The contemporary context of investment in the United States, part 1: introduction

The Great Recession (2007-2009) changed the context for investment in the United States in several ways. First, it created imbalances in the economy, in terms of losses and gains between economic (businesses, households, government) as well as industrial (agriculture, manufacturing, services) sectors. These losses and gains can be measured in terms of lost output, including employment. These are imbalances to the extent that contractions in output were unevenly shared across sectors.

Second, the political environment changed as new constituencies and alliances were formed, while others were made more obvious. An example of such a long-standing alliance that became stronger was the relationship between the Federal Reserve and large, globally-competitive financial companies. This relationship was codified in the emergency recapitalization (the TARP) and in the Dodd-Frank law. New constituencies emerged or became more pronounced, for instance as unemployment and homelessness rose, and they reflected a regional character (for the reason that the financial crisis and recession were, in fact, regional crises). These new constituencies and alliances generated pressures for different kinds of policy intervention, with varying success.

And finally, the macroeconomic context changed, given changes or stickiness in the informal rules of investment (such as tax rates, interest rates, the supply of credit). Similarly, economic development through the application of new technologies, discovery and extraction of fossil fuels, and global capital flows also have shaped the macroeconomic context.

Over the next several posts, I’m going to outline the context of investment in the US immediately before and since the financial crash in 2008. I will describe the nature of investment since the crash, with a focus on the distribution of investment activity between sectors (economic and industrial) as well as the nature of investment (private fixed assets: structures, equipment, intellectual property). Finally, I’ll briefly describe the kinds of companies and regions that were poised to reap the benefits of this changing context, and contrast them against those entities that have borne the greatest burden.

The next posts will frame investment in the US with the following specific questions. First, what do I mean when I talk about investment in the United States? I will outline that question by referencing JK Galbraith’s new book (The End of Normal) as well as borrowing some insights from Hyman Minsky. Let me add that I do not mean to advance any kind of coherent theory; that is way beyond my remit at the moment. Rather, I find it useful to identify useful metrics and relationships in the data that can eventually be situated within a wider theory, or can be used to advance or refute other ones.

Second, what are the current obstacles at the geopolitical and national levels to growth in investment within the United States? Off the top of my head, I can think of several important “obstacles”: the process of domestic credit allocation (including interest rates, integrity of too-big-to-fail banks, property development); the cost of raw materials (especially oil and gas); and, military commitments and the general financing of national security. Readers of Galbraith’s book will notice these topics are quite prominent in his account, while I have spent most of my very short academic career focused on the first.

Third, what is the current progress or state of the economic recovery since 2009? There are some subsidiary descriptive questions that point to my thinking here. Which economic sectors have returned to pre-crisis trends in output growth (contribution to GDP) and which remain stalled? Which industrial sectors? How did investment in private fixed assets respond to the crisis and aftermath? What about for investment in structures, equipment, and intellectual property? (If readers want to see what I’m getting at with these questions, take a look at this article from the NYT back in April: http://nyti.ms/1zZEVct; I am essentially expanding this kind of inquiry, which has been, incidentally, the empirical base upon which most of research has been conducted).

My doctoral dissertation advisor liked to say that a solid way to organize an argument follows the formula: what; why; and, so what. The what component here is really, where is investment happening in the USA (both in sector and locational terms). The why seeks to explain the relevant processes that propel the investment we see or hinder the investment we hope for (particularly, why it should be the case that despite there being a real recovery in material terms, there should be such a slow expansion in quality employment opportunities). The so what for me really comes down to distributional fairness. In other words, something that has been on my mind the last few years is whether the parties responsible for the financial crisis and disappointing recovery were the same parties that amortized its costs, or whether there has been a systemic and successful effort to push those costs onto other parts of society. Additionally, I want to explore the durability/sustainability of the investment that is happening. At the end of the day, we all want to be a part of a successful collective endeavor–the US economy. Hopefully I’ll find much to be proud and excited about as I dig through the data. Alternatively, hopefully I can provide some insight into how to rectify the processes that point to the contrary.

