Length of commute: Bankers are important people, and they cannot afford to waste too much time getting around. For each city, we found the average lengths of peoples' trips to work from the Selected Economic Characteristics table in the U.S. Census' 2012 American Community Survey. Length of commute makes up 20% of the full index.
Length of flight times to key cities: Bankers often have to travel to hubs like New York, London, and Hong Kong. For each city, we went to expedia.com and found the shortest flight available to those hub cities, and took the average of the three times. We made a minor exception for Stamford, and used the travel time for a Metro North train to Grand Central instead of a flight time. This measure is 15% of the full index.
Services: Bankers need access to various amenities to maintain their busy lives. We estimated the number of gyms (bankers need to stay in shape), maid services, late-night delivery places, and laundry/dry cleaning services by going to yelp.com and counting up the number of reviewed businesses of each type in each city. We took that number and divided by the city's population, to adjust for size. Services make up 25% of the full index.
Nightlife: Bankers need good places to take clients, and to unwind and blow off some steam. Again using yelp.com, we found the number of high end restaurants and bars (taking advantage of the cost filter and only counting the most expensive establishments), and the number of strip clubs in each city. As with services, we adjusted by the size of the city. Nightlife makes up 25% of the index.
High End Retail: Bankers need to dress the part, and so they need access to top of the line clothing. We chose eight high end clothing retailers - Brooks Brothers, Allen Edmonds, Ferragamo, Gucci, Hermes, J. Crew, Polo Ralph Lauren, and Hugo Boss - and, using the store locators on the retailers' websites, counted up the number of stores near each city. Retail makes up the final 15% of the index.
We converted each of the components into an index by dividing each city's value by the average over all the cities studied and multiplying by 100. For example, Chicago had 26 of the high-end retail stores, compared to an average value of 19.5 among all 16 cities, so Chicago's retail score was 100 × (26/19.5) = 133. This represents the measure as a percentage of the average, so Chicago's score tells us that it had 33% more of the high end retail stores than the average among the cities. These index scores are convenient because they allow us to more easily compare and combine the different components.
For the commute times and the flight times, we instead use the reciprocal of the above relationship, dividing the average by the value for each city. For example, Omaha had the shortest commute at 18.2 minutes, compared to the average of 27.6. So, Omaha's commute score was 100 × (27.6/18.2) = 152. This allows us to reward cities that have shorter commutes, and punish cities that have longer commutes.
The final step is to put the components together, taking a weighted average of the sub-indexes using the weights indicated above, giving us the complete Banker Livability Index.