Tag Archives: GDP

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.

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.

Economic Freedom, Growth, and What Might Have Been

Economists are obsessed with growth. And for good reason. Greater wealth doesn’t just buy us nicer vacations and fancier gadgets. It also buys longer life spans, better nutrition, and lower infant mortality. It buys more time with family, and less time at work. It buys greater self-reported happiness. And as Harvard economist Benjamin Friedman has argued, wealth even seems to make us better people:

Economic growth—meaning a rising standard of living for the clear majority of citizens—more often than not fosters greater opportunity, tolerance of diversity, social mobility, commitment to fairness, and dedication to democracy.

For much of my lifetime, brisk economic growth was the norm in the United States. From 1983 to 2000, annual growth in real (that is, inflation-adjusted) GDP averaged 3.67 percent. During this period, the U.S. experienced only one (short and mild) recession in the early ‘90s. The era was known among macroeconomists as the “great moderation.”

But starting around the turn of the millennium, things changed. Instead of averaging 3.67 percent growth, the U.S. economy grew at less than half that rate, 1.78 percent on average. To see the effect of this deceleration, consider the chart below (data are from the BEA). The blue line shows actual GDP growth (as measured in billions of chained 2009 dollars).

The red line shows what might have happened if we’d continued to grow at the 3.67 percent rate which prevailed for the two previous decades. At this rate, the economy would have been 30 percent larger in 2015 than it actually was.

This assumes that the Great Recession never happened. So to see what would have happened to GDP if the Great Recession had still occurred but if growth had resumed (as it has in every other post-WWII recession), I calculated a second hypothetical growth path. The green line shows the hypothetical path of GDP had the economy still gone through the Great Recession but then resumed its normal 3.67 percent rate of growth from 2010 onward. Under this scenario, the economy would have been fully 8 percent larger in 2015 than it actually was.

screen-shot-2016-09-16-at-11-31-02-am

(Click to enlarge)

So what happened to growth? One answer is economic freedom—or a lack thereof. Just yesterday, the Fraser Institute released its annual Economic Freedom of the World report. Authored by Professors James Gwartney of Florida State University, Robert Lawson of Southern Methodist University, and Joshua Hall of West Virginia University, the report assesses the degree to which people are free to exchange goods and services with one another without interference. As Adam Smith might have put it, it measures the degree to which we live under “a system of natural liberty.”

As the chart below shows, economic freedom was on the steady rise before 2000. This coincided with modest deregulation of a few industries under Carter and Reagan, tax cuts under Reagan and Clinton, free trade deals, and restrained growth in the size of government. But from 2000 onward, U.S. economic freedom has been in precipitous decline. This coincides with major new financial regulations under both Bush II and Obama, significant growth in government spending, and a steady erosion in measures of the rule of law.

screen-shot-2016-09-16-at-11-33-15-am

(Click to enlarge)

As I’ve noted before, the research on economic freedom is quite extensive (nearly 200 peer-reviewed academic studies use economic freedom as an explanatory variable). Moreover, meta-studies of that literature find “there is a solid finding of a direct positive association between economic freedom and economic growth.”

Perhaps the two charts have something to do with one another?

 

 

Where’s the growth?

In a famous Wendy’s commercial from 1984, three elderly women are examining a hamburger with a rather large bun when one of them asks “Where’s the beef?” in order to express her disappointment that the burger is all bun and no meat. When it comes to the economy growth is like the beef of a burger – without it all you’re left with is fluff and filler.

For the last 8 years the US economy has been mostly fluff and filler. Sure unemployment is down, but that is largely due to a lower labor force participation rate. Wage growth has been anemic and total GDP growth remains below the pre-recession long-run average of 3%.  GDP per capita growth is weak too.

