If you want to start a startup, go work for someone else
When you look online for advice about entrepreneurship, you will see a lot of "just do it":
The best way to get experience... is to start a startup. So, paradoxically, if you're too inexperienced to start a startup, what you should do is start one. That's a way more efficient cure for inexperience than a normal job. - Paul Graham, Why to Not Not Start a Startup
There is very little you will learn in your current job as a {consultant, lawyer, business person, economist, programmer} that will make you better at starting your own startup. Even if you work at someone else's startup right now, the rate at which you are learning useful things is way lower than if you were just starting your own. - David Albert, When should you start a startup?
This advice almost never comes with citations to research or quantitative data, from which I have concluded:
The sort of person who jumps in and gives advice to the masses without doing a lot of research first generally believes that you should jump in and do things without doing a lot of research first.
As readers of this blog know, I don't believe in doing anything without doing a ton of research first, and have therefore come to the surprising conclusion that the best way to start a startup is by doing a lot of background research first.
Specifically, I would make two claims:
It's unclear whether the average person learns anything from a startup.
It is clear that the average person learns something working in direct employment, and that they almost certainly will make more money working in direct employment (which can fund their later ventures).
I think these two theoretical claims lead to one empirical one:
If you want to start a successful startup, you should work in direct employment first.
Rather than boring you with a narrative, I will just present some choice quotes:
"We found that among the 24 possible success factors identified in the literature, 8 are homogeneous significant success factors for NTVs [New technology ventures]: ... (6) founders' marketing experience; (7) founders' industry experience... 5 [other factors] were not significant: ... (2) founders' experience with start-ups" Success Factors in New Ventures: A Meta-analysis
"Human capital variables [measured by things like past startup experience] have limited impact on startup performance, and the few significant effects are split equally between enhancing and impeding performance." Picking winners or building them? Alliance, intellectual, and human capital as selection criteria in venture financing and performance of biotechnology startups.
"We find that a spell of self-employment is associated with lower hourly wages compared to workers who were consecutively wage-employed. We also show, however,that this effect disappears - and even becomes positive in some settings - for formerly self-employed who find dependent employment in the same sector as their self-employment sector." Is self-employment really a bad experience?: The effects of previous self-employment on subsequent wage-employment wages
Entrepreneurs don't seem to learn much from their failures: "first-time entrepreneurs have only a 18% chance of succeeding [i.e. have a successful exit] and entrepreneurs who previously failed have a 20% chance of succeeding." Skill vs. luck in entrepreneurship and venture capital: Evidence from serial entrepreneurs.
"Our most important finding is that the reward to the entrepreneurs who provide the ideas and long hours of hard work in these startups is zero in almost three quarters of [startups], and small on average once idiosyncratic risk is taken into consideration"- The Burden of the Nondiversifiable Risk of Entrepreneurship
Even a stopped clock is right twice a day
It's interesting to think about what exactly the "people don't learn anything from a startup" hypothesis would look like. If we take the above cited numbers of everyone having a 20% chance of succeeding in a given startup, then even if each success is independent most people will have succeeded at least once by their fourth venture.
So the underlying message that many in the startup community say of "if you keep at it long enough, eventually you will succeed" is still completely true. I just think you could succeed quicker if you go work for someone else first.
But… Anecdata!
I am sure that there are a lot of people who sucked on their first startup, learned a ton, and then crushed it on their second startup. But those people probably also would've sucked at their first year of direct employment, learned a ton, and then crushed it even more when they did start a company.
There are probably people who learn better in a startup environment and you may be one of them, but the odds are against it.
Attribution errors
So if entrepreneurs don't learn anything in their startups, why do very smart people with a ton of experience like Paul Graham think they do? One explanation which has been advanced is the "Fundamental Attribution Error", which refers to "people's tendency to place an undue emphasis on internal characteristics to explain someone else's behavior in a given situation, rather than considering external factors." Wikipedia gives this example:
Subjects read essays for and against Fidel Castro, and were asked to rate the pro-Castro attitudes of the writers. When the subjects believed that the writers freely chose the positions they took (for or against Castro), they naturally rated the people who spoke in favor of Castro as having a more positive attitude towards Castro. However, contradicting Jones and Harris' initial hypothesis, when the subjects were told that the writer's positions were determined by a coin toss, they still rated writers who spoke in favor of Castro as having, on average, a more positive attitude towards Castro than those who spoke against him. In other words, the subjects were unable to properly see the influence of the situational constraints placed upon the writers; they could not refrain from attributing sincere belief to the writers.
