Silicon Valley venture-backed tech startups have had a wildly successful twenty years, coming to dominate the markets. But tech remains a relatively small sector in terms of the total number of businesses and employees, and by many measures entrepreneurship and small business have been in relative decline in the US during the 2000’s.
Covid accelerated many pre-existing trends, like the shift to remote work. But it reversed other trends, and seems to have led to a revival in entrepreneurship broadly.
This wasn’t all good at first- in 2020, the share of “necessity entrepreneurs” also reached record highs. These are people who start a business because they can’t get the job they want, not because they expect their business to be wildly successful. But in 2021, the rate of new entrepreneurs remained high while the share of “necessity entrepreneurs” and “opportunity entrepreneurs” returned to their normal balance.
Another good sign is that the share of businesses surviving at least a year is also at record levels:
This semester I am participating in a reading group with undergraduate students that focuses on the history and prospects for capitalism and socialism. Lately we have been reading Joseph Stiglitz, who has long argued that China’s transition to a market economy has gone much better than the former Soviet Union. Gradual transition is superior to “shock therapy,” according to Stiglitz.
There’s an extent to which this is true. If we just look at economic growth rates since, say, 1995, China has clearly outpaced Russia.
It’s hard to know exactly what year to start, since GDP figures for former planned economies immediately after transition aren’t reliable, but the start date is mostly irrelevant for everything I’ll say here (please play around with the start year in the charts to see if I’m cherry-picking years). 1995 seems a reasonable enough year to start for reliable post-transition starting point.
As we see above, while Russia has had a rough doubling of GDP per capita since 1995 (respectable, and yes, it’s all adjusted for inflation!), China has soared almost 600%. Wow! But this is something of a cheat. Despite all that growth, average income in China is still lower than Russia: only about 60% of Russia in 2020. China started from a much lower level, meaning that faster growth, while not guaranteed, is at least easier to achieve. In fact, if we go back to 1978, when China’s first reforms began, GDP per capita in the Former USSR was about 6 times as high as China (that’s according to the latest Maddison Project estimates, which will always be speculative for non-market economies, but are the best we have).
Furthermore, Russia hasn’t really transitioned to a democracy either. China clearly hasn’t, but no one doubts that. But despite having the outward symbols of democracy (elections, a legislature, etc.), Russia still scores low on most indexes of democracy and civil liberties. For example, Freedom House scores them at 19/100, a little better than China (9/100), but nothing like Western Europe.
So, did the quick transition to market economies fail? Not so fast. While it did fail in Russia, in most of Eastern Europe and the eastern part of the former USSR seems to have been a major success. Take a look at this chart, which shows the former Soviet Republics in and near Europe (I exclude Central Asian FSRs).
The saying that “The first casualty of war is the truth” has been credited to anti-war Senator Hiram Warren Johnson in 1918 and also to the ancient Greek dramatist Aeschylus. We have seen this played out dramatically with Russia’s invasion of Ukraine. From the Ukrainian side have come the predictable overinflated estimates of the enemy’s losses, and perhaps understated reporting of their own casualties. Also, on the first day or two of the war there was a raunchy defiant response of Ukrainian defenders to a “Russian ship” that was demanding their surrender; as far as I know that exchange was for real, but the initial report by Ukraine that all the heroic defenders were killed was not true. Maybe I am biased here, but these sorts of excesses are stretching some core truth, not trampling over it roughshod.
On the Russian side, perhaps because there is no even vaguely legitimate justification for their invasion, the lies have been simply ludicrous. Apparently, the Russian troops have been told that they are going there to rescue Ukrainians from the current regime which is a bunch of “neo-Nazis”. If Putin’s thugs had a sense of humor or perspective, they might have discerned the irony of characterizing the Ukrainian regime as “neo-Nazi” when the president (Zelenskyy) is a Jew, whose grandfather’s brothers died in Nazi concentration camps.
And the Russian lies go beyond ludicrous, to revolting and inhuman. Russian Foreign Minister Sergey Lavrov has dismissed concerns about civilian casualties as “pathetic shrieks” from Russia’s enemies, and denied Ukraine had even been invaded.
