Category Archives: Economic Growth

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.

Smart rule-breakers make the best entrepreneurs

A new paper in the Quarterly Journal of Economics (working version here) finds that the combination of intelligence and a willingness to break the rules as a youth is associated with a greater tendency to operate a high-earning incorporated business as an adult i.e. be an entrepreneur.

Previous work examining entrepreneurship that categorizes all self-employed persons as entrepreneurs has often found that entrepreneurs earn less than similar salaried workers. But this contradicts the important role entrepreneurs are presumed to play in generating economic growth. As the authors of the new QJE paper remark:

“If the self-employed are a good proxy for risk-taking, growth-creating entrepreneurs, it is puzzling that their human capital traits are similar to those of salaried workers and that they earn less.”

So instead of looking at the self-employed as one group, the authors separate them into two groups: those who operate unincorporated businesses and those who operate incorporated businesses. They argue that incorporation is important for risk-taking entrepreneurs due to the limited liability and separate legal identity it provides, and they find that those who choose incorporation are more likely to engage in tasks that require creativity, analytical flexibility and complex interpersonal communications; all tasks that are closely identified with the concept of entrepreneurship.

People who operate unincorporated businesses, on the other hand, are more likely to engage in activities that require high levels of hand, eye and foot coordination, such as landscaping or truck driving.

Once the self-employed are separated into incorporated and unincorporated, the puzzling finding of entrepreneurs earning less than similar salaried workers disappears. The statistics in the table below taken from the paper show that on average incorporated business owners (last column) earn more, work more hours, have more years of schooling and are more likely to be a college graduate than both unincorporated business owners and salaried workers based on two different data sets (Current Population Survey (CPS) and National Longitudinal Survey of Youth (NLSY)).

(click table to enlarge)

The authors then examine the individual characteristics of incorporated and unincorporated business owners. They find that people with high self-esteem, a strong sense of controlling one’s future, high Armed Forces Qualifications Test scores (AFQT)—which is a measure of intelligence and trainability—and a greater propensity for engaging in illicit activity as a youth are more likely to be incorporated self-employed.

Moreover, it’s the combination of intelligence and risk-taking that turns a young person into a high-earning owner of an incorporated business. As the authors state, “The mixture of high learning aptitude and disruptive, “break-the-rules” behavior is tightly linked with entrepreneurship.”

These findings fit nicely with some notable recent examples of entrepreneurship—Uber and Airbnb. Both companies are regularly sued for violating state and local ordinances, but this hasn’t stopped them from becoming popular providers of transportation and short-term housing.

If the founders of Uber and Airbnb always obtained approval before operating the companies would be hindered by all sorts of special interests, including taxi commissions, hotel industry groups and nosy neighbors. Seeking everyone’s approval—including the government’s—before operating likely would have meant never getting off the ground and the companies know this. It’s interesting to see evidence that many other, less well-known entrepreneurs share a similar willingness to violate the rules if necessary in order to provide their goods and services to customers.

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.

Government Spending and Economic Growth in Nebraska since 1997

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

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

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

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

NE GSP annual growth rates 1997-14

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

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

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

NE, US GSP growth since 1997

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

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

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

An Overview of the Virginia State Budget and Economy

By Adam Millsap and Thomas Savidge

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

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

Government Overview: Expenditures and Revenue

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

VA expend and rev 2006-16

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

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

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

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

VA expend by category 2006-16

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

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

Virginia Economic Growth & County Personal Income

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

VA GDP 2006-15

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

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

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

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

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

VA county pers. inc. map

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

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

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

US county pers. inc. map

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

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

Conclusion:

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

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


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

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

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

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

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

geography of inventiveness 1940

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

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

innovation, long run growth US states

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

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

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

pop density, innovation 1940-1960

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

innovation, bank deposits 1920-1940

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

innovation, transport costs 1920-1940

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

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

innovation, slavery 1880-1940

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

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

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

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

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

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

innovation, inc inequality 1920-1940

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

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

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

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

innovation, social mobility 1940

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

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

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

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

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

Why Do We Get So Much Regulation?

