Tag Archives: San Francisco

Today’s public policies exacerbate our differences

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

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

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

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

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

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

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

States with lower minimum wages will feel the impact of California’s experiment

California governor Jerry Brown recently signed a law raising California’s minimum wage to $15/hour by 2022. This ill-advised increase in the minimum wage will banish the least productive workers of California – teens, the less educated, the elderly – from the labor market. It will be especially destructive in the poorer areas of California that are already struggling.

And if punishing California’s low-skill workers by preventing them from negotiating their own wage with employers isn’t bad enough, there is reason to believe that a higher minimum wage in a large state like California will eventually affect the employment opportunities of low-skill workers in other areas of the country.

Profit-maximizing firms are always on the lookout for ways to reduce costs holding quality constant (or in the best case scenario to reduce costs and increase quality). Since there are many different ways to produce the same good, if the price of one factor of production, e.g. labor, increases, firms will have an incentive to use less of that factor and more of something else in their production process. For example, if the price of low-skill workers increases relative to the cost of a machine that can do the same job firms will have an incentive to switch to the machine.

To set the stage for this post, let’s think about a real life example; touch screen ordering. Some McDonald’s have touchscreens for ordering food and coffee and San Francisco restaurant eatsa is almost entirely automated (coincidence?). The choice facing a restaurant owner is whether to use a touch screen or cashier. If a restaurant is currently using a cashier and paying them a wage, they will only switch to the touch screen if the cost of switching and the future discounted costs of operating and maintaining the touch screen device are less than the future discounted costs of using workers and paying them a wage plus any benefits. We can write this as

D + K + I + R < W

Where D represents the development costs of creating and perfecting the device, K represents the costs of working out the kinks/the trial run/adjustment costs, I represents the installation costs, and R represents the net present value of the operating and maintenance costs. On the other side of the inequality W represents the net present value of the labor costs. (In math terms R and W are: R = [ ∑ (rk) / (1+i)^n from n=0 to N ] where r is the rental rate of a unit of capital, k is the number of units of capital, and i is the interest rate and W = [ ∑ (wl) /(1+i)^n from n=0 to N ] where w is the wage and l is the amount of labor hours. But if this looks messy and confusing don’t worry about it as it’s not crucial for the example.)

The owner of a restaurant will only switch to a touch screen device rather than a cashier if the left side of the above inequality is less than the right side, since in that case the owner’s costs will be lower and they will earn a larger profit (holding sales constant).

If the cashier is earning the minimum wage or something close to it and the minimum wage is increased, say from $9 to $15, the right side of the above inequality will increase while the left side will stay the same (the w part of W is going up).  If the increase in the wage is large enough to make the right side larger than the left side the firm will switch from a cashier to a touch screen. Suppose that an increase from $9 to $15 does induce a switch to touch screen devices in California McDonald’s restaurants. Can this impact McDonald’s restaurants in areas where the minimum wage doesn’t increase? In theory yes.

Once some McDonald’s restaurants make the switch the costs for other McDonald’s to switch will be lower. The reason for this is that the McDonald’s that switch later will not have to pay the D or K costs; the development or kinks/trial run/adjustment costs. Once the technology is developed and perfected the late-adopting McDonald’s can just copy what has already been done. So after the McDonald’s restaurants in high wage areas install and perfect touch screen devices for ordering, the other McDonald’s face the decision of

I + R < W

This means that it may make sense to adopt the technology once it has been developed and perfected even if the wage in the lower wage areas does not change. In this scenario the left side decreases as D and K go to 0 while the right side stays the same. In fact, one could argue that the R will decline for late-adopting restaurants as well since the maintenance costs will decline over time as more technicians are trained and the reliability and performance of the software and hardware increase.

What this means is that a higher minimum wage in a state like California can lead to a decline in low-skill employment opportunities in places like Greenville, SC and Dayton, OH as the technology employed to offset the higher labor costs in the high minimum wage area spread to lower wage areas.

Also, firm owners and operators live in the real world. They see other state and local governments raising their minimum wage and they start to think that it could happen in their area too. This also gives them an incentive to switch since in expectation labor costs are going up. If additional states make the same bad policy choice as California, firm owners around the country may start to think that resistance is futile and that it’s best to adapt in advance by preemptively switching to more capital.

