Employment update: the job creation meme

On the radio yesterday morning, some DJ quoted Hillary Clinton, who recently said that ‘business don’t create jobs’ or something along those lines. The statement is meant, I believe, to reflect the notion that consumers create jobs, through their demand for goods and services. Implicitly, the statement seeks to counter the argument that the governing plutocrats are benevolent and generous ‘job creators.’ I think it was a mistake for left/progressive commentators to adopt this theme, but it is here, so let’s first take a look at the process of job creation and then employment and job statistics. My question is: what do we know about job creation, conceptually and descriptively? The post here is going to tackle the first of these, and I’ll write-up another one with some data later.

Semantics

First of all, businesses change the number of jobs recorded in the formal economy by adding workers to or removing them from their payrolls. We know that businesses ‘create jobs’ because the government counts jobs as the number of workers who are employed by firms. So, purely from a methodological perspective, when we talk about jobs, we are talking about the gains and losses of salaried/employed workers at US firms (and occasionally at government agencies) as reported by payroll data (which in turn are reported to the almighty IRS).

Second, businesses do not want to create jobs. Employees are costly, and employers are really only going to hire a new employee after other efforts to cope with increasing workloads (overtime, or productivity-enhancing strategies, for instance) have failed to relieve the stresses from the rising demand for their goods and services. Clearly, there is an important relationship between demand for a firm’s output and the way that the firm adjusts to this changing demand. It is not necessarily the case that a firm will hire more workers as demand rises. Some owners are incompetent, for example. Some firms enter into long-term contracts that so structure their budgets that they simply cannot hire new workers, while other contracts make firing difficult. There is also a tremendous amount of uncertainty in the economy. The process of hiring and firing may be responding to events that are not fully formed or are ambiguous. So when a firm decides to hire/fire an employee, the process will usually be undertaken in the face of exigent circumstances. Again, business don’t want to do this. It’s expensive, it’s uncertain whether it will solve its workload problems, and it may be based on faulty information about its business prospects.

Third, the hiring/firing process is one of the miracles of the economy. I use ‘miracles’ advisedly because it is, in fact, a process that is extraordinary, a welcome but surprising event, and difficult to be observed or explained. Job creation statistics do not actually observe the hiring and firing process. Remember that those statistics simply count the reported number of employed workers at firms at a given time (beginning or end of the quarter, usually). If you want to actually see the phenomena of job creation, you will need to be either a manager or a (prospective/former) employee. The Census doesn’t see job creation; I can’t see ‘job creation’ happening directly. This is partly semantics, but also it should emphasize the fact that the process is personal and sociological. That is, hiring/firing really comes down to a host of economic, political, and cultural factors that are probably highly specific to context (national, regional, sectoral), with a dose of randomness as well. My point here is, if you really want to understand job creation, then you should absolutely be talking to prospective employees, recently hired employees, recently fired employees, and the people who hire and fire them. Then you can get a sense of how the process works.

These are some preliminary comments. In a post later I will elaborate on how job creation happens between sectors and how it varies depending on the quantitative measures utilized.

Should Lehman Brothers have been allowed to fail?

On the question, briefly

I was recently asked the question: “Should the investment bank Lehman Brothers have been allowed to fail in the summer of 2008”? I’d like to sketch out a couple of points about the question and its answer, mainly to make myself feel better after realizing I did not have an answer ready and responded so inarticulately.

Background

For a more thorough introduction to Lehman Brothers, I suggest scanning this post at Investopedia (http://bit.ly/1mJP8nM) and the Wikipedia entry entitled ‘Bankruptcy of Lehman Brothers’ (http://bit.ly/ZXfoBe). A search for ‘Lehman Brothers’ on Amazon reveals a number of books about the firm, none of which I have read. Here are some key points.

