On near and far skill transfer

Note: I wrote this as a response to a comment on one of my articles about how brain games don’t have enough evidence to really back up their claims that they improve general cognition, and it ended up getting weighty enough that I thought it merited its own (slightly edited) blog post.

Most of the confirmed effects of video games on cognition that I know of are near-transfer, mostly on a physically measurable scale. Strengthening hand-eye-coordination is a little more concrete and closer to being near transfer than far transfer, since the skill you’re training by reacting to an enemy on screen (quick response to visual stimuli) is very similar to skills you would need in other quick-response settings.

The problem with cognitive effects of games and other mental activities is that not only are they harder to measure by default, but they’re almost all examples of far transfer, or skill transfer between dissimilar domains. The existence of near transfer tasks, like playing video games and getting better at other video games, seems pretty intuitive, but extending that intuition to far transfer gets pretty hazy. It’s totally possible that playing chess does strengthen a certain part of our brain and leads to something neurologically measurable, but whether that then translates to improved performance at something like math or stock trading is difficult to figure out at best, and most studies so far haven’t found much evidence for it.

For example, here’s a recent meta-analysis on chess and music: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5724589/

So it’s really not just brain training games where the skills are non-transferable, it’s pretty much any cognitive activity. Playing one instrument will probably help you play other instruments better, but maybe it won’t be as helpful for learning Chinese. The issue with brain-training games isn’t that they’re useless, it’s more that they rely on the idea that far transfer works, and the only skill they really build is peoples’ ability to get good at their games. If the skill you want to train is puzzle-solving, you’re good to go; it’ll give you puzzle training + a few other cognitive perk-ups.

General brain activity is pretty scientifically proven to be a good thing (lots of studies have found correlations between high levels of brain activity leading to lower levels of dementia, for example), so brain training games are probably a fine way to keep your brain ticking. If you’re doing brain training hoping it’ll boost your general intelligence and help you pass an exam or something in the future, though, you’re better off just studying that thing. As far as we know, one type of cognitive activity is as good as another, so you might as well just directly target the skill you want.

Actual exercise might actually be a better option for improving overall brain function and memory, though! There’s a big literature coming out these days on how especially cardiovascular activity is strongly linked to better cognition, which points again to a lot of these more general factors being mostly physical, and perhaps marginally affected by cognitive input.

Nikola Tesla was right

Not about everything, but I’ve been reading up on far-field wireless energy transmission and it seems that Tesla was at least right about the possibility. It’s actually way further advanced than I’d previously thought–I knew it was possible to beam electricity, but I was only vaguely aware that it had left the lab. It turns out there are actually several different companies (Ossia, Energous, PowerCast, and Wi-Charge, to name a few) that have produced systems capable of figuring out where a receiver is and beaming power to it.

The biggest obstacle in this case is the FCC. Parts 15 and 18 of FCC Title 47 limit devices’ frequency emissions, both in slightly different ways, but with the net outcome that these devices are limited to a few watts of maximum output (for reference, phone chargers tend to be around 5 watts) each depending on the distance of the power transmisison (though there doesn’t appear to be a restriction on having multiple of these devices in one place). Since it’s a relatively new technology, isn’t really a life-or-death situation (as pharmeceutical approval processes can be), and presents relatively underexplored safety concerns, I don’t have a huge problem with that.

What really grabs me about this is that wireless electricity transmission is so close, and what’s really holding it back from being more exciting (i.e, being on the front page of most major news outlets) is compliance with FCC regulations. I’d only heard about it once or twice before deciding to look into it a little more deeply, and, at least to me, it seems like such a sci-fi technology that I find it hard to believe that it hasn’t gotten more coverage yet. As soon as I realized how advanced it was getting, I was instantly hooked and had to look into it more–but it’s also prompted me to wonder how many more things are happening that haven’t popped in front of my eyeballs yet.

The infoverse is growing exponentially, since it’s essentially a function of human population * technological innovation, and at some point, even the most passionate of infovores has to realize what they’re up against–learning huge amounts of neat stuff has never been so possible, but keeping up with that stuff is getting increasingly difficult.