Even in the extreme circumstance where people are explicitly told that an actor's performance is solely due to luck, they still believe that there must've been some internal characteristic involved. In the noisy world of startups where great ideas fail and bad ideas succeed it's no surprise that people greatly overestimate the effect of "skill". Baum and Silverman found that:
VCs... appear to make a common attribution error overemphasizing startups' human capital when making their investment decisions. - Picking winners or building them? Alliance, intellectual, and human capital as selection criteria in venture financing and performance of biotechnology startups
And if venture capitalists, who sole job consists of figuring out which startups will succeed, regularly make these errors then imagine how much worse it must be for the rest of us.
(It also doesn't bode well for this essay – I'm sure that even after reading all the evidence I cited most readers will still attribute their startup heros' success to said heroes' skill, intelligence and perseverance.)
I wrote this because I've become annoyed with the "just do it" mentality of so many entrepreneurs who spout some perversion of Lean Startup methods at me. Yes, doing experiments is awesome but learning from people who have already done those experiments is usually far more efficient. (Academics joke that "a month in the lab can save you an hour in the library.")
If you just think a startup will be fun then by all means go ahead and start something from your dorm room. But if you really want to be successful then consider apprenticing yourself to someone else for a couple years first.
(NB: I am the founder of a company which I started after eight years of direct employment.)
Baum, Joel AC, and Brian S. Silverman. "Picking winners or building them? Alliance, intellectual, and human capital as selection criteria in venture financing and performance of biotechnology startups." Journal of business venturing 19.3 (2004): 411-436.
Gompers, Paul, et al. Skill vs. luck in entrepreneurship and venture capital: Evidence from serial entrepreneurs. No. w12592. National Bureau of Economic Research, 2006.
Kaiser, Ulrich, and Nikolaj Malchow-Møller. "Is self-employment really a bad experience?: The effects of previous self-employment on subsequent wage-employment wages." Journal of Business Venturing 26.5 (2011): 572-588.
Song, M., Podoynitsyna, K., Van Der Bij, H. and Halman, J. I. M. (2008), Success Factors in New Ventures: A Meta-analysis. Journal of Product Innovation Management, 25: 7–27. doi: 10.1111/j.1540-5885.2007.00280.x
Also see Pablo's comment below
An Interactive Guide to Population Ethics
Population Ethics is the branch of philosophy which deals with questions involving - you guessed it - populations. Most of the problems that are solved by population ethics are things involving tradeoffs between quantity and quality of life. In bumper-sticker form, the question investigated in this post is:
Should we make more happy people, or more people happy?1
When a disaster occurs, most of us have the intuition that we should help improve the lives of survivors. But very few of us feel an obligation to have more children to offset the population loss. (i.e. our intuitions line up with making "more people happy" instead of "more happy people".) This is a surprisingly difficult position to defend, but it reminds me of Brian Tomasik's joke:
Bob: "Ouch, my stomach hurts."
Classical total utilitarian: "Don't worry! Wait while I create more happy people to make up for it."
Average utilitarian: "Never fear! Let me create more people with only mild stomach aches to improve the average."
Egalitarian: "I'm sorry to hear that. Here, let me give everyone else awful stomach aches too."
Negative total utilitarian: "Here, take this medicine to make your stomach feel better."
Limiting theorems
It turns out that population ethics has, to a certain extent, been "solved". This is a technical result, so uninterested readers can skip to the next section, but basically the various questions I discuss in this blog post are the only questions remaining. Specifically:
Let $\mathbf u = \left(u_1,u_2,\dots\right)$ be the utilities of people $1,2,\dots$ and similarly let $\mathbf u' = \left(u_1',u_2',\dots\right)$ be the utilities of a different population. Further, suppose we have a "reasonable" way of defining which of two populations is better. Then there is a "value function" $V$ such that population $\mathbf u$ is preferable to population $\mathbf u'$ if and only if $V(\mathbf u) > V(\mathbf u')$. Furthermore, $V$ has the form: $$V(\mathbf u)=f(n)\sum_{i=1}^{n}\left[ g(u_i)-g(c)\right]$$
The three sections of the blog post concern:
The concavity of $g$, which moderates our inequality aversion
The value of $c$, which is known as the "critical level"
And the form of $f$, which is the "number dampening"
I hope to write a post soon on why these are the only three remaining questions, but interested readers can see (Blackorby, Bossert and Donaldson, 2000) in the mean time.2
In the wake of the financial crisis, movements like Occupy Wall Street raised wealth inequality as a major political issue.
