Archive for the ‘public policy’Category

"The opening deal"

I liked this quote from economist Karthik Muralidharan, which is pulled from a conversation at Ideas for India with Kaushik Basu of the World Bank:

My own take on what is happening in economics as a profession, talking to people in other disciplines, is that our fundamental weakness at some level is that because the touchstone of policy evaluation is the idea of a Pareto improvement (is someone better off and no one worse off) – effectively, economists do not question the justice of the initial positions. You kind of take the initial position as granted and say that conditional on this, how do I improve things on the margin.

Given vast inequalities in the opening deal of cards, so to speak, there is obviously a deep political need to create the space for more pro-poor policy. I think because the professional economists have abdicated that space to saying that it is a philosophical debate and we have really nothing to say, the rights-based movement that has created the political space for pro-poor policy has also then occupied the space of how to design it because they are the people who have created the political movement.

My own view on this is that because economists have kind of been seen as apologists for the status quo in many settings, we have lost the credibility to say that we are as pro-poor as you are, but conditional on these objectives there are much better ways to design it.

Lots on poverty policy, inequality, etc at the link.

Formalizing corruption: US medical system edition

Oh, corruption. It interferes with so many aspects of daily life, adding time to the simplest daily tasks, costing more money, and — often the most frustrating aspect — adding huge doses of uncertainty. That describes life in many low-income, high-corruption countries, leading to many a conversation with friends about comparisons with the United States and other wealthy countries. How did the US “solve” corruption?

I’ve heard (and personally made) the argument that the US reduced corruption at least in part by formalizing it; by channeling the root of corruption, a sort of rent-seeking on a personal level, to rent-seeking on an institutional level. The US political and economic system has evolved such that some share of any wealth created is channeled into the pockets of a political and economic elite who benefit from the system and in turn reinforce it. That unproductively-channeled share of wealth is simultaneously a) probably smaller than the share of wealth lost to corruption in most developing countries, b) still large enough to head off — along with the threat of more effective prosecution — at least some more overt corruption, and c) still a major drain on society.

An example: Elisabeth Rosenthal profiles medical tourism in an impressive series in the New York Times. In part three of the series, an American named Michael Shopenn travels to Belgium to get a hip replacement. Why would he need to? Because health economics in the US is less a story of free markets and  more a story of political capture by medical interests, including technology and pharmaceutical companies, physicians’ groups, and hospitals:

Generic or foreign-made joint implants have been kept out of the United States by trade policy, patents and an expensive Food and Drug Administration approval process that deters start-ups from entering the market. The “companies defend this turf ferociously,” said Dr. Peter M. Cram, a physician at the University of Iowa medical school who studies the costs of health care.

Though the five companies make similar models, each cultivates intense brand loyalty through financial ties to surgeons and the use of a different tool kit and operating system for the installation of its products; orthopedists typically stay with the system they learned on. The thousands of hospitals and clinics that purchase implants try to bargain for deep discounts from manufacturers, but they have limited leverage since each buys a relatively small quantity from any one company.

In addition, device makers typically require doctors’ groups and hospitals to sign nondisclosure agreements about prices, which means institutions do not know what their competitors are paying. This secrecy erodes bargaining power and has allowed a small industry of profit-taking middlemen to flourish: joint implant purchasing consultants, implant billing companies, joint brokers. There are as many as 13 layers of vendors between the physician and the patient for a hip replacement, according to Kate Willhite, a former executive director of the Manitowoc Surgery Center in Wisconsin.

If this system existed in another country we wouldn’t hesitate to call it corrupt, and to note that it actively hurts consumers. It should be broken up by legislation for the public good, but instead it’s protected by legislators who are lobbied by the industry and by doctors who receive kickbacks, implicit and explicit. Contrast that with the Belgian system:

His joint implant and surgery in Belgium were priced according to a different logic. Like many other countries, Belgium oversees major medical purchases, approving dozens of different types of implants from a selection of manufacturers, and determining the allowed wholesale price for each of them, for example. That price, which is published, currently averages about $3,000, depending on the model, and can be marked up by about $180 per implant. (The Belgian hospital paid about $4,000 for Mr. Shopenn’s high-end Zimmer implant at a time when American hospitals were paying an average of over $8,000 for the same model.)

