An Open Source Alternative to No Bid Contracts

At Muniland, Cate Long reports that the US Treasury Department’s Office of the Comptroller of the Currency (OCC) awarded Municipal Market Advisors (MMA) a contract to evaluate the risk of municipal bond holdings by banks it regulates.  OCC did not find any credible alternatives and thus is awarding the contract to MMA on a no-bid basis.  Quoting at length from Cate’s excellent blog post:

So federal regulators, who can no longer use credit ratings for evaluations of the municipal bond holdings of the commercial banks that they regulate, just gave a no bid contract to MMA, a relatively small firm with four principals. In essence the OCC will be substituting the opinions of MMA for those of the credit ratings agencies. Federally chartered banks held $363 billion of municipal securities as of 4th quarter 2012 according to the Federal Reserve ... The federal bank regulator will essentially be substituting the work of credit rating agencies, which issue over 1 million individual municipal ratings, with “research” from a small private shop. Is this wise? I think restricting themselves to such limited information is short-sighted given that muniland has over 80,000 issuers with $3.7 trillion of municipal debt outstanding. High quality credit analysis for even the debt of 50 states requires a shop bigger than MMA. Let alone all the other issuers. … Of course all the folks at MMA are nice, informed market professionals. But this process of hiring independent municipal research is ridiculous. No bid contracts have no place in our new, more transparent, post Dodd-Frank regulatory framework. The municipal bond market is facing its toughest challenges since the Great Depression and this BPD/OCC process needs more public input and openness.

As I will discuss at Tuesday’s SEC Credit Ratings Roundtable, there is a better alternative to this kind of arrangement. For almost 50 years, academics have been churning out corporate default models.  This modeling effort could be extended to structured and government bonds.  If the modeling data and software were fully open, these academic tools could undergo rapid, iterative improvements through a process of mass collaboration:  like Wikipedia or Linux.  If the SEC were to create a standards board for open source credit models, a group of experts would be empowered to separate the wheat from the chaff among these open source products. Regulators could further encourage the development of such tools by allowing results of certified models to be used in lieu of ratings as a credit-worthiness standard – meeting the spirit of Dodd-Frank Section 939A.  I make this argument at greater length here.

What should supplement or replace ratings?  Confidential, non-reproducible findings from a proprietary vendor, or transparent tools developed using academic research protocols and benefiting from peer review? I think the answer is clear.

Dispelling a Myth or Two about the Ratings Lawsuits

Since the Justice Department sued S&P for fraud in February, the media has been awash with concerns as to whether a lawsuit against Moody's will inevitably follow - and questions about the lack of a lawsuit against Moody's pointing to the potential for bias on the side of the DOJ.  The inference drawn was that the DOJ might be targeting S&P because S&P downgraded the debt of the United States.

Meanwhile, market participants and researchers have honed in on the fact that other credit rating agencies issued “virtually identical” grades on the same securities that lie at the heart of the lawsuit.

Commenting on the fact that S&P alone has been sued (at this stage) by the DOJ, a member of S&P's general counsel reportedly remarked “The S&P ratings for the CDOs at issue in this lawsuit are identical to the ratings issued by other rating agencies. So we don’t have an explanation and you’ll have to ask the Department of Justice…”

Meanwhile, Edwin Groshans, a managing director, at Washington-based equity research firm Height Analytics LLC, reportedly commented that “Given that the ratings between S&P and Moody’s were identical, a loss by S&P would create significant uncertainty for Moody’s regarding whether the Department of Justice would take action against it also…”

Of course, while these arguments may have been carefully considered, and may even have some rational basis, they are built on a faulty premise.  Let us explain why. 

Straw Man Argument

The key distinction is that the DOJ is not suing for fraud in the rating provided.  The DOJ isn't saying the rating was wrong or imprecise or otherwise lacking in predictive content. Therefore, that Moody's provided the same or an equivalent rating has no import.

The DOJ is saying that in the context of these securities, it is concerned that S&P maneuvered its ratings process, in a manner neither objective nor unbiased, to achieve a necessary result (including the generation or maintenance of revenues).  As such, if Moody's already had achieved THAT result based on its then-current methodology, it would not have had to maneuver towards it.  No foul committed.

In the case of active ratings competition, it is often (but not always) the case that any jockeying done is done (or needs to be done) by the more severe rating agency/agencies, so as to allow them to compete with other raters who have a rosier view. 

An Example

Suppose we have a world with only 3 rating agencies – Moody's, Fitch and S&P – with two being selected to rate each bond.  If for example Moody's and Fitch were the two raters getting business because their methodologies resulted in the highest rating, S&P alone would have an incentive to maneuver so as to win new business or disrupt status quo.  If they did, and their actions resulted in their achieving the same view as say Moody's, then they may be included on certain deals.  But how does this translate into any misdemeanor on the side of Moody's?

Is S&P is being subjected to special attention, or targeted?  We don't know.  But it has nothing whatever to do with the nature of the lawsuit that, because Moody's may have rated the assets at similar levels, it ought similarly to be accused of improper conduct.