Wealth inequality in the US
An intuition that underlies these concerns is that the worse off people are, the more important it is to help them. We might donate to a charity to help starving people eat, but not one which helps rich yuppies eat even fancier food. The formal way to model this is to state that one person's utility has diminishing returns to society's overall well-being (i.e. additional utility to that person benefits society less and less as they become better off).
$g(x)=\sqrt{x}$
(As in the rest of this post, you can use the slider to modify the function and see how changing $g$ affects our ethical choices.)
One way of visualizing the impact this has on our decisions about populations is to use an indifference curve. In the chart below, the x-axis represents the utility of person X and the y-axis the utility of person Y. Each line on the chart indicates a set of points for which we are indifferent - for example, the blue line includes the point (50,50) and the point (100,0) since if we don't believe that utility has diminishing returns we don't care about how utility is divided up between the populace. (50 + 50 = 100 + 0).
You can see that the stronger we think returns diminish, the more inequality-averse we become. For example, if $g(x)=\sqrt{x}$ we are indifferent between $(60,10)$ and $(100,0)$ since $\sqrt{60} + \sqrt{10}\approx \sqrt{100} + \sqrt{0}$, meaning that a 40-point increase in person X's welfare is needed to offset the 10-point loss in person Y's welfare, since Y's welfare is so low. This is an important point, so I'll call it out:
Inequality aversion is a conclusion of population ethics, not an assumption3
Interlude - The Representation of Populations
We've just shown a very non-trivial result: if $g$ is concave (meaning that increasing utility has diminishing returns), then we are inequality-averse. (Conversely, if $g$ were convex then we would be inequality-seeking, but I don't know of anyone who has argued this.) One problem we're going to run into soon is that there are too many variables to easily visualize. So I want to bring up a certain fact about population ethics:
For any population $u$, there is a population $u'$ such that:
The number of people in $u$ and $u'$ are the same
Everyone in $u'$ has the same utility as each other (i.e. $u'$ is "perfectly equitable")
And we are indifferent between $u$ and $u'$
For example, if we believed utility did not have diminishing returns, we would be indifferent between $(75,25)$ and $(50,50)$ because the total utility is the same. This means that:
Any time we want to compare populations $p$ and $q$, we can instead compare $p'$ and $q'$ where both $p'$ and $q'$ are perfectly equitable (i.e. every person in $p'$ has the same utility as each other, and similarly for $q'$).
A perfectly equitable population can be parameterized by exactly two variables: the number of people in the population, and the average utility. While there are theoretical implications of this, the most relevant fact for us is that it means we can keep using two-dimensional graphs.
Critical Levels
Back to the topic at hand. The following assumption sounds very strange, but it's made quite frequently in the literature:
Even if your life is worth living to you and you don't influence anyone else, that doesn't mean the population as a whole benefits from your existence. Specifically, your welfare must be greater than a certain amount, known as the "critical level", before your existence benefits society.4
More formally:
Value to society = utility - critical level
Or $$V(\mathbf u)=\sum_{i=1}^{n} \left(u_i - c\right)$$ where $c$ is the critical level. (Note that $c$ is a constant, and independent of $\mathbf u$.) I think this is best illustrated with an example. Suppose we have a constant amount of utility, and we're wondering how many people to divide it up between. (As mentioned earlier, this is a perfectly equitable population, so everyone gets an equal share.) Here's how changing the critical level changes our opinion of the optimal population size:
c=10
The impact of critical levels can be summarized as:
Positive critical levels give a "penalty" for every person who's alive, whereas negative critical levels give a "bonus"
This is clear since $$V(\mathbf u)=\sum_{i=1}^{n} \left(u_i - c\right)=\left(\sum_{i=1}^{n} u_i\right)-nc$$ Here are indifference curves for different critical levels:
As the critical level gets lower, we are increasingly willing to decrease average utility in exchange for increasing the population size. The major motivation for having a positive critical level is that it avoids the mere addition paradox (sometimes known as the "Repugnant Conclusion"):
For any possible population of at least ten billion people, all with a very high quality of life, there must be some much larger imaginable population whose existence, if other things are equal, would be better even though its members have lives that are barely worth living.5
In tabular form:
Average Utility
(c=0)
(c = -10 -5 5 10 )
A 1,000 100 100,000 90,000
B 10,000,000 0.1 1,000,000 -99,000,000
C 1,000 -4 -4,000 -15,000
D 100 -1 -100 -1,100
Many people have the intuition that A is preferable to B. We can see that only by having a positive critical level can we make this intuition hold.