“The manufacturers do not have the right to sell an implant at a higher rate,” said Philip Boussauw, director of human resources and administration at St. Rembert’s, the hospital where Mr. Shopenn had his surgery. Nonetheless, he said, there was “a lot of competition” among American joint manufacturers to work with Belgian hospitals. “I’m sure they are making money,” he added.

It’s become a cliche to compare the US medical system to European ones, but those comparisons are made because it’s hard to realize just how systematically corrupt — and expensive, as a result — the US system is without comparing it to ones that do a better job of channeling the natural profit-seeking goals of individuals and companies towards the public good. (For the history of how we got here, Paul Starr is a good place to start.)

The usual counterargument for protecting such large profit margins in the US is that they drive innovation, which is true but only to an extent. And for the implants industry that argument is much less compelling since many of the newer, “innovative” products have proved somewhere between no better and much worse in objective tests.

The Times piece is definitely worth a read. While I generally prefer the formalized corruption to the unformalized version, I’ll probably share this article with friends — in Nigeria, or Ethiopia, or wherever else the subject comes up next.


08 2013

Advocates and scientists

A new book by The Idealist: Jeffrey Sachs and the Quest to End Poverty. The blurbs on Amazon are fascinating because they indicate that either the reviewers didn’t actually read the book (which wouldn’t be all that surprising) or that Munk’s book paints a nuanced enough picture that readers can come away with very different views on what it actually proves. Here are two examples:

Amartya Sen: “Nina Munk’s book is an excellent – and moving – tribute to the vision and commitment of Jeffrey Sachs, as well as an enlightening account of how much can be achieved by reasoned determination.”

Robert Calderisi: “A powerful exposé of hubris run amok, drawing on touching accounts of real-life heroes fighting poverty on the front line.”

The publisher’s description seems to encompass both of those points of view: “The Idealist is the profound and moving story of what happens when the abstract theories of a brilliant, driven man meet the reality of human life.” That sounds like a good read to me — I look forward to reading when it comes out in September.

Munk’s previous reporting strikes a similar tone. For example, here’s an excerpt of her 2007 Vanity Fair profile of Sachs:

Leaving the region of Dertu, sitting in the back of an ancient Land Rover, I’m reminded of a meeting I had with Simon Bland, head of Britain’s Department for International Development in Kenya. Referring to the Millennium Villages Project, and to Sachs in particular, Bland laid it out for me in plain terms: “I want to say, ‘What concept are you trying to prove?’ Because I know that if you spend enough money on each person in a village you will change their lives. If you put in enough resources—enough foreigners, technical assistance, and money—lives change. We know that. I’ve been doing it for years. I’ve lived and worked on and managed [development] projects.

“The problem is,” he added, “when you walk away, what happens?”

Someone — I think it was Chris Blattman, but I can’t find the specific post — wondered a while back whether too much attention has been given to the Millennium Villages Project. After all, the line of thinking goes, the MVP’s have really just gotten more press and aren’t that different from the many other projects with even less rigorous evaluation designs. That’s certainly true: when journalists and aid bloggers debate the MVPs, part of what they’re debating is Sachs himself because he’s such a polarizing personality. If you really care about aid policy, and the uses of evidence in that policy, then that can all feel like an unhelpful distraction. Most aid efforts don’t get book-length profiles, and the interest in Sachs’ personality and persona will probably drive the interest in Munk’s book.

But I also think the MVP debates have been healthy and interesting — and ultimately deserving of most of the heat generated — because they’re about a central tension within aid and development, as well as other fields where research intersects with activism. If you think we already generally know what to do, then it makes sense to push forward with it at all costs. The naysayers who doubt you are unhelpful skeptics who are on some level ethically culpable for blocking good work. If you think the evidence is not yet in, then it makes more sense to function more like a scientist, collecting the evidence needed to make good decisions in the longer term. The naysayers opposing the scientists are then utopian advocates who throw millions at unproven projects. I’ve seen a similar tension within the field of public health, between those who see themselves primarily as advocates and those who see themselves as scientists, and I’m sure it exists elsewhere as well.

