Tag Archives: migration

Exit, voice, and loyalty in cities

Economist Albert Hirschman’s 1970 book Exit, Voice, and Loyalty: Responses to Decline in Firms, Organizations, and States presents a theory of how consumers express their dissatisfaction to firms and other organizations after a decline in product or service quality. In terms of interjurisdictional competition exit is demonstrated by migration: dissatisfied residents migrate to a community that better matches their preferences for local government services, externality mitigation, and fiscal policy. Voice, on the other hand, requires staying in place and is usually manifested through voting. Other methods such as protests, letters, and public comments directed at officials may also be effective ways to create change.

Loyalty plays a role in whether voice or exit is employed. Someone who is loyal to a city will be less likely to exit due to a given deterioration in quality. Hirschman argues that loyalty serves an important function by limiting the use of exit and activating voice. If exit is too easy, the quality-conscious people most capable of using voice to elicit change at the local level will tend to leave early, sparking a “brain drain” and generating cumulative deterioration. If some of the most quality-conscious residents are loyal they will remain in place, at least initially, and try to fix a city’s problems from within i.e. they will use some method of voice.

The presence of loyalty within a city’s population has implications for city population decline and growth. The diagram below, based on one from Hirschman’s book on p. 90, shows the relationship between city quality and population.

Exit and loyalty diagram

Quality deteriorates as one moves up the y-axis and population increases along the x-axis, which enables a depiction of the relationship between quality and population similar to that of a traditional demand curve.

The example begins at point A. If quality declines from Q1 to Q2, the population will decline from Pa to Pb. The relatively small population decline relative to the decline in quality is due to the presence of loyalty. Loyalty can be conscious, meaning that the loyal residents are aware of the quality decline and are staying to try to improve the situation, or it can be unconscious, meaning that some residents are unaware that quality is deteriorating. These unaware residents appear loyal to outsiders, but in reality they have just not perceived the decline in quality. Perhaps the decline has not impacted their particular neighborhood or is so gradual that many people don’t realize it is happening. Hirschman notes that unconscious loyalty will not spark voice since by definition the resident is unaware that decline is occurring.

As quality continues to decline from Q2 to Q3 it becomes more observable and even the most loyal residents accept the fact that voice will not save their city. Additionally, the unconscious “loyal” residents will finally notice the decline. Both groups of people will then exit the city in order to reside somewhere else. This leads to a larger drop in population and is shown in the diagram as a movement from Pb to Pc.

This pattern is repeated as a city recovers. An initial quality improvement from Q3 to Q2 induces a relatively small amount of migration back to the city (Pc to Pd), since most people will need confirmation that the city has actually started down a path of sustainable improvement before they will return. Further improvement from Q2 to Q1 will generate a larger increase in population, represented by a movement from point D to point A (Pd to Pa).

What is interesting about this theoretical analysis is that it generates two different populations for the same level of quality. At quality Q2 the city’s population will be relatively large (Pb) if the city is declining in quality and it will be relatively small (point Pd) if the city’s quality is improving. This means that a declining city such as Detroit, Baltimore, Cleveland, Buffalo, etc. will have to make substantial quality improvements before they will see a large influx of people. So even if a city such as Cleveland returns to its 1970 level of relative quality we shouldn’t expect a drastic increase in population, as this model predicts that its population will be less than its actual 1970 population since it will be on the returning curve (CDA) rather than the exiting curve (ABC).

A city that is consistently losing population over a long period of time faces a variety of problems such as increased crime, declining housing values, a decline in the quality of public services, and higher costs in the provision of public services. Fixing these problems is often expensive and this model implies that the costs required for increasing quality from Q1 to Q2 will not result in substantial population gain, which means per capita costs to taxpayers are unlikely to decline by much and may even increase as the city begins to improve. This model predicts that revitalizing America’s struggling cities is a more difficult task than many politicians and policy makers are acknowledging.

