Are you an economics graduate student casting about for dissertation topics? I have a few ideas for you. As part of the rewriting of Freedom in the 50 States, I’ve been reviewing the economic literature on how various public policies affect consumer and producer surplus, deadweight loss, and so on. We use an estimate of “victim cost” for each policy to weight the policy. The weighted sum of policies comprises the freedom index. Interestingly, the literature on some public policy issues is quite thin. That makes it difficult for us to come up with weights, and sometimes we have to do no more than guess.

Without further ado, here is a list of research questions to which I for one would like to see credible answers:

  1. How productive are publicly owned hospitals relative to for-profit and charitable ones?
  2. How accurate are people in estimating how much of their state’s residents’ income is taken in state and local taxes?
  3. How do state-level regulatory takings compensation requirements affect local land-use regulation and economic outcomes such as housing prices and population growth?
  4. How does state-level eminent domain reform affect actual local eminent domain takings?
  5. How do laws forbidding employers from forbidding guns on their property affect employers’ liability insurance rates?
  6. What are the effects of state workers’ compensation laws on workers’ comp costs? What is the effect of employers’ WC coverage costs on employment and wages? How efficient are state funds relative to private insurers?
  7. What is the incidence of state short-term disability insurance coverage mandates? In other words, which workers lose, which ones win, and by how much? What is the effect on labor supply?
  8. How does mandatory paid family leave affect labor supply and wages?
  9. How do state mandatory E-Verify programs affect employment rates of natives and immigrants and aggregate economic growth? What are their administrative costs?
  10. How do state anti-discrimination laws in employment (above the federal minimum) affect lawsuits, liability insurance rates, and tort judgments against companies? How effective are they at reducing employment and wage differentials?
  11. How do state-mandated health insurance benefits affect premiums after the PPACA? How do direct access to provider mandates affect premiums and health care spending? What about standing referral mandates?
  12. How do video franchising reform and general telecom deregulation (along different dimensions) affect broadband rollout, competition, and prices?
  13. How does severity of occupational licensing (education and examination requirements especially), not just prevalence or existence of licensing, affect prices, wages, and deadweight loss?
  14. Here’s one for the political scientists: do state sunrise and sunset provisions affect occupational licensing burdens?
  15. What are the economic impacts of being inside versus outside the Nurse Licensure Compact? What are the effects of having a “nursing consultation exception” that permits some interstate practice?
  16. How does membership in the Interstate Insurance Product Regulation Compact affect the number of life insurance and annuity offerings in a state and their premiums? How does it affect wages of life insurance agents, independent versus employed? How do state form filing requirements affect these outcomes?
  17. How do certificate of need laws affect density of ambulatory surgery centers?
  18. What is the effect of state rate filing requirements for personal auto insurance and homeowners’ insurance on premiums and coverage rates (residual markets)? What about bans on certain kinds of rating, e.g., territorial?
  19. What is the effect of California’s Proposition 65 on prices of consumer products, manufacturing production, and manufacturing employment? How many products are not sold at all in California as a result of the law, and what are the economic losses?
  20. What is the effect of allowing direct-to-consumer Tesla sales on consumer surplus?
  21. What is the effect of requiring certificates of public convenience and necessity for household goods moving companies on prices, moving company profits, consumer surplus, and deadweight loss?
  22. What is the effect of general retail sales-below-cost laws on retailer margins, prices, and productivity?
  23. How effective are certain tort reforms, such as joint and several liability reform/abolition and punitive damages caps/abolition, on liability insurance costs?
  24. How does concealed carry license cost, including mandatory training, affect the number of license holders? How do various state gun regulations, such as assault weapons bans, high-capacity magazine bans, “Saturday night special” bans, licensing of gun buyers/owners, mandatory sale of locking devices, and state licensing of dealers, affect gun prices, sales, and ownership rates?
  25. How do happy hour bans affect alcohol sales and restaurant and bar profitability?
  26. How do direct-to-consumer wine shipping bans affect wine prices and sales?
  27. How does permitting wine and spirits sales in grocery stores affect sales?
  28. How does marijuana decriminalization affect consumption and producer and consumer surplus? What is the price elasticity of demand for marijuana?
  29. How do penalties for marijuana cultivation and distribution affect quantity supplied?
  30. How do Salvia divinorum bans affect consumption and producer and consumer surplus?
  31. What would Americans be willing to pay for greater privacy? E.g., not having fingerprints on driver’s licenses, not having SSN’s stored in government databases, not having to go through sobriety checkpoints, not being tracked by automated license plate readers, etc.?
  32. Do bans on home poker games affect the prevalence of such games? Do gambling penalties (felony vs. misdemeanor) affect the prevalence of illegal gambling?
  33. What is the price elasticity of demand for casino gambling? How do casino regulations/legalization affect consumer surplus?
  34. What is the size of the U.S. raw milk industry? What is the consumer and producer surplus? How would banning raw milk affect welfare?
  35. How have restaurant trans fat bans affected sales, prices, and consumer welfare?
  36. What is the consumer surplus from mixed martial arts? What are the economic effects of state legalization?
  37. How do mandatory state approval/accreditation and registration of private schools affect private school enrollment and competition? What about mandatory licensure of private school teachers?
  38. How do home school regulations affect the number of home schoolers by state?
  39. How do smoking bans in bars affect bar turnover, profitability, and competition? Believe it or not, no one has analyzed bars separately from restaurants, even though the effects on bars are hypothesized to be much larger.
  40. Do regulations on cigarette vending machines and Internet sales affect consumption?
  41. How did sodomy laws, gay marriage bans, and “super-DOMAs” affect the migration of gay couples? What can we infer about their effect on gay Americans’ well-being?
  42. How do campaign contribution limits affect would-be contributors’ welfare?

