We’ve received many questions on how we did the analysis behind our Storefront Index. This post will describe our dataset, our method, and how we created our visualizations. We hope that this will spur future research and new forms of visualizations, similar to the way in which the release of our Lost In Place data led to amazing reinterpretations of the dataset.
We used a database of businesses from Custom Lists American Business Directory. The Directory contains 2014 records on US businesses, their industry classification, and their address. Our aim was to understand how clusters of these quasi-private storefront spaces contribute to active streetscapes and generated steady flows of people — so we filtered our business dataset based on three criteria: 1) businesses in the largest metro areas; 2) businesses that have storefronts; and 3) lastly, a spatial filter based on clustering.
For the first filter, we simply chose business in the largest metro areas based on the 2013 definitions of CBSA. For the second filter, we selected businesses that fit into one of 44 industry classifications that would typically have customer-serving storefront. These include businesses like grocers, bookstores, and salons. A full list of our categories can be found on page 18 of the report.
Armed with a storefront business dataset, we next sought to find clusters of storefronts. Thus, for each business with a storefront, we needed to know the distance to the next closest storefront. Our first task was to geocode the addresses, turning the address into a latitude and longitude that we could map. (Luckily, we performed this geocoding in ArcMap just before they cut off access to their geocoding API.) We then used the NEAR function in GIS software, allowing us to calculate the distance to the next closest storefront in meters. To apply our filter, we then chose only storefronts that had another storefront within 100 meters, allowing us to identify clusters of destinations that would be easily walkable.
With our three filters applied, we created a set of map images (for the report) and an interactive map. We used a 3-mile buffer around the central business district of our metro areas of interest. For the images, we highlighted these buffers in white, and used only the points of our clustered storefront locations and the US Census Bureau’s Roads shapefile. For the interactive web map, we used a circles to represent the 3-mile buffer, the points of our clustered storefront locations, and the Mapbox library with the Stamen Toner basemap.
Our list of industry classifications (using Standard Industry Classifications) can be found here and GeoJSON shapefiles for each metro area (using FIPS codes for each metro) can be found here.
Given how much time media outlets, policy shops, and community groups have spent talking about America’s affordable housing crisis over the last few years, you might think that we’ve at least settled on a pretty good way to define what housing affordability actually is. After all, how can we talk about solving a problem if we don’t have a reliable way of determining who’s suffering, and where, and why?
Unfortunately, you’d be wrong.
As an illustration, picture yourself as an employee of a local supermarket, making $1,500 a month. You live with a friend in an outlying neighborhood, and your share of rent is $400, plus $300 a month for car expenses. After all that, you have $800 a month left over – which dwindles pretty quickly between child care, groceries, and prescriptions. When you get sick or your car breaks down, you can’t avoid racking up some credit card debt.
The front page of Craigslist for apartments in San Francisco.
Across town, a man who works as a VP in marketing makes $8,000 a month. He pays $3,000 in rent for a brand new loft apartment near downtown. Because he can walk to work and takes public transit most other places, he buys a monthly pass for $100 and doesn’t own a car. After those costs, he’s got $4,900 to spend every month, which buys lots of nice meals out and international vacations while leaving room for healthy retirement savings.
You’re having trouble making rent, and the marketing VP can make their payments easily. But according to our most common standard of housing affordability, it’s the VP who’s rent-burdened, and you’re doing fine.
That’s because those standards rely on a simple ratio: if you pay more than 30% of your income in housing costs, your housing is unaffordable. If you don’t, it’s not. And the supermarket worker pays just 27% ($400 of $1,500), while the marketing VP pays 38% ($3,000 of $8,000).
The supermarket/VP story is an extreme example, but it demonstrates several of the fundamental problems with the 30% threshold as a measure of housing affordability.
