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Water Infrastructure Schematic Diagram*

I put together the diagram above to help me explain how water is delivered and taken away from urban locations. The point I want to make with the diagram is that the infrastructure is designed to deliver water to ‘typical’ buildings and that this means people who are wandering around cities where buildings are all private also lack access to water. There is a political debate going on right now about whether or not access to water is a human right – the UN voted on this and decided water IS a human right but large countries like the US disagreed. When the US does not back UN resolutions, those UN resolutions tend not to mean as much.

So why would the US vote against this resolution? I am not altogether sure, but I believe it has something to do with the fact that many places have privatized their water. Privatization of water takes different faces. Sometimes a system like the one diagrammed above is privatized. Studies have shown that when this happens, the company that sets up a system like the one above delivers a poorer quality product – more sedimentation and other low level contaminants which are the typical results of choosing sources quite close to cities. The closer the source is to the delivery, the lower the expenditure for engineering and installation of water mains, monitoring stations along the route, and reservoirs. The other way in which water can be privatized is through bottling – bottled water in some parts of Africa is more expensive than Coca-Cola. And this in areas that may have no access to safe alternatives for drinking water. Nestle owns the Poland Springs brand and folks in Maine are scrambling to get hydrological studies performed that can prove Nestle’s water extractions are drawing down lake volumes on adjacent properties. The only way to fight Nestle, it seems, is to prove that they are damaging one’s own property and yet water sources – rivers, lakes, oceans, springs – technically do not belong to private individuals. The individuals or corporations can own the land surrounding them, but the water is a bit like air and cannot be owned. (Rights to the fish found in the water CAN be owned. As you can see this gets complicated quickly.)

The diagram above contains none of the politics of the discussion below. For me, it is important to attempt to create graphics that are not political, even when I am creating them for the express purpose of delivering a presentation that takes a side in a political fight. For me, the challenge is two-fold. First, I face the technical difficulty of creating any kind of complex diagram. I’ll leave questions about execution out of this particular discussion though feel free to comment on execution below. Second, when I know I have a political message that I want to keep out of my graphics, I am often too far into my own head to be able to step back and determine whether I have created something that is both comprehensive enough to tell a complete (but apolitical) story and one that does not drift into the political. As it is, this diagram seems to err on the side of being incomplete rather than being more fully detailed where the details start to carry politics with them. My larger point is that this is one way in which cities are exclusionary zones by design. It would be easy to find a way to provide the basic infrastructure to supply water outside of buildings – fire hydrants do just that. But maintaining the ‘last mile’ of infrastructure is almost always completely given over to the private sector. Individuals and companies maintain bathrooms with all of their fixtures, cleaning, and maintenance requirements. This is big business. Just about every shop and restaurant on the street in New York reserves the rights to the bathroom for customers only.

One of Starbucks redeeming qualities is that their bathrooms tend to be open to all, proving that it is possible to continue to service a relatively affluent clientele no matter who is in the bathroom.

Obama on Water

The word on the political street is that even though Obama’s stimulus efforts contain plans to address infrastructure, water infrastructure has been taken off the table at this point. Our water infrastructure is ageing; most of the current infrastructure is due to age out of acceptable functionality in the next ten years. Already there are an average of 240,000 water main breaks. Just yesterday the New York Times reported that a dam outside of Bakersfield is uncomfortably close to catastrophic failure, threatening the lives and livelihoods of thousands of people. There are another 4400 dams in the US that require work in order to fall within comfortable safety ranges. Some are publicly owned, some are privately owned. In either case, it is unclear which entities can foot the bill (projected at $16 billion dollars over 12 years).

*This diagram uses New York City as a guide. Not all cities have overflow valves that risk the release of raw sewage due to increases in rain. What’s more, in New York there are some other systems in place to recapture some of the overflow at the point of release. But this is a different kind of political discussion, one that focuses on the other typical focus of water discussions – the environment.

References

Ascher, Kate. (2005) The Works: Anatomy of a City. New York: The Penguin Press.

Bone, Kevin, ed. and Gina Pollara, Associate Ed. (2006) Water-Works: The architecture and engineering of the New York City water Supply. The Cooper Union School of Architecture, New York: The Monacelli Press.