The Associated Press snapped a picture in the besieged city of Mariupol a few days ago which went viral, showing a pregnant woman with a bleeding abdomen being carried out on a stretcher from a maternity hospital which the Russians had bombed. The local surgeon tried to save her and her baby, but neither one survived. The Russian side put out a string of bizarre and contradictory stories, claiming that they had bombed the hospital because it was a militia base (a neo-Nazi militia, of course) but also that no, they didn’t bomb it, the hospital had been evacuated and the explosions were staged by the Ukrainians, and the bloody woman in the photos was a made-up model. Ugh. I find it chilling to observe a regime in operation where there is absolutely no respect for what the truth actually is; rather, lies are manufactured to serve whatever purpose will suit the regime.
I know that some of that goes on even with Western democracies, but we are still usually ashamed of outright lying, and stand discredited when exposed. But with hardcore authoritarian regimes, there does not seem to be even this minimal respect for integrity.
Freedom of speech becomes even more critical as cynicism about truth becomes more widespread in the world, even in our own political discourse. Putin is trying to suppress the truth within Russia, now with very harsh penalties (fifteen years in prison) for those disseminating information contrary to the party line. All he needs to do is deem such talk as “treasonous”, and into the clink you go.
I do worry about similar trends towards censorship within the West. In our case, it is not so much governments (so far) doing the censorship, but Big Tech. If Google [search engine and YouTube] / Facebook/Twitter disapprove of your content, they can label it “hate speech” or whatever, and your voice disappears from public discourse. But what gives the high priests of big tech the authority and the powers of moral discernment to rule on what discourse is permissible? Also, the algorithms of social media sites usually direct you towards other sites that reinforce your own point of view, so you rarely get exposed to why the other side believes what it does. However annoying it may be to see various forms of nonsense circulating on-line, the time-tested democratic response is to allow (nearly) all points of view to be fairly stated, and to trust in the people to figure out where the truth lies. Otherwise, the truth can become a casualty of culture wars, as it is in shooting wars.
I had the title of this post sitting in “Drafts” for a couple months now, but Kris and Paul have given me good reason to actually write about it. These thoughts are largely off the cuff, but they do come from experience.
What is Agent-Based Modeling?
This is not actually as straight-forward question as one might think. If you define it broadly enough as, say, any model within which agents make decisions in accordance with pre-defined rules and assigned attributes, then the answer to the overarching question posed by this post becomes: well, actually, economics has been producing agent-based models for decades, but that answer is as annoying as it is useless.
Instead, let’s start with a minimal definition of an agent-based model:
They are composed of n >3 agents making independent decisions
Agents are individually realized within the model.
Decisions are made in accordance with pre-defined rules. These rules may or may not evolve over time, but the manner in which they evolve are themselves governed by pre-defined rules (e.g. learning, mutation, reproduction under selective pressures, etc).
If we stop at this minimalist definition, then the answer becomes only marginally less trivial, as essentially any dynamic programming/optimal control model within macroeconomics would meet the definition. This leads to what I consider the minimal definiton of an agent-based model as a distinct subclass of computational model:
Agents within the model are characterized by deep heterogeneity.
Agents exist within a finite environment which serves as a constraint in at least one dimension (lattice, sphere, network, etc).
Decisions are made sequentially and repeatedly over time
Now we’re getting farther into the weeds and beginning to differentiate from whole swaths of modern macroeconomics that either employ a “representative agent” or collapse agent attributes to the 1st and 2nd moments of distributions. But that doesn’t eliminate all of modern macro. If embracing heterogeneous agents in your models of macroeconomics, banking, etc, are of interest to you, there are scholars waiting to embrace you with open arms.
Which brings me to the final attribute that I believe fully distinguishes the bulk of the agent-based models and their advocates from modern economics:
Agent-based models exist as permanently dynamic creations, absent any reliance on equilibria as a final outcome, characterization, or prediction.
The departure from general or partial equilibria as outcomes or predictions is where the schism actually occurs and, I suspect, is where many purveyors found themselves with a research product they had a hard time selling to economists. Economics, perhaps more than any other social science, demands that theoretic predictions be testable and falsifiable. Agent-based models (ABMs) don’t always produce particularly tidy predictions that lend themselves to immediate validation. Which doesn’t preclude them from making a scientific contribution, but it puts them on unsteady footing for economists who are used to having a clear path from the model to the data.
OK, but really, why didn’t agent-based modeling happen?
As much as big, irreconcilable differences in scientific philosophy would make for a satisfying explanation, I suspect the most salient reasons are less sexy and, in turn, less flattering of the day-to-day realities of grinding out research in the social sciences. Here are a few.