And if you think that touch screen ordering machines aren’t a good example, here is a link to an article about an automated burger-making machine. The company that created it plans on starting a chain of restaurants that use the machine. Once all of the bugs are worked out how high does the minimum wage need to be before other companies license the technology or create their own by copying what has already been done?

This is one more way that a higher minimum wage negatively impacts low-skill workers; even if workers don’t live in an area that has a relatively high minimum wage, the spread of technology may eliminate their jobs as well.

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.

City population dynamics since 1850

The reason why some cities grow and some cities shrink is a heavily debated topic in economics, sociology, urban planning, and public administration. In truth, there is no single reason why a city declines. Often exogenous factors – new modes of transportation, increased globalization, institutional changes, and federal policies – initiate the decline while subsequent poor political management can exacerbate it. This post focuses on the population trends of America’s largest cities since 1850 and how changes in these factors affected the distribution of people within the US.

When water transportation, water power, and proximity to natural resources such as coal were the most important factors driving industrial productivity, businesses and people congregated in locations near major waterways for power and shipping purposes. The graph below shows the top 10 cities* by population in 1850 and follows them until 1900. The rank of the city is on the left axis.

top cities 1850-1900

 

* The 9th, 11th, and 12th ranked cities in 1850 were all incorporated into Philadelphia by 1860. Pittsburgh was the next highest ranked city (13th) that was not incorporated so I used it in the graph instead.

All of the largest cities were located on heavily traveled rivers (New Orleans, Cincinnati, Pittsburgh, and St. Louis) or on the coast and had busy ports (New York, Boston, Philadelphia, Brooklyn, and Baltimore). Albany, NY may seem like an outlier but it was the starting point of the Erie Canal.

As economist Ed Glaeser (2005) notes “…almost every large northern city in the US as of 1860 became an industrial powerhouse over the next 60 years as factories started in central locations where they could save transport costs and make use of large urban labor forces.”

Along with waterways, railroads were an important mode of transportation from 1850 – 1900 and many of these cities had important railroads running through them, such as the B&O through Balitmore and the Erie Railroad in New York. The increasing importance of railroads impacted the list of top 10 cities in 1900 as shown below.

top cities 1900-1950

A similar but not identical set of cities dominated the urban landscape over the next 50 years. By 1900, New Orleans, Brooklyn (merged with New York) Albany, and Pittsburgh were replaced by Chicago, Cleveland, Buffalo, and San Francisco. Chicago, Cleveland, and Buffalo are all located on the Great Lakes and thus had water access, but it was the increasing importance of railroad shipping and travel that helped their populations grow. Buffalo was on the B&O railroad and was also the terminal point of the Erie Canal. San Francisco became much more accessible after the completion of the Pacific Railroad in 1869, but the California Gold Rush in the late 1840s got its population growth started.

As rail and eventually automobile/truck transportation became more important during the early 1900s, cities that relied on strategic river locations began to decline. New Orleans was already out of the top 10 by 1900 (falling from 5th to 12th) and Cincinnati went from 10th in 1900 to 18th by 1950. Buffalo also fell out of the top 10 during this time period, declining from 8th to 15th. But despite some changes in the rankings, there was only one warm-weather city in the top 10 as late as 1950 (Los Angeles). However, as the next graphs shows there was a surge in the populations of warm-weather cities during the period from 1950 to 2010 that caused many of the older Midwestern cities to fall out of the rankings.

top cities 1950-2010

The largest shakeup in the population rankings occurred during this period. Out of the top 10 cities in 1950, only 4 (Philadelphia, Los Angeles, Chicago, and New York) were still in the top 10 in 2010 (All were in the top 5, with Houston – 4th in 2010 – being the only city not already ranked in the top 10 in 1950, when it was 14th). The cities ranked 6 – 10 fell out of the top 20 while Detroit declined from 5th to 18th. The large change in the rankings during this time period is striking when compared to the relative stability of the earlier time periods.

Economic changes due to globalization and the prevalence of right-to-work laws in the southern states, combined with preferences for warm weather and other factors have resulted in both population and economic decline in many major Midwestern and Northeastern cities. All of the new cities in the top ten in 2010 have relatively warm weather: Phoenix, San Antonio, San Diego, Dallas, and San Jose. Some large cities missing from the 2010 list – particularly San Francisco and perhaps Washington D.C. and Boston as well – would probably be ranked higher if not for restrictive land-use regulations that artificially increase housing prices and limit population growth. In those cities and other smaller cities – primarily located in Southern California – low population growth is a goal rather than a result of outside forces.

