Posts Tagged ‘states’

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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Having taken on left-liberals in my last post, it’s only fair to take a shot at the right too. Here‘s the Deseret News editorializing on why our recommendations for Utah are wrong:

The report’s authors are clear about their definition of freedom. “In our view, individuals should be allowed to dispose of their lives, liberties, and properties as they see fit, as long as they do not infringe on the rights of others,” they write. But few personal behaviors can intrude more on the rights of others than drinking alcohol and gambling… [T]he enormous alcohol industry, relentlessly pushing everything from glamorous images to new products such as sweet-flavored alco-pops, would, if left unfettered, eventually rob more people of freedoms.

The line taken here seems to be that if you make bad decisions that decrease your life satisfaction, you have lost freedom (to whom?). And if you encourage someone to make a decision that might be bad, you’ve violated his rights. For the benefit of the Deseret News, I’ve compiled a new list of policy recommendations for Utah based on this new definition of freedom:

1. The enormous credit card industry gets people hooked on cheap credit, and the debt they take on means less freedom. Enact a state monopoly of credit.

2. Television and books encourage people to sit at home rather than get up and exercise, resulting in an epidemic of obesity and, of course, violating their victims’ rights. Tightly regulate their use.

3. Many people get involved in mistaken relationships when they are young, sometimes resulting in children and often resulting in heartbreak. Clearly these young lovers have taken away each other’s freedoms. Ban fornication. Fund a virtue police to monitor young couples. Iran has a system that works, at least compared to decadent, unfree societies in the West.

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Matt Yglesias throws some scorn the way of Freedom in the 50 States 2011:

Reasonable people can disagree as to whether there’s more freedom in Los Angeles or Brooklyn, and there may be good reasons to move from either place to Sioux Falls, but obviously “for the freedom” is not one of those reasons. For the lower taxes? Sure. Because there’s less government regulation? Maybe so. But because there’s more freedom? Clearly not. They say that they “explicitly ground our conception of freedom on an individual rights framework” but all that goes to show is that their understanding of the individual rights framework offers an unsound conception of freedom. These answers are clearly and uncontroversially mistaken.

Because he doesn’t propose any alternative conception of freedom, it’s unclear precisely in what way he thinks that the libertarian conception of freedom is mistaken. But it’s even more perplexing how he comes to the conclusion that the ranking “refutes” the libertarian conception of freedom. California lost 4.4% of its 2000 population over the next 9 years to other states, on net. New York lost 8.9% of its 2000 population over the next 9 years to other states, on net. New Hampshire, by contrast, enjoyed a net gain of 2.8% of its 2000 population over the same period. South Dakota’s net in-migration was 0.8%. The study finds that freer states experience more net in-migration, controlling for climate.

So let’s get this straight: People are fleeing a state with gorgeous year-round climate, world-class universities, Silicon Valley, and Hollywood and flocking to a wintry, windswept state with… the Badlands. People are fleeing a state with Wall Street, the Met, the Yankees, and Broadway for a wintry, rural state with… the Old Man of the Mountain. Wait, he’s gone now too. The omitted variable? Libertarian freedom. And that makes all the difference.

So how do libgressives define freedom? They often seem to conflate freedom and utility. (See for instance the quotes at the end of this story.) But surely a man locked in a cell hooked to an experience machine isn’t really free, is he?

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I’ve just gotten back from a Cato Institute event discussing the new study, Freedom in the 50 States, with my coauthor William Ruger, John Samples, and Michael Barone. I’ll post the video when it’s available. The Mercatus site for the study allows you to download the study and to use a calculator to see how states would change on the index if they made certain policy reforms. They’ve also put together this nice little video for the project:

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Not long after the ratification of the Constitution, Madison came to have serious doubts about his former Federalist friends. Particularly, he came to suspect the sincerity of many who had asserted that the new government would possess only those powers specifically delegated to it.

