Tag Archives: economy

The unseen costs of Amazon’s HQ2 Site Selection

Earlier this year Amazon narrowed down the list of potential cities to site its second headquarters. Applicants are now waiting out the selection process. It’s unclear when Amazon will make its choice, but that hasn’t stopped many from speculating who the likely contenders are. Varying sources report Atlanta, Boston, and Washington D.C. at the top of the list. The cities that didn’t make the cut are no doubt envious of the finalists, having just missed out on the potential for a $5 billion facility and 50,000 jobs. The second HQ is supposed to be as significant for economic growth as the company’s first site, which according to Amazon’s calculations contributed an additional $38 billion to Seattle’s economy between 2010 and 2016. There is clearly a lot to be gained by the winner.  But there are also many costs. Whichever city ends up winning the bid will be changed forever. What’s left out of the discussion is how the bidding process and corporate incentives affect the country.

Although the details of the proposals are not made public, each finalist is likely offering some combination of tax breaks, subsidies, and other incentives in return for the company’s choice to locate in their city. The very bidding process necessitates a lot of time and effort by many parties. It will certainly seem “worth it” to the winning party, but the losers aren’t getting back the time and effort they spent.

This practice of offering incentives for businesses has been employed by states and localities for decades, with increased usage over time. Targeted economic development incentives can take the form of tax exemptions, abatements, regulatory relief, and taxpayer assistance. They are but one explicit cost paid by states and cities looking to secure business, and there is a growing literature that suggests these policies are more costly than meets the eye.

First, there’s the issue of economic freedom. Recent Mercatus research suggests that there may be a tradeoff to offering economic development incentives like the ones that Amazon is receiving. Economists John Dove and Daniel Sutter find that states that spend more on targeted development incentives as a percentage of gross state product also have less overall economic freedom. The theoretical reasoning behind this is not very clear, but Dove and Sutter propose that it could be because state governments that use more subsidies or tax breaks to attract businesses will also spend more or raise taxes for everyone else in their state, resulting in less equitable treatment of their citizens and reducing overall economic freedom.

The authors define an area as having more economic freedom if it has lower levels of government spending, taxation, and labor market restrictions. They use the Fraser Institute’s Economic Freedom of North America Index (EFNA) to measure this. Of the three areas within the EFNA index, labor market freedom is the most affected by targeted economic development incentives. This means that labor market regulation such as the minimum wage, government employment, and union density are all significantly related to the use of targeted incentives.

Economic freedom can be ambiguous, however, and it’s sometimes hard to really grasp its impact on our lives. It sounds nice in theory, but because of its vagueness, it may not seem as appealing as a tangible economic development incentive package and the corresponding business attached to it. Economic freedom is associated with a series of other, more tangible benefits, including higher levels of income and faster economic growth. There’s also evidence that greater economic freedom is associated with urban development.

Not only is the practice of offering targeted incentives associated with lower economic freedom, but it is also indicative of other issues. Economists Peter Calcagno and Frank Hefner have found that states with budget issues, high tax and regulatory burdens, and poorly trained labor forces are also more likely to offer targeted incentives as a way to offset costly economic conditions. Or, in other words, targeted development incentives can be – and often are – used to compensate for a less than ideal business climate. Rather than reform preexisting fiscal or regulatory issues within a state, the status quo and the use of targeted incentives is the more politically feasible option.

Perhaps the most concerning aspect of Amazon’s bidding process is the effect it has on our culture. Ideally, economic development policy should be determined by healthy economic competition between states. In practice, it has evolved into more of an unhealthy interaction between private interests and political favor. Economists Joshua Jansa and Virginia Gray refer to this as cultural capture. They find increases in business political contributions to be positively correlated with state subsidy spending. Additionally, they express concern over the types of firms that these subsidies attract. There is a selection bias for targeted incentives to systematically favor “flighty firms” or firms that will simply relocate if better subsidies are offered by another state, or potentially threaten to leave in an effort to extract more subsidies.