Bozzo, Sam. (2009) Blue Gold: World Water Wars [Documentary film, available streaming for free]

Davis, Mike. (2006) Planet of Slums. Brooklyn, NY: Verso Books.

Fountain, Henry. (2011) Danger Pent Up Behind Aging Dams. New York Times. 21 February 2011.

India’s Public Restrooms

In “Squatting with dignity: Lessons from India” Kumar Alok details efforts to increase the provision of sanitation at the household and community level. Along the course of the book, he presents all sorts of interesting data – some of his studies present information rarely gathered in the US. In the first table I’ve chosen (on page 293), Alok demonstrates that even after areas have received ‘Clean Village Awards’ (Nirmal Gram Puraskar = NGP and means Clean Village Award) there is generally still open defecation, one of the key elements that was supposed to have been eradicated in order to achieve the NGP status in the first place. He writes that the reason for this is multiple. Of course, the first problem is the lack of household toilets, then there’s the lack of public toilets both of which force people out into the open. The Clean Village Award was intended as a measure to increase investment in toilet infrastructure and winners in the first year were photographed shaking hands with the President of India. Alok’s deep research revealed that this photo opportunity caused leaders in many villages to seek the award just to have a photo with the President. This increased the number of awardee villages in the second year of the program so much that the President was not able to shake all the hands: “…only few were allowed to personally receive the award from the President of India. For the rest, the photographers did the magic. Using the modern computer tools, they produced photographs of individual PRI members shaking hand with the President of India….There was a serpentine queue outside the photo shops in Bengali Market immediately after the NHP award distribution ceremony was over.”

It is hard to predict just what will stand in the way of public toilet provision. I would never have guessed photoshop (and its knock-offs) could have led to an increase in pro-toilet hype while subverting actual investment in toilet infrastructure.

Where do people in India excrete?

The bars above are a bit clumsy as graphics, but they contextualize the information about open defecation by illustrating where else people might relieve themselves. The IHHL category refers to Individual Household Latrines and is the lowest section of the bars [the color coding does not come through at all]. Basically, this refers to people who use the toilet at home but does not indicate just how those toilets are plumbed. They may or may not be flush toilets. Many of them are not flush toilets. I find it useful to see how much variation there is across space. I also think it is worth noting that there are very few people who report using community or shared toilets. Open defecation is far more common than either of those two categories. In her film Q2P Paromita Vohra shows viewers that women have very few opportunities to use public or shared bathrooms. There are not many public facilities and where they do exist, the women’s areas are often taken over by men, leaving the women without a place to go. What’s more, where women’s rooms are still for women, the women have to pay to pee but men can use urinals in the men’s room for free.

Alok notes that another contributing factor to the relatively high proportion of open defecation is that not all toilets are being used. Sometimes open defecation is preferred. He writes that children are not deemed to need as much privacy as adults and that, furthermore, their feces is not thought to be as ‘dirty’ as adult feces. Thus, children are often allowed to go in the open rather than seeking out toilet facilities. As a result, “in only 51 per cent of the households either children are using toilets or child feces are disposed in the toilet. Forty-one per cent of households dispose feces in open space or along with solide waste, while 3 per cent drain out feces in the drain.” While it may at first seem a bit silly to think that children’s waste is somehow different than adult waste, think about what we do with dog waste. In the US people use hands covered with thin layers of plastic (or paper) to pick up dog poo and then dispose of it as if it were solid waste.

Alok continues to investigate sanitation practices, writing about hand-washing practices. This kind of information is something I would like to see for US-based populations. It is out there for hand-washing following the bathroom but there are not always break downs by what the person was doing (pooping or peeing) and I cannot recall coming across information about hand-washing before eating or after changing diapers…or picking up after dogs.

What works and needs work

My job here is to critique graphics and graphical representation of social science data. The tables and graphs here are not at all easy to use or beautiful to view. But the information is fascinating. It is almost always more important to get the information out in front of a public than to hide it away because it may not be formatted as well as you might like it to be.

References

Alok, Kumar. (2010) Squatting with Dignity: Lessons from India. New Delhi: Sage Publications India.

