We mostly limited this literature review to studies that used the Fraser Institute’s Economic Freedom of North America index and produced results related to labor markets to some degree.[*] But we also included recent academic papers that used Stansel’s MSA dataset, and more generally, the most recent papers to look for associations between economic freedom and some other metric.
Of all the empirical scholarship performed using the EFNA index through 2016, the labor market freedom component was more frequently associated positively with economic performance than the other two components: government spending and taxation levels. We use 2016 as a cutoff point because Stansel co-authored a literature review (with Meg Tuszynski) of academic papers employing EFNA and constructed the tally using papers through that year.
Their review, “Sub-National Economic Freedom: A Review and Analysis of the Literature,” found that 103 of the 155 empirical studies they examined had “unambiguously good” or “mostly good results,” meaning that economic freedom was found to have a positive relationship with an outcome that is considered to be “good” (e.g., faster economic growth, more entrepreneurial activity, increased employment, etc.). Only one study showed results that could be identified as “mostly bad.”
There were 80 papers they reviewed in which the authors used specific areas of the EFNA index, as opposed to (or sometimes in addition to) the overall index score. In 58 of those papers, there was one or more areas that had a statistically significant relationship with whatever outcome was being examined. The area that had the most consistent positive effect was labor market freedom. It was statistically significant in 36 of those 58 studies. Government spending was significant in 27 and taxation was in 23. The topics of the research included entrepreneurship, corruption, interstate migration, labor market productivity, labor force participation, wages, income inequality, economic and employment growth, venture capital and more. [†]
A 2013 paper, “Economic Freedom and Labor Market Conditions: Evidence from the States,” used EFNA’s dataset from 1981 through 2009 and examined economic freedom and labor market conditions across all 50 U.S. states. It included measures of the unemployment rate, labor force participation and the employment-to-population ratio. The statistical model it created to measure these impacts included control variables for such things as demographics (population percentage over the age of 65, percentage that lives in an urban area or is college educated or female, to name a few). It also controlled for each of the 50 states’ production of energy, on the grounds that being steeped in energy resources may affect the economic well-being of the state more positively.
The authors found statistically significant relationships existed between the unemployment rate and the overall economic freedom score of states, as well as several subcomponents of the index. They noted that “a one-point increase in economic freedom is associated with a 0.78 percentage point decrease in the unemployment rate for the overall EFNA measure.”
Labor force participation was also positively associated with economic freedom. That is, a one-point increase in a state’s overall index score was associated with a 1.5 percentage point increase in labor force participation. This held true for each of the three subcomponents of the EFNA index as well, though the labor market freedom area had the smallest impact. The same was true for the employment-to-population ratio the authors studied: The overall EFNA score and each subcomponent showed a positive relationship, with the labor component producing the smallest effect.
The authors conclude that there is a solid link between economic freedom and positive outcomes in the labor market. They also noted that the labor market freedom component of the EFNA index has smaller impacts than other subcomponents because it is comprised, in part, of state minimum wage mandates, “which typically applies to a small fraction of the labor force.”
The 2014 study, “A Longitudinal Analysis of the Impact of State Economic Freedom on Wages,” demonstrates a statistical link between economic freedom in the 50 states and increased wages. According to the study, (citing the EFNA), Michigan’s freedom score improved from 5.2 out of 10 in 1981 to 5.9 in 1990 and 6.8 in 2000.
To conduct the analysis, the author studied annual youth worker survey data from 1979 through 1994 and then for every two years thereafter, through 2000. This information was then paired to Bureau of Labor Statistics data, which allowed the author to track where each survey taker was located. Using a variety of statistical approaches, the author found that a one standard-deviation increase in a state’s freedom score was associated with a wage increase of between 2.5% and 8.6%.[‡] He also found that changes in the labor market freedom area had a stronger positive relationship with wages than government spending or taxation.
The 2011 European Journal of Political Economy article, “Panel Evidence on Economic Freedom and Growth in the United States,” found a positive and significant relationship between freedom and economic growth, as measured by inflation-adjusted gross state product. The authors examined both the levels of and changes to states’ freedom scores and determined that different areas of economic freedom impact economic growth differently. They conclude:
From a policy perspective, what emerges from these results is the importance of constraining excessive government expenditure within the economy and minimizing the tax burden faced by a nation’s citizens. Further we see the importance of maintaining an open labor market and, in particular, the cost from a growth perspective that may be associated with increases in state minimum wages.
Another 2011 paper looked at economic growth, as measured by changes in employment in U.S. states. That paper, “Economic Freedom and Employment Growth in U.S. States,” was published in the journal Review, a publication of the Federal Reserve Bank of St. Louis. The authors look at economic liberty in three distinct spans of time — 1980-1990, 1990-2000 and 2000-2005 — to explore whether the years in which economic freedom and employment are measured make a difference to their results. The authors used both the EFNA’s overall freedom scores and the three areas that comprise it. They found a positive relationship between economic freedom and employment growth in each of the three periods they examined: An increase of one point on the EFNA’s 0-10 scale was associated with an increase in employment growth rates of about 3-4% over a 10-year period.