The first disappointment came with Hamilton’s championing of the incorporation of the Bank of the United States in 1791. It sparked the formation of the first party system: Federalists who supported the bank versus Republicans (not the modern party by that name) who opposed it. Madison felt especially sensitive to this issue. He remembered that the power of incorporation had come up at the Philadelphia convention. Indeed, he remembered it so well because he had been the one to move for its approval. He also recalled that it had been roundly voted down.

To Madison’s thinking, the power to incorporate was a very particular and peculiar power. At the time he had proposed its inclusion in the Constitution, he was certain it could serve important national purposes, but having been voted down, he was just as certain that no such power had been given to the general government.

Hamilton took a different view. The bank, he argued, would be of such significant utility to the collection of taxes, the paying of obligations, the administration of finance, both public and private, and of the regulation of commerce and the value of coinage, that it achieved the level of an implied power. Its necessity was established by its usefulness, and as such, it was constitutional.

To Madison that way of thinking amounted to no limits at all. By such an assumption, anything deemed useful could be done by the federal government regardless of whether or not it had been specifically written down. Where then was the promise of reserved and delegated powers?

Madison summarized his concern poignantly on the floor of the House: “With all this evidence of the sense in which the con (more…)

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At The Monkey Cage, Andrew Gelman takes issue with my post on union density and tax collections by state. I argued that states with higher percentages of workers covered by collective-bargaining contracts have higher tax collections as a percentage of personal income, and that the relationship is probably causal. Gelman argues that it is inappropriate to infer causation from a correlation among observational data. My UB colleague Phil Arena offers a qualified defense of my post.

I more or less agree with the points Phil makes, as well as Gelman’s main point. Yes, correlation does not automatically mean causation, and in my original post I moved very quickly between the two without acknowledging the difference – not the sort of thing I would do in a journal article. Nevertheless, the most natural interpretation of my results is indeed causal. It does not seem plausible that higher taxes cause higher union densities (I can think of no reason why this should be the case). On the other hand, it is quite plausible that higher union densities cause higher taxes: in my state the education unions have been lobbying heavily against spending cuts and a proposed property tax cap. What about endogeneity due to omitted variables? Well, the most plausible one would be ideology: liberal states have higher unionization rates and higher taxes. But I controlled for ideology, and indeed even “overfitted” taxation to ideology with a squared term.

Finally, the dynamic analysis showed a correlation between unionization rates in 2000 and change in tax burdens over the next eight years, although it was not quite statistically significant. But because it’s a short time period, we shouldn’t expect taxes to change all that much. Most of the dependent variable is going to be statistical noise. The effect found is also substantively impressive, even if not statistically significant, and as Ziliak and McCloskey remind us, that’s often what we really want to know.

So yes, Gelman is right that correlation doesn’t automatically imply causation, but I nevertheless contend that the most plausible interpretation of the relationship between union densities and tax levels in the states is that the former are affecting the latter.

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One of the purposes of “right to work” legislation, currently being debated in Indiana, New Hampshire, and other states, is to reduce the percentage of the workforce covered by collective bargaining agreements. Leaving aside the ethics of collective bargaining as practiced in the U.S. today, what are the political and economic consequences? Since unions donate almost exclusively to Democratic candidates and lobby heavily for more government regulation and spending, it would be unsurprising if more unionized states ended up with bigger state governments.

To examine the evidence, I ran statistical models of unionization and taxation over the 2000-2008 period. The dependent variable in the first analysis is state and local tax collections as a percentage of state personal income, excluding mineral severance and gas taxes (since large and resource-abundant states will otherwise look like states with large tax burdens), in Fiscal Year 2007-8, the latest year for which data are available. The main independent variable is (more…)

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At Volokh, Ilya Somin presents the evidence that people vote for economic freedom with their feet internationally and domestically. Pileus on inter-state migration here.

Update:  Somin has more on this issue here.  And of course, please feel free to examine the original study comparing the states cited by both Eric Crampton and Somin.