The only cold-weather cities that were in the top 15 in 2014 that were not in the top 5 in 1950 were Indianapolis, IN (14th) and Columbus, OH (15th). These two cities not only avoided the fate of nearby Detroit and Cleveland, they thrived. From 1950 to 2014 Columbus’ population grew by 122% and Indianapolis’ grew by 99%. This is striking compared to the 57% decline in Cleveland and the 63% decline in Detroit during the same time period.

So why have Columbus and Indianapolis grown since 1950 while every other large city in the Midwest has declined? There isn’t an obvious answer. One thing among many that both Columbus and Indianapolis have in common is that they are both state capitals. State spending as a percentage of Gross State Product (GSP) has been increasing since 1970 across the country as shown in the graph below.

OH, IN state spending as per GSP

In Ohio state spending growth as a percentage of GSP has outpaced the nation since 1970. It is possible that increased state spending in Ohio and Indiana is crowding out private investment in other parts of those states. And since much of the money collected by the state ends up being spent in the capital via government wages, both Columbus and Indianapolis grow relative to other cities in their respective states.

There has also been an increase in state level regulation over time. As state governments become larger players in the economy business leaders will find it more and more beneficial to be near state legislators and governors in order to lobby for regulations that help their company or for exemptions from rules that harm it. Company executives who fail to get a seat at the table when regulations are being drafted may find that their competitors have helped draft rules that put them at a competitive disadvantage. The decline of manufacturing in the Midwest may have created an urban reset that presented firms and workers with an opportunity to migrate to areas that have a relative abundance of an increasingly important factor of production – government.

Berkeley, CA and the $15 – oops – $19 living wage

Berkeley, CA’s labor commission – in what should be an unsurprising move at this point in Berkeley’s history – has proposed raising the city’s minimum wage to an astounding $19 per hour by 2020! The labor commission’s argument in a nutshell is that Berkeley is an expensive place to live so worker’s need more money. And while Berkeley may be an expensive place to live, mandating that employers pay a certain wage doesn’t necessarily mean that the workers will get that money. As one Berkeley restaurant owner noted:

“We can raise our prices. But you can’t charge $25 for a sandwich,” said Dorothee Mitrani, who owns La Note. “A lot of mom-and-pop delis and cafes may disappear.”

The article states that Ms. Mitrani’s

…. full-service restaurant now subsidizes her take-out shop, which she said is running in the red as a result of the increases already in place. If the minimum rose to $19, she expects she would have to shut it down.

Of course, there are some politicians – and unfortunately some economists – who insist that raising the minimum wage doesn’t have adverse effects on employment, despite sound theoretical reasoning and empirical evidence to the contrary. My Mercatus center colleague Don Boudreaux has compiled an extensive collection of blog posts at Café Hayek debunking and refuting every pro-minimum wage argument out there, and I encourage interested readers to check them out.

The minimum wage most adversely effects low-skill, inexperienced workers, such as those without a high school degree, below the poverty level, between the ages of 16 – 19, and with some type of disability. So how do the people who fit into those categories currently fare in Berkeley’s labor market?

The table below shows the labor force rate and percentage employed for people 16 and over in each of those categories in the city of Berkeley in 2013 and 2014. The data is from the ACS 1-year survey. (American FactFinder table S2301)

berkely min wage employment 2013-14

As the table shows the labor force rate and the employment rate for each of those categories is already low compared to the overall labor force rate in Berkeley of 67% and employment rate of 62%. From 2013 to 2014 both the labor force rate and the employment rate declined for people without a high school degree, while the employment rate increased in the other categories. Nothing in this table leads me to believe that it would be a good idea to make the workers in these categories more expensive to hire, as it seems it is already difficult for them to find employment and it’s getting more difficult for some.

The table below compares Berkeley to the surrounding San Francisco MSA using only 2014 data.

berkeley min wage emp vs SF MSA

This table reveals that compared to the surrounding area, workers in these categories fare worse in Berkeley. The percentage of people with less than a high school degree who are employed was 11 percentage points lower in Berkeley, while the percentage with a disability was 0.8 points lower and the percentage below the poverty level was 1.5 points lower. Out of the four categories only 16 – 19 year olds had a better chance of being employed in Berkeley than in the surrounding MSA.