Fixing municipal finances in Pennsylvania

Last week I was a panelist at the Keystone Conference on Business and Policy. The panel was titled Fixing Municipal Finances and myself and the other panelists explained the current state of municipal finances in Pennsylvania, how the municipalities got into their present situation, and what they can do to turn things around. I think it was a productive discussion. To get a sense of what was discussed my opening remarks are below.

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Pennsylvania is the 6th most populous state in the US – just behind IL and in front of OH – and its population is growing.

PA population

But though Pennsylvania is growing, southern and western states are growing faster. According to the US census, from 2013 to 2014 seven of the ten fastest growing states were west of the Mississippi, and two of the remaining three were in the South (FL and SC). Only Washington D.C. at #5 was in the Northeast quadrant. Every state with the largest numeric increase was also in the west or the south. This is the latest evidence that the US population is shifting westward and southward, which has been a long term trend.

Urbanization is slowing down in the US as well. In 1950 only about 60% of the population lived in an urban area. In 2010 a little over 80% did. The 1 to 4 ratio appears to be close to the equilibrium, which means that city growth can no longer come at the expense of rural areas like it did throughout most of the 20th century.

urban, rural proportion

2012 census projections predict only 0.66% annual population growth for the US until 2043. The birth rate among white Americans is already below the replacement rate. Without immigration and the higher birth rates among recent immigrants the US population would be growing even slower, if not shrinking. This means that Pennsylvania cities that are losing population – Erie, Scranton, Altoona, Harrisburg and others – are going to have to attract residents from other cities in order to achieve any meaningful level of growth.

PA city populations

Fixing municipal finances ultimately means aligning costs with revenue. Thus a city that consistently runs a deficit has two options:

  1. Increase revenue
  2. Decrease costs

Municipalities must be vigilant in monitoring their costs since the revenue side is more difficult to control, much like with firms in the private sector. A city’s revenue base – taxpayers – is mobile. Taxpayers can leave if they feel like they are not getting value for their tax dollars, an issue that is largely endogenous to the city itself, or they can leave if another jurisdiction becomes relatively more attractive, which may be exogenous and out of the city’s control (e.g. air conditioning and the South, state policy, the decline of U.S. manufacturing/the economic growth of China, Japan, India, etc.). The aforementioned low natural population growth in the US precludes cities from increasing their tax base without significant levels of intercity migration.

What are the factors that affect location choice? Economist Ed Glaeser has stated that:

“In a service economy where transport costs are small and natural productive resources nearly irrelevant, weather and government stand as the features which should increasingly determine the location of people.” (Glaeser and Kohlhase (2004) p. 212.)

Pennsylvania’s weather is not the worst in the US, but it I don’t think anyone would argue that it’s the best either. The continued migration of people to the south and west reveal that many Americans like sunnier climates. And since PA municipalities cannot alter their weather, they will have to create an attractive fiscal and business environment in order to induce firms and residents to locate within their borders. Comparatively good government is a necessity for Pennsylvania municipalities that want to increase – or simply stabilize – their tax base. Local governments must also strictly monitor their costs, since mobile residents and firms who perceive that a government is being careless with their money can and will leave for greener – and sunnier – pastures.

Fixing municipal finances in Pennsylvania will involve more than just pension reform. Act 47 was passed by the general assembly in 1987 and created a framework for assisting distressed municipalities. Unfortunately, its effectiveness is questionable. Since 1987, 29 municipalities have been placed under Act 47, but only 10 have recovered and each took an average of 9.3 years to do so. Currently 19 municipalities are designated as distressed under Act 47 and 13 of the 19 are cities. Only one city has recovered in the history of Act 47 – the city of Nanticoke. The average duration of the municipalities currently under Act 47 is 16.5 years. The city of Aliquippa has been an Act 47 city since 1987 and is on its 6th recovery plan.

