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Today the Obama administration issued a statement calling for a ‘First Job’ funding initiative to connect young Americans with jobs.

The statement laments how difficult it is for young people to find employment and emphasizes how important a first jobs is for future career success:

“After the worst economic crisis of our lifetimes, the United States is in the midst of the longest streak of private-sector job growth in our history, with more than 14 million new jobs created during the past 70 months. But for too many young people, getting a first job—a crucial step in starting their career—is challenging.

When a young person struggles to get their first job, it can have a lasting negative impact on her lifetime income as well as her motivation, pride, and self-esteem.”  

I brought up this same issue 3 months ago in a previous blog post that highlighted the differences in teenage unemployment across cities. And unsurprisingly there are substantial differences – in 2012 teenage unemployment was over 45% in Atlanta and only about 26% in Houston.

So what’s the proposal? A $5.5 BILLION grab bag of grants, skills investment, and direct wage payments to put young people to work. Naturally, the most obvious solution to the teenage unemployment problem is never mentioned – eliminating the minimum wage. In fact, nowhere is it hinted at that the minimum wage may be contributing to teenage unemployment, despite several recent studies affirming this theory.

From a 2013 study:

“Thus, for older workers, the two effects offset one another, and there is little impact on their long-term employment rate. For teenagers, the extra reduction in hiring implies that their employment rates decline. The results are very similar for males and females.”

From a 2015 study:

Using three separate state panels of administrative employment data, we find that the minimum wage reduces job growth over a period of several years”

From a 2015 study:

We find that a higher minimum wage level is associated with higher earnings, lower employment and reduced worker turnover for those in the 14–18 age group. “ (My bold)

From a 2015 study:

I apply the estimator to estimate the impact of the minimum wage on the employment rate of teenagers. I estimate an elasticity of -0.10 and reject the null hypothesis that there is no effect.”

This glaring omission is unconscionable in light of the abundant evidence that the minimum wage harms the least skilled, least experienced workers, which includes teenagers.

As a Prof. David Neumark stated in a recent WSJ op-ed:

“…let’s not pretend that a higher minimum wage doesn’t come with costs, and let’s not ignore that some of the low-skill workers the policy is intended to help will bear some of these costs.”

An all too common occurrence in US policy is that government intervention causes a problem that the government then tries to solve with additional intervention, completely ignoring the possibility that the initial intervention was the source of the problem. In this case, price controls at the bottom of the labor-market ladder have prevented young people from getting on the first rung, so now the government wants to wheel over a $5.5 billion dollar stool to give them a boost.

While this series of imprudent events is not surprising, it’s still frustrating.

Saving for retirement is something most workers do – either on their own or through an employer – and most are aware that the rate of return on their retirement investment matters. For example, if I save $100 today and it earns 10% per year in interest for the next 20 years I will have $672.75 at the end of 20 years. If the money earns 6% instead I will only have $320.71 at the end of 20 years.

Moreover, if I wanted to have $672 at the end of 20 years and the interest rate was only 6% I would have to save $209.54 today rather than $100. This demonstrates that the higher the interest rate is, the less money I will have to save today in order to have a specific amount of money in the future. This simple truth has important implications for pension funding.

For many years state pension plans assumed average returns of around 8% per year when calculating pension liabilities. Assuming this relatively high rate of return meant that pension plans required less contributions today in order to meet their future goals. But this also came with significant risk – if the average rate of return fell short of 8% then the pensions would not be able to pay out the benefits that were promised. This is demonstrated in the previous example; if a person wanted $672 after 20 years and assumed a 10% rate of return they would have only saved $100. However, if the rate of return turned out to be 6% per year instead of 10%, they would have ended up over $300 short of their goal ($672 – $320 = $352).

It turns out that an expected rate of return of 8% was unachievable and many pension plans are lowering their expected returns. This can generate large pension shortfalls, since a lower rate of return means that more money needed to be saved all along. In many states the budget is tight and it’s not clear where the additional money will come from, but there’s a good chance that taxpayers are going to have make up the difference.

Assuming too high of a return is an obvious problem. But there is a more subtle issue that doesn’t get as much attention yet generates similar results; even if a pension plan gets an 8% return on average, the plan may still fall short of its goal. This is because different returns have different effects on the actual amount of money over time. The chart below provides a simple example, where the goal is to accumulate $100,000 in 10 years.

pension avg return table

Based on the $100,000 goal and an 8% yearly return one can calculate that (approximately) $6,400 must be contributed to the plan at the beginning of each year, which is the contribution amount I used. In each scenario in the table the average annual return is 8%, but not every plan returns 8% each year.

Scenario 1 is the most straightforward; the plan actually earns 8% each year and the $100,000 goal is reached by year 10. But while this is the simplest scenario, it’s also the most unrealistic. Anyone who follows the stock market knows that it’s volatile – some years it’s up, some years it’s down. Standard pension accounting, however, assumes scenario 1 will occur even though that’s incredibly unlikely.

In scenario 2, the plan earns 8% in each of the first two years, then loses 15% the third year. After that returns are above average and plan actually exceeds its goal of $100,000 at the end of 10 years. In scenario 3 the plan earns 8% for the first 6 years, then 14%, before losing 15% in year 8. In this scenario, even the exceptional gains in years 9 and 10 are not enough to reach the $100,000 goal. And finally, in scenario 4 the gains fluctuate more often – there are some high return years in the beginning and the loss year is relatively late (year 7). In this scenario the plan ends up over $7,500 short of its $100,000 goal.

There are infinite ways a plan could get an 8% return on average, but these 4 examples demonstrate the different dollar amounts that can result even if the average return goal is met. In two of the scenarios (3 and 4) the plan falls short of its actual dollar goal and is underfunded even though it met its return goal. This exemplifies the inherent risk in any pension plan that promises a specific amount of money in some future period, as defined benefit plans do. As the previous example shows, even if the required contributions are made each year AND the plan’s average return goal is met, there is still a chance the plan will be underfunded.

The risks associated with the variability in returns is another reason why many pension reform advocates recommend defined contribution plans rather than defined benefits plans. Defined contribution plans don’t promise a specific amount of benefits, which means they are not subject to the same underfunding risks as defined benefit plans. Switching from defined benefit plans to defined contribution plans needs to be a part of the solution to public sector pension problems. Otherwise there’s a good chance that taxpayers will be required to pick up the tab when plans inevitably miss their funding goals.

 

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.

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.

Are state lotteries good sources of revenue?

January 14, 2016

By Olivia Gonzalez and Adam A. Millsap With all the hype about the Powerball jackpot, we decided to look at the benefits and costs of state lotteries from the taxpayer’s perspective. The excitement around yesterday’s drawing is for good reason, with the jackpot reaching $1.5 billion – the largest thus far. But most taxpayers will […]

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We don’t need more federal infrastructure spending

January 6, 2016

Many of the presidential candidates on both sides of the aisle have expressed interest in fixing America’s infrastructure, including Donald Trump, Hilary Clinton, and Bernie Sanders. All of them claim that America’s roads and bridges are crumbling and that more money, often in the form of tax increases, is needed before they fall into further […]

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How effective are HUD programs? No one knows.

December 16, 2015

The Department of Housing and Urban Development, or HUD, has been in the news lately due to its policy proposal to ban smoking in public housing. HUD usually flys under the radar as far as federal agencies are concerned so many people are probably hearing about if for the first time and are unsure about […]

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Fixing municipal finances in Pennsylvania

November 24, 2015

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 […]

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Berkeley, CA and the $15 – oops – $19 living wage

November 13, 2015

Berkeley, CA’s labor commission – in what should be an unsurprising move at this point in Berkeley’s history – has proposed raising the city’s minimum wage to an astounding $19 per hour by 2020! The labor commission’s argument in a nutshell is that Berkeley is an expensive place to live so worker’s need more money. And […]

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Puerto Rico’s labor market woes

October 28, 2015

Puerto Rico – a U.S. territory – has $72 billion dollars in outstanding debt, which is dangerously high in a country with a Gross Domestic Product (GDP) of only $103.1 billion. The Puerto Rican government failed to pay creditors in August and this was viewed as a default by the credit rating agency Moody’s, which […]

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