Within a country as large as the US different regions are going to have different levels of GDP per capita and different growth rates for a variety of reasons including labor force characteristics, industry composition, weather, and geography. In order to examine the differences across the US, the graph below depicts the natural log of real GDP per capita in 2009 dollars for the 9 census divisions from 2001 to 2014. Because the natural log is on the y-axis the slope of the line corresponds to the growth rate between years. The black line is the US Metropolitan Area average and does not include rural areas.

ln real per cap gdp by cen div 2001-14

I created the census division average by generating a population weighted average of the real per capita GDP of the Metropolitan Statistical Areas located in each division. The weights are adjusted for each year in the data. Also, since the averages discussed in this post do not include rural areas one can think of them as the urban average in each census division. The population data for the weights and the real GDP per capita data are from the BEA.

As shown in the graph, the highest average real GDP per capita is in the New England division (orange) while the lowest is in the East South Central (purple), although as of 2014 the Mountain is not far ahead.

The slopes of the lines are steeper on average prior to the recession, indicating that the regions were growing faster during the pre-recession period. This is particularly noticeable in the Mountain and South Atlantic division, where real GDP per capita growth has essentially been zero (flat line) since 2009. Growth has also slowed considerably in the Pacific division (dark blue). Only in the East North Central (yellow) and West South Central (brown) does it appear that growth has reached or eclipsed its pre-recession rate.

The next graph below shows the average real per capita GDP by census division in three separate years – 2001, 2007, and 2014. This makes it easier to see the changes in levels over time.

real per cap gdp by cen div 2001,07,14

Real GDP per capita was higher in 2014 than in 2007 (year prior to the recession) in only three divisions – the Mid Atlantic, West North Central, and West South Central. The rest of the country has experienced either no gain or a decrease in the case of the South Atlantic and Mountain divisions. Together these graphs are hardly evidence of a strong economy.

High per capita GDP is not a perfect measure of economic prosperity but it is strongly correlated with many of the other things people care about. Countries with a higher level of per capita GDP are healthier, freer, and happier. The data presented here show that the US economy is struggling when it comes to growth, especially in the South Atlantic and Mountain divisions where people have become worse off on average. Whoever the next president is, he or she needs to come up with an answer to the question – Where’s the growth?

 

Economic freedom matters at the local level too

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

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

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

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

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

MSA econ freedom regressions

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

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

MSA econ freedom sum stats

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

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

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

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

We don’t need more federal infrastructure spending

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

The provision of basic infrastructure is one of the most economically sound purposes of government. Good roads, bridges, and ports facilitate economic transactions and the exchange of ideas which helps foster innovation and economic growth. There is certainly room to debate which level of government – federal, state, or local – should provide which type of infrastructure, but I want to start by examining US infrastructure spending over time. To hear the candidates talk one would think that infrastructure spending has fallen of a cliff. What else could explain the current derelict state?

A quick look at the data shows that this simply isn’t true. A 2015 CBO report on public spending on transportation and water infrastructure provides the following figure.

CBO us infrastructure spending

In inflation adjusted dollars (the top panel) infrastructure spending has exhibited a positive trend and was higher on average post 1992 after the completion of the interstate highway system. (By the way, the original estimate for the interstate system was $25 billion over 12 years and it ended up costing $114 billion over 35 years.)

The bottom panel shows that spending as a % of GDP has declined since the early 80s, but it has never been very high, topping out at approximately 6% in 1965. Since the top panel shows an increase in the level of spending, the decline relative to GDP is due to the government increasing spending in other areas over this time period, not cutting spending on infrastructure.

The increase in the level of spending over time is further revealed when looking at per capita spending. Using the data from the CBO report and US population data I created the following figure (dollars are adjusted for inflation and are in 2014 dollars).

infrastructure spend per cap

The top green line is total spending per capita, the middle red line is state and local spending with federal grants and loan subsidies subtracted out, and the bottom blue line is federal spending. Federal spending per capita has remained relatively flat while state and local spending experienced a big jump in the late 80s, which increased the total as well. This graph shows that the amount of infrastructure spending has largely increased when adjusted for inflation and population. It’s true that spending is down since the early 2000s but it’s still higher than at any point prior to the early 90s and higher than it was during the 35-year-construction of the interstate highway system.