Unfortunately, we can also see that having a positive value of c results in what Arrhenius has called the "sadistic conclusion": We prefer population C to population B, even though everyone in C is suffering and the people in B have positive lives. And if c is negative we have another sort of sadistic conclusion: We prefer C to D even though there are fewer people suffering in D and no one is better off in C than they are in D.
Some people will bite the bullet and prefer the Sadistic Conclusion to the Repugnant one. But it's hard to make a case for this being the less intuitive of the two, meaning we must have a critical level of zero.
Number Dampening
Canadian philosopher Thomas Hurka has argued for the two following points:
For small populations, we should care about total welfare
For large populations, we should care about average welfare
Independent of the question about whether people should care more about average welfare for large populations, it seems clear that in practice we do (as I've discussed before).
The way to formalize this is to introduce a function $f$:
$$V(\mathbf u)=f(n)\sum_{i=1}^{n}u_i$$ where $$f(n) = \left\{ \begin{array}{lr} 1 & : n \leq n_0 \\ n_0/n & : n > n_0 \end{array} \right.$$ If we have fewer than $n_0$ people (i.e. if the population is "small") then this is equivalent to total utilitarianism. If we have more (i.e. the population is "large") then it's equivalent to average utilitarianism. Graphically:
n0=50
The non-differentiability at $n=n_0$ is pretty ridiculous though, so instead of a strict cutoff we could claim that there are diminishing returns to population size, just like we claimed that there are diminishing returns to utility in the first section. For example, we could state that $$V(\mathbf u)=\frac{1}{\sqrt{n}}\sum_{i=1}^{n}u_i$$ This gives us a graph like:
$V(\mathbf u)=\frac{1}{\sqrt{n}}\sum_{i=1}^{n}u_i$
Even with this modification though, it still seems pretty implausible that population size has diminishing returns. The relevant fact is that $\sqrt{x+y}\not=\sqrt{x}+\sqrt{y}$, so we can't just break populations apart.6 Therefore, we have to consider every single person who has ever lived (and who ever will live) before we can make ethical decisions. As an example of the odd behavior this "holistic" reasoning implies:
Some researchers are on the verge of discovering a cure for cancer. Just before completing their research, they learn that the population of humans 50,000 years ago was smaller than they thought. As a result, they drop their research to focus instead on having more children.
An example will explain why this is the correct behavior if you believe in number-dampening. Say we're using the value function
$$V(\mathbf u)=\frac{1}{\sqrt{n}}\sum_{i=1}^{n}u_i$$
and we can either move everyone alive from having 10 utils up to 10.1 (discovering cancer cure) or else add a new person with utility 100 (have a child). Which option is best depends on the population size:
Population size
Value of society w/ cancer cure
Value of society w/ new child
500 $\frac{1}{\sqrt{500}}\left(500\cdot 10.1\right)=226$ $\frac{1}{\sqrt{501}}\left(500\cdot 10 + 100\right)=228$
5,000 $\frac{1}{\sqrt{5000}}\left(5000\cdot 10.1\right)=714$ $\frac{1}{\sqrt{5001}}\left(5000\cdot 10 + 100\right)=708$
Having a child is better if the population size is 500, but worse if the population size is 5,000.
