I donated $35 to offset my carbon footprint for this year

I want to start donating annually to offset my carbon footprint. I don’t really think of this as a charitable cost - instead it’s internalizing my externalities.

This is the first time I am systematically deciding to make an annual donation - I wanted to walk through my thinking in case it’s useful for anyone else! This post also serves as pro-Effective Altruism propaganda.

• How much carbon do I need to offset?

  • The average American seems to emit about 15-20T of CO2 per year (source, source, source). I’ll assume 20T.

  • But I travel a lot. A round-trip flight from London to New York emits ~1T of CO2. This year I took 5 international flights - most had multiple legs, so I’ll assume I emitted 15T more than the average American.

  • So let’s say I have to offset 35T of CO2 each year.

• Where should I donate?

  • I trust Vox’s Future Perfect on stuff like this. They recommend donating to a climate change fund such as the Climate Change Fund from Founder’s Pledge

• How much should I donate?

  • I’ll use the top recommended climate charity from Vox’s Future Perfect as a benchmark. As of December 2023, this is the Clean Air Task Force

  • Founder’s Pledge estimates that a donation to CATF can avert 1T of CO2 emissions for $0.1-$1

  • So that would put the amount I have to donate to offset all my emissions at $3.50-$35 per year

  • I’ll be on the safe side and assume I should donate $35

Conclusion: I just donated $35 to the Climate Fund from Founder’s Pledge to offset my yearly carbon footprint. I intend to make this donation annually going forward, and encourage you to as well!

—

Effective Altruism has been under some heat lately - with the collapse of FTX, and the drama around the OpenAI board ousting Sam Altman.

EA is both a philosophy and a community. I think the above exercise illustrates why both are really good, despite recent drama.

• The philosophy of Effective Altruism gave me the intellectual motivation to donate in the first place. And it informs my decision about where to donate: I should not just donate to what feels the best - I should donate where my dollar will have the highest impact in terms of tons of CO2-eq averted.

• The community of EA has created institutions (in this case Vox’s Future Perfect, and Founder’s Pledge) that help me quickly (1) identify a good donation opportunity, and direct my funds effectively. Also, a post on the the EA Forum provided extra social motivation to make this donation

Is this system perfect? No. Perhaps I could have spent more time finding a better charity to donate to. Perhaps I should be doing more in my lifestyle or in political activism to be addressing the problem of climate change.

But I think my actions here are a lot better than they would be if Effective Altruism did not exist (2). So overall I remain proud of Effective Altruism - both the philosophy and the community.

—

1. It only took 1 hour to do the research and decide to donate!

2. For what it’s worth, the philosophy and community of EA were also key motivators in my decision to become vegetarian

Pascal's roulette wheel, or how I won $20

I was at a casino last week. I was down on my luck, having lost $20 at the 7-card poker table.

Then I remembered when my friend Jesse had told me about the Martingale system which guarantees you to win at roulette! It goes like this:

• First bet $20 on red. If red comes up, you’ve won. Quit

• If you lose, double your bet to $40 and put it on red. If red comes up, you lost $20 the first round, but then won $40 the second round, so in total you won $20. Quit

• If you lose again, double your bet to $80…

• …and so on. Just keep betting red, doubling your bet each time, and eventually you are bound to win, taking home $20

This was clearly the only way to climb my way out of this $20 hole I was in. Given the table limit, the only way I could lose was if I lost roulette 5 games in a row (1). The odds of this were 4% - practically zero!! (2)

So I cashed in with $100 worth of chips and put $20 on red. I lost the first bet, but then bet again at $40 and won. I had won back at roulette the $20 I had lost at poker!

Jesse was a genius! I was half considering simply quitting my job and moving into a mattress in the corner of this casino to make my way as a roulette player.

Before making any final decision on my career and housing, I decided to crunch some numbers and calculate the expected value of carrying out the Martingale strategy into infinity.

As I should have expected given that casinos remain in business, the house still wins in the long run even if you use a Martingale strategy. This wasn’t a viable career option for me

The thing I hadn’t really considered was that if I lost 5 games in a row, I would have been losing around $600. Even with only a 4% chance of losing that much, this potential loss outweighs the 96% chance that I win $20 (3).

