Weekend long reads (Dec. 5, 2014)

Readers will notice that there hasn’t been much activity here since Thanksgiving. My absence is partly due to traveling I’ve had to do, being engrossed in my new book (The Power Broker by Robert Caro), and other academic obligations, which will continue next week. Nonetheless, I have provided some long reads here as they seem to one of the more popular types of posts. I hope to have the third part of the series on sectoral investment patterns up by the end of next week.

Fracking tantrums


Research and academics

Thanksgiving long reads (Nov. 27, 2014)

Happy Thanksgiving! Here are some articles I have lined up to read over the long weekend. Not all are recent; I’m trying to clear out the reading list.

1. The tech worker shortage doesn’t really exist. Bloomberg Businessweek

2. State unemployment map goes monochrome for October 2014. The Economic Populist. [Not actually monochrome, but close: no state observes an unemployment rate greater than 7.9% (US average is 5.9%), although underemployment is a separate problem. Also contains maps for employment-population ratio by state!]

3. Kuroda turns up the heat on Japan Inc.: turn profits into higher wages. WSJ

4. How the world’s most leveraged hedge fund got away with insider trading. Zerohedge

5. Oil at $75 means patches of Texas Shale turn unprofitable. Bloomberg [Good run-down of the economic-geography of fracking profitability]

6. Public relations and the obfuscation of management errors–Texas Health Resources dodges its Ebola questions. Health Care Renewal

7. Boomers, millennials and interest rates: a muni investor’s perspective. BlackRock blog

8. For middle-skill occupations, where have all the workers gone? Federal Reserve of Atlanta

9. Over at Project Syndicate: economic growth and the Information Age: Daily Focus. Washington Center for Equitable Growth

10. Jeff Henry, Verruckt, and the Men Who Built the Great American Water Park. Grantland.com [Schlitterbahn!]

On the book front, I’ve reading The Power Broker: Robert Moses and the Fall of New York by Robert Caro. I’ve been meaning to read it for a while and then found it at the bookstore, so here I go!

Link share: geography of a decade of job growth and decline

Check out this very well done interactive graphic from the blog of Austin-based consulting firm TIP Strategies!

I’ve filed this under “Terrible Cartography” (I have no category for the opposite), but rest assured this is the work of professionals.

Meso-level analysis using cluster methods: local economic structure among Texan counties, part II

This post continues from the previous one, presenting the empirical findings from the cluster analytical technique.

The four independent variables I settled on as a way to investigate the local economic structure of Texan counties were: urban size, unemployment (rate), poverty (rate), and the share of oil and gas employment in total employment. This selection captures generalized urban economies that arise from large size as well as diseconomies (unemployment and poverty), and a degree of specialization (in an industry of some importance to the state). Unemployment and poverty, by the way, are very different phenomena. Unemployment does not necessarily imply poverty, and vice-versa. Unemployment refers to civilians of adult age who could be in the labor force but are not (perhaps because they are not qualified for available work, for instance). In contrast, individuals in poverty may be gainfully employed, except that they have fewer opportunities for social advancement, are socially ostracized, and their incomes are so low and posses so few material possessions that they have difficulty feeding and clothing themselves without assistance.

Local economic structure, thus, reveals in broad terms the allocation of economies and diseconomies from urban size and specialization. Once emplaced on a map, we may be able to determine the spatial structure of the economy as well. The rest of the post presents the findings from the k-means cluster analysis.

Cartographic introduction

Before diving into the cluster results, however, it is important to provide a more basic introduction to the geography of Texas. The first map below records the population distribution by county (absolute numbers). Consider that with the exception of the far west, counties in Texas are all roughly the same size. This curious feature makes it easier for us infer population density and hence urbanization from the following map. There are a number of important points to draw out here.

  1. Houston is the largest urban area in Texas, with more four million people living in the core county of Harris; activity spills out from Houston into at least four adjacent counties
  2. Dallas-Fort Worth is the next largest, with two heavily populated counties surrounded by less populated areas, ostensibly suburbs.
  3. Central Texas features San Antonio and Austin as part of a rather long chain of urban areas pointing towards Dallas, with San Antonio at the bottom of the chain, and Austin closer to the middle.
  4. Outside of the more densely populated eastern portion is the south, which include Corpus Christi and Brownsville.
  5. In the far west is El Paso, which is almost as much a part of New Mexico as it is Texas.
  6. Several counties are scattered throughout the panhandle, where there is oil and gas extraction.


I have also included a map of the distribution of the Hispanic population in Texas. I do this in order to head off any attempt to associate the location of Hispanic people and unemployment or poverty. In almost half of the counties in Texas, over fifty pct of the population are of Hispanic descent.