It goes against our intuition that the population size in the distant past should affect our decisions about what to do today. One simple way around this is to just declare that "population size" is the number of people currently alive, not the people who have ever lived. Nick Beckstead's thesis has an interesting response:
The Separated Worlds: There are only two planets with life. These planets are outside of each other's light cones. On each planet, people live good lives. Relative to each of these planets' reference frames, the planets exist at the same time. But relative to the reference frame of some comet traveling at a great speed (relative to the reference frame of the planets), one planet is created and destroyed before the other is created.
To make this exact, let's say each planet has 1,000 people each with utility level 100. Then we have:
Dampening Amount
Value on both planets
Value on comet
None Square Root Cube Root Fourth Root $1$ $1$
None 200,000 200,000
How valuable a population is shouldn't change if you split it into arbitrary sub-populations, so it's hard to make the case for number dampening.
I started off by claiming (without proof) that for any "reasonable" way of determining which population is better, we could equivalently use a value function $V$ such that population $\mathbf u$ is better than population $\mathbf u'$ if and only if $V(\mathbf u) > V(\mathbf u')$. Furthermore, I claimed $V$ must have the form: $$V(\mathbf u)=f(n)\sum_{i=1}^n\left[g(u_i)-g(c)\right]$$ In this post, we investigated modifying $f,g$ and $c$. However, we saw that having $c$ be anything but zero leads to a "sadistic conclusion", and having $f$ be non-constant leads to the "Separated Worlds" problem, meaning that we conclude $V$ must be of the form $$V(\mathbf u) = \sum_{i=1}^n g(u_i)$$ Where $g$ is a continuous, monotonically increasing function. This is basically classical (or total) utilitarianism, with perhaps some inequality aversion.
It's common to view ethicists as people who just talk all day without making any progress on the issues, and to some extent this reputation is deserved. But in the area of population ethics, I hope I've convinced you that philosophers have made tremendous progress, to the point that one major question (the form of the value function) has been almost completely solved.
I'm sure I didn't come up with this phrase, but I can't find who originally said it. I'd be much obliged to any commenters who can let me know.
The obvious objection I'm ignoring here is the "person-affecting view", or "the slogan." I'm pretty skeptical of it, but it's worth pointing out that not all philosophers agree that population ethics must of this form.
Of course, if we came to the conclusion that inequality is good, we might start questioning our assumptions, so this is perhaps not completely true.
If the critical level is negative, then the converse holds (your life can suck but you'll still be a benefit to society). This is rarely argued.
From Parfit's original Reasons and Persons
This isn't just a problem with the square root - if $f(x+y)=f(x)+f(y)$ with $x,y\in\mathbb R$ then $f(x)=cx$ if $f$ is non-"pathological". (This is known as Cauchy's functional equation.)
An Improvement to "The Impossibility of a Satisfactory Population Ethics"
Why Inequality Can't Matter
On my inability to improve decision making
Summary: It's been suggested that improving decision making is an important thing for altruists to focus on, and there are a wide variety of computer programs which aim to improve clinician decision making ability. Since I earn to give as a programmer making healthcare software, you might naively assume that some of the good I do is through improving clinician decision making. You would be wrong. I give an overview of the problem, and suggest that the problems which make improving medical decision making hard are general, and might suggest low-hanging fruit is rare in the field of decision support.
Against stupidity the gods themselves contend in vain. - Friedrich Schiller
In 1966, the Massachusetts General Hospital Utility Multi-Programming System (MUMPS) was created as one of the first healthcare information technology platforms. Running on the "cheap" ($70,000) PDP-7, it spread to become one of the most common pieces of infrastructure in healthcare - to this day, if you walk into your doctor's office there's a good chance some part of what you see has MUMPS in its stack.
A few years later, researchers at Stanford using a computer with the approximate power of today's wristwatches created MYCIN, a program capable of outperforming human physicians in diagnosing bacterial infections. Unlike MUMPS, such programs are still far from use in everyday care today: when I go to the doctor's office I'm not diagnosed by computerized super-doctors but instead by the time-honored combination of human gut, skill and the occasional glance at a reference volume. Even "low-skill" jobs like calling patients to remind them about their appointments are still usually done by receptionists or temps with a printed call list; a process essentially indistinguishable from 50 years ago.