A 2016 review, published in the Cato Journal and titled “Subnational Economic Freedom and Performance in the United States and Canada,” found that economic freedom at the state and provincial levels of government in the U.S. and Canada is associated strongly with “higher levels of income per capita, lower rates of unemployment and higher income inequality.” They find that the relationship was strongest for the labor market freedom component, and that a one “standard deviation increase in labor market freedom is associated with a 1.34 standard deviation decline in the unemployment rate.”
“Economic Freedom and Migration Flows between U.S. States” is an oft-cited academic paper, published in the Southern Economic Journal in 2007, which looked at economic freedom and gross migration in the contiguous United States. The author concludes, “States with persistently more economic freedom will experience higher levels of income and more rapid employment growth.” He adds, “[I]n aggregate, the findings indicate that individuals migrate toward states with relatively higher government consumption expenditures, relatively lower tax burdens, and states with more freedom with respect to labor decisions in the form of less restrictive minimum wages, less concentration of unions, and less dependence on public employment.”
After the 2016 literature review by Stansel and Tuszynski appeared, scores of new academic papers have helped inform this new study.
“The Impact of Economic Freedom on Startups,” a 2021 study published in the Journal of Regional Analysis & Policy, looked at the impact of economic freedom on entrepreneurship between 2005 and 2015. Using the EFNA and the Kauffman Startup Activities Index, the authors found that increases in labor market freedom are “likely to cause significant increases in startup density (the rate at which businesses with employees are created in the economy) of entrepreneurial activities.” The authors find that minimum wage and government employment rates drive differences between states and suggest that easing labor market regulations would help increase start-up density.
In contrast, less government spending and lower tax burdens decrease startup density. Indeed, overall index scores show that an increase in economic freedom leads to drops in startup density. The authors note, however, that lower startup density is not necessarily a negative outcome. For instance, it could simply be caused by more startups surviving their first year, which would be a positive outcome.
A 2020 study in the Journal of Entrepreneurship and Public Policy, “The Impact of Labor Market Freedom on State-Level In-Migration in the US,” examined how labor market freedom affected interstate migration during the Great Recession and post-recession period from 2008 to 2016.
The authors postulate that more labor freedom suggests “greater labor-market efficiency as well as better opportunities for entrepreneurship and small business formation and success.” This in turn may induce greater in-migration. The scholars look at discrete components that determine a state’s EFNA labor market freedom score and control for economic phenomena that may otherwise impact their research output. These phenomena include state unemployment rates and the average temperature in January for each state.
They find that increasing a state’s overall labor market freedom component score by just one percentage point is associated with “a 2.8% increase in the gross in-migration rate.” In other words, a 10% increase in a state’s freedom score, say from 6 to 6.6, “would elevate the gross in-migration rate by approximately 28%.” Net in-migration was also positively associated with the overall freedom score.
Another research paper published in the same journal in 2017 and titled “The Impact of Labor Freedom on Geographic Cost of Living Differentials” finds that an increase in state labor market freedom was positively associated with a decrease in the cost of living in 2016. The authors find that cost of living “is a decreasing function of the overall degree of labor market freedom.” In other words, states with greater labor market freedom enjoy a lower cost of living. The authors assert that a one-unit increase in the state’s EFNA labor market freedom score “is found to reduce the overall cost of living by 13.9 percent.”
Digging deeper, the authors also examined the components of the EFNA’s labor market freedom index and controlled for variables that might influence these scores. They found a statistically significant positive relationship between cost of living and both government employment and union density. Better scores in these categories were associated with a lower cost of living. Specifically, they state that, on average, an increase in their measure of freedom from government employment (or “intrusion”) in the marketplace “reduces the cost of living in (a state) by 4.8%.” They found a similar drop in the cost of living (4.5%) for the union density part of the EFNA index.
A 2019 paper in the Journal of Economics and Finance, “Labor Market Freedom and Geographic Differentials in the Percentage Unemployment Rate in the U.S.,” examined the labor market freedom component of the EFNA and its three constituent parts. Using state-level data from 2008 to 2016, the author found that labor market freedom was negatively associated with the state unemployment rate, meaning states with freer labor markets had less unemployment.”
A 2018 paper, “Economic Freedom and Income Levels Across U.S. States,” pays particular attention to economic geography. The authors argue that “spatial spillovers” may influence the measured link between freedom and income. They conclude that a 10% increase in economic freedom is associated with a 5% increase in real per-capita gross state product (GDP at the state level, or GSP). Arguably more central to their paper’s research, they find that an increase in economic freedom in one state can have positive impacts on neighboring states. The authors also ran their analyses for each of the three areas in the EFNA and found a positive and significant link between labor market freedom and per-capita GSP. For every 10% rise in labor market freedom, there was an associated increase of 1.2% in state GSP.
A 2018 study in the Journal of Private Enterprise looked at all three components of the EFNA’s labor market freedom component and their relation to the cost-of-living differences between states. This has important implications for state migration. As the authors point out, other scholarship has found that as the cost of living rises, net in-migration falls. Titled “An Empirical Analysis of the Impact of the Three Labor Market Freedom Indices and Occupational Licensing on Interstate Living-Cost Differentials,” the study finds that the cost of living index for U.S. states is negatively associated with all three variables in the EFNA’s labor market freedom area. So, fewer restrictions in labor markets were associated with a lower cost of living and higher restrictions were associated with a higher cost of living.
