of private sector wages.

In 2010, the average salary in the securities industry in New York City grew by 16.1 percent to $361,330 (see Figure 5), which was 5.5 times higher than the average salary in the rest of the private sector ($66,120). In 1981, the average salary in the securities industry was only twice as high as in all other private sector jobs.

You be the judge. I think the graphic leaves a greater impact than the text alone. The two together are striking. Maybe we should…occupy Wall Street to demand a decrease in inequality?

The short report has a few more interesting graphs. First, they throw together a quick graph of Wall Street bonuses. These bonuses are tied to performance and so big that they often represent more than a finance worker’s annual salary. As you can see, they took a dip, but they didn’t disappear even though the US economy is still not great.

Wall Street Bonuses | New York State Comptroller's Report No. 12, 2011

The other interesting metric the report contains is a compensation-to-earnings ratio graph, which is the right context for this discussion. Bankers often defend their large salaries and even larger bonuses by pointing out how much money they have made for their banks. I agree with the bankers that this is the place to look. The question should not be: “How much are individual bankers making?” Rather, it should be, “How much does the banking sector make and is that the way we as a society want to distribute our surplus, primarily to banks and bankers through processes of financialization?”

Ratio of banker's (and insurer's) compensation-to-net-revenues | New York State Comptroller's Report No. 12, 2011

What needs work

The graphs are not attractive and the first one reads as cluttered. I generally go with line graphs for this kind of trend data to cut down on the clutter impact, something I have repeated again and again so I won’t hammer on that point too much. I like the information behind these graphs so I am not going to swat at them too much. Excel is not a graphic design tool for graphs; I have occasionally made some sweet tables with it.

I’m glad the report put these data points into graphs, glad that the report is available during the discussions brought on by the OccupyWallStreet crowd, and glad that the New York State Comtroller’s office rolled right on ahead with the release of some fairly damning evidence against the status quo.

Want more?

Another Society Pages blog, Thick Culture, ran a post including graphs that deal with the compensation and wealth differentials between the tippy-top echelon of financiers and the rest of us at Tax Gordon Gekko.

References

DiNapoli, Thomas and Bleiwas, Kenneth. (October 2011) “The Securities Industry in New York City” Report No. 12, Office of the State Comptroller.

See also: A blog I wrote – Americans estimate our wealth distribution and fail. Horribly. using a Dan Ariely graphic about how bad Americans are at estimating the distribution of wealth in this country. Teaser: we think it is much more equitable than it actually is.

The most popular blog post of all time on Graphic Sociology: Champagne Glass Distribution of Wealth

American Generation Age Timeline (Age measured in 2009) | Pew Research

What works

Pew Research has created a tidy series of interactive graphics to describe the demographic characteristics of American generational cohorts from the the Silent Generation (born 1928 – 1945) through the Boomers (born 1946 – 1964), Generation X (1965 – 1980), and the Millennial Generation (born 1981+). The interactive graphics frame the data well. They offer the timeline above as contextual background and a graphic way to describe what they .

Then users can flip back and forth between comparing each generation to another along a range of variables – labor force participation, education, household income, marital status – while they were in the 18-29 year old age group OR by looking at where each generation is now. The ability to interact makes the presentation extremely illustrative and pedagogically meaningful. It is much easier to understand patterns that are changing over time versus patterns that are life course specific.

Marital status

Marital status by generation measured when young | Pew Research

Marital status by generation measured in 2009 (snapshot) | Pew Research

For instance, marital trends have been hard to talk about because the age at first marriage moves up over time, so it’s hard to figure out at what age we can expect that people will have gotten married if they are ever going to do so (I tried looking at marriage here).

What I like about the Pew Research graphics is that they show us not only what the generations looked like when they were between 18 and 29 years old (above) and again what they look like now (below). Not only does it become obvious how many more people are choosing to remain unmarried, but it also becomes clear that in addition to divorce, widowhood is a major contributor to the end of marriage.

Labor force participation

Here’s another set of “when they were young” vs. “where they are now” comparisons, this time on labor force participation. It appears that the recession has walloped the youngest, least experienced workers the hardest. They have the highest unemployment rate AND the highest rate of educational attainment (and school loan debt), which leaves them much worse off as they start out than their parents were in the Boomer Generation. Even if their parents were in Generation X, they were still better off than today’s 20-something Millennials.

American labor force participation by generation (2009) | Pew Research

American Labor Force Participation by Generation (measured in 2009) | Pew Research

What needs work – Are generations meaningful?

