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Does Tax Increment Financing (TIF) generate economic development?

Tax increment financing, or TIF, is a method of financing economic development projects first used in California in 1952. Since then, 48 other states have enacted TIF legislation with Arizona being the lone holdout. It was originally conceived as a method for combating urban blight, but over time it has become the go-to tool for local politicians pushing economic development in general. For example, Baltimore is considering using TIF to raise $535 million to help Under Armor founder Kevin Plank develop Port Covington.

So how does TIF work? Though the particulars can vary by state, the basic mechanism is usually similar. First, an area is designated as a TIF district. TIF districts are mostly industrial or commercial areas rather than residential areas since the goal is to encourage economic development.

Usually, in an effort to ensure that TIF is used appropriately, the municipal government that designates the area as a TIF has to assert that economic development would not take place absent the TIF designation and subsequent investment. This is known as the ‘but-for’ test, since the argument is that development would not occur but for the TIF. Though the ‘but-for’ test is still applied, some argue that it is largely pro forma.

Once an area has been designated as a TIF district, the property values in the area are assessed in order to create a baseline value. The current property tax rate is applied to the baseline assessed value to determine the amount of revenue that is used for the provision of local government goods and services (roads, police, fire, water etc.). This value will then be frozen for a set period of time (e.g. up to 30 years in North Carolina), and any increase in assessed property values that occurs after this time and the subsequent revenue generated will be used to pay for the economic development project(s) in the TIF district.

The key idea is that municipalities can borrow against the projected property value increases in order to pay for current economic development projects. A simple numerical example will help clarify how TIF works.

In the table below there are five years. In year 1 the assessed value of the property in the TIF district is $20 million and it is determined that it takes $1 million per year to provide the government goods and services needed in the area (road maintenance, sewage lines, police/fire protection, etc.). A tax rate of 5% is applied to the $20 million of assessed value to raise the necessary $1 million (Tax revenue column).

TIF example table

The municipality issues bonds totaling $1 million to invest in an economic development project in the TIF district. As an example, let’s say the project is renovating an old business park in order to make it more attractive to 21st century startups. The plan is that improving the business park will make the area more desirable and increase the property values in the TIF district. As the assessed value increases the extra tax revenue raised by applying the 5% rate to the incremental value of the property will be used to pay off the bonds (incremental revenue column).

Meanwhile, the $1 million required for providing the government goods and services will remain intact, since only the incremental increase in assessed value is used to pay for the business park improvements. Hence the term Tax Increment Financing.

As shown in the table, if the assessed value of the property increases by $2 million per year for 4 years the municipality will recoup the $1 million required to amortize the bond (I’m omitting interest to keep it simple). Each $1 million dollars of increased value increase tax revenue by $50,000 without increasing the tax rate, which is what allows the municipality to pay for the economic development without raising property tax rates. For many city officials this is an attractive feature since property owners usually don’t like tax rate increases.

City officials may also prefer TIF to the issuance of general obligation bonds since the latter often require voter approval while TIF does not. This is the case in North Carolina. TIF supporters claim that this gives city officials more flexibility in dealing with the particular needs of development projects. However, it also allows influential individuals to push TIF through for projects that a majority of voters may not support.

While TIF can be used for traditional government goods like roads, sewer systems, water systems, and public transportation, it can also be used for private goods like business parks and sports facilities. The former arguably provide direct benefits to all firms in the TIF district since better roads, streetscapes and water systems can be used by any firm in the area. The latter projects, though they may provide indirect benefits to nearby firms in the form of more attractive surroundings and increased property values, mostly benefit the owners of entity receiving the development funding. Like other development incentives, TIF can be used to subsidize private businesses with taxpayer dollars.

Projects that use TIF are often described as ‘self-financing’ since the project itself is supposedly what creates the higher property values that pay for it. Additionally, TIF is often sold to voters as a way to create jobs or spur additional private investment in blighted areas. But there is no guarantee that the development project will lead to increased private sector investment, more jobs or higher property values. Researchers at the UNC School of Government explain the risks of TIF in a 2008 Economic Bulletin:

“Tax increment financing is not a silver bullet solution to development problems. There is no guarantee that the initial public investment will spur sufficient private investment, over time, that creates enough increment to pay back the bonds. Moreover, even if the investment succeeds on paper, it may do so by “capturing” growth that would have occurred even without the investment. Successful TIF districts can place an additional strain on existing public resources like schools and parks, whose funding is frozen at base valuation levels while growth in the district increases demand for their services.”

The researchers also note that it’s often larger corporations that municipalities are trying to attract with TIF dollars, and any subsidies via TIF that the municipality provides to the larger firm gives it an advantage over its already-established, local competitors. This is even more unfair when the local competitor is a small, mom-and-pop business that already faces a difficult challenge due to economies of scale.

There is also little evidence that TIF regularly provides the job or private sector investment that its supporters promise. Chicago is one of the largest users of TIF for economic development and its program has been one of the most widely studied. Research on Chicago’s TIF program found that “Overall, TIF failed to produce the promise of jobs, business development or real estate activity at the neighborhood level beyond what would have occurred without TIF.”

If economic development projects that rely on TIF do not generate additional development above and beyond what would have occurred anyway, then the additional tax revenue due to the higher assessed values is used to pay for an economic development project that didn’t really add anything. Without TIF, that revenue could have been used for providing other government goods and services such as infrastructure or better police and fire protection. Once TIF is used, the additional revenue must be used to pay for the economic development project: it cannot be spent on other services that residents might prefer.

Another study, also looking at the Chicago metro area, found that cities that adopt TIF experience slower property value growth than those that do not. The authors suggest that this is due to a reallocation of resources to TIF districts from other areas of the city. The result is that the TIF districts grow at the expense of the municipality as a whole. This is an example of the TIF working on paper, but only because it is pilfering growth that would have occurred in other areas of the city.

Local politicians often like tax increment financing because it is relatively flexible and enables them to be entrepreneurial in some sense: local officials as venture capitalists. It’s also an easier sell than a tax rate increase or general obligation bonds that require a voter referendum.

But politicians tend to make bad venture capitalists for several reasons. First, it’s usually not their area of expertise and it’s hard: even the professionals occasionally lose money. Second, as Milton Friedman pointed out, people tend to be more careless when spending other people’s money. Local officials aren’t investing their own money in these projects, and when people invest or spend other people’s money they tend to emphasize the positive outcomes and downplay the negative ones since they aren’t directly affected. Third, pecuniary factors don’t always drive the decision. Different politicians like different industries and businesses – green energy, biotech, advanced manufacturing, etc. – for various reasons and their subjective, non-pecuniary preferences may cause them to ignore the underlying financials of a project and support a bad investment.

If TIF is going to be used it should be used on things like public infrastructure – roads, sewer/water lines, sidewalks – rather than specific private businesses. This makes it harder to get distracted by non-pecuniary factors and does a better – though not perfect – job of directly helping development in general rather than a specific company or private developer. But taxpayers should be aware of the dangers of TIF and politicians and developers should not tout it as a panacea for jump-starting an area’s economy.

Washington DC is set to become the latest city to make it illegal for low-skill people to work

In the latest example of politics trumping economics, Washington DC’s city council voted to increase the city’s minimum wage to $15 per hour by 2020. The economic arguments against a minimum wage are well-known to most people so I won’t rehash them here, but if you want to read more about why the minimum wage is bad policy you can do so here, here, and here.

In a nutshell, the minimum wage prices lower-skill workers out of the market by setting the wage higher than the value they can produce for their employer; if a worker only produces $9 worth of value in an hour an employer can’t pay her $10 per hour and stay in business.

The minimum wage has the strongest impact on low-skill workers since they tend to produce the least amount of value for their employers. Two categories of such workers are teenagers, who lack experience and have yet to finish their education, and adults with less than a high school degree. The figures below depict the employment and unemployment rates for these two groups in the Washington DC metro area (MSA) and the city proper (District only) from 2009 to 2014 (most recent data available) using 5-year American Community Survey data from American FactFinder.

DC 16-19 employed

As shown in the figure only about 15% of DC’s 16 – 19 year olds were employed (orange) in 2014 compared to about 25% in the MSA as a whole. The percentage has fallen since 2009 and doesn’t appear to be recovering. Increasing the price of such workers certainly won’t help.

The next figure shows the percentage of people with less than a high school degree who were employed.

DC less HS employed

Again, the percentage has fallen in DC since 2009 and is far below the MSA as a whole. Less than half of adults with less than a high school degree are employed in DC compared to 67% in the Washington metro area. If employers relocate to other jurisdictions within the MSA once the minimum wage law takes effect it will make it more difficult for the less-educated adults of DC to find a job.

The next two figures show the unemployment rates for both groups in both areas. As shown, the unemployment rate is higher in DC than in the MSA for both groups and has been trending upward since 2009.

DC 16-19 unemp

DC less HS unemp

It’s outlandish to think that raising the minimum wage will improve things for the 35% of 16 – 19 year olds and 21% of high school dropouts who were looking for a job and couldn’t find one under the old minimum wage of only $9.50.

Politicians and voters are free to ignore economic reality and base their decision making on good intentions, but when doing so they should at least know the employment facts and be made aware of the futility of their intentions. I predict that we will see more automation in DC’s restaurants, hotels, and bars in the future as workers get relatively more expensive due to the higher minimum wage. This will only make it harder for DC’s teenagers and less-educated residents to find work, which as shown above is already a difficult task.

Baltimore’s misguided move to raise its minimum wage will harm its most vulnerable

Baltimore’s city council, like others around the country, is considering raising the city’s minimum wage to $15 per hour. This is an ill-advised move that will make it harder for young people and the least skilled to find employment, which is already a difficult task in Baltimore.

The figure below shows the age 16 – 19 labor force participation (LFP) rate, employment rate, and unemployment rate in Baltimore City from 2009 to 2014 (most recent data available). The data are from the American Community Survey table S2301.

baltimore 16-19 emp stats

As shown in the figure, the LFP rate declined along with the employment rate, which has caused the unemployment rate to hold steady at approximately 40% (red line). So 40% of Baltimore’s unemployed teens were searching for a job but couldn’t find one and only 20% of all teens were actually employed, a decline of 4 percentage points (blue line). How is increasing the minimum wage to $15 per hour going to help the 40% who are looking for a job find one?

The minimum wage increase may help some people who are able to keep their job at the higher wage, but for the 40% who can’t find a job at the current minimum wage of $8.25, an increase to $15 is only going to make the task harder, if not impossible. Who is standing up for these people?

The data are just as gloomy when looking at workers with less than a high school degree, which is another group that is severely impacted by a higher minimum wage. As the figure below shows, the employment rate is falling while the unemployment rate is rising.

baltimore lt hs emp stats

In 2009 over 42% of people in this skill group were employed (blue line). In 2014 only 37% were, a decline of five percentage points. Meanwhile, the unemployment rate increased from about 19% to over 25% (red line). And all of this occurred while the economy was supposedly improving.

Again we should ask; how is a higher minimum wage going to help the 25% of high school dropouts in Baltimore who are unemployed find a job? It won’t. Unemployed workers do not become more attractive as employees simply because the city council mandates a higher wage.

What’s going to happen is that more people in this skill group will become discouraged and leave the labor market entirely. Then they will earn $0 per hour indefinitely and be forced to rely entirely on family, friends, and public assistance to live. A $15 minimum wage destroys their chances of finding meaningful employment and unduly deprives them of opportunities to better their lives.

This is the unseen effect of minimum wage hikes that $15 supporters rarely acknowledge. When faced with the higher cost, firms will hire workers who can justify a $15 wage and those who cannot will be unable to find employment. Additionally, firms will start using more technology and automation instead of workers. This happens because consumers want low prices and high quality, and as the minimum wage increases technology and capital become the best way to give consumers what they want. Over time workers in states with lower minimum wages may be forced out of the labor market as well as new technologies spread from high minimum wage areas to low minimum wage areas.

Another common argument put forth by minimum wage supporters is that taxpayers subsidize firms that pay low wages. But this is not true. Firms like Wal-Mart, McDonalds, and the countless other large and small business that employ low-skill workers are doing their part by giving people an opportunity. Firm owners did not unilaterally decide that all Americans should have a minimum standard of living and they should not be required to provide it on their own. Ultimately, advocates of a higher minimum wage who worry that they are subsidizing firms will likely be forced to contribute even more tax dollars to social programs since the wage for unemployed workers is $0.

Furthermore, why $15 and not $20? The argument is that $15/ hour is the minimum necessary to maintain a basic standard of living for working Americans but that argument is subjective. In fact, it can be extended to other areas. For example, should new hires be paid more than an entry-level salary so they can pay off college debt and maintain the standard of living of their parents?

To the extent that Americans deserve a particular lifestyle, providing it is a collective burden that should be shared by everyone. Politicians, clergy, union heads and other minimum wage supporters who want to push the entire burden onto firms are abandoning the moral obligation they claim we all share.

While minimum wage supporters mean well they appear to be blind to those who are harmed by wage controls. And those who are harmed are some of the most vulnerable members of the workforce – high school drop-outs, recent immigrants and urban youth. The minimum wage is a misguided policy that consigns these vulnerable members of the labor force to the basement of the economy and prevents any escape.

States with lower minimum wages will feel the impact of California’s experiment

California governor Jerry Brown recently signed a law raising California’s minimum wage to $15/hour by 2022. This ill-advised increase in the minimum wage will banish the least productive workers of California – teens, the less educated, the elderly – from the labor market. It will be especially destructive in the poorer areas of California that are already struggling.

And if punishing California’s low-skill workers by preventing them from negotiating their own wage with employers isn’t bad enough, there is reason to believe that a higher minimum wage in a large state like California will eventually affect the employment opportunities of low-skill workers in other areas of the country.

Profit-maximizing firms are always on the lookout for ways to reduce costs holding quality constant (or in the best case scenario to reduce costs and increase quality). Since there are many different ways to produce the same good, if the price of one factor of production, e.g. labor, increases, firms will have an incentive to use less of that factor and more of something else in their production process. For example, if the price of low-skill workers increases relative to the cost of a machine that can do the same job firms will have an incentive to switch to the machine.

To set the stage for this post, let’s think about a real life example; touch screen ordering. Some McDonald’s have touchscreens for ordering food and coffee and San Francisco restaurant eatsa is almost entirely automated (coincidence?). The choice facing a restaurant owner is whether to use a touch screen or cashier. If a restaurant is currently using a cashier and paying them a wage, they will only switch to the touch screen if the cost of switching and the future discounted costs of operating and maintaining the touch screen device are less than the future discounted costs of using workers and paying them a wage plus any benefits. We can write this as

D + K + I + R < W

Where D represents the development costs of creating and perfecting the device, K represents the costs of working out the kinks/the trial run/adjustment costs, I represents the installation costs, and R represents the net present value of the operating and maintenance costs. On the other side of the inequality W represents the net present value of the labor costs. (In math terms R and W are: R = [ ∑ (rk) / (1+i)^n from n=0 to N ] where r is the rental rate of a unit of capital, k is the number of units of capital, and i is the interest rate and W = [ ∑ (wl) /(1+i)^n from n=0 to N ] where w is the wage and l is the amount of labor hours. But if this looks messy and confusing don’t worry about it as it’s not crucial for the example.)

The owner of a restaurant will only switch to a touch screen device rather than a cashier if the left side of the above inequality is less than the right side, since in that case the owner’s costs will be lower and they will earn a larger profit (holding sales constant).

If the cashier is earning the minimum wage or something close to it and the minimum wage is increased, say from $9 to $15, the right side of the above inequality will increase while the left side will stay the same (the w part of W is going up).  If the increase in the wage is large enough to make the right side larger than the left side the firm will switch from a cashier to a touch screen. Suppose that an increase from $9 to $15 does induce a switch to touch screen devices in California McDonald’s restaurants. Can this impact McDonald’s restaurants in areas where the minimum wage doesn’t increase? In theory yes.

Once some McDonald’s restaurants make the switch the costs for other McDonald’s to switch will be lower. The reason for this is that the McDonald’s that switch later will not have to pay the D or K costs; the development or kinks/trial run/adjustment costs. Once the technology is developed and perfected the late-adopting McDonald’s can just copy what has already been done. So after the McDonald’s restaurants in high wage areas install and perfect touch screen devices for ordering, the other McDonald’s face the decision of

I + R < W

This means that it may make sense to adopt the technology once it has been developed and perfected even if the wage in the lower wage areas does not change. In this scenario the left side decreases as D and K go to 0 while the right side stays the same. In fact, one could argue that the R will decline for late-adopting restaurants as well since the maintenance costs will decline over time as more technicians are trained and the reliability and performance of the software and hardware increase.

What this means is that a higher minimum wage in a state like California can lead to a decline in low-skill employment opportunities in places like Greenville, SC and Dayton, OH as the technology employed to offset the higher labor costs in the high minimum wage area spread to lower wage areas.

Also, firm owners and operators live in the real world. They see other state and local governments raising their minimum wage and they start to think that it could happen in their area too. This also gives them an incentive to switch since in expectation labor costs are going up. If additional states make the same bad policy choice as California, firm owners around the country may start to think that resistance is futile and that it’s best to adapt in advance by preemptively switching to more capital.

And if you think that touch screen ordering machines aren’t a good example, here is a link to an article about an automated burger-making machine. The company that created it plans on starting a chain of restaurants that use the machine. Once all of the bugs are worked out how high does the minimum wage need to be before other companies license the technology or create their own by copying what has already been done?

This is one more way that a higher minimum wage negatively impacts low-skill workers; even if workers don’t live in an area that has a relatively high minimum wage, the spread of technology may eliminate their jobs as well.

A $15 minimum wage will excessively harm California’s poorest counties

Lawmakers in California are thinking about increasing the state minimum wage to $15 per hour by 2022. If it occurs it will be the latest in a series of increases in the minimum wage across the country, both at the city and state level.

Increases in the minimum wage make it difficult for low-skill workers to find employment since the mandated wage is often higher than the value many of these workers can provide to their employers. Companies won’t stay in business long if they are forced to pay a worker $15 per hour who only produces $12 worth of goods and services per hour. Statewide increases may harm the job prospects of low-skill workers more than citywide increases since they aren’t adjusted to local labor market conditions.

California is a huge state, covering nearly 164,000 square miles, and contains 58 counties and 482 municipalities. Each of these counties and cities has their own local labor market that is based on local conditions. A statewide minimum wage ignores these local conditions and imposes the same mandated price floor on employers and workers across the state. In areas with low wages in general, a $15 minimum wage may affect nearly every worker, while in areas with high wages the adverse effects of a $15 minimum wage will be moderated. As explained in the NY Times:

“San Francisco and San Jose, both high-wage cities that have benefited from the tech boom, are likely to weather the increase without so much as a ripple. The negative consequences of the minimum wage increase in Los Angeles and San Diego — large cities where wages are lower — are likely to be more pronounced, though they could remain modest on balance.

But in lower-wage, inland cities like Bakersfield and Fresno, the effects could play out in much less predictable ways. That’s because the rise of the minimum wage to $15 over the next six years would push the wage floor much closer to the expected pay for a worker in the middle of the wage scale, affecting a much higher proportion of employees and employers there than in high-wage cities.”

To put some numbers to this idea, I used BLS weekly wage data from Dec. of 2014 to create a ratio for each of California’s counties that consists of the weekly wage of a $15 per hour job (40 x $15 = $600) divided by the average weekly wage of each county. The three counties with the lowest ratio and the three counties with the highest ratio are in the table below, with the ratio depicted as a percentage in the 4th column.

CA county weekly min wage ratio

The counties with the lowest ratios are San Mateo, Santa Clara, and San Francisco County. These are all high-wage counties located on the coast and contain the cities of San Jose and San Francisco. As an example, a $600 weekly wage is equal to 27.7% of the average weekly wage in San Mateo County.

The three counties with the highest ratios are Trinity, Lake, and Mariposa County. These are more rural counties that are located inland. Trinity and Lake are north of San Francisco while Mariposa County is located to the east of San Francisco. In Mariposa County, a $600 weekly wage would be equal to 92.6% of the avg. weekly wage in that county as Dec. 2014. The data shown in the table reveal the vastly different local labor market conditions that exist in California.

The price of non-tradeable goods like restaurant meals, haircuts, automotive repair, etc. are largely based on local land and labor costs and the willingness to pay of the local population. For example, a nice restaurant in San Francisco can charge $95 for a steak because the residents of San Francisco have a high willingness to pay for such meals as a result of their high incomes.

Selling a luxury product like a high-quality steak also makes it relatively easier to absorb a cost increase that comes from a higher minimum wage; restaurant workers are already making relatively more in wealthier areas and passing along the cost increase in the form of higher prices will have a small effect on sales if consumers of steak aren’t very sensitive to price.

But in Mariposa County, where the avg. weekly wage is only $648, a restaurant would have a hard time attracting customers if they charged similar prices. A diner in Mariposa County that sells hamburgers is probably not paying its workers much more than the minimum wage, so an increase to $15 per hour is going to drastically affect the owner’s costs. Additionally, consumers of hamburgers may be more price-sensitive than consumers of steak, making it more difficult to pass along cost increases.

Yet despite these differences, both the 5-star steakhouse in San Francisco and the mom-and-pop diner in Mariposa County are going to be bound by the same minimum wage if California passes this law.

In the table below I calculate what the minimum wage would have to be in San Mateo, Santa Clara, and San Francisco County to be on par with a $15 minimum in Mariposa County.

CA comparable min wage

If the minimum wage was 92.6% of the average wage in San Mateo it would be equal to $50.14. Using the ratio from a more developed but still lower-wage area – Kern County, where Bakersfield is located – the minimum wage would need to be $37.20 in San Mateo. Does anyone really believe that a $50 or $37 minimum wage in San Mateo wouldn’t cause a drastic decline in employment or a large increase in prices in that county?

If California’s lawmakers insist on implementing a minimum wage increase they should adjust it so that it doesn’t disproportionately affect workers in poorer, rural areas. But of course this is unlikely to happen; I doubt that the voters of San Mateo, Santa Clara, and San Francisco County will be as accepting of a $37 + minimum wage as they are of a $15 minimum wage that won’t directly affect many of them.

A minimum wage of any amount is going to harm some workers by preventing them from getting a job. But a minimum wage that ignores local labor market conditions will cause relatively more damage in poorer areas that are already struggling, and policy makers who ignore this reality are excessively harming the workers in these areas.

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.

Economic freedom matters at the local level too

Since 1996 the Fraser Institute has published an annual economic freedom of the world index that ranks countries according to their level of economic freedom. They also publish an economic freedom of North America Index that ranks the US states, Canadian provinces, and Mexican provinces using similar data.

Both of these studies have been used to show that countries and states/provinces with relatively high levels of economic freedom tend to be better off in several ways, including higher GDP per capita, longer life expectancy, and greater economic growth. Countries with higher levels of economic freedom tend to have higher quality democracies as well.

A quick google search reveals that there has been a lot of other research that looks at the relationship between economic freedom and various outcomes at the country and state level. However, substantially less research has been done at the local level and there are two main reasons for this.

First, it’s hard to gather data at the local level. There are thousands of municipalities in the US and not all of them make their data easily available. This makes gathering data very costly in terms of time and resources. Second, a lot of policies that impact economic freedom are enacted at the federal and state level. Because of this many people probably don’t think about the considerable effects that local policy can have on local economies.

There has been one study that I know of that attempts to create an economic freedom index for metropolitan areas (MSAs). This study is by Dr. Dean Stansel of SMU, a coauthor of the economic freedom of North America index. The MSA economic freedom index runs from 0 (not free) to 10 (very free) and was created with 2002 data. I am currently working on a paper with Dean that uses this index, but I was recently inspired to use the index in a different way. I wanted to see if economic freedom at the MSA level impacted subsequent employment and population growth, so I gathered BEA data on employment and population and ran a few simple regressions. The dependent variables are at the top of each column in the table below and are private, non-farm employment growth from 2003 – 2014, proprietor employment growth from 2003 – 2014, and population growth from 2003 – 2014.

MSA econ freedom regressions

I also included a quality of life index independent variable from another study in order to control for the place-specific amenities of each MSA like weather and location. This variable measures how much people would be willing to pay to live in a particular MSA; a positive number means a person would pay to live in an area, while a negative number means a person would have to be paid to live in an area. Thus larger, positive numbers indicate more attractive areas. The index is constructed with 2000 data.

As shown in the table, economic freedom has a positive and significant effect on both measures of employment and population growth. The quality of life index is also positive and significant for private employment growth (column 1) and population growth (column 3, only at the 10% level). We can calculate the magnitude of the effects using the standard deviations from the table below.

MSA econ freedom sum stats

Using the standard deviation from column 1 (0.84) we can calculate that a one standard deviation increase in economic freedom would generate a 2 percentage point increase in private employment growth from 2003 – 2014 (0.84 x 0.024), a 4.5 percentage point increase in proprietor employment growth, and a 2.9 percentage point increase in population growth.  A one standard deviation change would be like increasing San Francisco’s level of economic freedom (6.70) to that of San Antonio’s (7.53).

Similarly, a one standard deviation increase in the quality of life index would lead to a 2.1 percentage point increase in private employment growth from 2003 – 2014 (0.000011 x 1912.86) and a 1.9 percentage point increase in population growth. A one standard deviation change would be like increasing the quality of life of Montgomery, AL (-21) to that of Myrtle Beach, SC (1643).

I think the most interesting finding is that quality of life does not affect proprietor employment while economic freedom’s largest effect is on proprietor employment (column 2). According to the BEA proprietor employment consists of the number of sole proprietorships and the number of general partners. Thus it can act as a proxy for the level of entrepreneurship in an MSA. This result implies that economic freedom is more important than things like weather and geographic location when it comes to promoting small business formation and entrepreneurship. This is a good sign for cities located in colder regions of the country like the Midwest and Northeast that can’t do much about their weather or location but can increase their level of economic freedom.

Of course, correlation does not mean causation and these simple regressions omit other factors that likely impact employment and population growth. But you have to start somewhere. And given what we know about the positive effects of economic freedom at the country and state level it seems reasonable to believe that it matters at the local level as well.

Properly funding a defined benefit plan requires solid average returns and some luck

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.

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 $6,500 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.


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.