In my mind, 4% rounded down to “basically zero”. But in real life, things with 4% probability happen about 1 in every 25 times. In finance / probability, these are known as “tail risks”. And this tail risk had a disproportionally large consequence - losing more than $600.

—

DALL-E’s interpretation of Blaise Pascal playing roulette with God in a Chinatown casino

Even if there was no table limit and I could play the game infinite times, I still would be expected to lose. This was super hard for me to intuitively understand. The odds that red loses 20 times in a row are 0.0002%. If I had enough money to double my bets 20 times, that feels like that’s basically a guaranteed win, right?

No. What feels like a “guaranteed” win is actually only a 99.9998% chance. This is a small tail risk but with a huge consequence - if I did lose 20 games in a row, I would be losing $20M (4). Which, despite my lucrative recent side hustles in forex and casinos, is more money than I have.

I was falling victim to failures in thinking about tail risks that Nassim Nicholas Taleb describes in The Black Swan: “just as we tend to underestimate the role of luck in life in general, we tend to overestimate it in games of chance.”

Also created with DALL-E

This is the first time I’ve been confronted with tail risks in a way that feels salient. I think having played roulette will help me when thinking about things like existential risks, risky but high-impact ventures, and the existence of aliens.

So now I have a good excuse to keep going to the casino. I’m not throwing my money away. I’m on an epistemic quest to sharpen my intuitions about low-probability events.

It also makes me think of Blaise Pascal, who I think would have happily taken my money as I played roulette over and over again, delighting in the small chance of winning huge amounts from me.

—

Thanks to Binx for co-writing this post!

—

1. the table had a maximum bet of $500, so if I lost at $20, $40, $80, $160, and $320, I would not be able to double my bet again to $640

2. it was a single-zero roulette table, so the odds of winning on red were 18/37. Odds of losing 5 in a row were (19/37)^5 = 3.6%

3. Expected value: $20 * 0.96 - $630 * .04 = expected loss of -$6

4. $20 * 2^19 = $10M lost on the last bet, then another $10M for all I lost cumulatively on the previous bets

A Complete Taxonomy of Expats in Kenya

Abstract: It’s said there are three types of expats in Nairobi (1): Missionaries, Mercenaries, and Misfits. This widely used framework, however, is incomplete. In this post I propose a rigorous and complete taxonomy (2). Every under-35-year-old expat that you will meet in Nairobi can be easily sorted into one of 24 buckets based on four dimensions. There are no edge cases.

Methods: The Taxonomy was created using extremely rigorous methods based on anecdotal data of my  friends in Kenya. Who are of course, totally and completely representative of all under-35 foreigners living in Kenya.

Overview: Each expat can be assigned an letter from each of four categories:

• Purpose for being in Kenya: Impact (I) / market (M) / good time (G)

• B-school-adjacence: In-program (P) / no plans (N)

• Degree of entrapment in expat bubble: Practically local (L) / stuck (S)

• Weekend behavior: City (C) / travel (T)

For example, an impact-driven person on internship from business school who is not trapped in the expat bubble and generally stays in Nairobi would be an IPLC.

Detail on the Four Categories

Purpose for being in Kenya:

• I: Impact-driven (~”missionaries”)

• M: Interested in the market or political environment (~”mercenaries”)

• G: Just here for a good time man (~”misfits”)

• Discussion: The most obvious of the 4 dimensions, purpose, is captured in the pop wisdom “missionary/mercenary/misfit” trichotomy. This trichotomy is obviously stylized for humorous effect - not all M’s are motivated solely by money, and many G’s aren’t misfits at all in their home countries. But it captures a general truth about expats that is more precisely articulated in the I/M/G dimension.

B-school-adjacence (3): 

• P: In a master’s program or planning to apply next cycle (often business school)

• N: No plans for grad school (includes those who are post-grad school)

• Discussion: For the most part, someone’s adjacency to graduate school determines whether they plan to stay in Kenya longer than 6 months. No master’s plans = no concrete plans to leave Kenya.

Degree of entrapment in the expat bubble:

• L: Basically local

• S: Stuck in the bubble

• Discussion: L’s tend to go to Kenyan clubs, listen to afrobeats, and not exclusively hang out with fellow expats. S’s tend to plan trips to the coast with each other and avoid taking matatus at all costs. If a S is the clubbing type, Alchemist is their favorite club. As a show of dominance over S’s, L’s will often slip the odd Swahili word into conversation. 