Hopefully Berkeley’s city council reviews the labor market reality in their city and thinks about actual consequences vs. intentions before deciding to increase the price that low-skill workers are allowed to charge for their labor. It’s already difficult for low-skill, inexperienced workers to find a job in Berkeley and making it harder won’t help them.

Teenage unemployment in cities

New research that examines New York’s Summer Youth Employment Program (SYEP) finds that participation in the program positively impacts student academic outcomes. As the authors state in the introduction, youth employment has many benefits:

“Prior research suggests that adolescent employment improves net worth and financial well-being as an adult. An emerging body of research indicates that summer employment programs also lead to decreases in violence and crime. Work experience may also benefit youth, and high school students specifically, by fostering various non-cognitive skills, such as positive work habits, time management, perseverance, and self-confidence.” (My bold)

This is hardly surprising news to anyone who had a summer job when they were young. An additional benefit from youth employment not mentioned by the authors is that the low-skill, low-paying jobs held by young people also provide them with information about what they don’t want to do when they grow up. Working in a fast food restaurant or at the counter of a store in the local mall helps a young person appreciate how hard it is to earn a dollar and provides a tangible reason to gain more skills in order to increase one’s productivity and earn a higher wage.

Unfortunately, many young people today are not obtaining these benefits. The chart below depicts the national teenage unemployment rate and labor force participation rate (LFP) from 2005 to 2015 using year-over-year August data from the BLS.

national teen unemp, LFP

During the Great Recession teenage employment fell drastically, as indicated by the simultaneous increase in the unemployment rate and decline in the LFP rate from 2007 to 2009. From its peak in 2010, the unemployment rate for 16 to 19 year olds declined slowly until 2012. This decline in the unemployment rate coincided with a decline in the LFP rate and thus the latter was partly responsible for the former’s decline. More recently, the labor force participation rate has flattened out while the unemployment rate has continued to decline, which means that more teenagers are finding jobs. But the teenagers who are employed are part of a much smaller labor pool than 10 years ago – nationally, only 33.7% of 16 to 19 year olds were in the labor force in August 2015, a sharp decline from 44% in 2005.

Full-time teenage employment is unique in that it has a relatively high opportunity cost – attending school full time. Out of the teenagers who work at least some portion of the year, most only work during the summer when school is not in session. Some teenagers also work during the school year, but this subset of teenage workers is smaller than the set who are employed during the summer months. Thus a decline in the LFP rate for teenagers may be a good thing if the teenagers who are exiting the labor force are doing so to concentrate on developing their human capital.

Unfortunately this does not seem to be the case. From 2005 to 2013 the enrollment rate of 16 and 17 year olds actually declined slightly from 95.1% to 93.7%.  The enrollment rate for 18 and 19 year olds stayed relatively constant – 67.6% in 2005 and 67.1% in 2013, with some mild fluctuations in between. These enrollment numbers coupled with the large decline in the teenage LFP rate do not support the story that a large number of working teenagers are exiting the labor force in order to attend school full time. Of course, they do not undermine the story that an increasing amount of teenagers who are both in the labor force and attending school at the same time are choosing to exit the labor force in order to focus on school. But if that is the primary reason, why is it happening now?

Examining national data is useful for identifying broad trends in teenage unemployment, but it conceals substantial intra-national differences. For this reason I examined teenage employment in 10 large U.S. cities (political cities, not MSAs) using employment status data from the 5-year American Community Survey (ACS Table S2301. 2012 was the latest data available for all ten cities).

The first figure below depicts the age 16 – 19 LFP rate for the period 2010 – 2012. As shown in the diagram there are substantial differences across cities.

City teenage LFP

For example, in New York (dark blue) only 23% of the 16 – 19 population was in the labor force in 2012 – down from 25% in 2010 – while in Denver 43.5% of the 16 – 19 population was in the labor force. Nearly every city experienced a decline over this time period, with only Atlanta (red line) experiencing a slight increase. Five cities were below the August 2012 national rate of 34% – Chicago, Philadelphia, Atlanta, San Francisco, and New York.

Also, in contrast to the improving unemployment rate at the national level from 2010 – 12 shown in figure 1, the unemployment rate in each of these cities increased during that period. Figure 3 below depicts the unemployment rate for each of the 10 cities.