If people are better at making decisions, then we will be better at a whole range of things, making decision-support technology an important priority for altruists. It was listed as one of 80,000 hours top priorities, for example. I haven't seen many empirical examinations of how decision-making technology (fails to) improve our abilities, so I offer healthcare IT as a case study.
Different, not fewer, problems
Clinicians sometimes order the wrong thing. Perhaps they forget the dosing and accidentally order 200 miligrams instead of 200 micrograms, or they order penicillin because they forgot that the patient's allergic.
It's relatively easy to program a computer to warn the user when their prescription is off by an order of magnitude or contraindicates with an allergy, but it turns out that doctors are actually pretty good at what they do most of the time. If they order an unusually high dose, it's probably because the patient has an unusually severe case. If they order a med that the patient is allergic to, it's probably because they decided the benefits outweigh the risks. As a result, these warnings are almost always noise without a signal.
The result is familiar to anyone who used the version of Microsoft Office with Clippy: clinicians slam on the keyboard to close all message boxes without bothering to read the warnings, completely negating any possible benefits. This "alert fatigue" (as it is politely termed) sometimes stems from organization's fears of lawsuits keeping extraneous alerts around (Tiwari et al. 2013), but even in trials which are done specifically to improve health and are judged successful enough to publish, less than a fourth have any impact on patient outcomes (Hemens et al. 2011).
Anyone who's done computer learning is aware of the maxim "garbage-in, garbage-out". Even the most amazing prediction algorithm will give bad results if you give it bad input, and current medical algorithms are far from perfect.
Medical records are written of, by and for humans, and there is a large resistance to change. If your program requires someone with MD-equivalent skills to translate the patient's free-text chart into a discrete dataset that the software could analyse, then why would you use it? You might as well just hire the doctor to do the diagnosis herself.
This problem is largely what's held back programs like MYCIN. While they work great if your research grant provides for a grad student sweatshop to code data into your specialized format, it doesn't work so well in the real world.
Doctor-Hardness
To summarize these two problems: people had originally thought they could slice off just a tiny piece of clinicians' jobs and improve that without worrying about the rest. But it turned out that in order to do well in this tiny slice they needed to essentially replicate all of what a doctor does - in computer science terms, these problems are "doctor-hard".
What have we spent to get these minimal benefits?
The NIH's Biomedical Information Science and Technology initiative has funded about $350 million dollars worth of research (not all of it in clinical decision support), but this amount pales to to what governments have spent in getting IT into the hands of front-line physicians.
The HITECH Act (part of the 2009 US stimulus bill) is expected to spend about $35 billion on increasing the adoption of electronic medical records. On the other side of the pond, the NHS' troubled IT program ended up costing around £20 billion, up a mere order of magnitude from the original £2.3 billion estimate.
An explicit cost-benefit analysis of decision support research would require a lot more careful analysis of these expenditures, but my goal is just to point out that the lack of results is not due to lack of trying. Decades of work and billions of dollars have been spent in this area.
In retrospect, I think one argument we could have used to predict the non-cost-effectiveness of these interventions is to ask why they haven't already been invented. The pre-computer medical world is filled with checklists, and so if there was an easy way to detect mistyped prescriptions or diagnose bacterial infections, it would probably already be used.
This is to make a sort of "efficiency" argument - if there is some easy way to improve decision making, it's probably already been implemented. So when we're examining proposed decision support techniques, we might want to ask why it hasn't already been done. If we can't pin it on a new disruptive technology or something similar, we might want be skeptical that the problem is really so easy to solve.
Brian Tomasik proofread an earlier version of this post.
Ash, Joan S., Marc Berg, and Enrico Coiera. "Some unintended consequences of information technology in health care: the nature of patient care information system-related errors." Journal of the American Medical Informatics Association 11.2 (2004): 104-112. http://171.67.114.118/content/11/2/104.full
Hemens, Brian J., et al. "Computerized clinical decision support systems for drug prescribing and management: a decision-maker-researcher partnership systematic review." Implement Sci 6.1 (2011): 89. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3179735/
Reckmann, Margaret H., et al. "Does computerized provider order entry reduce prescribing errors for hospital inpatients? A systematic review." Journal of the American Medical Informatics Association 16.5 (2009): 613-623.