  • Lehman Brothers was an American, New York-headquartered investment bank–fourth largest in the States–with global operations. It had been part of American Express until that company decided to divest its banking and brokerage operations in 1994. The new entity, Lehman Brothers Holdings, Inc. was listed on the New York Stock Exchange. The company operated in three segments: capital markets; investment banking; and investment management.
  • In 2003 and 2004, the firm acquired mortgage lenders that were engaged in high-risk practices, namely subprime lending. The source of growing revenues in its capital markets division was securitization techniques, and revenues in this division surpassed other areas in the company shortly after acquisition. Record profits were reported from 2005 to 2007. Incidentally, rapid growth in profits is a tell-tale sign of an accounting control fraud.
  • Defaults on subprime mortgages began to increase over late 2006-early 2007. By August 2007, Lehman stock dropped substantially following the failure of two hedge funds associated with investment bank Bear Stearns. Lehman began shuttering its mortgage businesses, but continued to underwrite mortgage-backed securities. Global equities and fixed-income markets brushed off the hesitations earlier that year that profitability would suffer as mortgage and housing markets registered rising defaults.
  • Bear Stearns collapsed in March of 2008, and Lehman’s stock price dropped by almost 50 percent. Lehman was able to raise capital through preferred stock, however. In June, the company announced a loss of $2.8 billion but continued to raise capital from investors. It further announced it had increased liquidity, decreased assets, lowered exposure to mortgages, and decreased leverage. The stock price continued to drop over the summer and investors spurned management.
  • In September, the collapse in talks between Lehman and Korea Development Bank precipitated a new drop in the stock price as investors (hedge funds and short-term creditors) began to withdraw funds and limit exposure. The stock continued to drop as its cash reserves dwindled, losses and write-downs were reported, and restructuring programs were announced. A final attempt at a rescue by Barclays and Bank of America failed over the weekend of September 13 and on that Monday, the company declared bankruptcy.
  • Bear Stearns was in a similar position to Lehman Brothers, in that it was not a chartered institution, was publicly-listed, was an investment bank, and faced financial distress from its risky positions and unsound management practices. It was, however, the recipient of an emergency Federal Reserve loan via JP Morgan, earlier in the summer. The reasons for this, contrasted against the lack of such a loan to Lehman, are discussed below.

Is the question relevant?

My first comment is that the phrasing of the question “should Lehman have been allowed to fail” is actually quite misleading. A more pertinent question is “Could Lehman have been saved?” The answer, I think, is no. Recall that the rescue of Long-Term Capital Management in 1998 was orchestrated by the Federal Reserve of New York, but involved no public funds. That is, the Fed compelled creditors of Long-Term to recapitalize the firm. There were several attempts at a private sector recapitalization of Lehman Brothers; there was simply no appetite by its creditors or competitors to intervene.

Why is it the case that after the inability to recapitalize privately there was no recourse to a public recapitalization or any other preventative action by a government agency? Just as was the situation with Long-Term, Lehman was not a chartered bank. It’s primary regulator was the Securities Exchange Commission (SEC); the FDIC and (New York) Federal Reserve were not its supervisors. The SEC has no powers like the FDIC or Fed, and can really only intervene through review of a company’s risk management and investor protection policies. It can also levy fines on its supervised entities. In other words, it probably would have been illegal for the FDIC or the Fed to have used public funds to support Lehman Brothers. Furthermore, the SEC and the New York Fed have a long and combative history. Again, even though the Fed could not have provided any kind of balance sheet support, we should at least recognize that these turf wars prevented a lot of meaningful interagency communication.

In the absence of any consensus by private companies to support the bank, there was very little the state could do within the parameters of existing banking and macroprudential law to prevent the bankruptcy of Lehman. Specifically, with no chartered bank, the FDIC could not intervene by seizing the bank and its assets. The US Treasury could not have organized a direct loan nor could it have offered funds under various emergency programs related to the preserving the integrity of the dollar. And the SEC has no such powers in any event.

