Should Earners-to-Give Work at Startups Instead of Big Companies?
Confidence: Somewhat likely
Effective altruist earners-to-give might be able to donate more money if, instead of working at big companies for high salaries, they work at startups and get paid in equity. Startups are riskier than big companies, but EAs care less about risk than most people.
Working at a startup is easier than starting one. It doesn’t pay as well, but based on my research, it looks like EA startup employees can earn more than big company employees in expectation.
Does the optimal EA investment portfolio include a significant allocation to startups? To answer that question, I estimated the expected return and risk of startups by adding up the following considerations:
Find a baseline of startup performance by looking at historical data on VC firm returns.
VC performance is somewhat persistent. EAs can beat the average by working at startups that the top VC firms invest in.
Startup employees get worse equity terms than VCs, but they also don’t have to pay management fees, and they get meta-options. Overall, employees come out looking better than VCs.
Current market conditions suggest that future performance will be worse than past performance.
Startups are much riskier than publicly-traded stocks, and the startup market is moderately correlated with stocks (r=0.7).
All things considered, my best guess is that more earners-to-give should consider working at startups.
Framing the problem
Suppose you’re an effective altruist and you want to donate as much money as possible. Perhaps you’ve heard the arguments that EAs should start startups. Starting a startup is a lot of work and requires special skills, so you’d rather not. Maybe you’d like to invest in venture capital, but the really good VC firms won’t accept your money. However, you wouldn’t mind working at a startup. You could also work at a big company that pays a high salary. Which should you choose?
This is how I would think about the problem:
Say a startup offers you an equity package that’s worth $X per year at the current valuation. At the same time, a big company offers you a salary that’s $X higher than your salary would be at the startup. Both compensation packages have the same face value.
If you work at the startup, you get
$X per year of equity. Some number of years later, the startup might go public or get acquired, at which point you can sell your equity for
$Y. Over that time, your equity earned a return of
If you work at the big company, you could invest your extra
$X salary in the stock market. Will that investment earn a higher or lower return than the startup equity? Whichever you expect to earn a higher return is the one you should pick. (Well, that’s not really true. Read the next section to find out why not.)
The right question
Which do we expect to earn a higher return, startups or the public stock market?
We don’t have good data on the historical performance of startup employees. We do have data on VC returns, so we can use that.
“Have VCs historically outperformed the public market?” is the wrong question, because some VCs consistently outperform the average.
“Have top VCs historically outperformed the public market?” is the wrong question, because future expected performance probably isn’t the same as past performance.
“Can we expect top VCs to outperform the public market?” is the wrong question, because startup employees don’t earn the same returns as VCs. Employee equity has a worse liquidation preference than VC equity, but investors in VC firms have to pay fund fees, which employees don’t. And employees have the meta-option to keep vesting when their company does well, or to quit when it does poorly. We can use these considerations to estimate the value of employee equity compared to VC equity.
“Can we expect startup employees to outperform the public market?” is the wrong question, because we need to consider leverage. Investors in public markets can use leverage to increase their risk and expected return, but startup employees can’t.
“Can we expect startup employees to outperform the leveraged public market?” is the wrong question, because an effective altruist’s goal isn’t to maximize their own portfolio return, it’s to maximize the expected utility of the overall EA portfolio. If no other EAs work at the startup where you choose to work, then you’re adding better diversification than if you invest in the public market.
“Do startup employees contribute more expected utility to the EA portfolio than if they invested in the leveraged public market?” is the wrong question, because if they did work at a big company and invest their salary, they might be able to invest in something better than the broad market. For example, I have previously discussed investing in concentrated value/momentum/trend portfolios, and I made a rough attempt to calculate the expected utility of doing so. For us to prefer to become startup employees, startups would have to look better than the best possible public investment (whether that’s value/momentum/trend or something else).
“Do startup employees contribute more expected utility to the EA portfolio than if they invested in the optimal set of public investments?” is more or less the right question.
Modeling the solution
Do startup employees contribute more expected utility to the EA portfolio than if they invested in the optimal set of public investments?
The answer to this depends on two things:
How do we model the answer?
What values should we use for the model inputs?
#2 is hard. #1 is sort of hard, but luckily, it’s already a solved problem. I described an applicable model in Asset Allocation and Leverage for Altruists with Constraints. In short, we set up a mean-variance optimization problem where we assume 99% of the capital is controlled by other people, and we can decide how to allocate the remaining 1%. Suppose we can allocate between three investment choices:
A typical investment portfolio, such as a stock market index fund
The optimal (ex ante) public investment portfolio
The simplest method is to assume we should put all our money into just one choice. What is the overall expected utility if we put our money into choice 1, choice 2, or choice 3?
