Abstract A common belief held among researchers and policy makers is that regulatory reliance has inflated market demand for credit ratings, despite their decreasing informational value. Advances in information technology, coupled with reputation losses following the subprime crisis, renew the question of whether investors still rely on ratings to assess credit risk. Using Moody’s 2010 scale recalibration, which was unrelated to changing issuer fundamentals, we find that ratings still matter to investors and to issuers—apart from any regulatory implications. Our results commend improved disclosure to mitigate mechanistic reliance on ratings and inefficiencies due to rating standards that vary across asset classes. Received October 9, 2015; editorial decision June 7, 2017 by Editor Andrew Karolyi. The purpose of this paper is to test whether credit rating agencies (CRAs) remain relevant as information intermediaries in the modern information environment. There is a massive literature documenting correlation between credit ratings and securities prices; however, these papers commonly suffer an endogeneity problem.1 Specifically, it is difficult to determine whether investors respond directly to credit ratings, or if investors and CRAs merely observe and react to the same information about issuer fundamentals.2 Kliger and Sarig (2000) address this problem by showing that markets reacted when Moody’s Investors Service (Moody’s) added modifiers to its ratings scale in 1982. Much has changed since 1982. The speed and cost of information processing have exponentially increased and decreased, respectively, because of advancements in information technology and the advent of the internet. These developments have likely resulted in at least some investors who previously relied on CRAs to begin performing their own credit risk analyses. Even the less ambitious investors now have alternative sources of information—including market prices of credit default swaps (CDS)—that were not available in 1982. Further, both Moody’s and Standard and Poor’s (S&P) suffered significant loss of reputation capital as a result of the inaccurate ratings they produced in the run-up to the recent financial crisis. Although the ratings most relevant during the financial crisis were those of structured finance products, some question CRA viability more broadly. For example, during a post-crisis congressional hearing, Congressman Christopher Shays argued, “They have no brand, they have no credibility whatsoever. I can’t imagine any investor trusting them.”3 For these reasons, we revisit the question of whether credit rating agencies still matter. There are several more recent papers (reviewed in Section 1) indicating that credit ratings continue to have real economic effects. However, the evidence in these papers suggests that ratings now matter primarily (if not exclusively) due to their regulatory implications. Because ratings affect investment standards and capital requirements, they affect the value of securities to institutional investors, even if these investors do not rely on ratings to evaluate credit risk. The regulatory implications of ratings are particularly acute in markets dominated by regulated investors, including the more commonly studied corporate bond market. The unanswered research question we tackle here is whether investors continue to rely on credit ratings for information about credit risk, apart from the confounding effects of ratings’ regulatory implications. We find robust evidence that they do. We examine the impact of Moody’s recalibration of its municipal bond (muni) rating scale in the spring of 2010, after the dust of the recent financial crisis had settled. Historically, the criteria Moody’s used to assign municipal bond ratings was based on how likely the municipality was to require financial support from higher levels of government. These criteria were unique to municipal bonds; bonds in all other asset classes are rated according to their expected losses. When Moody’s recalibrated its ratings for municipal debt, it applied the expected loss criteria it uses for all other asset classes. This change in criteria resulted in upgrades of zero to four notches on $2.2 trillion of municipal debt. Importantly, Moody’s designed the recalibration to be uncorrelated with changes in issuer fundamentals. By changing the criteria, Moody’s provided a new perspective on the bonds’ credit risk, but the fundamentals of the bonds did not change. This event allows us to overcome the endogeneity challenge faced by most prior studies. Unlike the corporate bond market studied previously, the muni market is dominated by unregulated retail investors.4 By focusing on retail trades in the muni market, we avoid confounding regulatory effects. Because our setting involves rating changes that do not result from changes in fundamentals, and because the transactions in our sample are not executed by regulated investors, we are able to cleanly identify investor reliance on ratings to assess credit risk. An important feature of the recalibration is that not all munis were upgraded. Municipal issuers that were already “well calibrated” to the global scale for other asset classes serve as our control group in a difference-in-differences framework. These bonds provide reasonable benchmarks for