In other news, Disney Research has also made some wireless power breakthroughs. But that’s frankly less interesting than the fact that I just learned today that Disney Research is a thing. I’m not mad that they haven’t made any talking animals or flying carpets yet–just disappointed.

Georgian teachers and Indonesian experiments

I just came across a fascinating piece on a Georgian (the country, not the state) economics blog, Tbilinomics. It won’t really come as a surprise to anyone that teachers in Georgia are chronically underpaid, as they tend to be in many countries, but what’s really surprising is maybe just how much they are underpaid. They make under 60% of the average national of 1000 GEL (372 USD) at around 580 GEL (220 USD)–less than agricultural workers, retail workers, hotel workers… you get the picture.

This salary clearly doesn’t attract the best and brightest into the profession, and it shows in the test scores: Georgia is among the lowest-scoring countries on the international PISA ranking. Scores have been going up steadily, perhaps due to some earlier pay increases for teachers (yes, it used to be worse), and a new proposal would actually see teachers earning substantially more, up to 2 or 3 times their current salary, but would this actually have an effect?

Regardless of how incentivized they are to do better, even if they quit their side job and focus on teaching, pay increases won’t automatically create more qualified teachers, and the teacher’s union in Georgia is apparently pretty powerful. It’s unlikely that pay increases would pay off in the short term, since the money would be going to existing teachers without an accompanying incentive to actually improve instruction quality. Frankly, even if they’re motivated by the pay increase, old habits die hard–education systems all over the world struggle to reform with any sort of speed simply because it’s such a highly personal issue.

And, as Eric Livny points out in the article, this has been tried before: Indonesia ran a large multi-year experiment in teacher pay raises, working with the world bank to collect and analyze the data. When all was said and done, the study found no effect. Even with a custom-designed randomized controlled trial, they couldn’t find evidence that the pay increases improved instruction quality–though the teachers were substantially happier. That sort of goes to support my intuition: people don’t necessarily change because their pay does.

But I’d argue that this finding doesn’t conclusively mean that higher teacher salaries won’t improve teacher quality and student grades in the long run. As long as teacher salaries are coupled with more access to educational resources and student support services, there could certainly be a substantial effect down the line.

Max Planck once said that “A new scientific truth does not triumph by convincing its opponents and making them see the light, but rather because its opponents eventually die, and a new generation grows up that is familiar with it.” What he meant, of course, was that old ways are held by the old guard, and until they’re gone, the ways will just stick around. There’s not much we can do about it, and there’s not even much they can do about it–sticking to a paradigm isn’t just a psychological comfort mechanism; old ways are built on heuristics that we’ve been developing our entire lives, and wiping the slate clean isn’t so easy if you haven’t made a habit of doing it previously. 

The logic here, though, is that if we establish higher pay standards now, teaching will become a more attractive career for new entrants. Current teachers will probably stick to what they’re doing and we won’t see much improvement, but future teachers might decide to choose education because it’s a legitimately decent career option. Low salaries just don’t attract the best and brightest candidates (though PhDs seem to be an exception to this rule, particularly in the adjuncting phase), whereas a salary that’s at least average will probably attract a decent mix of above-average people who are passionate about education and people who weighed their financial options and personal aptitude and chose education over, say, nursing or fishing. I’m curious to see if the Indonesian World Bank experiment pays off by attracting more qualified candidates to job openings.

Unfortunately, this means we have to wait. Possibly a while. The old teachers will have to clear out of their jobs (perhaps this could be hastened by a retirement bonus) and the new teachers will have to go to school and get into the workforce.

That’s a lot of outlay for very little near-term benefit, though. The across-the-board pay increase might be the most politically effective way to get the teachers union and others on board, but it’s probably not the most efficient. Another scheme, such as offering different pay levels for different levels of qualification, might be more effective, followed by a gradual phase-in of higher pay across the board as average teacher quality rises.

If the increase is pushed through, however, I’d hope that it would be leveraged for its full research potential as was the case in Indonesia.



