One tip for a prosperous city economy

Local media over the course of the last several months have asked us variations on one question repeatedly: if our city wants to do better – be more productive, retain more young people, reduce poverty—how can it do that?

That’s a very complicated question of course, and each metro area and urban core has its own problems based on current policies and laws, history, and geography, among other factors. However, there is one indicator that above all else predicts success of city residents: college attainment rates. Even for those without a 4-year degree, this predicts success; essentially, if your neighbors are better educated, you are more likely to have a better income. With that, all of the correlates of a higher income such as health, educational opportunities for children, and even happiness—are higher.

It’s striking how strong and consistent the correlation between education and higher personal incomes is.  Economists attribute this to a number of factors.  Better educated workers command a high skill premium, because they’re more adaptable and productive, and are critical to growing knowledge based firms.  Education has important spillover benefits:  on average, workers of all education levels are more productive (and higher paid) if they live in cities that are better educated.  A well-educated population makes a city more resilient in the face of economic and technological change, and better able to quickly adapt to new circumstances and opportunities.

Cities around the nation pursue a range of different economic strategies–pursuing new industries, promoting innovation, encouraging entrepreneurship, expanding infrastructure, and building civic amenities.  While there are merits to all of these approaches, every one of them takes a back seat to improving educational attainment as a way to raise incomes.  Put another way, all of these strategies will work better in a place with strong educational attainment, and communities with weak educational attainment will find only meager returns.

Improving educational attainment isn’t the only economic strategy, but it’s a fundamental one, and if your city fails to move forward in this important area, it will find it more difficult to successfully implement all of its other tactics.

At CityObservatory, we track attainment rates closely, as we believe talent is the biggest driver of positive (or negative) change in a city. The figure above shows the most updated figures on educational attainment and per capita income, from the 2013 American Community Survey data. To learn more about how talent drives city success, go here, and be sure to check back often, as we will continue to discuss how talent and success are tied to complex urban problems (and solutions to those problems).

You are where you eat.

The Big Idea: Many metro areas vie for the title of “best food city.” But what cities have the most options for grabbing a bite to eat — and what does that say about where you live?


There are plenty of competing rankings for best food cities floating around the internet. You can find lists for cities with the most restaurants, the best restaurants, the most distinctive local restaurants… and of course none of these seem to agree (although the “winners” tend to be similar among these lists).

But what about the cities that provide the most dining options per person? And what does restaurant variety have to do with a city’s livability?

One of the hallmarks of a great city is a smorgasbord of great places to eat. Cities offer a wide variety of choices of what, where, and how to eat, everything from grabbing a dollar taco to seven courses of artisanally curated locally raised products (not to mention pedigreed chickens). The “food scene” is an important component of the urban experience.

Restaurants are an important marker of the amenities that characterize attractive urban environments. Ed Glaeser and his colleagues found that “Cities with more restaurant and live performance theaters per capita have grown more quickly over the past 20 years both in the U.S. and in France.”

Matthew Holian and Matthew Kahn have seen that an increase in the number of restaurants per capita in a downtown area has a statistically significant effect in reducing driving and lowering greenhouse gas production.

We’ve assembled data on the number of full service restaurants per capita in each of the nation’s largest metropolitan areas. These data are from the County Business Patterns data compiled by the US Census Bureau for 2012. Note that the “full service” definition basically applies only to sit down, table service restaurants, not the broader category that includes fast food and self-service. We’re also looking at metro-wide data to assure that the geographical units we’re comparing are defined in a similar fashion—political boundaries like city limits and county lines are arbitrary and vary widely from place to place, making them a poor basis for constructing this kind of comparison.

As you might guess, the metro areas with the most restaurants per capita are found predominantly in the Northeast and on the West Coast. Elsewhere, New Orleans and Denver score high as well. While the average metropolitan area has about seven full-service restaurants per 10,000 residents, the range is considerable. The San Francisco metropolitan area has more than 11 restaurants per 10,000, while Riverside has only five and seven other metropolitan areas have fewer than six.

The top five metropolitan areas on this indicator are San Francisco, Providence, Portland, New York, and Seattle. Each of these cities has nine or more full service restaurants per capita. With the possible exception of Providence, all of these are recognized as major food cities in the US. (And Portland achieves its high ranking without counting the city’s more than 500 licensed food carts.)