All of these policies show some variation across states, making it possible to do some research on their effects across states. Not all of them are hugely economically significant, but they are all at least somewhat controversial politically. How can legislators make informed decisions about these policies without at least rough estimates of their consequences for citizens?

In Which I Lose a Bet

Last year, I offered a bet that if an election were held this year in Catalonia, Catalan independentist parties would win a majority of valid, nonblank votes. One was, and they didn’t. The only person to take me up on the bet was Bernat Gispert, who bet me dinner next time I’m in Barcelona, hoping that he would lose. Alas, I owe him dinner now!

Why didn’t independentists win? Shortly after I offered that bet, in November 2014, support for independence declined precipitously, with a June 2015 survey from respected outfit CEO putting opposition to independence at 50-43%. I explored some reasons for that decline in another post, even as I predicted a rise in support before the September 27 election (I was right about that!).

In the rest of this post, I will explore the results from Sunday’s election in greater depth and what they imply about the views of the median voter in Catalonia. Here are the party results in votes and seats.
election results

The two independentist lists, JxS and CUP, between them won a majority of seats but only 48.0% of the valid, nonblank votes. The UDC is a Catalanist party that used to be in a longterm alliance with one of the constituent elements of the JxS list, leaving this year over the issue of independence. Their leaders have favored confederacy or freely associated state status for Catalonia, which international lawyers generally consider a form of independence. However, they opposed JxS’s roadmap to independence because it involved an illegal declaration of independence. Their campaign focused on “the power of good judgment (seny)” and was aimed at voters who might be pro-independence but are above all pro-stability and pro-business. The leader of the list has said that the UDC’s votes cannot be considered votes either for “yes” or for “no” to independence.

Catalonia Yes We Can, a radical-left list, supports a referendum on independence but is internally divided on whether Catalonia should actually become independent. The leader of their list has likewise said that their votes cannot be considered either “yes” or “no” on the issue.

Therefore, independentists are claiming that when one adds together the votes for the two independentist lists (JxS+CUP), they exceed the votes for the anti-independence lists (PSC+PPC+C’s), 48.0%-39.4%. JxS supporters, in particular, are claiming a mandate for the roadmap to independence. To their credit, the radical-left party CUP says that this is not enough for a unilateral declaration of independence. Because their seats are essential to an independentist coalition, CUP will likely be able to negotiate with JxS some amendments to the roadmap. One of those amendments is likely to be a definitive referendum, in which the Catalonian government agrees to respect whatever outcome the majority decides, declaring independence if a majority votes “yes” and shutting down the secessionist process for a generation if the majority votes “no.” Of course, Spain says such a referendum is illegal and will try to stop it by imprisoning officials, etc. It’s not clear that they’ll be able to prevent it from happening, however. Nor is it clear that the hardline unionists will boycott this vote as they did the 9-N consultation, if they know a declaration of independence will immediately follow a “yes” victory.

