Tag Archives: growth

City population dynamics since 1850

The reason why some cities grow and some cities shrink is a heavily debated topic in economics, sociology, urban planning, and public administration. In truth, there is no single reason why a city declines. Often exogenous factors – new modes of transportation, increased globalization, institutional changes, and federal policies – initiate the decline while subsequent poor political management can exacerbate it. This post focuses on the population trends of America’s largest cities since 1850 and how changes in these factors affected the distribution of people within the US.

When water transportation, water power, and proximity to natural resources such as coal were the most important factors driving industrial productivity, businesses and people congregated in locations near major waterways for power and shipping purposes. The graph below shows the top 10 cities* by population in 1850 and follows them until 1900. The rank of the city is on the left axis.

top cities 1850-1900

 

* The 9th, 11th, and 12th ranked cities in 1850 were all incorporated into Philadelphia by 1860. Pittsburgh was the next highest ranked city (13th) that was not incorporated so I used it in the graph instead.

All of the largest cities were located on heavily traveled rivers (New Orleans, Cincinnati, Pittsburgh, and St. Louis) or on the coast and had busy ports (New York, Boston, Philadelphia, Brooklyn, and Baltimore). Albany, NY may seem like an outlier but it was the starting point of the Erie Canal.

As economist Ed Glaeser (2005) notes “…almost every large northern city in the US as of 1860 became an industrial powerhouse over the next 60 years as factories started in central locations where they could save transport costs and make use of large urban labor forces.”

Along with waterways, railroads were an important mode of transportation from 1850 – 1900 and many of these cities had important railroads running through them, such as the B&O through Balitmore and the Erie Railroad in New York. The increasing importance of railroads impacted the list of top 10 cities in 1900 as shown below.

top cities 1900-1950

A similar but not identical set of cities dominated the urban landscape over the next 50 years. By 1900, New Orleans, Brooklyn (merged with New York) Albany, and Pittsburgh were replaced by Chicago, Cleveland, Buffalo, and San Francisco. Chicago, Cleveland, and Buffalo are all located on the Great Lakes and thus had water access, but it was the increasing importance of railroad shipping and travel that helped their populations grow. Buffalo was on the B&O railroad and was also the terminal point of the Erie Canal. San Francisco became much more accessible after the completion of the Pacific Railroad in 1869, but the California Gold Rush in the late 1840s got its population growth started.

As rail and eventually automobile/truck transportation became more important during the early 1900s, cities that relied on strategic river locations began to decline. New Orleans was already out of the top 10 by 1900 (falling from 5th to 12th) and Cincinnati went from 10th in 1900 to 18th by 1950. Buffalo also fell out of the top 10 during this time period, declining from 8th to 15th. But despite some changes in the rankings, there was only one warm-weather city in the top 10 as late as 1950 (Los Angeles). However, as the next graphs shows there was a surge in the populations of warm-weather cities during the period from 1950 to 2010 that caused many of the older Midwestern cities to fall out of the rankings.

top cities 1950-2010

The largest shakeup in the population rankings occurred during this period. Out of the top 10 cities in 1950, only 4 (Philadelphia, Los Angeles, Chicago, and New York) were still in the top 10 in 2010 (All were in the top 5, with Houston – 4th in 2010 – being the only city not already ranked in the top 10 in 1950, when it was 14th). The cities ranked 6 – 10 fell out of the top 20 while Detroit declined from 5th to 18th. The large change in the rankings during this time period is striking when compared to the relative stability of the earlier time periods.

Economic changes due to globalization and the prevalence of right-to-work laws in the southern states, combined with preferences for warm weather and other factors have resulted in both population and economic decline in many major Midwestern and Northeastern cities. All of the new cities in the top ten in 2010 have relatively warm weather: Phoenix, San Antonio, San Diego, Dallas, and San Jose. Some large cities missing from the 2010 list – particularly San Francisco and perhaps Washington D.C. and Boston as well – would probably be ranked higher if not for restrictive land-use regulations that artificially increase housing prices and limit population growth. In those cities and other smaller cities – primarily located in Southern California – low population growth is a goal rather than a result of outside forces.