how the prices on upgraded bonds would have behaved in the absence of Moody’s recalibration. Because credit ratings on insured bonds reflect the credit quality of the insurer, we include only uninsured bonds in our analyses. (Roughly 60% of the $\$$ 2.2 trillion sample munis are uninsured.) Our sample consists of roughly equally-sized treatment and control groups: $\$$640 billion of uninsured munis experienced upgrades due to recalibration, and $\$$601 billion did not. We find robust evidence that investors reacted to this event. Controlling for bond characteristics and a host of fixed effects, we find that upgraded bonds experience a decrease in credit spreads of 19 to 33 basis points (bp) relative to non-upgraded bonds. This is the main result in the paper. This reliance is economically meaningful, and our results are robust to a wide variety of alternative specifications. Although the municipal bond market is a natural setting to test whether investors rely on ratings to assess risk, we take seriously the possibility that our results could still reflect some regulation-based demand. Although our secondary market results focus exclusively on retail-size trades, we impose as a robustness check an additional filter that removes retail transactions for issues with any level of holdings by insurance companies facing ratings-based capital charges. Results and conclusions are unchanged. We make use of various institutional features to further test for regulation-based demand. For example, most ratings-based regulations distinguish between broad rating categories rather than individual notches within those categories. We exploit this feature by comparing results among bonds with equal-sized upgrades that do and do not cross into new broad rating categories. These tests provide at most limited evidence of regulation-based demand. Some regulations also use a “lowest rating binds” criterion, which employs the lower of Moody’s and Standard & Poor’s (S&P) ratings. We exploit this feature by comparing the impact of the recalibration among upgraded bonds with ratings from S&P that remain, versus become, lower than ratings from Moody’s. If the rating from S&P was lower than the rating from Moody’s before the recalibration and remained lower after Moody’s upgrades, then the recalibration should have no regulatory bite. However, if the Moody’s rating leapfrogged the S&P rating (and thus the S&P rating becomes the lower rating), then this upgrade should have regulatory implications. However, we find no differential impact of the recalibration on these two groups of upgraded bonds. Overall, the cross-sectional analysis of upgrades based on their likely regulatory effects provides corroborating evidence that investors’ reaction to Moody’s recalibration reflects primarily a reliance on ratings to price risk rather than an increase in regulation-based demand. We explore several other alternative explanations for our results. For example, Harris and Piwowar (2006) show that municipal bond liquidity increases with credit quality. If investor perception of bonds’ credit quality increases when ratings are upgraded, then the price changes we observe could reflect lower liquidity premiums. A consequence of this hypothesis is that upgraded bonds should experience permanent increases in liquidity. This is not what we find. Although upgraded bonds’ trading volume increases immediately after the recalibration relative to non-upgraded bonds, this increase is transitory. We find that upgraded bonds’ trading volume in the period three to six months after the recalibration is statistically indistinguishable from that prior to the recalibration. This finding indicates that upgraded bonds do not experience permanent increases in liquidity, despite their permanently higher ratings. Another possible explanation for our results is that the control group, the non-upgraded bonds, coincidentally experienced a decrease in returns around the time of the recalibration. If this is true, then our results could still obtain even if the market did not bid up the prices of the upgraded bonds. We find no evidence of this effect. If anything, the returns of the non-upgraded group are slightly higher (although not significantly so) around the recalibration in comparison to their own returns in a 180-day period preceding the recalibration. Next, we examine the possibility that our results could reflect shifts in demand for particular levels of governments’ bonds around the recalibration. For example, state-level issuers received some of the largest upgrades. If investors happened to experience an increase in demand for state-level (as opposed to county, city, or other levels of government) bonds around the time of the recalibration, then our results could reflect that demand shift instead of a response to the rating changes. We address this possibility by including in our regressions issuer-level-of-government fixed effects that vary before and after the recalibration. Our results are fully robust. Finally, we consider the possibility that our results could reflect differential changes in the fundamentals of the upgraded and non-upgraded groups. Moody’s (2010) states that the recalibration does not reflect changes in credit risk and that any ratings under review prior to the recalibration remained under