My first minor complaint is that the graphic does not make clear *exactly* what “when they were young” means. If we look at the first graphic in the series, the timeline, it appears that “when they were young” was measured when each generation was between 18 and 29 years old. I hope that is the case. I might have had an asterisk somewhere explaining that “when they were young = when they were 18-29 years old”.

The concept of generations, in my opinion, is a head-scratcher. What is the analytical purchase of looking at generations – strictly birth-year delimited groups that supposedly share a greater internal coherence than other affinal or ascribed statuses we might imagine? If we believe that social, technological, and most all kinds of change happen over time, of course there are going to be measurable differences between one generation and the next. I imagine, though I have never seen the comparison, that if social scientists split people into 10- or 20-year pools based on their birth years they would end up with the same sorts of results. So why not think of generations as even units? And is it clear that the meaningful changes are happening in 20-year cycles? Or would 10-year age cohorts also work?

The real trickiness comes in when we think about individuals. Say someone is like myself, born in a year on the border between one generation and the next. Am I going to be just as much like a person born firmly in the middle of my cohort as a person on the far end of it? Or will people like me have about as much in common with the people about 8 years above and below us, but less in common with the people 15 years older than us who are considered to be in the same generation, and thus to have many similar tendencies/life chances/characteristics?

A better way to measure the cohort effect would seem to be to consider each individual’s age distance from each other individual in the sample – the closer we are in age, the more similar we could be expected to be with respect to things like labor force participation and educational attainment. Large structural realities like recessions are going to hit us all when we have roughly similar amounts of work force experience, impacting us similarly (though someone 10 years older and still officially in the same generation will probably fare much better). Since it is computationally possible to run models that can take the actual age distances of individuals in the same into account, I don’t understand the analytical purchase of the concept of generations.

The take-away: great graphics, bad premise.

References

Taylor, Paul and Keeter, Scott, eds. (24 February 2011) The Millenials. Confident. Connected. Open to Change. [Full Report] [See also: Executive Summary and Interactive Infographic] Washington, DC: Pew Research Center.

New Drug Wave Takes Toll | Star Tribune "A Lethal Dose" series

This graphic was subset from a larger graphic. I trimmed off the third drug comparison because it was problematic for reasons I explain below.

New Drug Wave Takes Toll | Star Tribune "A Lethal Dose" series

What works

Tracking illegal behaviors can be extremely difficult because the people participating do not want to be arrested or fined. How then, do health investigators find out what risky behaviors people are doing in their leisure time? In this case, the investigative team on the Lethal Dose series at the Minneapolis – St. Paul Star Tribune newspaper used calls to the poison control center as a proxy for tracking the rise of newly available synthetic drugs. As journalists rather than, say, doctors, they do not have access to patient data. Using poison control center calls is not a perfect indicator of the spread of the new synthetic drugs, but they have followed up these charts with an entire series in which they interview parents and friends of victims as well as a retailer more than willing to defend his right to sell.

What works for me about this graphic is that the investigators found a fairly unbiased source of information about this drug use, something that helps tie the other articles in the series together. Interviewing stubborn retailers and grieving friends and family is part of what journalists do, but those interviews are so emotionally and politically charged that it I appreciate the presence of trend information.

Because these drugs are new, it was necessary to spell out active ingredients because the average person will not know. I appreciate that they included that in the graphic rather than in a footnote.

What needs work

The shading behind the bar graphs is frivolous. It adds no information and is not necessary to guide the eye. It could be dropped and nothing would be lost.

Trend data is better as a line graph than a bar graph because it is easier for the eye to follow a line and to compare one line to another line than to follow a series of steps and compare one series of steps to another.

This blog post focuses on two drugs that use the same axis. I would have kept the same axis for the third drug even though it’s use numbers are lower. Note that all of the drugs started with low numbers and rapidly climbed – perhaps the third drug family “synthetic chemicals” is simply lagging behind by a year or so. It is hard to make that comparison when the axis is so dramatically different from the other two. There is a danger in lying with graphics here – making the third graph seem comparable to the first two implies that the third drug poses an equal threat. The numbers do not support that assumption.

References

Star Tribune staff writers. (2011) A Lethal Dose: The war on synthetic drugs Investigative reporting series.

Star Tribune. (2011) “New Drug Wave Takes Toll” [Information Graphic] American Association of Poison Control Centers, DEA.

