This post will illustrate how users can customize the freedom index according to their own judgments about how various policies affect freedom. In particular, it will show how the weighting for tax burden can be significantly reduced and explores the consequences of this choice. It will also discuss briefly how abortion policies might be included in a customized index. Readers interested in customizing the freedom index should download the weighting spreadsheet at freedominthe50states.org.
The freedom index “weights” each policy variable by the dollar-terms amount of benefit received by victims of government intervention from a one-standard-deviation, nationwide shift in the variable in a freer direction. So the weight for taxation is simply the number of dollars represented by a one-standard-deviation shift in state and local tax burden as a percentage of personal income. The mean of tax burden is 0.095 (9.5% of personal income). The standard deviation is 0.0124. Therefore, the weight of the variable in the index is 0.0124 times national personal income, which was $12.357 trillion in 2010: $153.1 billion. That ends up being worth 28.6% of the total weights for all variables in the index.
That’s a lot. The numbers don’t lie, but we do note in the text one reason why this number may actually overestimate the true “loss of freedom” caused by taxation:
This index’s weight for tax burden assumes that all taxes take away freedom. But in fact some taxpayers consent to at least some of the taxes that they pay, as long as the taxes are legal and generally paid by others. Therefore, taxation is not wholly a violation of their freedom, as “freedom” is defined above. However, most criminal justice policies do not operate along these lines. For instance, an imprisoned drug possessor is no more likely to consent to being confined if others are as well, and a driver fined for not wearing a seat belt does not usually consent to being fined if others are, and so on.
Rather than trying to figure out how much of the observed taxation truly represents a diminution of freedom, this study uses aggressive estimates of the value of freedom from taxation and other fiscal policy measures, and then boosts the weighting of certain personal freedoms and economic regulations, as explained in the relevant sections below. The point is to make sure that the index is using an equally aggressive method for estimating the values of all the different freedoms it covers.
Now, one might believe that we have not gone far enough to adjust for this problem, and indeed that is the whole point of putting the spreadsheet online and encouraging reader customization. The freedom index as it currently stands is in some ways a libertarian’s index. If you think that all taxation diminishes freedom, you will like the weight it enjoys in the published study.
But what if you are a philosophically sophisticated progressive or “liberaltarian,” who does not have any personal issue with taxation, but who nevertheless thinks that negative liberty is part of justice, and that the costs that others associate with taxation are worth taking into account. What weight should you put on tax burden?
Let’s assume that the current tax burden in each state represents the ideal point of the median voter. Positive theories of democracy would suggest that this is as good a guess about where public opinion lies as any. Then 50% of voters would prefer a higher tax burden (and the services it would finance), and 50% would prefer a lower tax burden. Right away, we can slash the tax burden weight in half, because 50% of voters nationally would not see the taxes they currently pay as any diminution of their freedom at all. Now, this move assumes that the median-dollar taxpayer is the same as the median voter. That is unlikely to be the case. In fact, the median-dollar taxpayer is likely to be somewhat wealthier than the median voter and thus more ideologically conservative and more hostile to taxation. Thus, if anything, slashing tax burden in half on these grounds is somewhat too aggressive.
But we’re not done yet. Of the 50% of voters/taxpayers who would prefer a lower tax burden, most of them would not see all of the taxes they pay as a diminution of their freedom. That is, they would be fully willing to pay a lower tax burden that is greater than zero. To illustrate the logic, assume a normal probability density function over possible tax burdens, as follows:
On the X axis is tax burden, and on the Y axis is the proportion of the population corresponding to a particular view on tax burden. Fifty percent of the curve lies to the left or right of the mean of the tax burden distribution, which is 9.5, the actual national mean of state and local tax burden. (I have drawn the curve under the assumption of a standard deviation of 2.375, a fourth of the mean, but nothing that follows hinges on this assumption. Note that the standard deviation of voters’ views on taxation should be significantly greater than the standard deviation of actual state tax burdens, because each state tax burden roughly represents a median of a distribution.)