Weekend behavior:

• C: Prioritize city life in Nairobi 

• T: View traveling as the primary purpose of weekends

• Discussion: Typical interests of T’s are kite surfing, climbing, gathering “content” for “the gram”, hiking, applying sunscreen, and safaris. T’s tend to be “doers”, as in “We did Mt. Kenya last weekend”, “I’ve done Lamu 3 or 4 times”, and “Ah, I’ve been meaning to do Samburu again”. C’s, either out of fear of the outdoors, desire to build up a community in Nairobi, or revulsion to planning tend to enjoy taking advantage of all the restaurants, markets, and house parties that Nairobi city life has to offer.

Further work: None required. This topic is now closed (4).

Acknowledgements: Thanks to K and J1 for comments on this manuscript, and to A and J2 for fruitful discussions. Expats par excellence all.

—

1. As we all know, if you move from a poorer to a richer country, you’re an immigrant and if you move from a richer to a poorer country you’re an expat. This is right and just and no further interrogation of this fact is needed

2. Hence “The Taxonomy”

3. For brevity I have titled this dimension “B-school-adjacence”, although “Grad-school-adjacence” would be technically more accurate as there are P’s (particularly those from Europe) who are planning to go to grad school that is not business school

4. Tag urself. I’m an INSC

Nuclear power and cross-disciplinarity

Here’s an excerpt from the book Why Nuclear Power Has Been A Flop by Jack Devaney:

When we tried to make the argument for balanced limits to a group of Indonesian nuclear regulators, one member of the group had the honesty to stand up and say ‘I don't care what the problems with coal are. I'm a nuclear regulator. My job is to make nuclear as safe as possible.’ And under the instructions and incentives that he has been given, he's right. Unless these instructions and incentives are changed, horribly unbalanced regulation will continue to be the norm.

This crystalized an insight for me about the importance of thinking at a system level, and cross-disciplinarity.

• If your job incentivizes you to focus on a certain domain (in this case, safety of nuclear energy), then you will focus on doing that thing. 

• You will not focus on how, in the big picture, to achieve the theoretical goal of your work (in this case, safe and reliable electricity production). 

• But your work and theoretical goal are closely connected to work in other domains (in this case, making it difficult to build nuclear plants makes it relatively easier to build coal plants, which kills people)

• Unless someone is explicitly given the job of making trade-offs across domains, nobody will spend much time on it and we will not make the right trade-offs to achieve our goals (they also of course need the authority to ensure their evaluations enact change)

Here’s a running list of examples of where this sort of cross-domain thinking is missing. I’ll add more as I come across them. Let me know if you have additions!

• Energy regulation: tradeoffs between nuclear, coal, solar, etc.

• Pandemic response: Spending resources procuring vaccines vs. enforcing costly lockdowns

• Carbon emissions (management consulting): In management consulting, tradeoffs between doing projects helping oil companies extract oil more efficiently, and spending money on carbon offsets

We can debate what the right tradeoff is on all these issues. But my point is that it doesn’t even seem like anyone in a position of power is responsible for thinking at a system level about how actions in one area affect the other.

Who is responsible and has the authority for making these trade-offs across different highly technical domains of knowledge and expertise? Who is John Galt?

How Many Lives Could You Save by Fixing Potholes in Zanzibar?

Last weekend I was in Zanzibar, riding in a taxi through a rural part of the island, and noticed that there were a lot of potholes. I remembered a study overview I had read that showed that reducing the number of cars idling on a road has significant positive health impacts on the people who live nearby (1).

This made me think: Paving over these potholes would allow cars to travel faster on this road, which would reduce the pollution exposure of people living nearby. I wondered if it would be cost-effective for a charity to pave these potholes, in order to improve the health of people living nearby (not even counting the other benefits of having well-functioning roads).

I decided to do a back-of-the-envelop calculation to answer the question: Would it be cost-effective, purely from the standpoint of reducing low-birth-weight births via pollution reduction, for a charity to take on the task of paving these potholes?

In short, the answer is probably no - it would not be cost-effective. I think it’s still worth sharing my process, because I think this practice of doing these types of calculations is a good habit, and because knowledge about what health interventions don’t work can still be valuable (2).