If we tell our model the expected returns, standard deviations, correlations between these three portfolios, and a utility function, then the model will spit out the expected utility of each choice.
Let’s say the average EA investment portfolio equals the global equity market, which is at least sort of correct. What’s the expected return and standard deviation of global equities?
We have no idea how equities will perform in the short run. But in the long run, the market’s return is somewhat predictable. And over long time horizons, volatility stays pretty consistent, so we can simply assume the future standard deviation of global equities equals the historical standard deviation.
Similarly, we can approximate the future standard deviation of startups, and their correlation with global equities, by taking the historical standard deviation and correlation and assuming they will stay the same.
The most difficult input variable is the expected return of startups.
What factors determine the expected return of startups?
How to estimate the expected return of startups for employees:
Start with some index of VC returns, such as the Cambridge Associates Venture Capital Index.
These indexes usually provide returns net of fees. VC investors have to pay fees, but startup employees don’t. Add 2-and-20 fees back in to get the gross return.
Unlike with public market investors, VC firms that beat the market in the past tend to continue to beat the market. Employees can choose to work at startups with funding from top VCs. Add a premium to the expected return to account for this.
Maybe EAs can pick startups better than top VC firms. Possibly add a premium.
Employees get worse equity terms than VCs, so subtract some discount to account for tihs.
Startup employees get meta-options, which VCs don’t get. Add an appropriate premium.
Use the current market environment to forecast how future returns will look compared to past returns. (This is the sketchiest step, so we might skip this and just assume future returns equal historical returns. But I think we don’t want to skip this because we don’t even really know what historical returns were—more on that later.)
Giving now might be better than giving later. If so, that means we shouldn’t compare startups to public investments because public investments aren’t the best thing to do with money. Instead, we should compare startup equity to money donated now.
In the next four subsections, let’s break down each of these steps.
Returns for VC firms covers steps 1–3;
Returns for EAs covers steps 4–6;
Forecasting future returns covers step 7; and
Giving now vs. later covers step 8.
Returns for VC firms
I used Cambridge Associates’ Venture Capital Index 2020 report to find the aggregate historical return of VC firms from 1995 to 2018. According to this data set, VCs had a geometric mean return of 13.1% with a standard deviation of 18.9%. That’s a good starting point.
As the saying goes, a person with one clock always knows what time it is. Someone with two clocks is never quite sure. Cambridge Associates’ 2017 report has data from 1988 to 2016, which gives a geometric mean return of 14.9% and a standard deviation of 17.2%.
According to these data sets, VC experienced several regimes:
Strong performance 1988–2000, and especially 1998–2000 at the peak of the tech bubble.
Very bad returns 2001–2003.
Mediocre returns 2004–2013, with a mix of good years and bad years.
Strong returns 2014–2020.
The “historical performance of VC” substantially changes depending on which time period you look at. And we don’t know what sort of regime will come next.
Another problem: Other VC return databases give entirely different numbers. For example, I could have used the VentureXpert database, which some (e.g., Koch (2014)) claim is more accurate. (I used Cambridge Associates purely out of convenience.) Cambridge Associates tends to give higher VC returns than other databases (by a couple percentage points).
I will use the 2020 Cambridge Associates report as a starting point. Just be aware that the startup market will likely behave very differently in the future.
Now let’s convert net returns to gross returns. If we assume VCs usually charge 2-and-20 fees, this step is pretty easy. Using the Cambridge Associates 1995–2018 data, we find a gross historical return of 18.1% with a 23.5% standard deviation.
Some people (especially VCs) like to talk about how the “top quartile” of VC firms persistently beat the market. This is true, but potentially misleading. VC firms who beat the market in year N are more likely than chance to beat the market in year N+1. But top-quartile firms are by no means guaranteed to stay in the top quartile.
The table uses these terms:
IRR = internal rate of return, or the annual return achieved by investors.
PME = public market equivalent, or the total return of VCs relative to the S&P 500 (see Kaplan & Schoar (2005)). For example, if a VC fund earns a total return of 60% and the S&P earns 50% in the same period, then the PME is 60% / 50% = 1.2.
|Top-quartile VC, backward-looking||45.3%||2.60|
|Top-quartile VC, forward-looking||26.3%||1.70|
Results for VC firms 2001–2014:
|Top-quartile VC, backward-looking||30.0%||2.11|
|Top-quartile VC, forward-looking||14.7%||1.20|
(+) This figure is not provided by Harris et al.
In the full sample (1984–2014), top-quartile VCs retained about half of their outperformance out of sample, although they lost most of their relative outperformance (compared to the S&P 500). In the post-2001 sample, they lost most of their outperformance both in relative and absolute terms, but still showed nonzero persistence.