That is, of course, a caricature — few people fall completely on one side of the advocates vs. scientists divide. But I think the caricature is a useful one for framing arguments. The fundamental disagreement is usually not about whether evidence should be used to inform efforts to end poverty or improve health or advance any other goal. Instead, the disagreement is often over what the current state of knowledge is. And on that note, if you harbor any doubts on where Sachs has positioned himself on that spectrum here’s the beginning of Munk’s 2007 profile:

In the respected opinion of Jeffrey David Sachs…. the problem of extreme poverty can be solved. In fact, the problem can be solved “easily.” “We have enough on the planet to make sure, easily, that people aren’t dying of their poverty. That’s the basic truth,” he tells me firmly, without a doubt.

…To Sachs, the end of poverty justifies the means. By hook or by crook, relentlessly, he has done more than anyone else to move the issue of global poverty into the mainstream—to force the developed world to consider his utopian thesis: with enough focus, enough determination, and, especially, enough money, extreme poverty can finally be eradicated.

Once, when I asked what kept him going at this frenzied pace, he snapped back, “If you haven’t noticed, people are dying. It’s an emergency.”


via Gabriel Demombynes.

If you’re new to the Millennium Villages debate, here’s some background reading: a recent piece in Foreign Policy by Paul Starobin, and some good posts by Chris Blattman (one, two, three), this gem from Owen Barder, and Michael Clemens.

"Redefining global health delivery"

Jim Yong Kim, Paul Farmer, and Michael Porter wrote a piece called “Redefining global health delivery” for the Lancet in May. The abstract:

Initiatives to address the unmet needs of those facing both poverty and serious illness have expanded significantly over the past decade. But many of them are designed in an ad-hoc manner to address one health problem among many; they are too rarely assessed; best practices spread slowly. When assessments of delivery do occur, they are often narrow studies of the cost-effectiveness of a single intervention rather than the complex set of them required todeliver value to patients and their families. We propose a framework for global health-care delivery and evaluation by considering efforts to introduce HIV/AIDS care to resource-poor settings. The framework introduces the notion of care delivery value chains that apply a systems-level analysis to the complex processes and interventions that must occur, across a health-care system and over time, to deliver high-value care for patients with HIV/AIDS and cooccurring conditions, from tuberculosis to malnutrition. To deliver value, vertical or stand-alone projects must be integrated into shared delivery infrastructure so that personnel and facilities are used wisely and economies of scale reaped. Two other integrative processes are necessary for delivering and assessing value in global health: one is the alignment of delivery with local context by incorporating knowledge of both barriers to good outcomes (from poor nutrition to a lack of water and sanitation) and broader social and economic determinants of health and wellbeing (jobs, housing, physical infrastructure). The second is the use of effective investments in care delivery to promote equitable economic development, especially for those struggling against poverty and high burdens of disease. We close by reporting our own shared experience of seeking to move towards a science of delivery by harnessing research and training to understand and improve care delivery.

I think the overall thrust of the piece is something that is widely agreed upon by global health policy wonks, but I like that they lay out a more specific framework for thinking with this sort of systems approach. But, I’d love to see some more detail on putting it into practice on a national or subnational level.


07 2013

Someone should study this: Addis housing edition

Attention development economists and any other researchers who have an interest in urban or housing policy in low-income countries:

My office in Addis has about 25 folks working in it, and we have a daily lunch pool where we pay in 400 birr a month (about 22 USD) to cover costs and all get to eat Ethiopian food for lunch every day. It’s been a great way to get to know my coworkers — my work is often more solitary: editing, writing, and analyzing data — and an even better way to learn about a whole variety of issues in Ethiopia.

addis construction

Addis construction site (though not probably not government condos)

The conversation is typically in Amharic and mine is quite limited, so I’m lucky if I can figure out the topic being discussed.  [I usually know if they’re talking about work because so many NGO-speak words aren’t translated, for example: “amharic amharic amharic Health Systems Strengthening amharic amharic…“] But folks will of course translate things as needed.  One observation is that certain topics affect their daily lives a lot, and thus come up over and over again at lunch.