Act 47 bar graphAct 47 under pie chartAct 47 recovered pie chart

The majority of municipalities that have recovered from Act 47 status have been smaller boroughs (8 of 10). The average population of the recovered communities using the most recent data is 5,569 while the average population of the currently-under communities is 37,106. The population distribution for the under municipalities is skewed due to the presence of Pittsburgh, but even the median of the under cities is nearly double that of the recovered at 9,317 compared to 4,669.

Act 47 avg, med. population

This raises the question of whether Act 47 is an effective tool for dealing with larger municipalities that have comparatively larger problems and perhaps a more difficult time reaching a political/community consensus concerning what to do.

To attract new residents and increase revenue, local governments must give taxpayers/voters/residents a reason for choosing their city over the alternatives available. Economist Richard Wagner argues that governments are a lot like businesses. He states:

“In order to attract investors [residents, voters], politicians develop new programs and revise old programs in a continuing search to meet the competition, just as ordinary businesspeople do in ordinary commercial activity.” (American Federalism – How well does it support liberty? (2014))

Ultimately, local governments in Pennsylvania must provide exceptional long-term value for residents in order to make up for the place-specific amenities they lack. This is easier said than done, but I think it’s necessary to ensure the long-run solvency of Pennsylvania’s municipalities.

Local land-use restrictions harm everyone

In a recent NBER working paper, authors Enrico Moretti and Chang-Tai Hsieh analyze how the growth of cities determines the growth of nations. They use data on 220 MSAs from 1964 – 2009 to estimate the contribution of each city to US national GDP growth. They compare what they call the accounting estimate to the model-driven estimate. The accounting estimate is the simple way of attributing city nominal GDP growth to national GDP growth in that it doesn’t account for whether the increase in city GDP is due to higher nominal wages or increased output caused by an increase in local employment. The model-driven estimate that they compare it to distinguishes between these two factors.

Before I go any further it is important to explain the theory behind the author’s empirical findings. Suppose there is a productivity shock to City A such that workers in City A are more productive than they were previously. This productivity shock could be the result of a new method of production or a newly invented piece of equipment (capital) that helps workers make more stuff with a given amount of labor. This productivity shock will increase the local demand for labor which will increase the wage.

Now one of two things can happen and the diagram below depicts the two scenarios. The supply and demand lines are those for workers, with the wage on the Y-axis and the amount of workers on the X-axis. Since more workers lead to more output I also labeled labor as L = αY, where α is some fraction less than 1 to signify that each additional unit of labor doesn’t lead to a one unit increase in output, but rather some fraction of 1 unit (capital is needed too).

moretti, land use pic

City A can have a highly elastic supply of housing, meaning that it is easy to expand the number of housing units in that city and thus it is relatively easy for people to move there. This would mean that the supply of labor is like S-elastic in the diagram. Thus the number of workers that are able to migrate to City A after labor demand increases (D1 to D2) is large, local employment increases (Le > L*), and total output (GDP) increases. Wages only increase a little bit (We > W*). In this situation the productivity shock would have a relatively large effect on national GDP since it resulted in a large increase in local output as workers moved from relatively low-productivity cities to the relatively high-productivity City A.

Alternatively, the supply of housing in City A could be very inelastic; this would be like S-inelastic. If that is the case, then the productivity shock would still increase the wage in City A (Wi > W*), but it will be more difficult for new workers to move in since new housing cannot be built to shelter them. In this case wages increase but since total local employment stays fairly constant due to the restriction on available housing the increase in output is not as large (Li > L* but < Le). If City A output stays relatively constant and instead the productivity shock is expressed in higher nominal wages, then the resulting growth in City A nominal GDP will not have as large of an effect on national output growth.

As an example, Moretti and Hsieh calculate that the growth of New York City’s GDP was 12% of national GDP growth from 1964-2009. But when accounting for the change in wages, New York’s contribution to national output growth was only 5%: Most of New York’s GDP growth was manifested in higher nominal wages. This is not surprising as it is well known that New York has strict housing regulations that make it difficult to build new housing units (the recent extension of NYC rent-control laws won’t help). This makes it difficult for people to relocate from relatively low-productivity places to a high-productivity New York.