The feature that stands out most in comparing the total population and Hispanic population distributions is that the latter does not cluster in major urban areas. Instead, the main factor is proximity to the border with Mexico. This pattern stands in contrast, for example, to the distribution of black Americans in urban areas in the North during the last mid-century. Urban areas do not appear to be sites of particular concentrations of a given demographic group, in other words.

Finally, I present the results of the cluster technique described in the previous post.


There is quite a bit of information that needs to be parsed here. The first point is to note the frequency of each cluster group and their basic identities. The table below shows the frequency and the mean for each cluster group of the four dependent variables (population, unemployment rate, poverty rate, and share of oil and gas in total employment).

Cluster group Freq. Population Unemp. rate Poverty rate Oil and gas employment share
1 1 4,205,743 5.40% 18.63% 2.85%
2 34 31,413 5.29% 26.49% 1.14%
3 8 171,535 11.16% 32.08% 0.04%
4 25 208,060 5.19% 10.81% 0.94%
5 4 1,773,114 5.10% 17.97% 0.37%
6 58 16,835 3.46% 13.03% 4.63%
7 35 56,846 7.14% 20.75% 0.15%
8 89 39,826 4.83% 18.59% 1.02%
Total 254 100,199 5.14% 18.33% 1.70%

Cluster group 1 is clearly central Houston and cluster group 5 is the central areas of Dallas-Fort. Worth, San Antonio, and Austin. The next smallest group is cluster group 3, with 8 non-urbanized counties located along the border with Mexico. Cluster group 4 is the fourth smallest group, and it seems, based on the map, that these are suburban areas. It seems rather interesting that suburban areas of at least six metropolitan areas can be identified on the basis of the four dependent variables. This indicates there is uniformity in economic structure that relates to an area’s position within the urban hierarchy. All other cluster groups have memberships greater than 34 counties, the largest being the final cluster group (8) with 89 counties.

A glance at the map emphasizes that clustering is a common phenomena for almost all groups. This is especially the case for: group 8 in the central part of the state; group 7 in the eastern and northern parts of the state (a long chain of counties registering as 7 stretch from the border with Louisiana up to Dallas); group 6 in the panhandle and northwest of Dallas; and groups 2 and 3 along the border with Mexico.

For discussion, I think the most pressing question would be what are the differences between the most populated groups (particularly 2, 6, 7, and 8). Referring back to the above table, we can categorize those clusters as such:

  • Group 2 are small, poor areas with some oil and gas activity.
  • Group 6 are even smaller areas with low unemployment, relatively low levels of poverty and a substantial amount of oil and gas activity. Ostensibly, these counties observe an economic specialization in oil and gas extraction, and, being underpopulated, probably draw in skilled migrants for temporary work.
  • Group 7 counties are on average slightly larger than group 2 and 6 counties, but have higher rates of unemployment and poverty than group 2, which might be attributed to the very low presence of oil and gas.
  • Group 8 counties are slightly larger than group 2 counties, with slightly lower levels of unemployment and poverty, but also less oil and gas presence.

Obviously, we quickly encounter the explanatory limits of the four dependent variables. Thankfully, I collected more data than ended up going into the determination of clusters, so it is possible to fill out the analysis a bit more. Specifically, we can present the number of bank offices, deposits per capita, and median income. These indicators can reveal the financial characteristics of the clusters, such as access to financial services and accumulated personal savings. We should not necessarily expect that estimated median income will mirror deposits per capita precisely, for the reason that spending and saving patterns will vary between areas due to cost of living differences in addition to varying access to financial intermediaries. For instance, wealthier areas may hold their savings in 401(k) or brokerage accounts as well as bank deposits, while poorer areas may hold savings in cash. Other areas probably send savings back to Mexico in the form of remittances. So the financial indicators can raise some interesting hypotheses (again, cluster analysis does not manufacture evidence that allow us to make statements of causality; this is purely exploratory).

Cluster group Est. median income Bank offices Deposits per capita
1 51,298 1024 50,241
2 35,197 10 20,600
3 30,274 34 10,220
4 62,475 54 14,240
5 52,579 431 36,130
6 49,825 7 30,421
7 38,919 13 14,364
8 40,613 14 20,078
Total 43,815 27 21,209

I will take each cluster group in turn.