Interestingly, Las Vegas, which we think of as a tourism mecca, has fewer restaurants per capita than the average metropolitan area. A lot of this has to do with scale—the average restaurant in Las Vegas tends to be much larger than in other metropolitan areas. According to the Census Bureau, almost eight percent of Las Vegas restaurants employed more than 100 workers; nationally the average is only two percent.

This ranking doesn’t include anything about quality–simply quantity– but the higher restaurants per capita can indicate higher competition (and therefore better quality options), or higher demand (a signal that more diversity of options is valued, allowing for more valuable experiences). It is also highly correlated with per capita income, which makes sense: the more people that are able to afford frequent restaurant outings, the more restaurants there will be.

While this isn’t a perfect listing of best food culture — each person’s measure of the ‘best food town’ is subjective — it does settle the debate of where you should go to have the largest selection of eatery options. If you’re going to travel 2,000 miles for dinner, it might be wise to make a reservation. Or if you’re going to Portland, at least be ready to wait in line.


Photo courtesy of Janet at Flickr Creative Commons

How productive is your city?

Which metropolitan economies are the most productive?  Our broadest measure of economic output is gross domestic product — the total value of goods and services produced by our economy.  Economists usually compare the productivity of national economies by looking at GDP per worker or per employee.  At the sub-national level, the Bureau of Economic Analysis estimates an analogous concept “Gross Metropolitan Product” –the total value of goods and services produced in a metropolitan area.

If we divide metropolitan GDP by population, we get a rough idea of which metropolitan economies are the most productive on a per person basis.  Nationally, gross metropolitan product averages about $55,000 per person in the nation’s largest metropolitan areas.

The distribution is characterized by two distinct outliers: Riverside, CA on the low end, and San Jose on the high end. The two cities are 400 miles apart, but San Jose has a GDP per capita almost $75,000 more than Riverside (that’s more than most cities produce in a year per person).

In general, it’s clear that the productivity of a few big cities in the northeast and west coast is much higher than those in the middle of the country. Nine metros have gross domestic product over $65,000 per capita, and the only one of these not on the east or west coast is Houston.

It should be noted that this looks quite similar to the map of educational attainment: GDP per capita and educational attainment are highly correlated, and an increase in the level of talent in one’s city is associated with an increase in GDP:

We should keep in mind that gross product is a broad measure of economic activity:  it picks up the value of goods and services produced in an area, including the rental value of owner-occupied homes and returns to physical capital.  While most labor income in a metropolitan area goes to residents of that area, capital income often goes to owners who live elsewhere.  Since GMP measures the value of services where businesses are located, rather than where shareholders live, it apportions the capital returns for banks in New York, to New York, and for software firms in Seattle, to Seattle, rather than to the location of the shareholders of these firms.

Some technical notes:  The Bureau of Economic Analysis measures gross domestic product of metropolitan areas in chained 2009 dollars.  These data are for calendar year 2013; annual data for 2014 should be released in the third quarter of this year.  You can explore GDP by industry sector to see which industries make the biggest contribution to regional output in each metropolitan area.  Detailed data are available on the BEA website:

Is life really better in Red States (and cities)?

The red state/blue state divide is a persistent feature of American politics. Political differences among states are also associated with important economic differences, and a similar patterns hold across and within metro areas. Big cities are more likely to be blue, and smaller towns and rural areas are red. The more densely populated portions of every metro area are also more likely to be blue. Understanding and eventually bridging these fissures is a major challenge for the nation.

In an article in last week’s New York Times, urbanist Richard Florida seems to have, if perhaps only inadvertently, given aid and comfort to the persistent myth that people are somehow worse off in big cities compared with smaller towns and suburbs.

It could be that this impression is amplified by the headline writer’s provocative question: “Is life better in America’s Red States?” While he doesn’t directly answer this question, Florida seems to imply that because housing is on average cheaper in red states, people who live there must be better off.

But is it the case that cheap housing is a reliable marker of economic well-being?

While it’s true that average home prices are higher in blue states, it’s important to consider why that is, and what it signifies. First and most importantly, blue state housing prices are driven higher because incomes and economic productivity are higher in bluer states and bluer cities. GDP per capita tends to be higher in metro areas that favored President Obama’s re-election by the widest margin, as shown here:



Note: if you hover over the orange trend line, you will see that the p-value is low and significant at the 1% threshold. (The p-value measures the statistical likelihood that the relationship between vote margin and productivity –measured by GDP per capita–is different from zero).  It measures correlation and tells us nothing about causality.    You can see the now familiar red-blue pattern on the attached map; the size of circles for each city corresponds to GDP per capita:  

The question then is, are higher housing prices in blue places an indication that the standard of living is lower?