That Moody's achieved a similar rating to S&P does not imply that its ratings process was influenced in a manner similar to that described by the DOJ in its lawsuit against S&P.

It may be the case that in certain scenarios all 3 rating agencies simultaneously, knowingly, intentionally, lowered their ratings standards to compete for the same business, while advertising themselves as being independent investor services.  This is possible - but until that is known to have occurred, any argument suggesting Moody's ought to be similarly defensive to any charges alleged of S&P is, to us, simply an informal fallacy. 

Are Credit Ratings Opinions?

The Justice Department's lawsuit against S&P made us reconsider whether the ratings provided by S&P were "opinions."

Let's quickly look at the concept of an opinion: one often relates the word "opinion" to a judgment (which may be factually supported) but may not be "provable."  A fact, however, is closer to being provable.

Of course, the rating agencies have long argued that their ratings are opinions, but there's probably more to it than that.

At the very beginning, many rating agencies have the ability (and they exercise it) to assign a rating of "D" to defaulted issuers or assets.  Many (but perhaps not all) issuer or asset defaults, relative to their defined terms, are directly provable - in other words, at least certain ratings may be more fact, and less opinion.

Now let's dig deeper into the DoJ's argument.  One core argument alleged throughout the complaint was that S&P maneuvered its models/methodology to achieve the rating levels desired by the structuring bankers (the issuers).

"As set forth in detail … S&P's competition for ratings business, that is, its desire to maintain and increase market share and profits, and its resulting desire to maintain its relationships with issuers who drove its ratings business, improperly influenced S&P to favor issuers in its ratings of RMBS and CDOs. In particular, as alleged in detail … to maintain and increase its market share and profits, S&P limited, adjusted, and delayed updates to the ratings criteria and analytical models S&P used to assess the credit risks posed by RMBS and CDO tranches, thereby weakening those criteria and models from what S&P analysts believed was necessary to make them more accurate."

The argument could therefore be that the resulting rating wasn't the "opinion" formed as part of the ratings process: the required rating, known upfront, caused the determination as to which ratings process/model to apply.  In such a case, the rating would be the fact, and the process (to be used) is the judgment.

In other words, an argument may be that the resulting ratings were not the result of the ratings process.  The ratings processes used were the result of the rating required!  

(Think of this relative to the structuring process itself, one of the goals of which is to achieve a certain rating.  Thus it may be inferred -- if the DOJ's allegations hold true -- that both the banks and S&P were playing the role of engineering their analyses in such a way as to achieve the desired rating.)

Are the Rating Agencies in Sync?

This morning's Financial Times brought with it another wonderful article by Arturo Cifuentes.  Much of his commentary on ratings reform is not new - but it is important that we be reminded of the distance we have yet to travel.

We're going to examine one element of his piece, an element he has brought up before: the rather peculiar situation of credit rating agencies coming to the same conclusions (i.e., equivalent, mapped ratings) in the structured finance arena, despite the application of different methods and proprietary data (and different rating scales, and having different ratings meanings, but Prof. Cifuentes doesn't mention these here).

Digging a little deeper, we notice similar behavior in other spaces too.  In corporate finance space, Dell Corporation - the maker of laptop computers, among other things - had not had its rating visited by any of the "Big Three" credit rating agencies since 2007.  On February 5, 2013, all three rating agencies downgraded Dell. (See screenshots courtesy of Bloomberg LP.)


Equally interesting, the last time S&P and Fitch analyzed Dell was within a week of one another in August 2007.


Late last month, the Economist ran an article that tried to describe how the rating agencies rate sovereign debt.  What the article did do was show just how similar their opinions are of sovereign countries.


We did a back of the envelope analysis to establish whether they tend to share the same ways of looking at sovereign countries, by converting the ratings to a simple comparable scale, and measuring correlation using the excel function. (Moody's Aaa and S&P or Fitch AAA were all converted to a "21"; Moody's Aa1 and S&P/Fitch's AA+ were converted to a "20" and so on.)

We found the correlation to be extraordinary:


Of course, we're not saying the rating agencies always agree.  We covered in 2011 how in the realm of seasoned structured finance deals, they vary widely on their opinions.  But the concept here may be that there is less pressure to agree once the deal gets done or for securities over which the scrutiny is limited. When they're competing, however, they seem increasingly to agree.

Moody's "Expects" DoJ Lawsuit to Cost S&P Less than $10mm

Wow, we're finally talking "expect[ed] loss."

So here's the theory.  Moody's and Fitch have both been considering downgrading S&P's debt since September 2011.  Apparently there was much uncertainty which has been removed now that a multi-billion-dollar lawsuit has been filed, and both rating agencies have quickly downgraded S&P: Fitch downgraded from A- to BBB+ and Moody's from A3 to Baa2.  (How similar their opinions are!)