Do the majority of Catalans support independence? It’s impossible to be certain. Suppose half of the minor parties’ (PACMA, RC-EV, Ganemos, Pirata) electorates support independence. That adds 0.6% to the independentist total. Then add a mere 20% of the votes won by the anti-roadmap but pro-right-to-decide UDC and CSQEP. That adds another 2.3%. That extra 2.9% would give independence an extremely slender 50.9-49.1% majority. I don’t think it is plausible to think that fully half of CSQEP and UDC voters support independence.

A final issue is that of votes from Catalans abroad. The Spanish government was responsible for sending them ballots, but apparently the vast majority did not receive them. Catalans abroad are overwhelmingly pro-independence. The returns from international ballots for the Barcelona province allegedly show more than 65% voting for the independentist lists. Turnout from Catalans abroad was a mere 7%, so clearly there was some kind of snafu. The voting period for Catalans abroad has been extended to Friday, but it’s not clear this will resolve the problem for most.

How would 70% participation among the 200,000 Catalans living abroad affect the result of the election? (This is just below the 77% turnout overall for this election.) If we add 0.7*200000*0.65=91,000 votes to the independentist lists and 0.7*200000*.35=49,000 votes to the other lists, the independentists still end up with just 48.56% of valid, nonblank votes.

As a political scientist, I don’t believe in “mandates.” Electoral choices revolve around many different issues, differential turnout can affect results, voters are often ignorant of party platforms and how policies affect outcomes, and cyclical majorities and different preference intensities complicate any attempt to come up with a “will of the people” under the best of circumstances. Yet if Catalonia’s election tells us anything about the position of the median voter on independence, it is probably this: the median voter may well support independence, but not a roadmap that includes a unilateral declaration of independence (at least not yet). The new government of Catalonia, whenever it forms, would do well to proceed with caution.

On September 27, Catalonia, an “autonomous community” of Spain, votes in a regional election that will likely determine whether the region declares independence from Spain. The Economist and other global news outlets have generally not taken the movement very seriously, which is a grave mistake. According to a series of new polls, the independentists are likely to win this election, and if they do win, they will pursue a roadmap ending in a proclamation of independence within 18 months. It would be the first secession from an industrialized democracy since either Iceland (1944) or Ireland (1922), depending on how you count (Iceland had full internal self-government from 1918).

Catalan independence may well be a good outcome for the world. There are several reasons why Catalonia is likely to be more successful as an independent state than Scotland would have been.

First, Catalonia is significantly wealthier than the rest of the Spain and suffers a significant annual net fiscal drain to the Spanish treasury, on the order of 6-10% of GDP. Catalonia is also the least corrupt region of Spain.

The stock markets also suggest that independence might benefit or at least not hurt Catalonia. I examined the INDEXCAT produced by the Barcelona stock exchange, an index of all Catalan-owned, publicly traded companies on its exchange, to see how its prices responded to changes in the probability of independence. Since September 11, 2012, when the independence movement reached a popular crescendo on the streets, the INDEXCAT has grown 48.7%, compared to 26.2% for the IBEX 35 index of major Spanish firms, to 31% for the German DAX, to 24.4% for the Dow, and 21.6% for the EUROSTOXX index. This chart produced by the Catalan Business Circle shows that the fastest growth for the INDEXCAT occurred during the period when independence seemed most likely to occur, the late 2012 to late 2013 period when support for independence generally topped 55% of those expressing an opinion in yes-no questions and it still seemed possible a true referendum might be held.

Then I looked at how the INDEXCAT responded to the recent turnaround in polling for the September 27 elections. After several months of declining support for independence and independentist parties, public opinion started to turn around dramatically just two weeks ago. (Right after I predicted it would!) There have been three “polling shocks” since September 1. (The English-language Wikipedia article on these polls is rapidly and accurately updated.) The first and most significant occurred on the night of September 3, when three polls were released, all showing a pro-independence majority, after a series of July and August polls showing the independentist lists well short. We should expect investors to update their views about the likelihood of independence immediately and to trade on those views as soon as possible. Within a few minutes, the new market prices should reflect the public information. The INDEXCAT dropped just 0.31% between close on September 3 and five minutes after opening on September 4. That is consistent with a small negative impact of independence on major Catalan firms, but let’s look at the other shocks.