The exception is Section 13(3) of the Federal Reserve Act, which authorizes the Federal Reserve Board to permit a Federal Reserve Bank to extend loans under exigent and unusual circumstances. So essentially the question is really why Lehman Brothers was not offered an emergency loan as was Bear Stearns. What explains this discrepancy? In the words of Fed Chairman Ben Bernanke:

A public-sector solution for Lehman proved infeasible, as the firm could not post sufficient collateral to provide reasonable assurance that a loan from the Federal Reserve would be repaid, and the Treasury did not have the authority to absorb billions of dollars of expected losses to facilitate Lehman’s acquisition by another firm. Consequently, little could be done except to attempt to ameliorate the effects of Lehman’s failure on the financial system (underline mine).

The underlined portion is actually very important. Pursuant to Section 13(3) of the above stated Act, the Fed cannot extend a loan with the knowledge that the loan cannot be repaid. The perception on Wall Street prior to Lehman’s bankruptcy was that it was insolvent; it could not repay any loans extended to it. Certainly, this does not explain why the Fed did not extend such a loan many months before, but then hindsight is twenty-twenty, and there are limits to what these regulators know (especially in light of the turf wars just mentioned). So, the state was effectively unable to save Lehman Brothers.

So what?

The next pertinent question is, what does it matter that Lehman Brothers failed? Many would stipulate that its bankruptcy was the triggering mechanism of the banking panic that fall. I suggest this is wrong. There are several relevant points here.

First, housing markets began to buckle in late 2006 and early 2007. This manifested in liquidity problems at a host of non-bank financial institutions, including two hedge funds associated with Brazilian firm BNP Paribas in 2007 and Bear Stearns as mentioned. Saving Lehman would not have prevented housing markets from tanking or prevented the liquidity problems associated with the failure of mortgage markets and products. The Lehman bankruptcy was only one event in a long series of events that dried up credit and lending. Interbank lending in particular had already seized up.

Second, it is arguable that US financial markets were already in a state of panic before the weekend that Lehman failed. Again, note the experience of Bear Stearns. There are other examples: Countrywide Financial experienced a run in August 2007 before being acquired by Bank of America; IndyMac Bank suffered a run in July 2008 after Senator Charles Schumer stated to the media that he believed the bank was unstable; Washington Mutual witnessed massive withdrawals by its retail customers beginning on the same day as Lehman announced its bankruptcy. The financial panic, not only in the ‘shadow’ financial sector as well as in retail, therefore, was already underway when Lehman failed.

And finally, the stock markets “crashed” beginning on September 16, 2008, the day after Lehman declared bankruptcy. Indeed, during several trading sessions in September and October of 2008, US stock markets lost two to five percent of their value. However, again, equity markets had begun to decline beginning in 2007. If Lehman Brothers had not failed, then stock markets likely would have responded to similar triggering mechanisms, of which there were many.

These points support the conclusion that at a certain stage in the financial evolution of the US economy between 2003 and 2006, a financial crisis became inevitable. This was due to the construction of balance sheets, the dependencies erected between sectors of the economy, the rampant fraud and corruption in markets, etc. Lehman stands out principally due to, I suspect, the animus held by many on Wall Street for state regulation and action that doesn’t suit their wishes. The Lehman Brothers bankruptcy is partly an instance of scapegoating–‘the government failed to act’ narrative–and a failure of imagination, specifically the failure to grasp the roots of financial crises as endogenous and nonrandom.

The loan to deposit ratio and the trouble with US finance

 The three problems in US finance revisited

In an earlier post, I suggested there were three core problems with US finance: tendency towards dysfunction; uneven cost and access to financial services; and, waste, redundancy, and fraud. I was just browsing through blogs and saw a post at zerohedge (http://bit.ly/1so7Gd9). It is entitled ‘All that is broken with the US financial system in one chart’ and shows the loan to deposit ratio at JP Morgan from 2008 to now. The question I’m asking myself is: does JPM’s loan to deposit ratio really tell us everything wrong with US finance? Does it reveal the US tendency to undermine profitability and stability by way of incentives to misallocate credit? Does it reveal the uneven ability for consumers to access credit on good terms? Does it reveal the wastefulness of the US system, the high incidence of consumer fraud, and the high incidence of white-collar fraud?

