American financial dysfunction

Three problems in US finance

US financial dysfunction manifests itself outwardly in at least three characteristics. First, the system as a whole displays a remarkable tendency towards catastrophic failure, in terms of severe contractions in profitability, widespread insolvency of organizations, and periodic illiquidity. Second, access to and cost of credit is uneven, across regions and between social groups. And third, there is quite a bit of redundancy, waste, and fraud. The fraud exists at two levels. First is the problem of white-collar crime, where executives in banks and other financial organizations use firm competencies and knowledge as weapons for their personal aggrandizement; and the second refers to the kind of fraud that most consumers experience (theft of personal financial information) as well as attacks on the integrity of bank electronic and security systems.

My reason for framing US financial dysfunction in this way is that I don’t see a coherent reform program that tackles the main problems of US finance. Often we hear about ‘breaking up the banks’, or the problem of cognitive capture of the regulators, the lack of transparency in securities markets, etc. These are problems that we fixated on following the 2008 crisis, but it is worth mentioning that there were efforts at financial reform during the 2000s (Sarbanes-Oxley, the bankruptcy reforms in 2005, attempts to regulate credit rating agencies) that didn’t produce a more stable, secure, or efficient financial system. The troubles ran, and still run, deeper. My objection to the current proposals, besides the fact that they rarely appear together in any kind of blueprint that weighs how they will be implemented, the ramifications for doing so, and economic rationale, is that they do not do anything for the long-standing problems in US finance: tendency to crisis, access/cost of credit, and fraud.

Essentially, these three problems, being unique to the US, reflect the outcome of the ‘Game of Bank Bargains’ as it has played out in the US (based on the analysis by Charles Calomiris and Stephen Haber in their book Fragile by Design). Calomiris and Haber discuss the problems of tendency to crisis and cost of credit, but do not focus much on the fraud angle. They also do not spend much time on two elements that I find particularly important: the types of organizations that are permitted to allocate credit (public sources, private institutions, and their specific institutional profiles) and the types of organizations that are set up to supervise the allocation of credit (centralized/decentralized agency; independent agency, or democratically accountable; financed by appropriations, or not; and the rest of its institutional profile).

The point of this post is to gather some evidence for the first of these problems, and to determine how the US fits in amongst its cohort of rich countries. In subsequent posts, I will gather the evidence for the other two points, and then be at a point where I bring all the evidence together to determine whether or not there is a solid empirical basis for the following paradox: how is that a rich and (formerly?) prosperous country like the United States can feature such a destructive element at the core of its economy?

Hopefully as I analyze the data, the argument will evolve and become more nuanced, if not thrown out altogether. Nonetheless, I think it is a good hypothesis and I think it already has quite a bit of support. I believe the answer to why this paradox exists has to do with the characteristics just mentioned, but, more specifically, I think it is important to highlight how the structure of finance systematically displaces the costs of its excesses and failures onto other parts of the economy and society. This conclusion, of course, assumes that the paradox exists.

The historical tendency to crisis

We really should not be surprised that there is this highly destructive element at the center of the economy, because capitalism is propelled by creative destruction. Yet the US stands out because its level of financial dysfunction is so much greater than peer countries. One way of showing this is to compare the US to a group of countries that share many of its institutional and economic characteristics. I’ve selected Australia, Canada, and the United Kingdom for this brief example.

The first step is to establish that the US is more prone to crisis than these countries. Using data from Carmen Reinhart and Ken Rogoff (, I tally the number of years in which there was a stock market crash or banking crisis in the four countries between 1945 and 2010. The figures are collected below.

Years between 1945 and 2010 spent in a
Stock market crash Banking crisis
Australia 10 4
Canada 5 3
UK 11 9
USA 21 12

In their book This Time is Different, Reinhart and Rogoff define these crises are as such. A stock market crash is where real equity prices decline by 25 percent. A banking crisis features bank runs leading to government intervention, or government intervention into the banking sector (closure, merger, takeover by public sector of a single or group of financial institutions). Canada is by far the most stable, while the USA has spent the most number of years in a crisis (almost a third of the post-war period has witnessed dramatic crashes in equity markets!). The UK and Australia are about the same for number of years of stock market crashes, but the UK is closer to the USA for number of years in a banking crisis.