Economics was already a “model” social science
One of the reasons mobile phones caught on faster in Africa than North America was an absence of infrastructure. The value add of going from “no phones” to “mobile phones” is far larger than going from “reliable land lines in every edifice” to “mobile phones”, making it easier to justify both investments in relevant infrastructure and bearing of personal costs. Such a thing occurred across the social sciences with regards to ABMs.
Rational choice and mathematical sociology always had a limited following. Evolutionary biologists were often alone in their mathematical modeling, computational biology barely existed, and cultural anthropologists were more excited about Marx’s “exchange spheres” than they were about formal models of any kind. For a PhD student in these fields, the first time they saw a Netlogo demonstration of an agent based model, they were seeing something never previously available to their field: the ability to formalize their own theories in a way fully exogenous to themselves. There would be no fighting about what their words actually meant, whose ideas they were mischaracterizing, what they were actually predicting. Their critics, be it journal referees or thesis committee members, would have no choice but to confront their theory as an independent entity in the world.
This advantage of formality, of independent objectivity, in agent-based modeling was not something new to economics. While critics have many (often correct) complaints about modern economics, it’s rare to air concerns that economics is insufficiently formal or mathematized.
Too many “thought leaders”, not enough science
Axtell and Epstein wrote their landmark book “Growing Artificial Societies” in 1996. In it they produced a series of toy simulation models within which simple two-good economies emerged. This wasn’t revolutionary in it’s predictions by any means (whole swaths of macro models were able to make comparable predictions for two decades prior), but the elegance through which minimalist computer code could produce recognizable markets emergent from individual agent decisions was just incredible. The potential to readers was immediately obvious: if we can produce such things from 100 lines of code, what could we simulate the fully realized power of modern programming?
What came next was…still more people evangelizing and extolling the power of ABMs to revolutionize economics. What didn’t come were new models. Forget revolutionary, its hard to even find models that were useful or at least interesting. The ratio of “ABMs are gonna be great” books and articles to actual economic models is disappointing at best, catastrophic to the field at worst.
There were a couple early models that got attention (the artificial Anastazi comes to mind), but after a few years everyone noticed that same 2-3 models were still be brought up as examples by evangelists, and none of them had meaningful economic content. As for the new models that did end up floating out there, there was also an oversupply of “big models”, with millions (billions) of agents and gargantuan amounts of code that intended to make predictions about enormous chaotic systems. Models, such as the Santa Fe Artificial Stock Market, tried to broadly replicate the dynamics of actually stock markets across a large number of dimensions. Such ambitions were greeted with skepticism by economics for a variety of reasons, not least of which the “curse of dimensionality”, which limits what you can learn about underlying mechanisms when the number of modeler choices exceeds your ability to test them or, for that matter, verify their internal coherence. For better or worse, these models felt akin to amateurs trying to predict a town’s weather 30 days out.
Bad models drove out good
The problem of too few good models was closely followed by the over-supply of bad models. Agent-based modeling, for good and for ill, is not a technique with high entry costs. A successful macroeconomic theorist is effectively a Masters-level mathematician, bachelors level computer programmer, and PhD economist. Netlogo programming can be learned in a week. You can get really good at programming agent-based models in a dedicated summer.
This isn’t unto itself a problem, but I can tell you this: in my first 5 years as an assistant professor, I was asked to review at least 100 papers built around agent-based models. I’m not sure if any of them were any good. I am sure that many of them were extremely bad. Most concerning is that I don’t think I learned anything from any of them. The costs of producing bad ABM papers is much lower than the costs of producing bad theory papers based on pure math. Bad science is often evolutionarlily selected for in modern science, a dynamic that in the case of ABMs was only amplified by a lower cost supply curve.
Now, here’s the thing: there was probably huge selection effects into what I interacted with. I doubt I was getting the best papers sent to me for review given my status in the field. But the quantity of bad papers was astonishing. They were just too easy too churn out. I suspect that some decent papers were lost in haystack of ad hoc pseudoscience and, in turn, some decent scientific careers probably got lost in the shuffle. More than once I had the thought “Editors are going to start rolling their eyes every time they see the term agent-based modeling if this what keeps coming across their desks.” Combined with the fact that ABMs are tricky to evaluate because you really need to go through the code to know what is driving the results, I think a lot good modelers got lumped in with the dreck.