Advertisements

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.

tx-pop

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.

hisp

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.

tx-c8a-beta

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.

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

Quantitative analysis is never as neat and tidy as one would like it to be. At least, I’ve found that it is often hard to be graceful when you’re dealing with large amounts of data, with the need to clean it up, transform it, present it, and, finally, interpret it. And, then, of course, there is the issue of documenting your process and method, which can also be tedious. Data management and analysis are hard-to-master art forms.

Especially with geography, where you typically are looking at multiple aspects of a large sample of places, presenting the greatest amount of relevant information in the most convincing way to an audience makes for a messy task. Not only is it messy in the planning and development stages, but this carries on all the way to presenting it. (Life would be so much easier with a word processor like LaTeX, which gives much more control over presentation to the writer, except that so many people still use and edit documents collaboratively, which LaTeX doesn’t make easy).

My purpose here today is to share my workflow process, including its shortcomings and how I would like to improve it, as well as to continue the analysis of Texas, which I seem to be fixated upon at the moment. In this post, I outline my workflow process. In a subsequent post, I will present the data.

The method I describe here is cluster analysis. Cluster analysis is a technique that in theory condenses a tremendous amount of information for easier access, yet in practice is quite messy. Essentially, cluster analysis groups objects based on their similarity. It is mainly an exploratory technique, useful for generating hypotheses. There are many potential algorithms for sorting the observations. In a chapter of my dissertation, I used a hierarchical method, which began with all observations in their own cluster and then aggregated two clusters together at each stage. This process avoids the issue of selecting in advance the number of clusters, and employs ANOVA to determine the optimal distance between clusters with the aim of minimizing the sum of squares of a given pair of clusters. The method strives for maximum internal homogeneity in each cluster based on the dependent variables. Other post-estimation techniques are available to confirm the optimal number of clusters.

Here, I use k-means clustering, which has three steps. First, the number of clusters is specified. Second the location of each cluster is initialized. Third, each object is attributed to the nearest cluster. Convergence is achieved when the algorithm can no longer change the assignment of each observation for an optimal fit. It should be noted that, in this way, k-means clustering can group the same set of observations differently on each independent attempt. That is, I could run the same process today and discover an entirely new membership for each observation. Another main difference between k-means and hierarchical methods is that in the former the number of clusters are designated in advance. There are also differences in post-estimation tests.

Let me now move on to the more mundane aspects.

My workflow process

Here is the process by which I assembled data and performed a cluster analysis.