Obviously, the years are clustered in time because a crisis often spans many years. For example, in terms of episodes, the USA has much longer banking crises than the UK. In the US, there was the S&L crisis, which Reinhart and Rogoff record from 1984 to 1991, and the recent crisis from 2007 to 2010 (when the dataset ends). The UK, by contrast, has a three-year crisis beginning in 1974, and shorter crises in 1984, 1991, and 1995, and then the recent one from 2007 to 2009. For Australia, there was a four-year crisis beginning in 1989, and in Canada, a three-year crisis beginning in 1983.

Much of the difference might reflect the fact that the US has had historically a very large banking population, as a consequence of the New Deal reforms (intra- and inter-state banking restrictions). In the US, once a banking crisis begins, it can affect so many more organizations before burning out. I will return to this point in another post.

Another dataset on banking crises has been compiled by economists working at the IMF. This dataset includes only “systemic” banking crises (available here: These crises include the following elements: defaults of institutions in the corporate and financial sectors of a country; difficulty of institutions in these sectors in the timely repayment of contracts; an increase in non-performing loans; illiquidity; depressed asset prices following the crisis; rise in real interest rates; and, potentially, capital flow reversal. Such a definition is quite different from the banking crises in the Reinhart and Rogoff dataset, namely in that it attempts to include quantitative indicators.

Using that information, the IMF finds no systemic crises in Australia or Canada, and only in 2007 was the UK in a systemic crisis. In the USA, there were two crises beginning in 1988 and 2007. Even after adopting a stricter definition of a financial crisis, the USA surpasses its immediate peers in financial dysfunction.

Alternative indicators

Banking crises are more difficult to pinpoint historically because bank assets do not reveal conditions of crisis the same way that equity or real estate prices reveal a crisis in asset markets. This feature partly explains the loose definitions above and the reliance on qualitative indicators, such as government intervention. One criticism of using government intervention as an indicator of crisis is that it can become a self-fulfilling prophecy, while another is that there remains the question as to what level of intervention counts as a crisis. It may also be the case that individual countries or groups or types of countries (democratic, autocratic, young/old countries, English common law system, industrial-based) experience different types as well as different manifestations of crises. As eluded earlier, a banking crisis in the USA looks very different from one in Canada given the large number of unit-banks, the highly compartmentalized regulatory system, and the high level of capital mobility. My preference is to situate “crisis” in the context of these country-specific factors. Nonetheless, there is some value in comparative work.

Institutional change in lending

Who lends?

The declining share of deposit money banks in total credit lending indicates increasing diversification and specialization of capital markets. Organizations that are not deposit money banks include credit unions, money market funds, pension funds, insurance agencies, brokerages and other securities firms, financing companies, venture capital firms, hedge funds, and, at the lowest-tier of the financial structure, payday and other short-term, small amount lending.

Another way of interpreting the share of deposit money banks in total lending is as a share of lending that is not insured or supervised by the government. This is a bit misleading because there are public and private forms of insurance available to these organizations. Credit unions are insured by the National Credit Union Administration (NCUA, the equivalent of the FDIC), while most brokerage houses are insured by the Securities Investor Protection Corporation (SIPC), which has many of the same features as the FDIC. These programs insure depositors against losses; they do not include safety and soundness oversight. As such, the non-deposit money bank share is a reasonably good approximation of the amount of activity taking place outside the purview of public supervision.

The share of deposit money banks in lending is an important institutional relationship for a national financial system. Data for this is collected in the US by the Federal Reserve and other agencies, and the Bank of International Settlements has put together a national cross-sectional, time-series dataset that can be found online ( In the United States, this share rose from the 1940s until the 1970s, peaking below 60% before decreasing from the mid-1970s until today. It dropped substantially during the S&L crisis, as many hundreds of savings institutions but also commercial banks failed. The shares for the US, Australia, and Canada are presented in the figure below.