[Not for nothing, it wasn’t uncommon for ABM papers to spend the bulk of the paper describing model outputs, while having nearly nothing about model inputs (i.e. rules, code, math, etc). These models were essentially black boxes that expected you to take their coherence on faith. I should note here that I haven’t really kept up with the field in the past few years. Hopefully transparency norms have improved, particularly in biological, ecological, and anthropological modeling, where ABMs have thrived to a far greater extent.]
The empirical revolution took hold of economics
I’ve save the biggest reason for last, but honestly I think it dwarfs the others.
The same rise in cheap computational power that gave rise to other forms of computational modeling, including ABMs, came along with the plummeting cost of data creation, storage, analysis, and access. By 2010 it was already increasingly clear that theory was taking a backseat in economics. Not because we were becoming an a-theoretic discipline (far from it), but because the marginal contribution of theory against the body of broadly accepted economic framings was small compared to those made by empirically testing the predictions of the existing body of theories against real data. The questions were no longer “How do we mentally organize and make sense of the world”, but instead “What is the actual measured effect of X on Y?” Theory gave way to statistical identification. Modeling technique gave way to causal inference.
Agent-based models are hard to empirically evaluate and test
Which gives way to a sort of subsidiary problem. It is more difficult for agent-based models to take advantage of the new data-rich world we live in. They don’t produce neatly direct predictions the way that microeconomic theories do, nor do they lend themselves to measured empirical validation in the same way as general equilibrium predictions of macroeconomic models. Empirical validation is by no means impossible, but it requires the matching of observed dynamics or patterns, which is generally a taller order. In this way, agent-based computational models are a bit of a throwback to the days of “high theory”, making for interesting discussion but of secondary importance when it comes to the assigning of journal real estate that makes and breaks careers.
Bonus story
I once presented my ABM paper on emergent religious divides, only to have an audience member become extremely upset, closing with the denouncement that “This isn’t agent-based modeling, this is economics!” That was my first exposure to the theme of ABMs as “antidote” to the hegemony of economics and all of its false prophecies. The idea that the destiny of ABMs was to unseat economics as the queen of the social sciences was probably an effective marketing strategy in many hallways, but not so much in economics departments (well, maybe at The New School).
So why should economists give agent-based modeling another shot?
These workbooks are well designed. I’m not promising that your kids will not see it as a chore, but I think these books make practicing math and writing about as fun as it can be.
We found Brain Quest in a bookstore while we were looking for things to do in my son’s summer after kindergarten. The K-to- 1 summer workbook was fun and helped maintain what he had learned in kindergarten. He loved adding a new sticker to the adventure path after finishing each activity. You can finish it in one summer by doing about 5 pages per day, which only takes about 10 minutes.
We really love the summer map books but they also have schoolyear books. The First Grade school-year book is huge (320 pages). There aren’t as many stickers as the K-to-1, but they still have a way of marking off accomplishments that my son finds satisfying. It’s a kind of gamification, but it’s not more screen time.
These pages can be done after school on weekdays. What I like best is that it gives us some structure to leaning on weekends and holidays. It’s cheaper than tutoring.
Overall, I’ve been disappointed with the reporting on the US embargo against Russian oil. The AP reported that the US imports 8% of Russia’s crude oil exports. But then they and other outlets list a litany of other figures without any context for relative magnitudes. Let’s shine some more light on the crude oil data.*
First, the 8% figure is correct – or, at least it was correct as of December of 2021. The below figure charts the last 7 years of total Russian crude oil exports, US imports of Russian crude oil, and the proportion that US imports compose. That 8% figure is by no means representative of recent history. The average US proportion in 2015-2018 was 7.8%. But the US share as since risen in level and volatility. Since 2019, the US imports compose an average of 11.9% of all Russian crude oil exports.
As an exogenous shock, the import ban on Russian crude oil might have a substantial impact on Russian exports. However, many of the world’s oil importers were already refusing Russian crude. The US ban may not have a large independent effect on Russian sales and may be a case of congress endorsing a policy that’s already in place voluntarily.
Crypto is a lot of things- a store of value, a means of payment, a building block for other tools on the web. But while much of its value as a tool is yet to be realized, one big effect we see already is that it has made a lot of nerds very rich very young, even by the standards of tech and finance generally. These newly minted millionaires and billionaires have started giving their money away in very different ways than the traditional older philanthropists.