On the morning of September 9, a modest negative polling shock occurred, as following a string of four polls showing a clear independentist seats majority, a poll from the respected Spanish government research outfit CIS showed the slenderest of possible majorities for the independentists, just 68 out of 135 seats. It’s hard to figure out exactly when that information went public. A single tweet with the results went out at 9:00 AM exactly, but it seems to have broken an embargo, and those results weren’t confirmed until 9:30. In any event, between 9:00 and 9:10 AM, the INDEXCAT fell 0.22% and didn’t change much over the following hour. These results are consistent with a small positive impact of independence on major Catalan firms.

Finally, over last weekend a new series of polls seemingly have shown the CIS result to be an outlier, once again confirming a clear seats majority for independentists. Between market close on Friday and 9:05 AM Monday, INDEXCAT rose 0.26%. This outcome is consistent with a small positive impact of independence on major Catalan firms.

Unfortunately, I cannot calculate the expected value of independence for publicly traded Catalan firms as I did for Scottish companies, because there are no betting markets on Catalan independence or the majority in the coming election. (Unbelievable but true.) Still, on balance, the results suggest that investors expect Catalan companies to become more, not less, profitable with independence. In turn, that finding implies that the transition costs of independence are excessively hyped.

The second reason why I think Catalan independence may be good for the world is that the Spanish government has not given any concessions to Catalans to prevent them from voting for independence. To the contrary, Spain has tried to recentralize powers and has even hinted at using military force against Catalans (almost certainly a bluff). The contrast with Britain’s response to Scotland could not be stronger. If Catalans vote against independence, it would send a bad signal to Spain: that threats work to deter secessionism. Moreover, it would leave Catalonia and all the other autonomous communities vulnerable to even more severe recentralization policies. Unilateral disarmament more often invites aggression than defuses it.

The final reason why Catalan independence would be good for the world is that Spain’s existing pattern of decentralization is dysfunctional, as just about everyone recognizes. Spain’s autonomous communities racked up excessive debt during the 2000s boom and have required bailouts from the central government (PDF). Those bailouts establish a moral-hazard incentive for autonomous communities to continue profligate spending and rely on the central government for assistance when borrowing becomes difficult. Why did the autonomous communities rack up excessive debt in the first place? Stanford political scientist Jonathan Rodden has shown that when there are no external balanced-budget requirements on lower-level governments in decentralized systems, the only way to encourage fiscal discipline is to require the lower-level governments to pay for their own spending mostly out of own-source revenues and to make credible promises to let these governments go bankrupt if they cannot pay back their bondholders. The bond markets then provide fiscal discipline: subcentral governments maintain fiscal discipline because if they borrow too much, they will end up paying higher interest rates. But what happened in Spain was that the autonomous communities (with the exception of Euskadi and Navarre) had vast spending rights and responsibilities but few sources of independent income. They depended on central government grants, and thus had little incentive to spend the money responsibly. So you got things like this.

If Catalonia leaves Spain, it will be a significant fiscal shock to Spain. One relatively easy way for the Spanish central government to deal with the shock is to reduce transfers to the autonomous communities and allow them more independent taxation powers. The autonomous communities will complain about the burden-shifting, but the more nationalist communities will be happy to enjoy more fiscal autonomy. Moreover, fiscal competition between independent Catalonia and rump Spain could encourage both governments to adopt more efficient and less corrupt policies.

Catalonia isn’t a free-market paradise. For instance, the regional government passed a protectionist law limiting shop hours that the Spanish government wisely overruled. Politics throughout the Mediterranean region are toxic right now, and Catalonia is not immune. The European Central Bank’s unconscionable policies of monetary austerity have kept southern Europe in economic crisis for years, and the region’s voters have turned against wealth creation and free markets as a result. That’s a different problem with different solutions. But in the medium term, would you rather see Catalonia as part of a Spain ruled by a coalition between the corrupt left (PSOE) and the extreme left (Podemos), since the PP will lose the next election, or would you rather see an independent Catalonia in which the largest party has always been of the center-right (Convergence)?

I don’t blog much here anymore, in part because I’ve been too busy with Ethics & Economics Education, and in part because I find it easier to share quick thoughts on Twitter. Here’s a little tweetstorm I had recently on Catalonia’s independence vote next month:











In the U.S., states have full authority over local government. Some states strictly centralize power and leave local government little to do. For instance, Hawaii has a single school district for the entire state, so that different localities cannot choose to spend different amounts on the government schools. Michigan effectively has a similar system, because it requires every school district to spend the same amount of money per student and redistributes tax funds across districts to make that possible. Vermont has also centralized school funding.

