A friend recently sent me the following Actual Advice Mallard, containing a strategy for buying airline tickets that is the hot new thing on the interwebs:

A link from Chris Blattman’s blog indicates that the source of this claim is most likely this reddit post by /u/Valendr0s, who claims experience working in airline ticket pricing and was rewarded with reddit gold (worth $4) for his comment. So this is widely seen as a useful fact, and taken at face value. And I don’t believe it for one second.

My skepticism is motivated by two factors: 1) theory, from my perspective as an economist-in-training, and 2) evidence, from the fact that my mother works for the world’s largest airline and the related fact that I am cheap as hell.

Theoretically, what are airlines trying to do when they vary their prices? Well, probably a lot of things, but most importantly they’re trying to price discriminate. This means separating their customers into categories by how much they’re willing to pay for tickets, and charging them different prices. A simple case of price discrimination happens at movie theaters, which give discounts to students and seniors, both of whom tend to have less income than middle-aged adults.

The classic example of price discrimination for airlines is to charge more for a roundtrip ticket that returns on a Friday than one that returns on the following Sunday or Monday. Why? Business travelers don’t want to spend their weekends away from home, and tourists do. Since business travelers don’t care as much about the cost of their ticket, this price structure can extract more profit out of the market by dividing it. If you charged the same price, then you could either charge a low price tourists will pay (and lose out of extra profit from business travelers) or a high price to gouge business travelers (and lose out on all the sales to tourists). Charging two different prices is the best of both worlds.

How else can airlines price discriminate? I’ve often suspected that the seemingly-random fluctuations in ticket prices over short periods of time are part of another price discrimination strategy. For simplicity, let’s say the market comprises 50 cheap people like me, and 50 normal folks who don’t want to waste their time shopping. Normal people will happily pay the typical market price of $100 for a ticket, but cheap people won’t, they’ll shop around or reconsider if the price is above $40. The two groups are otherwise identical, so there’s in principle no way to differentiate them. If the airline charges $100/ticket, only the normal people buy tickets and it makes $5000.* If it charges $40/ticket, everybody jumps on the tickets and it makes $4000. It seems like there’s no way to do better.

But maybe there is: the airline could randomly offer a $60 discount, 10% of the time. If it does this, then it makes 45*$100+5*$40=4700 from the normal people, who will take either price they see. The cheap people (e.g. me) will hit reload on kayak.com until they see the price they want. All 50 of them will eventually get the $40 ticket offer, so they pay a total of $2000 and the airline’s total profit is $4700+$2000 = $6700. This approach, which I’ll call “randomized price discrimination” just to give it a name, is much more profitable than either of the two alternatives.

Now, you might object that everyone should keep shopping until they hit the jackpot, but the assumption that some people won’t is potentially quite realistic. First off, consumers would have to realize this is how the system works. If cheap people go to different sites instead of refreshing just one, they might get the discount without knowing why it happened. Second, higher-income people have more reason to value their time, and empirically appear to do so more.

It’s also possible that airlines could do both: offer discounts at random but also try to identify “desperate” passengers. But pure reloads, tracked by cookies, are going to confound the two. We need some data. 
I don’t have any data, but what I do have are a couple of strong anecdotes. By virtue of my mother’s employment with Delta, I’ve done the vast majority of my lifetime travel on non-revenue passes. These were free when I was younger, and now are steeply discounted relative to full-fare tickets. The only catch is that when I use them, I fly standby. That’s usually an added benefit: I don’t have to plan ahead and can arrive pretty late to the airport. Every once in a while, though, all the flights are full, occasionally for days on end.

I therefore have a fair bit of experience with the kind of “urgency” that Valendr0s says airlines try to exploit: twice in the past year or so, I’ve ended up with no ticket, already late for something I needed to do, and had to give up on standing by for flight after flight and bite the bullet on a full-fare ticket.