The only cold-weather cities that were in the top 15 in 2014 that were not in the top 5 in 1950 were Indianapolis, IN (14th) and Columbus, OH (15th). These two cities not only avoided the fate of nearby Detroit and Cleveland, they thrived. From 1950 to 2014 Columbus’ population grew by 122% and Indianapolis’ grew by 99%. This is striking compared to the 57% decline in Cleveland and the 63% decline in Detroit during the same time period.

So why have Columbus and Indianapolis grown since 1950 while every other large city in the Midwest has declined? There isn’t an obvious answer. One thing among many that both Columbus and Indianapolis have in common is that they are both state capitals. State spending as a percentage of Gross State Product (GSP) has been increasing since 1970 across the country as shown in the graph below.

OH, IN state spending as per GSP

In Ohio state spending growth as a percentage of GSP has outpaced the nation since 1970. It is possible that increased state spending in Ohio and Indiana is crowding out private investment in other parts of those states. And since much of the money collected by the state ends up being spent in the capital via government wages, both Columbus and Indianapolis grow relative to other cities in their respective states.

There has also been an increase in state level regulation over time. As state governments become larger players in the economy business leaders will find it more and more beneficial to be near state legislators and governors in order to lobby for regulations that help their company or for exemptions from rules that harm it. Company executives who fail to get a seat at the table when regulations are being drafted may find that their competitors have helped draft rules that put them at a competitive disadvantage. The decline of manufacturing in the Midwest may have created an urban reset that presented firms and workers with an opportunity to migrate to areas that have a relative abundance of an increasingly important factor of production – government.

Can historic districts dampen urban renewal?

Struggling cities in the Northeast and Midwest have been trying to revitalize their downtown neighborhoods for years. City officials have used taxpayer money to build stadiums, construct river walks, and lure employers with the hope that such actions will attract affluent, tax -paying residents back to the urban core. Often these strategies fail to deliver but that hasn’t deterred other cities from duplicating or even doubling down on the efforts. But if these policies don’t work, what can cities do?

Part of the answer is to allow more building, especially newer housing. One factor that may be hampering the gentrification efforts of many cities is the age of their housing stock. The theory is straightforward and is explained and tested in this 2009 study. From the abstract:

“This paper identifies a new factor, the age of the housing stock, that affects where high- and low-income neighborhoods are located in U.S. cities. High-income households, driven by a high demand for housing services, will tend to locate in areas of the city where the housing stock is relatively young. Because cities develop and redevelop from the center outward over time, the location of these neighborhoods varies over the city’s history. The model predicts a suburban location for the rich in an initial period, when young dwellings are found only in the suburbs, while predicting eventual gentrification once central redevelopment creates a young downtown housing stock.”

In the empirical section of the paper the authors find that:

… a tract’s economic status tends to fall rather than rise as distance increases holding age fixed, suggesting that high-income households would tend to live near city centers were it not for old central housing stocks.” (My bold)

This makes sense. High income people like relatively nicer, newer housing and will purchase housing in neighborhoods where the housing is relatively nicer and newer. In the latter half of the 20th century this meant buying new suburban homes, but as that housing ages and new housing is built to replace the even older housing in the central city high income people will be drawn back to central city neighborhoods. This has the power to reduce the income disparity between the central city and suburbs seen in many metropolitan areas. As the authors note:

Our results show that, if the influence of spatial variation in dwelling ages were eliminated, central city/suburban disparities in neighborhood economic status would be reduced by up to 50 percent within American cities. In other words, if the housing age distribution were made uniform across space, reducing average dwelling ages in the central city and raising them in the suburbs, then neighborhood economic status would shift in response, rising in the center and falling in the suburbs. (My bold)

To get a sense of the age of the housing stock in northern cities, the figure below depicts the proportion of housing in eight different age categories in Ohio’s six major cities as of 2013 (most recent data available, see table B25034 here).

age of ohio's housing stock

The age categories are: built after 2000, from 1990 and 1999, from 1980-89, from 1970-79, from 1960-69, from 1950-59, from 1940-49, and built prior to 1939. As the figure shows most of the housing stock in Ohio’s major cities is quite old. In every city except for Columbus over 30% of the housing stock was built prior to 1939. In Cleveland, over 50% of the housing stock is over 75 years old! In Columbus, which is the largest and fastest growing city in Ohio, the housing stock is fairly evenly distributed across the age categories. Columbus really stands out in the three youngest categories.