Here’s an overview of my calculation (detailed calculation at the Google Sheets worksheet here if you’re super interested):

• $500 to fix 1km of road’s potholes, based on cost to fix a pothole in the US and assuming 10 potholes per km

• 1 low birth weight eliminated per 10km of road patched, based on the birth rate and population density of Zanzibar, plus some wild assumptions about the equivalence of Zanzibarian Potholes to Pennsylvanian EZ Pass systems

• 7 years of life saved per low birth weight eliminated based on a study from Mozambique

So if we count a “life saved” if we save 57 years of life (57 being the life expectancy in Zanzibar), that gives us an estimated cost per life saved of ~$40k ($50k = $500 x 10 x 57 / 7).

This is not great compared to  the most effective charities (charity evaluator GiveWell estimates it costs $2k-$3k to save a life by distributing anti-malarial bednets). But my estimate was very rough - maybe fixing potholes is way cheaper in Zanzibar than in the US. And I only estimated lives saved due to low birth weight deaths - reducing pollution also has health benefits to people other than newborns that I’m not taking into account. On the other hand, maybe there’s way less traffic in Zanzibar than in Pennsylvania, so that pollution reduction effect is way lower than I estimated.

Another consideration is that maybe fixing potholes isn’t the kind of thing a charitable organization should get into - local governments should fix potholes and if a charity came in it would create a harmful cycle of dependency.

In any case, the potholes should probably be filled, even if the cost can’t be justified purely on the terms of reducing low-weight births. And the case isn’t totally closed against fixing potholes as a charitable opportunity. It may be worth a deeper dive more if you run an asphalt company and are interested in social impact, or if you’re just someone else who is bored some weeknight.

1. The study overview is only three pages and worth reading if you don’t often think about how harmful pollution is. The main takeaway is this: EZ-pass toll booths were installed on expressways in Pennsylvania and New Jersey. This meant that cars did not slow down and idle near the toll station. This reduced the amount of exhaust pollution that pregnant mothers who lived within 1km of the toll station were exposed to, which improved their health and caused the number of low-weight births to fall by 9%.

2. We all know what Edison said about lightbulbs or something

Labor is Very Cheap in Kenya

One of the most significant mental adjustments I’ve had to make in living in Nairobi is internalizing how incredibly cheap labor is (1). There aren’t lots of high-paying job opportunities here, so tons of people are willing to do low-skilled labor for very low costs (2). Here are some of the ways that the cheapness of labor manifests itself:

• A 15-minute Uber costs ~$2-3

• Food delivery is cheap (~50 cents for an UberEats order)

• Most apartments have maids / house managers who come in frequently to clean and do laundry. In my apartment we pay $100 per month for someone who came three times a week (this was excessive so we recently asked her to switch to twice a week, but are paying the same amount. We also had to explicitly ask her to stop cleaning the guest bathroom every week because we never have guests)

• My friend who works at a e-commerce company said that when they need new deliverymen (3) they don’t put out formal applications because “guys just materialize”. If you need an extra guy you just let your current deliverymen know, and one (or all) of them will have friends who are happy to take that spot (4)

• Many many buildings have multiple security guards who apparently do very little. Security is more of an issue here than in the US, but I feel like at very least a lot of these offices/apartments/houses would have only one security guard if guards were more expensive

• When staying in a hotel in Amboseli National Park, people cut the grass lawn by hand with a machete instead of with a lawn mower. It must take them at least 5 times as long to cut by hand as it would with a machine, but because the grass cutters are probably being paid less than $10 per day, management is fine if it takes them 3 days to cut all the grass by hand to avoid disturbing guests with a loud lawnmower

• People work long hours to make enough money. General working hours are 45-52 hours w week according to Kenyan labor law. Today a worker at a food mentioned how “we have to work so hard here - we can’t work just 8 hour days like people do in the US”

In the US I was always averse to paying for things like food delivery, Ubers, or cleaning. I felt that I was being lazy and wasteful by paying to avoid getting my food myself, biking to wherever I was going, or cleaning my own stuff. When I moved here I told my flatmate that I would probably do all the dishes, because I like doing them and I often did them for my family, housemates in college, and roommates in Chicago. But I’ve found that my enjoyment in doing the dishes has decreased dramatically now that I know that I always know our maid is coming sometime in the next few days.