None of these concerns even address the question of whether targeted incentives actually achieve their intended goals.  The evidence does not look good. In a review of the literature by my colleague Matthew Mitchell, and me, we found that of the studies that evaluate the effect of targeted incentives on the broader economy, only one study found a positive effect, whereas four studies found unanimously negative effects. Thirteen studies (half of the sample) found no statistically significant effect, and the remaining papers found mixed results in which some companies or industries won, but at the expense of others.

In addition to these unseen costs on the economy, some critics are beginning to question whether being chosen by Amazon is even worth it. Amazon’s first headquarters has been considered a catalyst for the city’s tech industry, but local government and business leaders have raised concerns about other possibly related issues such as gentrification, rising housing prices, and persistent construction and traffic congestion. There is less research on this, but it is worth considering.

It is up to each city’s policymakers to decide whether these trade-offs are worth it. I would argue, however, that much of the evidence points to targeted incentives – like the ones that cities are using to attract Amazon’s business – as having more costs than benefits. Targeted economic development incentives may seem to offer a lot of tangible benefits, but their unseen costs should not be overlooked. From the perspective of how they benefit each state’s economy as a whole, targeted incentives are detrimental to economic freedom as well as our culture surrounding corporate handouts. Last but not least, they may often be an attempt to cover up other issues that are unattractive to businesses.

Graduate School Opportunities Available Through Mercatus

One of the great parts of working at Mercatus is getting to interact with all of the bright and ambitious students that participate in our academic programs. Mercatus offers four unique graduate programs for students interested in political economy and public policy. The training and education that Mercatus provides are one of a kind.

As part of each program students get access to funding, practical experience, and a wide network of passionate, dedicated scholars. Many graduates from each program go on to develop successful careers in academia and public policy. Ninety-two percent of MA Fellowship graduates, for example, receive a job within 9 months of graduation. Whether you’re pursuing a Master’s, PhD, or law degree, there may be something for you at Mercatus.

The four programs and their details are below.  If you’re interested in learning more and applying, check out our website. Deadlines are right around the corner, with the PhD Fellowship deadline approaching at the end of this week.

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Mercatus PhD Fellowship

The PhD Fellowship is a competitive, full-time fellowship program for students pursuing a doctoral degree in economics at George Mason University. PhD Fellows take courses in market process economics, public choice, and institutional analysis and work on projects that use these lenses to understand global prosperity and change.

Students receive an award up to $200,000 (over five years) for full tuition support and a monthly stipend, as well as experience as a research assistant working closely with Mercatus-affiliated Mason faculty. The application deadline is February 1, 2018.

Mercatus MA Fellowship

The MA Fellowship is a tw0-year, competitive, full-time fellowship program for students pursuing a master’s degree in economics at George Mason University in preparation for a career in public policy. Fellows attend readings groups and career development workshops, spend at least 20 hours per week working with Mercatus scholars and staff, and complete a Mercatus Graduate Policy essay.

Students receive an award of up to $80,000 (over two years) for full tuition support and a monthly stipend, as well as practical experience conducting and disseminating research with Mercatus scholars and staff on pertinent policy issues. The application deadline is March 1, 2018.

Mercatus Adam Smith Fellowship

The Adam Smith Fellowship is a one-year, competitive fellowship program for PhD students at any university and in any discipline. The goal of this fellowship is to introduce students to a framework of ideas they may not otherwise encounter in their studies. Fellows meet a few times out of the year to engage in discussions on key foundational texts in the Austrian, Virginia, and Bloomington schools of political economy and learn how these texts may apply to their research interests.

Students receive a stipend up to $10,000 as well as travel, lodging, and all materials to attend workshops and seminars hosted by the Mercatus Center. The application deadline is March 15, 2018.

Mercatus Frédéric Bastiat Fellowship

The Frédéric Bastiat Fellowship is a one-year, competitive fellowship program for graduate students attending master’s, juris doctoral, and doctoral programs in a variety of disciplines. The goal of this fellowship is to introduce students to the Austrian, Virginia, and Bloomington school of political economy as academic foundations for pursuing contemporary policy analysis. Fellows meet a few times out of the year to engage in discussions on key foundational texts and interact with scholars that work on the cutting edge of policy analysis.