What works

Looking at change over time is often best when using simple trend lines. They are easy for the eye to follow – easier than if the same figures were depicted as bar graphs. Given that there are measurable and meaningful differences between the returns for men and women, it is a good idea to show two separate trend lines, as they have done here.

What needs work

The major problem is that returns to college education do not come only from the education received. This trend line is a simple construction that cannot sort out how much income can be attributed to college alone. Sociologists know that a combination of factors – from parents’ educational attainment and parents’ income to things like the student’s aptitude – impact measures of the student’s attainment (like income and wealth). A far more sophisticated model would estimate just how much income one could expect, all other things being equal, for each additional year of schooling. That’s a much tougher model to construct and it wouldn’t be something that could be plotted using trendlines.

In fact, one of the big problems with trend lines is that they are often overly simplistic. On the other hand, they can be excellent representations of the big picture, whatever that might be. There is no simple rule I can think of that would help sort out when a trend line is a great idea and when it is overly simplistic. In this case, change over time is hardly the main story. The real wrinkle when it comes to education is that it can be difficult to determine if students are receiving indoctrination into social networks, ways of acting, and professional networks while they are at college and that these are the advantages that lead to the later bump in income or if they are receiving important knowledge that makes them better, more qualified workers. What’s more, even if we will never be able to divorce the networking from the knowledge gained, we still wouldn’t know how much the background a student starts school with influences their later life choices. Think about this. If someone deeply embedded in a network of people who would usually be a college attender chose not to go to college but continued to hang out with the same people and therefore received much of the same college experience, social and professional networks, how would they fare later in life? Since social scientists cannot randomly assign some students to attend college and others not to, it is very difficult to answer this question. And in this case, a trend line is an oversimplification that misses the major questions about returns to higher education altogether.

Knowing what we know about the various influences on wages later in life and what we see in the trend line, we might assume that women are better able to use educational attainment to escape lower incomes that would have been predicted by, for instance, their parents’ education and/or income. But again, the suggestion that educational attainment has some kind of positive influence on wage premiums is correct, but incomplete. Any assertion about the relationship represented by this graphic is likely to be inaccurate and certain to be incomplete.

References

Blau and Duncan’s Status Attainment Model.

Nate Silver of 538 created this field map of the likely GOP candidates seeking the party’s nomination for President. I note, as does Mr. Silver, that none of these candidates have yet announced official intentions to run.

What works

Mr. Silver and I seem to share a fondness for two-axis field maps as a way to wrangle with a pool of qualitative information. Earlier, I used the same kind of strategy to sort my thoughts regarding peeing in public.

Here Mr. Silver is the field map approach (along with different sized/colored circles) to apply a system useful for thinking through the possible Republican nominees for President. As he explains, the x-axis is one of the most commonly used sorting devices for any candidate – political conservativism on the right, liberalism on the left. In this case, because all the candidates fall to the right of center, ‘moderate’ is used as the left hand label. Mr. Silver admits the y-axis need not have been the one he chose. But he decided to go with insider-outsider status because that will be an important element in this primary battle, given the claims made by the Tea Party.

The two axis field map works well for establishing some basic rules with which to sort out candidates who are attached to all manner of qualitative facts that may matter. The field map gives us a way to sort out messy, unmeasurable (qualitative, or quantitative but on different scales) information in a way that allows us a bit of clarity. If you want to use this as a tactic in your own work, I would suggest thinking through a number of different choices for axes. In this case, Silver was fairly confident about the x-axis (level of conservativism) but he was less sure that the y-axis was going to be the most meaningful compared to other choices. He didn’t discuss the other y-axes he might have considered – I can think of a few – but the point is that if you are using this approach in your own work, you need not limit yourself to coming up with one field map. In a situation like this one where you are reasonably certain about the x-axis, keep that same x-axis but redraw the field map with multiple y-axes. Maybe one of them will make the most sense. Likely they will all make some sense when it comes to explaining some things, but not as much when it comes to explaining something else. It is acceptable to end up with an array of field maps, not just one. The social world is a complicated places. Expecting it to fit into a two-axis field map is unrealistic, but helpful as a starting point.

Also, in this case, I like the use of different sized circles. The bigger the circle, the higher the odds of that candidate’s taking the nomination, according to a third party.

