Giving Credit Where It Is Due

This graphic was created by Andrew Price for GOOD Magazine, inspired by a paper written by Dan Ariely (Duke University) and Daniel Norton (Harvard Business School) entitled “Building a Better America – One Wealth Quintile at a Time”.

What works

Wealth in America is heavily – extremely – concentrated among people in the top quintile. It’s not that wealthy people have a bit more than the middle class and a lot more than the lowest quintile. No. Wealthy Americans own almost 85% of assets in America. That should be surprising to you because when Ariely and Norton surveyed people to find out how much wealth folks *think* the top quintile owns, they estimate about 58%. Even that inequality is too much, the respondents think. In an ideal world, some inequality is acceptable, but the top quintile of earners should only hold about 32% of America’s wealth. And the bottom quintile would get a slice of the pie too, though at right about 10.5% it’s only one-third as big as the slice at the top. This would be a huge improvement over actual numbers where the bottom 40% owns less than 1% of America’s wealth.

What needs work

I would have put the wealthy people on the right since we usually order things from left to right. And if I were specifically trying to be polemical, I might have rotated the entire graphic and had the wealthy people on top, squeezing everyone else into the smallest possible space, kind of like a trash compactor. But, you know, I’m not trying to be polemical.

If I were trying to be polemical, I might say something along these lines: is it possible that because the bottom of our income distribution usually (though not always) has enough food to eat and a safe place to sleep, maybe even a television and a mobile phone, we have been lulled into thinking that extreme inequality is acceptable? Maybe even that extreme inequality keeps everyone pulling at their own bootstraps, trying to keep up with the Joneses, striving for some impossible future in which folks from the bottom four quintiles might make it into that top quintile? These are blunt numbers barely containing a moral question. In a wealthy society, is eradicating absolute poverty (food, shelter, safety, health care) the most morally responsible way to organize public funds? Is reducing inequality a moral imperative? Or just a bunch of belly-aching by people who should be happy that they can sit around and blog about these things?

Unfortunately, as a society we have not even been able to guarantee even an eradication of absolute poverty, let alone ushered in a debate about the moral implications of pronounced inequality. Morality is not the kind of thing social scientists are supposed to mention. The deep philosophical tenets of what it means to do the right thing are also usually absent from political debates, despite all of the overblown lamentations and soap operatic cases of individual hopes and despairs. There are still many many people who go hungry because they cannot afford to pay rent, buy food, and keep the lights on. There are still many people who cannot access basic preventive health care, either. Weren’t we all raised as children, as religious adherents or good humanists, to help those who cannot help themselves? Well. Americans aren’t so good at that. It’s easier to think that the poor aren’t really that poor (as this chart so bluntly demonstrates), that with food stamps and affordable housing vouchers they seem to be doing just fine. Maybe your neighbor tells you that they someone simultaneously holding a cardboard sign and a mobile phone. The gall of it! How can someone who so obviously has money – they are paying for that phone, aren’t they – be asking for money? When you hear that, think of this chart. Wonder about what it does to a social fabric to have such a vast difference between the wealth of the wealthiest and the poverty of the poorest (Which, by the way, includes many many people who are working. Full-time. Maybe even yourself).

It’s not my place to settle the moral debate about absolute poverty and relative poverty. But it is my place, and the point of this graphic, to raise the question and to make sure that we look at the whole graphic. It’s good to be shocked at how much the top quintile controls. But as Americans in this potpourri of a country together, we spend way too much time marveling at the monstrous wealth and not nearly enough time wondering what might be done about the dire straights of the bottom 40%-60% of us. If the graphic does nothing else, perhaps it shows you who your closest economic neighbors are – and they aren’t the folks to the left.

References

Ariely, Dan and Norton, Michael. (2010) “Building a Better America – One Wealth Quintile at a Time” forthcoming in Perspectives on Psychological Science.