Focusing on dollars per square foot misses the important fact that, unlike our stone age ancestors, we don’t rely on shelter solely as a means of warding off the cold, dark and wild beasts. We don’t value houses just as boxes—location matters. The reason the price of a square foot of land in Manhattan is worth as much as an acre of farmland in North Dakota has everything to do with the access it provides to a range of services, experiences and goods.

To an economist, if people are willing to pay a higher price for something—like housing in Manhattan or San Francisco or Honolulu—it’s a good indication that it has a higher value. A big part of the reason housing prices are higher in bigger cities than small ones is that we value the personal and economic opportunities that come from being close to lots of other people. As University of Chicago economist and Nobel Laureate Robert Lucas famously put it: “What can people be paying Manhattan or downtown Chicago rents for, if not being near other people?”

Harvard’s Ed Glaeser, author of Triumph of the City, has explored this theme in great depth. Increasingly, he argues, the biggest driver of city growth is the consumption advantages of living in cities, with close proximity to a wide range of goods, services, experiences, social interactions and cultural activities. This “consumer city” theory means that cities increase the well-being of their residents by facilitating all kinds of consumption. Indeed there are whole categories of goods, and especially services that are simply unavailable at any price outside major cities: think of everything from diverse ethnic restaurants to specialized medical care to cutting edge live art and music.

Provocative new work by Jessie Handbury shows once you adjust for the variety and quality of goods available in different places, the cost of living in big cities is actually lower than smaller cities. Her work looks at variations in the price and availability of food. It’s almost certain that differences in services are even more skewed in favor of city residents.

Moreover, looking just at differences in housing costs ignores important city advantages of density, proximity and convenience. Higher rents invariably provide city residents with better physical access to jobs, shopping, culture and social interaction. As Scott Bernstein and his colleagues at the Center for Neighborhood Technology have shown, savings in transportation costs in cities largely, and in some cases fully, offset differences in rents.

People who live in blue cities drive much less on average than those who live in red cities, and the savings in time and expense are substantial. My own work shows that residents of blue Portland Oregon drive about 20 percent less than in other large metro areas, saving them more than a billion dollars a year in transportation costs.

Florida makes one point that we all ought to pay attention to: as a nation we’d be much better off if we created more opportunities for people to live and work in blue cities. Because residents in big blue cities are so much more productive than otherwise identical workers in smaller red cities, we take a substantial hit to national economic productivity and growth. Enrico Moretti estimates GDP would be 13 percent or so higher if it weren’t for constrained population growth in these highly productive cities.

There’s an old adage that claims than an economist is someone who knows the price of everything and the value of nothing. Assuming that difference in house prices per square foot across metropolitan areas accurately reflects cost of living differences is arguably wrong. Cheap houses entail high costs for other things—like transportation—and to believe cheap houses automatically equals better quality of life misses the huge and tangible differences in the price and availability of a whole range of goods, services and experiences that make life nicer.

The political message here ought to be the high prices for blue cities generally, and the growing market premium for housing in dense, urban neighborhoods particularly, is a signal that Americans want more cities, and more opportunities for urban living. It’s a fair criticism of blue cities to say that they haven’t done a good enough job of making it possible for more people to live there—and this has a lot to do with local land use planning. But it has also been amplified by decades of federal subsidies to sprawling low-density development.

One final addendum on Richard Florida’s political analysis: as troubling as the persistent red/blue divide is among states and cities, it’s probably wrong to attribute the 2014 election results to this dichotomy. The huge fall off in turnout, especially among younger voters compared to 2012, is clearly the big driver of November’s red tide. Not only was 2014 the lowest off-year election turnout—only 37 percent—in six decades, but the electorate skewed far older in 2014 than in 2012. Voters over 65 made up 22 percent of voters in 2014, up from 16 percent in 2012; voters under 30 made up 13 percent of the electorate down from 19 percent in 2012. The 2014 red surge wasn’t so much geographic as it was demographic.

Where are the food deserts?

One of the nation’s biggest health problems is the challenge of obesity:  since the early 1960s the number of American’s who are obese has increased from about 13 percent to 35 percent.

The problem is a complex, deep-seated one, and everything from our diet, to our inactive life-styles, to the built environment have been implicated as contributing factors.