The latest, and I believe biggest example is the FTX Future Fund. It plans to give away at least $100 million this year, funded primarily by 30-year-old Sam Bankman-Fried, the CEO of crypto exchange FTX. I recommend that everyone read their full list of the 35 types of projects that they’d like to fund, but I’ll highlight a few you wouldn’t see from older foundations:
Demonstrate the ability to rapidly scale food production in the case of nuclear winter
Biorisk and Recovery from Catastrophe
In addition to quickly killing hundreds of millions of people, a nuclear war could cause nuclear winter and stunt agricultural production due to blocking sunlight for years. We’re interested in funding demonstration projects that are part of an end-to-end operational plan for scaling backup food production and feed the world in the event of such a catastrophe. Thanks to Dave Denkenberger and ALLFED for inspiring this idea
Prediction markets
Epistemic Institutions
We’re excited about new prediction market platforms that can acquire regulatory approval and widespread usage. We’re especially keen if these platforms include key questions relevant to our priority areas, such as questions about the future trajectory of AI development.
Critiquing our approach
Research That Can Help Us Improve
We’d love to fund research that changes our worldview—for example, by highlighting a billion-dollar cause area we are missing—or significantly narrows down our range of uncertainty. We’d also be excited to fund research that tries to identify mistakes in our reasoning or approach, or in the reasoning or approach of effective altruism or longtermism more generally.
They also seem to be borrowing some of Tyler Cowen’s approach to Fast Grants and Emergent Ventures- the application is relatively short and simple, and they promise response times that will be measured in weeks, rather than the months or years typical of large funders.
But they expect applicants to be fast too- this fund was just announced a few days ago, and applications are due March 21st. Economists will be natural fits for some of their project ideas, since their areas of interest include “economic growth” and “epistemic institutions”. I’ll be applying with my book project on why US health care spending is so high. But they are clearly casting a wide net to find the best ideas, so I encourage everyone to check it out and consider applying.
Gasoline prices are high and rising. Anecdotally, they seem to be increasing at the pump by the hour. And indeed, in nominal terms they are now the highest they have ever been in the US (this is true with both the AAA daily price level and the EIA weekly price level). At over $4.10 per gallon, the price now exceeds the peaks briefly hit in 2008, 2011, and 2012. And it’s looking like this peak might not be so brief.
But we all know you can’t compare nominal dollars over long periods of time. We need some context for this price! Plenty of news stories provide what they think is the right context: adjust it for inflation! For example, USA Today reports that today’s price “would come to around $5.25 today when adjusted for inflation.”
$5.25: that’s a pretty concrete number. But it’s not really useful. OK, so clearly that’s higher than the current price, about 20% higher in fact. Still, it doesn’t really give us the right context.
As I argued in a previous post on housing costs, inflation adjustments aren’t always the best way to contextualize a historical number. Yes, when you want to compare income or wages over time, it’s good to adjust for inflation. It’s necessary, in fact. And a good economist will always do that.
However, when comparing particular prices over time, it doesn’t really make sense to adjust for other prices. All you are really saying is “if the price of gasoline increased at the same rate as the average price level, here’s what it would be.” Perhaps slightly useful, but it doesn’t really get at the thing we’re really try to address: is gasoline more or less affordable than in the past?
The best approach is to adjust the prices for changes in wages or income. Which measure of wages or income you choose is important, but it’s the best adjustment to make. No need to make any inflation adjustments, are worrying about whether the index you choose is properly accounting for quality changes, substitution effects, etc. If you want to know how affordable something is, compare it to income.
Here’s what I think is the best simple comparison for gasoline, which I’ll explain it below. In short, it tells us how many minutes the average worker would need to work to purchase one gallon of gasoline.
Since the price of gasoline is rising sharply every day lately, my chart will surely be out of date very soon. But right now, it’s the most current data I could provide with a comparable historical series: EIA weekly data current through March 7th, 2022 (Monday). We can see that at current prices, it takes about 9 minutes of work at the average wage to purchase a gallon of gasoline. At the peak in 2008, it took over 13 minutes of work to purchase a gallon, and it fluctuated between 10 and 12 minutes of work for much of 2011-2014.
There is a difference between healthy zeal for a basically good cause like reducing CO2 emissions, and unbalanced myopia. Back in September I wrote about the European power debacle (skyrocketing gas and electricity prices):
Shut down your old reliable coal and nuclear power plants. Replace them with wind turbines. Count on natural gas fueled power plants to fill in when the breeze stops blowing. Curtail drilling for your own natural gas, and so become dependent on gas supplied by pipeline from Russia or by tankers chugging thousands of miles from the Middle East. What could possibly go wrong?