In a free market for housing old housing would be torn down and replaced by new housing once the net benefits of demolition and rebuilding exceed the net benefits of renovation. But anyone who studies the housing market knows that it is hardly free, as city ordinances regulate everything from lot sizes to height requirements. While these regulations restrict new housing, they are a larger problem in cities where demand for housing is already high since they artificially restrict supply and drive up prices.

A potentially bigger problem for declining cities that has to do with the age of the housing stock is historic districts. In historic districts the housing is protected by local rules that limit the types of renovations that can be undertaken. Property owners are required to maintain their home’s historical look and it can be difficult to demolish old houses.

For example, in Dayton, OH there are 20 historic districts in a city of only 142,000 people. Dayton’s Landmark Commission is charged with reviewing and approving major modifications to the buildings in historic districts including their demolition.  Many of the districts are located near the center of the city and contain homes built in the late 1800s and early 1900s. Some are also quite large; St. Anne’s Hill contains 315 structures and the South Park historic district covers 24 blocks and contains more than 700 structures. The table below provides a list of Dayton’s historic districts as well as the year they were classified, number of structures, acreage, and whether the district is a locally protected district. Seventy percent of the districts are protected by a local historic designation while 30 percent are only protected by the national designation.

dayton historic districts table

I personally like old houses, but I also recognize that holding on to the past can interfere with revitalization and growth. Older homes, especially those built prior to 1940, are expensive to restore and maintain. They often have old or outdated plumbing systems, electrical systems, and inefficient windows that need to be replaced. They may also contain lead paint or other hazardous materials that were commonly used at the time they were built which may have to be removed. Many people can’t afford these upfront costs and those that can often don’t want to deal with the hassle of a restoration project.

Also, people have different tastes and historic districts make it difficult for some people to live in the house they want in the area they want. As this map shows, many of the Dayton’s historic districts are located near the center of the city in the most walkable, urban neighborhoods. The Oregon district and St. Anne’s Hill are both quite walkable and contain several restaurants, bars, and shops. If a person wants to live in one of these neighborhoods they have to be content with living in an older house. The design restrictions that come standard with historic districts prevent people with certain tastes from locating in these areas.

A 2013 study that examined the Cleveland housing market determined that it is economical to demolish many of the older, vacant homes in declining cities rather than renovate them. This is just as true of older homes that happen to be in historic districts.

Ultimately homeowners should be free to do what they want with their home and the land that it sits on. If a person wants to buy a historic house and renovate it they should be free to do so, but they should also be allowed to build a new structure on the property if they wish. When a city protects large swathes of houses via historic districts they slow down the cycle of housing construction that could draw people back to urban neighborhoods. This is especially true if the historic districts encompass the best areas of the city, such as those closest to downtown amenities and employment opportunities. Living in the city is appealing to many people, but being forced to purchase and live in outdated housing dampens the appeal for some and may be contributing to the inability of cities like Dayton to turn the corner.

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.

The cost disease and the privatization of government services

Many US municipalities are facing budget problems (see here, here, and here). The real cost of providing traditional public services like police, fire protection, and education is increasing, often at a rate that exceeds revenue growth. The graph below shows the real per-capita expenditure increase in five US cities from 1951 to 2006. (Data are from the census file IndFin_1967-2012.zip and are adjusted for inflation using the US GDP chained price index.)

real per cap spend

In 1951 none of the cities were spending more than $1,000 per person. In 2006 every city was spending well over that amount, with Buffalo spending almost $5,000 per person. Even Fresno, which had the smallest increase, increased per capita spending from $480 to $1,461 – an increase of 204%. Expenditure growth that exceeds revenue growth leads to budget deficits and can eventually result in cuts in services. Economist William Baumol attributes city spending growth to what is known as the “cost disease”.