Blog reading and writing by gender, 2000-2010 | Pew Internet Research

What works

This graphic was created using a wonderful, if not entirely complete, massive Excel spreadsheet summarizing interview results from the Pew Internet Project. There are many more questions than the three I looked at. I am primarily interested in how many adults write blogs and I was happy to see that the Pew Internet Research center has been asking adults about their blog reading and writing practices for about a decade. Just to give it context, I also plotted the percentage of adults using the internet at all.

I am also interested to see that women and men write blogs at about the same rate, these days, even though I know that they aren’t writing the same kinds of blogs. Food bloggers, for example, are overwhelmingly women as are baby bloggers (aka mommy bloggers, but using the term ‘mommy’ is too gender-restrictive). Political bloggers and tech bloggers tend to be male more often than not, though I know less about them.

What needs work

The interviews are different from year to year – some years I was averaging five or seven data points on the same question and some years I had only one (or, sadly, none). I wish there had been more years of data available on blog reading, for instance.

If I had one takeaway point it would be that we need to keep funding places like Pew to conduct detailed, ongoing research. I have found it invaluable to have access to their research and it makes the work I am currently conducting about food bloggers relatable to a wider body of practices.

References

Pew Center for Internet Research. Usage over time spreadsheet.
— If you cannot click on that link and automatically start a download, try downloading it from the Pew website

Food Blog Study | Web Crawler Progress Egg

Food Blog Study Update

I heard there was a graduate student once who used egg timers to break her dissertation down into writeable chunks. She had these timers all over the apartment, flipping one over to start a new bout of writing. Once it ran out, she might keep on writing since there was no buzzing or beeping to interrupt her. If she looked up and the sand had all run through, she would flip over another egg-timer to measure out a dose of ‘free-time’. Maybe I had her strategy in mind while I was trying to come up with a way to monitor progress on the food blog study. Large, long-term projects can envelope me, making it hard to see either where (and why) I started the project and where I mean to end up while I’m toiling away in the trenches of the day-to-day. This post is not about a final product. Rather it is about how I use information graphics to help me keep my mind on both the questions I started with and the place I mean to end up when all is said and done.

The food blog study is broken into three parts. The interviews (N=22) have all been conducted and are out being transcribed. The survey cannot begin until the web crawler has gotten to a stopping point. So where do things stand with the web crawler? That is not an easy question to answer except to say that it is doing what good bots do, chugging along finding food blogs to add to its growing collection with minor down times for maintenance here and there.

The graphic above demonstrates how the network set is growing – I simply used the file size of the daily cumulative db output to tell me how big to make each day’s egg. Still, looking at file size is kind of silly – it does not help me figure out when the network has been sufficiently crawled. It simply represents the absolute size of the database and because I do not have some target absolute size as my endpoint, knowing the current absolute size is mere trivia and not analytically useful.

Rather than considering absolute size or the linear growth of the network data, it is a lot more meaningful to examine the rate of change of new nodes from one day to the next. For comparison sake, I graphed both the linear growth of the network (top graph) and the number of nodes added per hour for each day in July (bottom graph) with the exception of July 17th when the crawler was down for maintenance. The linear growth is chugging along consistently enough with a few exceptions for reasons like maintenance and accidents (someone unplugged my computer from the internet for six hours one day. oops.). The rate of new food blogs added to the network set per hour is finicky, a pattern that is much easier to see in the bottom graph. That graph was calculated by taking the number of new food blogs added to the network during a given run and dividing it how long the run lasted to generate an hourly rate of growth. That hourly rate is what is plotted below – the crawler’s sweet spot seems to be when it is adding about 60 – 90 new food blogs per hour.

Food Blog Study | Graphs of Web Crawler Progress

The plunge in the rate of new blogs added per hour around the 18th of July is artificial. I happened to add a command that day which retroactively removed all of the blogs primarily focused on cocktails, wine, and beer. Their removal nearly outweighed the new food blogs that were added to the network that day so the overall rate of new blogs added appears to be extremely low at only 6 per hour.

This graph is extremely useful for keeping in mind where I started and helping me to figure out when I have gotten some where. I will know that the food blog bot is exhausting new nodes and that I have started to run into the bounds containing the food blog network when the rate of newly discovered food blogs per hour starts dropping and does not recover. Right now, the crawler is still pulling in new entries fairly rapidly so I know I am probably going to be babysitting it for at least another week. Thus far, the roughly-cleaned network includes about 32,000 nodes. Yes, folks, that means there are greater than 30,000 food blogs out there in the world. Probably a lot more, especially because the bot speaks food in English, Spanish, French, Italian, and German so the network under consideration is multi-national though not quite global.