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

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

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

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

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

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

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

QJE trust

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

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

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

gallup trust

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

pew trust

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

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

High-speed rail: is this year different?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.

What else can the government do for America’s poor?

This year marks the 20th anniversary of the 1996 welfare reforms, which has generated some discussion about poverty in the U.S. I recently spoke to a group of high school students on this topic and about what reforms, if any, should be made to our means-tested welfare programs.

After reading several papers (e.g. here, here and here), the book Hillbilly Elegy, and reflecting on my own experiences I am not convinced the government is capable of doing much more.

History

President Lyndon Johnson declared “War on Poverty” in his 1964 state of the union address. Over the last 50 years there has been some progress but there are still approximately 43 million Americans living in poverty as defined by the U.S. Census Bureau.

Early on it looked as if poverty would be eradicated fairly quickly. In 1964, prior to the “War on Poverty”, the official poverty rate was 20%. It declined rapidly from 1965 to 1972, especially for the most impoverished groups as shown in the figure below (data from Table 1 in Haveman et al. , 2015). (Click to enlarge)

poverty-rate-1965-72

Since 1972 the poverty rate has remained fairly constant. It reached its lowest point in 1973—11.1%—but has since fluctuated between roughly 11% and 15%, largely in accordance with the business cycle. The number of people in poverty has increased, but that is unsurprising considering the relatively flat poverty rate coupled with a growing population.

census-poverty-rate-time-series-2015

Meanwhile, an alternative measure called the supplemental poverty measure (SPM) has declined, but it was still over 15% as of 2013, as shown below.

poverty-rate-time-series

The official poverty measure (OPM) only includes cash and cash benefits in its measure of a person’s resources, while the SPM includes tax credits and non-cash transfers (e.g. food stamps) as part of someone’s resources when determining their poverty status. The SPM also makes adjustments for local cost of living.

For example, the official poverty threshold for a single person under the age of 65 was $12,331 in 2015. But $12,331 can buy more in rural South Carolina than it can in Manhattan, primarily because of housing costs. The SPM takes these differences into account, although I am not sure it should for reasons I won’t get into here.

Regardless of the measure we look at, poverty is still higher than most people would probably expect considering the time and resources that have been expended trying to reduce it. This is especially true in high-poverty areas where poverty rates still exceed 33%.

A county-level map from the Census that uses the official poverty measure shows the distribution of poverty across the 48 contiguous states in 2014. White represents the least amount of poverty (3.2% to 11.4%) and dark pink the most (32.7% to 52.2%).

us-county-poverty-map

The most impoverished counties are in the south, Appalachia and rural west, though there are pockets of high-poverty counties in the plains states, central Michigan and northern Maine.

Why haven’t we made more progress on poverty? And is there more that government can do? I think these questions are intertwined. My answer to the first is it’s complicated and to the second I don’t think so.

Past efforts

The inability to reduce the official poverty rate below 10% doesn’t appear to be due to a lack of money. The figure below shows real per capita expenditures—sum of federal, state and local—on the top 84 (top line) and the top 10 (bottom line) means-tested welfare poverty programs since 1970. It is from Haveman et al. (2015).

real-expend-per-capita-on-poverty-programs

There has been substantial growth in both since the largest drop in poverty occurred in the late 1960s. If money was the primary issue one would expect better results over time.

So if the amount of money is not the issue what is? It could be that even though we are spending money, we aren’t spending it on the right things. The chart below shows real per capita spending on several different programs and is also from Haveman et al. (2015).

expend-per-cap-non-medicaid-pov-programs

Spending on direct cash-assistance programs—Aid for Families with Dependent Children (AFDC) and Temporary Assistance for Needy Families (TANF)—has fallen over time, while spending on programs designed to encourage work—Earned Income Tax Credit (EITC)—and on non-cash benefits like food stamps and housing aid increased.

In the mid-1970s welfare programs began shifting from primarily cash aid (AFDC, TANF) to work-based aid (EITC). Today the EITC and food stamps are the core programs of the anti-poverty effort.

