In 1838, Charles Darwin was deciding whether or not to suggest to his cousin Emma Wedgewood. To assist make up his thoughts, he wrote down the entire causes for and in opposition to marriage. Causes in favour included “fixed companion (and good friend in outdated age)”, whereas causes in opposition to included “compelled to go to family members” and “horrible lack of time”.

Deciding which US cities are most tasty to overseas traders must be a much less emotionally fraught train. However in a single respect the choice isn’t that totally different from Darwin’s: it means selecting what issues, and the way a lot.

To provide the FT-Nikkei Investing in America rating, we compiled information on the financial, regulatory and social traits of US cities and the preferences of abroad traders. Combining that information into an total rating concerned making judgments about what issues to a various group of individuals with totally different particular person objectives. 

Total, we’re assured that our method has created an attention-grabbing and significant index. However you will need to recognise that different individuals might have made totally different selections with the identical information. So right here we clarify what we might have finished otherwise, and why we made the alternatives that we did.

Selecting the cities 

We restricted our choice to cities in 50 states and DC with a inhabitants higher than 250,000, based mostly on 2020 Census place information. 

Final 12 months, these cities captured 45 per cent of all new overseas enterprise tasks within the US, roughly a fifth of the nation’s greenfield overseas direct funding — that’s, cross-border investments that create new jobs and services — in response to fDi Markets, an data supplier owned by the Monetary Instances. This doesn’t imply that rural areas should not enticing locations for abroad traders. In reality, in the event you’re constructing a big warehouse, they could be extra enticing than the cities we’ve chosen.

However we wished to have a look at locations with a gentle stream of FDI, locations the place any trade might discover a dwelling. We additionally wished to have a look at locations with bigger and extra numerous populations, that are extra accessible and should really feel extra snug to individuals from all around the world.

We checked out Census place information as a result of many elements — enterprise regulation, workplace hire, faculty high quality — differ drastically inside a metropolitan space. Many New Yorkers most likely want they paid the housing prices of Newark, New Jersey, the place costs are 61 per cent cheaper.

We used metropolitan-area information when city-level information was not accessible or when traces had been tougher to attract. Dallas and Fort Price, for instance, share the identical worldwide airport. The variety of worldwide flights out of Dallas-Fort Price was assigned to each cities in our rating. 

Selecting the classes 

The very best place for overseas enterprise isn’t one-size-fits-all. A fintech agency and a producing plant can have very totally different priorities in terms of what they’re in search of in a location. 

By an evaluation of press releases, interviews and surveys, we recognized and measured some frequent options of any metropolis that make it shine for worldwide enterprise. Other than entry to particular markets, we discovered that expert employees and a pleasant enterprise surroundings had been high of the listing for overseas traders. Beneath is extra data on every class and the way we selected to measure it. 

Enterprise surroundings 

This class appears at taxes, regulation and prices. We in contrast cities by their company earnings tax, gross sales tax, property tax, tax incentives, hire and utility prices. We additionally performed a survey with the State Worldwide Growth Organizations (Sido) on how properly metropolis and state enterprise insurance policies supported FDI aims. 

Sources: Business Edge, FT-Nikkei and Sido Survey, GIS Planning, Gross sales Tax Clearinghouse, Tax Basis, US Census, Wavteq

International enterprise wants

This class appears at how a lot cities’ insurance policies and infrastructure assist worldwide enterprise. On this class, we in contrast cities by their variety of worldwide flights, distance to a port, web connectivity, and FDI companies. We partnered with Sido to trace what number of staff cities and states have devoted to attracting FDI, if they’ve investor platforms, and in the event that they help with website choice, market technique, provide chain procurement, regulation, and mergers and acquisitions. 

Sources: Broadband Now, FT-Nikkei and Sido survey, GIS Planning, OAG

Workforce and expertise

Expertise is on the high of the listing for overseas traders. Other than proximity to prospects, evaluation by fDi Markets of company bulletins confirmed {that a} expert workforce was probably the most cited purpose for FDI into the US in 2021. 

We in contrast cities by their share of school graduates, the dimensions of their working age inhabitants, the variety of close by universities, and the liberty of their labour market. Labour market freedom refers to how a lot leeway the personal sector has in terms of hiring and remunerating employees. It’s modelled after the financial freedom index compiled by Canadian think-tank the Fraser Institute and analysis by Dean Stansel, a professor at Southern Methodist College who additionally labored on the Fraser Institute’s index. 