Note on graphics

Could that egg have been perfectly round? Yes. And would perfectly round circles have been easier for average humans to measure with their eyes? Yes. So why did I choose an egg shape? Because I feel like this project is an incubation period. Data collection can be a delicate process – I would say that is especially true with respect to the web crawler because it was a tool custom-built for this project and thus has not been used and tested elsewhere. I also chose an egg because it is not important if viewers understand exact figures – this graphic was intended to provide an impressionistic view of the rate of growth of the network that the crawler is gathering. It grows incrementally, not by leaps and bounds. Like tree rings, the concentric nature of these eggs demonstrates that some days generate fatter rings than others.

As for the two graphs, I wanted to try using the same horizontal access because I wanted to make sure people understood that those two graphs are best understood as a pair. Basically, one is the derivative of the other, though there’s no need to pull out your calculus textbook just to understand these two. The top one just shows the total number of food blogs in the network so far. The bottom one shows how fast new blogs are being added from day to day. I didn’t want to clutter up the graphs with too many words so I opted to go with a single horizontal access, short titles, no labels for the vertical access (they are implied in the title), and I kept the two points about strange days outside of the bounds of the bottom graph. I don’t know if it is acceptable to stick asterisks in a graph, but I did it.

References

Noren, Laura. (2011) Food Blog Study.

How happy are parents vs. non-parents? | Graphic by Norén based on Margolis and Myrskyla

Kids and happiness

Thanks to my twitter feed I landed on Philip Cohen’s blog post “Children beget happiness, eventually” on his blog Family Inequality. In the post, Cohen discusses A Global Perspective on Happiness and Fertility which appeared in Population and Development Review last March.

Margolis and Myrskyla used the World Values Survey from 1981 – 2005 for a total of 201,988 responses across 86 countries to perform their inquiry into the relationship between having kids and being happy. They measured happiness by asking people “taking all things together, would you say you are very happy, quite happy, somewhat happy, or not at all happy?”. They controlled for all sorts of things that probably matter like socioeconomic status, country level effects, and state welfare regimes. This is global evidence, folks, not US-only.

Cohen included the graph below and discussed the author’s findings which, in summary, are as follows:

1. Having kids does not lead to happiness when parents are actively involved in raising said children.
2. Older parents consistently report being happier than their childless counterparts. [My editorial comment: It is reasonable to believe that, for the most part, the children are no longer living with their parents by the time their parents start to report increases in happiness. At the very least, the kids are at least spending more time out of the home by the time mom and dad are between ages 40 and 49. The majority of kids are almost surely out of the house by the time their parents are 50+ which is the 'happiest' time to be a parent. Perhaps it's because parents are proud of their kids' accomplishments, perhaps it's because the parents are no longer anxiously worrying about their kids well-being on a day-to-day basis. Who can say.]
3. Results in the 15 – 19 age cohort have fewer data points and are thus somewhat less representative. It’s hard to have three or four kids while in that age cohort.

Happiness and number of children by age of parent | Margolis and Myrskyla

An experiment

I used the exact same evidence to create the graph at the top of the blog because I wasn’t satisfied that the results were being clearly communicated by the graph above. Instead of plotting the happiness of age cohorts, I flipped it around and looked at happiness by number of children. Since I used the exact same information – pulling it directly from the graph because I couldn’t find a corresponding table in the paper – I do not have distinctly different findings to report. Duh. However, this is an excellent example of why visualizations are meaningful. It’s the same information, plotted in two different ways.

In my version, it is clearer to see that having 1 – 3 children represents extremely similar patterns of happiness across the life course. I discount the results at the very low age range because we know that the data at that end is less-than-representative. If we just look from the 20-29 cohort through to the 50+ cohort, we see that having more kids eventually represents more happiness for parents but that they are about equally unhappy during the most active years of child-rearing.

Having four or more kids breaks the pattern. This is evident in both graphic representations. In my opinion, it is more evident in the first version of the graph than the second version, as they appear in this post. I used a similar sensibility for the colors of 1, 2, and 3 children trends and a different kind of color for the 4+ kids scenario.

The graphs do not explain why having four (or more) kids would be so different than having, say, three kids. More study is needed.