I include Australia and Canada for the reason that the three countries share similar geographical, historical, and institutional characteristics. They are large, demographically-heterogenous, continental-sized economies that are former British colonies, and so have legal systems based on English common law. Natural resource extraction and trade are essential components of their economic growth. Their experiences with financial crises, however, are somewhat different. For instance, the US has seen two banking crises in the last thirty years (S&L and 2008), while Australia experienced a banking crisis in the early 1990s (when a couple of government-owned savings institutions failed and several other institutions), but was largely spared from the 2008 crisis. Canada has seen periodic but isolated failures of banking organizations, but was also spared from the 2008 crisis. In effect, only the US experienced a ‘systemic crisis’ during the last three decades, which was in 2008. This disparity is even more remarkable in light of the fact that in all three countries, property markets underwent an incredible boom leading up to the 2008 crash.

A institutional model of financial crises

The issue of who lends is vital when considering why some countries are more prone to financial dysfunction than others. Charles Calomiris and Stephen Haber have argued in their book Fragile by Design that the frequency with which a country experiences financial crises is a result of how its fundamental political institutions distribute power between competing groups in society. In the US, historically, populists have exerted a level of influence over the structure of banking markets due to the alliances they have formed with local bankers. In Canada, however, the federal government has insulated the banking sector from populist influence and created a more stable framework that allows its few, very large banks to control much of the lending markets while also providing abundant and cheap credit. The US, by contrast, ranks comparatively low among high-income countries in providing affordable credit (witness the lack of affordable and government-insured borrowing options for low-income Americans in poor neighborhoods and cities).

The conceptual model–the Game of Bank Bargains–that Calomiris and Haber offer is a good counterargument to explanations of financial crises that invoke ‘speculative euphoria’ or ‘banker folly’ or other aspects of human behavior. Their argument has its limits, though. For example, they spend a great deal of time repeating the conservative bugaboo about the 2008 crisis that Frannie Mae, Freddie Mac, and the Community Reinvestment Act of 1970 distorted lending incentives and caused a deterioration of underwriting standards during the housing bubble. They also suggest that community activists teamed up with mega-bank interests to push through the financial liberalization reform program of the 1990s in exchange for greater lending to urban minorities. I think in doing so they alienated a number of readers who might have been inclined to use their model but were put off by the polarizing and discredited notion that the victims of the 2008 crisis–urban minorities–were responsible for the crisis.

This is obviously a shame, because I think it is crucial that we have a more informed conversation about how financial markets and specific banking organizations are used by the state to advance the state’s interests as well as to advance parochial or regional interests. Nonetheless, it is possible to use their model of interest group collusion to tell a different story about the 2008 crisis and why credit is allocated the way it is. The central process in that story is the transformation of legal foundation of the banking sector and the change in contractual relations between households, the financial sector, and the state. I’m going to outline this story and emphasize its geographical component.

A brief application/reinterpretation of the Game of Bank Bargains

At the end of the S&L crisis, the surviving, well-capitalized financial organizations were able to take advantage of (1) a buyer’s market in distressed financial assets, and (2) an appetite for regulatory reform at the national level, aided by an accommodative Federal Reserve Board. The S&L crisis affected mainly (obviously) savings institutions in the south and southwest states. The surviving banks were money center and investment banks in New York and other northeast financial centers, as well as the so-called super-regionals. For a more in-depth review of this period, I suggest you read a working paper by Gary Dymski entitled “Genie out of the Bottle: The Evolution of Too-Big-to-Fail Policy and Banking Strategy in the US.”

Suffice it to say that the type of organizations that were poised to reap the benefits of the post-S&L environment very much shaped the nature of the legal reforms of the 1990s. That is, the reforms included repealing intra- and inter-state branching restrictions, removing separations between investment and retail activities, and repealing size limitations. Less talked about consequences of these reforms include that the role of the Federal Reserve took on greater importance. For example, under the Riegle-Neal Act of 1994, the Fed was charged with approving mergers-and-acquisition (M&A) activity, while the organizational model of choice for accomplishing M&A activity was the bank holding company. It so happens that the bank holding company is supervised by the Federal Reserve.