Air Pollution Fact Roundup

Recently, the city I’ve called home for the past few months (Chiang Mai, Thailand) has been making it to the top of the most “world’s most polluted” lists on a pretty much daily basis. Today, the PM10 meter maxed out at 999 μg/m3 in one area, and the PM2.5 meter wasn’t far behind at around 850. For reference, the EPA defines an “acceptable” level as being below 12 micrograms per cubic meter (μg/m3). 12-35 is moderate, 35-55 is unhealthy for sensitive groups, 55-150 is unhealthy for everyone, 150-250 is very unhealthy, and 250-500 is hazardous. The EPA’s scale tops out at 500, since that basically doesn’t exist in the U.S.

I’ve been getting throat and nose symptoms, a dull headache, and general tiredness out of this whole experience, which hasn’t been great. I lived in South Korea for a few years, where the air quality hasn’t been amazing recently (it can go above 100, but I didn’t see above 200 happening much), but I never felt symptoms there. I’m not exercising, barely leaving the house, and generally in kind of a bad mood as a result of all this, which has left me some free time to obsesively research air quality topics. One of the things that’s caught my attention is the “Air Quality Index.” It’s essentially a market basket of various pollutants developed to help people better understand the impact of air pollution on health, which is great, but also confusing, especially when you’d just prefer to know exactly how much PM2.5 is being pumped into the air, since that’s what we’re really worried about.

Air Quality Indexes vs PM2.5

When you look up the air quality in a certain city, you’ll probably see an AQI, or an Air Quality Index–not an actual measurement of any one pollutant. There are a few reasons this is confusing:

  1. Different countries have different air quality standards. In China, a 10 is fine. In Canada, 10 is basically “we can’t count the moose in our backyard.” These different metrics are all calculated using different mixes of pollutants, each of which may be weighted differently, measured over a different period of time, and interpreted differently.
  2. Many AQIs (the EPA’s in particular) are non-linear, as they’ve been normalized to fit the basket of pollutants into a scale ranging from 0-N. That means that multiplying a number on the scale by 2 does not necessarily mean that it will be twice as bad for you–it actually may be much more than twice as bad.
  3. Many AQI measures can be pushed up by less harmful pollutants, which makes them fairly misleading when you really just care about PM2.5.

In general, an air quality index is one good way to get a quick grasp on air pollution, and the equations do make sense. However, in a situation like the one I’m currently in, what I really care about is the mass concentration per cubic meter of PM2.5, and I think it should be more of a general standard than it is. It lets you directly know what the content of the air you’re breathing is, isn’t skewed by different equations or national standards, and it allows for pretty much any air situation to be immediately understood.

When it comes to air pollution data, which we use to make large and small decisions on a regular basis, simpler is better for the public-facing stuff, and in this case that doesn’t mean giving people a mystery number and a color. Ideally, different AQIs would be available as part of the data presented by air pollution information sites, but more emphasis on the concentration would really help improve general understandability. Going with concentration data is more scientific, it’s linear, and it’s universal.

In the meantime, if you see an EPA AQI number anywhere, you can use this calculator to find the actual concentrations of each pollutant.

On Nomaducation: could the next digital nomads be students?

“Digital nomad” is a term that’s been making its way increasingly into the mainstream. As internet access and speeds around the world increase and the economy increasingly goes digital, working remotely or on a freelance basis is becoming increasingly possible. Coincidentally, so is studying online: there are literally thousands of MOOCs (Massive Open Online Courses), more free or easy-to-access educational content than you could ever possibly consume, and even fully-accredited, inexpensive degree programs you can pursue via laptop no matter where in the world you are.

This is all fairly new, so it’s understandable that no community has really emerged around student nomads yet, but maybe that time is close. Maybe the “nomaducation” buzzword is just a few years away from becoming the next “digital nomad.”

I’ve been doing it. Albeit, not on a full-time basis at all: I got my undergrad in the U.S, moved to South Korea to teach for a few years (the savings were a great jump-start), moved back to the U.S, moved to Thailand, and am currently planning on moving to Tbilisi, Georgia. The whole time I’ve been working and studying at the same time, and while it eats up most of my free time it’s been going pretty great. I’ve built a lot of skills and identified some things I really want to work on more deeply. In a nutshell:

  • I’ve improved my math and stats skills,
  • gotten into programming,
  • developed my passion for economics,
  • studied data analysis and visualization,
  • cultivated in interest in behavioral sciences,
  • learned the basics of a few languages,
  • taught English as a foreign language (I actually got pretty good at it–after a while)
  • written for tech, finance, and blockchain publications,
  • learned how small the world can be
  • met people from all over
  • listened to hundreds of hours of podcasts
  • taken so many MOOCs
  • and way more

In terms of knowledge and experience accrued, it’s been a clear win. In terms of time, it’s certainly taking longer than if I’d gone straight to a masters program, but in retrospect, I’m glad I didn’t. I’ve explored so many new worlds since I started my journey that it’s starting to get hard to keep them all straight. That’s why I’ve decided I really need to start specializing–but I digress. Here’s the pitch:

  • Travel is its own form of education
  • Online degrees are getting more common and acceptable
  • Online work is becoming easier to find
  • Living in low-cost countries can often be cheaper
  • Resources to serve the digital nomad community are becoming readily available

Essentially, while it’s certainly not the right call for everyone, I’d argue that “nomaducation,” or studying and travelling at the same time, is a small trend just waiting for a name and a community. That community part is important, and it’s most of the reason I’m writing about this now: I’ll get to that in my next post. Suffice it to say that I haven’t found much of one, I miss it, and (as you might have guessed), I’m taking a shot at creating it.

Where are all the homebrewed bomb drones?

The title alone seems like enough to get me on a list, but this is a topic that’s been turning over in my head for a while now, and ever since the Gatwick incident in December 2018 when as-of-yet unnown drone operators paralyzed an entire airport by just casually flying around. The cheapness and availability of drones combined with humanity’s tendency to blow each other up seems like it should have sparked some sort of an epidemic of drones with explosives strapped to to them committing acts of terrorism.

That’s only happened a few times, though. All I was able to turn up were the following incidents:

Out of those, only ISIS and the Houthi rebels actually did any damage. The Houthi attack is too recent to know if it will be adopted as a future MO, but if ISIS didn’t find it especially effective, it may just not be that great of a way to take out targets. Though they did post a propaganda poster of a drone headed towards the Eiffel Tower [Autoplaying video behind link].


This is hardly a neglected threat–Nicholas Grossman has written a whole book on it, and FBI Director Christopher Wray has expressed his opinion that this will be a big issue in the future. The fact remains, though, that we have yet to see a large-scale, high-profile drone bombing in the vein of other terror attacks. A few possible reasons for this:

  • It lacks the impact of a personal attack: terrorism isn’t about eliminating valuable targets, it’s about sending a message, and sending in a drone just doesn’t say as much as sending in a person.
  • It’s actually not that easy: maybe figuring out how to get sufficient amounts of explosives onto a drone, getting the trigger mechanisms right, flying it into the right spot, and detonating it isn’t as easy as it sounds.
  • Maybe drones just haven’t caught on yet: sure, they’re a hit on the commercial market, but maybe the first domino hasn’t fallen to set off the terrorism market yet. Maybe all the pieces are there, but no one has seen them used destructively enough to copycat them.

Either way, it seems likely that we’re due for that first incident. Drones are easy to get and wiring explosives onto them surely can’t be harder than wiring them other places. A large-scale drone attack is probably coming sometime in the next decade, and that will likely be the start of a trend, which will, unfortunately, probably lead to lots of restrictions on drones and the increased government use of drones and drone countermeasures.

Honestly, I’m more surprised that, given humans’ historical fixation with implementing new and exciting ways of murdering each other, we haven’t already filled the skies with small exploding death-copters. Maybe the world actually is getting more peaceful.


Moving from Humanities to Data (and around the world)

My geographical and intellectual journeys aren’t directly related, but over the past few years, my lack of a stable location has certainly played into my sense of what’s possible, what’s measurable, and how best to understand the forces that shape the world. Long story short, I’ve been making the jump from math-averse humanities major towards a me that is comfortable with numbers, models, uncertainty, and testing hypotheses.

I’ve made a lot of different mental jumps in a lot of different places, though, and since my history education instilled in me a strong sense of narrative (something humans love to impose on the world, whether it belongs there or not), I feel driven to connect the two. In this post: how I apparently am choosing to explain some of my life choices.

tl;dr: I move around a lot, mostly between relatively affordable urban areas in different countries. Humans and cities can only be fully explained with data, which is why I’m going to be making a series of posts recording some interesting pieces of my now multi-year journey towards getting better at doing that. 