Another interesting thing that jumps out is that state and local governments provide the bulk of infrastructure spending. The graph below depicts the percentage of total infrastructure spending that is done by state and local governments.

infrastructure spend state, local as percent of total

As shown in the graph state and local spending on infrastructure has accounted for roughly 75% of total infrastructure spending since the late 80s. Prior to that it averaged about 70% except for a dip to around 65% in the late 70s.

All of this data shows that the federal government – at least in terms of spending – has not ignored the country’s infrastructure over the last 50 plus years, despite the rhetoric one hears from the campaign trail. In fact, on a per capita basis total infrastructure spending has increased since the early 1980s, driven primarily by state and local governments.

And this brings up a second important point: state and local governments are and have always been the primary source of infrastructure spending. The federal government has historically played a small role in building and maintaining roads, bridges, and water infrastructure. And for good reason. As my colleague Veronique de Rugy has pointed out :

“…infrastructure spending by the federal government tends to suffer from massive cost overruns, waste, fraud, and abuse. As a result, many projects that look good on paper turn out to have much lower return on investments than planned.”

As evidence she notes that:

“According to the Danish researchers, American cost overruns reached on average $55 billion per year. This figure includes famous disasters like the Central Artery/Tunnel Project (CA/T), better known as the Boston Big Dig.22 By the time the Beantown highway project—the most expensive in American history—was completed in 2008 its price tag was a staggering $22 billion. The estimated cost in 1985 was $2.8 billion. The Big Dig also wrapped up 7 years behind schedule.”

Since state and local governments are doing the bulk of the financing anyway and most infrastructure is local in nature it is best to keep the federal government out as much as possible. States are also more likely to experiment with private methods of infrastructure funding. As de Rugy points out:

“…a number of states have started to finance and operate highways privately. In 1995, Virginia opened the Dulles Greenway, a 14-mile highway, paid for by private bond and equity issues. Similar private highway projects have been completed, or are being pursued, in California, Maryland, Minnesota, North Carolina, South Carolina, and Texas. In Indiana, Governor Mitch Daniels leased the highways and made a $4 billion profit for the state’s taxpayers. Consumers in Indiana were better off: the deal not only saved money, but the quality of the roads improved as they were run more efficiently.”

It remains an open question as to exactly how much more money should be devoted to America’s infrastructure. But even if the amount is substantial it’s not clear that the federal government needs to get any more involved than they already are. Infrastructure is largely a state and local issue and that is where the taxing and spending should take place, not in Washington D.C.

 

 

Puerto Rico’s labor market woes

Puerto Rico – a U.S. territory – has $72 billion dollars in outstanding debt, which is dangerously high in a country with a Gross Domestic Product (GDP) of only $103.1 billion. The Puerto Rican government failed to pay creditors in August and this was viewed as a default by the credit rating agency Moody’s, which had already downgraded Puerto Rico’s bonds to junk status earlier this year. The Obama administration has proposed allowing Puerto Rico to declare bankruptcy, which would allow it to negotiate with creditors and eliminate some of its debt. Currently only municipalities – not states or territories – are allowed to declare bankruptcy under U.S. law. Several former Obama administration officials have come out in favor of the plan, including former Budget Director Peter Orszag and former Director of the National Economic Council Larry Summers. Others are warning that bankruptcy is not a cure-all and that more structural reforms need to take place. Many of these pundits have pointed out that Puerto Rico’s labor market is a mess and that people are leaving the country in droves. Since 2010 over 200,000 people have migrated from Puerto Rico, decreasing its population to just over 3.5 million. This steady loss of the tax base has increased the debt burden on those remaining and has made it harder for Puerto Rico to get out of debt.