An even less talked about event during this period, which likely had a strong effect on the nature of bank strategy, was the experience with Long-Term Capital Management. Without getting too deep into the details, the collapse of Long-Term in 1998 was marked by an incredible intervention by the Federal Reserve, which organized a private sector bailout by leaning on Long-Term’s creditors. Some of the lessons of that experience were: the Fed was willing to drastically intervene to prevent the failure of what it considered a systemically important institution; the use of complex instruments such as derivatives would continue as a risk management mechanism; and, a highly connected firm could be successfully resolved in the event of a crisis. I suspect the experience of Long-Term was on the minds of decision-makers at the Fed and elsewhere in the upper echelons during 2008 (it had only been a decade, after all).

So, who is at the table of the Game of Bank Bargains in the 1990s? The table is populated by New York investment and money center banks, super-regionals, the Federal Reserve, and various senators and administration officials representing those organizations. I think it is fair to say that there was nothing inevitable about this particular group advocating for the particular package of liberalization reforms. It seems quite opportunistic, in fact. The group’s identity reflect the survivors of a crisis in the housing finance and commercial property sectors in the 1980s and their strategic considerations.

Was there a viable alternative to their view of the ideal financial landscape? Not really. The savings association industry was in a shambles, and community, consumer, and housing advocates had made major gains in the reforms passed during and immediately after the S&L crisis (notably by imposing as much of the costs of the S&L resolutions on the industry itself). Their continued efforts were stymied by the George H.W. Bush administration. There doesn’t seem to be much in the way of any advocacy for a greater role of public options in banking, such as state banks (in the style of the Bank of North Dakota) or perhaps a postal savings system during this time.

So the legal foundation of the financial sector was radically altered between the early 1990s up to the early 2000s. The preferred organization for consolidation was the bank or financial holding company, the preferred regulator was the Federal Reserve, and the preferred solution to the bank profitability crisis witnessed in the 1980s was the removal of geographic barriers in banking markets. Another implicit element of the Game of Bank Bargains in this instance was the too-big-to-fail issue. The bailouts during the S&L crisis demonstrated the possibility that market mechanisms could be used to resolve failed assets, while the government emphasized its willingness to intervene directly into markets to prevent disruption. The effect of these reforms was actually, as shown in the figure above, to narrow the government’s zone of control over lending–new intermediaries popped up in the interstices between the big banks, which themselves were executing more trades and lending outside of their chartered, depository units.

The Bank Bargain of the 1990s was truly a new contract between a quasi-governmental agency (the Federal Reserve) and a collection of large institutions (many of which were already embedded within international financial markets) for control over the credit allocation process. It was guided by an ethos of liberalization that had been brewing before the S&L crisis, and in fact could arguably be seen as having instigated the crisis or at least made it worse (for instance, by deregulating interest rates under DIDMCA of 1990 and Garn-St. Germain Act of 1982).

Bottom line

One of the lessons for institutional change in lending, which is how I started this post, is that the choice of organization actually matters very much. Different types of organizations face different strategic considerations and observe specific locational distributions. Additionally, types of organizations fall more or less within the state’s influence. The Bank Bargain of the 1990s sought, given the ideological climate of the time, to limit the zone of control. One of the points I picked up on reading Calomiris and Haber’s account of the banking stability of Canada is that the state endeavored to maintain or increase the domain of lending covered by its preferred organizations (its large chartered banks) as new technologies and innovations arose. The example of REITs in the US (which precipitated a crisis during the 1970s, see Hyman Minsky’s Stabilizing an Unstable Economy) shows that while REITs were eventually legitimated, they remained an industry operationally separate from the chartered and regulated system.

These are only some preliminary thoughts, but I think they provide good support for a more thorough comparative analysis of the politics and institutions of credit allocation. The US, Australia, and Canada, I suggest, are excellent candidates for such an analysis. The conceptual model by Calomiris and Haber could be reapplied by adding the spatial perspective–the location of organizations, their structure, strategies, the competitive environment–and also by reframing the issue as the extent to which a country manages (and wants to manage) the quality of its lending.