review. Still, we address this potential concern by comparing S&P’s ratings to Moody’s ratings around the time of the recalibration. If the recalibration did indeed reflect changes in fundamentals, then we should observe S&P eventually change its ratings in a pattern similar to Moody’s. We start by constructing ratings transition matrices for bonds rated by both Moody’s and S&P around the recalibration. We find little similarity in the shape of S&P’s and Moody’s transition matrices. We also examine a time series of the two raters’ average muni ratings. We observe a sharp increase in Moody’s ratings relative to S&P in 2010 that remains fully intact through the end of our data availability. Overall, although we cannot disprove the possibility that the upgraded and non-upgraded bonds’ fundamentals were changing differently around the time of the recalibration, we find no evidence to support this possibility. We turn next to the primary market to test whether market reliance on ratings has real economic effects. We conduct this analysis at the issuer level, sorting issuers by whether their outstanding bonds were upgraded as a result of Moody’s recalibration. Using a multivariate difference-in-differences approach, we find that spreads on new issues by upgraded issuers decrease by 15 to 22 bp, relative to the control group. This magnitude is comparable to what we find in the secondary market, and it indicates that our findings are economically meaningful. The product of $\$$ 640 billion (the face value of uninsured municipal debt upgraded during recalibration) and 15 bp (our most conservative estimate of the recalibration effect on offer yields) is $\$$960 million. This back-of-the-envelope calculation provides an estimate of aggregate excess interest paid annually (in 2010 dollars) by U.S. taxpayers due to Moody’s previous dual-class rating system. We also observe that upgraded issuers see a larger increase in issuance volume than non-upgraded issuers in the years following the recalibration. This finding demonstrates that ratings have real economic effects; lower borrowing costs increase municipal borrowing (and presumably increase municipal investment)5. We extend our primary market analysis to test whether investors rely more heavily on credit ratings when the amount and quality of alternative sources of information are low. For example, we employ the issuer level of government as a proxy for issuer size and opacity. Consistent with an information effect, we find the results of the recalibration are weakest among state-level issuers and strongest among cities. The recalibration effect is also stronger among issuers in states with more opaque accounting practices, in states identified as more corrupt, and among municipalities without ratings from S&P. Combined, these additional results indicate that ratings are more influential when investors have less alternative information. Overall, our contribution to the literature is original evidence that investors rely on credit ratings to assess risk, and that this reliance is greatest among opaque issuers for which investors lack alternative sources of information. Our results bring new evidence to bear regarding the classic question of whether and how security prices depend on credit ratings. The results also shed light on how investors process information in the municipal bond market, a multi-trillion-dollar market that is relatively opaque and is beginning to receive greater attention from researchers. 1. Institutional Background and Literature Review 1.1 Moody’s dual class ratings Moody’s uses its Global Scale when rating corporate bonds, sovereign debt, and structured finance products. These bonds are rated according to their expected losses. Expected loss is the product of probability of default and loss given default. Historically, Moody’s rated municipal bonds according to separate criteria. Moody’s assigned municipal ratings based on how likely a municipality is to require extraordinary support from a higher level of government in order to avoid default; Moody’s (2007, 2). This changed in the spring of 2010, when Moody’s recalibrated its municipal bond rating criteria to match that of the Global Scale. Moody’s (2010, 1) clarifies that the recalibration is intended to enhance the comparability of ratings across asset classes, not to indicate a change in credit quality: Our benchmarking $$\ldots$$ will result in an upward shift for most state and local government long-term municipal ratings by up to three notches. The degree of movement will be less for some sectors $$\ldots$$ which are largely already aligned with ratings on the global scale. Market participants should not view the recalibration of municipal ratings as ratings upgrades, but rather as a recalibration of the ratings to a different scale. $$\ldots$$ [The recalibration] does not reflect an improvement in credit quality or a change in our opinion. Importantly for our study, Moody’s (2010) indicates that any ratings under review for upgrade or downgrade prior to recalibration would remain under review—not lumped into these massive ratings changes. As such, our sample does not include any natural upgrades associated with improving issuer fundamentals that would contaminate the estimates generated by our tests. The timeline of Moody’s recalibration is as