City teenage unemp rate

In August 2012 the national unemployment rate for 16 – 19 year olds was 24.3%, a rate that was exceeded by all 10 cities analyzed here. Atlanta had the highest unemployment rate in 2012 at 48%. Atlanta’s high unemployment rate and relatively low LFP rate reveals how few Atlanta teens were employed during this period and how difficult it was for those who wanted a job to find one.

The unemployment rate may increase because employment declines or more unemployed people enter the labor force, which would increase the labor force participation rate. Figures 2 and 3 together indicate that the unemployment rate increased in each of these cities due to a decline in employment, not increased labor force participation.

The preceding figures are evidence that the teenage employment situation in these major cities is getting worse both over time and relative to other areas in the country. To the extent that teenage employment benefits young people, fewer and fewer of them are receiving these benefits. From the linked article:

“The substantial drop in teen employment prospects has had a devastating effect on the nation’s youngest teens (16-17), males, blacks, low income youth, and inner city, minority males,” wrote Andrew Sum in a report on teen summer employment for the Center for Labor Market Studies at Northeastern University. “Those youth who need work experience the most get it the least, another example of the upside down world of labor markets in the past decade.”

Unfortunately, in many cities the response to this situation will only exacerbate the problem. Seattle and Los Angeles have already approved local $15 minimum wages, and a similar law in the state of New York that applies only to fast food franchises was recently approved by the state’s wage board. While many people still question the effect of a minimum wage on overall employment, there is substantial empirical evidence that a relatively high minimum wage has a negative effect on employment for the least skilled workers, which includes inner-city teenagers who often attend mediocre schools. Thus it is hard to believe that any of the seemingly well-intentioned increases in the minimum wage that are occurring around the country will have a positive effect on the urban teenage employment situation presented here. A better response would be to eliminate the minimum wage so that in the short run low-skilled workers are able to offer their labor at a price that is commensurate to its value. In the long run worker productivity must be increased which involves K-12 school reform.

Education, Innovation, and Urban Growth

One of the strongest predictors of urban growth since the start of the 20th century is the skill level of a city’s population. Cities that have a highly skilled population, usually measured as the share of the population with a bachelor’s degree or more, tend to grow faster than similar cities with less educated populations. This is true at both the metropolitan level and the city level. The figure below plots the population growth of 30 large U.S. cities from 1970 – 2013 on the vertical axis and the share of the city’s 25 and over population that had at least a bachelor’s degree in 1967 on the horizontal axis. (The education data for the cities are here. I am using the political city’s population growth and the share of the central city population with a bachelor’s degree or more from the census data linked to above.)

BA, city growth 1

As shown in the figure there is a strong, positive relationship between the two variables: The correlation coefficient is 0.61. It is well known that over the last 50 years cities in warmer areas have been growing while cities in colder areas have been shrinking, but in this sample the cities in warmer areas also tended to have a better educated population in 1967. Many of the cities known today for their highly educated populations, such as Seattle, San Francisco, and Washington D.C., also had highly educated populations in 1967. Colder manufacturing cities such as Detroit, Buffalo, and Newark had less educated workforces in 1967 and subsequently less population growth.

The above figure uses data on both warm and cold cities, but the relationship holds for only cold cities as well. Below is the same graph but only depicts cities that have a January mean temperature below 40°F. Twenty out of the 30 cities fit this criteria.

BA, city growth 2

Again, there is a strong, positive relationship. In fact it is even stronger; the correlation coefficient is 0.68. Most of the cities in the graph lost population from 1970 – 2013, but the cities that did grow, such as Columbus, Seattle, and Denver, all had relatively educated populations in 1967.

There are several reasons why an educated population and urban population growth are correlated. One is that a faster accumulation of skills and human capital spillovers in cities increase wages which attracts workers. Also, the large number of specialized employers located in cities makes it easier for workers, especially high-skill workers, to find employment. Cities are also home to a range of consumption amenities that attract educated people, such as a wide variety of shops, restaurants, museums, and sporting events.

Another reason why an educated workforce may actually cause city growth has to do with its ability to adjust and innovate. On average, educated workers tend to be more innovative and better able to learn new skills. When there is an negative, exogenous shock to an industry, such as the decline of the automobile industry or the steel industry, educated workers can learn new skills and create new industries to replace the old ones. Many of the mid-20th century workers in Detroit and other Midwestern cities decided to forego higher education because good paying factory jobs were plentiful. When manufacturing declined those workers had a difficult time learning new skills. Also, the large firms that dominated the economic landscape, such as Ford, did not support entrepreneurial thinking. This meant that even the educated workers were not prepared to create new businesses.