Harris et al. also did a regression analysis, and found that across all VC firms, one third of PME outperformance persisted.
(Note: Harris et al. used data from Burgiss, yet another source for VC returns.)
Returns for employees
Almost all startups give preferred shares to VCs and common shares to employees. Normally, preferred shares get a 1x liquidation preference. That means if the company exits, VCs are guaranteed to get back at least the money they put in before employees get anything. This makes employee equity worth less than it appears.
A startup raises funding at a $100 million valuation. VCs have $20 million of preferred shares; founders and employees have $80 million of common shares.
Later, the startup is acquired for $50 million.
VCs get back their $20 million. That leaves just $30 million to split among the common shareholders. The valuation went down by 50%, but employees lost 62.5% of their equity value.
This is basically standard practice. Some startups also give special advantages to VCs. There are lots of ways they can do this, such as:
a liquidation preference that’s higher than 1x (e.g., a 2x preference guarantees that VCs get to double their money before employees get anything)
a ratchet, which gives VCs protection against dilution at the expense of employees
These sorts of conditions are really bad for startup employees. You might just want to avoid any startup that offers terms like these. (If you work at a startup with no sketchy terms, and they raise a new round of funding that introduces sketchy terms, that alone might be enough reason to start looking for a new job.) For more on what to watch out for, read Ben Kuhn’s How bad are fundraising terms?
Even without any bad terms, employee stock options introduce some other problems:
If you don’t exercise your options, they could expire before the company exits.
If you don’t exercise your options and they go up in value, you might have to pay income tax or alternative minimum tax instead of capital gains tax.
You can avoid these problems by exercising your options as soon as they vest, or even early exercising if you can. But even if you do exercise, you might end up paying higher taxes when the startup exits because you’ll get pushed into a higher tax bracket. You can (at least partially) mitigate this by donating the stock instead of selling it.
(A lot of people can’t afford to exercise their employee stock options. Perhaps an EA org could make grants or loans to help EAs exercise their options. That would be difficult to set up and they’d have to carefully vet grant recipients, but maybe it could work.)
According to my rough estimate, a 1x liquidation preference reduces the expected value of common shares by about 10%. That equates to around 1–2% per year, depending on how long the company takes to exit. Let’s assume a 3% annual discount due to liquidation preference plus tax disadvantage.
Many startups pay below-market compensation by claiming that their equity is underpriced. Don’t buy it. The whole point of working at a startup is that your equity will earn (in expectation) above-market returns. If your employer adjusts for this by giving you less equity, that ruins the (monetary) advantage of working at a startup.
This essay is not about employee equity terms, but it’s an important topic for anyone considering working at a startup. For an in-depth guide, see The Open Guide to Equity Compensation by Joshua Levy et al. For something shorter, I recommend Ben Kuhn’s checklist for stock option offers.
Startup options are better than they look because employees get “meta-options”: your compensation package gives you the option to vest stock options at the current price for the next four years. If the company does well, you can “exercise” your meta-options by continuing to work there. If it doesn’t do well, you can quit. Neither startup founders nor startup investors can do this.
I piggybacked on Ben Kuhn’s meta-option model (my code here) and found that meta-options are worth an extra 16 percentage points of return (!!). I just did a quick calculation and didn’t perform a sensitivity analysis or anything, so this number could be way off, but let’s go with it for now. If correct, this number is so large that working at a startup looks more profitable than starting a startup, unless you believe you’d make for an unusually good entrepreneur.
It’s worth mentioning that you could work at a big company that offers equity compensation, which also behaves like a meta-option—albeit a much less valuable one, because big company stock is not as volatile. Using similar methodology, I found that meta-options at a big company are worth 5 percentage points. That means startup meta-options provide an extra 11 percentage points of value (16% – 5%).
As far as I know, Ben Kuhn invented the concept of meta-options, and no one has ever rigorously analyzed them. My own modification of his program could contain bugs or logical flaws. The value of meta-options could be large enough to dominate every other factor, or they could be worth nothing. This subject strongly warrants a deeper investigation.
If we can get around the practical concerns, EAs can easily match the returns of top VC firms by getting jobs at their portfolio companies. Can EAs do even better? Can we outperform VCs at picking winning startups?
Let me say up front that I don’t believe EAs in general can outperform top-quartile VC firms. But when I say I don’t believe it, what I really mean is I assign it less than a 50% probability. So it might still be worth trying.
(To be more precise, I would give an 80% probability that at least a handful of EAs could pick winning startups better than top VCs, but I don’t know how to identify those people in advance. I’d estimate a 30% chance that, if a group of EAs decide to go work at startups and make a conscious effort to pick winning startups, then they will do better than top-quartile VCs.)