Students receive a stipend of up to $5,000 as well as travel, lodging, and all materials to attend workshops and seminars hosted by the Mercatus Center. The application deadline is March 15, 2018.

 

 

Manufacturing employment and the prime-age male LFP rate: What’s the relationship?

Recently I wrote about the decline in the U.S. prime-age male labor force participation (LFP) rate and discussed some of the factors that may have caused it. One of the demand-side factors that many people think played a role is the decline in manufacturing employment in the United States.

Manufacturing has typically been a male-dominated industry, especially for males with less formal education, but increases in automation and productivity have resulted in fewer manufacturing jobs in the United States over time. As manufacturing jobs disappeared, the story goes, so did a lot of economic opportunities for working-age men. The result has been men leaving the labor force.

However, the same decline in manufacturing employment occurred in other countries as well, yet many of them experienced much smaller declines in their prime-age male LFP rates. The table below shows the percent of employment in manufacturing in 1990 and 2012 for 10 OECD countries, as well as their 25 to 54 male LFP rates in 1990 and 2012. The manufacturing data come from the FRED website and the LFP data are from the OECD data site. The ten countries included here were chosen based on data availability and I think they provide a sample that can be reasonably compared to the United States.

country 25-54 LFP rate, manuf table

As shown in the table, all of the countries experienced a decline in manufacturing employment and labor force participation over this time period. Thus America was not unique in this regard.

But when changes in both variables are plotted on the same graph, the story that the decline in manufacturing employment caused the drop in male LFP rate doesn’t really hold up.

country 25-54 LFP rate, manuf scatter plot

The percentage point change in manufacturing employment is across the top on the x-axis and the percentage point change in the prime-age male LFP rate is on the y-axis. As shown in the graph the relationship between the two is negative in this sample, and the change in manufacturing employment explains almost 36% of the variation in LFP rate declines (the coefficient on the decline in manufacturing employment is -0.322 and the p-value is 0.08).

In other words, the countries that experienced the biggest drops in manufacturing employment experienced the smallest drops in their LFP rate, which is the opposite of what we would expect if the decline in manufacturing employment played a big role in the decline of the LFP rate across countries.

Of course, correlation does not mean causation and I find it hard to believe that declines in manufacturing employment actually improved LFP rates, all else equal. But I also think the less manufacturing, less labor force participation story is too simple, and this data supports that view.

America and Italy experienced similar declines in their male LFP rates but neither experienced the largest declines in manufacturing employment over this time period. What else is going on in America that caused its LFP decline to more closely resemble Italy’s than that of Canada, Australia and the UK, which are more similar to America along many dimensions?

Whatever the exact reasons are, it appears that American working-age males responded differently to the decline in manufacturing employment over the last 20 + years than similar males in similar countries. This could be due to our higher incarceration rate, the way our social safety net is constructed, differences between education systems, the strength of the economy overall or a number of other factors. But attributing the bulk of the blame to the decline of manufacturing employment doesn’t seem appropriate.

Many working-age males aren’t working: What should be done?

The steady disappearance of prime-age males (age 25-54) from the labor force has been occurring for decades and has recently become popular in policy circles. The prime-age male labor force participation rate began falling in the 1950s, and since January 1980 the percent of prime-age males not in the labor force has increased from 5.5% to 12.3%. In fact, since the economy started recovering from our latest recession in June 2009 the rate has increased by 1.3 percentage points.

The 12.3% of prime-age males not in the labor force nationwide masks substantial variation at the state level. The figure below shows the percentage of prime-age males not in the labor force—neither working nor looking for a job—by state in 2016 according to data from the Current Population Survey.

25-54 males NILF by state 2016

The lowest percentage was in Wyoming, where only 6.3% of prime males were out of the labor force. On the other end of the spectrum, over 20% of prime males were out of the labor force in West Virginia and Mississippi, a shocking number. Remember, prime-age males are generally not of school age and too young to retire, so the fact that one out of every five is not working or even looking for a job in some states is hard to fathom.