At the other end of the spectrum, states like New Hampshire let local governments pretty much decide their own level of funding for schools and other programs (about half of all local spending in the U.S. goes toward schools), and towns differ widely. If you want to live in a low-tax, low-spending town or a high-tax, high-spending town, it isn’t terribly difficult to find one. In the middle are states like Texas, where local governments are responsible for their own tax and spending decisions, but the most important level of local government is the county, much larger than the town, and it is therefore difficult to choose where to live based on local taxes and services.

Can we measure how decentralized each state is? I’ve tried to do so. The first measure of decentralization looks at how important local taxes are compared to state taxes. It divides local taxes by state and local taxes put together. This is a familiar variable to scholars of “fiscal federalism,” and it is typically called “tax decentralization.” Here is how the states rank on tax decentralization, as of fiscal year 2011-12, the most recent year for which data on local taxes are available from the U.S. Census Bureau:

New Hampshire 0.62475539
Alaska 0.584999114
Texas 0.555497037
Colorado 0.5420195
New York 0.540915308
Louisiana 0.520062304
South Dakota 0.514664958
Florida 0.508526077
New Jersey 0.503867865
Georgia 0.502739018
Missouri 0.490816162
Nebraska 0.486587041
Rhode Island 0.483462474
Ohio 0.47233672
Virginia 0.468452418
Illinois 0.466955731
Wyoming 0.465238453
South Carolina 0.459566438
Maryland 0.451067476
Pennsylvania 0.449406333
Arizona 0.440699694
Iowa 0.437082825
Oregon 0.434834984
Kansas 0.434319401
Washington 0.431347838
Wisconsin 0.423486277
Tennessee 0.421965652
Utah 0.420621904
Maine 0.411333699
Massachusetts 0.398031363
Connecticut 0.397670719
Montana 0.389680799
California 0.387518844
Nevada 0.383740954
Oklahoma 0.383081024
New Mexico 0.382245601
Alabama 0.382121115
North Carolina 0.366066432
Michigan 0.361458412
Indiana 0.352963108
Kentucky 0.33512693
Idaho 0.325219717
North Dakota 0.312465478
Mississippi 0.306727915
West Virginia 0.29895431
Minnesota 0.282530032
Hawaii 0.258739008
Arkansas 0.220173834
Delaware 0.215201394
Vermont 0.152464302

This isn’t the only way we can measure decentralization, though. After all, some states have more “competing jurisdictions” from which a prospective homeowner can choose than others do. To get at this concept was a little more complicated. I first counted the number of county, municipal, and township governments for each state from the U.S. Census Bureau. Then I looked at what proportion of local taxes came from each level of government and created a weighted average of number of local governments for each state. So if a state had 100 towns, 10 counties, 0 townships, and towns raised 20% of local taxes, while counties raised 80% of local taxes, the formula for the weighted average would be 10*0.8+100*0.2. The formula “rewards” states for letting lower-level, more numerous governments raise more taxes.

Then I thought about the decision of a homeowner in choosing a government to live under. Typically, your general location is set by where you have a job, say, a metropolitan area. But there may be several jurisdictions in that metro area to choose from. So I divided the “effective number of competing jurisdictions” described in the last paragraph by the state’s privately owned land area in square miles and multiplied by 100. So the resulting variable is the effective number of competing jurisdictions per 100 square miles of privately owned land. Higher values mean there is a lot of choice among governments.

Here is how the states come out on this variable measuring choice among governments:

New Jersey 5.619216533
Massachusetts 4.644661232
Pennsylvania 4.458726121
Rhode Island 4.016477858
Connecticut 3.634408602
Vermont 3.315789474
New York 2.934484963
New Hampshire 2.529344945
Wisconsin 2.189851779
Illinois 1.823655675
North Dakota 1.699505873
Delaware 1.586429725
Ohio 1.522032431
Maine 1.515194346
South Dakota 1.21988394
Missouri 1.105963152
Iowa 1.092652689
Indiana 0.972491305
Michigan 0.968708835
Kentucky 0.818674996
Minnesota 0.789141489
Arkansas 0.724807709
West Virginia 0.709066369
Oklahoma 0.693505752
Alabama 0.684705931
Georgia 0.551970462
North Carolina 0.535369811
Tennessee 0.506450581
Maryland 0.49052107
Kansas 0.479065166
Virginia 0.475682594
Florida 0.453352937
Nebraska 0.444783283
South Carolina 0.431428983
Louisiana 0.427531008
Utah 0.382243912
Mississippi 0.375744252
Washington 0.373979057
Texas 0.326573652
California 0.301273953
Colorado 0.284535146
Idaho 0.275746556
Oregon 0.273392409
Arizona 0.094351369
New Mexico 0.087975845
Montana 0.077669113
Hawaii 0.070909413
Wyoming 0.058851844
Alaska 0.042298043
Nevada 0.036335668

In general, the northeastern states score highly, largely because of a historical legacy of strong town government.

We can multiply both variables, tax decentralization and effective number of competing jurisdictions per 100 sq mi, together to get a single measure of how decentralized each state is.

New Jersey 2.831342639
Pennsylvania 2.003779755
Rhode Island 1.941816321
Massachusetts 1.848720839
New York 1.587307839
New Hampshire 1.580221888
Connecticut 1.44529788
Wisconsin 0.927372178
Illinois 0.851566468
Ohio 0.718911807
South Dakota 0.627831517
Maine 0.623250495
Missouri 0.54282459
North Dakota 0.531036915
Vermont 0.505539528
Iowa 0.477579724
Michigan 0.350147957
Indiana 0.343253553
Delaware 0.341401889
Georgia 0.277497088
Kentucky 0.274360038
Oklahoma 0.265668894
Alabama 0.261640594
Florida 0.23054179
Minnesota 0.22295617
Virginia 0.222834661
Louisiana 0.222342761
Maryland 0.221258101
Nebraska 0.216425781
Tennessee 0.21370475
West Virginia 0.211978447
Kansas 0.208067296
South Carolina 0.198270281
North Carolina 0.195980916
Texas 0.181410696
Washington 0.161315058
Utah 0.160780162
Arkansas 0.159583693
Colorado 0.154223598
Oregon 0.118880584
California 0.116749334
Mississippi 0.115251251
Idaho 0.089678217
Arizona 0.041580619
New Mexico 0.03362838
Montana 0.030266162
Wyoming 0.027380141
Alaska 0.024744317
Hawaii 0.018347031
Nevada 0.013943484

New Jersey is the state where the taxpayer has the most choice of government. While local property taxes are generally high there, that may simply reflect the preferences of local homeowners who want to spend money on services. It would be unsurprising if there are also some local jurisdictions in New Jersey where taxes are especially low.

In general, northeastern states, which are mostly left of center and high-tax, have a heretofore unseen advantage in their fiscal systems, letting competing local governments do much or even most of the taxation, making them responsive to local property owners. Perhaps it is precisely because of that responsiveness that overall tax burdens are allowed to be high in some of these states (New Hampshire aside): homeowner voters are more content with the way government uses their tax money there.

Updated to include two scatter plots

Having examined which states have the most and least libertarians, I’ve decided to do something similar for the 239 populated towns of New Hampshire. Towns are the most important level of local government here, and therefore the degree of libertarian-ness should make some difference to policy at the town level.

The indicators I use for number of libertarians are as follows: percentage of the vote for Gary Johnson and Ron Paul (write-ins) in the 2012 presidential general election (Ron Paul won a nontrivial number of write-ins in New Hampshire); percentage of the vote for libertarianish gubernatorial candidate Andrew Hemingway in the 2014 Republican primary (he got over 37% of the vote); percentage of the vote for Ron Paul in the 2012 Republican primary; percentage of the vote for Ron Paul in the 2008 Republican primary; and the percentage of voters registered “undeclared” (independent). These are all measured at the town level.

As in my research on the states, I use principal component analysis to reduce the correlations among these variables to a single “best” variable expressing their underlying commonality. I also “weight” the observations by population, since New Hampshire has many small towns, where sampling error should be higher (lots of zeroes and high percentages in election results). In fact, weighting the observations this way yields better results, as revealed by the eigenvalue of the first extracted component.

These variables do in fact correlate with each other and all contribute positively, as expected, to the extracted component. The highest scoring coefficient goes to 2012 Paul primary vote (0.55) and the lowest to undeclared registration percentage (0.25).