Noren, Laura via Dalton Conley’s Intro to Soc text book: Champagne Glass Distribution of Wealth

What Works

I love that recessionary periods are included in this graphic. They are the lavender columns and it is obvious that recessions tend to correlate with increases in the number of people in poverty and that the current recession is really a doozy.

What saddens me the most is the graphic that depicts how poverty breaks down by age. First, note that people over age 65 have the lowest poverty rate of any age group. Then remember that they receive social security and health care. Now wonder what would happen to the economy if every US citizen were supported at that level or above. I cannot answer that question, but this graphic compels me to pose it.

Second, note that the age group most likely to be poverty stricken is children. Over 20 percent of people under the age of 18 are living in poverty. Think about it: if a parent with three kids loses his or her job, that means four people are negatively impacted from that single job loss. In this economy, I’m guessing that is part of the reason we see children sliding into poverty.

Third, have a look at this next graphic. Note that beyond the absolute number of poor kids and the rate of poverty among children, the proportion of impoverished Americans who are under 18 shows over-representation. Growing up poor is not only difficult for the kids, but it is not good for the future of the country. Being poor comes with all sorts of baggage for kids – they are more likely to live in poorer school districts with lower quality schools, they are more likely to live in more dangerous neighborhoods, they are more likely to have food insecurity (just try studying for a math test or writing a composition when you’re hungry), poor kids are more likely to be African or Hispanic American which might mean they are also dealing with face-to-face and institutional racism all throughout their lives, and so forth. Not trying to sound like Stevie Wonder here, but these kids are our future. As a country we’re doing a crap job at making sure they have the basic physical, social, and educational support they need to live up to their best potential. Quite stupid. Decision making made by people who have trouble seeing past the end of their own nose, perhaps?

Forgive me. I know I am supposed to keep politics out of my blog but it’s hard to see how making sure kids are not living in poverty is a political issue. It’s a human issue. I would hope we can at least agree kids should not be living in poverty. I realize that it is much more difficult to agree on how to go about getting them out of poverty and preventing others from becoming poverty-stricken in the first place.

What Needs Work

Right. So what needs work here is our economy. But that is not news so I’ll let that debate sit.

The New York Times article about this topic pointed out that what needs work is the way the poverty line is calculated. On one hand, at about $11,000 for a single adult and $22,000 for a family of four it’s awfully low. This is because when the original formula for calculating poverty was adopted, it was tied to food prices and food budgets now make up a smaller proportion of the overall family budget than they did when the formula was concocted. [Remember this example folks: equations are not unbiased.] Over the years, family food budgets have experienced a real drop due to subsidies (the true costs are not passed to consumers), technological advances (we can grow more for less $ with fertilizer, GMOs, antibiotics for livestock, and pesticides for greens/grains), and ‘advances’ in corporate agriculture (economies of scale, see Michael Pollan’s work, Eric Schlosser’s “Fast Food Nation”, Marion Nestle’s “Food Politics”). Other critical costs for surviving from day to day like housing and health care have risen. On the other hand, benefits from programs like food stamps are not included in ‘income’ so there might be a few people bouncing above that poverty line once we take their food stamps (and a few other benefits) into account. Then again, the poverty line is too low so even if food stamps sends a family above it, they are still likely to experience poverty even if they don’t fit the current fiscal definition of poverty. The other problem with the calculation is that it does not take into account differences in regional costs of living. Living in New York City is expensive. Living in a rural area may be less so though paying to own, insure, maintain, and fuel a car or two to drive to work, school, and the grocery store could hike up the rural cost of living more than I know. With an annual budget of $22,000 for a family of four, a car or two would be a real cost, one that an NYC resident would not need to handle.

There is a graphic in the report that shows where poverty rates would be if the poverty line were adjusted upward or downwards.

References

DeNavas-Walt, Carmen; Proctor, Bernadette; Smith, Jessica. (September 2010) Income, Poverty, and Health Insurance Coverage in the United States, 2009 US Census Bureau, Current Population Reports: Consumer Income.

Eckholm, Eric. (16 September 2010)
Poverty Rate Rose Sharply in 2009, Says Census Bureau. New York Times.