Over the past five years, a new term has crept into our common lexicon of cities:  food desert.  (Google Trends reports almost no use of the term prior to 2009, and mentions have grown steadily since then).

The image of a food desert conveys a strong, specific image:  people who live so far from a grocery store with healthy food that they have little alternative but to subsist on the unhealthy alternatives close at hand.  But what exactly is a food desert?  And how many Americans have poor diets because of the distance they have to travel to reach a grocery store?

Judged by proximity to grocery stores nearly all of rural America is a food desert.  Nathan Yau at FlowingData uses Google maps data to construct a compelling map of how far it is to the nearest grocery store across the entire nation. The bleakest food deserts are the actual deserts of the American West, in Nevada and Wyoming.

City dwellers, particularly those in the biggest, most dense cities tend to live closest to supermarkets and have the best food access.  At City Observatory, we’re big fans of WalkScore, the app that computes a walkability index for any residential address in the U.S. based on its proximity to common destinations like stores, parks and schools.  Earlier this year, WalkScore used their data and modeling prowess to develop some clear, objective images of who does (and doesn’t) have a good grocery store nearby.  They estimate that 72 percent of New York City residents live within a five-minute walk of a grocery store.  At the other end of the spectrum, only about five percent of residents of Indianapolis and Oklahoma City are so close.  If you want to walk to the store, this data shows the real food deserts are in the suburbs.

There are other ways of measuring food access and mapping food deserts.  The U.S. Department of Agriculture and PolicyMap have both worked to generate their own maps of the nation’s food deserts.  They use a combination of physical proximity (how far it is to the nearest grocery store) and measurements of neighborhood income levels.

While it’s clear that income plays a big role in food access, it’s far from clear how to combine income and proximity to define food deserts.  The USDA uses an overlay which identifies low-income neighborhoods with limited food access.  PolicyMap has a complicated multi-step process that compares how far low-income residents have to travel to stores compared to higher income residents living in similarly dense neighborhoods.

In practice, combining neighborhood income and physical proximity actually muddles the definition of food access.  First, and most important, it acknowledges that income, not physical distance is the big factor in nutrition.  Both of these methods imply that having wealthy neighbors or living in the country-side means than physical access to food is not a barrier.  Second, it is your household’s income, not your neighbor’s income, that determines whether you can buy food.  Third, these methods implicitly treat low income families differently depending on where they live.  For example, PolicyMap excludes middle income and higher income neighborhoods from its definition of “limited supermarket access” areas—and therefore doesn’t count lower income families living in these areas as having poor food access.

The fact that both of these systems use a different yardstick for measuring accessibility in rural areas suggests that proximity isn’t really the issue. Rural residents are considered by USDA to have adequate food access if they live within ten miles of a grocery story whereas otherwise identical urban residents are considered to have adequate access only if they live within a mile or half-mile of a store.

If we’re concerned about food access, we probably ought to focus our attention on poverty and a lack of income, not grocery store location.  The argument here parallels that of Nobel Prize winning economist Amartya Sen, who pointed out that the cause starvation and death in famines is seldom the physical lack of sufficient food, but is instead the collapse of the incomes of the poor.  Sen’s conclusion was that governments should focus on raising incomes if they wanted to stave off hunger, rather than stockpiling or distributing foodstuffs

It’s tempting to blame poor nutrition and obesity on a lack of convenient access to healthier choices, but the problem is more difficult and complex than that.  Poverty and poor education are strong correlates of poor nutrition and obesity.

Finally, it’s reasonable to question whether the physical proximity to healthier eating choices is the big driver of our hunger and nutrition problems.

Millions of Americans, rich and poor, walk right past the fresh vegetables and buy chips, soda, and other calorie-rich processed foods.  The “food desert” narrative is a convenient way of making it sound like personal choice doesn’t enter into the problem.  But studies show that there is no apparent relationship between a store’s mix of products and its customer’s body/mass index (BMI) (Lear, Gasevic, and Schuurman, 2013). Limited experimental evidence suggest that improving the supply of fresh foods seems to have limited impacts on food consumption patterns.  Preliminary results of a study of consumers in a Philadelphia neighborhood that got better supermarket access showed no improvement in fruit and vegetable consumption or body mass index even for those who patronized the new store.

Of course, we have good reasons to believe that the built environment does play an important role in obesity—but that may have more to do with how easy it is to walk to all our daily destinations, and not just the distance to the fresh food aisle.