1. Equity. Most obviously, it doesn’t take into account that, depending on how much money you start with, leaving 70% of your income for all non-housing expenses may be plenty – or not nearly enough. Affluent people have the luxury of deciding whether to spend relatively large proportions of their incomes to buy housing in a better location, or with particular amenities, without sacrificing other necessities like food or clothing. Low-income people generally don’t. In that way, comparisons between people with different earnings can turn out misleading or unfair, as in the example above.
Craigslist apartments in Boston.
But it can also fail in analyzing the burden of housing costs on people with similar incomes. Not everyone, after all, has the same non-housing obligations: for a healthy, childless twentysomething, a salary of $40,000 might easily cover housing, food, insurance, and other necessities. But someone who has to do much more non-housing spending – because of a chronic medical condition, say, or children with special needs – might struggle on the same income.
2. Other location-based costs. On top of that, there’s increasing recognition that housing choices are closely tied to other costs, which need to be considered part of the package. In other words, the cost of housing is less relevant than the total cost of a location. By far the most important of these other costs is transportation. While housing closer to the center of a metropolitan area is often more expensive, it also requires less driving – and often no driving at all, thanks to public transit – which saves a lot of money. According to Harvard’s Joint Center for Housing Studies, low-income people who manage to spend less than 30% of their income on housing actually end up paying $100 a month more on getting around, which eats into their savings, and sometimes erases them entirely.
Some organizations, like Chicago’s Center for Neighborhood Technology, have tried to take this into account. CNT’s H+T Index shows the total housing and transportation costs for various locations, set against a combined affordability standard of 45% of income. That’s a major step forward – but using a ratio like 45% still has all the other problems of the 30% ratio we’ve already covered.
3. Quality of housing. The 30% threshold can’t tell us anything about what a given household is getting for their money. Few of us would say that affordable housing needs are met by homes that are low in cost but lacking in basic modern amenities like heating or indoor plumbing. While those problems are now relatively rare in major metropolitan areas, many cities have a stock of affordable housing that is predominantly located in neighborhoods with high crime rates, failing schools, few options for fresh food, or other major quality of life issues. Do that housing satisfy our need for affordability?
Craigslist apartments in Memphis.
This is an especially important question if we care about housing for its effects on opportunity and mobility. As recent research from Raj Chetty has reinforced, the kind of neighborhood you live in can dramatically change your prospects for living a comfortable middle-class life. It seems odd, in light of those findings, to measure housing access without taking into account whether that access includes communities that offer a shot at economic stability in addition to cheap rent.
In conclusion, the way that we currently measure housing affordability – a simple 30% ratio of cost to income – is simply inadequate to the task. It fails to give us an equitable picture of who is in need and who isn’t; fails to consider the total cost of a location, missing housing-dependent payments, like transportation, that can add a significant burden to low-income households; and fails to consider questions of housing and neighborhood quality that exert significant influences on the life chances of the people who live there.
(Why, then, do we use it? This Bloomberg piece from last year, also pointing out the 30% ratio’s flaws, is probably correct that its durability has to do with simplicity.)
Tomorrow, we’ll look at an alternative way to measure housing affordability that addresses some of these problems.
A story published by the Washington Post’s Wonkblog last week made the headline claim that “The McMansion is back, and bigger than ever.” The article says that new homes are an average of 1,000 feet larger than in 1982, and that the “death of the McMansion” has been highly exaggerated, as have claims that development is shifting to smaller, more urban and more walkable development. The Wonkblog article echoes an 2014 post in CityLab –“The Increasingly Bloated American Dream”–which claimed that “American homes are getting bigger and bigger.”
While the data seem to superficially support this argument, a closer reading shows that the apparent surge in McMansions is actually a bit of a statistical mirage. These analysts have overlooked a key limitation of the reported data. It’s actually the case that American homes are only getting bigger if one believes that people living in multi-family housing either aren’t Americans or don’t have homes.
If instead of looking at the median, we look at the actual number of houses built, a different story emerges. As with all single-family housing, the market for big houses remains depressed—housing starts of 4,000 square feet or more are down 59 percent from the peak and are lower now than they were in 2001. Homebuilders built 137,000 of these huge homes in 2006, but only 56,000 in 2013, according to the Census Bureau.