  • Group 1 is central Houston, which as a median income almost 20 pct greater than the Texan average as well as the most number of bank offices and the largest stock of deposits per person, almost equal to estimated median income. I should note anecdotally, by the way, that the major urban areas do not necessarily have greater deposits per capita than rural or other areas. This really is a matter of financial literacy and access, which in turn is structured very differently between regions and demographic groups.
  • Group 2, a relatively poor grouping, has the lowest average median income, the second fewest average number of bank offices, but its average deposits per capita is roughly in line with the Texas average.
  • Group 3, along the Mexico border, which also had the highest rate of poverty, has lowest average median income, and the lowest average deposits per capita. However, it has a high average number of bank offices. One might be tempted to say that the number of bank offices reflects remittance activity, except that most banks in the US do not offer this kind of activity, but rather face a number of non-regulated, pseudo-bank competitors (wire services). So, I’m not sure how to interpret that high number. It may be the case that these banks are unit (stand-alone) banks, unlike the nationally-recognized banking conglomerates, who would be unlikely to locate in such areas anyway, leaving a more competitive banking market.
  • Group 4, the suburbs, have the highest average median income yet one of the lowest levels of deposits per capita. Perhaps this reflects my statement earlier about wealthier households having access to a larger and more sophisticated range of financial products, which would mean they put fewer of their assets in bank accounts. Another alternative is that their wealth is accumulated in material things, such as residential property, fixed assets if they are business owners, and other ‘stuff’. Perhaps they finance their expenditures with debts and mortgages as well, creating financial obligations that draw their money away from savings and into regular interest payments.
  • Group 5, the urban cores, are also high income areas, with an exceptional number of bank offices on average.
  • Group 6, mainly in the panhandle, based on the low readings of poverty and unemployment, appear to be relatively successful rural communities. The median income in the average group 6 county is over ten pct greater than the Texas average, with a high level of savings (deposits per capita). Furthermore, this group contains the lowest number of banks offices, which suggests an uncompetitive but perhaps stable banking system.
  • Groups 7 and 8 are quite similar, featuring lower incomes, deposits per capita, and number of banks than the Texas average.

A final indicator I would like to include is total job creation. Here, I simply took the average quarterly number of jobs created (job gains less job destruction) between 2009Q3 and 2013Q3 for each county. As a point of reference, I also calculated job creation as a share of total employment, to account for urban size.

Cluster group Avg. quarterly job growth Job growth relative to urban size
1 17,484 0.90
2 78 1.15
3 273 0.70
4 556 1.15
5 6,750 0.85
6 87 1.65
7 122 0.74
8 97 0.87
Total 320 1.09

The main point from the job growth figures is that the greatest relative amount of job growth was happening in clusters 2, 4, and 6. Recall that these are, in fact, quite a disparate group: group 2 is one of the poorest in terms of work opportunities and high rates of poverty, while group 6 are major oil and gas centers. Group 4, meanwhile, the suburbs, have comparatively low oil and gas presence but are generally wealthier. It is likely the case that further disaggregation of job creation statistics would be necessary to determine the quality of these jobs.

That concludes my case study using cluster analysis. After completing posts like this, I often sit and wonder to myself, “Now, just exactly why did I do all of this?” The point, for me anyway, is that context is important. Given the major economic and political themes going on in the US right now–fracking, the uneven recovery, the ongoing foreclosure crisis, immigration, too-big-to-fail, the competitiveness of US industry–it is as important to know the location and context of such events in order to further our understanding of why these topics are important, to whom they are important (in the sense that some groups will benefit from certain kinds of economic processes while others will bear the burden of them), and the long-term ramifications. Eventually, I am going to revisit the posts I have written on Texas and the oil and gas industry and try and bring together some of my insights into how that industry is shaping the US economic landscape.

Employment update: losses and gains since 2007 recession

In yesterday’s “2:00 pm Water Cooler” links at nakedcapitalism blog (http://bit.ly/1u1lJ9u), Lambert Strether of Corrente blog (http://correntewire.com/) included a Bloomberg map (from September of this year) of the recovery of employment by state since 2007 (http://bloom.bg/1zM4rC5). Specifically, Bloomberg writers purported to depict the ‘uneven recovery in states post-recession’ by showing the ‘percentage difference between a state’s maximum employment in 2014 and its recession high (reached between December 2007 and June 2009).” They sought to highlight only those states where employment remained below peak levels during the recession, coded using a couple shades of red.

As Strether pointed out, it is a rather confusing map, and I don’t think it conveyed the information in the best way. Why construct a choropleth map that only color-codes poorly performing areas? Why focus on individual peaks in employment during the recession? It would make more sense to depict cartographically the employment changes for all the states and to select a uniform starting date.

Being convalescent following my recent surgery means I have plenty of time to create some terrible cartography of my own. My topic here is total employment changes at the state level from 2007 to 2013. Descriptively, the question is: which states bore the brunt of the recession in terms of employment losses and which have experienced a recovery in employment. Before going right for the States, I start by presenting the ratio of employment in December 2013 to employment in December 2007 for the Census divisions (of which there are nine). [All data was drawn from the Bureau of Labor Statistics (http://data.bls.gov/cgi-bin/dsrv?sm)%5D.