EAs as a group are really smart. But professional investors are also really smart, and the overwhelming majority of them still fail to beat the market. Maybe EAs are smarter? Maybe EAs are more rational or clear-thinking in some way that most professional investors aren’t? I don’t know.
It’s possible that EAs could do a better job than VCs of identifying the best startups. On the other hand, EAs might also do a better job of identifying the best public investments. On the other other hand, startups are hard to invest in, so it might be easier to find underappreciated opportunities. If EAs have an edge in public markets, they probably have an even bigger edge in startups.
I don’t have strong evidence on this either way, so I’m leaning on my prior that almost nobody can beat the market. We do have at least some evidence, but I’m not sure how to interpret it.
The evidence we do have:
Over the past few years, EA investors have beaten the market. This is mostly driven by a single company (FTX), so I don’t know how much we can infer from this.
One person reviewed a few months’ worth of EAs’ proposed investing ideas and found that they had beaten the market over those few months. (I don’t want to go into specifics because this review was not shared publicly, but that’s the gist of it.)
In the rest of this essay, I will assume EAs can’t beat top-quartile VCs—not because I am confident that this is true, but because I don’t know how to evaluate the evidence. It could be a good idea to look into this in more depth.
Forecasting future returns
As discussed above, startup returns tend to vary a lot over time, so past performance does a poor job of predicting future performance. But we can’t choose between two investments (in this case, public investments vs. startups) unless we believe something about how they will perform. So what should we believe?
Public US equity and bond markets have unusually low return expectations right now, thanks to high valuations/low yields. When stocks and bonds look bad, money flows into alternatives, including VC. Therefore, it’s reasonable to expect VC to have worse future returns as well.
For the past few years, VCs have been investing much more money than the historical average (Statista, 2021). Crowdedness suggests low future returns. The only other time we saw similar crowdedness was in the year 2000, which was the beginning of a major losing streak for VC.
Private equity (that is, leveraged buyouts, not VC) has gotten more expensive over the last decade or so (Chingono & Rasmussen, 2015), which predicts muted future performance. And Harris et al.[5:1] found that, while top VC firms’ persistence persisted in their whole sample, top private equity firms’ returns stopped persisting after 2001. It’s not clear why private equity’s persistence didn’t persist, but whatever the reason, the same thing might happen to VCs.
The outlook for VCs looks worse than usual. The question is, how much worse? Should we expect future returns to be 1 percentage point lower per year? Or 20 percentage points?
Well, how much worse to US equities and bonds look? We can reliably predict bonds’ long-term returns using the yield. 5-year bonds currently yield around 1%, compared to a 1984–2014 average nominal return of 8% (Damodaran, 2021).
Stock returns are harder to predict. In the short term, they’re almost impossible to predict, but we can estimate their return over 10+ year periods with reasonable accuracy. Under a more EMH-y model that assumes no change in market valuation (e.g., AQR, 2021), forward-looking US equity return expectations look around 5 percentage points worse than they did from 1984 to 2014 (4% vs. 9% after inflation). According to a model that assumes valuations will revert to their historical average (such as Research Affiliates, 2021), returns look 10 percentage points worse (-1% vs. 9%). The truth is probably somewhere in the middle.
In theory, all asset classes should have the same risk-adjusted return. Startups are riskier than stocks or bonds. So if return expectations for stocks/bonds go down by some amount, expectations for startups should go down by more than that. But we don’t know if this holds in practice, and we don’t even know exactly how risky startups are. If bonds look 7% worse, and stocks look between 5% and 10% worse, then maybe we could assume VC will perform 7% to 10% worse in expectation.
As for top-quartile VCs: according to Harris et al., over the full historical sample, they outperformed average VCs by a full 11 percentage points. In the post-2000 era, they only outperformed by 4 percentage points, and had a public-market equivalent performance of 1.2 (which means they only weakly outperformed the S&P 500). It seems fair to assume that the future for VCs will look more like 2001–2014 than like 1984–2000, as the pre-2000 VC market was probably less efficient. We could simply assume top VCs will perform 4 percentage points better than average VCs going forward. But it’s also possible that the gap between average and top VCs will continue to narrow.
Giving now vs. later
Working at a startup is comparable to working for a salary and investing it. But if giving now beats giving later, then you wouldn’t want to invest your salary. Instead, you’d want to donate it right away. This makes working at a startup look worse because you can’t donate your equity until it becomes liquid.
This possibility makes the comparison more difficult, so I will mostly ignore it. It’s not as simple as applying a fixed discount rate to the value of your startup equity. Just be aware that my methodology for comparing startups vs. big companies only works if giving later is at least as good as giving now, at least for the next few years.