Local politicians often want to protect local firms in certain industries through favorable treatment and regulation. But often this protection harms newer, innovative firms since they are forced to compete with the older firms on an uneven playing field. Political favoritism fosters a stagnant economy since in the short-run established firms thrive at the expense of newer, more innovative startups. Famous political statements such as “What’s good for General Motors is good for the country” helped mislead workers into thinking that government was willing and able to protect their employers. But governments at all levels were unable to stop the economic forces that battered U.S. manufacturing.

To thrive in the 21st century local politicians need to foster economic environments that encourage innovation and ingenuity. The successful cities of the future will be those that are best able to innovate and to adapt in an increasingly complex world. History has shown us that an educated and entrepreneurial workforce is capable of overcoming economic challenges, but to do this people need to be free to innovate and create. Stringent land-use regulations, overly-burdensome occupational licensing, certificate-of-need laws, and other unnecessary regulations create barriers to innovation and make it more difficult for entrepreneurs to create the firms and industries of the future.

Rent control: A bad policy that just won’t die

The city council of Richmond, CA is thinking about implementing rent control in their city. Richmond is located north of Berkeley and Oakland on the San Francisco Bay in an area that has some of the highest housing prices in the country. From the article:

“Richmond is growing and becoming a more desirable place where people want to live, but that increased demand is putting pressure on the existing housing stock.”

It is true that an increase in the demand for housing will increase prices and rents. Unfortunately, rent control will not solve the problem of too little housing, which is the ultimate cause of high prices.

rent control 1

The diagram above depicts a market for housing like the one in Richmond. Without rent control, when demand increases (D1 to D2) the price rises to R2 and the equilibrium quantity increases from Q1 to Q*. However, with rent control, the price is unable to rise. For example, if the Richmond city council wanted prices to be at the pre-demand-increase level they would set the rent control price equal to R1. But with the increase in demand the quantity demanded at that price is Qd, while the quantity supplied is only Q1. Thus there is a shortage. This is the outcome of a price ceiling.

What this means is that some people will find a place to rent at the old, lower rental price (Q1 people).  But more people will want to rent at that price than there are units available, and since the price cannot rise due to the price control, the available apartments will have to be allocated some other way. This means longer wait times for vacant apartments and higher search costs. It also means lower quality apartments. Since the owners know there are more people who want an apartment than available apartments, they don’t have an incentive to maintain the apartment at the same level as they would if they had to attract customers.

With rent control, only Q1 people get an apartment. Without rent control, as the price rises more units are supplied over time and the new equilibrium has Q* (> Q1) people who get an apartment. Yes, they have to pay a higher price, but the relevant alternative is not an apartment at the lower price: The alternative is that some people who would have been willing to pay the higher price do not get an apartment.

Since Richmond has strict land-use rules like many communities in the San Francisco metro area (you can read all about their minimum lot size and parking space requirements here), rent control is adding to the housing woes of Richmond’s renters and any person who would like to move there.

rent control 2

Land-use restrictions decrease the amount of buildable land which subsequently increases the cost of housing. This is depicted in the diagram above as a shift from S1 to S2. The decrease in supply leads to a new equilibrium rent of R2 > R1 and a reduction in the equilibrium quantity to Q2 (< Q1). So land-use restrictions have already decreased the amount of available housing and increased the price.

If rent control is implemented, depicted in the diagram as the solid red line at the old price (R1), then the quantity supplied decreases even more to Qs. Again, with rent control there is a shortage as the quantity of housing demanded at R1 is Q1 (> Qs). So all of the same problems that occurred in the first example occur here, only here the quantity of housing is decreased not once, but TWICE by the government: Once due to the land use restrictions (Q1 to Q2) and then AGAIN when the rent control is implemented (Q2 to Qs). Restricting the amount of housing available does not help more people find housing, and restricting it again exacerbates the problem.

Trying to find an economist who doesn’t think that rent control is a bad idea is like trying to find a cheap apartment in a city with rent control; it can be done, but you have to spend a lot of time looking. In a Booth IGM poll question about rent control, 95% of the economists surveyed disagreed with the statement that rent control had a positive impact on the amount and quality of affordable rental housing. Yet despite basic economic theory, the agreement among experts, and the empirical evidence (see here, here, and here) rent control remains in some places and is often brought up as a viable policy for increasing the amount of affordable housing. This is truly a shame since what places like Richmond need is more housing, not less housing with artificially low prices.