The first thing I note is that I don’t think the 2008 to 2014 period is satisfactory for answering these questions. Furthermore, there is no comparison to other large banks, even though JPM on its own has market power and can be considered a special case worthy of analysis. I don’t post comments on that blog, but I did notice in the comments section someone asking if there was a chart for the whole financial system. The implicit criticism, I think, was that JPM is not indicative of the rest of the banking system, even though it is the largest bank in terms of deposits, or at least in the top four. I think a better starting point is asking whether or not the loan to deposit ratio can capture the problems in US finance, and then proceeding with individual banks to understand how the ratio is constructed organizationally and geographically.

I’ve attached below a chart created using FRED (http://research.stlouisfed.org/fred2/), showing the ratio of loans & leases to deposits at US commercial banks. It begins in 1973 and continues monthly to the start of October 2014.

Loan to deposit ratio, US commercial banks

It seems to me this is another important institutional relationship in US finance. Some notable features include: (1) the ratio currently stands at its lowest level since the late 1970s; (2) it peaked during the dot.com bubble, and even the mortgage lending boom could not raise the ratio to its previous peak (which suggests some interesting limits to leverage); (3) it increased rapidly starting in the mid-1990s economic boom; and, (4) it had been in decline from the panic in 2008 until earlier this year.

There are a number of different behaviors that affect the ratio. First is clearly the demand for credit, which is structured by access and cost. Demand, by this token, is affected by the organization of credit allocation, that is the specific institutions responsible for providing it (too-big-to-fail banks, savings associations, credit unions, payday lending). Second is the effect of interest rates, which affect willingness to take on risk by banks. Third is capital and other regulatory requirements.

So, does this institutional relationship reveal the problems in US finance? This question assumes that the three-problems are valid; they may not be, and this is a good exercise to work that issue out.

1. Tendency towards dysfunction

The ratio tends to drop around US recessions, but it is interesting to note that it did not drop as much during the S&L debacle. Leverage actually appears to have increased from 1985 onwards, even through 1987 when there was a sharp drop in profitability of US banks. Even during a banking crisis, leverage continued to grow. It did, however, respond dramatically to the latest recession and banking crisis, however it is hard to separate out those effects. Did leverage decline after 2008 because of demand or because of supply side factors? I don’t know. At the least, the ratio might anticipate collapses, in that periods of expansion in leverage are followed by contractions, which is to be expected based on periods of credit expansion.

2. Cost and access is uneven

In order for us to determine whether or not the ratio can reveal the problem of access, it would probably be best to disaggregate the ratio between types of organizations. First would be sector (commercial banks, savings associations, credit unions, non-regulated intermediaries); second would be size of organization; and third would be the region of operations. It would be great if there were data on the average ratio by banking markets served (which could then be related to other demographic features of those markets, such as income, population, etc), but that would be quite an undertaking. At least in general terms, the ratio suggests that there has been a significant pullback in leverage; as we cannot quantify the factors that contributed to that (it could be demand, regulation, and supply), it is difficult to know whether or not the pullback reflects lower willingness by consumers to take on debt or greater risk aversion by banks. The post at zerohedge argues that the flatlining in amount of loans outstanding indicates a conscious decision by the bank not to extend loans; I think we would need more data on the volume of loan applications as well as the rate at which applications are accepted. The Federal Reserve has survey data from banks, which speaks in a way to sentiment, but hard data would be more useful.

Another important point that comes to mind, and which returns to the issue of access and organization type, is that using commercial banks to determine lending might actually be misleading. As I’ve posted before, regulated banks are actually performing less lending. The ‘shadow’ financial sector has been encroaching on traditional lending markets for decades. It may be the case that the growth of shadow activities in financial markets has lowered the share of commercial banks in lending. To develop this line of inquiry further, one would need to explain that this pattern accelerated after 2008.

3. Waste and fraud

On the whole, the ratio really seems to be an indicator of system-wide leverage. It does not seem capable of tackling issues of consumer fraud, white-collar crime, etc.