My #1 take-away: do not have four or more children if you value your happiness.
My #2 take-away: Think twice about having any children at all if you would prefer to be happy for the twenty or so years it’s going to take those kids to move out.
My #3 take-away: Thanks, mom and dad. I hope you’re happy now.

References

Cohen, Philip. (14 May 2011) Children beget happiness, eventually [blog post] on Family Inequality.

Conley, Dalton. (2005) The Pecking Order: A Bold New Look at How Family and Society Determine Who We Are, New York: Vintage.

Margolis, Rachel and Mikko Myrskyla. (9 March 2011) A Global Perspective on Happiness and Fertility in Population and Development Review, Vol.37(1): 29-56.

Original Version of the bar graph

How is the scoring system determined?

British researchers affiliated with the Independent Scientific Committee on Drugs met for a one day workshop and constructed a composite scoring system to determine which drugs are most harmful both to individuals and to society collectively. Scores can range from 0 – 100. Authors David Nutt, Leslie King and Lawrence Phillips found that,

heroin, crack cocaine, and metamfetamine were the most harmful drugs to individuals (part scores 34, 37, and 32, respectively), whereas alcohol, heroin, and crack cocaine were the most harmful to others (46, 21, and 17, respectively). Overall, alcohol was the most harmful drug (overall harm score 72), with heroin (55) and crack cocaine (54) in second and third places.

The full list of factors that were included in the composite score are here:

• Mortality
• Damage
• Dependence
• Impairment of mental functioning
• Loss of tangibles
• Loss of relationships
• Injuries to others
• Crime increase
• Environmental degradation
• Family breakdowns
• International turmoil
• Economic cost
• Loss of community cohesion and reputation

Though it is possible to go into an explanation of how each of these was measured and subsequently combined to produce the composite scores, I am going to leave that discussion to the authors of the original study. There’s an overview graph below and the full article Drug Harms in the UK: A multi-criteria decision analysis is at the Lancet.

Composite scores showing contributions from harm to individuals and harm to society

What can be done?

I found it interesting that there was no attempt made to distinguish between legal and illegal drugs. Yes, of course, some drugs are not clearly legal or illegal. They are legal when prescribed and supervised by a doctor but illegal when used off-label or outside the medical authority system (like anabolic steroids, methadone, and marijuana in California). I assumed that most methadone users are under some kind of supervision but that most anabolic steroid users are using the steroids off-label (ie illegally). You can quibble with my choices below. The point here is that I found the graph to have more context if the legality issue was visually inscribed into it.

Photoshopped version of graph that highlights legal drugs

There are age limits and places where it’s illegal to smoke or drink, but for the most part everyone will be able to use alcohol and tobacco legally for most of their lives. Methadone is probably being used legally in most cases. That’s why I shaded those bars grey. I am not expert on methadone, but I see that it is much less harmful to users and to society than heroin, the drug it stands in for, so I guess if this were the only data I had to make a decision about continuing methadone treatment programs, I would keep them going. I would also call for close scrutiny of methadone programs. Something is clearly not working as well as it could be.

As for alcohol and tobacco…well…it’s hard to argue *for* the continuing legality of alcohol. How large do detriments to society have to be to trigger additional control mechanisms? The authors of the study noted that alcohol is part of society and it isn’t going anywhere. I agree. Prohibition was a failed experiment in this country and I’m not suggested we try it again. However, I would like to reopen the debate about how the negative impacts of alcohol can be alleviated. I recommend that all new cars must have breathalyzers in them. If the driver cannot blow a legal sample, the car won’t start. Yes, people could game that system by having their friends blow for them, but often one’s friends are also drunk. And hopefully, friends really wouldn’t let their friends drive drunk. Once upon a time, seatbelts were considered extraneous and seatbelt laws were considered constraints upon American’s rights to freedom and the pursuit of happiness. Well, when a drunk driver kills one of your family members, you might decide that the sudden loss of your mother or son or niece puts a much bigger crimp in your pursuit of happiness than a breathalyzer in your car ever would have. Will breathalyzers make cars cost more? Probably. But the cost of dealing with car accidents caused by drunken driving, even when they aren’t fatal, is absorbed by random individuals who happened to be in the wrong place/time as well as tax payers who pay to repair guard rails, subsidize public hospitals and EMTs, pay cops’ salaries, and so on.

References

Nutt, David J, Leslie A King, and Lawrence D Phillips. (6 November 2010) “Drug harms in the UK: a multicriteria decision analysis” The Lancet, Vol 376(9752): 1558 – 1565.