To get a sense of Puerto Rico’s situation, the figure below shows the poverty rate of Puerto Rico along with that of three US states that will be used throughout this post as a means of comparison: California (wealthy state), Ohio (medium-wealth state), and Mississippi (low-wealth state). All the data are 1-year ACS data from American FactFinder.

puerto rico poverty

The poverty rate in Puerto Rico is very high compared to these states. Mississippi’s poverty rate is high by US standards and was approximately 22% in 2014, but Puerto Rico’s dwarfed it at over 45%. Assisting Puerto Rico with their immediate debt problem will do little to fix this issue.

A government requires taxes in order to provide services, and taxes are primarily collected from people who work in the regular economy via income taxes. A small labor force with relatively few employed workers makes it difficult for a county to raises taxes to provide services and pay off debt. Puerto Rico has a very low labor force participation (LFP) rate relative to mainland US states and a very low employment rate. The graphs below plot Puerto Rico’s LFP rate and employment rate along with the rates of California, Mississippi, and Ohio.

puerto rico labor force

puerto rico employ rate

As shown in the figures, Puerto Rico’s employment rate and LFP rate are far below the rates of the US states including one of the poorest states, Mississippi. In 2014 less than 45% of Puerto Rico’s 16 and over population was in the labor force and only about 35% of the 16 and over population was employed. In Mississippi the LFP rate was 58% while the employment rate was 52%. Additionally, the employment rate fell in Puerto Rico from 2010-14 while it rose in each of the other three states. So at a time when the labor market was improving on the mainland things were getting worse in Puerto Rico.

An educated labor force is an important input in the production process and it is especially important for generating innovation and entrepreneurship. The figure below shows the percent of people 25 and over in each area that have a bachelor’s degree or higher.

puerto rico gt 24 education attain

Puerto Rico has a relatively educated labor force compared to Mississippi, though it trails Ohio and California. The percentage also increased over this time period, though it appears to have stabilized after 2012 while continuing to grow in the other states.

Puerto Rico has nice beaches and weather, so a high percentage of educated people over the age of 25 may simply be due to a high percentage of educated retirees residing in Puerto Rico to take advantage of its geographic amenities. The next figure shows the percentage of 25 to 44 year olds with a bachelor’s degree or higher. I examined this age group to see if the somewhat surprising percentage of people with a bachelor’s degree or higher in Puerto Rico is being driven by educated older workers and retirees who are less likely to help reinvigorate the Puerto Rican economy going forward.

puerto rico 25to44 educ attain

As shown in the graph, Puerto Rico actually fares better when looking at the 25 – 44 age group, especially from 2010-12. In 2012 Puerto Rico had a higher percentage of educated people in this age group than Ohio.

Since then, however, Puerto Rico’s percentage declined slightly while Ohio’s rose, along with Mississippi’s and California’s. The decline in Puerto Rico was driven by a decline in the percentage of people 35 to 44 with a bachelor’s or higher as shown in the next figure below.

puerto rico 35to44 educ attain

The percentage of 35 to 44 year olds with a bachelor’s or advanced degree fell from 32% in 2012 to 29.4% in 2014 while it rose in the other three states. This is evidence that educated people in their prime earning years left the territory during this period, most likely to work in the US where there are more opportunities and wages are higher. This “bright flight” is a bad sign for Puerto Rico’s economy.

One of the reforms that many believe will help Puerto Rico is an exemption from compliance with federal minimum wage laws. Workers in Puerto Rico are far less productive than in the US, and thus a $7.25 minimum wage has a large effect on employment. Businesses cannot afford to pay low-skill workers in Puerto Rico such a high wage because the workers simply do not produce enough value to justify it. The graph below shows the median individual yearly income in each area divided by the full time federal minimum wage income of $15,080.

puerto rico min wage ratio

As shown in the graph, Puerto Rico’s ratio was the highest by a substantial amount. The yearly income from earning the minimum wage was about 80% of the yearly median income in Puerto Rico over this period, while it was only about 40% in Mississippi and less in Ohio and California. By this measure, California’s minimum wage would need to be $23.82 – which is equal to $49,546 per year – to equal the ratio in Puerto Rico. California’s actual minimum wage is $9 and it’s scheduled to increase to $10 in 2016. I don’t think there’s a single economist who would argue that more than doubling the minimum wage in California would have no effect on employment.