Summary

I am weary of these reductionist articles that pop up on so many blogs and online news sites. The problems in the US financial system cannot be boiled down to any one factor. The sector is too heterogeneous for there to be even one set of factors. Clearly, there are more than three problems as well, but I’ve tried to make it so that other “sub-problems” can be filed under those categories. Again, the endeavor to determine just exactly what is wrong with US finance has to take place on a lot of levels–regional, sectoral, historical, institutional, etc. The key may be to generate case studies that are regionally and institutionally specific and see what kinds of patterns and differences can be identified. That said, I’m glad to add the ratio to the list of relevant institutions of finance.

Employment update: a work in progress

Inspired by infographics

Lying in bed this morning, browsing through my blog reader, I stumbled upon an infographic of the best-selling make and model of car in each US state. You see this type of infographic all the time, and they typically are attempting to argue that some kind of sub-national, regional patterns of cultural, social, and economic behavior exist. I enjoy these graphics, but they are generally not terribly scientific.

This gave me the idea to try to manufacture a map showing the largest sector by employment in each of the state. My hope was to be able to identify economic specializations, or perhaps even regional patterns. I was hoping there would be considerable differences. I visited the Quarterly Workforce Indicator (QWI) by the Census online (http://ledextract.ces.census.gov/) and for each state downloaded the first quarter 2012 employment at the 4-digit NAICS level, and then extracted the largest sector into a separate spreadsheet that would contain such information for all fifty states and the District of Columbia.

By the time I got to Colorado, I had to abandon my endeavor, or at least hit the brakes. The largest sector in the first six states was NAICS 6111–elementary and secondary schools. This sector was followed usually by NAICS 6221 (hospitals) or 7221 (full-service restaurants). This was not what I expected, although it seems intuitive now. Nonetheless, I think that in itself this is interesting. Oddly enough, even though I see them everyday, it never occurred to me that schools and hospitals would take up quite so much employment. Of course, the QWI, classifying employment according to NAICS, does not suggest anything about occupational structure, just the type of economic activity that those employed are engaged in. So, hospitals employ lots of people, but the NAICS cannot tell us what the division within hospitals looks like (doctors, nurses, administration, custodial, etc), just the number of workers in them.

As I wanted to identify differences between states and regional specializations, I returned to the QWI and selected only for private-sector employment. I also retreated to the three-digit NAICS level, sacrificing a layer of specialization. This, again, was mostly exploratory, so I just looked at the first five states. In the table below, I show the top five three-digit NAICS sectors by employment in these five states.

Alabama
NAICS Employment
Food service and drinking places 132267
Administrative and support services 94136
Professional services 93134
Ambulatory health care services 84443
General merchandise stores 62209
Alaska
NAICS Employment
Food services and drinking places 17779
Ambulatory health care services 16302
Professional services 14606
Hospitals 12020
General merchandise stores 10442
Arizona
NAICS Employment
Administrative support services 192147
Food services and drinking places 179828
Ambulatory health care services 138116
Professional services 122605
Hospitals 79049
Arkansas
NAICS Employment
Crop production 75732
Animal production 48223
Forestry and logging 46388
Fishing, hunting, and trapping 43540
Support activities for agriculture and forestry 42925
California
NAICS Employment
Professional services 1080843
Food services and drinking places 1044787
Administrative support services 829147
Ambulatory health care services 660011
Hospitals 383284

In all states except Arkansas, the largest (private) employers are generally the health care system, restaurants/bars, professional services and administration support, and merchandise stores. Arkansas (finally!) is quite different–all agricultural activities.

I think the next step for me is actually to return to something I did in my doctoral dissertation, which is to compile the sectoral employment data into an index that shows the divergence of a state’s employment profile from the average. That is, it indicates the extent to which the employment profile in an area diverges due to specialization in sectors. An index like that can summarize quite a bit of information, yet it is also vital to include what those specializations are as the index is incapable of revealing that. That will be the next step, which will be in a forthcoming post.