One subject that has come up repeatedly is housing. Middle class folks in Addis Ababa feel the housing shortage very acutely. Based on our conversations it seems the major limitation is in getting credit to buy or build a house.

The biggest source of good housing so far has been government-constructed condominiums, for which you pay a certain (I’m not sure how much) percentage down and then make payments over the years. (The government will soon launch a new “40/60 scheme” to which many folks are looking forward, in which anyone who can make a 40% down payment on a house will get a government mortgage for the remaining 60%.)

When my coworkers first mentioned that the government will offer the next round of condominiums by a public lottery, my thought was “that will solve someone’s identification problem!” A large number of people — many thousands — have registered for the government lottery. I believe you have to meet a certain wealth or income threshold (i.e., be able to make the down payment), but after that condo eligibility will be determined randomly. I think that — especially if someone organizes the study prior to the lottery — this could yield very useful results on the impact of urban housing policy.

How (and how much) do individuals and families benefit from access to better housing? Are there changes in earnings, savings, investments? Health outcomes? Children’s health and educational outcomes? How does it affect political attitudes or other life choices? It could also be an opportunity to study migration between different neighborhoods, amongst many other things.

A Google Scholar search for Ethiopia housing lottery turns up several mentions, but (in my very quick read) no evaluations taking advantage of the randomization. (I can’t access this recent article in an engineering journal, but from the abstract assume that it’s talking about a different kind of evaluation.) So, someone have at it? It’s just not that often that large public policy schemes are randomized.


12 2012

Still #1

Pop quiz: what’s the leading killer of children under five?

Before I answer, some background: my impression is that many if not most public health students and professionals don’t really get politics. And specifically, they don’t get how an issue being unsexy or just boring politics can results in lousy public policy. I was discussing this shortcoming recently over dinner in Addis with someone who used to work in public health but wasn’t formally trained in it. I observed, and they concurred, that students who go to public health schools (or at least Hopkins, where this shortcoming may be more pronounced) are mostly there to get technical training so that they can work within the public health industry, and that more politically astute students probably go for some other sort of graduate training, rather than concentrating on epidemiology or the like.

The end result is that you get cadres of folks with lots of knowledge about relative disease burden and how to implement disease control programs, but who don’t really get why that knowledge isn’t acted upon. On the other hand, a lot of the more politically savvy folks who are in a position to, say, set the relative priority of diseases in global health programming — may not know much about the diseases themselves. Or, maybe more likely, they do the best job they can to get the most money possible for programs that are both good for public health and politically popular.  But if not all diseases are equally “popular” this can result in skewed policy priorities.

Now, the answer to that pop quiz: the leading killer of kids under 5 is…. [drumroll]…  pneumonia!

If you already knew the answer to that question, I bet you either a) have public health training, or b) learned it due to recent, concerted efforts to raise pneumonia’s public profile. On this blog the former is probably true (after all I have a post category called “methodological quibbles“), but today I want to highlight the latter efforts.

To date, most of the political class and policymakers get the pop quiz wrong, and badly so. At Hopkins’ school of public health I took and enjoyed Orin Levine‘s vaccine policy class. (Incidentally, Orin just started a new gig with the Gates Foundation — congrats!) In that class and elsewhere I’ve heard Orin tell the story of quizzing folks on Capitol Hill and elsewhere in DC about the top three causes of death for children under five and time and again getting the answer “AIDS, TB and malaria.”

Those three diseases likely pop to mind because of the Global Fund, and because a lot of US funding for global health has been directed at them. And, to be fair, they’re huge public health problems and the metric of under-five mortality isn’t where AIDS hits hardest. But the real answer is pneumonia, diarrhea, and malnutrition. (Or malaria for #3 — it depends in part on whether you count malnutrition as a separate cause  or a contributor to other causes). The end result of this lack of awareness — and the prior lack of a domestic lobby — of pneumonia is that it gets underfunded in US global health efforts.