Tiwari, Ruchi, et al. "Enhancements in healthcare information technology systems: customizing vendor-supplied clinical decision support for a high-risk patient population." Journal of the American Medical Informatics Association20.2 (2013): 377-380. http://171.67.114.118/content/20/2/377.abstract
Williams, D. J. P. "Medication errors." JOURNAL-ROYAL COLLEGE OF PHYSICIANS OF EDINBURGH 37.4 (2007): 343. http://www.rcpe.ac.uk/journal/issue/journal_37_4/Williams.pdf
Why Charities Might Differ in Effectiveness by Many Orders of Magnitude
Summary: Brian has recently argued that because "flow-through" (second-order) effects are so uncertain, charities don't (on expectation) differ in their effectiveness by more than a couple orders of magnitude. I give some arguments here about why that might be wrong.
1. Why does anything differ by many orders of magnitude?
Some cities are very big. Some are very small. This fact has probably never bothered you before. But when you look at how cities sizes stack up, it looks somewhat peculiar:
Taken from Gibrat's Law for (All) Cities, Eeckhaut 2004.
The X-axis is the size of the city, in (natural) logarithmic scale. The Y-axis corresponds to the density (fraction) of cities with that population. The peak is around the mark of 8 on the X-axis, which corresponds to $e^8\approx 3,000$ people.
You can see that the empirical sizes of cities almost perfectly matches a normal ("bell curve") distribution. What's the explanation for this? Is mayoral talent distributed exponentially? When deciding to move to a new city do people first take the log of the new city's size and then roll some normally-distributed dice?
It turns out that this is solely due to dumb luck and mathematical inevitability.
Suppose every city grows by a random amount each year. One year, it will grow 10%, the next 5%, the year after it will shrink by 2%. After these three years, the total change in population is
$$1.10\cdot 1.05\cdot 0.98$$
As in the above graph, we take the log
$$\log\left(1.10\cdot 1.05\cdot 0.98\right)$$
A property of logarithms you may remember is that $\log(a\cdot b)=\log a + \log b$. Rewriting (2) with this property gives
$$\log 1.10+ \log 1.05+\log 0.98$$
The central limit theorem tells us that when you add a bunch of random things together, you'll end up with a normal distribution. We're clearly adding a bunch of random things together here, so we end up with the bell curve we see above.
2. Why charities might differ by many orders of magnitude
Some of Brian's points are about how even if a charity is good in one dimension, it's not necessarily good in others (performance is "independent"). The point of the above is to demonstrate that we don't need dependence to have widely varying impacts. We just need a structure where people's talents are randomly distributed, but critically their talents have a multiplicative effect.
There are some talents which obviously cause a multiplier. A charity's ability to handle logistics ("reduce overhead") will multiply the effectiveness of everything else they do. Their ability to increase the "denominator" of their intervention (number of bednets distributed, number of leaflets handed out, etc.) is another. PR skills, fundraising etc. all plausibly have a multiplicative impact.
More controversially, some proxies for flow-through effects might have a multiplicative impact. Scientific output is probably more valuable in times of peace than in times of war. GDP increases are probably better when there's a fair and just government, instead of the new wealth going to a few plutocrats.
Here's a simulation of charities' effectiveness with 10 dimensions, each uniformly drawn from the range [0,10].
The red line corresponds to Brian's scenario (where each dimension is independent) and as he describes effectiveness is very closely clustered around 50. But as the dimensions have more interactions, the effectiveness spreads out, until the purely multiplicative model (purple line) where charities differ by many orders of magnitude.
3. Picking winners
Say that impact is the product of measurable, direct impacts and unmeasurable flow-through effects. Algebraically: $I=DF$. By linearity of expectations
$$E[I]=E[DF]=E[D]E[F]$$
So if two charities differ by a factor of say 1,000 in their direct impact then their total impact would (on expectation) differ by 1,000 as well.
This isn't a perfect model. But I do think that it's not always correct to model impacts as a sum of iid variables, and there is a plausible case to be made that not only do charities differ "astronomically" but we can expect those differences even with our limited knowledge.
This post was obviously inspired by Brian, and I talked about it with Gina extensively. The log-normal proof is known as Gibrat's Law and is not due to me.
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