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The new, book-length edition of Freedom in the 50 States: Index of Personal and Economic Freedom will be released on March 28 by the Mercatus Center at George Mason University. In the days leading up to release, I will be “teasing” a few of the novel findings and methods from the study. Here at Pileus, I’ve already posted a couple of teasers over the past few months, linked here:

This post will explain the logic and method behind the weighting scheme in the new edition. Every index of freedom has to use some way of weighting its variables to come up with an aggregate measure of freedom. The Heritage Foundation’s “Index of Economic Freedom” and Fraser Institute’s “Economic Freedom of the World” and “Economic Freedom of North America” essentially weight each variable equally, either within categories that are themselves weighted equally in the overall index (Fraser) or across the index as a whole (Heritage). The most commonly used international indices of democracy, Polity IV and Freedom House, and the first two editions of Freedom in the 50 States use “arbitrary” weights, that is, the researchers weight the categories according to their own judgment using general criteria.

We were unsatisfied with all of these approaches, as well as with inductive statistical alternatives known as “principal component analysis” and “factor analysis.” Here is how we put the case in the book:

Because we want to score states on composite indices of freedom, we need some way of “weighting” and aggregating individual policies. One popular method for aggregating policies is “factor” or “principal component” analysis, which weights variables according to how much they contribute to the common variance—that is, how well they correlate with other variables.

Factor analysis is equivalent to letting politicians weight the variables, because correlations among variables across states will reflect the ways that lawmakers systematically prioritize certain policies. Of course, partisan politics is not always consistent with freedom (e.g., states strong on gun rights tend to be weak on gay rights). The index resulting from factor analysis would be an index of “policy ideology,” not freedom.

Another approach, employed in the Fraser Institute’s “Economic Freedom of North America,” is to weight each category equally, and then to weight variables within each category equally. Of course, this approach assumes that the variance observed within each category and each variable is equally important. In the large dataset used for the freedom index, such an assumption would be wildly implausible. We feel confident that, for instance, tax burden should be weighted more heavily than court decisions mandating that private malls or universities allow political speech.

Previous versions of this index used a subjective weighting system, based on a rough assessment of the importance of each policy in terms of the number of people affected and the value they were likely to place on their infringed freedom. We were dissatisfied with the imprecise and subjective manner in which we constructed those weights, and for this edition we have tried to use a much more objective and independent measure of the “value” of each freedom.

The new, “objective” method of weighting variables is what we call the “freedom value” approach. Here is how we describe it: (more…)

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1.  I’m not a big fan of CNN but it occasionally produces an interesting piece.  This one on a surrogate who rescued a baby with birth defects from the natural parents (or so she thought!) who wanted the baby aborted is a must-read and raises a lot of interesting questions about law and ethics.  It also highlights how states are still relevant actors in our lives despite the encroachments of the federal government (and see #3 below).

2.  One of the great benefits of government spending cuts (including the sequester) is that politicians and bureaucrats have to think more seriously about trade-offs.  Of course, the sequester cuts are absolutely tiny – as Nick Gillespie at Reason nicely points out - and thus don’t pinch those folks enough.  But this piece at the USNI site notes one potential benefit – the Navy may have to reduce its efforts in support of the drug war.  Of course, the article makes it sound like the possible shift is a bad one but this is yet another war the US won’t be winning.

3.  As citizens and visitors to the Tar Heel State know too well, North Carolina has a state liquor monopoly.  In this white paper, lawyer Jeannette Doran of the NCICL “addresses whether North Carolina’s monopoly system violates the State Constitutional provision which declares and mandates: ‘monopolies are contrary to the genius of a free state and shall not be allowed.'”  Here is a nice quotation from the conclusion of this short paper:

It is dangerous to permit the State to engage in monopolistic activity. To tolerate a government-sanctioned monopoly by any entity, including the State itself, is “contrary to the genius of a free state”, according to the common sense of our Constitution. If the State is given wide discretion to monopolize spirituous liquor sales on the justification that it is doing so to protect public health and safety, there is little constitutional barrier to the monopolization of other products and services.

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Which public policies make an economy better for business? One way to answer this question is to ask businesspeople. Two recent surveys ask businesspeople to rank the American states on their friendliness toward business.

Now, libertarians often remind us that friendliness toward business is not the same as friendliness toward markets. Indeed, libertarians believe that many of their favored policies, such as abolishing trade protection, corporate welfare, and regulations that privilege big business, will redound to the benefit of workers and small business owners. What’s so interesting about these two surveys is that they are of different types of business owners: CEOs of large companies and small businesspeople. The first survey was conducted by Chief Executive magazine and the second by thumbtack.com in partnership with the Kauffman Foundation. By relating respondents’ views about the friendliness of their states to those states’ actual policies, we can see where big and small businesses agree and disagree about which policies are most important for their success.

My first step was to draw out of these survey data those numbers that relate specifically to different states’ policy environments, as opposed to other aspects of the economic climate. From the CEO survey, therefore, I took the taxation/regulation score given for each state (higher is better). From the small business survey, I took the “Regulations” component grades. Unfortunately, the small business survey does not include raw scores for each state, so I simply quantified the grades as follows: A+ = 0, A = 1, A- = 2, and so on, up to F = 11. The small business survey only covers 45 states, but for these states, the correlation between CEO and small business scores was -0.76. Since higher is better in the CEO survey and lower is better in the small business survey, that high correlation indicates a surprising degree of agreement between large and small businesses about states’ friendliness toward their businesses.