In his 1967 paper, Baumol argues that municipalities will face rising costs of providing “public” goods and services over time as the relative productivity of labor declines in the industries controlled by local governments versus those of the private sector. As labor in the private sector becomes more productive over time due to increases in capital, wages will increase. Goods and services traditionally supplied by local governments such as police, fire protection, and education have not experienced similar increases in capital or productivity. K-12 education is a particularly good example of stagnation – a teacher from the 1950s would not confront much of a learning curve if they had to teach in a 21st century classroom. However, in order to attract competent and productive teachers, for example, local governments must increase wages to levels that are competitive with the wages that teachers could earn in the private sector. When this occurs, teacher’s wages increase even though their productivity does not. As a result, cities end up paying more money for the same amount of work. Baumol sums up the effect:

“The bulk of municipal services is, in fact, of this general stamp [non-progressive] and our model tells us clearly what can be expected as a result…inexorably and cumulatively, whether or not there is inflation, administrative mismanagement or malfeasance, municipal budgets will almost certainly continue to mount in the future, just as they have been doing in the past. This is a trend for which no man and no group should be blamed, for there is nothing than can be done to stop it.” (Baumol, 1967 p.423)

But is there really nothing than can be done to cure the cost disease? Baumol himself later acknowledged that innovation may yet occur in the relatively stagnant sectors of the economy such as education:

“…an activity which is, say, relatively stagnant need not stay so forever. It may be replaced by a more progressive substitute, or it may undergo an outburst of innovation previous thought very unlikely.” (Baumol et al. 1985, p.807).

The cure for the cost disease is that the stagnant, increasing-cost sectors need to undergo “an outburst of innovation”. But this raises the question; what has prevented this innovation from occurring thus far?

One thing that Baumol’s story ignores is public choice. Specifically, is the lack of labor-augmenting technology in the public-sector industries a characteristic of the public sector? The primary public sector industries have high rates of unionization and the primary goal of a labor union is to protect its dues-paying members. The chart below provides the union affiliation of workers for several occupations in 2013 and 2014.

union membership chart

In 2014, the protective service occupations and education, training, and library occupations, e.g. police officers and teachers, had relatively high union membership rates of 35%. Conversely, other high-skilled occupations such as management, computer and mathematical occupations, architecture and engineering occupations, and sales and office occupations had relatively low rates, ranging from 4.2% to 6.5% in 2014. Installation, maintenance, and repair occupations were in the middle at 14.6%, down from 16.1% in 2013.

The bottom part of the table shows the union membership rate of the public sector in general and of each level of government: federal, state, and local. The highest rate of unionization was at the local level, where approximately 42% of workers were members of a union in 2014, up from 41% in 2013. This is about 14 percentage points higher than the federal level and 12 percentage points higher than the state level. The union membership rate of the private sector in 2014 was only 6.6%.

In addition to the apathetic and sometimes hostile view unions have towards technological advancement and competition, union membership is also associated with higher wages, particularly at the local-government level. Economists Maury Gittleman and Brooks Piece of the Bureau of Labor statistics found that local-government workers have compensation costs 10 – 19% larger than similar private sector workers.

The table below shows the median weekly earnings in 2013 and 2014 for workers in the two most heavily unionized occupational categories; education, training, and library occupations and protective service occupations. In both occupation groups there is a substantial difference between the union and non-union weekly earnings. From the taxpayer’s perspective, higher earnings mean higher costs.

union median wage chart

There needs to be an incentive to expend resources in labor-saving technology for it to occur and it is not clear that this incentive exists in the public sector. In the public sector, taxpayers ultimately pay for the services they receive but these services are provided by an agent – the local politician(s) – who is expected to act on the taxpayer’s behalf when it comes to spending tax dollars. But in the public sector the agent/politician is accountable to both his employees and the general taxpayer since both groups vote on his performance. The general taxpayer wants the politician to cut costs and invest in labor-augmenting technology while the public-employee taxpayer wants to keep his job and earn more income. Since the public-employee unions are well organized compared to the general taxpayers it is easier for them to lobby their politicians/bosses in order to get their desired outcome, which ultimately means higher costs for the general taxpayer.

If Baumol’s cost disease is the primary factor responsible for the increasing cost of municipal government then there is not an easy remedy in the current environment. If the policing, firefighting, and education industries are unreceptive to labor-augmenting technology due to their high levels of unionization and near-monopoly status, one potential way to cure municipalities of the cost disease is privatization. In their 1996 paper, The Cost Disease and Government Growth: Qualifications to Baumol, economists J. Ferris and Edwin West state “Privatization could lead to significant changes in the structure of supply that result in “genuine” reductions in real costs” (p. 48).