1. The median income in Kenya is only ~$1,000 per year (compared to $30,000 in the US)

2. People can also survive on low incomes, because things are cheap (and the main reason things are cheap is because labor is cheap)

3. I have yet to see a deliverywoman, and have had only 1 woman Uber driver in 2 months

4. Another factor allowing this to happen is the low degree of formal employment (5 times as many people in informal employment as in formal). Since most of these delivery people didn’t have formal jobs, switching costs are very low. It’s easy for them to just start doing deliveries tomorrow, instead of having to decide if they’re burning bridges by leaving, giving 2 weeks notice, etc.

Back of the envelope math on pausing AstraZeneca vaccinations

European countries, led by Germany, have paused administration of the AstraZeneca vaccine. The US has not even begun to administer it yet.

There are 7 instances of blood clotting in 1.6M AstraZeneca shots in Germany. IF that’s causal ,which it looks like it isn’t, that means you have a .0004% chance of getting blood clotting if you get the AstraZeneca vaccine

Extremely conservatively, let’s say AstraZeneca reduces your chances of dying by 60%.  Currently 74K people  have died in Germany from COVID, out of a population of 80M. So getting the shot can reduce your chance of COVID death by 0.05%.

Given that we know COVID is killing people, and it looks like you’re 100 times more likely to die of COVID if you don’t get the vaccine than you are to get blood clotting if you do get the vaccine (which does not necessarily even kill you), it makes far more sense to err on the side of continuing (or beginning @USA) administration of the vaccine. By all means investigate the blood clots, but don’t stop administration of something we know will protect us from the virus until we get convincing evidence that halting vaccinations is safer than continuing them.

Goodreads review of The Complacent Class

Today I wrote a Goodreads review that was long enough to make me feel satisfied with my “amount of time spent thinking critically and writing” for this weekend, so I’m just posting it here.

If you’re reading this and use Goodreads, I’d love to add you!

The Complacent Class: The Self-Defeating Quest for the American Dream by Tyler Cowen
My rating: 4 of 5 stars

Cowen makes a compelling case that technological innovation and social innovation and political competency have slowed down dramatically over the past 3 years, that individuals are largely okay with this, and that our institutions make it difficult to change this. This complacency has benefits such as increased safety and increased ability to satisfy certain kinds of preferences (like cheap access to nearly unlimited movies, books, and music), and makes sense for individuals in the short-term. But in the long-term it means that we will improve less as a society, making life worse for people down the line.

Cowen makes some of his points by leaning on his own intuition for how society has changed (e.g., when he says that the U.S. is more segregated in terms of “overall feel”). But these intuitive judgement are important to making his arguments mentally sticky, and I believe that he has both the statistics and the credentials as a generally open-minded devourer of cultural information to back up these sorts of claims. In fact I think that what is jarring is not that he leans on intuition when making arguments, but that he is so much more open about the cases where he does this than are other social science authors.

The book was published in 2016, and two 2020 issues jumped out to me after reading the book:
1. If you accept this account of complacency, it's very predictable that we would be terribly-prepared for something like COVID-19. Cowen writes “At some point this country will face an immediate crisis, and there won’t quite be the resources or more fundamentally the flexibility, to handle it...Building good institutions and capabilities very quickly is no longer something the American public sector is very good at.” And “As soon as Americans have to rely on their government to do something new and concrete — whether at home or in the realm of foreign policy or public health or the environment — low levels of trust will make that more difficult.” All this, unfortunately, checks out as we've seen.
2. Cowen calls out the civil unrest in Ferguson and Baltimore as potential signs that change is coming - that people are truly acting out their frustration instead of simply voicing it online. The protests and riots we’ve seen this summer in the aftermath of George Floyd’s killing are a stronger sign that at the very least complacency with racial injustice may be on its way out.

View all my reviews

Additional facts from the book that might be of interest:

• The crime rate hasn’t gone down as much since the 90s as people often think, because internet crime became possible during the 90s, and there is a lot of internet crime as people think because a lot of it moved online

• Greater inequality is correlated with reduced protest participation

• Segregation is up by race and income And distressingly, the most segregated cities (in terms of working class/non-working class) are often those seen as the very trendy cities: LA, Austin, Dallas-Fort Worth, DC, Raleigh, SF. Even though people say they don’t like segregation by race or class, they vote with their feet by moving to very segregated cities

Key drivers of the growth of global capitalism

The past 300 years have seen an incredible amount of overall progress in terms of quality of life. Many of the drivers of that project have also been the drivers of immense suffering. Much of this progress and suffering is tied to what we call “global capitalism”.