So, how to improve pneumonia’s profile? Today, November 12th, is the 4th annual World Pneumonia Day, and I think that’s a great start. I’m not normally one to celebrate every national or international “Day” for some causes, but for the aforementioned reasons I think this one is extremely important. You can follow the #WPD2012 hashtag on Twitter, or find other ways to participate on WPD’s act page. While they do encourage donations to the GAVI Alliance, you’ll notice that most of the actions are centered around raising awareness. I think that makes a lot of sense. In fact, just by reading this blog post you’ve already participated — though of course I hope you’ll do more.

I think politically-savvy efforts like World Pneumonia Day are especially important because they bridge a gap between the technical and policy experts. Precisely because so many people on both sides (the somewhat-false-but-still-helpful dichotomy of public health technical experts vs. political operatives) mostly interact with like-minded folks, we badly need campaigns like this to popularize simple facts within policy circles.

If your reaction to this post — and to another day dedicated to a good cause — is to feel a bit jaded, please recognize that you and your friends are exactly the sorts of people the World Pneumonia Day organizers are hoping to reach. At the very least, mention pneumonia today on Twitter or Facebook, or with your policy friends the next time health comes up.

Full disclosure: while at Hopkins I did a (very small) bit of paid work for IVAC, one of the WPD organizers, re: social media strategies for World Pneumonia Day, but I’m no longer formally involved. 


11 2012

Obesity pessimism

I posted before on the massive increase in obesity in the US over the last couple decades, trying to understand the why of the phenomenal change for the worse. Seriously, take another look at those maps.

A while back Matt Steinglass wrote a depressing piece in The Economist on the likelihood of the US turning this trend around:

I very much doubt America is going to do anything, as a matter of public health policy, that has any appreciable effect on obesity rates in the next couple of decades. It’s not that it’s impossible for governments to hold down obesity; France, which had rapidly rising childhood obesity early this century, instituted an aggressive set of public-health interventions including school-based food and exercise shifts, nurse assessments of overweight kids, visits to families where overweight kids were identified, and so forth. Their childhood obesity rates stabilised at a fraction of America’s. The problem isn’t that it’s not possible; rather, it’s that America is incapable of doing it.

America’s national governing ideology is based almost entirely on the assertion of negative rights, with a few exceptions for positive rights and public goods such as universal elementary education, national defence and highways. But it’s become increasingly clear over the past decade that the country simply doesn’t have the political vocabulary that would allow it to institute effective national programmes to improve eating and exercise habits or culture. A country that can’t think of a vision of public life beyond freedom of individual choice, including the individual choice to watch TV and eat a Big Mac, is not going to be able to craft public policies that encourage people to exercise and eat right. We’re the fattest country on earth because that’s what our political philosophy leads to. We ought to incorporate that into the way we see ourselves; it’s certainly the way other countries see us.

On the other hand, it’s notable that states where the public has a somewhat broader conception of the public interest, as in the north-east and west, tend to have lower obesity rates.

This reminds me that a classmate asked me a while back about my impression of Michelle Obama’s Let’s Move campaign. I responded that my impression is positive, and that every little bit helps… but that the scale of the problem is so vast that I find it hard seeing any real, measurable impact from a program like Let’s Move. To really turn obesity around we’d need a major rethinking of huge swathes of social and political reality: our massive subsidization of unhealthy foods over healthy ones (through a number of indirect mechanisms), our massive subsidization of unhealthy lifestyles by supporting cars and suburbanization rather than walking and urban density, and so on and so forth. And, as Steinglass notes, the places with the greatest obesity rates are the least likely to implement such change.


11 2012

Bad pharma

Ben Goldacre, author of the truly excellent Bad Science, has a new book coming out in January, titled Bad Pharma: How Drug Companies Mislead Doctors and Harm Patients

Goldacre published the foreword to the book on his blog here. The point of the book is summed up in one powerful (if long) paragraph. He says this (emphasis added):

So to be clear, this whole book is about meticulously defending every assertion in the paragraph that follows.