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At the NY Times‘ Economix blog, Ed Glaeser takes up explanations for the relative population growth enjoyed by Texas, Florida, Georgia, Arizona, and Nevada, compared to relative decline in New York, Massachusetts, and Connecticut. If we ignore international migration, which tends to increase the population of Mexican border states especially, and natural increase, then the population shift is really the result of inter-state migration. So why have people been leaving most of the Northeast and fleeing to the South and Southwest?

Glaeser notes two explanations: sunshine and public policy. People tend to leave cold states for warm states. But that fact alone can’t explain all or even most of the migration that we see. Here’s Glaeser on the policy explanation: (more…)

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Clearly, the recession caused state revenues to fall short of projections, opening up budget deficits. However, some states dealt with more serious fiscal problems than others. California’s, New York’s, and Illinois’ woes have been in the news quite a bit lately.

A new paper by Matt Mitchell at the Mercatus Center finds that states with more less spending as a percentage of income, more growth in spending per capita in the two decades prior, less stringent balanced budget requirements, and less economic freedom have had bigger budget gaps. From the study:

Using Jason Sorens and William Ruger’s measure of economic freedom, I found that other factors being equal, the most-economically free states tended to have budget gaps that were 25 percentage points smaller than the least-free states.

One implication of this research, it seems to me, is that federal bailouts of highly indebted states encourage more spending and less economic freedom in the future.

(Disclosure: Work on the Ruger-Sorens Index of personal and economic freedom was funded by the Mercatus Center.)

UPDATE: corrected & clarified findings on government spending.

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In the third and final installment of this series (part 1 here, part 2 here), I investigate the truth of that hackneyed Margaret Mead quotation, “Never doubt that a small group of thoughtful, committed citizens can change the world. Indeed, it is the only thing that ever has.”

Is that really true when it comes to libertarians? To recap, in parts 1 and 2 of this series, I investigated whether votes for Ron Paul, per capita donations to Ron Paul, Libertarian presidential votes in 1996-2004, and Libertarian presidential votes in 2008 all correlate together at the state level. If they do consistently track together across the states, that fact implies that there is some underlying factor explaining all of them. It turns out that they do correlate together rather strongly, and I interpret the extracted common factor as the size of the liberty bloc in each state. So the question is – does this liberty bloc have any real influence on politics, or does its minority status doom it to irrelevance?

To test the political influence of libertarians, I model state respect for individual freedom as a function of libertarian constituency, liberal constituency, political institutions, and some demographic controls. In short, I’m trying to find out whether states with more libertarians are freer. The standard and most plausible way to interpret a correlation between state ideology and policy is causal: libertarians influence the political process in their states. It is also possible that libertarians tend to move to states that are freer to begin with, but most of us are not that footloose. Many of us end up stuck in places like New York. (Cough.)

The dependent variable in this regression model is state-level freedom, including both economic and personal freedom, as measured by the “Ruger-Sorens Index” (RSI) in our study “Freedom in the 50 States.” However, I’m going to use the latest and greatest data that haven’t been published yet (next version of the study coming out in January). This is the regression equation:

“Unionization” is the percentage of workers covered by collective bargaining contracts in 1977 (I chose an early year because freedom can have reciprocal effects on unionization), “PctBlack” is the percentage of the state population that is black (the reason for including this variable is to capture well-known “racial threat” dynamics, whereby whites in states that have larger black populations are more racist), and “LegProf” is legislative professionalism, a technical term for how similar a state legislature is to Congress in terms of salary, staff, and session length. I expect states with more union members and racists to be less free, and states with well-paid legislators, large legislative staffs, and long sessions to be less free. Both the “liberal” and “libertarian” variables (defined in part 2 of this series) have been rescaled from 0 to 1.