It's easier to dig a little deeper on Moody's side -- Moody's rating speaks directly to an expected loss.  

According to Bloomberg data, McGraw Hill has two debt issues outstanding, each for $400mm, with one maturing in 2017 and the other in 2037.  The ratings are identical for each issuance, irrespective of its maturity.

For the 2017 bond, the rating downgrade from A3 to Baa2, maps to an increased expected loss estimate change from approximately  0.3% to roughly 0.66% (using 4-year maturity as an estimate).  The difference is 0.36%, which comes out to about $1.5mm on a $400mm bond.

For the 2037-maturity bond, the downgrade maps to an expected loss estimate that increases to 6.35% from 4.17%  (using 24-year maturity as an estimate).  Even that's not too much - roughly $8.7mm.

In sum, Moody's is saying that thanks to the DoJ's filing, S&P's bondholders are "expected" to lose the present value of less than $10mm down the road. ("Less than" - because S&P was already on watch for downgrade prior to the Dept. of Justice's filing.)

We are the first to agree this analysis is imperfect, but it's worth discussion and we welcome refutations!  Tell us why we're wrong.

The S&P Lawsuit: Can It Fix the Rating System?

The government's lawsuit against S&P has triggered speculation about why DOJ singled out just one agency  and whether cases against the other two (Moody's and Fitch) will be forthcoming. One theory is that S&P was chosen because it downgraded US Treasury bonds in 2011. Two other options seem more likely: (1) the other two agencies may still be in settlement talks with DOJ, or (2) DOJ has a better case against S&P.

I was in Structured Finance at another rating firm in 2006 and 2007, and recall the headiness of the time. Revenues were exploding and half the money fell to the bottom line. Analysts were under pressure to keep up with the rapid flow of new securitization deals pouring in from Wall Street. Management had trouble hiring good people in the highly competitive environment. Meanwhile, everyone was aware that business could quickly be lost to competing rating agencies if investment banking clients were dissatisfied with the speed or nature of our conclusions.

In short, it was an environment that encouraged the cutting of corners - in terms of research quality, and, if the government allegations hold true, in terms of ethics - in fact, if the allegations are true, it seems S&P transcended the realm of ethical lapses and entered the land of outright fraud.

According to the complaint, S&P management instructed employees not to publish software and data updates that would have resulted in lower ratings. For example, pages 42-48 of the complaint detail how S&P management suppressed an update to the agency's LEVELS tool that relied on a much larger and more representative set of mortgages. (Recall the US Senate testimony of former S&P analyst Frank Raiter: “…S&P had developed better methods for determining default which did capture some of the variations among products that were to become evident at the advent of the crisis. It is my opinion that had these models been implemented we would have had an earlier warning about the performance of many of the new products that subsequently lead to such substantial losses. That, in turn, should have caused the loss estimates mentioned above to increase and could have thus caused some of these products to be withdrawn from the market as they would have been too expensive to put into bonds.”).

By cancelling a previously announced upgrade to LEVELS at the end of 2004, S&P was allegedly able to perpetuate the use of a flawed methodology which allowed investment banks to create deals with insufficient collateral subordinated to the senior AAA tranche. While cancelling this upgrade allowed S&P to remain competitive with Moody's and Fitch, it (allegedly) did a huge disservice to AAA investors such as the Western Federal Credit Union, on whose behalf the government filed its complaint.

Naturally, S&P denies this allegation and it remains to be seen whether the government can prove its case. While the gory details of who knew what will undoubtedly fascinate, I hope that the debate around this lawsuit has room for a discussion about how to solve the fundamental rating agency problem. Rather than merely consider who is to blame, we should focus on how to change the institutional structure of the industry to incent more positive behavior.

First, why should we even care about the rating agency business enough to bother reforming it? After all, as depicted by Michael Lewis in The Big Short and in other financial crisis chronicles, rating agency employees are just a bunch of bottom feeders wearing J.C. Penney suits and sucking up to the investment bankers who might one day hire them.

Whatever we think of rating agency employees (I, for one, never shopped at Penney's), the inescapable fact is that their output shapes much of our financial conversation. Discussion around the US budget deficit and the Eurozone sovereign debt crisis often focuses on how rating agencies will respond to political measures. S&P's upgrade of California - raising it above Illinois in state bond rating purgatory - was a major local news story last week. Enron filed for bankruptcy because it lost its investment grade rating. And, of course, toxic assets poisoned the financial system in the years leading up to 2007 because of the high ratings they received.

Ratings are essential to the financial system because they help direct the flow of capital. By bucketing debt instruments into different risk categories, rating agencies help determine their interest rates. This function - if executed well - optimizes the use of society's savings and thus contributes to economic growth.

Given the importance of ratings, we need alternatives to the way they are now produced, i.e. by for profit companies with known conflicts of interest using proprietary data and analytics together with closed door rating committee meetings.

