The advent of conversations I thought I’d never have

Even teaching at a place like the University of Colorado, marijuana use was never something I discussed with my students or professors. It didn’t really come up among my fellow graduate students (except the ones who played Ultimate, let’s be honest). Outside of one friend who owned a medical marijuana dispensary and a few pothead friends (mostly my guitar teacher, who taught me some Bob Marley before anything else), it just wasn’t something I talked about that much.

The passage of Amendment 64, however, has suddenly turned the conversation on its head in ways I never thought possible. More than once this semester, I have had my principles of micro students ask about marijuana legalization and how we tax it. (The answer was I didn’t know, turns out that in Colorado it’s taxed at a higher rate (regular sales tax) than other pharmaceuticals (exempt from sales and use taxes), which, from an elasticity and deadweight loss perspective in a simple supply and demand model, is certainly the way to go).

Another student asked me this afternoon about moving to Denver after graduation, where he might have a job opportunity. I, of course, lauded Denver’s many highlights and, to my surprise, added, “well, and you can smoke pot legally if you want.” He responded it was not his thing, but we then delved into a conversation on the relative economic merits and costs of legalization. I know that people have been having conversations like this forever, it just seems like they’ve suddenly become much more mainstream.

Finally, on Saturday, I got together with some friends and one friend’s very conservative, elderly, immigrant parents, perhaps the last people on Earth I thought I would have a conversation with about marijuana. But, they grow orchids, and according to the new law, her dad told the group, he can grow six plants and so can his wife, so why not get started? He didn’t want to smoke it, he said. I’m still not sure what he planned to do with it, sell it, keep it just because he could, give it away? But both of my friend’s parents were extremely excited about the possibility. We went back and forth on the specifics of the law all while laughing uproariously at the insistence of two elderly Chinese that they wanted to grow pot and were looking for a consultant to help them, while their daughter tried to convince them not to because she didn’t want to take care of the plants while they’re not in town.

Not your run-of-the-mill cocktail chatter, for sure, but suddenly, it is. Brave New World.

Job lising of the month

I’m wrapping up my job-applying, at least for the big pre-December 1 deadline push, and am now mostly in the process of looking back at jobs I didn’t apply for in places I’d really like to live. Unsurprisingly, Denver is one of the places, and despite an apparent hiring spree by Colorado schools this year, I’m not a particularly good fit for the faculty positions that are open.

I’m curious, though, if there’s actually anyone who fits this University of Denver opening for an assistant professor of Economics: “Must show promise of distinction in research and publications in the fields of the Chinese economy, environmental economics, and feminist economics.” (emphasis mine.)

Not just heterodox, but feminist, examining questions of environment, and concentrated in an area where those that run the economy are largely indifferent to both feminist and environmental concerns. It kind of boggles the mind. I’m really curious to see who they end up hiring. In fact, I’d like to meet her; she sounds like a rockstar.

Spatial auto-correlation is not causation

There’s a strong tendency in human nature to draw distinctions along dichotomous lines. Good and evil, black and white, ugly and pretty. We all know that these distinctions only really work in children’s fiction, and even then tend to fall flat, but we try anyway. In teaching, particularly a new subject, those dichotomies are both useful and can lead to the downfall of a lesson.

In that vein, the instructor in my spatial econometrics workshop last week presented two significant data issues that a researcher might encounter in using spatial data: spatial heterogeneity and spatial dependence.

By way of definition: spatial heterogeneity is simply that there is something about an area or a piece of space that is different than the spaces around it. My dichotomizing, learning mind went immediately to the idea of observables. Clearly, if we are trying to include spatial information–location–in a regression, we know that the area has certain characteristics. As long as we explicitly control for these in our regression (and believe they are accurately measured), it doesn’t present much of a problem.

However, this is not always the case due to the level of analysis problem. In a general econometric specification, we control for the unit of spatial analysis that is relevant–county, Metropolitan Statistical Area (MSA), state, whatever it may be. By choosing the level and assigning a dummy variable, perhaps, we assume that all those characteristics are captured uniquely, but also that they are assigned independently to the spatial unit. Take for instance the distribution of the African-American population in the United States. Regression analysis that uses that variable as a covariate assumes that the number of African-Americans in Georgia is independent from the number of African-Americans in South Carolina, which makes little intuitive sense. Both were states with large plantation economies that employed Black slaves from Africa in production of goods. It makes sense that these two states, spatially proximate, would also have similar factors leading to their demographic makeup. Thus, spatial heterogeneity: areas in the South have higher Black populations than in the North.

The corollary to spatial heterogeneity is spatial dependence. Like spatial heterogeneity, we see patterns occur in certain variables, but rather than an outside, perhaps observable and easily measurable factor that accounts for the clustering, there’s something inherent about the place itself that causes proximate areas to change their realization of some variable. Think of housing prices. Housing prices are higher in places with certain amenities (close to transportation, mountains, whatever), but housing prices are also higher in areas with higher housing prices. Perhaps homeowners see their neighbors selling their houses for more and thus put them on the market for more. Or buyers see houses in the area with higher values and thus are willing to spend more. This spills over county and other lines, too.

Both of these problems, regardless of how strict that line is between the two, manifest in spatial auto-correlation. The variation we see in each variable for two spatially proximate observations is less than the variation for two independently observations because the information comes from the same place. Some of this we can control for, some of it we can’t, and some of it we can try to control for with the tools I’ll discuss in coming days.