Tracking Neighborhood Change: How we made “Lost In Place”

In this post, we’ll go over the data and mapping steps that were used to create our Lost In Place report on the concentration of poverty and the interactive web map. This post is one of several commentary posts that accompany the report, including an examination of how poverty has deepened.

Data for our report is provided at the Census Tract geography for US Metropolitan Statistical Areas (MSAs) with a 2010 population of over 1 million people — 51 in total. Our online map and report are based off two reported data points across five Census years: population and poverty levels in 1970, 1980, 1990, 2000 and 2010. Unfortunately for data analysis, Census Tract boundaries changed each census year between 1970 and 2010 as the geography of people changed. Fortunately, John Logan of Brown University and his colleagues released the Longitudinal Tract Database (LTDB) and have estimated tract-level Census counts from historical Census data from 1970 through 2010 using Census 2010 tract boundaries.

Two additional steps were necessary: we needed to determine which Census Tracts were part of our MSAs of interest, and in order to create maps and determine which tracts are within 10 miles of each MSAs central business district (CBD), we need to merge the Census tract data with their corresponding Census tract polygons. First, each 2010 Census Tract number is composed of a 2-digit identifier for the state, a 3-digit county identifier, followed by a 6-digit tract identifier. For example, Census Tract number 41051010600 can be decomposed State 41 (Oregon), County 051 (Multnomah) and Tract 010600. Using the Census Metropolitan Statistical Area Definition Files, we see that any Census Tract that starts with 41051 (often referred to as the FIPS State-County) is within the MSA 38900 (Portland-Vancouver-Hillsboro, OR-WA). Using this information, we filtered the our tracts list to only those within our MSAs of interest.

Second, using only this filtered set of Census Tracts, we matched the Census Tract numbers to the Census Tract numbers of Census 2010 tract shapefiles. Using a list of CBDs for each of the metro areas, we calculated the distance from the CBD to the nearest point of each MSAs Census Tract polygon. Once this spatial relationship is calculated, we could calculate totals for core MSA and the MSA as a total.

Using the existing literature, we developed the typology of tracts in poverty featured in the report. We used QGIS to create GEOJSON-based shapefiles with the geographic data in them. This allows us to host the files on github, making them available for download. But first, we needed to shrink the size of the shapefiles in order to serve an entire metro areas tract files quickly. We did this by simplifying the geography using rgeos in R. Additionally, by having a shapefile for each metropolitan area, we can quickly and dynamically load shapefiles for each metro area. To create the interactive maps for each metro, we used a combination of Mapbox.js and the Stamen Design basemaps.

We hope that both the data and the analysis that we develop at City Observatory help advance the understanding of cities.  Please feel free to contact us with your questions and comments.

Our dataset can be downloaded here.

How Poverty Has Deepened (part 2)

Recently, we discussed the growth in the number of urban high-poverty neighborhoods, which we illustrated by examining the distribution of poverty rates among census tracts. This analysis showed that high poverty neighborhoods are becoming more common in urban areas. Today we will use this distribution to discuss what few of us have directly experienced: extremely concentrated poverty. There is a small but growing segment of the population that lives in significantly differently conditions than the rest of the population; this group is one that sees extreme hardship and the highest rates of poverty nationally.

As you can see, while next to no one lived in urban census tracts had an 80% or higher poverty rate in 1970, over 72,000 people lived in neighborhoods with an 80% or higher poverty rate 40 years later. 20,000 lived in tracts with a 90% or higher poverty rate. It is extremely difficult for many to imagine living in a neighborhood where 1 in 2 people live in poverty (notice that this number tripled 1970 to 2010), but to live in a place where almost no one lives on more than $22,050 a year- for a family of 4- has profound implications for those residents.

While 72,000 is not a lot in the scope of the millions of residents of urban neighborhoods nationwide, it is still deeply concerning to have that many live in pockets of such highly concentrated poverty. To understand more about what 80% poverty means, I examined a census tract in Cleveland with an 83% poverty rate. It is census tract # 39035109801, and it is part of the Central neighborhood in Cleveland. In 1970, it had over a 50% poverty rate. Google Earth shows this picture of it:

cleveland google maps

It is a mix of empty land, industrial warehouses, and low-income housing units. The 2010 Census data provides the following demographic breakdown:

These are extremely atypical demographics. For example, the national median household income was $50,046 in 2010, compared to $7,654 in this neighborhood. Even with pictures, numbers, and demographic profiles, it is very difficult for most people to understand how difficult it can be to live in a place with this level of poverty.