In these situations, I’ve done exactly what the advice mallard discourages: continually hit refresh on travel search websites until I find something cheap. I generally look for roundtrip tickets, which are cheaper than one-way ones,** and move the return date around as I don’t plan to use the second leg.*** Consistent with my theory of randomized price discrimination, I was eventually able to find a ticket discounted by over 50% relative to the median, departing on the same day I bought the ticket. These prices I ended up paying would have been excellent even if I’d bought the tickets weeks in advance. My strategy focused almost solely on repeated searches in the Kayak app for my iPhone.

It might still be possible to rescue the Valendr0s model: maybe moving the return date around helps, or maybe there’s something special about the Kayak app. Maybe I “got lucky”, although that seems equivalent to the randomized price discrimination model being true. More important, I only really have two datapoints on this. If I were more ambitious, I might try to set up a data scraper to collect price offers under different conditions (incognito, cookies off, normal) for a range of different flights and days and see if we can sort out these different models.

Failing that, though, I’d actually recommend doing the opposite of what Valendr0s recommends. If you want cheap airline tickets: 1) don’t clear your cookies; 2) hit refresh, over and over; and most important, 3) signal thriftiness, and prove it by revealing the low value you put on your time.

Hat tip: BTK

*I’m ignoring any marginal cost per passenger here (that is, the increased fuel and soft drink costs associated with another person on the plane) but the result doesn’t depend on that.

**I still don’t have a good explanation for why this is the case. My only theory is that it’s a way of price discriminating between more- and less-informed travelers.

***Even if I wanted to use it, I could just pay the change fee later on.

In an article on Slate, Jenny Trinitapoli and Alexander Weinreb argue that the decline in HIV incidence in Africa can be attributed to religious leaders preaching a pragmatic message of sexual morality and caution. As evidence, they cite different behaviors and beliefs among congregants at churches that discuss AIDS and sex compared with the rest of the population, based on their own research in Malawi.

This idea is consistent with some of my own personal experiences in Malawi, but the first question that comes to mind is what kind of person chooses to attend one of these particular churches, and why they do it. Church membership is anything but random, and it’s conceivable that self-selection is driving a lot of the differences they are picking up. Even if that’s not an important factor, there’s also the issue of the epidemiological importance of those affected by these religion-based messages: HIV epidemics are driven by high-activity subpopulations, who don’t strike me as particularly likely to show up at church. Instead, I’d assume they are more like the people Trinitapoli and Weinreb describe in this passage:

Many of us drink in the comfort of our own homes (often accompanied by our loving partners). But consumption of alcohol across Africa tends to be more public and to occur in places that provide opportunities for unsafe sex: Women working at bars and bottle shops often double as prostitutes.

Another question is what other factors should also receive credit for any decline in HIV incidence. Mother-to-child transmission prevention efforts now often involve putting HIV-positive mothers on permanent antiretroviral therapy, which we now know to have pretty huge benefits in terms of reducing HIV transmission.

I’m also curious about their claims about changes in sexual behavior and HIV incidence – the authors cite incidence declines for Africa as a whole, but those don’t match what I understand to be the case for Malawi in particular. UNAIDS estimates show the incidence (new cases) to be fairly steady at 1% of the overall population.

Maybe you can read a 25% (not percentage-point) decline since 2001 off that graph if you squint at it, but that would be something like 1.25% to 1%, a pretty small change in actual magnitude. If you do a back-of-the-envelope calculation, with a life expectancy of 10 years after infection, 1% incidence is consistent with a stable prevalence of 10% of the population. We can take this a step further: imagine following a cohort of 15-year-olds over time. Each year, 1% contracts HIV, so that the average across all people 15-49 is 1%. While increased mortality among HIV-positive people will keep the prevalence at any given time near 10%, by the time people reach 49 their total chance of HIV infection is 34%.* One in three. I’m not sure that it’s time to figure out whose needs to get credit for our huge success yet.