Sources: Financial Coverage Institute, GIS Planning,,, US Census

Bar chart of Survey asked foreign companies considering or conducting business in the US what matters most to them when it comes to workforce and talent acquisition  showing Education and diversity are among the top workforce concerns for prospective foreign investors in the US

High quality of life

We targeted right here on the fundamentals: price of dwelling, commute occasions, crime threat and faculty high quality.

Many issues matter to individuals when deciding on a spot to stay and work—good climate, entry to nature, political affiliation, proximity to household and pals. Most of the constructive elements that we might have preferred to incorporate can be exhausting to measure or troublesome to replace yearly. With different elements, even denoting them as constructive or unfavorable would in impact be a political resolution.

Sources: Utilized Geographic Options, GIS Planning, Area of interest


This class appears at range. We measured cities by the dimensions of their foreign-born inhabitants and their racial range rating — the possibility that individuals of various races can be chosen in any random pattern of two.

Supply: US Census

This class appears at how properly cities attracted funding in 2021. We in contrast cities based mostly on their greenfield overseas and home direct funding per capita. 

Supply: fDi Markets


Making certain firms are supported as soon as they’ve been established can go a great distance in attracting FDI. 1 / 4 of all greenfield FDI tasks within the US final 12 months had been expansions of current investments.

By our partnership with Sido, we surveyed cities on whether or not they had officers devoted to supporting firms’ long-term wants and speaking regulatory modifications. We additionally tracked whether or not they helped relocate and combine employees, suggested on new investments, or offered export and promotion companies. 

Sources: FT-Nikkei and SIDO survey

What about. . . ?

There have been many variables we wished to incorporate that didn’t make the lower. There have been additionally many variables that we didn’t have the chance to discover that we would have used. In the end, we constructed our rating with variables that had been related, well timed and available.

We wished to stay as goal as potential when measuring and rating cities. The salience of points within the political realm — abortion, gun management, marijuana laws — is one thing that solely particular person companies can determine.

Combining the info

A key problem we confronted in developing the index was deciding how you can mix various kinds of information. The variables in our dataset characterize totally different sorts of issues: individuals, cash, distance, time. A number of the variables are percentages, some are scores on different indices, others are ordinal scales, the place some cities rating greater than others.

Every of those variables must be represented on the identical scale, in order that they are often added collectively. This can be a totally different downside to deciding how a lot weight to present to various things. It’s about selecting how you can characterize various kinds of information in an equal means.

A typical method to this kind of activity is to measure, for every variable, how far every metropolis’s rating falls from the common rating, relative to the variation within the scores. This works properly for variables which can be symmetrically centred across the common, nevertheless it works much less properly for variables that don’t have this “regular” form.

Think about two variables the place the scores fall between 0 and 100. In a single, the common is 50 and most cities rating between 30 and 70. Within the different, most cities rating between 0 and 20, however some cities rating between 20 and 50, and some outliers rating between 50 and 100. 

Within the first case, roughly half the cities rating under the common. Within the second, greater than half the cities rating under the common. The median metropolis — the one within the center in the event you listing them from lowest to highest rating — will rating decrease within the second case than the primary, and the most effective metropolis will rating greater. Some cities would achieve an unfair benefit due to variations within the form of the variables.

A method of coping with that is to disregard the uncooked scores and as a substitute use the ranks of the cities. This solves the issue of otherwise formed information, however at a excessive price, as a result of you find yourself throwing away the entire details about the dimensions of the variations between cities.

Ideally, you want to strike a stability between these two extremes, and the method now we have taken goals to just do that. First, we rework skewed variables in order that they’ve a extra regular form. Second, we measure the variation of the scores across the median fairly than across the imply. Third, we place a restrict on the minimal and most standardised scores, so any cities whose scores fall outdoors this vary have their scores pulled into the perimeters.

This reduces the impact of variations within the distribution of variables, and stops outliers gaining a disproportionate benefit, whereas conserving a lot of the details about the dimensions of the variations between the cities inside every measure.

The standardised scores for the variables inside every class are mixed as a weighted sum to create a class index. Every class index is scaled from 0 to 100, and the general rating is a weighted sum of the class indices.

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