Putting together the expected return
If we combine all the numbers I came up with in the previous section, we get:
13% after-fees historical return to VC firms, or 10% after inflation.
15% real historical return before fees.
Add 4% for the persistence of top-quartile VCs, giving 19%.
Add 0% for EAs’ extra outperformance. Still 19%.
Subtract 3% for employee equity terms, giving 16%.
Add 11% for meta-options, giving 27%.
Subtract 9% for the relatively poor market outlook, giving 18%.
Ignore giving now vs. later. Still 18%.
Thus, I predict a 18% real return for startup employees who try to maximize their earnings (by working at startups with funding from top VCs, getting good equity terms, and exercising their meta-options when necessary).
How big are the error bars on each of these numbers? In order:
Historical return depends a lot on what time period you look at. Wide error bars.
Calculating before-fees return just requires knowing fees, which are usually 2-and-20. Narrow error bars.
It wouldn’t be too surprising it top-quartile VCs had as much as 11% extra return or as little as 0%. Wide error bars.
Even if we have good reason to expect EAs to do better at picking startups than top-quartile VCs, it seems unlikely that they could perform much better. Narrow error bars.
Liquidation preference matters relatively little. Narrow error bars.
The concept of meta-options is complicated and has received hardly any attention. My best-guess estimate for their value is very large, but I could be way off. Extremely wide error bars.
Future performance is really hard to predict, even over long time horizons. Wide error bars.
By my estimate, startup employees’ expected returns could optimistically be as high as 52% (!); or they could be as low as −2%. (Remember, these are expected returns. Realized returns could fluctuate by much more than this. Startups in aggregate could easily realize a 100% return next year, and I wouldn’t find that surprising, but I would be crazy to expect it to happen.)
Risk and correlation of startups
Expected return alone isn’t what we care about. We really want to know risk-adjusted return.
And we don’t just care about the risk-adjusted return of startups in isolation. We want to know how they fit into a broader EA investment portfolio.
There are two equivalent ways of looking at this:
Find the risk of startups, and their correlation to the aggregate EA investment portfolio. Then we can calculate whether EAs on the margin should work at startups instead of big companies.
Find the alpha of startups relative to the EA portfolio. If alpha > 0, that means at least some EAs should work at startups.
I will focus on the first because I find it more intuitive. I also calculated the second and got similar results (not presented in this essay).
I’m looking at the risk and correlation of the startup industry, rather than the average risk/correlation of a single startup. We can think of it as a collective decision by many EAs to work at a diversified group of startups, rather than the decision of a single person.
As with expected return, we have no way to know the future risk of startups, or their correlation to the EA portfolio. But with risk and correlation, we get to make some simplifying assumptions.
We can’t learn much about the future return of an asset class by looking at its past return. Markets are reasonably efficient, so if an asset class performs well, more money floods in and performance reverts to the mean. But the efficient market hypothesis doesn’t say risk mean-reverts. Studies show that, at least for public equities, historical volatility is a pretty good predictor of future volatility (e.g., Dreyer & Hubrich, 2017).
Three different data sets all give similar(ish) numbers for startups’ standard deviations:
|Data Set (Gross)||Standard Deviation|
|Cambridge Associates, 1988–2016||21.4%|
|Cambridge Associates, 1995–2018||23.5%|
|Harris et al., 1984–2014||28.5%|
Note that, according to Cambridge Associates, top-quartile VCs have higher standard deviations than average VCs (25% or 28%, depending on which time horizon you use). So if we only work at top startups, we should bump these numbers up by a few percentage points. Also, startups don’t have public prices the same way stocks do, and VCs have some leeway to value their portfolios however you want. I expect that they tilt their portfolios toward low volatility to make themselves look better, so the “true” volatility is probably higher.
Woodward (2009) argues that, because startup valuations tend to lag the market, a naive regression doesn’t show the true relationship between startups and public equities. The paper finds that startups have a stock market beta of a little over 2, which corresponds to a standard deviation of about 35%.
For correlation, as with standard deviation, we can assume the future looks the same as the past. Historical correlation between startups and public equities was around r=0.7. (My own analysis found a correlation of 0.6, and Woodward (2009)[14:1] found a correlation of around 0.7–0.8 using more limited data but better methodology. Woodward’s analysis suggests that the naive approach underestimates the true correlation. So let’s use 0.7.)
Many EA investors probably want to use leverage. But startup employees can’t leverage their equity: they get however much they get based on their employment contract, and there’s no way to borrow money to get more equity. Instead of comparing startup equity to a public investment portfolio, we should compare startup equity to an optimally leveraged public investment portfolio (taking into account that leverage typically costs more than theoretical models assume).