In three of the most intensely land-regulated cities: New York, San Francisco, and San Jose, the accounting contribution to national GDP growth was 19.3%. But these cities actual contribution to national output as estimated by the authors was only 6.1%. Contrast that with the Rust Belt cities (e.g. Detroit, Pittsburgh, Cleveland, etc.) which contributed -28.5% according to the accounting method but +6.1% according to the author’s model.

The authors conclude that less onerous land-use restrictions in high-productivity cities New York, Washington D.C., Boston, San Francisco, San Jose, and the rest of Silicon Valley could increase the nation’s output growth rate by making it easier for workers to migrate from low to high-productivity areas. In an extreme migration scenario where 52% of American workers in 2009 lived in a different city than they actually did, the author’s calculate that GDP per worker would have been $8,775 higher in 2009, or $6,345 per person. In a more realistic scenario (only 20% of workers lived in a different city) it would have been $3,055 more per person: That is a substantial increase.

While I agree with the author’s conclusion that less land-use restrictions would result in a more productive allocation of labor and thus more stuff for all of us, the author’s policy prescriptions at the end of the paper leave much to be desired.  They propose that the federal government constrain the ability of municipalities to set land-use restrictions since these restrictions impose negative externalities on the rest of the country if the form of lowering national output growth. They also support the use of government funded high-speed rail to link  low-productivity labor markets to high-productivity labor markets e.g. the current high-speed rail construction project taking place in California could help workers get form low productivity areas like Stockton, Fresno, and Modesto, to high productivity areas in Silicon Valley.

Land-use restrictions are a problem in many areas, but not a problem that warrants arbitrary federal involvement. If federal involvement simply meant the Supreme Court ruling that land-use regulations (or at least most of them) are unconstitutional then I think that would be beneficial; a broad removal of land-use restrictions would go a long way towards reinstituting the institution of private property. Unfortunately, I don’t think that is what Moretti and Hsieh had in mind.

Arbitrary federal involvement in striking down local land-use regulations would further infringe on federalism and create opportunities for political cronyism. Whatever federal bureaucracy was put in charge of monitoring land-use restrictions would have little local knowledge of the situation. The Environmental Protection Agency (EPA) already monitors some local land use and faulty information along with an expensive appeals process creates problems for residents simply trying to use their own property. Creating a whole federal bureaucracy tasked with picking and choosing which land-use restrictions are acceptable and which aren’t would no doubt lead to more of these types of situations as well as increase the opportunities for regulatory activism. Also, federal land-use regulators may target certain areas that have governors or mayors who don’t agree with them on other issues.

As for more public transportation spending, I think the record speaks for itself – see here, here, and here.

Freedom in the 50 States and Migration

In last month’s publication of Freedom in the 50 StatesWill Ruger and Jason Sorens point to net domestic migration as an indicator that Americans demonstrate their preferences for more libertarian states by where they choose to live. They explain, ”

In each case, the bivariate relationship between freedom and migration is positive. However, it is strongest for fiscal freedom and weakest for personal freedom.”

The authors go on to use regression analysis to control for some of the other variables that likely cause people to move from one state to another:

We also try a regression specification including state cost of living from 2000, as estimated by political scientists William D. Berry, Richard C. Fording and Russell L. Hanson.7 This is an index variable linked to a value of 10 for the national average in 2007, the last date for which a value is available. There is some concern that this variable is endogenous to freedom. For instance, it correlates with the Wharton land-use regulation variable at r = 0.67, implying that strict land-use regulation drives up the cost of living. It also correlates with fiscal freedom at −0.35, perhaps implying that taxation can also drive up cost of living.