This isn’t to say that the idea isn’t really valuable – based on what they’re finding, I think somebody should try a pretty simple experiment where the treatment is training preachers on the sex and AIDS messaging that appears to be working.

*You might assume that by spreading the incidence over the 15-to-49 age range differently one could get a different answer, but I’m pretty confident that it doesn’t matter. If I had the inclination I could probably prove it mathematically.

Healthline has a press release about a paper by Orpinas et al. that studies the dating trajectories of adolescents (gated) and shows that people who date early (starting from middle school) have worse academic performance. The press release is titled “It’s Best to Wait”, and it does its best to pick and choose quotes from Professor Orpinas to make it look like she and her coauthors have found a causal relationship:

“When the couple splits, they have to continue to see each other in class and perhaps witness the ex-partner dating someone else. It is reasonable to think this scenario could be linked to depression and divert attention from studying.”

This is frustrating. The paper itself is really interesting, and not that hard to summarize: we can group adolescents into a set of just a few distinct dating trajectories, and people who follow different trajectories also differ in their academic performance, dropout rates, and drug use in the ways you might imagine they would. The graphs are really nice, too, to the point where I wish they’d made more. Here’s an example showing the different dating trajectories – note the “high middle school” group, which goes down and then back up.

What I dislike about the Healthline report is that as far as I can tell, the authors make no causal claims of the kind the press release makes. They show an interesting association, and Orpinas is right that early dating could partly be a cause of poor academic performance or drug use. But to paraphrase something my advisor once said about a potentially causal relationship, “A lot of other stuff could be going on.” To pick one likely omitted variable, middle schoolers might start dating young because of a permissive home environment that also allows them to slack off in school.

“Early dating may be partly to blame for poor grades but they both emerge from a complicated system that shapes students’ lives so any causal effect is probably small” doesn’t sell well. I get it. You need a nice headline. But I’ve got a bit of a vested interest in this fight: I have several personal friends whose parents thought they needed to prevent them from dating in order to improve academic performance. I’ve always thought – and continue to think – that this attitude is bizarre, unjustified, probably harmful to social development, and poisonous to the parent-child relationship.

Hat tip: probably reddit.

Ever since the early 1990s, a number of influential economists have argued that many epidemiological models need to be modified to account for endogenous changes in transmission. Traditional epidemiology assumes that individual choices can be ignored: models do not allow for individual agency, and the people being modeled just act like marbles bouncing of each other at random. More recently, the rules for these agency-free models have gotten more sophisticated in separating out different types of individuals, but the current paradigm in epidemiology (which I was exposed to during Jim Koopman’s excellent course on the topic in Fall 2011) still doesn’t have the people being modeled actually making any decisions.

Economic epidemiology has continued to develop as its own cross-disciplinary field, and has consistently focused on HIV as its most important case study. Starting with Philipson and Posner, economists have argued that models that account for risk compensation do a better job of forecasting the spread of HIV than those that ignore it. This pattern is stronger among gay men in the US than in sub-Saharan Africa, where responses tend to be small or even statistically indistinguishable from zero. However, recent work by my advisor and her coauthors (Godlonton, Munthali and Thornton 2012) and Dupas (2011) has shown that people in Africa do respond by changing their sexual behavior when they are taught facts about the relative risks of HIV transmission across population groups. At the same time, there’s increasing evidence (from Godlonton et al. and other work by Anglewicz and Kohler) that people in Malawi badly misunderstand HIV risks across all dimensions: they overestimate the prevalence of the virus, it’s transmission rate, and how quickly it kills you. I’m not aware of evidence that’s quite as systematic for developed countries, but preliminary research by Thornton, Foley and myself looking at US college students finds similar overestimates.

This is perplexing: it’s easy to see how the respondents in the Godlonton et al. and Dupas studies could respond to risks, because they were actively taught what those risks were. But how can we explain the famous* example of gay men in San Francisco reducing their sexual risk-taking in line with rising HIV prevalence if, as I suspect, they weren’t really aware of what the prevalence was? I suspect that many studies that compare individual-level behavior with factual information about disease risks are in fact picking up an intermediate variable, which is policy responses to the epidemic. The one group who is likely to understand the actual risks is the health authorities, who can enact provisions in response such as ad campaigns that aim to change social norms.