Startups vs. public equities
Now that we’ve discussed the main considerations, we can return to the original question: is it better for earners-to-give to work at high-paying companies and invest their salaries in the market, or to work at startups and “invest” in startup equity?
Some additional assumptions:
Our goal is to maximize the geometric return of the overall EA investment portfolio. (This is consistent with logarithmic utility of money.)
We can only invest in two things: public equities or startups.
We control 1% of the EA portfolio. We can’t affect the other 99%.
EAs currently invest all their money in public equities, and none in startups. (The latter is obviously false, but it’s also sort of true: on the margin, earners-to-give can consider working for startups that don’t already have any EAs working for them. That set of startups has 0% EA investment.)
If we buy public investments, we can use up to 2:1 leverage.
Public equities earn an expected real return of 3% with a standard deviation of 16%.
Recall from above that we’re giving startup equity an 18% expected real return, a 35% standard deviation, and a 0.7 correlation to public equities.
Our two choices:
Work at a big company. Invest our salary in public equities with 2:1 leverage.
Work at a startup.
Given all the stated assumptions, working at a startup is more than four times better than working at a big company (37 expected utility vs. 200 expected utility, according to a scaled logarithmic utility function).
Suppose we hold everything else constant but reduce the expected real return of startups. The return needs to be as low as 1% before the startup looks like the worse choice. (Notice that that’s lower than the 3% expected return of the public stock market, even before accounting for leverage. Startups with a 2% expected return are still (barely) preferable to public equities with a 3% return because we’re assuming startups have a lower correlation to the EA portfolio.) So even under a much more pessimistic projection for startup returns, they still look preferable to big companies.
Startups vs. an optimized public investment portfolio
Buying an index of public equities might not be the best way to invest one’s big-company salary. I personally prefer to invest in concentrated value, momentum, and trend strategies. Some EAs believe cryptocurrency or AI stocks will beat the market. The specifics don’t matter too much. What matters is that if you believe some other investment has substantially better expected performance than a broad index fund, then you should use that other investment as your benchmark instead. And startups need to look better than that benchmark.
My best guess is that a concentrated value/momentum/trend portfolio will earn an expected real return of 6% with a standard deviation of 11%. (Of course, as with my estimates for startup returns, these numbers are not remotely robust.) If we also use 2:1 leverage, then value/momentum trend still looks somewhat worse than startups, although not by a as big of a margin (126 expected utility points vs. 200). If startups returned 10% instead of 18%, then value/momentum/trend would be the better choice.
Alternative: Predictionless approach
Alternatively, take the same basic model as above, but don’t try to predict the future. Instead, assume asset classes will perform exactly as well in the future as they performed in the past. As I discussed above, this approach has issues—performance fluctuates a lot over time, so past performance doesn’t tell us what will happen in the future. But there’s also something appealing about this method. Trying to predict future performance leaves lots of room for you to bias the outcome toward what you (perhaps subconsciously) want. It’s harder to introduce bias if you just use past performance.
For the predictionless approach, I estimated the expected return to employee equity as:
13% after-fees historical return to VC firms, or 10% after inflation
15% real historical return before fees
Subtract 2% for employee liquidation preference, giving 13%
Add 11% for meta-options, giving 24%
Add 4% for the persistence of top-quartile VCs, giving 28%
For public equities and for my value/momentum/trend portfolio, instead of making projections, I used the (estimated) historical return after inflation from 1995 to 2018:
The three choices have the following expected utilities:
Public equities: 142 utility
Val/Mom/Trend: 288 utility
Startups: 279 utility
Startups look preferable to public equities, but slightly worse than value/momentum/trend.
Alternative: Models are bad. What if we don’t use a model?
I love using quantitative models like the one in this essay. I think more people should use them. But most models are bad, including mine. They depend on lots of assumptions, and you can change the model output by making small changes to the assumptions.
How could we reason through this decision without using an explicit model? Let’s review some arguments, both pro- and anti-startup.
Argument from risk preferences: Most startup employees don’t want to donate all their equity. That makes them much more risk-averse than EAs who work at startups. If they’re acting rationally, we should expect them to demand higher equity to compensate for the risk. Therefore, startup equity should look particularly compelling to EAs.
Argument from inefficiency: The market for startups is illiquid and has high barriers to entry. We might reasonably expect it to be less efficient than public markets, which means we have a better chance of identifying startups that will outperform.
Argument from investability: The most reputable VC firms usually don’t accept new investors. Even if they can beat the market, you can’t invest with them, so it doesn’t matter. But there’s nothing stopping you from getting jobs at top VCs’ portfolio companies.
Argument from overpopularity of startups:
A lot of people want to work at startups because startups are cool, and they’re willing to accept below-market compensation.
Total VC investment dollars have increased a lot over the past few years, even though the number of startups hasn’t changed much. So the startup market might be overinflated.