Local land-use restrictions harm everyone

In a recent NBER working paper, authors Enrico Moretti and Chang-Tai Hsieh analyze how the growth of cities determines the growth of nations. They use data on 220 MSAs from 1964 – 2009 to estimate the contribution of each city to US national GDP growth. They compare what they call the accounting estimate to the model-driven estimate. The accounting estimate is the simple way of attributing city nominal GDP growth to national GDP growth in that it doesn’t account for whether the increase in city GDP is due to higher nominal wages or increased output caused by an increase in local employment. The model-driven estimate that they compare it to distinguishes between these two factors.

Before I go any further it is important to explain the theory behind the author’s empirical findings. Suppose there is a productivity shock to City A such that workers in City A are more productive than they were previously. This productivity shock could be the result of a new method of production or a newly invented piece of equipment (capital) that helps workers make more stuff with a given amount of labor. This productivity shock will increase the local demand for labor which will increase the wage.

Now one of two things can happen and the diagram below depicts the two scenarios. The supply and demand lines are those for workers, with the wage on the Y-axis and the amount of workers on the X-axis. Since more workers lead to more output I also labeled labor as L = αY, where α is some fraction less than 1 to signify that each additional unit of labor doesn’t lead to a one unit increase in output, but rather some fraction of 1 unit (capital is needed too).

moretti, land use pic

City A can have a highly elastic supply of housing, meaning that it is easy to expand the number of housing units in that city and thus it is relatively easy for people to move there. This would mean that the supply of labor is like S-elastic in the diagram. Thus the number of workers that are able to migrate to City A after labor demand increases (D1 to D2) is large, local employment increases (Le > L*), and total output (GDP) increases. Wages only increase a little bit (We > W*). In this situation the productivity shock would have a relatively large effect on national GDP since it resulted in a large increase in local output as workers moved from relatively low-productivity cities to the relatively high-productivity City A.

Alternatively, the supply of housing in City A could be very inelastic; this would be like S-inelastic. If that is the case, then the productivity shock would still increase the wage in City A (Wi > W*), but it will be more difficult for new workers to move in since new housing cannot be built to shelter them. In this case wages increase but since total local employment stays fairly constant due to the restriction on available housing the increase in output is not as large (Li > L* but < Le). If City A output stays relatively constant and instead the productivity shock is expressed in higher nominal wages, then the resulting growth in City A nominal GDP will not have as large of an effect on national output growth.

As an example, Moretti and Hsieh calculate that the growth of New York City’s GDP was 12% of national GDP growth from 1964-2009. But when accounting for the change in wages, New York’s contribution to national output growth was only 5%: Most of New York’s GDP growth was manifested in higher nominal wages. This is not surprising as it is well known that New York has strict housing regulations that make it difficult to build new housing units (the recent extension of NYC rent-control laws won’t help). This makes it difficult for people to relocate from relatively low-productivity places to a high-productivity New York.

In three of the most intensely land-regulated cities: New York, San Francisco, and San Jose, the accounting contribution to national GDP growth was 19.3%. But these cities actual contribution to national output as estimated by the authors was only 6.1%. Contrast that with the Rust Belt cities (e.g. Detroit, Pittsburgh, Cleveland, etc.) which contributed -28.5% according to the accounting method but +6.1% according to the author’s model.

The authors conclude that less onerous land-use restrictions in high-productivity cities New York, Washington D.C., Boston, San Francisco, San Jose, and the rest of Silicon Valley could increase the nation’s output growth rate by making it easier for workers to migrate from low to high-productivity areas. In an extreme migration scenario where 52% of American workers in 2009 lived in a different city than they actually did, the author’s calculate that GDP per worker would have been $8,775 higher in 2009, or $6,345 per person. In a more realistic scenario (only 20% of workers lived in a different city) it would have been $3,055 more per person: That is a substantial increase.

While I agree with the author’s conclusion that less land-use restrictions would result in a more productive allocation of labor and thus more stuff for all of us, the author’s policy prescriptions at the end of the paper leave much to be desired.  They propose that the federal government constrain the ability of municipalities to set land-use restrictions since these restrictions impose negative externalities on the rest of the country if the form of lowering national output growth. They also support the use of government funded high-speed rail to link  low-productivity labor markets to high-productivity labor markets e.g. the current high-speed rail construction project taking place in California could help workers get form low productivity areas like Stockton, Fresno, and Modesto, to high productivity areas in Silicon Valley.