Schools, police, and fire services are not true public goods and thus economic efficiency does not dictate that they are provided by a government entity. Schools in particular have been successfully built and operated by private funds for thousands of years. While there are fewer modern examples of privately operated police and fire departments, in theory both could be successfully privatized and historically fire departments were, though not always with great success. However, the failures of past private fire departments in places like New York City in the 19th century appear to be largely due to political corruption, an increase in political patronage, poorly designed incentives, and the failure of the rule of law rather than an inherent flaw in privatization. And today, many volunteer fire departments still exist. In 2013 69% of all firefighters were volunteers and 66% of all fire departments were all-volunteer.

The near-monopoly status of government provided education in many places and the actual monopoly of government provided police and fire protection makes these industries less susceptible to innovation. The government providers face little to no competition from private-sector alternatives, they are highly unionized and thus have little incentive to invest in labor-saving technology, and the importance of their output along with the aforementioned lack of competition allows them to pass cost increases on to taxpayers.

Market competition, limited union membership, and the profit-incentive are features of the private sector that are lacking in the public sector. Together these features encourage the use of labor-augmenting technology, which ultimately lowers costs and frees up resources, most notably labor, that can then be used on producing other goods and services. The higher productivity and lower costs that result from investments in productive capital also free up consumer dollars that can then be used to purchase additional goods and services from other industries.

Privatization of basic city services may be a little unnerving to some people, but ultimately it may be the only way to significantly bring down costs without cutting services. There are over 19,000 municipal governments in the US, which means there are over 19,000 groups of citizens that are capable of looking for new and innovative ways to provide the goods and services they rely on. In the private sector entrepreneurs continue to invent new things and find ways to make old things better and cheaper. I believe that if we allow entrepreneurs to apply their creativity to the public sector we will get similar outcomes.

State government spending hits new heights

There is a large literature in macroeconomics that examines the extent to which federal spending “crowds out” investment in the private sector. Basic theory and common sense lead to the conclusion that government spending must replace some private sector spending. After all, dollars are scarce – if the government taxes Paul and uses his money to build a road Paul necessarily has less money to invest in his landscaping business. In theory government spending on public goods like roads could be a net gain. This would occur if the additional value produced by spending one more dollar on roads was greater than the additional value produced by investing one more dollar in Paul’s landscaping business. But even in this scenario, Paul himself may be worse off – he’s one dollar poorer and he may not use the new road – and there is still a dead-weight loss due to the tax.

In reality, the federal government does a lot more than build roads, especially productive ones. In 2014, only 1.9% of federal income tax revenue was spent on transportation. And most of the other stuff that the government does is way less productive, like shuffling money around via entitlement programs – Medicare, Medicaid, and Social Security – and investing in businesses that later go bankrupt like Solyndra. So while it is possible that a dollar spent by the government is more productive than a dollar spent by a guy like Paul, in a country with America’s spending habits it’s unlikely to be the case.

The same crowding out that occurs at the federal level can occur at the state level. In fact, in many states state spending as a percentage of gross state product (GSP) exceeds federal spending as a percentage of GDP. The graph below shows state spending as a percentage of GSP for all 50 states and Washington D.C. in 1970, 1990, and 2012 (data). The red, dashed line is federal spending as a percentage of GDP in 2012 (21.9%).

state spending gsp graph

As shown in the graph, nearly every state increased their spending relative to GSP from 1970 – 2012 (triangles are above the X’s). Only one state, South Dakota, had lower spending relative to GSP in 2012 than in 1970. In 2012, 15 of the 50 states spent more as a percentage of GSP than the federal government spent as a percentage of GDP (states where the triangle is above the red, dashed line). In 1990 only two states, Arizona and Montana, spent at that level.