If this is what’s really going on, it would go a long way toward reconciling the finding of small risk responses in Africa (e.g. Oster 2012) with the larger ones seen in the US. Maybe individual responses are always small on average, and public health authorities are just much more active and responsive in the developed world (which would be consistent with their relative levels of funding).

This would also change the whole discussion of what economic epidemiologists have been measuring. If we are picking up responses by health officials, rather than individuals, then we can’t argue that our results are policy-invariant and hence a guide to optimal disease prevention.

NB I’ve been writing this on my iPhone as I weather a lengthy transit delay, thus the lack of supporting links. I hope to go back and throw some in later on.

*A textbook example of Kerwin’s Razor, which states that anything that needs to be titled as “famous” cannot in fact be famous. You wouldn’t say “the famous singer, Justin Timberlake”, because you don’t need to point out his fame. Everybody knows who JT is: he’s famous.

From the Economist’s article on the death of Hugo Chavez comes this little bit of ridiculousness:

“What the hell is this supposed to be showing?” you might ask. According to the article, “In real terms, between 2000 and 2012 Venezuela’s total oil revenues were more than two and a half times as great as those of the preceding 13 years—even though output declined after 2000 (see chart 1).” That is, the right end of the blue line immediately precedes the left end of the black line. And I guess the point is that the black line is higher? Those two lines both show oil revenues for the same country (Venezuela), but for different time periods, with the X axis giving the number of years since the beginning of a given period. Since Chavez took office at the beginning of 1999, they logically divided the graph at the beginning of 2000 instead. This graph manages to do a bad job of illustrating the basic thing it was going for – that oil revenues went up.

It gets better: to see the point made in the actual text, you would need to eyeball the areas underneath those two curves. Luckily, they don’t happen to cross, but I’m not sure I’d guess that the area under the black curve is 2.5 times the area under the blue one. It looks more like double to me. If only someone had invented a way to illustrate the relative proportions of two totals.

All I can think is that someone told the poor person responsible for making this graph that it needed to be (roughly) square. The Economist’s graphic design people, by revealed preference, clearly love square-shaped graphs.

The World Bank just announced a new initiative (the Africa Gender Innovation Lab) to support innovative ideas to promote gender equality. I like this idea a lot – as I’ve pointed out before, a lot of ideas about the role of gender inequities in developing Africa, and what to do about them, are pretty uninformed and old-fashioned. Identifying where the key problems really lie, and what to do about them, is key if we’re going to make actual progress on gender relations, just like every social issue.

Markus Goldstein’s post announcing the initiative also alludes one reason why everyone – even sexists – should support gender equality in Africa. A lot of research has found that female farmers have lower crop yields than men. One could imagine a situation in which this is an efficient  outcome: maybe there are increasing returns to labor over the relevant range of effort, and there’s some reason women can’t put in as many hours (e.g. they are forced to spend more time on childcare). This would still be a very unjust outcome, but at least it wouldn’t be wasteful. And I don’t think this story is my unique, hypothetical invention – variants underpin the back-slapping attitude among the privileged in societies around the world: “things are unfair but that’s what makes the world go round.”

The problem with this argument is that there’s no evidence that’s what’s really going on. Chris Udry’s classic paper on gender and agricultural production studies an area in Burkina Faso where men and women from the same household control separate plots of land. Women’s land is less productive, but labor and in particular fertilizer use are heavily concentrated on male-owned land. Households could get 6% boost to total crop production by evening things out. The main reason this happens is because men control the use of fertilizer and don’t share it with their wives. In a counterfactual world where women had more bargaining power, we’d expect to see not just more equity but more total production: sexism is inefficient.