Now, what are the losses experienced by those who prefer a lower tax burden than what currently exists in their state? The loss curve will look like a mirror image of the left side of the normal density function. Those who want zero taxation will see all 9.5% of income taxed away as a loss of freedom. Those who want taxation of 2.5% of income will see 7% of income taxed away as a loss of freedom. And so on. Because the loss function is a mirror image of the probability density function, the area under the loss curve is also 0.5. So only 4.75% of personal income, in total, is a loss to those who prefer lower taxation. We can divide tax burden’s weight by two again, or by four in total.
The way to do this in the weighting spreadsheet is as follows. On the 2001-2011 worksheet, you can find all the standard deviations and weights of the variables in column GW. The weight for tax burden (“ainctot3″) is in cell GW10. You can divide the value there by four to create a new weight. All the other weighting cells automatically recalculate, and you now see in cell GV10 that tax burden is now worth just 9.19% of the index. (Why not one-fourth of 28%? Because reducing taxation’s weight also reduces the sum of all weights.) Fiscal policy as a whole is now worth just 17% of overall freedom, while personal freedom is 42%, and regulatory policy is 41%.
Note that all of the measures we took to boost personal freedom in the study remain in place, so this approach really does aggressively reduce the importance of taxation. I’ll call this new, nerfed-taxation index “Sandals,” as contrasted with the published index, which I’ll call “Suits.” How do the rankings of states differ between “Suits” and “Sandals”? See the table below.
|1. North Dakota||1. North Dakota|
|2. South Dakota||2. Indiana|
|3. Tennessee||3. New Hampshire|
|4. New Hampshire||4. Tennessee|
|5. Oklahoma||5. Nevada|
|6. Idaho||6. South Dakota|
|7. Missouri||7. Utah|
|8. Virginia||8. Iowa|
|9. Georgia||9. Delaware|
|10. Utah||10. Georgia|
|11. Arizona||11. Idaho|
|12. Montana||12. Nebraska|
|13. Alaska||13. Virginia|
|14. Texas||14. Missouri|
|15. South Carolina||15. Kansas|
|16. Indiana||16. Arizona|
|17. Delaware||17. Colorado|
|18. Alabama||18. Oklahoma|
|19. Colorado||19. North Carolina|
|20. Nevada||20. Alaska|
|21. New Mexico||21. Maine|
|22. Nebraska||22. Texas|
|23. Florida||23. South Carolina|
|24. North Carolina||24. Minnesota|
|25. Iowa||25. Wyoming|
|26. Kansas||26. Massachusetts|
|27. Kentucky||27. Oregon|
|28. Oregon||28. Montana|
|29. Washington||29. Florida|
|30. Massachusetts||30. Ohio|
|31. Pennsylvania||31. Pennsylvania|
|32. Arkansas||32. Wisconsin|
|33. Ohio||33. New Mexico|
|34. Minnesota||34. Kentucky|
|35. Michigan||35. Vermont|
|36. Wyoming||36. Washington|
|37. Louisiana||37. Michigan|
|38. Wisconsin||38. Connecticut|
|39. Maine||39. Arkansas|
|40. Connecticut||40. Alabama|
|41. Mississippi||41. Rhode Island|
|42. West Virginia||42. Louisiana|
|43. Vermont||43. Maryland|
|44. Maryland||44. West Virginia|
|45. Illinois||45. Hawaii|
|46. Rhode Island||46. Illinois|
|47. Hawaii||47. Mississippi|
|48. New Jersey||48. New Jersey|
|49. California||49. California|
|50. New York||50. New York|
The two rankings still look pretty similar! Three of the same states are in the top five in both indices, and the bottom three are identical as well. Indiana moves up from #16 to #2 between “Suits” and “Sandals,” and Nevada moves up from #20 to #5. Meanwhile, Oklahoma falls from #5 to #18, and Alabama falls from #18 to #40. But those are some of the biggest changes in rank; most states stay in a pretty similar location. It turns out that even a left-leaning index of negative liberty puts red and purple states at the top and deep blue states at the bottom.
Abortion policies have to be imported from another spreadsheet in order to be included in the freedom index. A little more Excel mastery is helpful here. The abortion policy spreadsheet is available at statepolicyindex.com (p_abor_11.xls).