UPDATE: Here are two charts of Andrew Hemingway 2014 percentage against Ron Paul 2012 percentage, by town. The first limits to towns and cities with at least 700 population, the second to towns and cities with at least 10,000 population. As you can see, the correlation is strong.



And now for the lists of most and least libertarian towns…

Top 10:

Town Score
Richmond 11.2
Grafton 9.4
Wentworth 7.4
Alexandria 6.1
Lyman 6.0
Dorchester 5.7
Marlow 5.6
Clarksville 5.3
Croydon 5.2
Benton 5.1

Most of these are in Grafton County, where I also live. They are all small and rural. The most libertarian large town (over 5000 population) is Plymouth (score of 4.5), a left-leaning college town (also in Grafton Co.). The most libertarian-leaning municipality with a city form of government is Franklin in Merrimack County (score of 2.0). Almost all of the towns where libertarian candidates are most popular are in the west, especially northwest, of the state. Three exceptions are Francestown (5.0), Mason (4.3), Hill (4.0), and New Ipswich (3.9), but even these are west of I-93, which bisects most of the state. The top town east of I-93 is Pittsfield (3.2).

Here are the bottom 10:

Dixville -5.9
Hale's Location -4.7
New Castle -3.9
Rye -3.5
Jackson -3.2
Bedford -3.1
Waterville Valley -3.1
Atkinson -3.0
Stratham -3.0
New London -2.7

Four out of these 10 are in Rockingham County on the seacoast. Dixville and Hale’s Location are truly tiny. Bedford is a staunchly Republican suburb with a population over 20,000. In fact, many of the least libertarian places are well-to-do suburbs that are strongly establishment-Republican (Bedford, New London, Hooksett, Hampstead, Windham).

Examining the towns that are right in the middle of the spectrum will give us a sense of which places are most “representative” in their libertarian-ness. Here are those, filtering down to places with more than 1000 population:

Derry 0.2
Littleton 0.2
Goffstown 0.1
Keene 0.1
Manchester 0.1
Lee 0.0
Chester 0.0
Claremont -0.0
Sandown -0.2
New Boston -0.2

Some of these are not representative of the state in a left-right sense, however. New Boston, Goffstown, Littleton, and Chester are all firmly Republican, while Keene, Lee, and Claremont are if anything even more firmly Democratic. Derry (R-leaning), Manchester (D-leaning), and Sandown (R-leaning) could be considered somewhat representative of the state.

A few years ago, I did a statistical analysis of which states had the most libertarians, using data from 2004 and 2008 Libertarian Party vote shares and 2008 Ron Paul vote shares and contributions. David Boaz has prodded me to update these numbers in light of the 2012 election. This post does just that.

To come up with a single, valid indicator of how many libertarians are in each state, I use a technique called principal component analysis (PCA), which extracts the vector of data that best explains the correlations among multiple variables. Say I have a number of different measures of the number of libertarians by state. Using PCA, I can convert those different measures into a single measure. A crude way of doing this would be to simply standardize and average all of the different variables, but that method assumes that each variable is an equally reliable measure of the underlying concept. PCA actually tells us which variables are most reliable measures and weights them more heavily.

To see which states have the most libertarians, I use six measures: Libertarian Party presidential vote share in 2008 and 2012, Ron Paul contributions as a share of personal income in 2007-8, Ron Paul and Gary Johnson contributions as a share of income in 2011-12, and “adjusted” Ron Paul primary vote share in 2008 and 2012. Ron Paul vote shares are adjusted for primary vs. caucus, calendar, number of other candidates, and the like (for details see this post). Hawaii and Wyoming are excluded because they did not collect vote shares in the 2008 presidential primary. D.C. is included.