Finally, we also try including growth in personal income from 2000 to 2007 from the Bureau of Economic Analysis, adjusted for change in state cost of living from Berry, Fording, and Hanson. This variable is even more clearly endogenous to economic freedom, as well as to migration (more workers means more personal income). Nevertheless, we want to put the hypothesis that freedom attracts people to the strictest reasonable tests.

With this more in-depth analysis, the authors find that the three types of freedom they study — fiscal, regulatory, and personal — are all positively associated with net migration (PDF p. 97). In particular, the relationship between land use regulation and migration strikes me as an interesting one. States with the strictest land use regulations prevent in-migration by disallowing new housing development. According to Census data, New York City grew by about 2-percent between 2000 to 2010, including natural growth and foreign immigration. This is a significant slowdown from the 1990s. While the Big Apple wouldn’t be expected to attract new residents through libertarian policies, it does offer many economic and cultural opportunities that people might value. Ed Glaeser explains that by preventing new development, city- and state-level restrictions have prevented more people from being able to move to New York City:

The high prices that persist in New York City suggest that the demand for city living isn’t falling. Case-Shiller data, which captures the metropolitan area rather than the city, shows that the New York area’s prices have risen by 67 percent since 2000 (32 percent in real terms), more than any metropolitan area in the sample except Los Angeles.

But the combination of economic strength and high prices need not lead to population growth if an area doesn’t build many more units. In that case, high housing demand leads only to higher prices — not more people.

[…]

The Bloomberg administration has worked hard to allow more building, but the recent Census numbers seem to suggest that a combination of slow growth and continuing high prices implies that New York’s barriers to building, such as a complex zoning code and ever more Historic Preservation Districts, are still shutting out families that would like to move to the city.

This is just one city-level example, but New York City demonstrates that locations with the strictest land use regulations are not just discouraging in-migration with policies that limit residents’ freedom, they are also preventing people from moving to their jurisdictions by restricting growth in housing stock.

Third Edition of Freedom in the 50 States

Today the Mercatus Center released the third edition of Freedom in the 50 States by Will Ruger and Jason Sorens. In this new edition, the authors score states on over 200 policy variables. Additionally, they have collected data from 2001 to measure how states’ freedom rankings have changed over the past decade. While several organizations publish state freedom rankingsFreedom in the 50 States is the only one that measures both economic and personal freedoms.

Ruger and Sorens have implemented a new methodology for measuring freedom. While previously the authors developed a subjective weighting system in which they sought to determine how significantly policies limited the freedom of how many people, in this edition they have use a victim-cost method, assigning a dollar value to each variable that restricts freedom measuring the cost of restricting freedom for potential victims. The authors’ cost calculations are designed to measure the value of the states’ freedom for the average resident. Since individuals measure the cost of policies differently, readers can put their own price on each freedom variable on the website to find the states that best match their subjective policy preference.

In addition to an overall freedom ranking, Freedom in the 50 States includes a breakdown of states’ Fiscal Policy Ranking, Regulatory Ranking, and Personal Freedom Ranking. On the overall freedom ranking, North Dakota comes in first followed by South Dakota, Tennessee, New Hampshire, and Oklahoma.  At the bottom of the ranking, New York ranks worst by a significant margin, with rent control and burdensome insurance regulations dragging down its regulatory freedom score. New York is behind California at 49th, then New Jersey, Hawaii, and Rhode Island.

The authors note that residents respond to the costs of freedom-reducing policies by voting with their feet. Between 2000 and 2011, New York lost 9% of its population to out-migration. In addition to all types of freedom being associated with domestic migration, the authors find that regulatory freedom in particular is associated with states’ growth in personal income. They conclude:

Freedom is not the only determinant of personal satisfaction and fulfillment, but as our analysis of migration patterns shows, it makes a tangible difference for people’s decisions about where to live. Moreover, we fully expect people in the freer states to develop and benefit from the kinds of institutions (such as symphonies and museums) and amenities (such as better restaurants and cultural attractions) seen in some of the older cities on the coasts.