A much better alternative would be a system based on open source rating software, with fully transparent inputs and outputs, and no rating committee discretion. This fully open, fully deterministic approach controls biases regardless of whether the analysis is funded by investors, issuers, foundations or governments. It also allows a distributed peer review process to occur over the internet. An excellent case for open source ratings appeared recently on Naked Capitalism. PF2 has advanced this idea by supporting my Public Sector Credit Framework - a simulation tool for rating government bonds.

The first question I get when I propose such an approach is how are you going to make money? I have thoughts about that, but let me respond here with another question:  why aren't more academics, pundits, politicians and regulators thinking of ways to make this operational model work?

Universities and foundations could fund rating transparency projects. The only such example right now is the National University of Singapore's Risk Management Institute. I have yet to find any foundation or academic willing to create such an institution in North America or Europe. 

Easy to blame a bunch of greedy people at rating agencies for the financial crisis. Much harder to put the proper incentives in place, to do the heavy intellectual lifting needed to really fix the rating system.

California v Ontario - The Deep Dive

In a previous blog post, I reported some figures showing that California is in significantly better fiscal condition than Canada’s largest province, Ontario. Those findings appeared in a Fraser Institute study published on January 31. In this post, I supplement the Fraser report with a comparison of health, expenditure and pension costs borne by these two systemically important sub-sovereign issuers.

Health and Education Expenditures

In both Ontario and California, health and education are the two largest categories of spending. Health is the largest category in Ontario while education (including post-secondary education) is the largest category in California.

In fiscal 2012, health accounted for 38% of Ontario’s overall spending and 41% of programmatic spending. Over the last 30 years, Ontario health expenditures grew at an annual rate of 7.2% from $5.776 billion in fiscal 1982 to $46.476 billion in fiscal 2012. According to StatCan data, consumer price inflation during this period averaged 2.8% annually while Ontario’s population growth averaged 1.4%. Thus, the province’s long term health expenditure growth cannot be explained exclusively by increasing population and general inflation: real per capita health spending increased 2.9% annually over the 30 year period.

While a number of factors are driving Ontario’s health cost escalation, one contributor is hard for policymakers to address: population aging. Since 1982, the population of those over 65 has increased by 2.7% annually – in contrast to the 1.4% increase for the overall population. Given the aging of the postwar baby boom and today’s relatively low birth rates, Ontario is likely to see a further rise in the proportion of senior citizens. Since this group makes more intensive use of health services, cost pressures on Ontario’s health system are likely to continue.

While California also faces high and rising health costs, it only funds health services for certain categories of residents. Most of the state’s health spending is in the MediCal program – California’s implementation of the federally sponsored Medicaid system. Under Medicaid, California and the federal government share responsibility for the cost of providing care to several groups of economically disadvantaged residents, especially low income mothers and children. Under the 2009 Patient Protection and Affordable Care Act more people will become eligible for Medicaid. Also, some moderate income individuals without employer health insurance will become eligible for state and federal insurance subsidies when purchasing coverage on a new state administered health insurance exchange. Finally, MediCal pays for nursing home care once seniors exhaust most of their assets. It is only this last category of MediCal spending that is substantially exposed to population aging.

In the US, most government health expenditures benefiting senior citizens are directly incurred at the federal level through the nation’s Medicare program. While there is wide agreement that this program is fiscally unsustainable, it does not directly affect the budget of California or any other US state. That said, states do provide medical coverage to their retired employees. But this cost burden is limited by two factors: (1) state workers only account for 0.9% of the population and (2) these workers are also eligible for Medicare. The impact of the second point is complex. Since most state workers can retire prior to becoming eligible for Medicare, the state is wholly responsible for their healthcare costs in the years immediately following their retirement. Further, since state retirees are eligible for a better benefit package than that provided under Medicare, the state still has to pay for insurance that provides incremental coverage to its Medicare-eligible retirees. Finally, it is worth noting that Ontario also pays for supplemental retiree health benefits, including dental and supplementary hospital costs.

Despite these advantages relative to Ontario, California also has a serious disadvantage: it is burdened by the high rate of US health cost inflation. Over the last 30 fiscal years, Canadian health care CPI has risen 3.2% annually while US medical CPI has grown 5.2% per year. Overall consumer price inflation has been quite similar in the two countries – 2.8% in Canada versus 2.9% in the US.

The sum of California’s MediCal and state retiree health costs rose from $5.419 billion in 1982 to an estimated $46.673 billion in 2012, representing an annualized increase of 7.4%. Real per capita cost rose 2.9%, similar to Ontario’s cost trend. However, because California’s health care responsibilities are less comprehensive than Ontario’s, health expenditures comprise a lower proportion of its overall spending – roughly 24% in fiscal 2012.

In fiscal 2012, education, post-secondary education and training accounted for 25% of Ontario’s overall spending and 27% of programmatic spending. Over the last 30 years, Ontario expenditures in these categories grew at an annual rate of 6.5% from $4.715 billion in fiscal 1982 to $30.709 billion in fiscal 2012. By contrast, education spending in California rose at an annual rate of only 4.7%.