Well, now we know what can go wrong.
In January I noted more specifically, “This energy shortage also makes Europe very vulnerable to Russia, at a time when Putin is menacing Ukraine with invasion.” Now it has come to pass. All the huffing and puffing about economic sanctions on Russia is mainly just hot air. Because Europe is utterly dependent on Russian gas, massive “carve-outs” have been made in sanctions in order to continue these purchases to continue. The vaunted SWIFT restrictions on Russian banks have been carved down to practical irrelevance. While sanctions may impact the lifestyles of oligarch playboys, this flow of euros to Russia ensures that Putin will not run short of money for his war.
Ecomodernist Michael Shellenberger writes that behind the Ukraine military drama “is a story about material reality and basic economics—two things that Putin seems to understand far better than his counterparts in the free world and especially in Europe.” Shellenberger asks, “How is it possible that European countries, Germany especially, allowed themselves to become so dependent on an authoritarian country over the 30 years since the end of the Cold War?” and then answers this question in his trademark style:
Here’s how: These countries are in the grips of a delusional ideology that makes them incapable of understanding the hard realities of energy production. Green ideology insists we don’t need nuclear and that we don’t need fracking. It insists that it’s just a matter of will and money to switch to all-renewables—and fast. It insists that we need “degrowth” of the economy, and that we face looming human “extinction.” (I would know. I myself was once a true believer.)
… While Putin expanded Russia’s oil production, expanded natural gas production, and then doubled nuclear energy production to allow more exports of its precious gas, Europe, led by Germany, shut down its nuclear power plants, closed gas fields, and refused to develop more through advanced methods like fracking.
The numbers tell the story best. In 2016, 30 percent of the natural gas consumed by the European Union came from Russia. In 2018, that figure jumped to 40 percent. By 2020, it was nearly 44 percent, and by early 2021, it was nearly 47 percent.
…The result has been the worst global energy crisis since 1973, driving prices for electricity and gasoline higher around the world. It is a crisis, fundamentally, of inadequate supply. But the scarcity is entirely manufactured.
Europeans—led by figures like Greta Thunberg and European Green Party leaders, and supported by Americans like John Kerry—believed that a healthy relationship with the Earth requires making energy scarce. By turning to renewables, they would show the world how to live without harming the planet. But this was a pipe dream. You can’t power a whole grid with solar and wind, because the sun and the wind are inconstant, and currently existing batteries aren’t even cheap enough to store large quantities of electricity overnight, much less across whole seasons.
In service to green ideology, they made the perfect the enemy of the good—and of Ukraine.
There we have it. It’s not just the Europeans. As I write this, shells are raining down on Ukrainian cities but the U.S. is not restricting its imports of Russian oil, lest our price of oil go even higher. The present oil shortage (even before the Ukraine invasion) is what happens when a president on his first day in office signs an executive order to cancel a pipeline expansion which would have enabled increased oil production from Canada’s massive oil sands, and the whole ESG movement hates on investing in projects for producing oil or gas.
All that said, what the West gives with one hand it may take back with the other. Although energy exports from Russia are theoretically permitted, Western private enterprises, including finance arms, are pulling back from any dealings with Russia. This means in practice, lots of wrenches are being thrown into the machinery of international finance, such that energy exports from Russia are being slowed, though not stopped. But in turn, the Russians are getting higher prices per barrel for the oil that does get exported. There are many moving parts to all this, so we will see how it all shakes out.
Russia planning and logistical failures mean a continuing heavy invasion may not be sustainable, leading instead to a long runing siege. If this is the case, then it becomes all the more important to get basic humanitarian resources in now in order to minimize the suffering caused by the siege and minimize the odds of Russian success.
Ukrainian resistance depends as much on morale as it does lethal resources. Knowing their families are fed and receiving basic healthcare is critical.
If the micro-returns protecting a Ukrainian soldier or feeding a Ukrainian family aren’t enough for you, here’s a macro one: if the autocratic leader of an increasingly fascist regime with the strategic advantage of a nuclear arsenal is rebuffed in the Ukraine by a heroic local resistance partnered by global economic sanctions, it will serve as a signal to every leader with similar aspirations that success is less likely than they previously estimated. If your donation can help force a Bayesian update on dangerous autocrats and strongmen everywhere, that seems like nothing less than a perfectly rational act of utility maximization to me.
Economist Writing Every Day 