Since 1996 the Fraser Institute has published an annual economic freedom of the world index that ranks countries according to their level of economic freedom. They also publish an economic freedom of North America Index that ranks the US states, Canadian provinces, and Mexican provinces using similar data.

Both of these studies have been used to show that countries and states/provinces with relatively high levels of economic freedom tend to be better off in several ways, including higher GDP per capita, longer life expectancy, and greater economic growth. Countries with higher levels of economic freedom tend to have higher quality democracies as well.

A quick google search reveals that there has been a lot of other research that looks at the relationship between economic freedom and various outcomes at the country and state level. However, substantially less research has been done at the local level and there are two main reasons for this.

First, it’s hard to gather data at the local level. There are thousands of municipalities in the US and not all of them make their data easily available. This makes gathering data very costly in terms of time and resources. Second, a lot of policies that impact economic freedom are enacted at the federal and state level. Because of this many people probably don’t think about the considerable effects that local policy can have on local economies.

There has been one study that I know of that attempts to create an economic freedom index for metropolitan areas (MSAs). This study is by Dr. Dean Stansel of SMU, a coauthor of the economic freedom of North America index. The MSA economic freedom index runs from 0 (not free) to 10 (very free) and was created with 2002 data. I am currently working on a paper with Dean that uses this index, but I was recently inspired to use the index in a different way. I wanted to see if economic freedom at the MSA level impacted subsequent employment and population growth, so I gathered BEA data on employment and population and ran a few simple regressions. The dependent variables are at the top of each column in the table below and are private, non-farm employment growth from 2003 – 2014, proprietor employment growth from 2003 – 2014, and population growth from 2003 – 2014.

I also included a quality of life index independent variable from another study in order to control for the place-specific amenities of each MSA like weather and location. This variable measures how much people would be willing to pay to live in a particular MSA; a positive number means a person would pay to live in an area, while a negative number means a person would have to be paid to live in an area. Thus larger, positive numbers indicate more attractive areas. The index is constructed with 2000 data.

As shown in the table, economic freedom has a positive and significant effect on both measures of employment and population growth. The quality of life index is also positive and significant for private employment growth (column 1) and population growth (column 3, only at the 10% level). We can calculate the magnitude of the effects using the standard deviations from the table below.

Using the standard deviation from column 1 (0.84) we can calculate that a one standard deviation increase in economic freedom would generate a 2 percentage point increase in private employment growth from 2003 – 2014 (0.84 x 0.024), a 4.5 percentage point increase in proprietor employment growth, and a 2.9 percentage point increase in population growth. A one standard deviation change would be like increasing San Francisco’s level of economic freedom (6.70) to that of San Antonio’s (7.53).

Similarly, a one standard deviation increase in the quality of life index would lead to a 2.1 percentage point increase in private employment growth from 2003 – 2014 (0.000011 x 1912.86) and a 1.9 percentage point increase in population growth. A one standard deviation change would be like increasing the quality of life of Montgomery, AL (-21) to that of Myrtle Beach, SC (1643).

I think the most interesting finding is that quality of life does not affect proprietor employment while economic freedom’s largest effect is on proprietor employment (column 2). According to the BEA proprietor employment consists of the number of sole proprietorships and the number of general partners. Thus it can act as a proxy for the level of entrepreneurship in an MSA. This result implies that economic freedom is more important than things like weather and geographic location when it comes to promoting small business formation and entrepreneurship. This is a good sign for cities located in colder regions of the country like the Midwest and Northeast that can’t do much about their weather or location but can increase their level of economic freedom.

Of course, correlation does not mean causation and these simple regressions omit other factors that likely impact employment and population growth. But you have to start somewhere. And given what we know about the positive effects of economic freedom at the country and state level it seems reasonable to believe that it matters at the local level as well.