We know that high poverty rates– even at the levels of 30% instead of 50% or higher- can have long-lasting implications for residents. Specifically,

“Concentrated poverty is associated with negative social effects (higher crime, worse mental and physical health), and lower economic prospects (both for current residents now and their children over their lifetimes). Concentrated poverty tends to be self-reinforcing: low-income communities have fewer fiscal resources (despite greater needs), producing low-quality public services. A lack of strong social networks undercuts the political efficacy of these citizens. There are a number of studies that review the extensive literature on the negative effects of concentrated poverty (Jargowsky & Swanstrom, 2009; Sard & Rice, 2014; Kneebone, Nadeau, & Berube, 2011).”Lost in Place: Why the persistence and spread of concentrated poverty—not gentrification—is our biggest urban challenge.

Most troubling is the direct impact on children in poverty. 26% of children are living in poverty, almost double the national rate (14.5%). In the neighborhood I just highlighted, 897 children 14 and younger—or 45% of the population according to the census—grew up in a place where if they were not impoverished, almost all of their neighbors were. The effects of a poor neighborhood, including sub-standard public services, worse schools, a lack of social ties necessary to get better jobs, and the many other well-noted direct social and economic impacts of living in concentrated poverty all mean that a growing number young children are growing up with less of a chance than they would have had 40 years ago.

More unfortunately, this also means that demographically, the growing concentration of poverty and income segregation has resulted in lower opportunities for people of color. Poverty and lowered income mobility disproportionately affects children of color; 71% of black infants and toddlers are low income, as are 67% of Hispanic infants and toddlers. (The Central neighborhood above is an even more extreme example of this disproportionality.) New evidence by Reardon, Fox and Townsend (2014) examines economic integration and racial segregation, noting that:

“The racial disparities in neighborhood income distributions are particularly troubling because these are differences that are present even among households with the same incomes. If long-term exposure to neighborhood poverty negatively affects child development, educational success, mental health, and adult earnings (and a growing body of research suggests it does, as noted above), then these large racial disparities in exposure to poverty may have long-term consequences. They mean that black and Hispanic children and families are doubly disadvantaged—both economically and contextually—relative to white and Asian families.

Given the self-reinforcing nature of poverty and its effects, this is a very concerning trend for black and Hispanic communities. The more common experience of urban poverty, and its concentration and deepening, doesn’t add up to a promising future- for any of us.

(At CityObservatory, we will continue to explore themes of poverty, economic integration, and policy solutions aimed at alleviating these issues. We also hope to illuminate growing evidence that increasing the welfare of low-income citizens has positive implications for middle and higher-income citizens. To read more about income segregation and economic opportunity, go here. For some links to articles we find particularly relevant go here and here.)

How Should Portland Pay for Streets?

For the past several months, Portland’s City Council has been wrestling with various proposals to raise additional funds to pay for maintaining and improving city streets. After considering a range of ideas, including fees on households and businesses, a progressive income tax, and a kind of Rube Goldberg income tax pro-rated to average gasoline consumption, the council has apparently thrown up its hands on designing its own solution.

The plan now is for the street fee solution to be laid at the feet of Portland voters in the form of a civic multiple choice test: Do you want to pay for streets with a monthly household street fee, a higher gas tax, a property tax, an income tax or something else entirely?

Given voter antipathy of taxes of any kind, it’s likely that “none of the above” would win in a landslide if it’s included as an option on the ballot (not likely).

All of these options have their own merits and problems, and it’s doubtful that there is a majority consensus for any one of them. How, how much, and who pays for streets is a key issue for every city. From an urbanist and public finance perspective, and as a guide to thinking about which—if any—of these approaches Portland should adopt, here are my eight suggested rules for paying for streets:

1. Don’t tax houses to subsidize cars. Despite mythology to the contrary, cars don’t come close to paying for the cost of the transportation system. The Tax Foundation estimates that only 30% of the cost of roads is covered by user fees like the gas tax. Not only do cars get a free ride when it comes to covering the cost of public services—unlike homes, they’re exempt from the property tax—but we tax houses and businesses to pay for car-related costs. Here are three quick examples: While half of storm runoff is from streets, driveways and parking lots, cars aren’t charged anything for stormwater—but houses are. A big share of the fire department’s calls involve responding to car crashes—and cars pay nothing toward fire department costs. Similarly, the police department spends a significant amount of its energy enforcing traffic laws—this cost is borne largely by property taxes—which houses pay, but cars don’t. If we need more money for streets, it ought to be charged on cars.