What needs work

I am unconvinced by the use of color. Silver himself wasn’t sure that it made sense to color-code these folks by their region of origin, but he threw in the color just because region of origin has mattered in some elections in the past. Again, if one variable doesn’t quite jive with what you think matters, I might try another. For instance, as the primary race heats up, maybe Silver would want to drop the concern with region-of-origin in favor of something like ‘attitude towards gun control’ or ‘attitude towards abortion’. Since neither of those are binary issues, he might be able to get away with using a single hue and darkening it for ardent supporters while moderate supports end up with lighter hues. Clearly, that graphic technique could be used to represent any kind of platform issue.

Further Reading

I encourage you to read Silver’s full post if you are interested in figuring out why he put the candidates where he did. No need to rehash what he has to say – he does a better job of explaining himself than I could.

Tenure dies, three graphics

Tenure is declining. There are many reasons for this, most of which are economic. Tenured professors are very expensive compared to, say, adjuncts and graduate student TAs. Once upon a time in departments far far away, even recitation/discussion sessions were led by tenured faculty members. The only experience I ever had with such a situation was in a department (physics) heavily funded by dollars from the Department of Defense. Don’t say the militaristic state never gave you anything, my fellow classmates. I’m not bragging, but I do think I am pretty clever when it comes to Newtonian physics, at least for a sociologist.

The story here is clear in the graphics…or is it?

This first graphic ran in The Chronicle of Higher Education last July in an article written by Robin Wilson who asked:
“What does vanishing tenure mean for higher education? For starters, some observers say that college faculties are being filled with people who may be less willing to speak their minds: contingent instructors, usually working on short-term contracts….But others argue that the disappearance of tenure is actually not the worst thing that could happen in academe. The competition to secure a tenure-track job and then earn tenure has become so fierce in some disciplines that academe may actually be turning away highly qualified people who don’t want the hassle. A system without tenure, but one that still gave professors reasonable pay and job security, might draw that talent back.”

It’s not my place to get into that discussion here, but I do want to interrogate the graphic that ran with the story to see if it captured the essence of the tenure story.

First, the Chronicle’s graphic has numbers that do not add to 100%. So I went back to the report from the American Association of University Professors that the Chronicle had pulled their numbers from and came up with this:

This report clearly has more detail – we can see where those missing numbers are (full-time non-tenured faculty) – as well as understand the distinction between full-time already tenured faculty and those who are in the process of seeking full-time tenured positions.

I decided to compile this information into a line graph for two reasons. First, a line graph is the best way to show trends over time. Second, the data were collected at odd intervals so the eye would not have an easy time just stringing together a line connecting the bar graphs to understand the pattern. I imposed a grid. I added in the missing category. I gave it some color (darker colors correspond to more reliable, financially sound employment categories; lighter colors refer to more fleeting or otherwise less remunerative employment categories).

References

(16 December 2010) The disposable academic: Why doing a phd is often a waste of time The Economist. Accessed online but it ran in the print edition.

Relevant quote:
“The earnings premium for a PhD is 26%. But the premium for a master’s degree, which can be accomplished in as little as one year, is almost as high, at 23%. In some subjects the premium for a PhD vanishes entirely. PhDs in maths and computing, social sciences and languages earn no more than those with master’s degrees. The premium for a PhD is actually smaller than for a master’s degree in engineering and technology, architecture and education.”

Wilson, Robin. (4 July 2010) Tenure, RIP. In The Chronicle of Higher Education.

The Annual Report on the Status of the Profession, 2007. The American Association of University Professors. Fact Sheet: 2007.

What works

Mapping the Measure of America is a social science project that deliberately includes information graphics as a communication mechanism. In fact, it is the primary tool for communicating if we assume that more people will visit the (free) website than buy the book. And even the book is quite infographic dependent. I support this turn towards the visual. I also support the idea that they hired a graphic designer to work with them. Often, social scientists do not do well when left to their own under-developed graphic design skill set. Fair enough.

The website presents a unified view of the three images above. I couldn’t get them to fit in the 600 pixel width format, so I presented them one at a time. I encourage you to go to the website because one of the greatest strengths of this approach is the interactivity and layering. I happen to have picked Massachusetts, but each state plus DC has it’s own graphics available. There are other charts and whatnot available, but I think that this set of graphics (which you see all at once) are the strongest.