The preceding figures do not paint a rosy picture of Puerto Rico: Its poverty rate is high and trending up, less than half of the people over 16 are in the labor force and only about a third are actually employed, educated people appear to be leaving the country, and the minimum wage is a severe hindrance on hiring. Any effort by the federal government to help Puerto Rico needs to take these problems into account. Ultimately the Puerto Rican government needs to be enabled and encouraged to institute reforms that will help grow Puerto Rico’s economy. Without fundamental reforms that increase economic opportunity in Puerto Rico people will continue to leave, further weakening the commonwealth’s economy and making additional defaults more likely.

 

 

The cost disease and the privatization of government services

Many US municipalities are facing budget problems (see here, here, and here). The real cost of providing traditional public services like police, fire protection, and education is increasing, often at a rate that exceeds revenue growth. The graph below shows the real per-capita expenditure increase in five US cities from 1951 to 2006. (Data are from the census file IndFin_1967-2012.zip and are adjusted for inflation using the US GDP chained price index.)

real per cap spend

In 1951 none of the cities were spending more than $1,000 per person. In 2006 every city was spending well over that amount, with Buffalo spending almost $5,000 per person. Even Fresno, which had the smallest increase, increased per capita spending from $480 to $1,461 – an increase of 204%. Expenditure growth that exceeds revenue growth leads to budget deficits and can eventually result in cuts in services. Economist William Baumol attributes city spending growth to what is known as the “cost disease”.

In his 1967 paper, Baumol argues that municipalities will face rising costs of providing “public” goods and services over time as the relative productivity of labor declines in the industries controlled by local governments versus those of the private sector. As labor in the private sector becomes more productive over time due to increases in capital, wages will increase. Goods and services traditionally supplied by local governments such as police, fire protection, and education have not experienced similar increases in capital or productivity. K-12 education is a particularly good example of stagnation – a teacher from the 1950s would not confront much of a learning curve if they had to teach in a 21st century classroom. However, in order to attract competent and productive teachers, for example, local governments must increase wages to levels that are competitive with the wages that teachers could earn in the private sector. When this occurs, teacher’s wages increase even though their productivity does not. As a result, cities end up paying more money for the same amount of work. Baumol sums up the effect:

“The bulk of municipal services is, in fact, of this general stamp [non-progressive] and our model tells us clearly what can be expected as a result…inexorably and cumulatively, whether or not there is inflation, administrative mismanagement or malfeasance, municipal budgets will almost certainly continue to mount in the future, just as they have been doing in the past. This is a trend for which no man and no group should be blamed, for there is nothing than can be done to stop it.” (Baumol, 1967 p.423)

But is there really nothing than can be done to cure the cost disease? Baumol himself later acknowledged that innovation may yet occur in the relatively stagnant sectors of the economy such as education:

“…an activity which is, say, relatively stagnant need not stay so forever. It may be replaced by a more progressive substitute, or it may undergo an outburst of innovation previous thought very unlikely.” (Baumol et al. 1985, p.807).

The cure for the cost disease is that the stagnant, increasing-cost sectors need to undergo “an outburst of innovation”. But this raises the question; what has prevented this innovation from occurring thus far?

One thing that Baumol’s story ignores is public choice. Specifically, is the lack of labor-augmenting technology in the public-sector industries a characteristic of the public sector? The primary public sector industries have high rates of unionization and the primary goal of a labor union is to protect its dues-paying members. The chart below provides the union affiliation of workers for several occupations in 2013 and 2014.

union membership chart

In 2014, the protective service occupations and education, training, and library occupations, e.g. police officers and teachers, had relatively high union membership rates of 35%. Conversely, other high-skilled occupations such as management, computer and mathematical occupations, architecture and engineering occupations, and sales and office occupations had relatively low rates, ranging from 4.2% to 6.5% in 2014. Installation, maintenance, and repair occupations were in the middle at 14.6%, down from 16.1% in 2013.