The difference appears to arise from California’s balanced budget requirement. When state revenues fall during recession years, the governor and legislature cut education spending. These cuts have been especially noticeable at the post-secondary level, where state colleges and universities have imposed tuition increases to offset reduced state funding. The overall effect has been a long term shift in the revenue mix away from taxpayer support and toward student funding. This trend has also been evident in Ontario, but to much more limited extent.

For example, in the University of California (2011a, 2012b) system, average annual in-state undergraduate tuition has risen from $938 in the 1981-1982 academic year to $12,192 in 2011-2012, representing an 8.9% annual rate of increase. By contrast, StatCan figures show that annual tuition in Ontario for domestic, undergraduates increased from $936 in 1981-1982 to $6815 in 2011-2012 – an annual increase of 6.8%. According to the University of California (2012) budget, “All tuition and fee increases since 1990-91 have been a direct result of inadequate and volatile State support (p. 101).” Historical data from the California Legislative Analyst Office (2012) show reductions in state aid to the University following the 1991-1992, 2001 and 2007-2009 recessions.

Pension Obligations

Pension provision is one area in which Ontario outperforms California.

As shown the accompanying table, four of the five funds to which the Province contributes are fully funded. By contrast, California’s Public Employees Retirement Fund was only 80% funded as of 2010, and the California State Teachers’ Defined Benefit Fund was just 72% funded at that time. But the difference in these ratios understates the real discrepancy. California’s largest pension funds base their funding ratios on more aggressive return assumptions. CalPERS (the California Public Employee Retirement System) and CalSTRS (the California State Teachers’ Retirement System) use 7.75% return assumptions while Ontario’s provincially supported funds use assumptions ranging from 5.40% to 6.75%. If California funds used similar assumptions to their Ontario counterparts, their funding ratios would be significantly lower. According to calculations published by Nation (2011), CalPERS and CalSTRS funding ratios would each fall by about 15% if they used a 6.2% return assumption rather than the current 7.75% rate.

During recessions, California politicians are often tempted to meet balanced budget requirements by skipping or reducing actuarially required or even statutorily required pension contributions. On the other hand, public employee unions and the courts apply pressure to maintain funding and meet legal commitments. For example, in 2003 the state withheld a required a $500 million payment to CalSTRS. The pension plan sued, and a Superior Court judge compelled the state to make the contribution (CalSTRS, 2005).

The state’s two biggest pension obligations – payable to CalSTRS and CalPERS – are both subject to important limitations. In the case of CalPERS, only about three in ten of the system’s members are state employees; local governments in California are responsible for the remaining obligations. In the case of CalSTRS, primary responsibility rests with local school districts; the state’s contribution is a fixed percentage of covered payroll. By contrast, the provincial government makes the vast majority of Ontario Teacher’s Pension Plan employer contributions.

Although Ontario has done a superior job of funding its pensions relative to California, it is worth noting that the province also has a greater obligation due to its higher rate of public sector supported employment. According to StatCan data (series 183-0002), Ontario had 87,851 general government and 238,905 health and social service public employees in March 2012, which works out to 24.1 provincially supported workers per 1000 residents. In California, total state government employment was 343,767 for fiscal 2011-2012, or 9.1 state workers per 1000 residents (California, 2012). Both of these calculations exclude teachers.

Sources

California (1983). Governor’s Budget, 1983-84. http://archive.org/details/governorsbudget1983cali.

California (2012). Governor’s Budget, 2012-13, Schedule 6. http://www.ebudget.ca.gov/pdf/BudgetSummary/BS_SCH6.pdf.

California Legislative Analyst Office (2012). Historical Data. http://www.lao.ca.gov/laoapp/LAOMenus/lao_menu_economics.aspx.

California Public Employees Retirement System (2012). Comprehensive Annual Financial Report for the Year Ended June 30, 2011. http://www.calpers.ca.gov/eip-docs/about/pubs/comprehensive-annual-fina-report-2011.pdf.

California State Controller’s Office (2011). Comprehensive Annual Financial Report, 2011. http://www.sco.ca.gov/ard_state_cafr.html.

California State Teachers’ Retirement System (2005). Text of the Judgment in TEACHERS' RETIREMENT BOARD, ET AL. v. CAMPBELL, ET AL. Attachment to Agenda Item 15 for the Teacher Retirement Board Meeting of June 2, 2005. http://www.calstrs.com/About%20CalSTRS/Teachers%20Retirement%20Board/AGENDAS/BOD0605PDF/Regular0602/rm0615%20SBMA.pdf.

California State Teachers’ Retirement System (2011). Comprehensive Annual Financial Report for the Fiscal Year Ended June 30, 2011. http://www.calstrs.com/help/forms_publications/printed/CurrentCAFR/cafr_2011.pdf.

Canada Department of Finance (2012). Fiscal Reference Tables. http://www.fin.gc.ca/pub/frt-trf/index-eng.asp.