Drugs are tested by the people who manufacture them, in poorly designed trials, on hopelessly small numbers of weird, unrepresentative patients, and analysed using techniques which are flawed by design, in such a way that they exaggerate the benefits of treatments. Unsurprisingly, these trials tend to produce results that favour the manufacturer. When trials throw up results that companies don’t like, they are perfectly entitled to hide them from doctors and patients, so we only ever see a distorted picture of any drug’s true effects. Regulators see most of the trial data, but only from early on in its life, and even then they don’t give this data to doctors or patients, or even to other parts of government. This distorted evidence is then communicated and applied in a distorted fashion. In their forty years of practice after leaving medical school, doctors hear about what works through ad hoc oral traditions, from sales reps, colleagues or journals. But those colleagues can be in the pay of drug companies – often undisclosed – and the journals are too. And so are the patient groups. And finally, academic papers, which everyone thinks of as objective, are often covertly planned and written by people who work directly for the companies, without disclosure. Sometimes whole academic journals are even owned outright by one drug company. Aside from all this, for several of the most important and enduring problems in medicine, we have no idea what the best treatment is, because it’s not in anyone’s financial interest to conduct any trials at all. These are ongoing problems, and although people have claimed to fix many of them, for the most part, they have failed; so all these problems persist, but worse than ever, because now people can pretend that everything is fine after all.

If that’s not compelling enough already, here’s a TED talk on the subject of the new book:


10 2012

Three new podcasts to follow

  •, a podcast on data visualization.
  • Pangea, a podcast on foreign affairs —  including “humanitarian issues, health, development, etc.” created by Jaclyn Schiff (@J_Schiff).
  •  Simply Statisics, a podcast by the guys behind the blog of the same name.

Why we should lie about the weather (and maybe more)

Nate Silver (who else?) has written a great piece on weather prediction — “The Weatherman is Not a Moron” (NYT) — that covers both the proliferation of data in weather forecasting, and why the quantity of data alone isn’t enough. What intrigued me though was a section at the end about how to communicate the inevitable uncertainty in forecasts:

…Unfortunately, this cautious message can be undercut by private-sector forecasters. Catering to the demands of viewers can mean intentionally running the risk of making forecasts less accurate. For many years, the Weather Channel avoided forecasting an exact 50 percent chance of rain, which might seem wishy-washy to consumers. Instead, it rounded up to 60 or down to 40. In what may be the worst-kept secret in the business, numerous commercial weather forecasts are also biased toward forecasting more precipitation than will actually occur. (In the business, this is known as the wet bias.) For years, when the Weather Channel said there was a 20 percent chance of rain, it actually rained only about 5 percent of the time.

People don’t mind when a forecaster predicts rain and it turns out to be a nice day. But if it rains when it isn’t supposed to, they curse the weatherman for ruining their picnic. “If the forecast was objective, if it has zero bias in precipitation,” Bruce Rose, a former vice president for the Weather Channel, said, “we’d probably be in trouble.”

My thought when reading this was that there are actually two different reasons why you might want to systematically adjust reported percentages ((ie, fib a bit) when trying to communicate the likelihood of bad weather.

But first, an aside on what public health folks typically talk about when they talk about communicating uncertainty: I’ve heard a lot (in classes, in blogs, and in Bad Science, for example) about reporting absolute risks rather than relative risks, and about avoiding other ways of communicating risks that generally mislead. What people don’t usually discuss is whether the point estimates themselves should ever be adjusted; rather, we concentrate on how to best communicate whatever the actual values are.

Now, back to weather. The first reason you might want to adjust the reported probability of rain is that people are rain averse: they care more strongly about getting rained on when it wasn’t predicted than vice versa. It may be perfectly reasonable for people to feel this way, and so why not cater to their desires? This is the reason described in the excerpt from Silver’s article above.

Another way to describe this bias is that most people would prefer to minimize Type II Error (false negatives) at the expense of having more Type I error (false positives), at least when it comes to rain. Obviously you could take this too far — reporting rain every single day would completely eliminate Type II error, but it would also make forecasts worthless. Likewise, with big events like hurricanes the costs of Type I errors (wholesale evacuations, cancelled conventions, etc) become much greater, so this adjustment would be more problematic as the cost of false positives increases. But generally speaking, the so-called “wet bias” of adjusting all rain prediction probabilities upwards might be a good way to increase the general satisfaction of a rain-averse general public.