It used to be the case that state and local spending was primarily focused on classic government services like roads, water/sewer systems, police officers, firemen, and K-12 education. But state spending is increasingly looking similar to federal spending. Redistributive public welfare expenditures and pension expenditures have increased substantially since 1992. As an example, the tables below provide a breakdown of some key spending areas for two states, Ohio and Pennsylvania, in 1992 and 2012 (1992 data here, 2012 data here). The dollar per capita amounts are adjusted for inflation and are in 2009 dollars.

ohio spending table

penn spending table

As the tables show, spending on public welfare, hospitals, and health increased by 120% in Ohio and 86% in Pennsylvania from 1992 to 2012. Pension expenditures increased by 83% and 125% respectively. And contrary to what many politicians and media types say, funding for higher education – the large majority of state education spending is on higher education – increased dramatically during this time period; up 250% in Ohio and 199% in Pennsylvania. Meanwhile, funding for highways – the classic public good that politicians everywhere insist wouldn’t exist without them – has increased by a much smaller amount in both states.

The state spending increases of the recent past are being driven in large part by public welfare programs that redistribute money, pensions for government employees, and higher education. While one could argue that higher education spending is a productive public investment (Milton Friedman didn’t think so and I agree) it is hard to make a case that public welfare and pension payments are good investments. This alone doesn’t mean that society shouldn’t provide those things. Other factors like equity and economic security might be more important to some people than economic productivity. But this does make it unlikely that the marginal dollar spent by a state government today is as economically productive as that dollar spent in the private sector. Like federal spending, state spending is likely crowding out productive private investment, which will ultimately lower output and economic growth in the long run.

Education, Innovation, and Urban Growth

One of the strongest predictors of urban growth since the start of the 20th century is the skill level of a city’s population. Cities that have a highly skilled population, usually measured as the share of the population with a bachelor’s degree or more, tend to grow faster than similar cities with less educated populations. This is true at both the metropolitan level and the city level. The figure below plots the population growth of 30 large U.S. cities from 1970 – 2013 on the vertical axis and the share of the city’s 25 and over population that had at least a bachelor’s degree in 1967 on the horizontal axis. (The education data for the cities are here. I am using the political city’s population growth and the share of the central city population with a bachelor’s degree or more from the census data linked to above.)

BA, city growth 1

As shown in the figure there is a strong, positive relationship between the two variables: The correlation coefficient is 0.61. It is well known that over the last 50 years cities in warmer areas have been growing while cities in colder areas have been shrinking, but in this sample the cities in warmer areas also tended to have a better educated population in 1967. Many of the cities known today for their highly educated populations, such as Seattle, San Francisco, and Washington D.C., also had highly educated populations in 1967. Colder manufacturing cities such as Detroit, Buffalo, and Newark had less educated workforces in 1967 and subsequently less population growth.

The above figure uses data on both warm and cold cities, but the relationship holds for only cold cities as well. Below is the same graph but only depicts cities that have a January mean temperature below 40°F. Twenty out of the 30 cities fit this criteria.

BA, city growth 2

Again, there is a strong, positive relationship. In fact it is even stronger; the correlation coefficient is 0.68. Most of the cities in the graph lost population from 1970 – 2013, but the cities that did grow, such as Columbus, Seattle, and Denver, all had relatively educated populations in 1967.

There are several reasons why an educated population and urban population growth are correlated. One is that a faster accumulation of skills and human capital spillovers in cities increase wages which attracts workers. Also, the large number of specialized employers located in cities makes it easier for workers, especially high-skill workers, to find employment. Cities are also home to a range of consumption amenities that attract educated people, such as a wide variety of shops, restaurants, museums, and sporting events.

Another reason why an educated workforce may actually cause city growth has to do with its ability to adjust and innovate. On average, educated workers tend to be more innovative and better able to learn new skills. When there is an negative, exogenous shock to an industry, such as the decline of the automobile industry or the steel industry, educated workers can learn new skills and create new industries to replace the old ones. Many of the mid-20th century workers in Detroit and other Midwestern cities decided to forego higher education because good paying factory jobs were plentiful. When manufacturing declined those workers had a difficult time learning new skills. Also, the large firms that dominated the economic landscape, such as Ford, did not support entrepreneurial thinking. This meant that even the educated workers were not prepared to create new businesses.

Local politicians often want to protect local firms in certain industries through favorable treatment and regulation. But often this protection harms newer, innovative firms since they are forced to compete with the older firms on an uneven playing field. Political favoritism fosters a stagnant economy since in the short-run established firms thrive at the expense of newer, more innovative startups. Famous political statements such as “What’s good for General Motors is good for the country” helped mislead workers into thinking that government was willing and able to protect their employers. But governments at all levels were unable to stop the economic forces that battered U.S. manufacturing.

To thrive in the 21st century local politicians need to foster economic environments that encourage innovation and ingenuity. The successful cities of the future will be those that are best able to innovate and to adapt in an increasingly complex world. History has shown us that an educated and entrepreneurial workforce is capable of overcoming economic challenges, but to do this people need to be free to innovate and create. Stringent land-use regulations, overly-burdensome occupational licensing, certificate-of-need laws, and other unnecessary regulations create barriers to innovation and make it more difficult for entrepreneurs to create the firms and industries of the future.

More reasons why intergovernmental grants are harmful

In a recent blog post I explained how intergovernmental grants subsidize some businesses at the expense of others. But that is just one of several negative features of intergovernmental grants. They also make local governments less accountable for their fiscal decisions by allowing them to increase spending without increasing taxes. The Community Development Blog Grant (CDBG) money that local governments spend on city services or use to subsidize private businesses is provided by taxpayers from all over the country. Unlike locally raised money, when cities spend CDBG money they don’t have to first convince local voters to provide them with the funds. This lack of accountability often results in wasteful spending.

These grants also erode fiscal competition between cities and reduce the incentive to pursue policies that create economic growth. If local governments can receive funds for projects meant to bolster their tax base regardless of their fiscal policies, they have less of an incentive to create a fiscal environment that is conducive to economic growth. The feedback loop between growth promoting policies and actual economic growth is impaired when revenue can be generated independently of such policies e.g. by successfully applying for intergovernmental grants.

Some of the largest recipients of CDBG money are cities that have been declining since the 1950s. The graph below shows the total amount of CDBG dollars given to nine cities that were in the top 15 of the largest cities in the US by population in 1950. (Click on graphs to enlarge. Data used in the graphs are here.)

CDBGs 9 cities 1950

None of these cities were in the top 15 cities in 2014 and most of them have lost a substantial amount of people since 1950. In Detroit, Cleveland, St. Louis, and Buffalo the CDBG money has not reversed or even slowed their decline and yet the federal government continues to give these cities millions of dollars each year. The purpose of these grants is to create sustainable economic development in the recipient cities but it is difficult to argue that such development has occurred.

Contrast the amount of money given to the cities above with that of the cities below:

CDBGs 9 cities 2014

By 2014 the nine cities in the second graph had replaced the other cities in the top 15 largest US cities by population. Out of the nine cities in the second graph only one, San Antonio, has received $1 billion or more in CDBG funds. In comparison, every city in the first graph has received at least that much.

While there are a lot of factors that contribute to the decline of some cities and the rise of others (such as the general movement of the population towards warmer weather), these graphs are evidence that the CDBG program is incapable of saving Detroit, Buffalo, St. Louis, Cleveland, etc. from population and economic decline. Detroit alone has received nearly $3 billion in CDBG grants over the last 40 years yet still had to declare bankruptcy in 2013. St. Louis, Cleveland, Baltimore, Buffalo, and Milwaukee are other examples of cities that have received a relatively large amount of CDBG funding yet are still struggling with population decline and budget issues. Place-based, redistributive policies like the CDBG program misallocate resources from growing cities to declining cities and reduce the incentive for local governments to implement policies that encourage economic growth.

Moreover, if place-based subsidies, such as the CDBG program, do create some temporary local economic growth, there is evidence that this growth is merely shifted from other areas. In a study on the Tennessee Valley Authority, perhaps the most ambitious place-based program in the country’s history, economists Patrick Kline and Enrico Moretti (2014) found that the economic gains that accrued to the area covered by the TVA were completely offset by losses in other parts of the country. As they state, “Thus, we estimate that the spillovers in the TVA region were fully offset by the losses in the rest of the country…Notably, this finding casts doubt on the traditional big push rationale for spatially progressive subsidies.” This study is further evidence for what other economists have been saying for a long time: Subsidized economic growth in one area, if it occurs, comes at the expense of growth in other areas and does not grow the US economy as a whole.

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.

State and local spending growth vs. GDP growth.

A few years ago, I produced a figure which showed inflation-adjusted state and local expenditures alongside inflation-adjusted private GDP.

It’s been some time since I made that chart and so I thought I might revisit the question. This time around, I compared state and local expenditures with overall GDP, not just private GDP.

The results are below (click to enlarge).

State and Local expenditures vs. GDPAfter adjusting for inflation, the economy is about 5.79 times its 1950 size. This is a good thing. It means more is being produced and more is available for consumption. And since the population has only doubled over this period, it means that per capita production is way up.

Over the same time period, however, state and local government expenditures have not just gone up 5 or 6 or even 8 times. Instead, after adjusting for inflation, state and local governments are spending about 12.79 times as much as they spent in 1950.

State and local governments, of course, depend entirely on the economy for their resources. As I put it when I produced the original chart, this is like a household whose income has grown about 6-fold but whose spending habits have grown nearly 13-fold.

What would a business-cycle balanced budget rule look like in Illinois?

A few years ago, I testified before the U.S. House Judiciary Committee. I’d been invited to talk about the design of a federal balanced budget amendment and much of my testimony drew on the lessons offered from state experience. Since 49 of the 50 states have such requirements, and since these requirements vary from state to state, I noted that federal lawmakers could learn from the state laboratory.

The best requirement, I argued, would have the following characteristics:

  1. Require balance over some period longer than a year. This effectively disarms the strongest argument against a balanced budget amendment: namely, that it would force belt-tightening in the middle of a recession. In contrast, if budgets need to balance over a longer time period, then Congress is free to run deficits in particular years as long as they are countered by surpluses in others.
  2. Allow Congress some time to come into compliance. You don’t have to be a Keynesian to worry that a 45 percent reduction in the deficit overnight might be a shock to the system.
  3. Minimize the gamesmanship associated with revenue estimation: Across the country, states with balanced budget requirements have to estimate revenue throughout the year (I’m a member of Virginia’s Joint Advisory Board of Economists and our responsibility is to pass judgment on the validity of these estimates). But this invites all sorts of questions: what model to use for the economy, should revenue be scored dynamically or statically, etc. One way to sidestep all of these questions is to make the requirement retrospective: require that spending this year not exceed revenue from years past.

Michigan Republican Justin Amash has proposed an amendment along these lines. It would be phased-in over 9 years and from there on out would stipulate that outlays “not exceed the average annual revenue collected in the three prior years, adjusting in proportion to changes in population and inflation.” Because it requires balance over three years rather than one, Amash calls it the “business cycle balanced budget amendment.”

Writing in Time, GMU’s Alex Tabarrok points to Sweden’s positive experience with a similar rule. And economists Glenn Hubbard and Tim Kane also endorse such a rule in their book, Balance.

Now, some Illinois state lawmakers have put together a proposal for a state rule that appears to be largely based on this model. It requires:

Appropriations for a fiscal year shall not exceed the average annual revenue collected for the 3 prior years, adjusting in proportion to changes in population and inflation.

(Unlike the Amash plan, however, the Illinois plan is not phased in over a number of years. Rather, it takes effect immediately upon passage of the bill.)

To see how it might work in a state, I decided to take the Amash Amendment for a test drive, using Illinois data. The solid blue line in the figure below charts Illinois’s actual general revenue from 1990 to 2012 in billions of current dollars. The dashed blue line phases in an Amash-type “business cycle” balanced budget rule. Once fully phased-in, it would limit spending to the average revenue of the three previous years, with an adjustment for inflation and population growth.

BCBBA

Notice three things:

  1. From 1990 to 2002, and from 2004 to 2007, the rule would have kept Illinois spending in line with Illinois revenue, and would have even allowed the state to run surpluses.
  2. In lean years (like 2008) when revenue levels off, the limit actually continues to rise. That’s because it is based on a longer time trend. This means that it wouldn’t require the sort of draconian budget cuts that balanced budget critics often fear. The accumulated surpluses from previous years could also be used to soften the blow.
  3. Lastly, note the (9 percent) revenue uptick from 2011 to 2012. The amendment would prudently make legislators wait a few years before they can go out and spend that money.