  • Data collection
    1. I gathered data from four datasets: employment (Quarterly Workforce Indicators, www.ledextract.ces.census.gov); unemployment (Bureau of Labor Statistics, http://www.bls.gov/lau/); poverty and income (Small Area Income & Poverty Estimates, US Census, http://www.census.gov/did/www/saipe/data/statecounty/); and, number of banks and deposits (from FDIC Summary of Deposits, https://www2.fdic.gov/sod/).
    2. The geography was Texan counties, of which there are 254 (big sample).
    3. I downloaded the relevant data for the most earliest available date, which differs somewhat between each dataset. Sometimes the data were not all available for all observations, so I retrieved the earliest dataset that had complete data.
      1. Employment data: end of 2012
      2. Population (total size, Hispanic): end of 2012
      3. Unemployment: June 2014
      4. Banking: June 2014
    4. I then merged each of the datasets using STATA and saved this as the master file.
  • Running the cluster analysis
    1. The first step is to standardize the variables, in order to place all variables on the same scale and to prevent distortions.
    2. There was a long trial-and-error process here, where I would select various combinations of variables and then perform post-estimation tests and create maps to determine what worked best. Bear in mind, I am trying to sort counties according to local economic structure, which explains why I initially included different sectors. To make a long story short, I ultimately prevailed upon four variables: total population; the unemployment rate; the poverty rate (all ages); and, the share of oil and gas employment in total employment (all variables were standardized). Often, the maps produced from each iteration would just not seem right, so I would return to the drawing board and repeat the process. Scrutinizing a map may not sound particularly scientific, but the bigger problem is that there is no hard rule about what constitutes an appropriate threshold for the post-estimation tests, so my subjective knowledge is as valid as a seemingly objective mathematical test.
    3. For each set of variables, I would always run the analysis with at least two and at most 13 cluster groups. I would then invoke a cluster stop rule, which produces an F-index. When comparing the F-index values for each cluster, the point is to locate the highest values. You would then gather the cluster groups with the greatest F-index values and look at the frequency with which observations appear in each group (for example, important questions here are: does one group have almost all the observations, or are there many groups with only one observation?). Then you could summarize the means of the dependent variables for each cluster and see how different they are.
    4. Then, if you really wanted to be rigorous, you could run ANOVA tests with the cluster group identifier as the independent variable regressed on each of the dependent variables (population, unemployment, poverty, oil/gas). A statistically-significant F-test in ANOVA can tell us whether or not the clusters are distinct from each other. However, again, there are no tools that have been recognized by scientific consensus as being the most accurate or reliable in determining the number of clusters. Some users of cluster analysis would pursue further statistical techniques, whereas my choice is to generate maps and utilize my knowledge of geography to gauge reliability.
    5. I will justify the selection of the four variables in the next post.
  • Creating tables and maps to present the information
    1. The final cluster analysis I performed identified eight cluster groups for the four dependent variables. I created several tables:
      1. One to show the mean values of the dependent variables, as well as number of counties, for each cluster group.
      2. Another to show the mean values on a set of variables not used in constructing the cluster schematic, partly as a further test of robustness but also as a means of analysis.
      3. And finally, a table showing the average quarterly change in number of jobs (not employment; this is the flow measure for ‘job creation’)  from 2009Q3 to 2013Q3. Again, including this data allows for a comparative analysis for hypothesis-testing.
    2. I exported the county identifiers as well as their cluster group identities into a .csv file and then converted that file into a .dbf file. This step is necessary for correctly adding the county cluster identities to a shapefile for Texas counties. I combined the .dbf file with the shapefile using QGIS and then moved the new shapefile over to TileMill, where I tinkered with the color scheme and added a legend (TileMill is less clunky than QGIS).

This is the process by which data was collected, tuned up, and applied for the purpose of cluster analysis. In the next section, I present the results and then advance some hypotheses and discuss some thoughts of mine on the workflow process and also the empirical findings.

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.

census-div-ratio-emp-07-13

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.

states-ratio-07-13

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.

states-emp-chg-07-09

states-emp-chg-09-11

states-chg-emp-11-13

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.

Reading lists (Nov. 11, 2014)

I thought I would add a few links to articles I am currently reading, about some of the stories that inspire me and keep me informed/interested.

1. An article on Market Basket, the New England grocery chain that recently saw its family-owners duke it out over the future of the company. It’s workers took to picketing (non-unionized) and with additional community pressure managed to reinstate their beloved CEO. Here, a number of scholars discuss lessons they learned from the case: http://bitly.com/11a1sVu

2. Obamacare architect admits need to conceal details of the reform from the public in order to pass it: http://bit.ly/1wQIwHm

3. A yet-to-read short paper on coops and other worker-owned organizations, and how they might provide a successful Lincolnian plank for the GOP: http://bit.ly/1tEexfW

The last one has given me an idea to compare credit unions in the States with commercial banks (location/clustering, profitability, how they affect cost of and access to credit, stability during times of macro-distress, etc). I’ll get around to that one day.

I am also in the middle of reading Master of the Senate by Robert Caro and The New Industrial State by Galbraith. Paper not electronic form, which is a nice vacation.