Global variables

Since graduating, I’ve lived long-term (6+ months) in several parts of the US, two cities in South Korea, one city in Thailand, and I’ll be moving on to Tbilisi in the Republic (not state) of Georgia pretty soon. Since I’ve sort of fallen into online work, it makes sense for me to live in places that offer more amenities and a lower cost of living than the US.

This has also given me a lot of opportunities to encounter a lot of different ways that things work, as well as radically different perspectives on the way they should work. I’ve generally come around to the idea, though, that these differences exist more on the surface than the photographs and travelogues would have you believe: humans tend to be humans. All other things being equal, the reasons for our behavior tend to change more than the behavior itself does.

The common currency I’ve fixed upon in trying to understand these forces is data. Obviously, you can’t plug variables into a regression and predict every aspect of a country’s culture–but you can make a pretty darn good guess about how things work in that country. As complex as our constructs are, there are global variables underlying them.

The data density of cities

That may be why my primary target in a new country is always the cities: nowhere can you find a higher density and diversity of available data. Walking around a new city for a few days, with open eyes and random feet, is basically skydiving for a certain type of data nerd. You won’t discover everything, and a lot of your impressions will be wrong (they’ll be wronger the less time you spend), but if you pay attention you’ll end up with a collection of means and standard deviations for everything from the price of a beer to the general quality of life experienced by residents.

My infovorous (what is the adjective form of “infovore”?) tendencies probably explain why, despite my rural upbringing, I’m an urbanite at heart. That’s an increasingly expensive thing to be in the states, where urban density and mixed-use zoning tend to meet stiff resistance, which, for better or for worse, has pushed me to venture out into other countries. Most of the cities I find attractive aren’t the ones with idyllic suburban neighborhoods or adorably preserved downtown boulevards, but the ones where you can find a new apartment building going up every corner, gradually being surrounded by the shops and restaurants its residents demand. As far as I’m concerned, aesthetics take a clear back seat to affordability and convenience.

A personal geography

This personal preference for efficiency over beauty probably explains a lot about what frustrated me with humanities (a tendency to emphasize the subjective and unquantifiable aspects of human experience) and what I find attractive about the prospect of engaging with data (the drive to measure what can be measured, to quantify the unquantifiable where possible, and a certain level of comfort with error). Neither extreme is preferable, of course: purely data-based decisions are likely to ignore things that are difficult to measure, while purely qualitative decisions are likely to be subject to a wide array of human psychological errors and biases.

I’m very happy to fall somewhere in the middle of the qual-quant spectrum, as that’s where a lot of truth (with varying confidence intervals) tends to lie, and especially since that’s where I’m likely to remain in terms of my abilities. I’m decidedly weaker on quantitative skills than I’d like, though, which is why, since graduating university in 2014, I’ve been on a journey to improve them. It’s been slower than I’d like, hindered by things like having to “earn money” and “live life,” but I know I’m not the only one trying to reconcile their idealistic teenage degree choices with the facts of a rapidly expanding reality, which is why I’m hoping to make this a series detailing my steps and missteps, the resources I’ve used, the progress I’ve made, and the gravities that have pulled me into various orbits.

This post hopefully takes care of a lot of the “why,” and in future ones I’ll mostly be focusing on the “how.”

Analyzing Facebook Data: Part 1

To get some practice using Python for data analysis, and to get more familiar with a few data visualization tools, I’ve decided to do a series of projects on random datasets and see what I can get out of them. For my first project, I’m taking a look at the data dump that Facebook gives you.

The mission:

  • Download all my data from Facebook, figure out what’s interesting about it, analyze it, and visualize it.

There will be two main dimensions to the analysis: a look at the general type and structure of data that Facebook keeps, and a delve into my own personal data to see what’s really going on there.


I graduated with degrees in history and sociology, so I’m incapable of starting a project without developing some research questions. Though they’ll be refined once I get into the data, initially, I’m looking to find out:

  • What are the main types of data Facebook has (interests, location, advertising, interaction history, etc)?
  • How extensive is this data, exactly? How far back does it go? What gets kept around?
  • What can I found out about myself (or someone else, theoretically) with unfettered access to this data dump?
    • Interesting sub-question: if I was to reconstruct my life using only Facebook data, what would it look like, and how closely does it match my real life?

