Tracking Retail Closures

At Fourth Economy we have been tracking the news about retail store closures.  These store closures often can leave significant redevelopment challenges for local community and economic development officials. In future posts we will highlights some of the ways that communities are dealing with these buildings. According to Business Insider more than 5,000 store closures have been announced so far, with the potential for nearly 9,000 store closures by the end of 2017. These store closings are the most physical manifestation of the challenges facing the retail sector.

As a resource to the community, Fourth Economy has started to identify and compile a list of retail store closings. Tracking down the locations has proven to be a challenge, but we have identified 1,768 of these closings so far.  You can see the results in the above Working Map of Retail Closings, created in Tableau Public.  We are providing this as a resource to the community and will continue to update it as closings are announced and locations identified.  If you know of any closings in your area, please send them to and we will update the map.

Stay tuned for more.

Co-authors: Jamie Reese & Katie Grauer

Fourth Economy’s Jerry Paytas Appears on “Workforce Central” Podcast

I recently had the opportunity to appear on the Workforce Central podcast, where I discussed the current state of the economy, trends that workforce boards should be aware of, and my thoughts about the impact of the upcoming election on the overall U.S. economy. Workforce Central is the official podcast of the National Association of Workforce Boards.  The podcast is hosted by Ron Painter, President of the NAWB.

Numbers Behind the News: The Truth about CEO Pay


CEO pay has been in the news a lot lately.  The AFL-CIO released its annual report showing that the pay gap between the top CEOs and average workers was a stunning 354:1 in 2012, compared to 1982 when the top CEOs earned on average only 42 times the average worker.   In 1982, the CEO would have to work 48 hours to earn as much as the average employee.  By 2012, the CEO would only need to work 6 hours.  Continue reading “Numbers Behind the News: The Truth about CEO Pay”

Numbers Behind the News: What is Driving Unemployment?

U.S. unemployment peaked at 15.4 million persons in October 2009 and has been falling back towards 12 million ever since.    Unemployment has always been the most troublesome statistic because it is one of the most widely recognized and flawed of the economic indicators. The recent drop has brought claims that the numbers have been manipulated.  Of course, this would be very hard to do.  Unemployment numbers are reported by companies to state bureaus that are staffed generally by civil servants.  In 29 states, the Governors are Republican and it is not very likely that they would be manipulating numbers to make Obama look good. Continue reading “Numbers Behind the News: What is Driving Unemployment?”

Numbers Behind the News: 2,764

This is not exactly a “Number Behind the News” as we have traditionally used it in this series, but it should be.  2,764 is the number of immigrant investors admitted to the U.S. in 2011.  In 2002 that number was 51, so we have seen an incredible increase in these “job creators.”  Much of the boost is a result of the EB-5 Immigrant Investor program.  Fourth Economy has been very excited by the prospect of this new tool for development and the potential it has to inject both new capital and new energy into regional development activities.

Continue reading “Numbers Behind the News: 2,764”

Numbers Behind the News: $100 Billion


Facebook has moved forward with an IPO that could be among the largest in the U.S. and at the least will dwarf Google’s 2004 IPO.  Facebook was launched only six months before Google’s August 2004 IPO.  The pessimistic outlook suggests that Facebook would hit a market value of $75 billion, which would make it the 35th largest company, but if it breaches the $100 billion barrier it could move ahead of Merck for the 25th spot.  Some analysts expected to see revenues around $4.3 billion and were disappointed in the  $3.71 billion sales figures released in the IPO.

Some other notable numbers from the IPO:

  • $1 billion profit (fails make the top 50)
  • 845 million users worldwide (would be the world’s 3rd largest if it were a nation)
  • 250 million photos are shared per day (a printed stack of these photos would more than 27 miles high)
  • $114 million spent on R&D (would be the 123rd largest research university in the U.S.)


Numbers Behind the News: Development Insights from Big Data

IBM estimates that we create 2.5 quintillion bytes of data every day.  They do not, however, estimate how much of this data is duplicated – all of the documents emailed between friends and coworkers that get stored on personal devices, corporate servers and cloud machines.  By my own unscientific and completely arbitrary estimate, at least 55 percent of our daily data production is duplication.

Big Data includes a lot of transactional data – what you purchase from stores as well as your Google searches or the fact that Person A sent an email to Person B,  as well as the content of that email.  There is also data from sensors used in industrial production as well as climate, weather and traffic monitoring.  It includes Twitter and other social media posts, digital photos, Wikipedia entries and data produced by researchers, scientists, corporations and government agencies.

Big Data is often unstructured but it is usually timely.   It is not simply an aggregation of a bunch of data.  The challenge is to structure this data and make sense of it.  Economists and regional developers have been behind in tapping into Big Data but it can be useful in a number of ways.  Much of it enables firms to better segment customers or develop next generation products.  It can also provide value in itself by selling access to that data for specific types of users and uses.

One of the problems we have with a lot of economic data is that it is too structured or aggregated to make it useful for data mining and other Big Data analytical techniques.  For instance, our use and definition of industry sectors (NAICS) hampers analysis of emerging industries.  This structure is used to provide anonymity and confidentiality but it also distorts the kind of variation that is useful to better understanding how our economy works.  For one, we have no idea what happens within a nondisclosed NAICS code.  But even within a sector we don’t know how many firms are growing or declining or the magnitude of those changes.  For most economic developers working within a local or regional economy, it can make a big difference whether an apparent “industry trend” is broadly shared by companies in the sector or if there are diverging patterns.