What needs work

Maps. Maps are too often used. Here’s why I think maps are a problem. Look, folks, political boundaries are meaningful when it comes to making policy or otherwise dealing with state-based funding. And that’s about it. Political boundaries occasionally coincide with geographical boundaries, but not always. Geographical boundaries are meaningful for some things – life opportunities may be based on natural resources or on historical benefits accruing to natural resources. But political boundaries and maps are often not all that useful because they imply that the key divisions are the divisions between states or counties or neighborhoods. Like I said, sometimes this is true because funding tends to be like the paint bucket tool – it flows right up to the boundaries and not beyond, even if the boundaries are arbitrary or oddly shaped. But where the issues are not heavily dependent on funding, thinking in terms of political boundaries makes it harder to see patterns that are organized along other axes. For instance, I wonder what would have happened if some of these categories – education, longevity, income – had been split between urban, suburban, and rural areas. Or urban and ex-urban areas if you prefer that perspective on the world as we know it.

In the end, I think the title is both accurate and disappointing: “Mapping the Measure of America”. Figuring out how to do information graphics well means figuring out which variables are the key variables. In this case, it seems that the graphic options might have determined the display of the information. Maps are easy enough – they appear to offer a comparison between my local and other people’s local. Those kinds of comparisons offer readers an easy way to access the information because everyone is from somewhere and there is a tendency to want to compare self to others. But ask yourself this: to what degree do you feel that state-level information is a reflection of yourself? Do you see yourself in your state?

References

Burd-Sharps, Sarah and Lewis, Kristen. (2010) Mapping the Measure of America with the American Human Development Project. Site design credit goes to Rosten Woo and Zachary Watson.

What works

This is the most graphic of graphic sociology so far. For those of you with delicate constitutions, give yourself a pat on the back for taking a deep breath and deciding to read the rest of this post without tossing it upon first glance.

This was published in 1976 in a book that is now out of print called The Bathroom by Alexander Kira, an architect and professor at Cornell. He was interested in the bathroom as a design challenge with an eye to the ergonomics of the fixtures and spaces commonly encountered in standard bathrooms, home to standard fixtures. The bathroom is not exactly a hotbed of design revolution so many of his ideas are not only still relevant, but still fresh. This particular diagram was used to help sort out how one might go about designing a urinal for women (if not a unisex urinal that could serve both women and men, not at the same time, though).

I usually find the use of photographs in information graphics to be superfluous. Generally, there is some graph about, say poverty or out of wedlock birth and the photograph paired with the graph takes a person and turns them into a token. The homeless man as icon of poverty; the mother and child (usually a woman of color) as icon of poignant nurturance. That sort of reductive photography has no place in information graphics. Quite frankly, I’d be happy never to see predictable, reductionist photography like that anywhere.

But in this case, Kira used a grid in the photo shoot turning the resulting photograph into an infographic. Did I mention that his ideas still seem fresh? With the grid, we have a much easier time making the visual comparison between trajectories of urine between women and men.

Imagine you are a urinal designer. Ask yourself: how would I use these diagrams to help me design a urinal that works for women? Realize that you would either pursue a trough strategy or, better, a urinal that women do not face. They could stand with their backs to it and bend forward like the woman in the third panel is doing. Of course, there are sartorial concerns. Backing up to a urinal works just fine if you are naked, like our urination model is. But what if she’s wearing clothing? That’s a different design challenge. I would be interested to see what would be possible by relocating pants’ zippers so that they open between the legs rather than in the front.

What needs work

I apologize that in some of these panels it is hard to see the stream of urine, which is a necessary piece of information. With the women, it’s pretty much straight down except when bent over at the waist. For the men, it is slightly in front of the body unless he is holding his penis in which case the trajectory is quite a bit in front of him — it leaves the photographic frame.

Reference

Kira, Alexander. (1976) The Bathroom New York: Viking Adult. [out of print]

What Works

It’s finals season so this one seems appropriate. And I always like it when people tell jokes with graphs. Extra funny. Maybe only extra funny if you are a nerd.