The second reason one might want to adjust the reported probability of rain — or some other event — is that people are generally bad at understanding probabilities. Luckily though, people tend to be bad about estimating probabilities in surprisingly systematic ways! Kahneman’s excellent (if too long) book Thinking, Fast and Slow covers this at length. The best summary of these biases that I could find through a quick Google search was from Lee Merkhofer Consulting:

 Studies show that people make systematic errors when estimating how likely uncertain events are. As shown in [the graph below], likely outcomes (above 40%) are typically estimated to be less probable than they really are. And, outcomes that are quite unlikely are typically estimated to be more probable than they are. Furthermore, people often behave as if extremely unlikely, but still possible outcomes have no chance whatsoever of occurring.

The graph from that link is a helpful if somewhat stylized visualization of the same biases:

In other words, people think that likely events (in the 30-99% range) are less likely to occur than they are in reality, that unlike events (in the 1-30% range) are more likely to occur than they are in reality, and extremely unlikely events (very close to 0%) won’t happen at all.

My recollection is that these biases can be a bit different depending on whether the predicted event is bad (getting hit by lightning) or good (winning the lottery), and that the familiarity of the event also plays a role. Regardless, with something like weather, where most events are within the realm of lived experience and most of the probabilities lie within a reasonable range, the average bias could probably be measured pretty reliably.

So what do we do with this knowledge? Think about it this way: we want to increase the accuracy of communication, but there are two different points in the communications process where you can measure accuracy. You can care about how accurately the information is communicated from the source, or how well the information is received. If you care about the latter, and you know that people have systematic and thus predictable biases in perceiving the probability that something will happen, why not adjust the numbers you communicate so that the message — as received by the audience — is accurate?

Now, some made up numbers: Let’s say the real chance of rain is 60%, as predicted by the best computer models. You might adjust that up to 70% if that’s the reported risk that makes people perceive a 60% objective probability (again, see the graph above). You might then adjust that percentage up to 80% to account for rain aversion/wet bias.

Here I think it’s important to distinguish between technical and popular communication channels: if you’re sharing raw data about the weather or talking to a group of meteorologists or epidemiologists then you might take one approach, whereas another approach makes sense for communicating with a lay public. For folks who just tune in to the evening news to get tomorrow’s weather forecast, you want the message they receive to be as close to reality as possible. If you insist on reporting the ‘real’ numbers, you actually draw your audience further from understanding reality than if you fudged them a bit.

The major and obvious downside to this approach is that people know this is happening, it won’t work, or they’ll be mad that you lied — even though you were only lying to better communicate the truth! One possible way of getting around this is to describe the numbers as something other than percentages; using some made-up index that sounds enough like it to convince the layperson, while also being open to detailed examination by those who are interested.

For instance, we all the heat index and wind chill aren’t the same as temperature, but rather represent just how hot or cold the weather actually feels. Likewise, we could report some like “Rain Risk” or “Rain Risk Index” that accounts for known biases in risk perception and rain aversion. The weather man would report a Rain Risk of 80%, while the actual probability of rain is just 60%. This would give us more useful information for the recipients, while also maintaining technical honesty and some level of transparency.

I care a lot more about health than about the weather, but I think predicting rain is a useful device for talking about the same issues of probability perception in health for several reasons. First off, the probabilities in rain forecasting are much more within the realm of human experience than the rare probabilities that come up so often in epidemiology. Secondly, the ethical stakes feel a bit lower when writing about lying about the weather rather than, say, suggesting physicians should systematically mislead their patients, even if the crucial and ultimate aim of the adjustment is to better inform them.

I’m not saying we should walk back all the progress we’ve made in terms of letting patients and physicians make decisions together, rather than the latter withholding information and paternalistically making decisions for patients based on the physician’s preferences rather than the patient’s. (That would be silly in part because physicians share their patients’ biases.) The idea here is to come up with better measures of uncertainty — call it adjusted risk or risk indexes or weighted probabilities or whatever — that help us bypass humans’ systematic flaws in understanding uncertainty.

In short: maybe we should lie to better tell the truth. But be honest about it.