Employment update: Texas employment growth

There is nothing I enjoy more than reading through spreadsheets of employment data. And I am certainly not being sarcastic here, this is the life of a nerd.

Part of the reason I enjoy reading through them so much is that when you deal with sector-level classification schemes (the North American Industrial Classification System, NAICS, is the one with which I have the most experience from my graduate work), you encounter some very funny occupations. They are funny, on one hand, because so many occupations are activities that you though were obsolete already, and there is something a little funny about the past (buggy-drivers, for instance, are a little funny; see what I mean?). On the other hand, they are funny in the way that bureaucracy is funny, like what you would read in Kafka or see in Terry Gilliam movie. Bureaucrats are uptight and fussy, which is pretty funny when you think about it; and you definitely get a sense of that fussiness reading through these spreadsheets.

As a short example, I’ve got in a figure below the ten industry sectors that grew the most in terms of employment in Texas from the third quarter of 2009 to the third quarter of 2013, and the ten sectors that contracted the most. This is mainly due to the influence of the book I am currently reading at the moment, Rough Country by R Wuthnow about the role of religion in Texas history. I am routinely sidetracked by my thoughts while reading, and this blog post follows one of the occasions.

Here are the largest contractions.

Contracting Industries NAICS Emp 2009 Emp 2013 Chg
Funds, Trusts, and Other Financial Vehicles 525 8356 78 -0.9906654
Electronics and Appliance Stores 443 38704 5352 -0.8617197
Rail Transportation 482 74 46 -0.3783784
Private Households 814 21290 16544 -0.2229216
Motion Picture and Sound Recording Industries 521 2026 1584 -0.2181639
Broadcasting (except Internet) 515 22571 18789 -0.1675601
Administration of Human Resource Programs 923 36668 32386 -0.1167776
Printing and Related Support Activities 323 29100 25732 -0.1157388
Apparel Manufacturing 315 4894 4370 -0.1070699
Publishing Industries (except Internet) 511 44231 40151 -0.092243

Some comments. I find it hard to believe the figures for 525; this must have something to do with counting error, which I’ve encountered a few times elsewhere in NAICS statistics. Second largest drop: electronics stores. That’s RadioShack, I guess. What’s incredible is the drop: from 38 thousand to five thousand! That is low-skilled service work after all, so ostensibly those workers can find jobs in other retail activities, although that isn’t much of a consolation. There are five thousand fewer people working in private households, so it seems that the wealthy have cut back on their excesses, out of solidarity with the working classes no doubt. Also four thousand cut from Administration of Human Resource Programs; so the rationalizers were rationalized. Printing also by four thousand. This classification is a little ironic; as printing of papers declines, printing of materials is growing (3-D), but it really seems to be evolving into just another large electronic item you would buy for your house, like a washing machine or entertainment system. Finally, Publishing Industries (except Internet) also declined by about four thousand. Several sectors include the “excluding the Internet” qualifier; economic history in the making.

Here are the largest positive employment changes.

Growth Industries NAICS Emp 2009 Emp 2013 Chg
Fabricated Metal Product Manufacturing 332 116206 138366 0.1906958
Oil and Gas Extraction 211 89965 107668 0.1967765
Securites, Commodity Contracts, and Other Financial Investmens and Related Activities 523 49272 62066 0.2596607
Machinery Manufacturing 333 86382 109960 0.2729504
Truck Transportation 484 104578 134310 0.2843045
Forestry and Logging 114 415 535 0.2891566
Wholesale Electronic Markets and Agents and Brokers 425 58507 75664 0.293247
Support Activities for Mining 213 100794 177155 0.7575947
Sporting Goods, Hobby, Book and Music Stores 451 39248 72612 0.8500815
Postal Service 491 325 856 1.633846