It’s impossible to know whether this shift has resulted in more or less poverty than what would have occurred without it. We cannot reconstruct the counterfactual without going back in time. But many people think that more direct cash aid, in the spirit of AFDC, is what’s needed.

The difference today is that instead of means-tested direct cash aid, many are calling for a universal basic income or UBI. A UBI would provide each citizen, from Bill Gates to the poorest single mother, with a monthly cash payment, no strings attached. Prominent supporters of a UBI include libertarian-leaning Charles Murray and people on the left such as Matt Bruenig and Elizabeth Stoker.

Universal Basic Income?

The details of each UBI plan vary, but the basic appeal is the same: It would reduce the welfare bureaucracy, simplify the process for receiving aid, increase the incentive to work at the margin since it doesn’t phase out, treat low-income people like adults capable of making their own decisions and mechanically decrease poverty by giving people extra cash.

A similar proposal is a negative income tax (NIT), first popularized by Milton Friedman. The current EITC is a negative income tax conditional on work, since it is refundable i.e. eligible people receive the difference between their EITC and the taxes they owe. The NIT has its own problems, discussed in the link above, but it still has its supporters.

In theory I like a UBI. Economists in general tend to favor cash benefits over in-kind programs like vouchers and food stamps due to their simplicity and larger effects on recipient satisfaction or utility. In reality, however, a UBI of even $5,000 is very expensive and there are public choice considerations that many UBI supporters ignore, or at least downplay, that are real problems.

The political process can quickly turn an affordable UBI into an unaffordable one. It seems reasonable to expect that politicians trying to win elections will make UBI increases part of their platform, with each trying to outdo the other. There is little that can be done, short of a constitutional amendment (and even those can be changed), to ensure that political forces don’t alter the amount, recipient criteria or add additional programs on top of the UBI.

I think the history of the income tax demonstrates that a relatively low, simple UBI would quickly morph into a monstrosity. In 1913 there were 7 income tax brackets that applied to all taxpayers, and a worker needed to make more than $20K (equivalent to $487,733 in 2016) before he reached the second bracket of 2% (!). By 1927 there were 23 brackets and the second one, at 3%, kicked in at $4K ($55,500 in 2016) instead of $20K. And of course we are all aware of the current tax code’s problems. To chart a different course for the UBI is, in my opinion, a work of fantasy.

Final thoughts

Because of politics, I think an increase in the EITC (and reducing its error rate), for both working parents and single adults, coupled with criminal justice reform that reduces the number of non-violent felons—who have a hard time finding employment upon release—are preferable to a UBI.

I also support the abolition of the minimum wage, which harms the job prospects of low-skilled workers. If we are going to tie anti-poverty programs to work in order to encourage movement towards self-sufficiency, then we should make it as easy as possible to obtain paid employment. Eliminating the minimum wage and subsidizing income through the EITC is a fairer, more efficient way to reduce poverty.

Additionally, if a minimum standard of living is something that is supported by society than all of society should share the burden via tax-funded welfare programs. It is not philanthropic to force business owners to help the poor on behalf of the rest of us.

More economic growth would also help. Capitalism is responsible for lifting billions of people out of dire poverty in developing countries and the poverty rate in the U.S. falls during economic expansions (see previous poverty rate figures). Unfortunately, growth has been slow over the last 8 years and neither presidential candidate’s policies inspire much hope.

In fact, a good way for the government to help the poor is to reduce regulation and lower the corporate tax rate, which would help economic growth and increase wages.

Despite the relatively high official poverty rate in the U.S., poor people here live better than just about anywhere else in the world. Extreme poverty—think Haiti—doesn’t exist in the U.S. On a consumption rather than income basis, there’s evidence that the absolute poverty rate has fallen to about 4%.

Given the way government functions I don’t think there is much left for it to do. Its lack of local knowledge and resulting blunt, one size fits all solutions, coupled with its general inefficiency, makes it incapable of helping the unique cases that fall through the current social safety net.

Any additional progress will need to come from the bottom up and I will discuss this more in a future post.