Recovery can be interpreted in a couple ways. First, it may refer to replacing lost activity, to the point that employment levels in 2013 equal those in 2007. Alternatively, recovery may refer to a kind of resilience. This term can indicate whether an area has returned to its pre-crisis trend, such that not only has that area recovered employment losses but it has added enough jobs that its employment levels are what would be expected had there not been any output losses. To calculate whether an area indeed was resilient and returned to a kind of equilibrium growth (or whether such a return is even possible!) is beyond my remit for the moment. However, the answer to whether an area is ‘resilient’ in this sense or not has much to do with whether there has been structural change in the economy (labor-saving technology, increases in productivity, further transition out of industry towards services). I highly recommend JK Galbraith’s new book The End of Normal for anyone interested in exploring this question.

As a point of reference, the ratio of 2013 employment to 2007 employment for the nation as a whole was exactly 1. The map (for the lower 48 states; Alaska and Hawaii are part of the Pacific division) shows that the West South Central states (Texas, Oklahoma, Arkansas, and Louisiana—major oil and gas states) performed best, with employment bases roughly six pct larger than they were in 2007. These can be deemed resilient. The West North Central (Great Plains states, where there is also quite a bit of fracking activity) and the Middle Atlantic (Pennsylvania, New Jersey, and New York) were second-best performing. The worst were the Mountain states and East South Central (Kentucky, Tennessee, Mississippi and Alabama), whose employment bases remained three pct below their levels at the end of 2007. These areas are clearly not resilient.

The next map depicts the above ratio of employment levels for all 50 states (maps not to scale). Clearly the Census divisions obscure some important differences within divisions, that is, between states. Census divisions do not always capture coherent economic-units, such as metropolitan areas or industrial districts, particularly in areas along and east of the Mississippi. A more apt unit of analysis for that would be the metropolitan statistical area, however these in turn do not necessarily have a single, coherent governing entity. As such, the US state, with different taxation regimes, varying receipts of federal moneys, bank regulation, local investment and labor force policies, etc, remains an important political-economic unit. The major takeaway, as I see it, is that resilient areas are either oil/gas producing (Alaska, North Dakota and Great Plains more generally, Gulf Coast) or are financial centers (New York and Massachusetts). Meanwhile, the diversified industrial and commercial economies of California, Washington, Virginia, Florida, Georgia, Pennsylvania, and New Jersey remain below their 2007 levels. That is just a hunch. From my academic research, other important factors include exposure to subprime mortgages and the foreclosure epidemic.


The next set of maps show change in employment for two-year increments beginning in 2007.I tried to apportion the states into quantiles, but there were quite a few shared values, and I also wanted to identify some of the outliers. The two-year increments correspond, more or less, with the most recent recession, then a nominal recovery period, and, perhaps finally, a stagnation period. These, incidentally, correspond to the stylized Minskiyan stages of the economic cycle (crisis/crash, recession, recovery, stagnation, economic boom, rinse, repeat). I won’t go into much depth here; I’ll leave readers to gander at these maps to their hearts’ content.




The maps, of course, rely on relative changes. I have also included below a table showing the ten states with greatest absolute employment losses from 2007 to 2009 and then the ten biggest gainers from 2009 to 2013.

Biggest Losers (from 2007 to 2009)
State Chg in employment (000s)
California -1,199
Florida -782
Illinois -409
Texas -403
Michigan -402
Ohio -401
North Carolina -329
Georgia -327
New York -288
Arizona -286
Biggest Gainers (from 2009 to 2013)
State Chg in employment (000s)
California 1,235
Texas 1,128
Florida 583
New York 546
Michigan 331
Ohio 291
Illinois 269
North Carolina 255
Georgia 250
Pennsylvania 230

Though the order in which these states appear varies somewhat, most of the states that lost the most employment also gained much of it back. The exception is Arizona, which lost over a quarter of a million employed workers but did not appear on the gains list. That state’s employment base grew by less than 170,000 from 2009 to 2013. In contrast, Pennsylvania lost close to a quarter of million jobs, but grew by 230, placing it at tenth in the gainer list.

In a previous post, I discussed the differences between employment and job growth. I emphasize here that I have looked at the stock of employed labor, not job growth. The quality of jobs is as important as the quantity, and job growth statistics provide great insights into employee turnover, job stability, and duration of employment. Additionally, I stress the importance of considering the sectoral component, which reveals comparative specialization and thus may indicate how an area is clued into larger financial networks and global supply chains. The utility of these maps is the clarity with which they can generate insights into the material distribution of burdens and benefits following the recession.