Regardless, it’s important to remember that the realization of spatial heterogeneity and spatial dependence is the same mathematically. Statistically, we cannot differentiate between whether some unobservable variable caused everything to be higher, or whether each observation is exerting an effect on its neighbors (a butterfly flaps its wings…). So, even with acknowledgement of these problems, we have not established causation.

A familiar refrain is, thus, minimally modified: spatial auto-correlation is not causation.

A note on correlation and causation: (see Marc Bellemare’s primer for a more detailed explanation)

Anyone who has ever taken a statistics course is familiar with the refrain that correlation is not causation. It’s a common refrain because it’s something that is often ignored when statistics are cited in news articles and personal anecdotes. My favorite example of this is that ice cream sales and murder rates are highly correlated. Only the biggest of scrooges would believe that ice cream sales caused murder rates to increase. In the abridged words of Elle Woods, happy people don’t kill people. And in my words, ice cream makes people happy.

They do move together, though, which is essentially the definition of correlation. When ice cream sales go up, murder rates go up; when murder rates go down, ice cream sales go down. Not because one causes the other, but rather because of the seasonality of both variables. More homicides occur in the summertime, and more ice cream is sold in the summertime.

An introduction to spatial analysis

After my first, rather disastrous, year of graduate school in Boulder, I almost transferred to Geography. Or at least, I thought a lot about it. While the math in Economics was kind of kicking my butt, everyone working with graphs and maps seemed so blissfully happy. Ultimately, I stuck it out in Economics, and am extremely glad that I did, but I haven’t lost my love of maps and have always been curious about spatial research.

Next week, I’ll be doing a three-day workshop at the University of Colorado‘s Institute of Behavioral Science. Many of my economics professors were associated with IBS, but none really did spatial analysis, so I was left to find out some of it on my own. A few years ago, I helped design a survey on handwashing and other hygiene behaviors for a group building latrines and protecting water sources in Nepal. The data are fascinating and though we started analyzing it, everyone had limited use of one of the two tools necessary to do spatial regression. I had the Stata skills and my coauthors had limited GIS skills, but combining them wasn’t going to happen. This short course is hopefully the next step in getting those papers off the ground and into journals, but also more importantly, back to the community where we did the research. Though we’ve presented some findings to them, I’m sure there are many more insights to be had with these data.

With that, I’ll be reading a lot of spatial analysis papers over the next week. The syllabus has hundreds of pages of reading, much of which I’ve printed out and am planning for my long trip back to Colorado next week, but I’m willing to share the “lite” version with you all.

For definitional purposes, spatial analysis is “the formal quantitative study of phenomena that manifest themselves in space,” according to Luc Anselin. More informatively, I think, spatial analysis allows us to “interpret what ‘near’ and ‘distant’ mean in a particular context” and showcase whether and how proximity or location have an effect on an outcome we’re interested in.

Anselin divides spatial analysis into two categories–data-driven analysis and model-driven analysis, and highlights the challenges of each, which I imagine will get plenty of air time next week and are a little bit daunting to a student and devotee of econometrics:

Indeed, the characteristics of spatial data (dependence and heterogeneity) often void the attractive properties of standard statistical techniques. Since most EDA techniques are based on an assumption of independence, they cannot be implemented uncritically for spatial data…As a result, many results from the analysis of time series data will not apply to spatial data.

Model-driven analysis seems much more up my alley and suited to regression, but the main problem, which I encountered in my own research, “is how to formalize the role of ‘space.'”

Just like this basic the ideas and tools used in spatial regression seem fairly consistent with my view of econometrics in general. There are tradeoffs to employing different models and assumptions, and measurement error is alive and well. Notably, although this could be out of date by now: “Spatial effects in models with limited dependent variables, censored and truncated distributions, or in models that have count data have been largely ignored…multivariate dependent distributions other than the normal are highly complex.” More to come, I’m sure. My colleague has already told me I have to teach him in the Fall, and I’m hoping to be able to incorporate some of this into my Methods class, so get ready for some spatial econometrics here.

As an aside, if you happen to be in Colorado, check out these cool solar events that are happening, including a world-record-braeking attempt at the most people in one place to watch a solar eclipse together at CU’s Folsom Stadium. Or, well, you could just go look at it where you are, too.

Referenced: Anselin, Luc. 1989. “What Is Special about Spatial Data? Alternative Perspectives on Spatial Data Analysis.” Conference Proceedings, Spatial Statistics: Past, Present, and Future. Institute of Mathematical Geography, Syracuse University.

A totally different country

A New York Times article yesterday details the growth of craft brewing around the US, but particularly in Denver. I, naturally, loved this article, but my favorite line in it has nothing to do with beer. The article quotes a craft brewer, who enjoys working for himself despite the long hours, and

who opened the Strange Brewing Company in 2010 in an old medical marijuana growing warehouse.

Colorado has a lot of quirky laws about alcohol. And it also has some quirky laws about marijuana. But what is so telling about this quote is that the medical marijuana growers moved on. In my limited experience with the industry (I know a guy who owns a medical marijuana store), the industry is growing like gangbusters. These growers probably went and got a bigger place.

But regardless, the nonchalance with which it’s included is priceless.

H/t @price_laborecon