The only reason these big houses have increased as a share of total new housing is because the market for affordable, smaller single family homes has done even worse. The smaller yet still catastrophic decline in McMansions is hardly evidence of a growing, or even a continuing consumer love-affair with big houses.
Medians are funny measures—they’re highly dependent on the composition of the population being measured. If the housing market were so bad that only Bill Gates had the wherewithal to build a house, the “median” new home would balloon to 66,000 square feet (the size of his Lake Washington mansion). While that’s an extreme example, that’s the kind of thing that has happened to the U.S. housing market since the bubble days of last decade.
When the housing market collapsed, the bottom fell out. The big decline has been in smaller houses. The apparent popularity of the McMansion is a statistical artifact of the misleading median in a still very depressed housing sector. If anything, the rising median size of new homes is more a testament to the continued growth of income inequality in the U.S., coupled with tougher (i.e. more realistic) lending standards by banks.
This becomes apparent when you look at the actual number of new houses built in the U.S. The growth in the share of new single family homes is not due to some burgeoning increase in the demand for McMansions—rather, it represented the bottom falling out of market for single-family homes. Since the housing bubble peaked in 2007, single-family housing construction is down 66 percent. The construction of 4,000 square foot and larger homes—the McMansions—is down 59 percent. Smaller single-family homes under 1,800 square feet are down 75 percent. Meanwhile, the number of multi-family homes constructed has been increasing steadily, and is now back to pre-recession levels. Multi-family housing now makes up 40 percent of new home starts, up from 20 percent a decade ago. If we recalculated the median new home size including both multi- and single-family homes, the increase in the McMansion share would look much smaller.
We’re far from having what by historical standards would be considered a “healthy” housing market. Total housing constructed over the past five years is lower than any five-year period in the past 50 years. Does anyone believe that if the single-family housing market boomed back to 1.5 million housing starts, that the demand would come proportionately from McMansions? Of course not: the only way to get unit growth in single family housing is by getting households of more modest means back into homeownership—if that ever happens. They will be buying smaller houses.
Unlike the old days of NINJA (no income, no job or assets) lending, where even those with poor credit could qualify for loans, today’s credit standards are much higher. The other key factor has been the demise of the trade-up market. Because most people buy their new homes in significant part with the accumulated appreciation on their existing home, the decline in home values meant that very few middle-income households were in any position to trade-up in the real estate market.
There’s another problem with this median measure: it only looks at single-family housing, not all housing. The one bright spot in the housing market is not in single-family homes, but in multi-family units. By excluding the smaller multi-family homes, this automatically biases the median measure upward.
So in large measure, the only healthy segment of the single-family market is for those with very high incomes. Even here, “health” is a relative thing. Compared to the peak of the housing bubble years, sales of McMansions were lower in 2013 than any year since 2001.
If anything, the growth of the median size of new houses is evidence of the continued and growing impact of income inequality. With growth in incomes occurring mostly among those with the highest incomes, it figures that to the extent there is demand for housing, it’s coming disproportionately from those in the highest income brackets who can afford larger homes, and who qualify for credit.
An accurate measure of the popularity of McMansions would look at the extent to which high-income households are buying large new houses. We don’t have a good annual public data series on wealth by household, but a number of private firms estimate the number of high-net-worth households that form the market for these very large single-family homes. The Spectrem Group has estimated the number of U.S. households with net financial worth of $5 million or more (exclusive of the value of their principal home). By their reckoning there are about 1.24 million such households in the U.S. The number fluctuates from year to year, chiefly due to changes in financial markets.
We can get a good contemporaneous gauge of the popularity of McMansions by dividing the number of new 4,000 plus square foot homes sold by the number of households with a net worth of $5 million or more: call it the McMansion/Multi-Millionaire ratio. (There’s no universally accepted definition of McMansion, but since the Census Bureau reports the number of newly completed single-family homes of 4,000 square feet or larger, most researchers take this as a proxy for these over-sized homes.)