[…]

These things take time, but the same kind of dynamic freedom enjoyed in Chicago or New York in the 19th century — that led to their rise — might propel places in the middle of the country to be a bit more hip to those with urbane tastes.

New Edition of Rich States, Poor States out this Week

The fifth edition of Rich States, Poor States  from the American Legislative Exchange Council is now available. Utah took the top spot in the ranking of states’ economic competitiveness, as it has every year the study has been produced. Utah excels in the ranking system because it is a right-to-work state, it has a flat personal income tax, and no estate tax, among other factors considered in the study.

The other states that round out the top ten for Economic Outlook include South Dakota, Virginia, Wyoming, North Dakota, Idaho, Missouri, Colorado, Arizona, and Georgia. On the bottom end of the ranking, the states with the worst Economic Outlook are Hawaii, Maine, Illinois, Vermont, and New York at number 50 for the fourth year in a row.

Several measures of economic competitiveness offer supporting evidence that these states have some of the worst policies for business including Mercatus’ Freedom in the 50 States and the Tax Foundation’s State Business Tax Climate Index.

The authors of Rich States, Poor States, Arthur Laffer, Stephen Moore, and Jonathan Williams demonstrate Tiebout Competition in action. They find a strong correlation between the states that have high Economic Outlook rankings with the states that are experiencing the highest population growth through domestic migration. Likewise, the states that experienced the largest losses due to out-migration include Ohio and New York, ranking 37th and 50th respectively.

The study draws attention to the role that unfunded pension liabilities play for states’ future competitiveness, as this debt will require difficult and unpopular policy decisions as current tax dollars have to be used to fund past promises. Laffer, Moore, and Williams draw a comparison between Wisconsin’s recent reforms that put it on a more sustainable path compared to its neighbor Illinois:

In stark contrast to Wisconsin’s successes, the story in Illinois is not so uplifting. Over the last 10 years, Illinois legislators have continuously ignored the pension burden in their state—so much so that Illinois has one of the worst pension systems in the nation, with an estimated unfunded liability ranging from $54 billion to $192 billion, depending on your actuarial assumptions. Furthermore, the official state estimates do not include the $17.8 billion in pension obligation bond payments that are owed. In addition, Illinois policymakers have spent beyond their means, borrowed money they don’t have, and made promises to public employee unions that they cannot fulfill. Not only did Illinois face significant unfunded pension liabilities, but also lawmakers had to confront large deficits and potential cuts to state programs.

While the policies that improve state economic competitiveness are clear, the path to achieving them is difficult after voters grow accustomed to programs that their states cannot afford. However the bitter medicine of reform is worthwhile, as we know that economic freedom is not only better for business, but evidence shows it also improves individuals’ well-being.

Fastest growing cities in America

Joel Kotkin and Wendell Cox have an analysis in Forbes of new Census data about where Americans are living. They look at metro regions not only central cities. Las Vegas and Raleigh, N.C. were two of the fastest growing regions this decade due in part to job growth. Several regions in Texas are growing and are attracting people from California. Housing prices help to explain the migration of people from the northeast (Boston and New York) to Raleigh.

And Washington D.C. has “defied all market logic” as a relatively expensive area with growth. The authors suggest this is due to the “ever-expanding scope of federal government and its…growing legions of parasitic private corporations.”

For more on why other areas have lost population check out the article.

Freedom in the 50 States

Bryan Caplan likes William Ruger and Jason Sorens’s Freedom in the 50 States index. He writes:

Overall, it’s an impressive set of results.  Given the intranational mobility of labor and capital – and the ability of real estate prices to adjust – I wonder how predictive their measures will be for things like economic growth and migration.  I also suspect that states like New York and California mask the social benefits of freer policies.  Due to their big non-policy perks – focal location for New York, great weather for California – they feel like they can get away with less economic freedom – and they’re not entirely mistaken. 