The Buiter formula and distributive conflict

A stable debt to GDP ratio

James K. Galbraith spends some time in his new book (The End of Normal) playing around with Willem Buiter’s formula for the debt to GDP ratio (in a 2010 paper for Citigroup entitled, “Games of chicken between the monetary and fiscal authority: Who will control the deep pockets of the central bank?”). I have not yet read that paper. The formula is specified as follows (after Galbraith 2014; Kindle location around 3227):

Δd = -s + d*[(r – g)/(1 + g)]

where: d is the debt to GDP ratio, s is the primary surplus (deficit) as a share of GDP, r is the real interest rate, and g is the rate of GDP growth.

Galbraith specifically examines the potential for a stable/sustainable ratio, which contra the Congressional Budget Office he demonstrates is possible if one challenges the assumptions of growth, interest rates, and inflation rates going forward. Additional points are in order. First, a definition: ‘unsustainable’ refers to a ‘[policy] path … that must be changed eventually.’ Second, sustainability in this context is descriptive, not normative. As long as the ratio ‘eventually stabilizes’, this is enough for our purposes, regardless of the actual level of the ratio. Implicit behind Galbraith’s reasoning is some doubt as to whether the formula provides a reasonable basis for the making of public policy. This is debatable. So where is the utility of this equation?

I suspect that this is an important formula, in particular for the way we think about distributive conflict. That is, I think there is some unique insight from the formula into key institutional relationships in government financing. For the purposes of government financing, in using the formula we must accept, first of all, that debt reduction is a worthwhile endeavor. Let’s say it is, despite the fact that austerity during periods of deflation is an unambiguous error that will prolong a low or negative growth environment.

One of the first lessons from playing around with this formula is that we recognize immediately that there are several moving parts in the battle to reduce the debt burden. One organization (the monetary authority, the fiscal authority) or perhaps one sector (financial markets, households, government) can disrupt the whole process. The Fed could raise interest rates, which could cause refinancing problems for non-financial businesses and even initiate a recession. The Congress could impose further budgetary austerity, which might decrease the primary deficit or might erect an additional barrier to growth. And all sorts of other factors–demographic change, consumer preferences, the savings rate, business sentiment, the development of new technology–could also affect the growth rate.

We can tinker with the assumptions and determine under what conditions we might achieve some sustainability (as Galbraith does). Or perhaps we can try to determine the effort it would take (and by whom) to compel a reduction in the deficit. The three conditions–average or greater growth, low interest rates, and budget restraint–are necessary for achieving a significant decline in the debt to GDP ratio, as I explore below. Pragmatically, it is unclear whether any of the growth factors mentioned could sustain a growth rate greater than the long-term (thirty-year) average (of three percent) for a period of time during which interest rates simultaneously remained negative or low (due to restraint by the Fed in raising rates and a steady rate of inflation) and budgetary austerity managed to put the federal books on a supposedly sounder footing.

So, let’s outline some scenarios of debt reduction.

Tinkering with the assumptions

Let’s use Galbraith’s assumptions in our first example. According to Galbraith, the current debt to GDP ratio is 74 percent (this is in line with CIA World Factbook estimate; the IMF sets the figure at 104.5 percent, which includes external debt obligations). Real interest rates (the interest rate less the rate of inflation) are negative one percent. The primary deficit is five percent of GDP. Let’s say these conditions are fixed in perpetuity. The trajectory of the debt to GDP ratio begins to flatten out in the late 2040s. It passes 120 percent in 2061, and then begins to flatline. Clearly, as Galbraith concludes, this is a stable pathway, irrespective of the level of the ratio.

However, how about we be more slightly ambitious. Let’s try an extreme example. Perhaps we embark on a grand experiment to return to the glorious pre-war years. The debt to GDP ratio target is set at 30%. What intensity of budgetary austerity would be required to bring the debt to GDP ratio (including external obligations) to 30% within the next couple of decades? In other words, how much austerity for how long? Again, we keep real interest rates at the current level of negative one percent.