The McMansion to Multi-Millionaire ratio started at about 12.5 in 2001 (the oldest year in the current Census home size series)—meaning that the market built 12 new 4,000 square foot-plus homes for every 1,000 households with a net worth of $5 million or more. The ratio fluctuated over the following few years, and was at 12.0 in 2006—the height of the housing bubble. The ratio declined sharply thereafter as housing and financial markets crashed.
Even though the number of high-net-worth households has been increasing briskly in recent years (it’s now at a new high), the rebound in McMansions has been tepid (still down 59 percent from the peak, as noted earlier). The result is that the McMansion/Multi-Millionaire ratio is still at 4.5–very near its lowest point. Relative to the number of high-net-worth households, we’re building only about a third as many McMansions as we did 5 or 10 years ago. These data suggest that even among the top one or two percent, there’s a much-reduced interest in super-large houses.
There are a couple of key lessons here for thinking about the state of the U.S. housing market. Don’t be fooled by the misleading median, and don’t overlook the big rebound in multi-family housing.
Great public spaces make great cities. But so do great private spaces. They provide opportunities for people to socialize, and provide the character that make a city more livable and unique. We have already talked about how restaurants add value to a city– but thought we’d look at bars in the same way.
Now, what makes a great bar depends on who you talk to- but regardless of if you prefer a wine bar with small plates, a gastropub, or a dive bar with ski ball (or without ski ball)—bars contribute to a city’s livability and an individual’s experience within a city. Trying to argue that one is better than another is, well, a way to start a barroom brawl. So while we can’t resolve which cities have the best bars, we can at least count which cities have the most bars.
We used County Business Patterns data to analyze the number of bars per 10,000 workers each the top 51 most populous metro areas. (The latest data is from 2012, reflected below):
It’s no surprise that New Orleans comes up first—it is renowned for its bars. (In case it wasn’t on your calendar, Mardis Gras is right around the corner..) There’s no particular rhyme or reason for the rest of the ranking; a variety of things can influence bar culture, such as policy and availability of licenses, availability and strength of public transit, age of residents (younger residents will desire more bars than older ones), and weather (colder places have seen higher consumption rates of alcohol).
Of course, the number of bars per capita isn’t a measure of quality (much like it isn’t for restaurants, either). More bars may reflect loose regulations, a higher city-wide per capita income, and of course, a desire for variety. For example, Pittsburgh’s historical blue-collar workforce may desire its older dive bars, but its new population of young engineers and medical workers allow for hip and more expensive wine, whiskey, and champagne bars to flourish.
Bars are just one way in which a city can make itself more livable for its residents. Livability is important—it attracts residents, and therefore tax payers, and helps to retain younger, talented workers. Ed Glaeser, Harvard economist and author, has encouraged the city of Boston in its efforts to allow bars to stay open later. He sees it as a matter of making Boston “livelier.” A city’s liveliness, to which bars certainly contribute, may not be of the utmost importance to all residents—but it’s clear it’s important to some, and can be a strategic advantage to cities. (To read more about how city distinctiveness and its placemaking efforts can benefit cities, go here and here.)
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).
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: http://www.bea.gov/regional/index.htm
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.
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.
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.
Neighborhood change is by definition a highly local process, and everyone wants to know how their city is performing: What about their city? Their neighborhood? Nationally, the number of high-poverty neighborhoods tripled, and the number of people in poverty in those neighborhoods have doubled, but this is not the pattern in every city. In Detroit, the numbers are even more staggering–the population living in poverty is more than 228,000, from less than 40,000 40 years previous. A few places like Virginia Beach saw an actual decline in concentrated poverty. Rebounding neighborhoods have been more common in some metros like New York and Chicago.
If you want to see the data for individual metros, we’ve created a city-specific dashboard. Just select the city of interest, and you’ll see a comprehensive set of indicators showing how your metro performed between 1970 and 2010.
