What is the Liquidity Trajectory Index?

There are three angles to understanding the equity portion of a compensation package:

"How big is the bag?" (what is the size of the equity portion);
"How real is the bag?" (will you ever have a chance to turn paper money into cash money?); and
"How often do you get the bag?" (is this equity package an active, reliable part of your compensation, or is the program full of gotchas).

The "size of the bag" is a straightforward question that we simply ask our respondents: the annualized equity value in their package. For the other two questions, Salary Confidential uses indexes to make equity compensation more easily comparable across respondents.

The Liquidity Trajectory Index (LTI) estimates how likely a respondent’s equity is to eventually become liquid, helping put equity compensation packages from different companies on a more comparable footing.

The problem at hand: 'How real is the bag?'

Early-stage or bootstrapped equity may never become liquid. Even late-stage equity is never guaranteed, but statistically the failure rate of investor-backed organizations diminishes as companies raise additional funding rounds or attract institutional investors. Bootstrapped companies can also be mature and profitable, but their equity does not always have reliable pathways to liquidity.

Knowing this, we wanted a way for requesters and respondents reading a benchmark to better understand what two equity packages might actually mean relative to the likelihood of a liquidity event turning that equity into real money.

For example, you may see that Response 1 reports a $30K equity package while Response 5 reports $70K. At first glance, it looks like Response 5 is doing much better. But if Response 1's LTI is 0.98 and Response 5's is 0.23, it is entirely possible that Response 5 will never see the color of that $70K equity.

LTI speaks only to the present moment. It does not predict the future — it reflects how far along a company appears to be on a plausible trajectory toward liquidity given the signals available today.

How LTI is calculated

In the Extended Equity portion of the survey, we collect information about the respondent’s company stage (for example pre-seed through Series C+, private equity–backed, or bootstrapped). Behind the scenes, these are translated into non-linear scores based on common statistics about failure rates as companies progress through funding stages. For example, failure rates tend to drop significantly after Series B.

We also collect the age of the company. Age acts as a weight on top of the stage score. Regardless of other factors, longevity is a signal of survival. However, this weight is intentionally non-linear: the early years contribute very little additional signal, and the scale fully saturates after about eight years. Beyond that point, more years mostly just mean… more years.

Finally, we consider organization size, but only after a certain threshold. We recognize that companies can grow headcount for many reasons, including poor management, so size does not act as a continuous signal. Instead, once a company reaches a sufficiently large headcount, it receives a one-time bump. This helps distinguish, for example, a long-established bootstrapped company that has grown to a meaningful scale from a bootstrapped company of the same age that remained very small.

At all times, LTI focuses specifically on the likelihood that a company will eventually reach a liquidity event. It is not a general measure of survival — it is narrowly concerned with pathways to liquidity for the equity itself.

What LTI is not: it is not a way to calculate the "real" value of an equity package

Equity liquidity is a bit like Schrödinger's cat. Either the equity eventually becomes liquid, or it doesn’t. In practice, the outcome is binary: zero or one.

For that reason, LTI is not a weight you should apply to an equity package.

If an equity package is reported as $30K and the LTI is 0.33, it does not mean the package is "worth" $10K today. That would be a misunderstanding of what the index measures. The package is still exactly what it was reported to be: $30K per year if it becomes liquid, or effectively zero if it never does.

What LTI reflects is how far along the company appears to be on a trajectory toward a liquidity event, based on the signals we observe today. So an LTI of 0.33 means we currently have limited confidence that such an event will occur (our midpoint threshold is around 0.5), though the company may still be on a path where it eventually could (for comparison, a one-year-old pre-seed company is about 0.15 in our model).

Why we didn't just use "Company Stage"

Company stage carries the largest weight in LTI, but it is a blunt instrument. When we tested using only company stage, too many realistic scenarios produced misleading results.

Some examples we tested:

A Series A company that is five years old — should it necessarily look weaker than a Series B company that just raised a round at age three? and how should it look relative to a 2 year old Series A.
A bootstrapped company that has existed for twelve years — should it automatically look worse than a Series B company that is only three years old? That bootstrapped company might be a comfortable cash machine.