A fifty percent reduction in the primary deficit per year for ten years would balance the budget. At that time, the debt to GDP ratio would sit at 57.08%. To put this into perspective, the primary deficit currently sits around $850 billion (based on a primary deficit of 5% of GDP, which is currently estimated at $16,800 billion). To achieve savings of $425 billion, we could start by denying the requests of the Air Force for $68 billion and Navy for $58 billion in funding for maintenance and modernization of their fleets. Another source of savings could be found in denying the funds to the US nuclear force (at $23 billion) for the next year (their requests total $355 between now and 2023). For the remaining $276 billion, it is fairly easy to find on the internet other suggestions by officials and citizens for cutting waste out of the defense budget and shuttering various executive departments; the savings from closing the Department of Energy, for example, have been estimated to be around $7.1 billion (see These suggestions are plainly ludicrous, but then that’s the point: the goal here is to demonstrate the futility of an exercise of debt to GDP targeting. Even after achieving balanced books (and keeping them there), not until 2040 would we reach the ideal debt to GDP ratio.

Let’s try a scenario that doesn’t require the wholesale and rapid liquidation of government operations. Let’s assume that Congress enacts an annual program of budgetary austerity that decreases the primary deficit (as a share of GDP) by two percent each year, beginning at current levels of 5%. This implies a total freeze in new spending. Now let’s breathe some life into the Federal Reserve, which has been sulking in the corner up to this point due to our fixing real interest rates at -1%. The Fed decides to incrementally increase the interest rate while effectively combating any inflationary tendencies, such that the real interest rate increases by 0.25 percent each year. By 2018, the real interest rate would be zero, and increases by a full percentage point every four years. The figure below shows the annual percentage change in the debt to GDP ratio under this scenario.


What you find is that the two mechanisms of rising interest rates and budgetary austerity are completely at odds. In fact, the rate of increase in debt to GDP accelerates. This scenario, furthermore, ignores the possibility that a rise in interest rates triggers a contraction, as Galbraith points out. We assume that there are no interactions between the components of the equation; this notion is a fantasy. That is, neither tinkering with the deficit nor the interest rates are supposed to have an effect on the growth rate–this is a bold assumption indeed.

There are many hidden assumptions built in that have not yet been mentioned. For instance, we take it for granted that the organizations responsible for implementing action (cutting budgets, fighting inflation, setting interest rates and thus affecting lending behavior by banks) will be effective. There are no exogenous shocks here, such as a spike in the cost of resources or a war, and no endogenous ones either, like a financial crisis. The necessary assumptions and the implied restraint and efficacy on the part of key organizations and sectors to deliver us to a sustainable growth path are enormous. After you play around with the Buiter formula, debt reduction as a public policy goal becomes even more horrifying.

The likelihood of distributive conflict

I do not think that the formula and recommendations based upon it should be taken as any kind of foundation of public policy decision-making. I do not believe that the appropriate object of our attention and effort should be the debt ratio; more socially-optimal outcomes include improving the public health, public safety, and fostering peace and prosperity in general. Does the debt to GDP ratio get us any closer to achieving any of these objectives in a fiscally sustainable manner? I would argue that as long as the debt to GDP ratio cannot be objectively determined to have any bearing on the likelihood of sovereign default or financial crisis, it should stay in the pages of academic journal articles and white papers. That is, be wary of its application outside those texts, especially if it is used as a political technology.

From my perspective, though, the formula does say a lot about the potential for distributive conflict. Any scenario involving continued austerity, battling inflationary tendencies in the economy, and changing interest rates (as set out in the final scenario above) already implies a situation of distributive conflict between recipients of government services, savers, and the financial sector. The Buiter formula makes this situation obvious. And this can all happen without actually producing a decline in the debt to GDP ratio. So, the potential that efforts at debt reduction merely stoke conflict for the purpose of advancing parochial interests is quite high. This raises an important question: is debt reduction pursued for its own sake, or for other reasons?

Pragmatically, here are some of the key institutional relations that matter for the purposes of debt reduction: budget appropriation processes, the process of interest-rate setting at the Federal Reserve, and the federal bureaucracy (namely, its cooperation with the other authorities and the level of influence and attention of its constituencies). Being a geographer, I am sensitive to the fact that a program of deficit reduction is essentially a series of spatial processes. That is, constituencies will be mobilized, certain organizations will be called upon to implement action, and policies will be decided upon and then enacted in certain locations. There’s quite a lot to work through here–the possibility for intervention in multiple venues exists.