Minor point on interest rates

I am currently writing a post that uses cluster analytical techniques to identify different economic structures at the county level in Texas, which I hope to complete soon. The point is to demonstrate the utility of a common but underutilized tool in economic-geography and also to continue my case study on the economic success story of Texas.

In the meantime, I just wanted to make a point about low interest rates. Quite frequently in the business press, one will stumble across a phrase like “in today’s low interest rate environment…” and it is typically quickly followed by a reference to our “accommodative” or “activist” Federal Reserve. Many would have us believe that low interest rates are somehow “unnatural,” which is an attractive idea because interest rates are in fact set by a committee, for all intents and purposes. The article that set me off today in a waiting room referred to the growth of fracking, and the whole thing was prefaced by the boilerplate statement about low interest rates.

But the United States is a country full of risk-hungry women and men. Do commentators truly expect us to believe that if interest rates were, say, five percent instead of near zero, high-risk endeavors and investments would not be undertaken? The causal effect of interest rates, I think, are totally overblown with respect to operational decisions of businesses and entrepreneurs to pursue high-risk projects or investors to purchase high-yielding securities whose underlying assets are less stable and cash flows less predictable. The while thing sounds like a lazy cop out.

People don’t make investment decisions exclusively on the basis of the Fed’s monetary policy. It is certainly one factor, but not the only one and an over-emphasized one at that. Entrepreneurs and corporate decision-makers don’t go into hibernation when interest rates rise or rev up when they drop. They make their decisions after careful consideration of risk, expected return, current capabilities, and a host of market factors.

Wouldn’t it be more interesting to know who (which entities/organizations, what sort of investors) make high-risk investments? And where those investments are located? Using what kind of evaluative technologies? What sort of collateral they put up, how they finance their investments? These are processes that depend on the structure of markets and organizations, and these in turn do not flow from the Fed’s interest rate dicta.

Databases of financial crises

On the economic history of financial crises

In my dissertation defense, my examiners and I spent a great deal of time talking about what kind of economic history emerges from the variable-based approach that is employed by economists such as Carmen Reinhart and Ken Rogoff. I’m not trained as an economist, but economic history is an important part of my discipline as well. The variable-based approach refers to long-term databases of dummies that indicate the presence of absence of a variety of financial crises. For example, Reinhart and Rogoff have a book called This Time is Different (http://www.reinhartandrogoff.com/) where they describe a variety of crises (banking, stock market, inflation, sovereign debt default, currency) for the last 800 years, in a large sample of countries.

I wrote a chapter on my dissertation on their dataset, and I used their sample of crises on a dataset of employment in nine economic sectors. In brief, however, such work contains serious problems with defining and determining thresholds for financial crises. One problem is that, in Reinhart and Rogoff’s book, a banking crises involves state intervention into the banking sector. Obviously, this makes a banking crisis as political as they are financial. Collecting the data is another problem, as it demands reviewing quite a bit of history.

The Wall Street Journal last week had a post on its MoneyBeat blog (http://blogs.wsj.com/moneybeat/2014/10/31/romer-and-romer-vs-reinhart-and-rogoff/) about a new database documenting financial distress on an index for a sample of advanced countries from 1967 and 2007, by Christina Romer and David Romer. Christina Romer previously served on Pres. Obama’s Council of Economic Advisers, and she recommended the President support and execute a spending program, which she helped to put together, in early 2009. That alone should suggest that the Romers take very different stances—in wider political circles but also within the discipline of economists—than do Reinhart and Rogoff, who, by the way, advocated for austerity measures and produced research (now determined to suffer from debilitating flaws in their data collection and analysis) to support their reasoning. I am very glad that we have this new dataset, because it is based on a very different method than the financial crisis dataset of Reinhart and Rogoff.

I’ll be writing up analysis in the coming days, as I fiddle with the Romer dataset. I need to read the paper that accompanies the dataset and also construct some databases.