At the same time, we do not think a two-year-old bootstrapped company should look identical to that twelve-year-old one. A two-year-old bootstrapped company may simply be three founders hacking away nights and weekends. It may eventually raise a strong round — but today it is still a young bootstrapped organization.

Combining stage, age, and size helps LTI better reflect these kinds of realities.

We were also mindful of privacy

We collect company stage, age, and size, but reporting all of that information directly in results could quickly become de-anonymizing for respondents.

Using an index allows us to incorporate those signals without exposing the raw inputs. The underlying information influences the analysis, but is not revealed directly.

And we care deeply about these things around here.

The edge cases LTI cannot solve

LTI is not a perfect index. In fact, we are aware of some cases where it breaks down. But they tend to be far outside common scenarios.

We think of 0.5 (the midpoint of the index) as roughly equivalent to a Magic 8-Ball saying “signs point to yes.” So we tested the model against a few well-known companies to see how it would behave.

Mailchimp — Mailchimp famously stayed bootstrapped for 17 years before being acquired by Intuit for $12 billion. Even factoring organization size (Mailchimp in 2017 was above the threshold where size contributes a bump), the model would still place Mailchimp slightly below our 0.5 threshold. In other words, LTI would slightly underestimate Mailchimp’s true trajectory.
Clubhouse — The COVID-era Silicon Valley darling that raised funding up to a Series C and was once valued at $4 billion. Because Series C companies statistically fail far less often, LTI would assign Clubhouse a strong index. But Clubhouse didn't quite happen (it's still around though). LTI has no hindsight — it only reflects the signals visible at the time and Clubhouse to this day would continue to receive a strong LTI in our model.

Stress cases LTI does handle well

We also tested the model against harder scenarios where LTI performed well

Taboola — Taboola spent almost ten years at Series D before eventually going public, remaining profitable throughout that time. A Series D company that old clearly sits on a strong trajectory toward liquidity. LTI would assign Taboola about 0.92 shortly before its IPO, while a comparable public company would receive roughly 0.98.
Bootstrapped longevity — The model handles the difference between long-established bootstrapped companies and young bootstrapped companies well. Longevity matters — but mostly after the early years, and not linearly.
Hypothetical anomalies — A hypothetical pre-seed company that somehow existed for ten years would still receive a very low score (around 0.15). Whatever is happening there, age alone cannot compensate for the structural reality that the company remains pre-seed.

The model could resolve some edge cases with additional inputs

And we even know what input would improve the model’s ability to distinguish companies like Mailchimp from more speculative Silicon Valley stories.

That missing input is revenue.

Revenue tells a powerful story. If we added revenue to the model, it would help resolve cases where a company has strong fundamentals but lacks typical venture signals.

But we deliberately chose not to include it.

Salary Confidential surveys are designed to take only a few minutes to complete. Many employees do not actually know their company’s revenue. Others may know a number — but not the right one (for example ARR versus total revenue). Some companies do not communicate revenue internally at all.

In those cases, bad data would be worse than missing data.

So instead, we prefer to be transparent about where LTI is imperfect rather than pretend we have built a bulletproof index. Within a wide range of real-world scenarios, the model performs well — and understanding where it fails actually helps clarify where it works.

A second indicator: the respondent’s own signal

When respondents complete a survey, we also ask whether they personally consider their equity to be a fully qualified component of their compensation.

Respondents know things we did not ask about. For example, a company may be private equity–backed but internally struggling, with investors planning to dismantle it. Employees may know this reality even if it is invisible to our model.

Rather than trying to capture that complexity through dozens of additional questions, we simply record whether the respondent chose to include equity in their Total Compensation (TC).

You can think of this as a quiet vote of (no)confidence from the respondent themselves.

If a response excludes equity from TC and LTI is also low, the signals align.
If equity is included in TC but LTI is low, either the respondent is optimistic or the model is missing something.
If equity is included in TC and LTI is high, the structured signals we measure line up with the respondent’s own judgment.

That alignment — or disagreement — can be an interesting signal in itself.