Bottom line

I think that examining the institutional relationships behind macro-economic formulae might be a good entry-point for evaluating the effects of territory/space/place on decision-making. This post has provided only a preliminary look; I am still thinking about the necessary conceptual and methodological foundations for an analysis of distributive conflict. The Buiter formula, though, seems to me an ideal candidate for the repurposing of concepts and models from the field of economics for more critical use. In any case, playing around with the assumptions of the equation makes for a fun if not pessimistic sideshow.

Employment update

I have created two choropleth maps of the state of employment in US metropolitan statistical areas (MSAs). The first map displays the unemployment rate in about 380 US MSAs. The second displays the percent change in employment (not unemployment) between June of 2007 (before the recession began) and June of 2014 (latest available data). To make the maps a little clearer, I’ve included state and coastal boundaries.

I used data from the BLS (, a shapefile from the US Census (, fussed with the data in various spreadsheet programs, and then plugged these variously into QGIS, TileMill, and MapBox Studio. The maps here are a final product of MapBox.

I’d like to able to create and share maps here without always going into the methodology. I add two notes about methodology here that are rather important before discussing the substance of the maps.

Map-maker, map-maker, make me a map

I’ll be the first to admit that my maps are an example of terrible cartography. Part of this comes from my technical inaptitude, but part comes from the decision to look at MSAs. In theory, metropolitan areas are a useful unit to study the structure of economy, but, practically, few people are familiar with their size, boundaries, relative location, and even constituent units. This lack of familiarity with the MSA is compounded by the fact that the unit is not defined in much of a standardized way. For example, Los Angeles MSA consists only of Los Angeles and Orange counties, its population was approx. 12 million in 2010, and its area is 4,850.3 sq. mi., according to Wikipedia. New York MSA, by contrast, consists of 25 counties in over three states, its population was close to 20 million in 2012, and its area is 13,318 sq. mi., also according to Wikipedia. So, we’re dealing with fairly arbitrary statistical creations.

While it may be difficult for most people to really read these maps (that is, to determine what the exact figures are for each and every MSA on the map), there are other patterns that can be picked up on more easily. One example is clustering of similar levels of unemployment and employment change at various scales: within-states or between them, or across larger regions. In my view, the map is not necessarily the final output; it is descriptive, not explanatory. These maps are meant to provide a basic snapshot and starting point for additional, more specific analysis.

And the lights all went out in Massachusetts

You might notice that no MSA located in the New England states is displayed. This absence comes from at least three quirks in US government statistics, which are worth mentioning for the important limits they impose on the maps. First, there aren’t actually any “MSAs” in New England; they are called “New England City and Town Areas (NECTAs).” This immediately introduces some confusion and if you dig around the Census, Bureau of Labor Statistics (BLS), and other US government sources, you’ll notice that New England states, and Massachusetts especially, consistently make odd appearances in federal statistics (often times, figures for these areas are not reported at all, or have a significant delay in their release). I’m not sure what’s going on here. As a result, we lose valuable information especially on Boston, one of the largest US cities, and Connecticut, which contains quite a bit of the US financial and insurance industry.

Second, the shapefiles I used to create the maps use “core based statistical area (CBSA),” which differ somewhat from MSAs. The two units are quite similar. CBSAs are typically amalgamations of “micropolitan” and “metropolitan” areas. The distinction is that metropolitan areas possess a population greater than 50,000. There are over 500 micropolitan areas and around 350 metropolitan areas. The employment data (retrieved from the BLS via the Department of Labor), it seems, covers mainly metropolitan and not micropolitan areas. Although, one of the key problems maybe that the BLS data is in fact organized according to MSAs, whereas the shapefile is organized by CBSA.

Again, the trade-off in using urban-level economies is that the way the data are organized and collected is something of a mess. I think, however, that looking at states does not give the same picture of activity, and nor do counties. And, more importantly, I’m not attempting to be too scientifically rigorous right now.

Final caveat: the maps display only the lower 48 states.