Land-use restrictions are a problem in many areas, but not a problem that warrants arbitrary federal involvement. If federal involvement simply meant the Supreme Court ruling that land-use regulations (or at least most of them) are unconstitutional then I think that would be beneficial; a broad removal of land-use restrictions would go a long way towards reinstituting the institution of private property. Unfortunately, I don’t think that is what Moretti and Hsieh had in mind.

Arbitrary federal involvement in striking down local land-use regulations would further infringe on federalism and create opportunities for political cronyism. Whatever federal bureaucracy was put in charge of monitoring land-use restrictions would have little local knowledge of the situation. The Environmental Protection Agency (EPA) already monitors some local land use and faulty information along with an expensive appeals process creates problems for residents simply trying to use their own property. Creating a whole federal bureaucracy tasked with picking and choosing which land-use restrictions are acceptable and which aren’t would no doubt lead to more of these types of situations as well as increase the opportunities for regulatory activism. Also, federal land-use regulators may target certain areas that have governors or mayors who don’t agree with them on other issues.

As for more public transportation spending, I think the record speaks for itself – see here, here, and here.

Local control over transportation: good in principle but not being practiced

State and local governments know their transportation needs better than Washington D.C. But that doesn’t mean that state and local governments are necessarily more efficient or less prone to public choice problems when it comes to funding projects, and some of that is due to the intertwined funding streams that make up a transportation budget.

Emily Goff at The Heritage Foundation finds two such examples in the recent transportation bills passed in Virginia and Maryland.

Both Virginia Governor Bob McDonnell and Maryland Governor Martin O’Malley propose raising taxes to fund new transit projects. In Virginia the state will eliminate the gas tax and replace it with an increase in the sales tax. This is a move away from a user-based tax to a more general source of taxation, severing the connection between those who use the roads and those who pay. The gas tax is related to road use; sales taxes are barely related. There is a much greater chance of political diversion of sales tax revenues to subsidized transit projects: trolleys, trains and bike paths, rather than using revenues for road improvements.

Maryland reduces the gas tax by five cents to 18.5 cents per gallon and imposes a new wholesale tax on motor fuels.

How’s the money being spent? In Virginia 42 percent of the new sales tax revenues will go to mass transit with the rest going to highway maintenance. As Goff notes this means lower -income southwestern Virginians will subsidize transit for affluent northern Virginians every time they make a nonfood purchase.

As an example, consider Arlington’s $1 million dollar bus stop. Arlingtonians chipped in $200,000 and the rest came from the Virginia Department of Transportation (VDOT). It’s likely with a move to the sales tax, we’ll see more of this. And indeed, according to Arlington Now, there’s a plan for 24 more bus stops to compliment the proposed Columbia Pike streetcar, a light rail project that is the subject of a lively local debate.

Revenue diversions to big-ticket transit projects are also incentivized by the states trying to come up with enough money to secure federal grants for Metrorail extensions (Virginia’s Silver Line to Dulles Airport and Maryland’s Purple Line to New Carrolton).

Truly modernizing and improving roads and mass transit could be better achieved by following a few principles.

  • First, phase out federal transit grants which encourage states to pursue politically-influenced and costly projects that don’t always address commuters’ needs. (See the rapid bus versus light rail debate).
  • Secondly, Virginia and Maryland should move their revenue system back towards user-fees for road improvements. This is increasingly possible with technology and a Vehicle Miles Tax (VMT), which the GAO finds is “more equitable and efficient” than the gas tax.
  • And lastly, improve transit funding. One way this can be done is through increasing farebox recovery rates. The idea is to get transit fares in line with the rest of the world.

Interestingly, Paris, Madrid, and Tokyo have built rail systems at a fraction of the cost of heavily-subsidized projects in New York, Boston, and San Francisco. Stephen Smith, writing at Bloomberg, highlights that a big part of the problem in the U.S. are antiquated procurement laws that limit bidders on transit projects and push up costs. These legal restrictions amount to real money. French rail operator SNCF estimated it could cut $30 billion off of the proposed $68 billion California light rail project. California rejected the offer and is sticking with the pricier lead contractor.