A study from 2018 and another from 2020 tackled economic freedom and migration at the MSA level. The first, “Economic Freedom, Migration and Income Change among U.S. Metropolitan Areas,” was published in the academic journal Current Urban Studies. The author concluded that “economic freedom seems to be an important determinant of migration and have positive effects on aggregate and per-capita income.”
The study divided 361 MSAs into three groups based on their economic freedom score from 2002 — low, medium and high — and used IRS data from 2011 to 2015 to analyze the effects of economic freedom on migration and personal income changes. Those MSAs in the high freedom group saw a net, inbound migration of nearly one million people. The medium and lower categories, combined, lost about that same amount. The high freedom MSA category was associated with total income gains from net migration, while MSAs in the medium and low categories were associated with lower levels of income.
The findings on per-capita income gains were more complex. The MSAs in the high economic freedom category saw average increases in per-capita income because of migration. The lower freedom categories also had net increases in per-capita income on average as “outmigrants had, on average, lower income than stayers.”
The second economic freedom and migration-related MSA paper was titled “Economic Freedom and Migration: A Metro Area-Level Analysis.” Using data for 1993 through 2014, the authors found that “a 10% increase in economic freedom of a destination MSA, relative to the economic freedom of an origin MSA, was associated with a 27.4% increase in net migration from the origin MSA to the destination MSA.” (To put that in perspective, a 10% increase in economic freedom would move the Detroit MSA from 36th of the 53 largest MSAs to 24th, putting it on par with New Orleans.)
They found in-migration to be equivalent to adding 22 people each year from each of the other 381 MSAs in the study. Assuming that each of those migrants earn the U.S. median wage ($47,216), the authors calculate that those additional workers migrating into an MSA would represent more than $1 million in adjusted gross income every year. Note that that assumption is probably too strong. Some of those migrants will be either children or retirees, but regardless, that level of in-migration has significant implications for the level of income in an MSA.
A 2021 study by economist Daniel Bennett, “Local Economic Freedom and Creative Destruction in America,” was published in Small Business Economics: An Entrepreneurial Journal. It looked at how economic freedom encourages marketplace disruptions that lead to more businesses and jobs. Bennett used local economic freedom’s impact on dynamism of economies, and his dataset includes almost 300 cities from 1972 through 2012.
To construct his statistical model, Bennett used data from both the Metropolitan Area Economic Freedom Index (developed by Stansel) and measures of dynamism created with data from the U.S. Census Bureau’s database Business Dynamism Statistics. The dynamism data runs from 1977 through 2014. Bennett’s research, like others described here, controlled for factors that may impact his findings, in this case, related to jobs and firms. These included such things as “shares of adults that are living below the poverty line, racial minorities, female, married, immigrants, Hispanic, college graduates” and more.
After running several specifications of his model and tests to examine the strength of his findings, Bennett concluded, “[E]conomic freedom is positively associated with firm and job creation, but it has no effect on firm and job destruction.”
[*] As this paper was being prepared for print, a new review of the academic literature using the Economic Freedom of the World Index was pre-released. It reviewed more than 700 studies published between 1996 and 2022. A majority of the papers found positive results associated with economic freedom, while only 5% reported a bad outcome. Robert Lawson, “Economic Freedom in the Literature: What Is It Good (Bad) For?” (Fraser Institute, 2022), https://perma.cc/BD3G-22QD.
[†] The reader will note the authors’ early reference to the Fraser Institute’s Economic Freedom of the World. This was the institute’s first major effort to objectively measure economic freedom, and it led to the Economic Freedom of North America index and other freedom indexes. In 2014, Joshua Hall and Robert Lawson published a literature review of academic articles that use the EFNA. Of the more than 400 articles surveyed, nearly 200 used the ENFA index as a key variable. According to Hall and Roberts, more than 66% of those articles “found economic freedom to correspond to a ‘good’ outcome such as faster growth, better living standards, more happiness, etc.” They summarized: “The balance of evidence is overwhelming that economic freedom corresponds with a wide variety of positive outcomes with almost no negative tradeoffs.” Joshua C. Hall and Robert A. Lawson, “Economic Freedom of the World: An Accouting of the Literature,” Contemporary Economic Policy 32, no. 1 (2014): 1, https://perma.cc/7AKT-E645.
[‡] Jeffrey J. Yankow, “A Longitudinal Analysis of the Impact of State Economic Freedom on Individual Wages,” The Journal of Regional Analysis & Policy 44, no. 1 (2014): 64,66, https://perma.cc/JBJ2-2SV2. To help explain the magnitude of the relationship between the dependent variable (in this case, wages) and a particular independent variable (in this case, economic freedom), economists calculate what are called marginal effects. These are estimates of how much the dependent variable will change (based on the statistical analysis of the data) for a particular change in the independent variable. A common scale used for the latter is one standard deviation. The standard deviation is a statistical measure of the degree to which a set of values is spread above and below the average of those values.