Canadian Taxpayers Association (2004). Ontario Superior Court Filing No. 04-CV-269781 CM1. http://taxpayer.com/sites/default/files/downloadable/73.pdf.

Nation, J. (2011). Pension Math: How California’s Retirement Spending is Squeezing the State Budget. Stanford Institute for Economic Policy Research. http://siepr.stanford.edu/system/files/shared/Nation%20Statewide%20Report%20v081.pdf.

Ontario Ministry of Finance (1982). Public Accounts 1982.

Ontario Ministry of Finance (2012). Public Accounts 2012. Retrieved from http://www.fin.gov.on.ca/en/budget/paccts/2012/.

Ontario Pension Board (2012). 2011 Annual Report. http://www.opb.ca/portal/ShowBinary?nodePath=/OPBPublicRepository/OPB/Publications/Investments/AnnualReports/en/Annual%20Report%202011.

Ontario Teachers’ Pension Plan (2012). Annual Report 2011. http://docs.otpp.com/annual_report/PDF2012/AnnualReport2011.pdf.

OPSEU Pension Trust (2012). Delivering Sustainability: Annual Report 2011. http://www.optrust.com/AnnualReports/AR2011/OPTrust_AR_2011.pdf.

University of California (2011a). Historical Fee Levels 1975 - Present. http://budget.ucop.edu/fees/documents/history_fees.pdf.

University of California (2011b). 2011-12 Tuition and Fee Levels as Approved by the Regents. http://budget.ucop.edu/fees/201112/documents/2011-12rev.pdf.

University of California (2012). Budget for Current Operations: Summary and Detail, 2012-13. http://budget.universityofcalifornia.edu/files/2011/11/2012-13_budget.pdf.

University of California Retirement System (2011). Annual Financial Report 10/11. http://www.universityofcalifornia.edu/finreports/index.php?file=ucrp/ar11ucrp.pdf.

The California Ontario Ratings Paradox

Today, the Fraser Institute published a compendium entitled “The State of Ontario’s Indebtedness” which includes my research comparing Canada’s largest province to California, America’s largest state. While media reports often suggests that California is on the verge of bankruptcy, the Golden State appears to be a model of fiscal probity when compared to Ontario. Consider these 2011 statistics from the report:

Indicator	Ontario	California
Total Bonded Debt	$236.6 billion	$143.9 billion
Bonded Debt-to-GDP	38.6%	7.7%
Bonded Debt Per Capita	$17,922	$3,833
Interest Expense	$9.5 billion	$ 5.5 billion
Interest Expense to Revenues	8.9%	2.8%
Deficit (Fiscal 2011)	$14.0 billion	$2.6 billion

Source: Fraser Institute based on California Comprehensive Annual Financial Report and Ontario Public Accounts. For comparability. California debt includes that of separately reporting component units.

Now, guess which of these sub-sovereigns has a lower rating. While reason suggests Ontario, the fact is California is rated below Ontario by the three major rating agencies. Here are the ratings:

Agency	Ontario	California
Moody’s	Aa2	A1
Standard & Poor’s	AA-	A-
Fitch	AA	A-

Rating agencies have admitted to applying a different, harsher, scale to US municipal bond issuers – including states –compared to other types of debt. In testimony to a US Congressional Committee, Moody’s Managing Director Laura Levenstein reported that this dual scale (i.e., one more severe rating system for US municipal bonds and another, less punitive scale for all other long term instruments) originated when John Moody first issued municipal bond ratings over 90 years ago.

In an attachment to written testimony to the same Congressional committee, California State Treasurer Bill Lockyer reported that when the state issued a taxable bond in 2007, Moody’s assigned a rating of A1 on its municipal scale and Aaa on its global scale. The implication is that Moody’s would have assigned California its highest rating – above that of Ontario – if it employed a single rating scale.

After being sued by Connecticut Attorney General Richard Blumenthal (now a US Senator), Moody’s and Fitch rescaled their municipal bond ratings, while S&P claimed that no such adjustment was necessary.

Not only was such a rescaling sorely needed, but it appears that the rescaling that was performed was insufficient. As I’ve discussed on ExpectedLoss previously, the inconsistency between municipal and other ratings is harmful to taxpayers. Monoline insurers arbitraged this discrepancy by selling unneeded insurance to general obligation issuers that have a long-term historic default rate on the order of 0.1%. If corporate and municipal ratings reflected similar default risk, it would have been impossible for an undercapitalized insurance provider to sell a wrapper to the nation’s largest state. As long as municipal and corporate ratings remain inconsistent, the risk of the monoline insurance business returning persists.

Also, besides ensuring that ratings for different asset classes have consistent definitions in default probability (or expected loss) terms, rating agencies should improve their monitoring efforts by using models that can be automatically updated as new fiscal data becomes available.

For example, we recently learned that California’s budget deficits have been closed through a mixture of tax increases and spending cuts. Yet the state’s ratings remain fixed in single A territory. If rating agencies ran new revenue and expenditure figures through a fiscal simulation model - like our Public Sector Credit Framework - they would be able to adjust their ratings more promptly. 