Read Bryan’s full post here.

Someday I’d like to study the degree to which governments extract locational rents in the way that Bryan hypothesizes. In the meantime, I am working on a project using the Ruger and Sorens index to study a slightly different question. Stay tuned. 

Forbes’ map of intercounty migration patterns

Forbes has put together a nifty interactive map of nationwide county-by-county migrations patters. See Tiebout sorting at work! The map shows inward and outward migration numbers, and the income of the households doing the moving. To take one example, here’s what it looks like for Arizona’s Maricopa County:

Via Radley Balko.

Not Connecting the Dots

Public policy often seems that it should be intuitive. If a state needs more revenue, the easiest way to raise some is to increase taxes (easiest for elected officials, that is). Who has the most money to appropriate? Millionaires, obviously. Connect the dots, and raise taxes on millionaires.

Maryland did just that, but their experiment shows why political common sense and real life common sense are distinctly separate things. From the Wall Street Journal:

We reported in May that after passing a millionaire surtax nearly one-third of Maryland’s millionaires had gone missing, thus contributing to a decline in state revenues. The politicians in Annapolis had said they’d collect $106 million by raising its income tax rate on millionaire households to 6.25% from 4.75%. In cities like Baltimore and Bethesda, which apply add-on income taxes, the top tax rate with the surcharge now reaches as high as 9.3%—fifth highest in the nation. Liberals said this was based on incomplete data and that rich Marylanders hadn’t fled the state.

Well, the state comptroller’s office now has the final tax return data for 2008, the first year that the higher tax rates applied. The number of millionaire tax returns fell sharply to 5,529 from 7,898 in 2007, a 30% tumble. The taxes paid by rich filers fell by 22%, and instead of their payments increasing by $106 million, they fell by some $257 million.

Don’t feel sorry for the poor poor millionaires; that’s not the point I’m trying to make. Taxes are a serious driver of out-migration, be it small states like Maine, or more populous states like New Jersey:

New Jersey out‐migrants tend to move to states that have much lower property values (35% lower), property taxes (41% lower) and overall costs of living (17%lower). Destination states also have notably lower average incomes, substantially higher crime rates, higher infant and child mortality; slightly lower school quality, but somewhat warmer winters. Overall, it appears that net out‐migration is due to the high cost of living (especially the high cost of housing and property tax) in New Jersey.

Policy makers and their hangers-on have often regard taxpayers as little more than fiscal sheep, and periodically shear them. But people, unlike sheep, can vote with their wallets and feet. Usually the powers that be see this as something akin to letting the home team down, or not doing one’s “fair share.” The word “selfishness” is also thrown around.

Policies like the levels of taxes, services, and entitlements that a government prescribes are hardly a form of science. Law makers and interest groups would like to portray them as a serious commitments, and not self-interested social experiments. Again from the Journal:

Thanks in part to its soak-the-rich theology, Maryland still has a $2 billion deficit and Montgomery County is $760 million in the red. Governor Martin O’Malley’s office tells us he wants the higher rates to expire “as scheduled at the end of 2010.” But there are bills in both chambers of the legislature to extend the surcharge. The state’s best hope is that politicians in other states are as self-destructive as those in Annapolis.

The “Soak the Rich” phenomenon is a common-sense argument for redistributive policies, but it has significant flaws beyond the simple fact that it doesn’t work. Take a look at this chart of how tax burdens are distributed in Federal taxation. (here, either insert or link to this: http://www.mint.com/blog/wp-content/uploads/2009/11/MINT-TAXES-R4.png)

Libertarians and liberals can mostly agree that there is too much money and influence in politics, but the policy prescriptions each group suggests are dramatically different. Advocates of punishing the rich ignore the simple fact that when a certain group bears so much of the tax burden, they have massive incentives to care about and influence politics. It’s that or leave the country, or just stop making money (by, for instance, not hiring new employees.)

See this graphic (or click below) for a good visual explanation: