Government Spending and Economic Growth in Nebraska since 1997

Mercatus recently released a study that examines Nebraska’s budget, budgetary rules and economy. As the study points out, Nebraska, like many other states, consistently faces budgeting problems. State officials are confronted by a variety of competing interests looking for more state funding—schools, health services and public pensions to name a few—and attempts to placate each of them often leave officials scrambling to avoid budget shortfalls in the short term.

Money spent by state and local governments is collected from taxpayers who earn money in the labor market and through investments. The money earned by taxpayers is the result of producing goods and services that people want and the total is essentially captured in a state’s Gross Domestic Product (GSP).

State GSP is a good measure of the amount of money available for a state to tax, and if state and local government spending is growing faster than GSP, state and local governments will be controlling a larger and larger portion of their state’s output over time. This is unsustainable in the long run, and in the short run more state and local government spending can reduce the dynamism of a state’s economy as resources are taken from risk-taking entrepreneurs in the private sector and given to government bureaucrats.

The charts below use data from the BEA to depict the growth of state and local government spending and private industry GSP in Nebraska (click on charts to enlarge). The first shows the annual growth rates in private industry GSP and state and local government GSP from 1997 to 2014. The data is adjusted for inflation (2009 dollars) and the year depicted is the ending year (e.g. 1998 is growth from 1997 – 1998).

NE GSP annual growth rates 1997-14

In Nebraska, real private industry GSP growth has been positive every year except for 2012. There is some volatility consistent with the business cycles over this time period, but Nebraska’s economy has regularly grown over this period.

On the other hand, state and local GSP growth was negative 10 of the 17 years depicted. It grew rapidly during recession periods (2000 – 2002 and 2009 – 2010), but it appears that state and local officials were somewhat successful in reducing spending once economic conditions improved.

The next chart shows how much private industry and state and local GSP grew over the entire period for both Nebraska and the U.S. as a whole. The 1997 value of each category is used as the base year and the yearly ratio is plotted in the figure. The data is adjusted for inflation (2009 dollars).

NE, US GSP growth since 1997

In 2014, Nebraska’s private industry GSP (red line) was nearly 1.6 times larger than its value in 1997. On the other hand, state and local spending (light red line) was only about 1.1 times larger. Nebraska’s private industry GSP grew more than the country’s as a whole over this period (57% vs 46%) while its state and local government spending grew less (11% vs. 15%).

State and local government spending in Nebraska spiked from 2009 to 2010 but has come down slightly since then. Meanwhile, the state’s private sector has experienced relatively strong growth since 2009 compared to the country as a whole, though it was lagging the country prior to the recession.

Compared to the country overall, Nebraska’s private sector economy has been doing well since 2008 and state and local spending, while growing, appears to be largely under control. If you would like to learn more about Nebraska’s economy and the policies responsible for the information presented here, I encourage you to read Governing Nebraska’s Fiscal Commons: Addressing the Budgetary Squeeze, by Creighton University Professor Michael Thomas.

An Overview of the Virginia State Budget and Economy

By Adam Millsap and Thomas Savidge

Virginia’s economy has steadily grown over time in spite of expenditures outpacing revenues each year since 2007. However, economic growth within the state is not evenly distributed geographically.

We examine Virginia’s revenue and expenditure trends, highlighting the sources of Virginia’s revenue and where it spends money. Then we discuss trends in state economic growth and compare that to recent personal income data by county.

Government Overview: Expenditures and Revenue

Figure 1 shows Virginia’s general spending and revenue trends over the past ten years. According to the Virginia Comprehensive Annual Financial Report (CAFR), after adjusting for inflation, government expenditures have outpaced revenue every single year as seen in Figure 1 below (with the exception of 2006). The red column represents yearly expenditures while the stacked column represents revenues (the lighter shade of blue at the top represents revenue from “Federal Grants and Contracts” and the bottom darker shade of blue represents “Self-Funded Revenue”).

VA expend and rev 2006-16

During the recession in 2009, expenditures climbed to $40 billion. Expenditures hovered around this amount until 2015 when they reached $41 billion. Then in 2016 expenditures dropped to just under $37 billion, a level last seen in 2006.

On the revenue side, the majority of Virginia’s government revenue is self-funded i.e. raised by the state. Self-funded revenue hovered between $24 and $29 billion over the ten year period.

However, revenue from federal contracts and grants steadily increased over time. There were two sharp increases in federal contracts and grants: 2008-2009 jumping from $8 to $10 billion and then 2009-2010 jumping from $10 to $13 billion. While there was a drop in federal contracts and grants from 2015-2016, the amount of revenue received from federal contracts and grants has not returned to its pre-2009 levels.

What is the state of Virginia spending its revenue on? According to the Virginia CAFR, state spending is separated into six major categories: General Government, Education, Transportation, Resources & Economic Development, Individual & Family Services, and Administration of Justice. The spending amounts from 2006-2016 (adjusted for inflation) are depicted in Figure 2.

VA expend by category 2006-16

As shown, the majority of spending over the ten year period was on Individual and Family Services. Prior to 2008, spending on Education closely tracked spending on Individual and Family services, but from 2008 to 2010 spending on the latter increased rapidly while spending on education declined. From 2010 through 2015 spending on Individual & Family Services was just over $15 billion per year. It dropped from 2015 to 2016, but so did spending on education, which maintained the gap between the two categories.

During the ten year period, Education spending hovered between $10 and $12 billion until it dropped to $9 billion in 2016. With the exception of Transportation (steadily climbing from 2010-2016), spending on each of the other categories remained below $5 billion per year and was fairly constant over this period.

Virginia Economic Growth & County Personal Income

After examining Virginia’s revenue and expenditures in Part 1, we now look at changes in Virginia’s economic growth and personal income at the county level. Data from the Bureau of Economic Analysis (BEA) shows that Virginia’s GDP hovered between $4 and $4.5 billion dollars (after adjusting for inflation), as shown in Figure 3 below. The blue columns depict real GDP (measured on the left vertical axis in billions of chained 2009 dollars) and the red line depicts percent changes in real GDP (measured on the right vertical axis).

VA GDP 2006-15

While Virginia’s GDP increased from 2006-2015, we’ve condensed the scale of the left vertical axis to only cover $3.9-4.35 billion dollars in order to highlight the percent changes in Virginia’s economy. The red line shows that the percent change in real GDP over this period was often quite small—between 0% and 1% in all but two years.

Virginia’s GDP rose from 2006-2007 and then immediately fell from 2007-2008 due to the financial crisis. However, the economy experienced larger growth from 2009-2010, growing from roughly $4.07-$4.17 billion, a 2.3% jump.

Virginia’s economy held steady at $4.17 billion from 2010 to 2011 and then increased each year up through 2014. Then from 2014-2015, Virginia’s economy experienced another larger spike in growth from $4.24-$4.32 billion, a 2% increase.

Virginia’s economy is diverse so it’s not surprising that the robust economic growth that occurred from 2014 to 2015 was not spread evenly across the state. While the BEA is still compiling data on county GDP, we utilized their data on personal income by county to show the intra-state differences.

Personal Income is not the equivalent of county-level GDP, the typical measure of economic output, but it can serve as a proxy for the economic conditions of a county.[1] Figure 4 below shows which counties saw the largest and smallest changes in personal income from 2014 to 2015. The red counties are the 10 counties with the smallest changes while the blue counties are the 10 counties with the largest changes.

VA county pers. inc. map

As depicted in Figure 4 above, the counties with the strongest personal income growth are concentrated in the north, the east and areas surrounding Richmond. Loudon County in the north experienced the most personal income growth at 7%. The counties surrounding Richmond experienced at least 5.5% growth. Total personal income in Albemarle County grew by 5.7% while the rest of the counties—Hanover, Charles City, Greene, Louisa, and New Kent—experienced growth between 6.2% and 6.7%.

With the exception of Northumberland, the counties in which personal income grew the least were along the western border and in the southern parts of the state. Four of these counties and an independent city were concentrated in the relatively rural Southwest corner of the state—Buchanan, Tazewell, Dickenson, Washington and the independent city of Bristol. In fact, Buchanan County’s personal income contracted by 1.14%.

Cross-county differences in personal income growth in Virginia from 2014 to 2015 are consistent with national data as shown below.

US county pers. inc. map

This map from the BEA shows personal income growth by county (darker colors mean more growth). Nationwide, personal income growth was lower on average in relatively rural counties. Residents of rural counties also have lower incomes and less educational attainment on average. This is not surprising given the strong positive relationship between human capital and economic growth.

And during the most recent economic recovery, new business growth was especially weak in counties with less than 100,000 people. In fact, from 2010 to 2014 these counties actually lost businesses on net.

Conclusion:

Government spending on Individual and Family Services increased during the recession and has yet to return to pre-recession levels. Meanwhile, spending on education declined while spending on transportation slightly increased. This is consistent with other research that has found that state spending on health services, e.g. Medicaid, is crowding out spending in other areas.

Economic growth in Virginia was relatively strong from 2014 to 2015 but was not evenly distributed across the state. The counties with the smallest percentage changes in personal income are relatively rural while the counties with the largest gains are more urban. This is consistent with national patterns and other economic data revealing an urban-rural economic gap in and around Virginia.


[1] Personal Income is defined by the BEA as “the income received by, or on behalf of, all persons from all sources: from participation as laborers in production, from owning a home or business, from the ownership of financial assets, and from government and business in the form of transfers. It includes income from domestic sources as well as the rest of world. It does not include realized or unrealized capital gains or losses.” For more information about personal income see https://www.bea.gov/newsreleases/regional/lapi/lapi_newsrelease.htm

Innovation and economic growth in the early 20th century and lessons for today

Economic growth is vital for improving our lives and the primary long-run determinant of economic growth is innovation. More innovation means better products, more choices for consumers and a higher standard of living. Worldwide, hundreds of millions of people have been lifted out of poverty due to the economic growth that has occurred in many countries since the 1970s.

The effect of innovation on economic growth has been heavily analyzed using data from the post-WWII period, but there is considerably less work that examines the relationship between innovation and economic growth during earlier time periods. An interesting new working paper by Ufuk Akcigit, John Grigsby and Tom Nicholas that examines innovation across America during the late 19th and early 20th century helps fill in this gap.

The authors examine innovation and inventors in the U.S. during this period using U.S. patent data and census data from 1880 to 1940. The figure below shows the geographic distribution of inventiveness in 1940. Darker colors mean higher rates of inventive activity.

geography of inventiveness 1940

Most of the inventive activity in 1940 was in the industrial Midwest and Northeast, with California being the most notable western exception.

The next figure depicts the relationship between the log of the total number of patents granted to inventors in each state from 1900 to 2000 (x-axis) and annualized GDP growth (y-axis) over the same period for the 48 contiguous states.

innovation, long run growth US states

As shown there is a strong positive relationship between this measure of innovation and economic growth. The authors also conduct multi-variable regression analyses, including an instrumental variable analysis, and find the same positive relationship.

The better understand why certain states had more inventive activity than others in the early 20th century, the authors analyze several factors: 1) urbanization, 2) access to capital, 3) geographic connectedness and 4) openness to new ideas.

The figures below show the more urbanization was associated with more innovation from 1940 to 1960. The left figure plots the percent of people in each state living in an urban area in 1940 on the x-axis while the right has the percent living on a farm on the x-axis. Both figures tell the same story—rural states were less innovative.

pop density, innovation 1940-1960

Next, the authors look at the financial health of each state using deposits per capita as their measure. A stable, well-funded banking system makes it easier for inventors to get the capital they need to innovate. The figure below shows the positive relationship between deposits per capita in 1920 and patent production from 1920 to 1930.

innovation, bank deposits 1920-1940

The size of the market should also matter to inventors, since greater access to consumers means more sales and profits from successful inventions. The figures below show the relationship between a state’s transport cost advantage (x-axis) and innovation. The left figure depicts all of the states while the right omits the less populated, more geographically isolated Western states.

innovation, transport costs 1920-1940

States with a greater transport cost advantage in 1920—i.e. less economically isolated—were more innovative from 1920 to 1940, and this relationship is stronger when states in the far West are removed.

The last relationship the authors examine is that between innovation and openness to new, potentially disruptive ideas. One of their proxies for openness is the percent of families who owned slaves in a state, with more slave ownership being a sign of less openness to change and innovation.

innovation, slavery 1880-1940

The figures show that more slave ownership in 1860 was associated with less innovation at the state-level from 1880 to 1940. This negative relationship holds when all states are included (left figure) and when states with no slave ownership in 1860—which includes many Northern states—are omitted (right figure).

The authors also analyze individual-level data and find that inventors of the early 20th century were more likely to migrate across state lines than the rest of the population. Additionally, they find that conditional on moving, inventors tended to migrate to states that were more urbanized, had higher bank deposits per capita and had lower rates of historical slave ownership.

Next, the relationship between innovation and inequality is examined. Inequality has been a hot topic the last several years, with many people citing research by economists Thomas Piketty and Emmanuel Saez that argues that inequality has increased in the U.S. since the 1970s. The methods and data used to construct some of the most notable evidence of increasing inequality has been criticized, but this has not made the topic any less popular.

In theory, innovation has an ambiguous effect on inequality. If there is a lot of regulation and high barriers to entry, the profits from innovation may primarily accrue to large established companies, which would tend to increase inequality.

On the other hand, new firms that create innovative new products can erode the market share and profits of larger, richer firms, and this would tend to decrease inequality. This idea of innovation aligns with economist Joseph Schumpeter’s “creative destruction”.

So what was going on in the early 20th century? The figure below shows the relationship between innovation and two measures of state-level inequality: the ratio of the 90th percentile wage over the 10th percentile wage in 1940 and the wage income Gini coefficient in 1940. For each measure, a smaller value means less inequality.

innovation, inc inequality 1920-1940

As shown in the figures above, a higher patent rate is correlated with less inequality. However, only the result using 90-10 ratio remains statistically significant when each state’s occupation mix is controlled for in a multi-variable regression.

The authors also find that when the share of income controlled by the top 1% of earners is used as the measure of inequality, the relationship between innovation and inequality makes a U shape. That is, innovation decreases inequality up to a point, but after that point it’s associated with more inequality.

Thus when using the broader measures of inequality (90-10 ratio, Gini coeffecieint) innovation is negatively correlated with inequality, but when using a measure of top-end inequality (income controlled by top 1%) the relationship is less clear. This shows that inequality results are sensitive to the measurement of inequality used.

Social mobility is an important measure of economic opportunity within a society and the figure below shows that innovation is positively correlated with greater social mobility.

innovation, social mobility 1940

The measure of social mobility used is the percentage of people who have a high-skill occupation in 1940 given that they had a low-skill father (y-axis). States with more innovation from 1920 to 1940 had more social mobility according to this measure.

In the early 20th century it appears that innovation improved social mobility and decreased inequality, though the latter result is sensitive to the measurement of inequality. However, the two concepts are not equally important: Economic and social mobility are worthy societal ideals that require opportunity to be available to all, while static income or wealth inequality is largely a red herring that distracts us from more important issues. And once you take into account the consumer-benefits of innovation during this period—electricity, the automobile, refrigeration etc.—it is clear that innovation does far more good than harm.

This paper is interesting and useful for several reasons. First, it shows that innovation is important for economic growth over a long time period for one country. It also shows that more innovation occurred in denser, urbanized states that provided better access to capital, were more interconnected and were more open to new, disruptive ideas. These results are consistent with what economists have found using more recent data, but this research provides evidence that these relationships have existed over a much longer time period.

The positive relationships between innovation and income equality/social mobility in the early 20th century should also help alleviate the fears some people have about the negative effects of creative destruction. Innovation inevitably creates adjustment costs that harm some people, but during this period it doesn’t appear that it caused widespread harm to workers.

If we reduce regulation today in order to encourage more innovation and competition we will likely experience similar results, along with more economic growth and all of the consumer benefits.

Why Do We Get So Much Regulation?

Over the past 60 or 70 years, levels of regulation in the United States have been on the rise by almost any measure. As evidence, in the year 1950 there were only 9,745 pages in the US Code of Federal Regulations. Today that number is over 178,000 pages. There is less information about regulation at the state level, but anecdotal evidence suggests regulation is on the rise there too. For example, the Commonwealth of Kentucky publishes its regulatory code each year in a series of volumes known as the Kentucky Administrative Regulations Service (KARS). These volumes consist of books, each roughly 400 or 500 pages or so in length. In 1975, there were 4 books in the KARS. By 2015, that number had risen to 14 books. There are many different theories as to why so much regulation gets produced, so it makes sense to review some of those theories in order to explain the phenomenon of regulatory accumulation.

Perhaps the most popular theory of regulation is that it exists to advance the public interest. According to this view, well-intended regulators intervene in the marketplace due to “market failures”, which are situations where the market fails to allocate resources optimally. Some common examples of market failures include externalities (cases where third parties are impacted by the transactions involving others), asymmetric information (cases where buyers and sellers possess different levels of information about products being sold), public goods problems (whereby certain items are under-provided or not provided at all by the market), and concentration of industry in the form of monopoly power. When market failure occurs, the idea is that regulators intervene in order to make imperfect markets behave more like theoretically perfect markets.

Other theories of regulation are less optimistic about the motivations of the different participants in the rulemaking process. One popular theory suggests regulators work primarily to help powerful special interest groups, a phenomenon known as regulatory capture. Under this view—commonly associated with the writings of University of Chicago economist George Stigler—regulators fix prices and limit entry into an industry because it benefits the industry being regulated. An example would be how regulators, up until the late 1970s, fixed airline prices above what they would have been in a competitive market.

The interest groups that “capture” regulatory agencies are most often thought to be businesses, but it’s important to remember that agencies can also be captured by other groups. The revolving door between the government and the private sector doesn’t end with large banks. It also extends to nonprofit groups, labor unions, and activist groups of various kinds that also wield significant resources and power.

The “public choice theory” of regulation posits that public officials are primarily self-interested, rather than being focused on advancing the public interest. Under this view, regulators may be most concerned with increasing their own salaries or budgets. Or, they may be focused primarily on concentrating their own power.

It’s also possible that regulators are not nearly so calculating and rational as this. The behavioral public choice theory of regulation suggests regulators behave irrationally in many cases, due to the cognitive limitations inherent in all human beings. A case in point is how regulatory agencies routinely overestimate risks, or try to regulate already very low risks down to zero. There is significant evidence that people, including regulators, tend to overestimate small probability risks, leading to responses that are disproportionate to the expected harm. For example, the Environmental Protection Agency’s evaluations of sites related to the Superfund clean-up project routinely overestimated risks by orders of magnitude. Such overreactions might also be a response to public perceptions, for example in response to high-profile media events, such as following acts of terrorism. If the public’s reactions carry over into the voting booth, then legislation and regulation may be enacted soon after.

One of the more interesting and novel theories as to why we see regulation relates to public trust in institutions. A 2010 paper in the Quarterly Journal of Economics noted that there is a strong correlation between trust in various social institutions and some measures of regulation. The figure below is an example of this relationship, found in the paper.

QJE trust

Trust can relate to public institutions, such as the government, but it also extends to trust in corporations and in our fellow citizens. Interestingly, the authors of the QJE article argue that an environment of low trust and high regulation can be a self-fulfilling prophecy. Low levels of trust, ironically, can lead to more demand for regulation, even when there is little trust in the government. One reason for this might be that people think that giving an untrustworthy government control over private affairs is still superior to allowing unscrupulous businesses to have free rein.

The flip-side of this situation is that in high-trust countries, such as Sweden, the public demands lower levels of regulation and this can breed more trust. So an environment of free-market policies combined with trustworthy businesses can produce good market outcomes, more trust, and this too can be a self-fulfilling, allowing some countries to maintain a “good” equilibrium.

This is concerning for the United States because trust has been on the decline in a whole host of areas. A Gallop survey has been asking questions related to trust in public institutions for several decades. There is a long-term secular decline in Gallup’s broad measure of trust, as evidenced by the figure below, although periodically there are upswings in the measure.

gallup trust

Pew has a similar survey that looks at public trust in the government. Here the decline is even more evident.

pew trust

Given that regulation has been on the rise for decades, a decline in trust in the government, in corporations, and in each other, may be a key reason this is occurring. Of course, it’s possible that these groups are simply dishonest and do not merit public trust. Nonetheless, the US might find itself stuck in a self-fulfilling situation, whereby distrust breeds more government intervention in the economy, worse market outcomes, and even more distrust in the future. Getting out of that kind of situation is not easy. One way might be through education about the institutions that lead to free and prosperous societies, as well as to create a culture whereby corruption and unscrupulous behavior are discouraged.

There are a number of theories that seek to explain why regulation comes about. No theory is perfect, and some theories explain certain situations better than others. Nonetheless, the theories presented here go a long way towards laying out the forces that lead to regulation, even if no one theory can explain all regulation at all times.

High-speed rail: is this year different?

Many U.S. cities are racing to develop high speed rail systems that shorten commute times and develop the economy for residents. These trains are able to reach speeds over 124 mph, sometimes even as high as 374 mph as in the case of Japan’s record-breaking trains. Despite this potential, American cities haven’t quite had the success of other countries. In 2009, the Obama administration awarded almost a billion dollars of stimulus money to Wisconsin to build a high-speed rail line connection between Milwaukee and Madison, and possibly to the Twin Cities, but that project was derailed. Now, the Trump administration has plans to support a high-speed rail project in Texas. Given so many failed attempts in the U.S., it’s fair to ask if this time is different. And if it is, will high-speed rail bring the benefits that proponents claim it to have?

The argument for building high-speed rail lines usually entails promises of faster trips, better connections between major cities, and economic growth as a result. It almost seems like a no-brainer – why would any city not want to pursue something like this? The answer, like with most public policy questions, depends on the costs, and whether the benefits actually realize.

In a forthcoming paper for the Mercatus Center, transportation scholar Kenneth Button explores these questions by studying the high-speed rail experiences of Spain, Japan, and China; the countries with the three largest systems (measured by network length). Although there are benefits to these rail systems, Button cautions against focusing too narrowly on them as models, primarily because what works in one area can’t necessarily be easily replicated in another.

Most major systems in other countries have been the result of large public investment and built with each area’s unique geography and political environment kept in mind. Taking their approaches and trying to apply them to American cities not only ignores how these factors can differ, but also how much costs can differ. For example, the average infrastructure unit price of high-speed rail in Europe is between $17 and $24 million per mile and the estimated cost for proposals in California is conservatively estimated at $35 million per mile.

The cost side of the equation is often overlooked, and more attention is given to the benefit side. Button explains that the main potential benefit – generating economic growth – doesn’t always live up to expectations. The realized growth effects are usually minimal, and sometimes even negative. Despite this, proponents of high-speed rail oversell them. The process of thinking through high-speed rail as a sound public investment is often short-lived.

The goal is to generate new economic activity, not merely replace or divert it from elsewhere. In Japan, for example, only six percent of the traffic on the Sanyo Shinkansen line was newly generated, while 55 percent came from other rail lines, 23 percent from air, and 16 percent from inter-city bus. In China, after the Nanguang and Guiguang lines began operating in 2014, a World Bank survey found that many of the passengers would have made the journey along these commutes through some other form of transportation if the high-speed rail option wasn’t there. The passengers who chose this new transport method surely benefited from shorter travel times, but this should not be confused with net growth across the economy.

Even if diverted away from other transport modes, the amount of high-speed rail traffic Japan and China have generated is commendable. Spain’s system, however, has not been as successful. Its network has only generated about 5 percent of Japan’s passenger volume. A line between Perpignan, France and Figueres, Spain that began services in 2009 severely fell short of projected traffic. Originally, it was expected to run 19,000 trains per year, but has only reached 800 trains by 2015.

There is also evidence that high speed rail systems poorly re-distribute activity geographically. This is especially concerning given the fact that projects are often sold on a promise of promoting regional equity and reducing congestion in over-heating areas. You can plan a track between well-developed and less-developed regions, but this does not guarantee that growth for both will follow. The Shinkansen system delivers much of Japan’s workforce to Tokyo, for example, but does not spread much employment away from the capital. In fact, faster growth happened where it was already expected, even before the high-speed rail was planned or built. Additionally, the Tokyo-Osaka Shinkansan line in particular has strengthened the relative economic position of Tokyo and Osaka while weakening those of cities not served.

Passenger volume and line access are not – and should not be – the only metrics of success. Academics have exhibited a fair amount of skepticism regarding high-speed rail’s ability to meet other objectives. When it comes to investment value, many cases have resulted in much lower returns than expected. A recent, extreme example of this is California’s bullet train that is 50 percent over its planned budget; not to mention being seven years behind in its building schedule.

The project in California has been deemed a lost cause by many, but other projects have gained more momentum in the past year. North American High Speed Rail Group has proposed a rail line between Rochester and the Twin Cities, and if it gets approval from city officials, it plans to finance entirely with private money. The main drawback of the project is that it would require the use of eminent domain to take the property of existing businesses that are in the way of the planned line path. Private companies trying to use eminent domain to get past a roadblock like this often do so claiming that it is for the “public benefit.” Given that many residents have resisted the North American High Speed Rail Group’s plans, trying to force the use of eminent domain would likely only destroy value; reallocating property from a higher-value to a lower-value use.

Past Mercatus research has found that using eminent domain powers for redevelopment purposes – i.e. by taking from one private company and giving to another – can cause the tax base to shrink as a result of decreases in private investment. Or in other words, when entrepreneurs see that the projects that they invest in could easily be taken if another business owner makes the case to city officials, it would in turn discourage future investors from moving into the same area. This ironically discourages development and the government’s revenues suffer as a result.

Florida’s Brightline might have found a way around this. Instead of trying to take the property of other businesses and homes in its way, the company has raised money to re-purpose existing tracks already between Miami and West Palm Beach. If implemented successfully, this will be the first privately run and operated rail service launched in the U.S. in over 100 years. And it doesn’t require using eminent domain or the use of taxpayer dollars to jump-start that, like any investment, has risk of being a failure; factors that reduce the cost side of the equation from the public’s perspective.

Which brings us back to the Houston-to-Dallas line that Trump appears to be getting behind. How does that plan stack up to these other projects? For one, it would require eminent domain to take from rural landowners in order to build a line that would primarily benefit city residents. Federal intervention would require picking a winner and loser at the offset. Additionally, there is no guarantee that building of the line would bring about the economic development that many proponents promise. Button’s new paper suggests that it’s fair to be skeptical.

I’m not making the argument that high-speed rail in America should be abandoned altogether. Progress in Florida demonstrates that maybe in the right conditions and with the right timing, it could be cost-effective. The authors of a 2013 study echo this by writing:

“In the end, HSR’s effect on economic and urban development can be characterized as analogous to a fertilizer’s effect on crop growth: it is one ingredient that could stimulate economic growth, but other ingredients must be present.”

For cities that can’t seem to mix up the right ingredients, they can look to other options for reaching the same goals. In fact, a review of the economic literature finds that investing in road infrastructure is a much better investment than other transportation methods like airports, railways, or ports. Or like I’ve discussed previously, being more welcoming to new technologies like driver-less cars has the potential to both reduce congestion and generate significant economic gains.

Economic policies and institutions matter

Economists often talk about the important role institutions and policies play in generating economic growth. A new paper that examines the role of urban governance and city-level productivity provides some additional, indirect evidence that institutions and policies impact economic productivity at the local level. (The focus of the paper is how administrative fragmentation affects city-level productivity, not what I present here, but I thought the following was interesting nonetheless.)

The authors graph the correlation between city population and city productivity for five different countries. There is a positive relationship between population and productivity in all of the countries, which is consistent with other studies that find a similar relationship. This relationship is largely due to agglomeration economies and the greater degree of specialization within large cities.

One of the figures from the study—for the U.S.—is shown below. City productivity is measured on the y-axis and the natural log of city population is on the x-axis. (Technical note for those interested: city productivity is measured as the coefficient on a city dummy variable in an individual-level log hourly wage/earnings regression that also controls for gender, age, age squared, education and occupation. This strips away observable characteristics of the population that may affect city productivity.)

US city productivity

Source: Ahrend, Rudiger, et al. “What makes cities more productive? Evidence from five OECD countries on the role of urban governance.” Journal of Regional Science 2017

 

As shown in the graph there is a relatively tight, positive relationship between size and productivity. The two noticeable outlies are El Paso and McAllen, TX, both of which are on the border with Mexico.

The next figure depicts the same information but for cities in Germany.

german city size, product graph

What’s interesting about this figure is that there is a cluster of outliers in the bottom left, which weakens the overall relationship. The cities in this cluster are less productive than one would expect based on their population. These cities also have another thing in common: They are located in or near what was East Germany. The authors comment on this:

“In Germany, the most noteworthy feature is probably the strong east-west divide, with city productivity premiums in eastern German cities being, on the whole, significantly below the levels found in western German cities of comparable size. In line with this finding, the city productivity premium in Berlin lies in between the trends in eastern and western Germany.”

The data used to construct these figures are from 2007, 17 years after the unification of Germany. After WWII and until 1990, East Germany was under communist control and had a centrally planned economy, complete with price controls and production quotas, while West Germany had a democratic government and market economy.

Since 1990, both areas have operated under the same country-level rules and institutions, but as shown above the productivity difference between the two regions persisted. This is evidence that it can take a considerable amount of time for an area to overcome damaging economic policies.

Why the lack of labor mobility in the U.S. is a problem and how we can fix it

Many researchers have found evidence that mobility in the U.S. is declining. More specifically, it doesn’t appear that people move from places with weaker economies to places with stronger economies as consistently as they did in the past. Two sets of figures from a paper by Peter Ganong and Daniel Shoag succinctly show this decline over time.

The first, shown below, has log income per capita by state on the x-axis for two different years, 1940 (left) and 1990 (right). On the vertical axis of each graph is the annual population growth rate by state for two periods, 1940 – 1960 (left) and 1990 – 2010 (right).

directed migration ganong, shoag

In the 1940 – 1960 period, the graph depicts a strong positive relationship: States with higher per capita incomes in 1940 experienced more population growth over the next 20 years than states with lower per capita incomes. This relationship disappears and actually reverses in the 1990 – 2010 period: States with higher per capita incomes actually grew slower on average. So in general people became less likely to move to states with higher incomes between the middle and end of the 20th century. Other researchers have also found that people are not moving to areas with better economies.

This had an effect on income convergence, as shown in the next set of figures. In the 1940 – 1960 period (left), states with higher per capita incomes experienced less income growth than states with lower per capita incomes, as shown by the negative relationship. This negative relationship existed in the 1990 – 2010 period as well, but it was much weaker.

income convergence ganong, shoag

We would expect income convergence when workers leave low income states for high income states, since that increases the labor supply in high-income states and pushes down wages. Meanwhile, the labor supply decreases in low-income states which increases wages. Overall, this leads to per capita incomes converging across states.

Why labor mobility matters

As law professor David Schleicher points out in a recent paper, the current lack of labor mobility can reduce the ability of the federal government to manage the U.S. economy. In the U.S. we have a common currency—every state uses the U.S. dollar. This means that if a state is hit by an economic shock, e.g. low energy prices harm Texas, Alaska and North Dakota but help other states, that state’s currency cannot adjust to cushion the blow.

For example, if the UK goes into a recession, the Bank of England can print more money so that the pound will depreciate relative to other currencies, making goods produced in the UK relatively cheap. This will decrease the UK’s imports and increase economic activity and exports, which will help it emerge from the recession. If the U.S. as a whole suffered a negative economic shock, a similar process would take place.

However, within a country this adjustment mechanism is unavailable: Texas can’t devalue its dollar relative to Ohio’s dollar. There is no within-country monetary policy that can help particular states or regions. Instead, the movement of capital and labor from weak areas to strong areas is the primary mechanism available for restoring full employment within the U.S. If capital and labor mobility are low it will take longer for the U.S. to recover from area-specific negative economic shocks.

State or area-specific economic shocks are more likely in large countries like the U.S. that have very diverse local economies. This makes labor and capital mobility more important in the U.S. than in smaller, less economically diverse countries such as Denmark or Switzerland, since those countries are less susceptible to area-specific economic shocks.

Why labor mobility is low

There is some consensus about policies that can increase labor mobility. Many people, including former President Barack Obama, my colleagues at the Mercatus Center and others, have pointed out that state occupational licensing makes it harder for workers in licensed professions to move across state borders. There is similar agreement that land-use regulations increase housing prices which makes it harder for people to move to areas with the strongest economies.

Reducing occupational licensing and land-use regulations would increase labor mobility, but actually doing these things is not easy. Occupational licensing and land-use regulations are controlled at the state and local level, so currently there is little that the federal government can do.

Moreover, as Mr. Schleicher points out in his paper, state and local governments created these regulations for a reason and it’s not clear that they have any incentive to change them. Like all politicians, state and local ones care about being re-elected and that means, at least to some extent, listening to their constituents. These residents usually value stability, so politicians who advocate too strongly for growth may find themselves out of office. Mr. Schleicher also notes that incumbent politicians often prefer a stable, immobile electorate because it means that the voters who elected them in the first place will be there next election cycle.

Occupational licensing and land-use regulations make it harder for people to enter thriving local economies, but other policies make it harder to leave areas with poor economies. Nearly 13% of Americans work for state and local governments and 92% of them have a defined-benefit pension plan. Defined-benefit plans have long vesting periods and benefits can be significantly smaller if employees split their career between multiple employers rather than remain at one employer. Thus over 10% of the workforce has a strong retirement-based incentive to stay where they are.

Eligibility standards for public benefits and their amounts also vary by state, and this discourages people who receive benefits such as Temporary Assistance for Needy Families (TANF) from moving to states that may have a stronger economy but less benefits. Even when eligibility standards and benefits are similar, the paperwork and time burden of enrolling in a new state can discourage mobility.

The federal government subsidizes home ownership as well, and homeownership is correlated with less labor mobility over time. Place-based subsidies to declining cities also artificially support areas that should have less people. As long as state and federal governments subsidize government services in cities like Atlantic City and Detroit people will be less inclined to leave them. People-based subsidies that incentivize people to move to thriving areas are an alternative that is likely better for the taxpayer, the recipient and the country in the long run.

How to increase labor mobility

Since state and local governments are unlikely to directly address the impediments to labor mobility that they have created, Mr. Schleicher argues for more federal involvement. Some of his suggestions don’t interfere with local control, such as a federal clearinghouse for coordinated occupational-licensing rules across states. This is not a bad idea but I am not sure how effective it would be.

Other suggestions are more intrusive and range from complete federal preemption of state and local rules to federal grants that encourage more housing construction or suspension of the mortgage-interest deduction in places that restrict housing construction.

Local control is important due to the presence of local knowledge and the beneficial effects that arise from interjurisdictional competition, so I don’t support complete federal preemption of local rules. Economist William Fischel also thinks the mortgage interest deduction is largely responsible for excessive local land-use regulation, so eliminating it altogether or suspending it in places that don’t allow enough new housing seems like a good idea.

I also support more people-based subsidies that incentivize moving to areas with better economies and less place-based subsidies. These subsidies could target people living in specific places and the amounts could be based on the economic characteristics of the destination, with larger amounts given to people who are willing to move to areas with the most employment opportunities and/or highest wages.

Making it easier for people to retain any state-based government benefits across state lines would also help improve labor mobility. I support reforms that reduce the paperwork and time requirements for transferring benefits or for simply understanding what steps need to be taken to do so.

Several policy changes will need to occur before we can expect to see significant changes in labor mobility. There is broad agreement around some of them, such as occupational licensing and land-use regulation reform, but bringing them to fruition will take time. As for the less popular ideas, it will be interesting to see which, if any, are tried.

Decreasing congestion with driverless cars

Traffic is aggravating. Especially for San Francisco residents. According to Texas A&M Transportation Institute, traffic congestion in the San Francisco-Oakland CA area costs the average auto commuter 78 hours per year in extra travel time, $1,675 for their travel time delays, and an extra 33 gallons of gas compared to free-flow traffic conditions. That means the average commuter spends more than three full days stuck in traffic each year. Unfortunately for these commuters, a potential solution to their problems just left town.

Last month, after California officials told Uber to stop its pilot self-driving car program because it lacked the necessary state permits for autonomous driving, Uber decided to relocate the program from San Francisco to Phoenix, Arizona. In an attempt to alleviate safety concerns, these self-driving cars are not yet driverless, but they do have the potential to reduce the number of cars on the road. Other companies like Google, Tesla, and Ford have expressed plans to develop similar technologies, and some experts predict that completely driverless cars will be on the road by 2021.

Until then, however, cities like San Francisco will continue to suffer from the most severe congestion in the country. Commuters in these cities experience serious delays, higher gasoline usage, and lost time behind the wheel. If you live in any of these areas, you are probably very familiar with the mind-numbing effect of sitting through sluggish traffic.

It shouldn’t be surprising then that these costs could culminate into a larger problem for economic growth. New Mercatus research finds that traffic congestion can significantly harm economic growth and concludes with optimistic predictions for how autonomous vehicle usage could help.

Brookings Senior Fellow Clifford Winston and Yale JD candidate Quentin Karpilow find significant negative effects of traffic congestion on the growth rates of California counties’ gross domestic product (GDP), employment, wages, and commodity freight flows. They find that a 10% reduction in congestion in a California urban area increases both job and GDP growth by roughly 0.25% and wage growth to increase by approximately 0.18%.

This is the first comprehensive model built to understand how traffic harms the economy, and it builds on past research that has found that highway congestion leads to slower job growth. Similarly, congestion in West Coast ports, which occurs while dockworkers and marine terminal employers negotiate contracts, has caused perishable commodities to go bad, resulting in a 0.2 percentage point reduction in GDP during the first quarter of 2015.

There are two main ways to solve the congestion problem; either by reducing the number of cars on the road or by increasing road capacity. Economists have found that the “build more roads” method in application has actually been quite wasteful and usually only induces additional highway traffic that quickly fills the new road capacity.

A common proposal for the alternative method of reducing the number of cars on the road is to implement congestion pricing, or highway tolls that change based on the number of drivers using the road. Increasing the cost of travel during peak travel times incentivizes drivers to think more strategically about when they plan their trips; usually shifting less essential trips to a different time or by carpooling. Another Mercatus study finds that different forms of congestion pricing have been effective at reducing traffic congestion internationally in London and Stockholm as well as for cities in Southern California.

The main drawback of this proposal, however, is the political difficulty of implementation, especially with interstate highways that involve more than one jurisdiction to approve it. Even though surveys show that drivers generally change their mind towards supporting congestion pricing after they experience the lower congestion that results from tolling, getting them on board in the first place can be difficult.

Those skeptical of congestion pricing, or merely looking for a less challenging policy to implement, should look forward to the new growing technology of driverless cars. The authors of the recent Mercatus study, Winston and Karpilow, find that the adoption of autonomous vehicles could have large macroeconomic stimulative effects.

For California specifically, even if just half of vehicles became driverless, this would create nearly 350,000 additional jobs, increase the state’s GDP by $35 billion, and raise workers’ earnings nearly $15 billion. Extrapolating this to the whole country, this could add at least 3 million jobs, raise the nation’s annual growth rate 1.8 percentage points, and raise annual labor earnings more than $100 billion.

What would this mean for the most congested cities? Using Winston and Karpilow’s estimates, I calculated how reduced congestion from increased autonomous car usage could affect Metropolitan Statistical Areas (MSAs) that include New York City, Los Angeles, Boston, San Francisco, and the DC area. The first chart shows the number of jobs that would have been added in 2011 if 50% of motor vehicles had been driverless. The second chart shows how this would affect real GDP per capita, revealing that the San Francisco MSA would have the most to gain, but with the others following close behind.

jobsadd_autonomousvehicles realgdp_autonomousvehicles

As with any new technology, there is uncertainty with how exactly autonomous cars will be fully developed and integrated into cities. But with pilot programs already being implemented by Uber in Pittsburgh and nuTonomy in Singapore, it is becoming clear that the technology’s efficacy is growing.

With approximately $1,332 GDP per capita and 45,318 potential jobs on the table for the San Francisco Metropolitan Statistical Area, it is a shame that San Francisco just missed a chance to realize some of these gains and to be at the forefront of driving progress in autonomous vehicle implementation.

Today’s public policies exacerbate our differences

The evidence that land-use regulations harm potential migrants keeps piling up. A recent paper in the Journal of Urban Economics finds that young workers (age 22 – 26) of average ability who enter the labor force in a large city (metropolitan areas with a population > 1.5 million) earn a wage premium equal 22.9% after 5 years.

The author also finds that high-ability workers experience additional wage growth in large cities but not in small cities or rural areas. This leads to high-ability workers sorting themselves into large cities and contributes an additional 3.2% to the urban wage-growth premium.

These findings are consistent with several other papers that have analyzed the urban wage premium. Potential causes of the wage premium are faster human capital accumulation in denser, more populated places due to knowledge spillovers and more efficient labor markets that better match employers and employees.

The high cost of housing in San Francisco, D.C., New York and dozens of other cities is preventing many young people from earning more money and improving their lives. City officials and residents need to strike a better balance between maintaining the “charm” of their neighborhoods and affordability. This means less regulation and more building.

City vs. rural is only one of the many dichotomies pundits have been discussing since the 2016 election. Some of the other versions of “two Americas” are educated vs. non-educated, white collar vs. blue collar, and rich vs. poor. We can debate how much these differences matter, but to the extent that they are an issue for the country our public policies have reinforced the barriers that allow them to persist.

Occupational licensing makes it more difficult for blue-collar manufacturing workers to transition to middle-class service sector jobs. Federal loan subsidies have made four-year colleges artificially cheap to the detriment of people with only a high school education. Restrictive zoning has made it too expensive for many people to move to places with the best labor markets. And once you’re in a city, unless you’re in one of the best neighborhoods your fellow citizens often keep employers and providers of much needed consumer staples like Wal-Mart out, while using eminent domain to build their next playground.

Over time people have sorted themselves into different groups and then erected barriers to keep others out. Communities do it with land-use regulations, occupations do it with licensing and established firms do it with regulatory capture. If we want a more prosperous America that de-emphasizes our differences and provides people of all backgrounds with opportunity we need more “live and let live” and less “my way or the highway”.

Solving the Public Pension Crisis

Last week I had the pleasure of attending a public policy conference that brought together many scholars who study public pensions to share what they have learned from their research. The crisis – growing unfunded pension liabilities and resulting fiscal distress for states and municipalities – laid as the foundation of the day. Hosted by GMU’s Law & Economics Center, the conference featured several panel discussions framed around different aspects of how to both diagnose the cause of this growing problem and hopefully find solutions to address the problem.

Professor Robert Inman of the University of Pennsylvania presented a helpful categorization of the different avenues to address the public pension crisis. He explained that as a reformer, you can either put stock in (1) courts, (2) markets, or (3) politics to solve the public policy problem. The next question is, which avenue is most effective at making pensions solvent while also keeping promises to beneficiaries?

First, take the courts. In municipal bankruptcy cases like that of Central Falls, Rhode Island; Stockton, California; and Detroit, Michigan, courts have ruled that reductions in benefits of current public workers and retirees are legally allowed. Until these rulings, however, it was thought to be almost impossible do such a thing. These cities employed reforms ranging from cutting payments to reducing current benefit formulas. By contrast, the state supreme court of Illinois has ruled similar cuts unconstitutional. It will be interesting to see how these conflicting legal precedents will affect future cases and what it will mean for the benefits of public workers.

However this legal discussion unfolds, it will certainly affect the courts as an avenue for solving the pension crisis. Strict rulings prevent states from cutting pension benefits of current workers, but they also require states to keep their promises, especially when it is politically hardest – during times of fiscal stress.

Times of fiscal stress are often prompted by a combination of factors. Growing unfunded liabilities, not enough cash in reserves, and poorly structured tax systems can all come together to really put policymakers in a tough spot and often leaves a large bill for taxpayers. A struggling economy on top of all of this can really exacerbate the situation. The main difference between the first three things and a struggling economy is that the latter is largely out of a policymaker’s control.

Despite this, many policymakers rely on the market to get them out of tough times. From the policymaker’s perspective “relying on the market” to solve the pension crisis usually means something different than what it means for an economist. This phrase for the policymaker usually entails reaping the benefits of more taxes generated from an economic boom or relying on high investment returns to improve the performance of pension funds.

Not only are the timing of economic booms fairly unpredictable, but they also don’t guarantee to solve all of your problems when they do occur. The growing city of Austin, Texas, for example, is facing budgetary pressures and only has enough money to pay for about two-thirds of the benefits workers have already earned, demonstrating that even good economic times don’t exempt you from pension problems.

The good news is that what we learn from market interactions can be transferred to the political sphere in order to increase our understanding. One lesson we learn from markets is that individuals respond to incentives and that the institutional structure in which they act influences how this occurs. The importance of incentives and rules doesn’t change when going from markets to politics, but the way they manifest does.

At the Law and Economics conference, Anthony Randazzo of the Reason Foundation explained how there is a tangled web of factors causing inappropriate pension funding behavior. These factors create misaligned incentives between fiduciaries and taxpayers. One way this has manifested is that the pension funding policy process has been captured by elected officials who are more concerned with near-term budget allocation than long-term solvency.

My colleague Eileen Norcross and her co-author Sheila Weinberg expanded more on the type of behavior that Randazzo spoke of. In their paper titled “A Judge in their Own Cause: GASB 67/68 and the continued mis-measurement of public sector liabilities” they review how policymakers are incentivized by state and local accounting guidelines to underreport the true value of their pension liabilities. Two new accounting rules were implemented in fiscal year 2015 in an attempt to improve this, but as Norcross and Weinberg’s findings suggest, they have not had their intended effects.

For example, there is evidence that one of the rules, GASB 67, is creating incentives for pension actuaries to project robust funding levels far into the future in order to avoid calculating and reporting large unfunded liabilities in the present.

They sum up the effects of both rules in their conclusion:

“Though these measures are justified in providing flexibility and practicality for governments, they only contribute to an artificial picture of state’s true fiscal results and thus affect important decisions on how states use resources.”

Their analysis demonstrates just how important it is to study the incentives present in both the measurement of and the governance of public pension funds. Luckily, there is also work being done that attempts to understand exactly what type of rules can improve incentives facing policymakers.

Another paper, presented by Professor Odd Stalebrink of Penn State, touched upon this by examining how governance structures affect the investment performance of public pension funds. He found that pension systems are more likely to meet their performance targets if they are governed by an institutional structure that (1) extends plan autonomy, (2) places emphasis on transparency, and (3) limits inefficient investment practices. In states that exhibit more corruption, however, Stalebrink noted that plans might actually be better off with less autonomy, while still focusing on transparency and improving efficiency.

The discussion of these papers along with many others at the conference underscored that pension problem in the states multifaceted one. The question of what avenue to employ reform efforts through does not have a simple answer. Growing unfunded pension liabilities are a result of many factors across market, political, and legal spheres. It only makes sense that effective solutions will revolve around an understanding of all three areas.

Proceedings of the conference will be published in a special symposium issue of Scalia Law School’s Journal of Law, Economics & Policy.