The bottom part of the table shows the union membership rate of the public sector in general and of each level of government: federal, state, and local. The highest rate of unionization was at the local level, where approximately 42% of workers were members of a union in 2014, up from 41% in 2013. This is about 14 percentage points higher than the federal level and 12 percentage points higher than the state level. The union membership rate of the private sector in 2014 was only 6.6%.

In addition to the apathetic and sometimes hostile view unions have towards technological advancement and competition, union membership is also associated with higher wages, particularly at the local-government level. Economists Maury Gittleman and Brooks Piece of the Bureau of Labor statistics found that local-government workers have compensation costs 10 – 19% larger than similar private sector workers.

The table below shows the median weekly earnings in 2013 and 2014 for workers in the two most heavily unionized occupational categories; education, training, and library occupations and protective service occupations. In both occupation groups there is a substantial difference between the union and non-union weekly earnings. From the taxpayer’s perspective, higher earnings mean higher costs.

union median wage chart

There needs to be an incentive to expend resources in labor-saving technology for it to occur and it is not clear that this incentive exists in the public sector. In the public sector, taxpayers ultimately pay for the services they receive but these services are provided by an agent – the local politician(s) – who is expected to act on the taxpayer’s behalf when it comes to spending tax dollars. But in the public sector the agent/politician is accountable to both his employees and the general taxpayer since both groups vote on his performance. The general taxpayer wants the politician to cut costs and invest in labor-augmenting technology while the public-employee taxpayer wants to keep his job and earn more income. Since the public-employee unions are well organized compared to the general taxpayers it is easier for them to lobby their politicians/bosses in order to get their desired outcome, which ultimately means higher costs for the general taxpayer.

If Baumol’s cost disease is the primary factor responsible for the increasing cost of municipal government then there is not an easy remedy in the current environment. If the policing, firefighting, and education industries are unreceptive to labor-augmenting technology due to their high levels of unionization and near-monopoly status, one potential way to cure municipalities of the cost disease is privatization. In their 1996 paper, The Cost Disease and Government Growth: Qualifications to Baumol, economists J. Ferris and Edwin West state “Privatization could lead to significant changes in the structure of supply that result in “genuine” reductions in real costs” (p. 48).

Schools, police, and fire services are not true public goods and thus economic efficiency does not dictate that they are provided by a government entity. Schools in particular have been successfully built and operated by private funds for thousands of years. While there are fewer modern examples of privately operated police and fire departments, in theory both could be successfully privatized and historically fire departments were, though not always with great success. However, the failures of past private fire departments in places like New York City in the 19th century appear to be largely due to political corruption, an increase in political patronage, poorly designed incentives, and the failure of the rule of law rather than an inherent flaw in privatization. And today, many volunteer fire departments still exist. In 2013 69% of all firefighters were volunteers and 66% of all fire departments were all-volunteer.

The near-monopoly status of government provided education in many places and the actual monopoly of government provided police and fire protection makes these industries less susceptible to innovation. The government providers face little to no competition from private-sector alternatives, they are highly unionized and thus have little incentive to invest in labor-saving technology, and the importance of their output along with the aforementioned lack of competition allows them to pass cost increases on to taxpayers.

Market competition, limited union membership, and the profit-incentive are features of the private sector that are lacking in the public sector. Together these features encourage the use of labor-augmenting technology, which ultimately lowers costs and frees up resources, most notably labor, that can then be used on producing other goods and services. The higher productivity and lower costs that result from investments in productive capital also free up consumer dollars that can then be used to purchase additional goods and services from other industries.