In Part II of this blog post, I will provide some comparative information on California and Ontario education, health and pension costs.

The Weak Underbelly of Capitalism

Appraisals. Auditing. Equity Research. Credit Ratings.

These four seemingly unrelated disciplines serve a common purpose. They inform investors about the value and risk of potential investments. If executed well, these services ensure that capital is invested wisely and in a way that promotes economic growth. If executed poorly, these services produce inefficiencies that hinder growth and, at worst, trigger recessions.

Headlines from the last two decades provide us with ample reason to believe that these services are not always performed well. Shoddy audits of Enron were a major enabler of that company’s massive fraud. The internet bubble was abetted by compromised research issued by analysts receiving a share of underwriting fees. Exaggerated appraisals and lenient credit ratings created the subprime bubble and heightened the magnitude of the reversal in home prices - the collapse of which still resonates.

The problem is that these four (supposedly independent) “gate-keepers” are often compromised by business considerations. The people who conduct these types of analysis are rarely at the top of the food chain in the industries they serve. They can be bullied or bribed by rainmakers at their firms or by clients to distort their findings. While outright fudging of the numbers often occurs, the more widespread problem is the selective use of “facts” to produce a desired result in line with preconceived notions. The product may not be an obvious, outright fraud, but even if it’s not, it is often harmful. Fraudulent and incomplete analysis causes the ongoing misappropriation of trillions of dollars of savings. One might call this situation “the weak underbelly of capitalism” – if you are willing to apply the term “capitalism” to today’s economic system.

The problem of biased, inadequate analysis is difficult to address through regulation alone. Even the best regulators can’t be in the room every time an analyst is encouraged to “massage” his or her findings. Much of the analysis is specialized and complex, rendering it difficult for individual regulators to identify shortcomings. Further, like analysts themselves, regulators are also not at the top of the financial industry food chain. Since both analyzing and regulating don’t offer the maximal compensation afforded by managing and rainmaking, members of the first two groups are often outsmarted or manipulated by those in the latter two groups.

Although these four services are products of the market, they can nonetheless be healed through market processes. How? It is often said that “sunshine is the best disinfectant.” In the financial industry, intermediaries maintain their margins by keeping information to themselves. But if more eyes are available to review any given analysis, the biases and distortions affecting this analysis are more likely to be identified and fixed. Further, best practice in each analysis profession can evolve rapidly through peer review, just as the highest visibility Wikipedia articles evolve rapidly toward accuracy and completeness.

The internet, and the Wikis and open source projects it nurtures, can provide the remedy to the “weak underbelly of capitalism” identified here. By making analyses public, and thus subject to widespread review, discussion and editing, these work products can converge toward an optimum.

This outlook motivated me to create an open source government bond assessment tool, the Public Sector Credit Framework (PSCF). This framework enables a user to build a multi-year budget simulation for any government and to use the results to estimate a default probability as well as an implied rating for that government. All source code for PSCF is posted on GitHub, a popular open source repository.

While I was getting started on this project, I learned about a parallel effort launched by a Swiss-based mathematician named Dorian Credé. His web site, Wikirating, directly applies Wiki technology to assessing a broad range of credit instruments. In November, Dorian and I announced a content sharing partnership. Maybe this can be the beginning of a network of mass collaboration efforts focused on improving the quality of credit ratings. And, perhaps, lessons learned in these endeavors can be applied to the other disciplines that inform investors.

Credit ratings, appraising, auditing and securities analysis are all important functions that need reform. Rather than seek top down solutions to improve these services – solutions which often come with adverse unintended consequences – let’s use the organizing power of the internet to find voluntary, collaborative alternatives.

We welcome any responses, and look forward to working with any academics or market participants out there who share a similar interest in creating an alternative, transparent framework that supports investment analysis.

An earlier version of this post appeared on The Progress Report.

A New Free Sovereign Risk Database

Happy New Year Readers!

Today we are introducing a free, public database of historical sovereign risk data. It is available at http://www.publicsectorcredit.org/sovdef.

The database contains central government revenue, expenditure, public debt and interest costs from the 19th century through 2011 – along with crisis indicators taken from Reinhart and Rogoff’s public database.

Why This Database?

Prior to the appearance of This Time is Different, discussions of sovereign credit more often revolved around political and trade-related factors. Reinhart and Rogoff have more appropriately focused the discussion on debt sustainability. As with individual and corporate debt, government debt becomes more risky as a government’s debt burden increases. While intuitively obvious, this truth too often gets lost among the multitude of criteria listed by rating agencies and within the politically charged fiscal policy debate.