The substance

Let’s begin with the distribution of unemployment as of June 2014.  The worst-performing areas are located in Oregon, California, Arizona, around the Great Lakes (particularly around in Illinois and Michigan), and the southern states (particularly Alabama, Georgia, and Florida). There are several clusters of contiguous metro areas where unemployment is concentrated, including California’s Central Valley, the US-Mexico border region in the southwest, the greater Chicago, Detroit, and Atlanta areas, and also the southern tip of New Jersey (Atlantic City). The best-performing areas include Salt Lake City, the large cities of Texas (San Antonio, Dallas, Houston), and metro areas in Oklahoma, Louisiana, Iowa, Minnesota, and South Carolina.

A few features stand out. First, city size does not imply better or worse unemployment prospects(Chicago and New York versus Houston and Washington, DC, for instance). Perhaps this has to do with the specialized industrial and commercial base of individual areas.

Second, though statistical analysis could analyze this more precisely, just eyeballing the map suggests that the level of variation within states is less than the level of variation between states. That is, there is probably an independent effect of US states, possibly related to differences in state/municipal public spending/austerity, state tax regimes, or to the disproportionate allocation of federal aid to the states. Doing an econometric analysis of metropolitan performance is made trickier when using states as an independent effect, because so many MSAs located on and east of the Mississippi sit in more than one state.

Third, it is apparent that there are regional patterns; that is, some patterns appear consistent across many states. There seem to be three main groups. First, the west coast states as well as Nevada and Arizona; second, the Great Lakes area; and third, the southeast. Possible explanations for the first and third are the high concentration of distress from the collapse in real estate and banking (in the west coast, thrift) markets. Indeed, some of the largest bank failures of the 2008-2010 period were west coast-based savings institutions (IndyMac, Wachovia). Economic structure may also be a factor, such as the high level of specialized industrial activity as a share of total activity in the Great Lakes. However, I was under the impression that California, Georgia, and Florida are actually quite diversified economies (agriculture, industry, FIRE, professional services). Diversity ostensibly delivers greater resilience to economic downturns. So, this issue needs to be fleshed our more.


The next map displays a very different set of dynamics. Where unemployment provides an indication into the mismatch between the civilian population able and willing to work and the supply of jobs, employment growth is actually quite different. At the macro-level, employment growth reflects demographic change, business sentiment, consumer preferences, the savings rate, and there is also an important sectoral component.

This difference in the underlying dynamics explains why unemployment can remain very high, such as in the California Central Valley area, while employment growth is actually expanding. We require more regional- and sectoral-specific data to test whether this discrepancy arises from a skills mismatch (for example, a hypothesis could be that professional or information sectors are expanding while the construction or agriculture industries continue to contract, leaving the comparatively under-skilled workers in the latter unprepared for work in the former) or perhaps because of austerity measures that rationalized the public sector, or perhaps some other hypothesis.

At the very least, we have a reinforced sense of the regional patterns of growth and contraction. Texas, Louisiana, and, to an extent, Oklahoma are growing–this region is one of the main sites of the mineral extraction-natural gas-shale fracking boom of the last few years. The former industrial heartlands continue their long-term process of collapse. The greater New York area registers a contraction, although it seems the worst of it was pushed to the more peripheral areas in New Jersey, Pennsylvania, and the New York suburbs as opposed to the core area. The Washington, DC area–dare we go so far as to include Richmond, VA here?–is booming, so at least austerity has been good to some people.


Bottom line

There are two chief points I’d like to make, unrelated to the specific content of the maps. First, I think one of the next steps is a set of exploratory econometric analyses that can test the effect of states, proximity between metropolitan areas, industrial structure, and metro-level real estate and housing market distress during the Great Crash of 2007-2009. Cluster-based analytical techniques would also be a useful way of identifying patterns, although neither econometric analysis nor cluster techniques reveal much in the way of explanation.

Second, to get at the causes of differential economic performance would require a series of case studies. An appropriate scale for such case studies, I suggest, is “regional”: not necessarily an entire state, but not necessarily only the areas within a state. Again, cluster-based techniques might be a good first step in deciding which areas are similar or distinct enough to merit study as a group. Interestingly, the Federal Reserve regional banks are a fairly reliable source of this kind of study–each Fed region is composed of three or so states, and every now and then their economists produce a report of regional conditions.