Several researchers have investigated the absence of these men from the labor force and there is some agreement on the cause. First, demand side factors play a role. The decline of manufacturing, traditionally a male dominated industry, reduced the demand for their labor. In a state like West Virginia, the decline of coal mining—another male dominated industry—has contributed as well.

Some of the most recent decline is due to less educated men dropping out as the demand for their skills continues to fall. Geographic mobility has also declined, so even when an adjacent state has a stronger labor market according to the figure above—for example West Virginia and Maryland—people aren’t moving to take advantage of it.

Of course, people lose jobs all the time yet most find another one. Moreover, if someone isn’t working, how do they support themselves? The long-term increase in female labor force participation has allowed some men to rely on their spouse for income. Other family members and friends may also help. There is also evidence that men are increasingly relying on government aid, such as disability insurance, to support themselves.

These last two reasons, relying on a family member’s income or government aid, are supply-side reasons, since they affect a person’s willingness to accept a job rather than the demand for a person’s labor. A report by Obama’s Council of Economic Advisors argued that supply-side reasons were only a small part of the decline in the prime-age male labor force participation rate and that the lack of demand was the real culprit:

“Reductions in labor supply—in other words, prime-age men choosing not to work for a given set of labor market conditions—explain relatively little of the long-run trend…In contrast, reductions in the demand for labor, especially for lower-skilled men, appear to be an important component of the decline in prime-age male labor force participation.”

Other researchers, however, are less convinced. For example, AEI’s Nicholas Eberstadt thinks that supply-side factors play a larger role than the CEA acknowledges and he discusses these in his book Men Without Work. One piece of evidence he notes is the different not-in-labor-force (NILF) rates of native born and foreign born prime-age males: Since one would think that structural demand shocks would affect both native and foreign-born alike, the difference indicates that some other factor may be at work.

In the figure below, I subtract the foreign born not-in-labor-force rate from the native born rate by state. A positive number means that native prime-age males are less likely to be in the labor force than foreign-born prime age males. (Note: Foreign born only means a person was born in a country other than the U.S.: It does not mean that the person is not a citizen at the time the data was collected.)

25-54 native, foreign NILF diff

As shown in the figure, natives are less likely to be in the labor force (positive bar) in 34 of the 51 areas (DC included). For example, in Texas the percent of native prime-age men not in the labor force is 12.9% and the percentage of foreign-born not in the labor force is 5.9%, a 7 percentage point gap, which is what’s displayed in the figure above.

The difference in the NILF rate between the two groups is also striking when broken down by education, as shown in the next figure.

25-54 native, foreign males NILF by educ

In 2016, natives with less than a high school degree were four times more likely to be out of the labor force than foreign born, while natives with a high school degree were twice as likely to be out of the labor force. The NILF rates for some college or a bachelor’s or more are similar.

Mr. Eberstadt attributes some of this difference to the increase in incarceration rates since the 1970s. The U.S. imprisons a higher percentage of its population than almost any other country and it is very difficult to find a job with an arrest record or a conviction.

There aren’t much data combining employment and criminal history so it is hard to know exactly how much of a role crime plays in the difference between the NILF rates by education. Mr. Eberstadt provides some evidence in his book that shows that men with an arrest or conviction are much more likely to be out of the labor force than similar men without, but it is not perfectly comparable to the usual BLS data. That being said, it is reasonable to think that the mass incarceration of native prime-age males, primarily those with little formal education, has created a large group of unemployable, and thus unemployed, men.

Is incarceration a supply or demand side issue? On one hand, people with a criminal record are not really in demand, so in that sense it’s a demand issue. On the other hand, crime is a choice in many instances—people may choose a life of crime over other, non-criminal professions because it pays a higher wage than other available options or it somehow provides them with a more fulfilling life (e.g. Tony Soprano). In this sense crime and any subsequent incarceration is the result of a supply-side choice. Drug use that results in incarceration could also be thought of this way. I will let the reader decide which is more relevant to the NILF rates of prime-age males.