Nevertheless, there may remain some important differences in which policies large and small businesses prioritize. To get a handle on this question, (more…)

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The Institute for Justice has just released a new study of occupational licensing requirements in the 50 states and D.C. These requirements disproportionately harm low- and moderate-income people who are seeking to ply a trade.

License to Work finds that Louisiana licenses 71 of the 102 occupations, more than any other state, followed by Arizona (64), California (62) and Oregon (59). Wyoming, with a mere 24, licenses the fewest, followed by Vermont and Kentucky, each at 27. Hawaii has the most burdensome average requirements for the occupations it licenses, while Pennsylvania’s average requirements are the lightest.

Arizona leads the nation with the worst combination of number of licenses and burdensome requirements to secure those licenses, followed by California, Oregon, Nevada, Arkansas, Hawaii, Florida and Louisiana. In those eight states it takes on average a year-and-a-half of training, an exam and more than $300 to get a license, a tremendous burden for would-be entrepreneurs and workers.

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Once upon a time, local governments accounted for the lion’s share of economic policy-making in the United States. Before World War I, not only was the federal government’s economic policy-making activity strictly limited to areas such as international trade, management of federal lands, trust-busting, and food and drug regulation, but state governments themselves were also internally decentralized. In 1913, local government own-source revenues (revenues raised autonomously by local governments, thus excluding grants) as a percentage of total state and local revenues (including federal grants to state and local governments) stood at a whopping 82%, according to my calculations based on historical Census Bureau data. If we assume that revenues track economic policy activity closely, this figure implies that four-fifths of all state and local economic policy activity occurred at the local level.

Today, of course, local governments are quite limited in their economic policy autonomy, with the most important remaining policy role left largely to local governments being K-12 education. Local revenue decentralization (the variable described in the last paragraph) was just 38% in 2008. This chart shows the evolution of local revenue decentralization over time for the U.S. as a whole:

So who killed local autonomy in the U.S.? The answer is: (more…)

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I want to piggy-back here on Mark’s great post on urban planning and the poor. I’ve been playing around with some state-level data on local land-use regulations and cost of living. The last decade in the U.S. has been one of very slow productivity growth. As a result, fast-growing states tend to be those with low growth in cost of living. This explains not just states like Texas but North Dakota as well (and at the other end, California). Take a look at the list of states with highest annualized real personal income growth over 2000 to 2007 (the deflator, a state cost of living index, comes from the newest, 2009 Berry et al. data, which explains why the series ends at 2007):

1. Louisiana – 2.8%
2. Wyoming – 2.8%
3. North Dakota – 2.6%
4. South Dakota – 2.1%
5. Oklahoma – 1.9%
6. Arkansas – 1.8%
7. Mississippi – 1.5%
8. Nebraska – 1.5%
9. Montana – 1.5%
10. Kansas – 1.4%

Surprised? These are hardly “knowledge economies.” In some cases, mining or energy accounts for strong growth, and indeed in multiple regression mining share of GDP in 2000 does strongly explain subsequent real personal income growth (per capita or total). And in Louisiana’s case Hurricane Katrina chased away a lot of low-income people. But part of the story is elastic housing supply leading to low growth in house prices during the 2000-2007 bubble. A better measure of state economic success is arguably total rather than per capita income growth, which rewards states that attract people. Here are those numbers:

1. Wyoming – 3.6%
2. Nevada – 3.1%
3. Florida – 3.0%
4. South Dakota – 2.8%
5. Arizona – 2.8%
6. Arkansas – 2.6%
7. Texas – 2.6%
8. North Dakota – 2.6%
9. Oklahoma – 2.5%
10. Louisiana – 2.5%

Again, these states have in common slow growth in housing prices during the bubble. And what explains slow growth in housing prices? Land-use regulation. I use the Gyourko et al. land-use regulation variable to predict both cost of living in 2000 and growth in cost of living over 2000-2007. It is an extremely strong predictor of both (statistical significance>99.99%). When net 2000-2007 in-migration (% of 2000 population) is regressed on both 2000 cost of living and the land-use regulation index along with controls (economic and personal freedom, 2000 accommodations GDP per capita), both are highly statistically significant and negative. When total personal income growth is regressed on migration, controls, and the land-use regulation index, land-use regulation is insignificant while migration is highly significant and positive. No surprise there – land-use regulation doesn’t reduce total factor productivity, but it does discourage labor inflows.

So here’s the big story of growth in those states that have experienced it in the last decade: lack of land-use regulation –> slow growth in cost of living –> more in-migration –> more income growth. Highly regulated states like California (-0.4% annual growth), Oregon (0.1%), Massachusetts (0.1%), and New Jersey (0.3%) could learn something. If we are entering a “great stagnation,” we may have to squeeze increases in living standards out of lower prices rather than innovation for a while.

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Noel Johnson, Matt Mitchell, and Steve Yamarik have a new working paper answering that question in the affirmative. They look at state fiscal and regulatory policies and find that Democrats generally like to increase taxes and spending when in control of state houses and Republicans do the reverse. But when states have tough balanced-budget requirements called “no-carry rules,” Democrats and Republicans don’t differ much on fiscal policy. Instead they try to appeal to their constituencies by pursuing regulatory policies – in general, Democrats increasing regulation and Republicans cutting it. As the paper’s still in the working draft stage, they are looking for comments on it.

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