The book Empire of Cotton charts the rise of the global cotton trade, which drove much of the development of global capitalism. In the spirit of learning from what has successfully fostered human development and condemning what hasn’t, I’d like to provide a summary of what, to my understanding, have been the most important factors in the development of global capitalism.

Development of markets to determine allocation of capital: The development of industry networks, contract law, and financial instruments like credit, insurance, and futures, allow capital to be allocated efficiently (“efficiently” not in a normative sense, but in the sense of producing the most outputs for the least inputs). This makes possible innovation that can allow processes such as farming, manufacturing, and shipping to be done even more efficiently.

Land seizure and clear private property rights: The ability for a large company to grow lots of cotton requires lots of land, and a state that can enforce clear property ownership laws. State capacity was built up with the support of industry leaders and merchants, and land was seized by force from native peoples in places such as India and the U.S. 

Specialization of land and labor: Before the 1800s, many people in the world lived through subsistence farming. They grew what they needed, and bought relatively little. Colonial powers, largely through coercion, forced a shift towards growing cotton for export and then buying your own food. This specialization allows goods to be produced more efficiently and increases demand for those goods, but comes at the cost of increased instability (e.g., higher likelihood of famines when low cotton prices means inability for workers to buy food).

All-compassing control of labor including slavery: The extremely low prices of finished cotton goods require incredibly low production costs. To keep these production costs low, cotton manufacturers kidnapped people from Africa to work on farms as slaves (euphemistically referred to as “elasticity of the labor supply”). They took children from orphanages to work in factories for 12 hours a day. They built dormitories for factory workers to live in and locked the doors at night so they could not escape.

Government protectionism, investment, and coordination: Government tariffs and bans on imports are needed to allow development of nascent manufacturing industries. Infrastructure such as railroads and canals allow goods to be grown in more locations and to be transported more quickly (reductions were dramatic: Getting cotton to the coast in Togo took 15 days in 1900 and just a few hours in 1907). Governments also served a coordination function in the gathering and dissemination of market data.

Commoditization of goods: Manufacturing products at a large scale requires getting resources from many different suppliers. This is very difficult if each supplier gives you a slightly different version of the good. Commoditization, as typified in the development of quality standards during the early 1800s, allows a manufacturer in 1820 Liverpool to buy 200 bales of “choice prime cotton” coming from many different growers in the U.S. and India, and trust that they will all be usable.

Amartya Sen’s Cohesive View of Development

In this post I will sketch out the conception of justice that Amartya Sen puts forth in his 1999 book Development as Freedom.

Sen makes two overarching and interconnected arguments in Development as Freedom: a philosophical argument, and a practical argument

Philosophical: Human development should be seen as the increase of individual liberty, which is a multifaceted object

Practical: It is important to take into account all the facets of individual liberty when measuring or trying to improve a country’s development

The philosophical argument is a claim about the nature of justice in a society. Individual liberty under Sen’s conception is at its core tied to the opportunities we have. A free person is one who has more options about how to live her life. So being truly free means having a plethora of different kinds of freedoms such as political freedom, economic mobility, health, education, and access to open markets.

Sen sees his view of justice as being distinct from the 3 standard theories of justice (utilitarianism, libertarianism, and Rawlsian liberalism). Utilitarianism does not respect individual liberty except as a means to the end of welfare — Sen rejects this, seeing political rights as being important in themselves and not only as a means to greater human happiness. Libertarianism has the opposite problem, viewing liberty as the end-all and be-all without regard for what the consequences (e.g. the economic consequences) of individual liberty are. And Rawls views certain individual rights as always taking precedence over other types of well-being, while Sen says that it would be better for someone to be well-fed and oppressed than starved and free to vote (though he argues that real instances of these kinds of tradeoffs are rare).

