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.

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.

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.


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

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?

February 17, 2017

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 […]

Read the full post →

High-speed rail: is this year different?

February 8, 2017

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 […]

Read the full post →

Economic policies and institutions matter

February 3, 2017

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 […]

Read the full post →

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

January 26, 2017

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 […]

Read the full post →

Decreasing congestion with driverless cars

January 17, 2017

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 […]

Read the full post →

Today’s public policies exacerbate our differences

January 3, 2017

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 […]

Read the full post →