References

Chan, Jorge. (27 October 2010) Procrastination

Hi all. I put together the following biblio after some of the folks in the audience during my presentation at The Image conference (UCLA). They were wondering how to create better information graphics.

This list of resources is good if you are sick of using excel and killing yourself trying to use Word to make graphics or wondering what you should be aiming for within a categorical type (what’s a good bar graph, anyways?). Admittedly, none of the resources are perfect, most do not tell you how to use which software programs. But there are good pieces of advice and instructions in just about all of them.

The list is available for download here. And in html below.

Infographics Biblio – emphasis on how-to

Cleveland, William. (1994) Elements of Graphing Data. Summit, NJ: Hobart Press.
+ Table of Contents and Chapter 1: http://hobart.com/Elements.PDF

— (1993) Visualizing Data. Summit, NJ: Hobart Press.
+ Table of Contents and Chapter 1: http://hobart.com/Visualizing.PDF

Few, Stephen. (2004) Show Me the Numbers: Designing Tables and Graphs to Enlighten. Oakland, CA: Analytics Press.
+ Perceptual Edge blog

Graff, Gerald and Catherine Birkenstein. (2009) “They Say/I Say”: The moves that matter in academic writing. New York: W. W. Norton.
+ This book is not about graphics. I find that it offers a useful framework for figuring out
which contextual information needs to be included in a graphic in order to provide
enough context for a useful discussions. If academics creating infographics include some
history of an argument or predict what critics might say, they will create a stronger, clearer
graphic just the way writers create stronger, clearer arguments if they situate their
argument within a field and address predicted criticism before they arise.

IBM Research: Many Eyes Visualization Tool.

Roam, Dan. (2009) Unfolding the Napkin: The hands-on method for solving complex problems with simple pictures. New York: Portfolio Trade, a division of Penguin.

Rosling, Hans. (2005-present) GapMinder Visualizations and tools to make your own visualizations.

Seagram, Toby and Jeff Hammerbacher. (2009) Beautiful Data: The stories behind elegant data
solutions. Sebastopol, CA: O’Reilly Media.
+ Table of contents

Steele, Julie and Noah Iliinsky. (2010) Beautiful Visualization: Looking at data through the eyes of experts. Sebastopol, CA: O’Reilly Media.

Tufte, Edward. (2006) Beautiful Evidence. New Haven, CT: The Graphics Press.
— (2001) The Visual Display of Quantitative Information, 2nd ed. New Haven, CT: The Graphics Press.
— (1990) Envisioning Information. New Haven, CT: The Graphics Press.
— (1997) Visual Explanations: Images and Quantities, Evidence and Narrative. New Haven, CT: The Graphics Press.

Ware, Colin. (2004) Information Visualization: Perception for Design, 2nd ed. Morgan Kaufmann.

Wong, Dona M. (2010) The Wall Street Journal Guide to Information Graphics: The dos and don’ts of presenting data, facts, and figures. New York: W. W. Norton.
+ Table of contents and sample pages

What Works

This graphic comparison in The Economist is an excellent piece of evidence in support of the use of logged scales. If you are an economist or quantitative sociologist reading this, you probably just fell asleep because you know about log scales already. Still you have to agree that the graphs here do an excellent job of visually explaining why log scales are better than linear scales in this case.

One of the general rules in multi-variable models involving per capita income data is that this data should be logged. The above graphs visually describe what happens when linear wage data is logged. That is the only change made between these two graphs. On the left, the wage data is measured just as it comes, on a linear scale which assumes that the difference between one dollar of per capita GDP is the same between no dollars of per capita GDP and that very first dollar of GDP as it is between the 10,000th dollar of GDP and the 10,001 dollar of GDP. The graph on the right logs the per capita GDP. This changes the assumptions about the distance between the zeroth and first dollars of income and the distance between the 10,000th and 10,001st dollars of income. In the graph on the right, logging the per capita GDP gives us a scale that is far more sensitive to differences when integers are small than when they are large. That difference between having no per capita GDP and having just one dollar of per capita GDP, or between one dollar and ten dollars has a relatively greater impact than the difference between 10,000 and 10,001 (or between 10,000 and 10,010). Logged values are sensitive to differences in orders of magnitude. There is an order of magnitude change between 1 and 10, then not again until we get to 100, not again until we get to 1000, and not again until we get to 10,000. The distance between each of these milestones grows successively larger. That’s the mathematical logic behind logged scales. Why do they tend to produce better fit lines for per capita income level data than the linear scale does?

