How To Become An Executive

(Co-authored by Link Gan and Alan Fritzler)

If you do a quick search for “How to become an executive,” you’ll be inundated with countless resources that promise to help you climb to the top of the corporate ladder. Most of the advice is based on qualitative data. Millions of executives have mapped out their career paths on their LinkedIn profiles, so we decided to see if we could quantitatively identify their keys to success.

"My recommendation for those of you who want to become executives is to work across as many job functions as possible."

We started by analyzing the career paths of about 459,000 LinkedIn members globally who worked at a Top 10 consultancy (per Vault Consulting Rankings) between 1990 and 2010 and became a VP, CXO, or partner at a company with at least 200 employees. About 64,000 members reached this milestone. Then we analyzed both observable and inferred traits from LinkedIn member profiles -- like educational background, gender, work experience, and career transitions. At the end of the day, the probability of becoming an executive is merely 14 percent.

What We Discovered

• Changing Job Functions Is Good, Changing Industries Is Not So Good -- Working across job functions, like marketing or finance, provides the well-rounded understanding of business operations that are needed to become an executive. Each additional job function provides a boost that’s, on average, equal to three years of work experience. But switching industries -- perhaps because of the learning curve or relationships lost -- has a slight negative impact to moving up the corporate ladder.
• MBAs Are Worth The Investment, But Pedigree Matters -- For the average ex-consultant in our sample, having an MBA from a top five program (per U.S. News and World Report) provides a net boost that’s equivalent to 13 years of work experience. In contrast, a non top ranked MBA provides a net boost of only five years. And while other advanced degrees -- like a PhD, Juris Doctor, or Master’s -- have increased in popularity among consultants and help with becoming an executive, they don’t provide the same boost as an MBA in our models. Nearly 80 percent of these non-MBA advanced degrees are some type of Master's -- typically in business, like economics and accounting, or technology, like computer science and IT.
• Location Matters -- In the U.S., working in New York City increased the chances of becoming an executive, while Houston and Washington, D.C., decreased the chances. Internationally, Mumbai and Singapore provided the greatest boost, while Sao Paulo and Madrid had the most negative effects. This could be in part due to the nature of the industries that are prevalent in these cities (for example, the financial services industry tends to be more hierarchical than the high-tech industry), as well as the higher concentration of company headquarters in those regions.
• Men Have A Slight Advantage -- All of the factors have the same effect on both men’s and women’s careers. But a woman who has the same profile as a man needs an average of 3.5 more years of work experience to reach the same probability of becoming an executive.

How Choices Impact Your Likelihood Of Becoming An Executive

The easiest way to interpret the findings of our model that predicts the probability of becoming an executive is through a comparison of three different former consultant profiles, all of whom have 15 years of work experience:

• Person A is male, has an undergraduate degree and no master’s degree, lives in Tulsa, Oklahoma, and has worked in one job function for three companies in three industries. His probability of becoming an executive is six percent.
• Person B is male, has an undergraduate degree from a top international school and a non-MBA Master's, lives in London, and has worked in two different job functions for two companies in two industries. His probability of becoming an executive is 15 percent.
• Person C is female, has an undergraduate degree from a top 5 U.S. program and an MBA from a top 5 U.S. program, lives in New York City, and has worked across four different job functions for four companies in a single industry. Her probability of becoming an executive is 63 percent.

Now let’s see what would happen if Person B made the following choices:

• If he worked in one industry rather than two, his probability of becoming an executive would increase three percentage points.
• If he got an undergraduate degree from a top 10 U.S. undergraduate program rather than a top international school, his probability would increase another three percentage points.
• If he worked for four companies rather than two, his probability would increase four percentage points.
• If he moved from London to New York City, his probability would increase four percentage points.
• If he worked across four job functions rather than two, his probability would increase eight percentage points -- a 1.5X effect.
• If he worked for 10 more years, his probability would increase 15 percentage points -- a 2X effect.
• If he got an MBA from a Top 5 U.S. program rather than a non-MBA Master’s, his probability would increase 21 percentage points -- a 2X effect.

Conclusion

Had Person B made all of the choices highlighted above, his probability of becoming an executive would increase from 15 percent to 81 percent. Our analysis reveals that getting an MBA from a top 5 U.S. program is the choice that would have most significantly increased Person B’s likelihood of becoming an executive. But getting an MBA from a top 5 U.S. program isn’t feasible for most people. So my recommendation for those of you who want to become executives is to work across as many job functions as possible.

Neil Irwin at the New York Times published this story today featuring our research. It includes an interactive graphic that calculates your likelihood of becoming an executive. The New York Times is offering everyone unlimited access to stories this weekend, so be sure to check it out! I’ve also listed a few common LinkedIn member personas below. See which one is most comparable to your career, and what your likelihood of becoming an executive is. And then check out the more than 10,000 executive job openings on LinkedIn.

Methodology

Our sample is comprised of LinkedIn consultants who worked at a Top 10 consultancy (per Vault Consulting Rankings) between 1990 and 2010 (See Appendix A1). Consultants were defined as those having job titles like (or variations of) ‘Consultant’, ‘Analyst’, or ‘Associate’ tied to their consulting stints. To ensure we only evaluated management consultants, we also added filters to exclude other types of junior workers at these firms -- like auditors and accountants.

After excluding career consultants, and controlling for other outliers, we ended up with a sample of ~459K ex-consultants globally. For these individuals, we defined ‘success’ as reaching VP/CXO/Partner seniority at a company with at least 200 employees. About 14% of sample reached this milestone.

The model includes the following features: metro region, graduate education type, undergraduate education type, # of industry sector worked, # of functions, years of experience, and gender.

The features have the following definitions:

• Metro Region: Indicates whether a member has worked in one of the top 10 largest cities in the U.S. or globally. We used LinkedIn membership as a proxy for population density derive the top 10 list. 
• Graduate Education Type: Indicates whether a member has an MBA from a top U.S. program, an MBA from a non-top program, an MBA from a top non-U.S. program, or another advanced degree. The 2016 U.S. News MBA rankings were used for U.S. schools, and the Financial Times MBA rankings were used for international schools.
• Undergraduate Education Type: Indicates whether a member has attended a school from the 2016 U.S. News national university rankings; or a top 10 school from the 2016 U.S. News liberal arts colleges rankings; or, a top 10 non-U.S. school listed in the U.S. News global university rankings.
• Company Count: The number of different companies in which a member has worked.
• Function Count: The number of different job functions in which a member has worked (for example, marketing and finance). We count when members switch functions while staying at the same company.
• Industry Sector Count: The number of different industries in which a member has worked (for example, government and finance). This can only be counted when a member switches companies.
• Years of Experience: The total years of work experience, including years in consulting, for a member.
• Gender: Inferred from member names. Categories are ‘unknown’, ‘either’, ‘male’, or ‘female’.

We used a logistic regression model to measure the effect of various career choices on the probability of ‘success’. The model has the following specification: log(p/1-p) = logit(success) = intercept + gender + metro region + graduate education type + undergraduate education type + industry sector count + function count + years of experience.