Adding a further charge on houses to subsidize car travel only worsens a situation  in which those who don’t own cars subsidize those who do. One in seven Portland households doesn’t own a car, and because they generally have lower incomes than car owners, fees tied to housing redistribute income from the poor to the rich.

2. End socialism for private car storage in the public right of way. Except for downtown and a few close-in neighborhoods, we allow cars to convert public property to private use for unlimited free car storage. Not asking those who use this public resource to contribute to the cost of its construction and upkeep makes no sense and ultimately subsidizes car ownership and driving. This subsidy makes traffic worse and unfortunately—but understandably—makes it harder and more expensive to build more housing in the city’s walkable, accessible neighborhoods. If, as parking expert Don Shoup has suggested, we asked those who use the streets for overnight car storage to pay for the privilege, we’d go a long way in reducing the city’s transportation budget shortfall—plus, we’d make the city more livable.  We should learn from the city’s success in reforming handicapped parking that getting the prices right makes the whole system work better.

3. Reward behavior that makes the transportation system work better for everyone. Paying for the transportation system isn’t just about raising revenue—it should be about providing strong incentives for people to live, work and travel in ways that make the transportation system work better and make the city more livable. Those who bike, walk, use transit, and who don’t own cars (or own fewer cars) actually make the street system work better for the people who do own and use cars. We ought to structure our user fee system to encourage these car-free modes of transportation, and provide a financial reward to those who drive less. The problem with a flat-household fee or an income tax is it provides no incentive for people to change their behavior in a way that creates benefits for everyone.

4. Prioritize maintenance. There’s a very strong argument that we shouldn’t let streets deteriorate to the point where they require costly replacement. Filling potholes and periodically re-surfacing existing streets to protect the huge investment we’ve already made should always be the top priority. Sadly, this kind of routine maintenance takes a back seat to politically sexier proposals to expand capacity. We need an ironclad “fix it first” philosophy. Also, we need to guard against “scope creep” in maintenance. There’s a tendency, once a “repair” project gets moving, to opt for the most expensive solution (see bridges: Sellwood, Columbia River Crossing). That’s great if your project gets funded, but a few gold-plated replacements drain money that could produce much more benefit if spread widely.  We need to insist on lean, cost-effective maintenance.

5. Don’t play “bait and switch” by bonding revenue to pay for shiny, big projects. There’s an unfortunate and growing tendency for those in the transportation world to play bait-and-switch with maintenance needs. They’ll tell us about the big maintenance backlog to justify tax and fee increases. Then they bond two or three decades worth of future revenue to pay for a shiny new project; the Sellwood Bridge and the local share of the Portland-Milwaukie light rail have been funded largely by tying up the increase in state gas tax revenue,vehicle registration fees, and flexible federal funds for the next two decades. The state, which routinely financed construction on a pay-as-you-go basis, has also maxed out its credit card: in 2002 ODOT spent less than 2% of its state revenue on debt service; today, it spends 35%. Now it is pleading poverty on highway maintenance. Politically, this makes a huge amount of sense.  You get to build the projects today, and pass the costs into the future. Unfortunately, in practice it leads to a few gold-plated projects now, while jeopardizing the financial viability of the transportation system in the long run.

6. Promote fairness through the “user pays” principle. We all want the system to be “fair.” In the case of general taxes, we often put a priority on progressivity—that taxes ought to be geared toward ability to pay. But for something like transportation (as with water rates, sewer rates, or parking meter charges), fairness is best achieved by tying the cost to the amount of use, or what economists call the “benefit principle.” Charges tied to use are fair for two important reasons: higher income people tend to use (in this case, drive) more than others, and therefore will end up paying more. Also, charges tied to use enable people to lower the amount they pay by changing their behavior.

7. Don’t buy the phony safety card. We’ll hear all about the need to spend money to make our streets safer. The safety argument is an all-purpose smokescreen to justify almost any expenditure, no matter how distantly related to safety. (Ostensibly, the $3.5 billion Columbia River Crossing project was justified as a “safety” project, even though the I-5 bridge had a lower crash rate than the Fremont Bridge). Here’s the key fact of street safety: Smaller, slower streets are safer. Metro’s region-wide analysis of crash data showed that fast-moving, multi-lane arterials are by far the most dangerous streets in the region for cars, cyclists, and pedestrians . The more we get people out of cars, the more crashes and injuries decline. The most effective thing we can do to improve safety turns out to be the cheapest: implement features that slow and calm traffic, and make walking, cycling, and transit more attractive.