Some comments. These are mostly big industries. I notice that Texas has a lot of people in manufacturing (about 130 thousand in fabricated metal and another 100 thousand in metal machinery). It sure has an enormous pool of skilled labor, which is even more interesting in the context of it being a right-to-work state; how many of these workers are members of a union? What are their wages? What is the level of labor turnover? Oil and gas extraction as well as mining support together make up over another quarter of a million workers; all growing after crisis by significant amounts. Yet another very large sector with skilled and highly regulated/licensed labor is truck transportation; obviously on account of US trade with Mexico and the great amount of industrial production. The securities sector has also expanded by leaps and bounds, in the wake, oddly enough, of a major financial panic. I suspect mostly this labor is located in Dallas and perhaps Houston. Maybe this has something to do with fracking as well, as big oil is hugely preoccupied with risk management through financial markets. Fracking boom in Texas may translate into risk management boom as well. FInally, I am slightly relieved by the expansion in Sporting Goods, Hobby, Book, and Music Stores–hopefully the workers displaced from Electronic Goods Stores have found their way into these stores, which hopefully will enjoy a long life. Unless Amazon wipes them out first.

In short, quite a story of economic success. These figures give an interesting insight into why Texas has prospered after the crash. That story still needs to be developed.

I place a lot of value in government statistics like employment figures because as an undergraduate, I majored in Near Eastern archaeology. Being able to read into the lives of past civilizations is so worthwhile; you get such a sense of that when you have an idea as to what people were doing and how they were organized. It’s hard to make historical employment and economic figures interesting, but economic history is fascinating.

Too-big-to-fail profitability

This is a short post, comparing basic financial information of the too-big-to-fail banks (these are: Bank of New York Mellon, Bank of America, Citigroup, Goldman Sachs, JP Morgan, Morgan Stanley, State Street, and Wells Fargo). Of these organizations, three are not headquartered in New York (BOA in Charlotte; State Street in Boston; WFC in San Francisco/Minneapolis).

The information I have included is the change in stock price over the 2008 crisis and price performance since the trough up to now; return on assets; a liquidity ratio (from the balance sheet, cash as a ratio to total current liabilities); and, profit as a share of total revenue. All this information can be collected from the balance sheets and income statements of the banks, which I retrieved using Google. The figure below contains the information.

Stock price change ROA Liquidity (Cash/Current liabilities) Profit/Revenue
2007-2009 2009-2014 June2014 June2013 avg 2013 avg 2012 avg 2013
BNYM -63% 58% 0.74% 0.56% 0.5 0.49 0.137
BOA -94% 129% 0.37% 0.30% 0.39 0.4 0.112
Citi -95% 42% 0.52% 0.51% 0.35 0.32 0.180
Goldman -77% 71% 0.87% 0.91% 0.29 0.285 0.235
JPM -70% 267% 0.66% 1.03% 0.35 0.275 0.188
MS -83% 70% 0.53% 0.19% 0.3 0.51 0.090
State Street -75% 74% 0.85% 1.02% 0.88 0.93 0.216
WFC -77% 563% 1.50% 1.48% 0.44 0.42 0.265
Average -80% 181% 0.82% 0.86% 0.44 0.46 0.196

There are a few points I see as especially relevant. First, almost none of the banks have restored the price of their stock to levels seen around 2007, the stock market peak. Not all of the banks, it should be said, were trading at historical peaks in 2007; nonetheless, they all responded to the crash in 2008. Bank of New York Mellon, State Street, JP Morgan, and Wells Fargo had below-average declines, but only JP Morgan and Wells had recovered their losses. The worst-performer has been Citigroup, which comes as no surprise seeing as it is easily the worst-run organization on the planet.

Second, the range in ROA is quite large. I’ve spent a lot of time looking at ROA at lots of other banks of various sizes and specialization; a previous post here put US total average bank profitability currently at around 1%. Wells is the only company reporting an ROA greater than the US average (in 2014; many more were closer to the average about a year ago). I’m not sure what the patterns are–perhaps there are regional trends, but I suspect they have much more to do with target deposit markets and product lines, etc–but this variability in ROA encourages further analysis.