Here are the results:

Regression with robust standard errors                 Number of obs =      50
F(  6,    43) =   23.16
Prob > F      =  0.0000
R-squared     =  0.7344
Root MSE      =  .11918

|               Robust
freedom     |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
libertarian |   .1731908   .0760284     2.28   0.028     .0198649    .3265167
liberal     |   .5440847   .2185759     2.49   0.017     .1032845    .9848849
liberalsq   |  -.8819435   .1994393    -4.42   0.000    -1.284151   -.4797358
union77     |   -.010695   .0028045    -3.81   0.000    -.0163508   -.0050391
estbkpct    |  -.0055999   .0019713    -2.84   0.007    -.0095754   -.0016244
legprof     |    -.37702   .1805852    -2.09   0.043    -.7412048   -.0128351
_cons       |   .9625205   .0842816    11.42   0.000     .7925504    1.132491

All my hypotheses are confirmed, and most interestingly, we see that states with more libertarians are freer. The effect of liberalism on freedom is less clear from a quick look at the table, so a plot will be more effective. Here is how liberalism affects freedom, when all other variables are set to their means:

So a little bit of liberalism might help, but a lot really hurts. Incidentally, I also tried including urbanization rate and percentage religious/Christian/evangelical, but none of those variables had any effect on freedom. The common perception among libertarians that more urbanized areas are less free is simply not true, once you control for things like unionization and liberal ideology.

To conclude, then, libertarians do make a difference, though not as much as liberals and conservatives make. If Idaho had only as many libertarians as Illinois, it would no longer be the fifth-freest state in the country; instead, it would be only about as free as Florida, Iowa, or North Dakota, slotting in at #11. If it had only as many libertarians as the least libertarian state, Mississippi, Idaho would be very close to Utah, around #20.


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This post is the first part of a Nate Silver-esque miniseries of posts reporting the results of statistical analysis on a macropolitics topic: the size of the “liberty constituency” in each state. Essentially, what I’m trying to estimate here is the relative percentage of the voting population in each state that would consistently prefer libertarian candidates. It’s similar to what David Boaz and David Kirby have done to estimate the “libertarian vote” nationally, but the main differences are that a) I am ranking states, not giving an absolute percentage for the nation as a whole; and b) the numbers are based on actual voting and donation behavior, rather than responses to questions about issue positions.

Readers should be careful not to interpret these results as giving a ranking of the “most libertarian states.” Any such designation would have to be based on an examination of the entire ideological distribution of voters. We cannot assume identical distributions in each state. To take an extreme example, imagine a state composed of 20% hardcore anarcho-capitalists and 80% stark raving Hitler lovers. Would this be a more or less libertarian state than one comprised of 15% moderate libertarians, 15% populists, 35% conservatives, and 35% liberals? Probably less. I’m only measuring the proportions of libertarians in each state.

The three indicators I will use are: vote percentage for libertarian candidates in the 2008 presidential general election (Bob Barr, Ron Paul in Louisiana only (where he was on the ballot), and George Phillies in New Hampshire (where he was on the ballot)); per capita donors to the Ron Paul presidential campaign (from ronpaulgraphs.com); and “adjusted” percentage vote for Ron Paul in the 2008 presidential primaries. Of course, many if not most libertarians did not vote for or donate to any of these candidates. However, the size of the libertarian constituency in each state should correlate strongly with the percentage of voters that did. That’s all we need to come up with a relative ranking of states on size of libertarian constituency.

The first step I want to take is to adjust Ron Paul’s 2008 primary results for state institutional context. Some states have caucuses or conventions rather than primaries, and of course these elections took place at different points in the electoral cycle. Ron Paul did much better in caucuses and conventions than primaries, because his supporters were particularly motivated compared to the rest of the Republican field. He also did better when turnout was lower. Two states that held conventions, Hawaii and Wyoming, do not have results available. If a state held both caucuses/conventions and a primary, I use the primary results.