What works

Thank you, Pew Research, for all of your hard work.

This set of lines does not tell a story about marriage and wages, it poses a question. Let me first take a moment to stop and praise the graph maker for choosing lines instead of bars. This is basically a series of timelines presented on the same axes. When displaying trends, lines are better than boxes. A line can travel over time, a box just sits there. Of course, then, for time series data, unless there is a compelling reason to discourage people from feeling a sense of movement over time, then go with a line. You might want to choose a box or series of boxes if you have reason to believe your dataset is not truly continuous.

Second, let me say that I enjoy the way the context provided here forces the viewer to wonder why it was that the wages of single people flattened out. While it might be tempting – and some have done it – to assume that getting married makes you rich, looking at the trends presented the way they are here makes it hard to jump to that conclusion. We can see changes over time in the relative wages of married and single men and women, but we cannot see any reason to think that it is marriage that leads to increased wages. Folks who study marriage and wages (Andrew Cherlin, Betsy Stevenson and Justin Wolfers, Kathleen Gerson, and many others) have long pointed out that even though there has long been a correlation between marriage and wages (married people tend to have higher wages) we have no idea whether being married leads to higher wages or having higher wages leads to getting/staying married. The set of lines above does a good job of making sure it is difficult to jump to a causal conclusion.

Karen Sternheimer at Everyday Sociology blog which is part of Norton publishing covered this question a long time ago, but she focused on the gender difference in wage returns. It used to be that women benefited economically by getting married but now that women’s and men’s salaries are getting closer to parity, men see a bit more of a per capita bump than do women when they get married.

This still does not explain why single people make so much less or whether marriage preceeds the wage increase or the actual or promised high wages attract marriage partners.

Dalton Conley, in Elsewhere, USA, pointed out that what could be more alarming than the distance between single and married people is the way that equality in marriage partners (folks are starting to equalize their strategy for choosing mates – more and more we all want to marry wealthy, attractive people who are likely to continue to be wealthy and attractive. This holds regardless of whether we are men or women.). This means that folks with high incomes marry other folks with high incomes and increase the distance between top earning households and lower earning households. He calls it doubling down, though I suppose if you are a high earner married to another high earner you might consider it doubling up. Either way, the distance between the haves and the have-nots may actually be exacerbated (in some ways) by the sexual revolution, especially if single people’s wages flatten out. I’m thinking in particular of single parents, who are going to be raising kids on sole salaries lower than their married counterparts, for whatever reason. Their kids are competing for spots in the good high schools and colleges with all the kids whose high earning parents doubled up.

I love graphics that make me ask questions.

What needs work

The married men’s trend line ends up looking like a shadow of the married women’s trend line even though men are not actually women’s shadows. I would have recommended a different color scheme to make sure we don’t read men as existing in women’s shadows.

References

Sternheim, Karen. (2010, February) “Men and Marriage” at Everyday Sociology by W.W. Norton Publishing.

Away message

Maps of public transportation are my favorite visual shorthand for any major city, not only because I have to rely on mass transit where ever I go, but also because these highly stylized versions of cities contain much more than the bare minimum amount of information to get from one point to the next. I will be in Tokyo checking out the public transit system and attending the 4S conference through the end of the month.

See you back here in September.

What works

What I like most about this graphic is that it summarizes great research from Pew that many folks would not have perused by reading Pew’s publicly available reports. That’s always one of the reasons I tout information graphics – they make information accessible and interesting to people who don’t have the drive/access/time to read full reports and the graphics often give more detail than do executive summaries. Clearly, any summary cannot give all the granularity of the report, but I assume most people do not read full reports. This comprehensive visual summary packs in more information than would a journalistic article about the research that have to include the requisite interview with a teen who texts or the parent who pays her bill or the person who was injured by a texting driver (or the guilty driver). Only sprinkled among the vox populi would we see a couple of quotes from a couple of ‘experts’ who conducted the survey. And nobody can summarize all that much in a total of four-ish quotes. I am still weighing the pros and cons of recommending that standard executive summaries be replaced by (accompanied by?) information graphics like this, at least in the case of survey-based reports.