Now, there are a few things to note about state abortion laws. Most state abortion laws that are actually enforced do not do much to limit first- and second-trimester abortions. Because of Roe v. Wade, states do not have the right to prohibit abortions before fetal viability. However, some abortion policies we code, like requiring that only licensed physicians perform abortions, requiring that abortions be performed in a hospital, restricting private insurance coverage of abortions, and imposing waiting periods for abortions, can raise the effective cost of getting even an early abortion. Some pro-choicers, particularly libertarians, might well see certain state restrictions, such as prohibiting Medicaid funding for abortions, restricting partial-birth and late-term abortions, and requiring parental notification for minors’ abortions, as justifiable.
The variable “pabor” gives a summary indicator of state abortion laws based on principal component analysis. It is available only for 2006-2010 because one of the constituent variables is unavailable for 2000. States scoring higher on “pabor” have more abortion restrictions, including limits on public funding. To insert the variable into the freedom index, simply create two new rows in the freedom index spreadsheet and paste the “pabor” values into the first row (values/transpose). Since abortion laws affect personal freedoms on any interpretation, you may wish to include abortion policies with the personal freedoms, for instance on rows 139 and 140. You may wish to carry 2006/7 values back to 2001.
Next, you need to adjust the raw values of “pabor” to put them on a standardized scale with other variables. Every other row of the spreadsheet consists of these adjusted values. The adjusted values lie right below the raw values of each policy variable. If you think fewer abortion restrictions enhance freedom, then you think that higher values on “pabor” are worse. Find another variable like that — “tpubfin” is an example on rows 125-126. You can copy and paste the formula for adjusted “tpubfin” values to adjust the “pabor” values. If you think fewer abortion restrictions threaten freedom, then you think that higher values on “pabor” are better. Find another variable like that — “tgprp” on rows 133-134 is an example. Copy and paste the “adjusted” row.
Next, make sure that the mean and standard deviation of the variable are calculated in columns GV and GW. Below the mean and standard deviation are the weights. For the purposes of this exercise, I’ll give abortion a weight equal to same-sex partnerships, about $10.4 billion. Make sure that the percentage weight is calculated in column GV by copying and pasting one of the bolded percentage weights from another variable (it doesn’t matter which). Also make sure that the summed weights is updated by changing the formula at the bottom of column GW (row 243 after inserting two rows for abortion). Make sure that the dollar weight for abortion laws is included.
Finally, update the personal freedom scores. For instance, go into GU143 and type at the end of the parenthetical expression: “+GU140*$GV140″ (without quotes). That updates Wyoming’s score. Then just drag the formula all the way to the left. Personal freedom scores are all updated, and overall freedom updates automatically.
Now what does the freedom ranking look like? I’ve taken the steps to create a pro-choice ranking that also nerfs taxation. Here it is:
|1. New Hampshire|
|2. North Dakota|
|7. South Dakota|
|19. North Carolina|
|24. South Carolina|
|29. New Mexico|
|41. Rhode Island|
|42. West Virginia|
|48. New Jersey|
|50. New York|
Not all that different. I’ve taken all the assumptions most favorable to a “liberaltarian” conception of negative liberty, and most states do not jump or fall very many places in the ranking. I don’t say this to tweak liberaltarians, but to point out how robust the freedom ranking is to even drastic changes of assumptions. It’s such a big dataset that seemingly big changes have small effects on the end result. New York, California, and New Jersey really are the most regulated states, no matter how you slice it. The Dakotas, Tennessee, and New Hampshire really are among the least regulated states. “Conservatarians” may be distressed by the low placement of states like Mississippi, West Virginia, and Louisiana in the published index. My guess is that the freedom ranking will be equally robust to changes in more right-wing direction, such as by nerfing many of the bonuses we gave to personal freedom variables, including abortion restrictions as a plus for freedom, and so on.
Although the freedom index is reasonably robust to changing assumptions about which freedoms matter how much, we still encourage readers to tinker with customizing the index. For one thing, very radical changes may well have radical effects. If you are interested in marijuana laws and business regulations but not at all in taxation, gun laws, or tobacco laws, your freedom index might look quite different after all. Our freedom index is tailored to the “average American” adversely affected by government intervention, but the “average American” is a statistical construct that probably corresponds to no actual person.