I took the log of Ron Paul’s percentage of the vote in each state (plus D.C.) and regressed it on an estimate of turnout (total votes cast divided by population – an ideal denominator would be registered voters, but that would be difficult to acquire for all 50 states, and it should make very little difference to the results), a dummy variable for caucus/convention, a dummy variable for whether the election was held after McCain clinched, and the log of the number of candidates in the race. (Taking the log of the dependent variable is necessary to make it impossible for predicted vote share to fall below zero and to ensure normality. I also tested for heteroskedasticity in this regression and found no evidence of it.) These are the results:

lnrp     |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
turnout  |  -.0070837   .0221658    -0.32   0.751     -.051756    .0375885
caucus   |   1.060498   .1955968     5.42   0.000     .6662991    1.454698
clinched |   .6133622   .1627105     3.77   0.000     .2854407    .9412838
lncand   |  -.2069483   .1333964    -1.55   0.128     -.475791    .0618944
_cons    |   1.999169   .2588205     7.72   0.000     1.477551    2.520788

Controlling for everything else, turnout actually does not predict Ron Paul’s vote share, but the results demonstrate that Paul did much better in caucuses than primaries and after McCain had clinched – and perhaps when the number of candidates on the ballot was smaller, although this result is not quite statistically significant. These last two results suggest that Paul was a protest vote for some people, and/or that some rather pro-Paul voters ended up going for one of the other candidates when it might have made a difference, and an agreeable alternative candidate was in the race (for instance, some libertarians supported Fred Thompson and Rudy Giuliani).

Now that we have estimated the effects of electoral institutions, we can adjust Ron Paul’s vote shares in each state accordingly and come up with a prediction of just how “pro-Ron Paul” each state was. Let us assume that every state had the exact same electoral institutions: primary not caucus, pre-clinching, with 5 candidates in the race, and a turnout of 6.27%. These are the median values on each variable. An “average” state (right on the regression line) would be predicted to give Ron Paul 5.06% of the vote under these conditions. We can add to this each state’s residual from the regression above (and convert out of logarithms) to get the percentage of the vote that Ron Paul would have won in that state under these conditions.

Here are the results:

State Prediction
New Hampshire 11.19589
Idaho 10.77407
South Dakota 8.623769
Washington 8.184763
District of Columbia 7.824208
Montana 7.780539
Pennsylvania 7.763993
Michigan 7.200534
North Dakota 7.124513
Maine 6.846468
New York 6.721992
Maryland 6.68655
Oregon 6.681799
Vermont 6.418495
Louisiana 6.35258
California 6.294377
Tennessee 6.275179
New Mexico 6.256058
Rhode Island 6.05923
Nebraska 5.881125
Alaska 5.555577
Illinois 5.435824
Missouri 5.199485
Nevada 5.072649
Minnesota 5.00343
Arkansas 4.858305
New Jersey 4.845448
Virginia 4.657678
Wisconsin 4.583703
Texas 4.53022
Ohio 4.441385
Arizona 4.346241
South Carolina 4.294139
Delaware 4.239972
Oklahoma 4.010873
Connecticut 4.000432
Kansas 3.990187
Indiana 3.976637
Iowa 3.830344
Kentucky 3.772909
Florida 3.756037
North Carolina 3.704741
Georgia 3.390078
Utah 3.010131
Massachusetts 2.954111
West Virginia 2.91586
Alabama 2.855571
Colorado 2.666248
Mississippi 1.943739

New Hampshire and Idaho were the most pro-Ron Paul states, while Mississippi was the least. These results give us some insight into the composition of the Republican Party in each state. States with a more “establishment” bent, especially those in the South, gave fewer votes to Ron Paul, while states with more of an anti-Washington bent gave him more votes. Ron Paul’s good score in the District of Columbia helps demonstrate my point about ideological distributions. D.C. is a hostile place to libertarianism overall, but there is a small contingent of very politically aware libertarians there, and they made a noticeable mark on the (tiny) Republican primary there.

Of course, this is just one of three indicators I will use to compile an aggregate measure of size of the liberty constituency in each state. If there are some quirks in these data (I am surprised by how low Colorado scored), they should drop out when combined with other, independent measures of the concept. I will discuss how that can be done in Part 2 of the series.


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