As you look at individual cities, keep these overall trends in mind:
Most cities only had 1 or 2 “rebounding” neighborhoods, or neighborhoods that were previously high poverty, and by 2010 were below the national average rate of poverty (15%).
Nationally, the number of high-poverty tracts tripled.
Overall, the number of poor people in those high-poverty tracts doubled.
High-poverty neighborhoods that didn’t rebound weren’t stable: they lost, on average, 40 percent of their population over 40 years (both of poor and non-poor persons). This means most “chronic high poverty” neighborhoods saw a dramatic reduction in population by 2010.
The majority of the increase in those living in high poverty were in newly poor or “fallen star” neighborhoods. (Fallen stars are neighborhoods that had poverty rates below the national average in 1970, but have poverty rates of 30 percent or higher today). The number of fallen stars exceeded the number of rebounding neighborhoods 12 to 1.
The process of neighborhood change is often difficult and disruptive, and poverty and gentrification are sensitive topics. Each city is different and has unique challenges; however, most cities follow the national trend of increasing concentrated poverty. If we are serious about bettering the lives of the poor (and we should be), we need to carefully examine the data about change and look for solutions that are fully grounded in the facts of neighborhood change.
If you want to look at each city’s specific tract-level data, go to the report here and scroll to the maps. We will also be sharing an informational post about how these were made soon- check back in a couple days!
To celebrate the Census Bureau’s release of the 5-year American Community Survey estimate, we decided to do a quick analysis of some of its information. So for some light Friday afternoon reading, we present you with an analysis of unemployment rates by gender throughout the country.
The 2009-2013 data spans the Great Recession and its aftermath, and as such much of its data reflects trends during that time. It has been widely noted that female unemployment rates were actually lower during the recession (although, men caught up at the end, and it’s important to note that lower unemployment rates don’t necessarily mean higher incomes). We decided to look at this phenomenon in the country’s 51 biggest metros to see if we could find any patterns. We also examined how poverty and the women’s share of the overall workforce intersected with gender differences in unemployment.
Our main findings were that generally, female unemployment rates were lower than male unemployment rates. Women experienced higher unemployment rates in the south, and where poverty rates were higher.. While higher unemployment rates were associated with higher poverty, this holds for both men and women. Finally, it is worth noting that female labor participation rates mean that even while women had higher employment rates than men, it is likely that any given metro had higher numbers of employed men than women. See the full analysis below:
Some technical details: We examined unemployment rates for those 16 and over. Poverty rate was defined as percentage of all people in the metro below the poverty level.
This was just one snapshot of the data the ACS has to provide- you can explore it more here — it has information on demographics, employment, housing, social characteristics, and much more.
A few months back our friends at CityLab published the results of a survey looking at differences in attitudes about cities and suburbs under the provocative headline, “Overall, Americans in the suburbs are still the happiest.”
Their claim is buttressed with a reported finding that 84 percent of all the respondents in suburbs said that they were “satisfied with their communities”, while only 75 percent of those who reported living in cities felt the same.
While at first glance, this seems to be pretty cut and dried, a closer look at the data suggests that the answer is far less clear.
As with all surveys, it’s worth paying very close attention to the actual question asked, the size of the survey’s margin of error, and the other factors that determine how respondents answer particular questions.
When we consider each of these factors, it actually turns out that it is difficult to make a strong claim that suburban residents are happier than their urban kin.
First, consider the question asked in the State of the City survey. It isn’t about “happiness”—it’s actually about satisfaction. This is more akin to a consumer satisfaction. There’s actually a well-developed happiness literature that asks people about their overall level of happiness. The conventional question is very internally focused, and doesn’t refer to place. The Pew Center has a good introduction to this subject here. So when we interpret these data, we should think of them not showing whether people are more or less happy than others living elsewhere, but whether they are satisfied or dissatisfied with their communities.