Moreover, this result is very general – it derives not from some special aspect of agriculture in Burkina Faso but from the law of diminishing returns. If a society allocates inputs based on arbitrary, discriminatory rules, this will in general lead to not just unfairness but worse economic outcomes. Suppose our education system funded schools in a way that was driven by race, and favored whites.* $1000 in education spending in a white neighborhood where most kids finish high school doesn’t go nearly as far as $1000 spent in a poor black neighborhood with a high dropout rate. Over the long run, such a policy will reduce economic growth and hurt society as a whole.

*Say, hypothetically, that much of the funding came from local property taxes, that school assignments are based on geography, and that people self-sort into racially-segregated neighborhoods. You know, as a thought experiment.

For an economist, I put relatively little time or effort into sophisticated, mathematical theory, but that still makes me a fairly high outlier in the development and public health research communities. The stock in trade of an economist is the use of mathematical models of behavior that involve agents maximizing utility. These days, with economics having long since left behind its origins as the pure study of markets, this approach to modeling rational decisionmaking is one of the few things that unites the field as a whole.

Sometimes the effort seems a bit silly. It’s a favorite graduate student cocktail party wheeze to suggest that those ivory tower economists have failed miserably, because their models don’t account for (irrationality, free will, psychology, power dynamics, etc.) and hence are useless. I think that reasoning is misguided in a lot of ways, which I won’t get into here, but there’s a deeper problem: what exactly is the point of all that math? Couldn’t we do all of this better as prose? The snide response is to look at discussions of Keynes’s General Theory or the works of Karl Marx: instead of making any progress toward establishing actual scientific facts, academics spent their time arguing about what some particular Great Sage really meant.

A deeper analysis, argued eloquently in an essay by Paul Krugman about the sad history of theory in development economics, is that math forces us to be precise about exactly what we mean. It also helps lead our logic toward conclusions that might not be obvious while keeping us from ending up at apparently-obvious conclusions that don’t follow from our premises. And, he claims, model-making is the not just the only way to be successful in developing scientific theories, but the only way to even try to theorize:

There are many intelligent writers on economics who are able to convince themselves — and sometimes large numbers of other people as well — that they have found a way to transcend the narrowing effect of model-building. Invariably they are fooling themselves. If you look at the writing of anyone who claims to be able to write about social issues without stooping to  restrictive modeling, you will find that his insights are based essentially on the use of metaphor. And metaphor is, of course, a kind of heuristic modeling technique.

The entirety of his essay, The Fall and Rise of Development Economics (written way back in 1994 – this is economist-era Krugman, not editorialist-era Krugman), is readable and interesting. Even mathematically-discinclined readers will be able to understand the simple model he uses as an example (to formalize the Rosenstein-Rodan Big Push theory of development) without too much trouble. Highly recommended.

The public school systems in both Malawi and Uganda (the two countries where I recently spent time doing fieldwork) revolve around a set of massively-important exams that determine whether you get to move on from one level of education to another, and often eligibility for jobs as well. One of the people I was working with in Uganda described primary school there as spending seven years studying for a single test.

It’s hard to overstate the importance of these tests. Uganda’s first such exam is the Primary Leaving Examination, or PLE, which you take after Primary 7 (roughly equivalent to 6th grade as there is no kindergarten). A more-or-less universal practice in the schools I visited in Northern Uganda was to kick out all the poorly-performing P6 pupils before the beginning of P7, leaving just a core of all-stars who spend the year prepping for the test. I’m guessing this is done, in large part, to optimize how good the school looks relative to its competition.

Pressure is high on the pupils as well – it’s common for the names of top performers to be published in newspapers (and hence, by process of elimination, everyone knows who did badly as well). An op-ed I read while in Lira – which I wish I’d cut out and kept, as I can’t find it online – pointed out that this pressure has a cost, and proposed a neat experiment that could be carried out on a grand scale. It’s clear that pupils and their families enjoy the immediate fame of having their names show up in the paper, the author said, but how do they fare down the road? The author proposed that someone should follow up to see how many newspaper-famous PLE success stories end up making it through secondary school.