Out with the written executive summary, in with the infographic summary? Please debate.

What needs work

I couldn’t find the actual references so I added some of my own where you can corroborate things like the Finnish PM who broke up with a girlfriend over text and the story of the first text message sent by Neil Papworth. My guess is that the bulk of the information comes from Pew while a lot of the fun facts come from the other sources. But I couldn’t find that out for sure without a great deal of effort (like tracing back every single datapoint in each of the components of this graphic).

The interwebs has a social policy of hyperlinking to sources. Please folks, keep that going someway, somehow. Otherwise we risk plagiarism which is bad in itself (see my dissertation 2011). Additionally, when it is not possible to check facts, exaggerations, methodological mistakes, made up info, and just plain lies are harder to ferret out.

References

Pew Internet and American Life Project
   Report on Mobile Access (7 July 2010)
   Report on Teens and Mobile Phones (20 April 2010)

shanesnow. (18 August 2010) “US and Worldwide Texting Trends” Original post at mashable.

Boyes, Roger. (14 March 2007) How potato love affair with Finnish PM went off the boil. The Sunday Times online.

BBC News Online. (3 December 2002) Hppy Bthdy Txt.

Just thought this was cool

This map of New York was created by Eric Fisher. He gathered the geotags of the photos uploaded to flickr. The colors work like this: blue photos were taken by locals (deemed to be local because they had taken pictures in the same location over an extended period of time), red indicates photos taken by tourists (people taking photos outside of their frequent-photo-taking-zone), and the yellow ones were indeterminate (taken by people who hadn’t uploaded any photos in the previous 30 days though we guess they might be tourists because they may be the kind of people who only take photos while on vacation).

I like the aesthetic and the method so that’s why I decided to share.

What works

These graphics accompany the graphic in my previous post about the counts of humanitarian images in Time and Newsweek. They are meant to give context to the methods section which describes these two magazines in terms of a few demographic variables and circulation information. I do not have access to the original source so I could not go back and get more demographic information besides household income and readers’ ages. It is possible that those were the only two pieces of information available in that source about reader demographics.

What needs work

The big question is: do you like the graph of the demographic data or should I just leave it in a table? I won’t tell you which way I’m leaning so as not to prejudice your opinions.

Go ahead, feel free to leave a one word comment (the one word being graph, table, or neither). If you’re feeling especially motivated, it would be nice if you explained your reasoning. But it’s August, so I’ll cut you some slack if all you can muster is a single word.

References

American Community Survey – 2008.

Mediamark Research & Intelligence (MRI). 2008 (Fall). Magazine Audience Estimates. New York: MRI.

What works

1. Menlo is my favorite font of the moment for information graphics.
2. I have no idea why I haven’t seen this Venn diagram before. In my humble opinion, if you are a social scientist and you are attempting to display a concept that may or may not have solid numbers to back it up, start with the Venn diagram because:
a. Venn diagrams are easy to make.
b. Venn diagrams are easy to understand.
c. Venn diagrams are not expected to represent solid numbers. They certainly can be employed in that way, but they are not always employed in that way so you are not likely to mislead readers that you are backing your claim up with census data.
3. I am doing a bit of research on marriage and I have run up against many arguments that seem to believe that marriage and childbearing always go together, or at least that they OUGHT to always go together. News flash: 36.9% of children are born out of wedlock (Cherlin, 2008). Other adults get married but do not have children. Yet other adults get married, have children, and then end up unmarried again because divorce and death ended their marriage. The above graphic should help clear up what actually happens in the world. Marriage and child raising frequently have no overlap.

What needs work

I was so upset that I didn’t stop and look up the actual data for each of these segments. In part, I wanted to leave it as a universal concept and NOT tie it to US data. But yeah, I realize it would be better if I had sat down and figured out how many people are in each of these three areas. That’s coming in the article version. And after I take a deep breath to disperse the anger I feel at people who make illogical arguments.