Second, in interpreting survey results, it’s important to consider the sample size and the sampling distribution of error. The overall survey included 1,656 respondents and the reported margin of error for the survey was plus or minus 3.4 percentage points. But that margin of error holds only for the entire sample—subgroups of the population (like just the one-third or so of respondents living in cities) are fewer in number and therefore have a larger percentage point margin of error. That number isn’t reported. But differences of less than four or five percentage points between sub-groups of the sample are likely to be borderline significant at best. When differences between sub-groups are small, we shouldn’t make too much out of them.
Third, we know that happiness (or in this case, satisfaction) is correlated with income. Higher-income people are more likely to say they are happy; lower-income people less likely. So if suburbs have more higher-income people and cities have more lower-income people, the apparent difference in reported satisfaction could be the product of income, rather than location. This appears to be the case for the data reported here.
Like published happiness research, the State of the City survey shows that reported satisfaction is highly correlated with income. Some 88 percent of those with incomes over $75,000 said their community was excellent or good; only 66 percent of those with incomes of less than $30,000 said the same. It’s worth noting here that the impact of income is larger than the impact of location in influencing satisfaction (a 9 percent difference between city and suburb as opposed to a 22 percent difference between high and low-income groups. These data suggest that the real headline finding of this survey should be “higher income people are more satisfied with the places they live.” The unsurprising takeaway here is that more income enables you to afford to live in a community that makes you happy. We could get a more direct answer to this question by looking directly at income data. While the CityLab article didn’t publish the findings on income by city and suburb, we can observe the differential effect of income on satisfaction levels in cities and suburbs by looking at two other variables—education and home ownership—which tend to be correlated with income.If we look just at the college educated, we find that the differences between cities and suburbs shrink by about half: College-educated urban residents are almost exactly as satisfied as college educated suburban residents (80 percent v. 85 percent).
If we look just at homeowners—who in general have higher incomes than renters—we find that the differences between cities and suburbs almost entirely disappear. Urban homeowners, for example, are almost exactly as satisfied as suburban homeowners (84 percent v. 87 percent).
Finally, we might want to consider how the race of respondents influences community satisfaction. When you dig into the data by race and ethnicity, the entire difference in reported levels of satisfaction appears to be the result of the differential racial and ethnic composition of cities and suburbs. Non-Hispanic whites living in cities were almost exactly as likely to report being satisfied with their communities (84 percent) as non-Hispanic whites living in suburbs (83 percent).
It’s definitely worth looking hard at data on personal happiness and community satisfaction, but in doing so, it’s critical that we take care to understand what the data are—and aren’t telling us.
If you’ve ever contemplated getting up at 3, 4, or 5 am only to brave large crowds to fight over scarce merchandise, well, think again. Instead of looking into census data this Thanksgiving, we thought we’d look at more “fun” data, specifically, Black Friday shopping habits– and whether or not they make sense.
Recently, several articles have talked about whether or not it’s worth it to brave the early morning ritual, especially given the widespread use of online shopping and success of “Cyber Monday.” Why go out when you can line up your shopping cart in your PJ’s, and hit a button to order and send? Moreover, especially since the Great Recession, many of the best times to go seasonal shopping haven’t been during Black Friday. Some years, when volume lags below expectations, retailers offer even bigger discounts later in the season. While it’s impossible to know for sure when to get the best deals, there are a few things we do know.
First, I went online to several large retailers to see what sales they were promoting for the day. I found several items popular amongst multiple retailers, such as an iPad, Beats headphones, tv’s, and an Xbox One. Prices were largely similar across retailers, and on average were discounted about 21-23% from regular prices. (Keep in mind these are on big ticket items- Beats headphones can be on sale for more than 50% off, but an Xbox One has a max savings of 17%. While that’s still significant given the price of an Xbox, it is likely that smaller-ticket items will have less than 20% savings.) IBM reports that last year the average amount spent on Black Friday was $120 ($200 for consumer electronics) — meaning a typical shopper saved approximately $25 according based on our estimate of average Black Friday discounts.