This practical argument says basically that we should not collapse down our view of individual liberty into one metric to be used to compare countries along a linear scale of “how developed they are”. You could try to make such a metric by taking measures of each component of individual liberty (life expectancy, infant mortality, democracy index, GDP per capita, median years of schooling, etc.), assigning each of them weights, and summing them. But to simply try to maximize this metric would be to bake in the assumptions you are making about the relative importance of each of these factors in a manner that is not very transparent. Sen instead says that when making decisions, we should look at how our decision will affect each of the components of individual liberty that we care about, and consciously make any necessary tradeoffs.

I admire the nuance Sen uses when approaching both the philosophy and application of development principles. I understand that, out of a desire to quickly communicate about complex issues, people often use shorthand measurements for development, such as GPD or health indices. But I believe that on balance, discussions around development could use the kind of nuance Sen brings. I’d love to see more economics papers framed within this cohesive view of human development, emphasizing that any one particular developmental metric we look at is only part of the story.

How many people do I kill with COVID-19 when I go to get groceries?

Quick post today: A back-of-the-envelope calculation of how worried I should be about going to the grocery store that I’m giving somebody COVID-19.

(These numbers are a little bit out-dated now — I originally did this calculation a week ago, when I was in Chicago. I am now in Des Moines IA, and am not going to the grocery store any more.)

Chances I kill somebody through COVID-19 when I go to the grocery store = (chance I have COVID-19) x (chance I transmit the virus if I have it) x (chance the person I give the virus to dies of the virus)

1. There is a 0.4% chance I am carrying COVID-19

   • 1.7% of people in Cook County are carrying the disease

     • 5.2M people live in Cook County

     • 93,000 likely carriers in Cook County

       • 186 deaths in Cook County (and 8,034 confirmed cases)

       • .2% death rate (Source). 186 / .2% = 93,000 carriers

     • 93,000 / 5.2M = 1.7%

     • ~1/4 people who have the disease are asymptomatic, and I am asymptomatic. So there is a 1.7%/4 = 0.4% chance I am carrying COVID

2. 4.5% chance that I give someone COVID when I go to the store if I have COVID-19

   • 2.5% chance I transmit by coughing on someone

     • This number is totally made up. It is likely lower, because I have not been coughing at all when going out, have been wearing a cotton mask when near people, and have been staying more than 6 feet away from people

   • 2% chance I transmit by touching someone with my hands. These numbers are all totally made up as well 

     • 15% chance virus goes onto something I touch with my bare hands

     • 15% chance someone else picks up virus after touching that thing

   • 2.5% + 2% = 4.5%

   • Sense check: R0 is ~1, you have the disease for 14 days, so the average odds you give someone else the disease each day is ~7% if you have COVID. I would estimate that I’ve had far less social contact with people since the outbreak started than most of the people who are driving the spread of the disease, so my personal R0 would probably be much lower than 1 

     • (I’m not sure if “my personal R0” is a meaningful thing to say — maybe R0 is only defined for a population? But I can’t fit the research that would be needed to find that out on the back of this envelope)

3. .6% chance that person dies from COVID-19

   • .2% chance someone dies if they have COVID-19

     •  Source

   • Of course, if they have the disease, then there is some chance they give it to someone else, and on it goes. So let’s multiply this number by 3 to get .6%. Perhaps it should be much higher —  I think this is the biggest weakness in my methodology

This means my chances of killing someone when I to the grocery store is 1.1 x 10^-6

For context, this is approximately equal to your chances of killing someone while driving ~100 miles if you get in the average number of fatal car accidents / mile.

For a different kind of context, if a human life is valued at $10M (I believe this is what the department of energy uses when doing nuclear power cost/benefit calculations), then the equivalent cost of my trip to the grocery store is $11.

Now I’m not saying that the value of human lives can be directly translated to dollars, but I think that these kinds of utilitarian calculations are useful to get a sense of the magnitude of what we’re talking about. I don’t really have a sense for what 1.1 x 10^-6 means  — my imagination isn’t that good. But I know what $11 means. It means that if I would be willing to pay an $11 “grocery shopping fee” then it’s probably alright to go get groceries. I should be moderately worried about going outside, but if I get groceries or go for a run, I don’t need to feel waves of guilt, as long as I am taking precautions.