Getting started

Downloading your Facebook data is pretty easy, though the files aren’t tiny. That’s probably because you’re not just getting text data and code—you’re getting every picture and video on your profile.

I opted to download all my data in both the JSON and HTML formats, just for kicks. Opening up the unzipped JSON folder with Jupyter Notebook reveals a bunch of folders with names like “friends,” “ads,” “location_history,” and a bunch of other stuff. The JSON files within the folders are, as you would expect, full of information on you in raw text format.

The HTML folder is a bit more user-friendly, so it should be good for answering some of my initial questions and getting an idea of what I should be looking for. The file structure is identical—same folder names and everything—but opening the HTML files gives you clear human-speak language in a nice format.

What are the main types of data?

Okay, first question: what’s in the box? (WHAAAAT’S IN THE BOOOOX?!”) Some big categories jump out right away:

  • Profile data: about you, pages, profile information
  • Friend data: Friends, groups, following and followers
  • Timeline/interaction data: Comments, events, likes and reactions, messages, photos and videos, posts
  • Advertising data: ads
  • Location/security: location history, security and login information
  • Stuff you looked for: search history, saved items

Upon inspection, it turns out that this is pretty much your entire timeline. The lion’s share of data here is your posts, comments, likes, photos, videos, and messages, which is all stuff you’ve generated and which has stuck around as a mini-history of you. This stuff will be interesting to look at from a behavioral/statistical standpoint. What’s my posting frequency by year? Average post length? Of course, I could get one of those funky little Facebook analysis apps to do this for me, but the whole point here is to analyze it myself.

What I’m really looking forward to here is the stuff I didn’t generate, because I don’t know exactly what’s in there. Do they know everywhere I’ve been? Do they keep a record of every login? What’s my advertising profile like? And search history? Yeah, I generated that, but are they keeping a record of every single search I’ve ever done? This is where it gets interesting, mostly because it’s a little creepy.

Over the next few weeks, I’m going to go piece by piece, looking at the data with both human eyes and some Python code. With any luck, I’ll produce some interesting data and some kind of data visualization on everything I look at. Coming up:

  • A personal analysis
    • My posting history
    • My comment/reaction history
    • Differences between my messages and posts
  • Photos
    • I noticed that the JSON files included a fair bit of photo metadata, so I’ll see what I can do with that.
  • Creep factor
    • My location history (Facebook also logs IP addresses in a separate location, so it probably goes back a long way)
    • My search history
    • Advertising: what are they looking at? Who is advertising to me?

Towards a theory of coffee pricing in Southeast Asia

Country Average price of 1 hot Americano
Indonesia (Bali) 1.60 USD
Singapore 2.25 USD
Thailand 1.60 USD
Laos 1.75 USD


Country Average price of a budget restaurant meal
Indonesia (Bali) 2.50 USD
Singapore 4.50 USD
Thailand 2.00 USD
Laos 2.50 USD
A cup of coffee in Cambodia, part of Southeast Asia I ironically did not include in this article

DISCLAIMER: These tables are based on my own experiences travelling on a budget through SE Asia; they are not actually representative of a random sample of coffee shops and are very subjective. But if you are travelling on a budget in SE Asia around late 2017, feel free to use this as a rough guide of what you’ll pay for coffee and food.

I lived in America for most of my life, and Korea after that, so something about the tables above is strange to me. I’ll give you a chance to guess what I think is odd.






Did you guess price ratios? Because if you did, you’re right, and I’m not apparently alone in thinking it’s odd.

In Indonesia you can buy a cup of coffee for about 65% of what you would pay for a meal. Singapore offers a more reasonable 45% (these numbers are skewed by my very driven efforts towards budget eating, though). In Thailand, a coffee will run you about 65% of your meal. Laos is about 65%. (Disclaimer again, these are fairly subjective numbers, but I have about 95% confidence that a cup in Thailand will cost you between 1.00 and 2.15, given that you aren’t above buying coffee from a street vendor and are avoiding anyplace that looks “too fancy.”)