References

Cherlin, Andrew. (2008) “The Marriage Go-Round.” New York: Vintage.

Revisions

I realized after I posted this that I had forgotten to include Australia and New Zealand. Big oops. I forgot them because they were not represented in any of photos in the humanitarian articles in Time or Newsweek during 2007-2008. This does not mean there were no humanitarian crises in Australia or New Zealand during those years, just that Time and Newsweek could not or did not cover those stories with photography (if at all).

I had also not had time to include one more layer of information which is the percentage of images that were ‘crisis’ images. The best way I can think to explain this goes like this: In any humanitarian crisis there are victims so victims appear in just about all the images but some images also include people trying to help. So, for instance, if Latin American countries ever made it into the news weeklies (and those countries are under-covered as it is) 86% of the time they were depicted as facing an impending or ongoing crisis. They weren’t depicted as helping themselves or anyone else. In the US, only 16% of the figures in the images were depicted as victims of impending or ongoing crises. The US, and developed countries in general, were more often depicted as helping out than as being victims.

An Original Creation – Draft Only

Jen Telesca and Nandi Dill, my fellow research assistants at the Institute for Public Knowledge, presented a paper last year based on data they gathered doing visual content analysis of Time and Newsweek during the years 2007 and 2008. They looked through each issue, identified the articles that were humanitarian in nature, and then coded those images according to geography, the type of situation depicted, and the purported status of the individuals in the image (military actor, activist, politician, celebrity, etc). I am helping create the graphics and I thought I would share this one even though it isn’t yet complete.

As per usual, I welcome your comments and criticisms with open arms. Tear it apart, but be specific.

Methods and Findings

There were a total of 130 articles containing 363 images. The above graphic is supposed to help viewers come to the realization that not all areas are equally represented. I assumed – and this is a wild leap here – that there is some baseline level of social disease and natural disaster plaguing any population. More people = more trouble though we know the relationship is imperfect. Poor areas may experience a natural disaster as a humanitarian crisis leading to orphanhood, starvation, lack of adequate food and shelter where another region would have experienced the same natural disaster as a major inconvenience but one that insurance policies would more or less cover. A natural disaster does not always become a humanitarian disaster. Variables like wealth, racism, literacy, and so forth do play a role and I cannot capture those elements by showing a simple population statistic.

Am I forgetting something major? Am I taking Time and Newsweek to be tellers of the truth, representers of the world as it is, completely objective and unbiased by budgetary constraints or political agendas? Not really. I’m also not trying to push those issues too hard. One could assume from this graphic that some regions are more likely to be represented as suffering from (or aiding in the recovery from) humanitarian emergencies than others for reasons that have nothing to do with the frequency of these kinds of emergencies.

I hope that the graphic leads you to wonder why some regions appear more frequently than others but that it does not beat you over the head with the claim that Time and Newsweek like to depict Africa and the Middle East as sufferers and the US as altruistic helpers far more than a random sample of suffering or aid giving would indicate. Just look at Europe. They appear neither to suffer from or aid in humanitarian crises much compared to how many people live there. One theory is that US based magazines prefer to show US citizens performing acts of altruistic heroism rather than showing Europeans lending a hand. To what degree is Africa over represented because there are simply more humanitarian emergencies there versus being over represented in images because in this particular moment, in these two magazines, Africa equates well with the typical imagination of victimhood?

The graphic cannot answer all those questions. Mostly it just intends to raise them. What do you think?

Please send me comments

I will post the next draft when it is ready. I’ll tell you right now that it will include an indication of how often impending or ongoing crises were associated with each region. That should make it easier to tell which geographies are shown to be full of victims and which are full of altruists.

[There is a future graphic that uses the same dataset to show that being a victim and being an altruist are more or less mutually exclusive. For instance, stories involving crises in Africa almost never show Africans helping Africans. Instead, folks from wealthy countries are usually the ones depicted doing the helping.]