What’s interesting about this idea is that we could, conceivably, not only do a raw comparison, but actually isolate the causal effect of passing the PLE versus failing it (or of getting a higher grade versus a lower one). The idea is that these exams have hard score cutoffs for passing (or getting a certain grade) and if administered honestly, students can’t control their exact score. Consider a group of exam-takers who are all basically right at the cutoff. Idiosyncratic events on the day of the exam, or random errors, will push them above or below the passing mark. Hence if you look just at that group, you effectively have random assignment to the “pass, name in the paper, life of success and riches” condition or the “fail, no newspaper fame, everybody feels bad for you” condition. You can see how much passing the exam impacts, for example, your wages later in life or the number of kids you have or how many years of school you eventually finish. This approach is called a “regression discontinuity” or “RD” design, and it’s pretty hot in education research these days.

The cool thing about doing this in Malawi or Uganda is that it’s not just a particular school or program – you could study the impact of passing an exam that basically everyone in the country takes. But you’d need the exam scores, plus followup data with a random subset (or all) of the pupils in the country. I can think of ways to do this, but none of them are feasible – unless, to pick one example, some folks at UNEB and the Ugandan Census want to let me at their raw, identifiable data.

Aine McCarthy has a beautifully-written tale of trying to free her car from a river in Tanzania, as a storm moves in:

So, we get out of the truck and start to push. This includes me, Loi (field assistant, driver, friend), two distributors with babies on back, and the family planning training facilitator, who is a nut. A handful of Witamhiya residents are standing around at the riverbank watching us, washing, watching us, watching their cattle. So we ask someone to go get village leader. They send the traditional healer. He is pretty useless. His friend, however, gets two oxen to pull the truck and one more strong man. We try to push while the oxen pull (literally attached to the tow of the front of the truck). Oxen=Tanzanian AAA? Not exactly. It still doesn’t budge and the front tire is getting deeper into the sand.

Also, there is a lot of cow poop. We are basically standing around in a warm poop-green river.  They send for two more oxen. We sit around the river talking about Obama. Four oxen pulling and eight people pushing does nothing for the truck. No cell service in Witamhiya. More sitting around and looking at the oxen. By now, it is 6pm and as if on cue, a dark cloud appears upstream. Huge and growing. The sun is setting and the awesome yet ominous wind that smell of rain starts to blow in our direction.

The whole thing is great, and will ring true to anyone who has tried to navigate crappy developing-country roads in the face of imminent rain. It joins The Economist’s classic tale of a Guiness delivery truck in Cameroon, “The Road to Hell is Unpaved”, as one of my favorite articles about infrastructure in Africa.

It used to be that if you ran an internet scam, the game was to lure people in with the possibility of gaining a small fortune. Now scammers are appealing to our moral compasses to make money. Case in point: I got a very confusing email recently, and following the lead of social science blogging super-hero Andrew Gelman, I’m posting a redacted version here.

Hi Jason,

My name is [name removed] and I came across nonparibus.wordpress.com after searching for people that have referenced or mentioned climate change and global warming. I am part of a team of designers and researchers that put together an infographic showing how bad climate change has gotten and how it’s contributing to the destruction of our planet. I thought you might be interested, so I wanted to reach out.

If this is the correct email and you’re interested in using our content, I’d be happy to share it with you. 🙂

Thank you,

[name removed]

I think this is some kind of meta-blogspam: get me to post their infographic, then include Amazon referral links or something to make money.

There’s almost zero chance this is legitimate or sincere. My only post that mentions climate change is one pointing out that it’s overstated as a cause of fluctuations in rainfall in Malawi and that that is probably be a bad thing. However, if the probably-a-scambot climate change activist reader who emailed me wants to explain why this isn’t bogus, I’m all ears.