But what about those things that detract from Black Friday– how do you account for the “cost” of going? The answer to an economist may be obvious, but to others, probably not: your time, in the classical sense, is worth money. Every minute you spend shopping, watching tv, or generally spending your leisure time, you are not spending time on work. In theory, the time you don’t spend on work is costing you the money you could be making if you were working. Hence, time is money. Frankly, however, our behavior doesn’t seem to to imply that people believe their time is worth much; people drive 20 minutes to get a slightly better deal on gas, spend days repairing a car when a mechanic could fix it in under an hour … or shop for hours to get the best price. A person earning $25,000 a year, working 50 weeks for 5 days a week and 8 hours a day, makes $12.50 an hour. By the same logic, someone making $50,000 a year nets $25 an hour, and someone making $75,000 earns $37.50 an hour. If people actually valued their time at these rates per hour, it wouldn’t make sense for them to spend hours getting to the store, shopping, and getting home on an early Friday morning. Basically, if someone saved $25, but spent 3 hours (at $12.50 an hour) shopping, they effectively lost $12.50. If you spend $200 (the average electronics bill), your net savings are only $4.50.
Check it out for yourself: at what point does it make sense for you to go out on Black Friday?
(Just slide the amounts to the right and left to estimate how much you believe your time is worth, how much you typically spend, etc.)
What’s worse: this doesn’t account for basic costs like gas and mileage, or more rare costs such as getting into a wreck because you’re too tired, or the occasional (but real) occurrence of injury due to in-store conflict like trampling. It doesn’t account for psychic costs like aggravation with other shoppers or crowds, or include the fact that actually being leisurely on vacation time– instead of spending it at a crowded store– can make you more productive at work. Finally, it doesn’t allow for the fact that very often, the items that Black Friday shoppers most want are taken by the first few shoppers, and the items with the biggest savings are then no longer available. (As an aside: we could build this into the model with relative ease- and you could too! Contact us if you want more information on how we built this.)
Of course, many like the ritual– and it’s hard to quantify happiness associated with that. So even if you value your time anywhere close to your wage, you could still value the time with your family (or away from them) more than that time.
Whether or not you go out on Black Friday is of course your prerogative. But remember that your time is valuable- and frequenting your local stores could save you time and effort. Happy Thanksgiving!
At CityObservatory, we strive to make data the driving force behind our operations. We know that many of you share our keen interest in digging through the data, and we strongly believe that everyone benefits when data sources and methods are as transparent as possible. In the spirit of open data, we’ve created this page as a one-stop shop for the data we’ve used to generate our CityReports. We invite you to download and use this data in your city to further explore the factors that drive city success.
If you have any further questions, please don’t hesitate to email info@cityobservatory.org.
Young and Restless
Our Young and Restless report provides data on the number of four-year college graduates aged 25-34, and 25 and older for the nation’s 51 largest metropolitan areas, and for close-in urban neighborhoods in those metros. Data are from Census 2000, and the American Community Survey. Data can be downloaded here.
Posts: The report is here, and the overview blog post is here.
Lost in Place
Our Lost in Place data is a subset of the Brown University US 2020 Longitudinal Tract Data Base. We present tract level data on population and poverty for 1970, 1980, 1990, and 2000 for areas within 10 miles of the center of the nation’s 51 largest metropolitan areas.
Posts: The report is here, and the overview blog post is here. An individual city dashboard is featured here, and maps for each metro are available here.
Other content: A post explaining how we did our analysis is here, our technical appendix here, and a deeper dive into the data is here and here.
Surging Center City Growth
We used the Census Bureau’s Local Employment and Housing Dynamics (LEHD) dataset to compile employment statistics for 41 of the nation’s 51 largest metropolitan aras for the years 2002, 2007, and 2011. Here we report data for the city center of each metro (an area encompassed by a 3-mile radius around the center of the region’s major central business district). Our techniques and methodology are spelled out in the appendix to this report.