In addition to emphasizing the importance of debt sustainability, Reinhart and Rogoff showed the virtues of considering a longer history of sovereign debt crises. As they state in their preface:

“Above all, our emphasis is on looking at long spans of history to catch sight of ’rare’ events that are all too often forgotten, although they turn out to be far more common and similar than people seem to think. Indeed, analysts, policy makers, and even academic economists have an unfortunate tendency to view recent experience through the narrow window opened by standard data sets, typically based on a narrow range of experience in terms of countries and time periods. A large fraction of the academic and policy literature on debt and default draws conclusions on data collected since 1980, in no small part because such data are the most readily accessible. This approach would be fine except for the fact that financial crises have much longer cycles, and a data set that covers twenty-five years simply cannot give one an adequate perspective…”

Reinhart and Rogoff greatly advanced what had been an innumerate conversation about public debt, by compiling, analyzing and promulgating a database containing a long time series of sovereign data. Their metric for analyzing debt sustainability – the ratio of general government debt to GDP – has now become a central focus of analysis.

We see this as a mixed blessing. While the general government debt to GDP ratio properly relates sovereign debt to the ability of the underlying economy to support it, the metric has three important limitations.

First, the use of a general government indicator can be misleading. General government debt refers to the aggregate borrowing of the sovereign and the country’s state, provincial and local governments. If a highly indebted local government – like Jefferson County, Alabama – can default without being bailed out by the central government, it is hard to see why that local issuer’s debt should be included in the numerator of a sovereign risk metric. A counter to this argument is that the United States is almost unique in that it doesn’t guarantee sub-sovereign debts. But, clearly neither the rating agencies nor the market believe that these guarantees are ironclad: otherwise all sub-sovereign debt would carry the sovereign rating and there would be no spread between sovereign and sub-sovereign bonds - other than perhaps a small differential to accommodate liquidity concerns and transaction costs.

Second, governments vary in their ability to harvest tax revenue from their economic base. For example, the Greek and US governments are less capable of realizing revenue from a given amount of economic activity than a Scandinavian sovereign. Widespread tax evasion (as in Greece) or political barriers to tax increases (as in the US) can limit a government’s ability to raise revenue. Thus, government revenue may be a better metric than GDP for gauging a sovereign’s ability to service its debt.

Finally, the stock of debt is not the best measure of its burden. Countries that face comparatively low interest rates can sustain higher levels of debt. The United Kingdom avoided default despite a debt/GDP ratio of roughly 250% at the end of World War II. The amount of interest a sovereign must pay on its debt each year may thus be a better indicator of debt burden.

Our new database attempts to address these concerns by layering central government revenue, expenditure and interest data on top of the statistics Reinhart and Rogoff previously published.

A Public Resource Requiring Public Input

Unlike many financial data sets, this compilation is being offered free of charge and without a registration requirement. It is offered in the hope that it, too, will advance our understanding of sovereign credit risk.

The database contains a large number of data points and we have made efforts to quality control the information. That said, there are substantial gaps, inconsistencies and inaccuracies in the data we are publishing.

Our goal in releasing the database is to encourage a mass collaboration process directed at enhancing the data. Just as Wikipedia articles asymptotically approach perfection through participation by the crowd, we hope that this database can be cleansed by its user community. There are tens of thousands of economists, historians, fiscal researchers and concerned citizens around the world that are capable of improving this data, and we hope that they will find us.  To encourage participation, we have supplied a comments feature and plan to add more participatory functionality in late January.

Sources and Acknowledgements

Aside from the data set provided by Reinhart and Rogoff, we also relied heavily upon the Center for Financial Stability’s Historical Financial Statistics. The goal of HFS is “to be a source of comprehensive, authoritative, easy-to-use macroeconomic data stretching back several centuries.” This ambitious effort includes data on exchange rates, prices, interest rates, national income accounts and population in addition to government finance statistics. Kurt Schuler, the project leader for HFS, generously offered numerous suggestions about data sources as well as connections to other researchers who gave us advice.

Other key international data sources used in compiling the database were:

• International Monetary Fund’s Government Finance Statistics
• Eurostat
• UN Statistical Yearbook
• League of Nation’s Statistical Yearbook
• B. R. Mitchell’s International Historical Statistics, Various Editions, London: Palgrave Macmillan.
• Almanach de Gotha
• The Statesman’s Year Book
• Corporation of Foreign Bondholders Annual Reports
• Statistical Abstract for the Principal and Other Foreign Countries
  
  

For several countries, we were able to obtain nation-specific time series from finance ministry or national statistical service websites.

We would also like to thank Dr. John Gerring of Boston University and Co-Director of the CLIO World Tables project, for sharing data and providing further leads as well as Dr. Joshua Greene, author of Public Finance: An International Perspective, for alerting us to the IMF Library in Washington, DC.

A number of researchers and developers played valuable roles in compiling the data and placing it on line. We would especially like to thank Charles Tian, T. Wayne Pugh, Amir Muhammed and Anshul Gupta, as well as Karthick Palaniappan and his colleagues at H-Garb Informatix in Chennai, India for their contributions.

Finally, we would like to thank the National University of Singapore’s Risk Management Institute for the generous grant that made this work possible.