Currently there are few sources of Big Data for economic development analysis, but job postings, social media feeds and patent data are a few that Fourth Economy has been working on to yield new insights on economic trends.  Patent data has been particularly ripe for this analysis, in part because it is so unstructured that it is difficult to analyze with traditional tools and techniques.  These can be frustrating times for analysts and for anyone seeking answers.  There is a wealth of data out there, but too often we aren’t able to hammer it into useful information.

We’ve set up a quick poll to gather some data of our own. It’s only one question, so share your thoughts.


Numbers Behind the News: The Fountain of Youth

William Frey of The Brookings Institution analyzed new Census data to identify the places that are attracting young people.   Keep in mind that young people are not moving in the numbers they once did, but the ones who do are choosing places that “have a certain vibe—college towns, high-tech centers, and so-called ‘cool cities’.

Top Region’s for Young Migrants (Employment growth Sep 2010 – Sep 2011)

  • Denver (0.4%)
  • Houston (2.6%)
  • Dallas (2.5%)
  • Seattle (1.6%)
  • Austin (2.2%)
  • Washington D.C. (0.3)
  • Portland (1.3%)

Older movers are choosing places like Phoenix, AZ and Riverside, CA, once booming regions where the bloom has faded but not disappeared.  It is not clear if this signals a long term trend in the making.  Frey’s data compares the trend from 2005-2007 to 2008-2010 so you can’t attribute too much to such limited data.  However, the gains made in attracting young movers can have long term payoff as they become adults and put down more roots in the community.

Another interesting element is that two of the cities, Denver and Washington, D.C. have had lower job growth than the national average (1.2%) from Sep 2010 to Sep 2011.  What attracts young people to these places must be something more than short-term or cyclical opportunities.  It is either that, or young people are making bad decisions.




Numbers Behind the News: 1,052!


1,052 is not a magic number but it is a very good number.  This number represents the average annual gain in young people (20-34 years) in the City of Pittsburgh from 2000 to 2010.  This is a remarkable turn around for a region that has suffered from decades of population loss and steep out-migration from young people especially.  In The Root of Pittsburgh’s Population Drain, Bob Gradeck showed how the loss of young adults in the 1980s robbed the region of the next generation.  Young adults have been the critical raw material missing from the region.  They are important because they start families and start businesses, both of which can have lasting impact on our population and employment trends.

Figure 1:  Average Annual Growth in Population Aged 20-34, 2000-2010


Numbers Behind the News: The Noise on Jobs and Buffet Taxes


The analogy of job-killing taxes is dramatic but it is not supported by evidence.  Michael Chase writing for The Rational Middle not only dissects the argument but also provides a how-to guide for readers to check the data themselves.  Yet we can expect to hear a lot more about job-killing taxes, especially now with Obama’s proposed Buffet Tax. In all of this noise we never hear how the taxes will kill jobs, or why we’ve lost jobs when we’ve cut taxes.  We must simply equate taxes with the “death” of jobs.

Businesses hire and fire based on their need to produce more, or less, to meet the demands of their customers.  Taxes might shift the marginal threshold of those decisions, but taxes are generally insufficient to “kill” jobs.  Taxes do take money out of people’s pockets so taxes do reduce consumer demand, but taxes do not take money out of the economy.  The money circulates back into the economy, sometimes more efficiently than our individual spending.  An individual might spend their tax cut largesse on dining out or going to a sporting event, whereas government makes investments in infrastructure and education that are much more productive.  Not all government spending is more productive, there is certainly money that is wasted, or where the marginal gain is wiped out by the transaction costs of transferring that money.  It also depends on whether the money is being used for consumption or investment.


Figure 1

Note:  All data indexed to 1998 values.


Enough rhetoric, it is time to turn to the data.  For more historical perspective, you can turn to Michael Chase again or go directly to the National Income and Product Account.  Because employment and taxes are on vastly different scales, the numbers here have been indexed to 1998 values.  During this period, employment has been stable while we have had two major spikes in tax revenue (Figure 1).  You might argue that taxes have acted like a prophylactic – preventing the creation of jobs that might otherwise have been created.  That is possible but we need to explore someother data.


Figure 2

Note:  All data indexed to 1998 values.


If taxes are killing jobs – then they do so by siphoning off profits that might otherwise be converted into jobs.  Yet when we look at taxes versus profits the linkage goes the other way – rising profits produce more tax revenue (Figure 2).  There is no connection between higher taxes reducing profits.  Beginning in 2003, profits began to increase much more rapidly than taxes.


Figure 3

Note:  All data indexed to 1998 values.


Investment income, or capitals gains, are taxed at a lower rate than labor income because we desire that income to be re-invested. The problem with this logic is that there is nothing magical about a lower tax rate that encourages that re-investment, and the data show that very little of those gains are being reinvested in ways that generate jobs.

The linkage between profits and employment has been broken for some time.  Private industry is generating profit but it is not translating that profit into jobs (Figure 3).  Businesses have been able to wring greater productivity and profit out of an increasingly stressed labor force.  There is significant variation around the country with some regions doing better in aligning growth in profits and output with job growth, but in many regions and many industries that is simply not happening.

Neither additional stimulus spending nor steeper spending and tax cuts are going to kickstart job creation until we fix the gap between wealth, growth and employment.  Right now the outlook is grim for any real progress on employment.