Criminal justice reform in the sense of fewer arrests and incarcerations would likely improve the prime-age male LFP rate, but the results would take years to show up in the data since such reforms don’t help the many men who have already served their time and want to work but are unable to find a job. Reforms that make it easier for convicted felons to find work would offer more immediate help, and there has been some efforts in this area. How successful they will be remains to be seen.

Other state reforms such as less occupational licensing would make it easier for people— including those with criminal convictions—to enter certain professions. There are also several ideas floating around that would make it easier for people to move to areas with better labor markets, such as making it easier to transfer unemployment benefits across state lines.

More economic growth would alleviate much of the demand side issues, and tax reform and reducing regulation would help on this front.

But has something fundamentally changed the way some men view work? Would some, especially the younger ones, rather just live with their parents and play video games, as economist Erik Hurst argues? For those wanting to learn more about this issue, Mr. Eberstadt’s book is a good place to start.

Mutant Capitalism rears its ugly head in Arlington

Confectionery-giant Nestlé plans to move its U.S. headquarters from California to 1812 North Moore in the Rosslyn area of Arlington in the next few years. This should be great news for the people of Arlington—a world-famous company has decided that Arlington County is the best place to be in the U.S. This must be due to our educated workforce and high quality of life, right?

Maybe. The real attraction might also be the $6 million of state handouts to Nestlé, along with an additional $6 million from Arlington County. Government handouts like these have become a way of life in the U.S. even though the results are often underwhelming.

Federal programs such as the New Markets Tax Credit Program have had at best small effects on economic development, and there is a good chance they just reallocate economic activity from one place to another rather than generate new economic activity. Local programs like Tax Increment Financing appear to largely reallocate economic activity as well. These programs might be good for the neighborhood or city that gets the handout, but it doesn’t help the residents of nearby places who are forced to contribute via their tax dollars.

In the Nestlé case, all of Virginia’s taxpayers are paying for Nestlé to locate in Arlington, which already has a relatively strong economy and is one of the wealthiest counties in Virginia. Why should taxpayers in struggling counties such as Buchanan or Dickenson County be forced to subsidize a company in Arlington? Government handouts to firms are often regressive since companies rarely want to locate in areas with a low-skill—and thus low-income—workforce. Everyone pays, but the most economically successful areas get the benefits.

Government officials often praise the jobs that these deals create and the Nestlé deal is no different: According to the performance agreement, Nestlé must create and maintain 748 new full-time jobs. And even if we ignore the fact that jobs are an economic cost, not a benefit, a closer look reveals that projections and reality usually diverge. For example, Buffalo awarded hundreds of millions of dollars to SolarCity, which promised to create 5,000 jobs. They have since revised that number down to 1,460. There are numerous other examples where the cost per job turned out to be higher than initially projected.

The grant performance agreement also estimates that Nestlé will provide $18.2 million in taxes to the county over the next 10 years, more than enough to offset the grant expenditure. But this doesn’t take into account what would have happened absent the handout. Perhaps some other company would have relocated here for free. Or a local company, or collection of companies, would have eventually rented out the space.

Government grants may also distort the real estate market: There’s a good chance no company had occupied 1812 North Moore because the rent was too high. If so, part of this grant is a handout to the owners of the building, Monday Properties, since now it does not have to lower its rent to attract a tenant. This may lead other property companies to lobby for and expect government handouts to help them find tenants.

Government grants often distort the economy by treating out-of-state companies differently than in-state companies. They encourage relocation by subsidizing it, which discourages expansion. A better strategy is to create a simple, non-intrusive business environment that treats all businesses equally.

Government grants are a characteristic of what my colleague Chris Koopman calls Mutant Capitalism and are antithetical to real capitalism and free enterprise. Capitalism involves businesses competing for consumers on an even playing field—there is no room for government favors that tilt the playing field towards one business or another.

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

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.