Imagine this: you have no money and someone gives you $10. That is quite meaningful. Now you are able to take the subway, get something to eat, and make a call at a pay phone, three things you would not have been able to do when you had nothing. Those $10 mean a whole lot to you in a way they wouldn’t if you had $10,000 and I gave you $10. With your $10,000 you would already have been able to do all the things I mentioned above. Having an extra $10 would not make much meaningful change in your immediate material conditions or your investing options. The point here is that when folks have no income, they are a lot more sensitive to small changes in income than they are when they have a measurable income. The more income they have, the less sensitive they are to small (or even moderate) changes in income. This is why economists and quantitative social scientists almost always log measures of income. The assumptions I just explained are almost always true.

In the graphs, once the per capita GDP (which isn’t exactly a measure of income, but it is closely correlated) is logged, the relationship between income and happiness is much clearer. The model fits better when per capita GDP is logged and it appears that there may be a positive relationship between money and happiness after all.

What needs work

These happiness measures are rather uninspiring. Happiness is quite possibly culturally specific – what makes my mother happy, for instance, is my singleness. What makes mothers in other places happy might be that their 30-year-old daughters are married and have healthy children. I can hear you all saying, ‘But wait! Your mom is weird, what makes her happy is singular’. And that is just exactly my point. Happiness is contingent upon so many other things that trying to measure it is difficult – what makes a person happy changes over time and place so we cannot measure happiness based on easily observed objective measures. Some people like to think they can measure levels of depression or even serotonin to figure out who’s happy or not. But I simply don’t buy it. In places where there is more health care, more people are going to be diagnosed with depression. But does that mean that a population with a high level of reported cases of depression (a seemingly scientific diagnosis of unhappiness) is any less happy than a place in which seeking a diagnosis for mental illness bears a prohibitively high financial or social cost such that people do not even seek diagnoses in the first place? Perhaps the people getting treated for depression are now happier than they were before they were treated and thus the place with a high collective rate of diagnosed depressives is actually happier than a place where people are not being treated for their depression?

Dalton Conley was on a panel I recently attended that was called together to offer thoughts on THE MEASURE OF AMERICA 2010-2011: MAPPING RISKS AND RESILIENCE”. Someone from the audience pointed out that the book tends to use measures like health, education, income, and mortality but that these may be missing the right question. The right question was something along the lines of, “But are people happy?” Dalton pointed out that this is a normative question (and thus not the point of the volume which is demographic in nature) and that it is methodologically nearly impossible. The reason the information in the book is meaningful is that the measures that have been established can be rigorously measured across time and place. And they HAVE been measured across time so we are able to see patterns. The problem with any new measure is that there isn’t much to compare it against for a couple decades. More importantly, there is no objective way to measure happiness. A pound is a pound where ever you weigh it on the face of the earth (OK, yes, there are some exceptions to this but those are for physicists). A dead person is a dead person just about no matter where they are so mortality tends to be a good measure, too. But happiness does not fit well into a measurement framework. And even if it did, we’d be back to Dalton’s first point, which is that all we could do with that information is become normative.

This increasing desire to find the roots of happiness seems both misguided and heavy handed. Just as people appreciate seasonality in nature, I tend to think there is something to be said for having a full set of emotions. If that is true, there is no particularly good reason to run around trying to doggedly pursue happiness. There are benefits to being sad and introspective just as there are benefits to being happy. What is *with* all this fixation on happiness?

You’ve heard plenty from me at this point so I’m shutting up. I would like to hear your thoughts about both log scales and measuring happiness.

References

(25 November 2010) Money and Happiness [Daily Chart] The Economist online.

Lewis, Kristen and Burd-Sharps, Sarah. (2010) The Measure of America 1900-2010: Mapping Risks and Resilience. with an introduction by Jeffrey Sachs. New York: NYU Press. Part of the American Human Development Project of the Social Science Research Council.