Third, it is illuminating to see the liquidity ratio comparison if only because it introduces the challenge faced by regulators and supervisors on macro-prudential matters. Current liabilities consist of accounts payable, short-term/current long-term debt, and ‘other current liabilities.’ Each of the banks, in turn, have different concentrations in each of these areas. The amount of cash (and current liabilities) also differs substantially between companies. Obviously, one of the more quiet fights by regulators over the last few years has been to raise capital ratios (one way to ensure that costs of crisis resolution fall as much on the private sector as possible). Some of these businesses simply operate in riskier markets; this may explain why there is such variation in liquidity ratios. I wonder how this unevenness is looked at by the regulators themselves?

Finally, the profit as a share of revenues for all too-big-to-fail banks is close to 20 cents on the dollar. I’m not sure what this number is for all Fortune 500 companies, but I remember hearing an interview with the CEO of ExxonMobil some years ago where he said that Exxon’s profit per revenue was 10 cents on the dollar, and that this put Exxon ‘right in the middle’ of the Fortune 500. If you look at the most recent Fortune 500 list, the top four companies (oil majors and conglomerates) are all less than 10%, but then Apple is at 21.6%. What this all suggests to me is that financial conglomerates enjoy profit margins that are much higher than capital goods producers and energy companies, but then there remains quite a bit of difference. Again, I suspect this has to do with product and market specializations but also company structure.

It is easy to clump the too-big-to-fail firms together, but in fact they serve quite different markets. When we devise strategies and policies to control them, we need to take into account this heterogeneity and plan accordingly.

Bank profitability compared

I’m trying to find quantitative indicators of bank profitability and activity that might be compiled into an index of macro-prudential stability at sub-national, regional scales. Ideally, such an index or set of indicators would be able to identify thresholds around which sector profitability is endangered or to identify mid-term growth potential for the area at large.

At the national-level, data for aggregate bank profitability is available. The World Bank collects data ostensibly from national agencies on the return on bank assets. The graph below was created using the FRED (http://research.stlouisfed.org/fred2/) function of the St. Louis Federal Reserve. It compares the return on bank assets for Australia, Canada, the United Kingdom, and the United States.

bank-return-assets-four

The dataset appears to go back only to 1998 and is annual, which is a shame as quarterly fluctuations can be informative. It is interesting that Canada has demonstrated a relatively consistent upward trend since 2000, while the United States had a steady level of profitability from 2000 to 2006. Australia and the United Kingdom are characterized by rather large swings.

The Federal Financial Institutions Examination Council (FFIEC) is responsible for collating similar data for US banks, which is no small task as there are several thousands of banks in the US. That data is collected quarterly, and extends back to the early 1980s. The graph representing US bank profitability (also return on assets) is available below.

us-bank-return-ffiec

This graph indicates that American banks enjoyed a long period of stable profits. The S&L crisis is clearly evident in the late 1980s, although it seems that despite a brief recovery, for several years after the immediate crisis years, profits remained depressed. It was not until around 1991/2 that profitability shifted to a higher plateau. Also notable is that profitability increased before the economic boom from the information technology revolution, which did not really begin in earnest until 1994/5. An additional important feature is that, although profitability had returned to positive levels after the 2008 crisis, it remains below its average levels during the preceding twenty-year period.

While profitability is a good indicator of stability, it has its limits. From my perspective, I wonder how possible it would be to disaggregate profitability between regions and types of banks (for instance, depending on their loan portfolio concentrations [commercial, industrial, residential, etc]). That is, it would be very enlightening to know how banks in different parts of the country are contributing to or perhaps retarding economic growth in general through their impact on the cost and type of loans, raising deposits, and their management of non-performing loans. In particular, it would be interesting to examine the extent to which bank activity in a given area relies on deposit-taking or recourse to capital markets.

In the meantime, I’m digging around for comprehensive datasets that may have this information. Otherwise, it would be seem to be the case that one would have to aggregate this data from bank call reports, which given the number of banks and the time scale necessary for meaningful analysis, is a truly onerous task.