In most of my experience, coffee rarely costs anything close to what the meal does, even if you are eating on a budget in America. I should point out that none of those average coffee prices above include visits to Starbucks or similar chains, because then all bets are off and you may as well stop eating altogether the way some of South Korea’s doenjang-hyoja (bean-paste girls: girls who eat very cheap food in order to afford fancy clothes and Starbucks) do.

So this made me curious–why are coffee and food such similar prices in Southeast Asia, and why is most coffee priced not that much lower than what you’d pay in the US?


Coffee beans are an imported input

In a way, the second question answers the first. Coffee prices worldwide don’t vary as much as food prices because coffee is difficult to produce domestically. The price of a coffee bean bought from a plantation in Brazil or Indonesia is about the same for a U.S importer as for a Thai one.

This means that the cost of coffee-making inputs remains fairly standard between countries, with some variability based on distance and perhaps grade of coffee purchased.


Making coffee isn’t very labor-intensive

But then you have the variable cost–people have to work to get that coffee bean to the store, keep the lights to the cafe on, grind the coffee, brew it, and serve it, and wages in America should definitely affect the cost of the coffee. It does, but the price of coffee in America definitely doesn’t rise proportional to the cost of labor/utilities/infrastructure in America versus Thailand.

I’m sure better people than I could do more than speculate, but my general theory is that for your average budget price of coffee, variable cost stays fairly low worldwide–it’s not too labor-intensive to make, so as long as you can pay the price of the coffee bean, you don’t have to invest too much in skilled workers to painstakingly prepare the perfect coffee. In Thailand, you pay a little less because the labor and operating overhead are cheaper. But you’re paying a lot for the coffee bean itself in both countries.


Food is domestically produced and labor-intensive

Food is, relative to coffee, a high-labor product. A single barista could churn out quite a few coffees in an hour, whereas a single chef probably couldn’t approach the same number of meals. So when you get a dish of pad see ew in Thailand, you can be sure it took more time to make than a coffee, and you’d expect the price to reflect the higher labor cost.

But you’d also expect the greater quantity and complexity of materials to be part of that price. A noodle dish needs meats, vegetables, sauces, seasonings, and all sorts of other things that require individual preparation and combination.

The big difference is that almost all of these materials can be produced close by, especially since the local cuisine tends to use cheap and available ingredients. There’s no single country exporting rice noodles or kale–the restaurant has access to a much more competitive, local market to source its materials from, and as such the cost of materials doesn’t include higher labor costs in the exporting country or the exporting costs themselves, or import duties, or supplier monopolies, or any of that.

And once you have those cheaper materials, you can use the cheap domestic labor to prepare them. The price is always higher than a cup of coffee for the reasons stated above (food requires more materials and labor), but the profit margin may be fairly similar–it took more labor to make your fried rice, and more ingredients, but both of those were cheap, whereas in the coffee equation only one of those was cheap.

So my basic, and almost definitely flawed, theory of coffee:meal relative pricing in Southeast Asia is this: With coffee you’re buying a little labor and a little of something expensive; with meals you’re buying a lot of labor and a lot of something cheap.

Stay tuned for my New York Times bestseller on the topic.

Some additions given new information

I’ve written a few speculative posts about Thailand–some updates on those, given my new experiences in Laos:

  1. In Laos, currently the poorest country in SE Asia, I’ve been seeing a higher ratio of “Asian  pickups,” and also a larger Korean and Japanese presence in the market in general. This leads me to believe that there are probably factors beyond necessity playing into the high Thai usage of “American” pickups, but the ratio I’ve observed in Laos has still been roughly 3:1 American:Asian pickups, so my guess may still hold some weight. There’s also a lot of Korean and Japanese aid going to Laos, so it’s possible that their pickup preference may derive from convenience over preference. I’m not ready to make this my master’s thesis yet though, so I’ll stop myself short of any actual research.
  2. ATM fees in Laos are about $2 USD, though given relative Lao income, this is still a fairly steep fee. Again, I believe it is only levied on foreign cards. We did once pay $4 since we were unsure of where the next ATM was going to be and we needed cash, thus contributing another data point to the elasticity of demand equation.