Here are the results of the PCA on these six variables:

. pca resid12 resid08 lp12 lp08 rpcpi08 libcpi12

Principal components/correlation Number of obs = 49
Number of comp. = 6
Trace = 6
Rotation: (unrotated = principal) Rho = 1.0000

Component | Eigenvalue Difference Proportion Cumulative
Comp1 | 2.81582 1.49201 0.4693 0.4693
Comp2 | 1.32382 .517957 0.2206 0.6899
Comp3 | .805859 .266932 0.1343 0.8242
Comp4 | .538928 .0754767 0.0898 0.9141
Comp5 | .463451 .411326 0.0772 0.9913
Comp6 | .0521252 . 0.0087 1.0000

Principal components (eigenvectors)

Variable | Comp1 Comp2 Comp3 Comp4 Comp5 Comp6 | Unexplained
resid12 | 0.1159 0.7527 0.1699 0.3288 0.5308 -0.0354 | 0
resid08 | 0.3400 0.5441 0.1240 -0.3297 -0.6750 0.0934 | 0
lp12 | 0.4360 -0.1868 0.3962 -0.6239 0.4133 -0.2408 | 0
lp08 | 0.3628 -0.3001 0.6360 0.5552 -0.1895 0.1724 | 0
rpcpi08 | 0.5218 -0.0665 -0.4366 0.2925 -0.1052 -0.6604 | 0
libcpi12 | 0.5263 -0.0897 -0.4513 -0.0152 0.2117 0.6828 | 0

“Resid*” is adjusted Ron Paul vote share, “lp*” is LP vote share, and the last two variables are contributions as a share of personal income. What this output tells us is that one single component has lots of explanatory power for the correlations among these six variables: we can interpret this component as the number of libertarians in a state. The method doesn’t give us a number interpretable as an absolute count of libertarians, but a number that we can interpret as representing how many libertarians each state has compared to all the others.

The second table of output shows how each variable contributes to each component. To the first extracted component, the one of interest to us here, the contributions variables actually contribute the most, while adjusted Ron Paul vote shares, especially in 2012, contribute the least. I have found elsewhere that in 2012 Paul did really well in states with lots of liberal voters, as he expanded his base beyond libertarians to antiestablishment liberals and moderates. As a result, his cross-state performance in 2012 isn’t actually a good measure of how libertarian each state is. Still, it contributes a little something to our measure.

Here is the extracted component, with all the states ranked from most to least libertarian:

state libertarians
Montana 5.504036
New Hampshire 4.163368
Alaska 3.586032
New Mexico 3.319092
Idaho 2.842685
Nevada 2.477748
Texas 1.632528
Washington 1.568113
Oregon 1.180586
Arizona 1.0411
North Dakota 0.7316829
Indiana 0.6056806
California 0.5187439
Vermont 0.4731389
Utah 0.2056809
Colorado 0.1532149
Kansas 0.107657
South Dakota 0.0328709
Maine -0.0850015
Pennsylvania -0.2063729
Iowa -0.3226413
Georgia -0.3296589
Virginia -0.3893113
Maryland -0.4288172
Rhode Island -0.470931
Tennessee -0.4882021
Missouri -0.4912609
Arkansas -0.5384682
Louisiana -0.5897537
Nebraska -0.6350928
Minnesota -0.7662109
Michigan -0.7671053
North Carolina -0.811959
South Carolina -0.8196676
Illinois -0.9103957
Ohio -0.9599612
Delaware -1.057948
Florida -1.072601
District of Columbia -1.091851
New York -1.225912
Kentucky -1.330388
Massachusetts -1.342607
Wisconsin -1.410286
New Jersey -1.431843
Connecticut -1.606663
Alabama -1.863769
Oklahoma -1.93511
West Virginia -2.244921
Mississippi -2.519249

Mississippi and West Virginia have the fewest libertarians, while Montana and New Hampshire have the most. Note that Montana and New Mexico will be overstated on this measure, because I have added half of the Montana Constitution Party’s vote share to the Libertarian Party vote share in 2008, because they listed Ron Paul on their general election ballot. No other state had the opportunity to run Ron Paul in the general election, however, so this choice overstates how many libertarian voters are in Montana. But excluding Ron Paul from Montana’s vote share would hurt them because he presumably drew lots of votes away from Bob Barr, the LP candidate, in that state. If I do exclude Ron Paul’s votes entirely from Montana 2008, then New Hampshire ends up just pipping them for most libertarian state. New Mexico is overstated because it is Gary Johnson’s home state, who did very well there both on contributions and on vote share.

These results are quite similar to those I found back in 2010, perhaps unsurprisingly since I included 2008 data on both occasions. Still, there are some small differences. New Hampshire has now easily passed Alaska for the #2 spot. Vermont, Maine, Kentucky, and Texas have gained, while Michigan, Idaho, Indiana, and Georgia have fallen.


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