You can plug in your own updated numbers or use different assumptions to get a different result for your likelihood of killing people when you do various activities. It’s fun! And allows you to compare the relative riskiness of different activities, and to prioritize your worrying towards the things that are truly the most worrisome.

Advanced Market Commitments! What Are They Good For?

Advance Market Commitments are a method for countries or NGOs to incentivize the production of vaccines for developing countries. This helps overcome the basic problem that pharma companies are less likely to develop vaccines for illnesses that people won’t be able to pay high prices for.

An well-functioning AMC has two main purposes:

• Ensures that vaccine companies invest in the capacity to sell their vaccines in low-income countries

• Ensures that the price of those vaccines is low enough for it to get to the people who need it

An AMC is basically a contract between a funder and a company. The funder gives the company a bunch of money, and the company agrees to provide a certain number of vaccines, at a certain price, for a certain amount of time. After that time, it’s theoretically possible that the company could jack up its prices so that the poorest people would no longer be able to afford the vaccine, or that it would simply stop selling the vaccine. But the likelihood of either of these happening is low if the AMC was designed well. Competitive and PR pressures would prevent the company from jacking up its prices. And because the marginal costs of production for a dose of the vaccine are low, it’s unlikely that the company would leave the market after having already put in the up-front costs needed to produce, distribute, and sell the vaccine.

The AMC is a relatively new idea, and the first “pilot” AMC program has just completed. From 2010-2019, the GAVI Alliance paid companies who agreed to sell a pneumococcal conjugate vaccine (PCV) to developing countries. In 2007 when the program was announced, a vaccine to treat PCV in developing countries was in late-stage trials, so the idea of the AMC was not to incentivize the development of an entirely new vaccine, but rather to incentivize the scaling up of production and distribution capabilities for a vaccine that would be available soon. If a company agreed to provide 20 million doses of the vaccine per year for ten years at $3.50 per dose, they got $150M. This $150M is in addition to the $3.50 they already get per dose.

The AMC appears to have been incredibly successful. Three companies (GSK, Pfizer, and Serum Institute) are distributing the vaccine, and charging <$3 per dose (so they are charging even less than they are mandated by the terms of the ACM, making it seem unlikely that they will phase out production of the drug now that the 10 years are over). Over 50 million children received the vaccine per year, and an estimated 700,000 lives were saved. That’s astounding — almost as many people as died in the Civil War. And all saved in the past 10 years by a vaccine I knew nothing about until 2 weeks ago.

This is very exciting to me as someone who wants to see more of the world’s talent and capabilities being efficiently allocated towards our toughest problems. A great idea + organizational knowledge and international cooperation + generosity of donors + power of markets led to hundreds of thousands of lives saved. Potential solutions are out there — it just takes dedication, work, and cooperation to bring them to life.

—

All my information comes from this working paper by Kremer, Levin, and Snyder.

Some other interesting tidbits:

Countries participating in the program had to cover part of the vaccine cost themselves roughly $0.20 per dose. Kremer et al. show that from a strictly financial perspective this doesn’t make that much sense — the 20 cents that GAVI saves per dose are not worth the possibility that takeup of the drug will be lower than it would have been if the vaccine was free. But they note that it still may be worth it to have small copayments to function as a market test to see if there is really a demand for these vaccines.

The WHO standard for a cost-effective intervention is $1,638 per DALY (DALY = Disability Adjusted Life Year. Basically, if you can allow someone to live an extra year for $1,638, that’s a cost-effective intervention). Kremer et al. don’t provide an estimated cost per DALY for this program because they don’t have the data to do so, and they’re good economists. But I’m not not a good economist and a lack of data isn’t going to stop me from making a back-of-the-envelop estimate: 

The program cost $1.5B plus $3.50 per dose + ~$1 per dose in administration. In 2016 there were 160M doses distributed. So let’s say the overall program cost over the 10 years is $8.9B. ~700,000 lives were saved by the PVC vaccine. We don’t know how quickly and at what price the vaccine would have been brought to market in the absence of the AMC, but let’s say that the vaccine would have been on the market in each country just two years later without the AMC (and at the same $3.50 price). Then we should only count one-fifth of the 700,000 lives as being saved by the AMC, and we’d be looking at $63,571 per life saved, or ~$1,000 per DALY (assuming 60 year lifespan). So it seems reasonable to claim that the program was cost-effective by WHO standards.