Argument from underappreciated risk: In my experience, almost nobody understands how risky individual startups are. Even medium-sized companies are about 3x as risky as the S&P 500. I don’t have sufficiently granular data on startups, but startup-sized public companies are about 5–6x as volatile as the S&P 500, and my guess is startups are even worse. When I see people discussing the value of startup equity, they almost never properly account for this.
Argument from diversification: If you get a job at a startup where no other EAs work, you’re adding an entirely new investment to the EA portfolio. That could be a good thing even if that particular investment has a worse expected value than the market. On the other hand, there are other ways of diversifying that might be better.
These qualitative arguments don’t obviously lean one way or the other. My intuition from my time working at startups and knowing lots of startup employees is that most people overvalue startups and underestimate risk, which means they probably push down the market rate for equity compensation. But even if most startup employees don’t behave consistently with their personal risk appetite, they still might behave more risk-aversely than EAs ought to.
If more EAs want to work at startups, there are some ways that people or organizations could support this effort, such as:
Maintain a list of startups with funding from top VCs, or startups that look particularly promising for whatever reason.
Coordinate to identify companies that don’t already have EAs working at them, or that might provide the most diversification benefits to the EA portfolio.
Help EAs review employment contracts from prospective employers.
Make loans or grants to EAs to help them exercise stock options as soon as they vest.
Career support/recruiting services for EAs who want to work at startups.
Support for EAs whose startups fail. Maybe even offer some kind of insurance to reduce risk, e.g., if you go work for a startup and it fails, we will pay you to compensate for the earnings you could have had.
Help people negotiate for better equity terms.
Some of these ideas are logistically difficult, maybe even impossible. I’m not sure the best way to provide support for earners-to-give who choose to work at startups, but it’s something to consider. I believe it would be valuable if an organization existed that helped EAs with these practical details.
Assuming my model is approximately correct, what type of person might want to work at a startup?
Someone who wants to earn to give.
Someone who doesn’t have the right skills or temperament to start a startup, but still might want to work at one.
Someone with special insight into a field who thinks they can identify the most promising companies.
Perhaps someone who can’t invest with leverage, or who doesn’t want to use leverage, but who is comfortable with the risk of startup equity.
Who might not want to work at a startup?
Someone who’s not comfortable with the risk (equity risk or career risk or both).
Someone who believes they can see particularly good investment opportunities /outside/ of startups, and wants to earn a high salary so they can invest in those other opportunities.
Someone who thinks donating now is significantly better than donating a few years from now, and therefore doesn’t want to wait for startup equity to vest.
My analysis suggests that working at a startup has good expected value under ideal conditions. If you get a job offer from a startup, remember to pay attention to the specifics of the offer:
Is your total compensation competitive with what you’d get at a big company? (Taking startup equity at face value)
Does your equity contract include any sketchy terms?
etc. (Too many specifics to list all of them)
Areas for further research
Many subjects warrant a deeper investigation:
Historical VC returns
Historical returns earned by startup employees
How employee equity terms affect the value of equity
Value of meta-options
How most startup employees decide where to work—most importantly, how sensitive are they to the value of equity?
Why EAs might or might not expect to beat the market
How Woodward (2009)[14:2]’s analysis looks if you update it to include more recent data
Relevance of giving now vs. later
Career capital from working at startups. Does working at a startup train you to start a startup or a nonprofit?
Other considerations worth including
Thanks to Linchuan Zhang for commissioning this research project and providing support. Thanks to Charles Dillon for feedback.
Appendix A: Startups for founders and investors
This essay has looked at startups from the perspective of employees. How do startups look for other types of people?
Founders: Similar to employees in many ways. The upside is you get a lot more equity. (Hall & Woodward (2012) found that VC-backed startup founders on average made much more money than salaried employees.) The downside is you have to actually start a startup, which is much harder and may require an entirely different skillset.
Other people have written about whether EAs should start startups:
Applied Divinity Studies, Life Advice: Become a Billionaire
Brian Tomasik, Calculator for Expected Utility of Founding a Startup
VC limited partners: If you give your money to a VC firm to invest, this is probably worse than being a startup employee (although it does have the advantage that you don’t need to get a new job). You have to pay VC fees and you don’t get meta-options. You do get a better liquidation preference, but that’s usually not worth as much. For a more detailed discussion on investing in VC, see Mulcahy et al. (2012).
Angel investors: If you become an angel investor, you don’t have to pay VC fund fees, but you do have to evaluate startups on your own.
Appendix B: Some important tangents
The points below are all important, but they distract from the thesis of this essay, so I’m not commenting on them in detail.