Correction:  Commissioner Steve Novick points out correctly  that his proposal contains a specific list of laudable safety projects that he proposes undertaking with street fee proceeds if his proposal is adopted.  These projects don’t fall into the “phony safety” category outlined above.  My apologies if this commentary implied otherwise.  Still, voters should consider two other things.  First, while the proposed list is a good one, it is “preliminary and subject to change” and isn’t binding on future city commissions, and the “safety” category is an elastic one.  Safety projects are defined as those that “reduce the likelihood of a person being killed or injured and address the perception of risk.”  Second, transportation money is very fungible.  Its always possible to re-arrange the budget to tell someone that this “new” money is only being used for good purposes.   The larger question is the overall priorities for the entire transportation budget.  If safety spending out of current revenues is reduced, the net gain could be less than advertised.(Revised, 10.20 PM January 8).

8. Don’t write off the gas tax yet. There’s a widely repeated shibboleth that more fuel-efficient vehicles have made the gas tax obsolete. Despite its shortcomings as a revenue source—chiefly that it bears no relationship to the time of day or roadway that drivers use—there’s nothing wrong with the gas tax as a way to finance street maintenance that a higher tax rate wouldn’t solve. While other methods like a vehicle-miles-traveled fee make a lot of sense, the reason they’re popular with the transportation crowd is because they would be set high enough to raise more money. And there’s the rub: people are opposed to the gas tax not because of what is taxed, but because of how much they have to pay. As an incremental solution to our maintenance funding shortfall, there’s a lot to like about a higher gas tax: it requires no new administrative structure, it’s crudely proportionate to use, and it provides some incentives for better use of streets. So when very serious people gravely intone that the gas tax is “obsolete” or “politically impossible”—you should know what they’re really saying is that people simply don’t want to pay more for streets.

Transportation and urban livability are closely intertwined. Over the past few decades it has become apparent that building our cities to cater to the needs of car traffic have produced lower levels of livability. There are good reasons to believe that throwing more money at the existing system of building and operating streets will do little to make city life better. How we choose to pay for our street system can play an important role in shaping the future of our city. As Portlanders weigh the different proposals for a street fee in the coming months, they should keep that thought at the top of their minds.

How Poverty Has Deepened (part 1)

Many talk about poverty—its causes, its effects, and its possible remedies. There is literature on this issue from almost every social science, and no one can summarize it all in one blog post. However, there’s one aspect of our most recent report that I wanted to highlight: the deepening of poverty. Not only are we seeing much more highly concentrated poverty than we used to–but this has the most profound effect on children.

As a quick background if you haven’t read it: the report looks at concentrated poverty in urban neighborhoods (census tracts within 10 miles of the central business district), and concludes that a full three-quarters of neighborhoods that were high-poverty neighborhoods 40 years ago are still mired in poverty today. Additionally, the number of new high poverty neighborhoods has tripled, and the number of poor people in them has doubled, a figure that amounts to 3.2 million people.

smaller infographic

Another way to look at this change is to examine the distribution of poverty rates across both neighborhoods (or census tracts), and population within those neighborhoods. (For those of you that aren’t stats-oriented: a distribution– generally graphically shown as a histogram—gives you an idea of what ‘normal’ looks like, and what kind of variation you would see. The highest point on the histogram is the most common  outcome of any given variable.) The following histograms chart out the poverty rate both in terms of population and census tracts. (So, there were 15 million people in neighborhoods with a 0-5% poverty rate in 1970, 5,000 neighborhoods with a 5-10% poverty rate in 1970, and so on.)


As you can see, the general shape is the same across both time periods, and both peak at the 5-10% poverty rate range. However, there seems to be a spreading of both distributions in 2010. That is, while the majority of urban census tracts were in the 5-10% poverty range, the height of this norm was smaller, and there were more people and neighborhoods in the 10% and over bins. This is an indicator that not only are more people in poverty—and economically segregated into high-poverty neighborhoods—but that the experience of high poverty had become more common. The Equality of Opportunity Project has found that intergenerational income mobility is much lower in places with high levels of income segregation; the growing income segregation that we see over this time period means that millions of Americans will not be able to achieve the American dream.

To see the extent of this shift, we examined the tail end of the distribution—basically, the most impoverished neighborhoods. We will discuss this—and its implications—in a post later this week.