# Luck, Talent and Effort in Business and Career Success

**Combined report: research literature and simulation study** · 13 November 2026

*One-page executive summary of the full report, `report/final_report.md` (also available as `final_report.docx` and `final_report.pdf`).*

## Executive summary

**The question.** When some people or businesses do far better than others, how much is due to talent, skill and effort, and how much to luck?

**How we answered it.** We combined two pieces of work. The first is a review of published research, drawing on tax and social-security records, business registers, startup-investment data and psychology studies. The second is a computer simulation of working lives: 1,000 runs of 1,000 people for each scenario, with settings taken from that research wherever it had evidence.

**Short answer.** It depends on what you measure. Everyday outcomes, such as yearly pay or total lifetime earnings, are mostly shaped by lasting differences between people. Extreme outcomes, such as a huge startup sale or great wealth, are mostly shaped by luck. Talent and skill raise your odds; they do not decide your outcome. And many "personal" advantages (genes, family, the year you start work) were themselves luck.

| # | Main finding | Confidence |
|---|---------------------------------------------|-----------------------------------------|
| 1 | The more extreme the outcome, the larger luck's role. | Literature: **High**. Simulation agrees: traits fixed at age 23 explain 62% of the variation in lifetime earnings, but only 31% of the variation in final wealth (in proportional terms). |
| 2 | For lifetime earnings, about 55–65% of the differences between people are already in place at age 23; the other 35–45% come from later events. | Literature: **Medium** (other models range up to 90/10). The simulation was built to match this, so it is not an independent check. |
| 3 | For any single startup, the founder's lasting qualities explain only a minority of its success; chance and timing dominate. One large venture-capital firm's own scores correlated only about 0.1 with results. | Literature: **Medium**. Simulation agrees: even perfect knowledge of talent correlates only 0.16 with a venture's payoff. |
| 4 | Skill is real, builds up over time and can partly be learned. It shifts the odds. | Literature: **High**. Simulation: talent helped in all 60 variants of the full model. The most talented tenth reach the top 1% 5.53% of the time (least talented tenth: 0.017%), yet 44.7% of the top 1% come from outside the most talented tenth. |
| 5 | If genes, family and birth timing count as luck, luck's share is larger than any figure above. | Literature: **Medium** (depends on definitions). Not tested by the simulation. |
| 6 | People credit themselves for success and blame circumstances for failure. | Literature: **High** (266 studies). Not tested by the simulation. |
| 7 | A steady income from work is the strongest equaliser: it reduces inequality and ties outcomes more closely to talent. | Simulation only: the direction held in all 33 of 33 paired tests; the size depends on assumptions. Fits the literature's finding that repeated, everyday outcomes are less luck-driven. |
| 8 | Two disagreements. (a) The literature suggests the very top performers are weaker evidence of skill than the next tier; the simulation never found this. (b) "Take more shots" raises the average and the chance of a big win, but lowers the typical outcome unless you can survive the losses. | Simulation results that depend on the model. They show the literature's points are not automatic, not that they are wrong. |

**Key caveats.**

- Luck and skill cannot be observed directly. Every estimate comes from a model, and the answer depends on its assumptions and on what counts as "luck". No single "luck percentage" is reliable.
- The simulation was built from the literature, so where they agree this is partly by design. 34 of its 60 settings are assumptions. The two that matter most for inequality (how often people get risky opportunities, and how much they stake) have no direct evidence behind them.
- Most evidence is from the U.S., and some figures come from secondary sources. Minor inconsistencies in both source files are listed in Section 7.3 of the full report; none changes these conclusions.