Privatization of basic city services may be a little unnerving to some people, but ultimately it may be the only way to significantly bring down costs without cutting services. There are over 19,000 municipal governments in the US, which means there are over 19,000 groups of citizens that are capable of looking for new and innovative ways to provide the goods and services they rely on. In the private sector entrepreneurs continue to invent new things and find ways to make old things better and cheaper. I believe that if we allow entrepreneurs to apply their creativity to the public sector we will get similar outcomes.

State government spending hits new heights

There is a large literature in macroeconomics that examines the extent to which federal spending “crowds out” investment in the private sector. Basic theory and common sense lead to the conclusion that government spending must replace some private sector spending. After all, dollars are scarce – if the government taxes Paul and uses his money to build a road Paul necessarily has less money to invest in his landscaping business. In theory government spending on public goods like roads could be a net gain. This would occur if the additional value produced by spending one more dollar on roads was greater than the additional value produced by investing one more dollar in Paul’s landscaping business. But even in this scenario, Paul himself may be worse off – he’s one dollar poorer and he may not use the new road – and there is still a dead-weight loss due to the tax.

In reality, the federal government does a lot more than build roads, especially productive ones. In 2014, only 1.9% of federal income tax revenue was spent on transportation. And most of the other stuff that the government does is way less productive, like shuffling money around via entitlement programs – Medicare, Medicaid, and Social Security – and investing in businesses that later go bankrupt like Solyndra. So while it is possible that a dollar spent by the government is more productive than a dollar spent by a guy like Paul, in a country with America’s spending habits it’s unlikely to be the case.

The same crowding out that occurs at the federal level can occur at the state level. In fact, in many states state spending as a percentage of gross state product (GSP) exceeds federal spending as a percentage of GDP. The graph below shows state spending as a percentage of GSP for all 50 states and Washington D.C. in 1970, 1990, and 2012 (data). The red, dashed line is federal spending as a percentage of GDP in 2012 (21.9%).

state spending gsp graph

As shown in the graph, nearly every state increased their spending relative to GSP from 1970 – 2012 (triangles are above the X’s). Only one state, South Dakota, had lower spending relative to GSP in 2012 than in 1970. In 2012, 15 of the 50 states spent more as a percentage of GSP than the federal government spent as a percentage of GDP (states where the triangle is above the red, dashed line). In 1990 only two states, Arizona and Montana, spent at that level.

It used to be the case that state and local spending was primarily focused on classic government services like roads, water/sewer systems, police officers, firemen, and K-12 education. But state spending is increasingly looking similar to federal spending. Redistributive public welfare expenditures and pension expenditures have increased substantially since 1992. As an example, the tables below provide a breakdown of some key spending areas for two states, Ohio and Pennsylvania, in 1992 and 2012 (1992 data here, 2012 data here). The dollar per capita amounts are adjusted for inflation and are in 2009 dollars.

ohio spending table

penn spending table

As the tables show, spending on public welfare, hospitals, and health increased by 120% in Ohio and 86% in Pennsylvania from 1992 to 2012. Pension expenditures increased by 83% and 125% respectively. And contrary to what many politicians and media types say, funding for higher education – the large majority of state education spending is on higher education – increased dramatically during this time period; up 250% in Ohio and 199% in Pennsylvania. Meanwhile, funding for highways – the classic public good that politicians everywhere insist wouldn’t exist without them – has increased by a much smaller amount in both states.

The state spending increases of the recent past are being driven in large part by public welfare programs that redistribute money, pensions for government employees, and higher education. While one could argue that higher education spending is a productive public investment (Milton Friedman didn’t think so and I agree) it is hard to make a case that public welfare and pension payments are good investments. This alone doesn’t mean that society shouldn’t provide those things. Other factors like equity and economic security might be more important to some people than economic productivity. But this does make it unlikely that the marginal dollar spent by a state government today is as economically productive as that dollar spent in the private sector. Like federal spending, state spending is likely crowding out productive private investment, which will ultimately lower output and economic growth in the long run.