Within the context of my model, some big companies’ compensation packages behave more like startups’. Companies such as Facebook and Google offer equity to employees. Unlike with startups, you can sell the equity as soon as you get it. But you usually have to wait a year, and a big company’s equity grant still behaves like a meta-option.
There are many non-monetary pros and cons to working at a startup. For instance, see Big companies vs. startups and Early career EA’s should consider joining fast-growing startups in emerging technologies.
An unimportant point that I nonetheless want to address: Startup employees’ equity will get diluted by future fundraising rounds. This doesn’t matter because VCs will get diluted by the same amount, so it doesn’t make employee equity look worse relative to VC equity (unless the employment contract contains sketchy terms around who gets diluted, in which case maybe you shouldn’t work there). Normally, VCs have the option to invest more money in future rounds to negate their dilution, but this also doesn’t matter because it doesn’t change the return on their initial equity purchase.
The report includes VC returns up to 2020, but it only includes detailed data up to 2018. So when I did my analysis, I used the 1995–2018 data.
Somewhat concerningly, these two data sets show different numbers even for years where they overlap. For example, the 1988–2016 data set quotes a 60.09% return for the year 1996, whereas the 1995–2018 data set claims a 63.46% return for the same year. This discrepancy is at least partially because the 1995–2018 series includes more VC firms, but I haven’t read the Cambridge Associates reports in enough detail to say if that’s the only reason.
Koch (2014). The risk and return of venture capital.
Woodward (2009). Measuring risk for venture capital and private equity portfolios.
Harris, Jenkinson, Kaplan & Stucke (2020). Has Persistence Persisted in Private Equity? Evidence from Buyout and Venture Capital Funds
Figures are copied or inferred from Harris et al. (2020), Table 1, Table 2, and Table 4.
Kaplan & Schoar (2005). Private Equity and Performance: Returns, Persistence, and Capital Flows.
At least in the United States, if you donate stock to charity, you can only deduct up to 30% of your income. If you get lucky and make a bunch of money when your startup exits, your startup equity could account for something like 90% of your income. You can only deduct 30%, so you’re stuck paying taxes on the other 60%.
Very roughly, a startup has a 70% chance to be worth 0x, 20% chance of 0–1x, and 10% chance of >1x. Liquidation preference only matters in the 0–1x case, where common shares are worth about half as much as their face value.
If you don’t filter out sketchy terms, the appropriate discount is more like 36%.
Actually, I know a few people who I believe could do a good job of identifying top startups if they took the time to conduct lots of interviews and due diligence. But they’re not going to do that because they’re busy doing other important EA-related activities.
Chingono & Rasmussen (2015). Leveraged Small Value Equities.
For the optimistic estimate, I assumed: top-quartile VCs’ average out-of-sample return of 26% (or 23% real) fully persists; EAs perform 5% better than top VCs; and meta-options are worth 20% (which follows from optimistic assumptions about how meta-options behave).
For the pessimistic estimate, I assumed: public equity valuations fully mean revert, and startups perform even worse due to higher risk; top quartile VCs’ returns do not persist at all; and meta-options are worthless (probably because there’s some flaw with them that I haven’t thought of).
Dreyer & Hubrich (2017). Tail Risk Mitigation with Managed Volatility Strategies.
Woodward (2009). Measuring Risk for Venture Capital and Private Equity Portfolios.
Technically, if you get employee stock options, your equity is leveraged. But the amount of leverage approaches zero as the stock price increases. And most stock options are only a little bit leveraged to begin with. For example, if your company stock was last valued at $4 and you get options with a $1 strike price, that’s only 1.33:1 leverage. If the stock price doubles to $8, now you only have 1.14:1 leverage.
In keeping with my previous work on leverage, I assume that borrowers must pay 1% plus the risk-free rate.
This is the average of the projections by AQR and Research Affiliates as of October 2021.
The utility function takes the geometric mean return as the utility and multiplies by 100,000 to make the numbers more readable. As a baseline, it calculates the expected utility of the EA portfolio without your investment, and then subtracts that from the total expected utility of the EA portfolio including your investment.
Still not impossible, because you could pick the data set or the time series that most closely matches the outcome you want.
For public equities, I used the historical return of the total US stock market, assuming zero fees or trading costs. To find the historical return of Val/Mom/Trend, I created a hypothetical portfolio that invested 80% in Alpha Architect’s Value Momentum Trend Index and 20% in AQR’s Managed Futures Index, which roughly reflects how I actually invest my money. Both indexes subtract estimated fees and trading costs. The historical returns are hypothetical, not actual. I didn’t have data for 2018, so I calculated summary statistics over 1995–2017 instead.
Hall & Woodward (2012). The Burden of the Nondiversifiable Risk of Entrepreneurship.