follows. In 2008, Moody’s revealed its intention to recalibrate its municipal bond rating scale.6 This announcement, however, contained no information regarding which bonds’ ratings would change or by how much. On March 16, 2010, Moody’s announced the particulars of the recalibration. On this date, Moody’s published a white paper containing its Primary Algorithm. The Primary Algorithm indicated which bonds’ ratings would be upgraded and by how much. The two characteristics that Moody’s used to recalibrate muni ratings were preexisting ratings and sectors. Moody’s categorizes bonds into four sectors. Figure A.1 in the Internet Appendix reproduces the Primary Algorithm. The actual recalibration was enacted on four dates. The first recalibration date was April 16, 2010, one month after the publication of the Primary Algorithm. The second, third, and fourth recalibration dates were April 23, May 1, and May 7, 2010, respectively. We provide details in Section 2 on the number and par values of bonds that were upgraded (and not upgraded) on each date. We test whether the market reacted to this event. If the market does not rely on Moody’s ratings to assess risk, then we should see no reaction. However, the change in criteria applied by Moody’s potentially allows the market to learn new information about the bonds. Consider the following analogy. Imagine a student who earns a B on a mostly qualitative exam. If the student soon after earns an A on a mostly quantitative exam in the same class, then the professor will update her opinion on the student’s aptitude. Yet the student’s aptitude does not change from one exam to the next. What changes are the criteria used to evaluate aptitude. In the same way, the market might react to Moody’s recalibration even though the bonds’ fundamentals do not change around the time of the recalibration. What changes are the criteria used to evaluate credit risk. Credit risk is not one-dimensional. If we find a response to the recalibration, then we can infer that investors updated their views about the bonds’ credit risk after Moody’s evaluated the bonds under the new criteria. Just as the professor takes a more favorable view of the student’s aptitude when using criteria that reward quantitative ability, the market might change its view of munis when Moody’s uses criteria based on expected losses. 1.2 Credit ratings and financial regulation Financial regulators have historically relied on credit ratings to establish capital requirements and prudent investment guidelines. This regulatory reliance on ratings dates to at least a ruling by the U.S. Comptroller of the Currency in 1931. Under Rule 5b-3 of the Investment Company Act, the United States Securities and Exchange Commission (SEC) treated Aaa-rated bonds as equivalent to Treasuries. Pension fund investment guidelines established by the Employee Retirement Income Security Act (ERISA) and bank capital requirements established by the Basel Committee on Banking Supervision have likewise been ratings-based. Under the Standardized Approach in Basel II, single A-rated munis carry a higher charge (20% risk weight) than Aa- or Aaa-rated munis (0% risk weight).7 Capital charges established by the National Association of Insurance Commissioners (NAIC) range from 3.39% to 19.5% for speculative grade (SG) bonds compared with 0.30% to 0.96% for investment grade (IG) bonds. This body of regulation creates incentives for regulated investors to respond to ratings, irrespective of whether they rely on ratings to evaluate risk. Indeed, the state of the credit ratings literature suggests that ratings matter primarily due to their regulatory implications. For example, Ellul, Jotikasthira, and Lundblad (2011) document fire sales by insurance companies when bonds in their portfolios are downgraded from IG to SG. The liquidity premiums associated with these sales indicate that the sales were attributable to capital charges rather than any information communicated by the downgrade. Becker and Ivashina (2015) further document regulatory arbitrage by insurance companies chasing yield in a ratings-based regulatory environment. Because the NAIC sets capital charges based on ratings, savvy insurance companies circumvent their regulatory capital charges by over-allocating capital to the bonds with the highest credit risk within a particular credit rating category. Stanton and Wallace (2013) similarly conclude that overinvestment in high-risk commercial mortgage-backed securities (CMBS) is attributable to such regulatory arbitrage in a ratings-based environment, and Cornaggia, Cornaggia, and Hund (2017) simulate potential regulatory arbitrage among banks subject to Basel II capital requirements. Opp, Opp, and Harris (2013) provide a formal model and conclude that regulatory implications of ratings are of first-order concern for marginal investors. In addition to official regulation, Chen et al. (2014) document the reliance on credit ratings in private investment mandates, asset management policies, and informal procedures that employ ratings to restrict holdings by mutual funds and investment advisors. Perhaps Ekins and Calabria (2012, 1) summarize the evolution of CRA relevance most succinctly in their conclusion: “Government regulatory use of credit ratings inflated the market demand for NRSRO ratings, despite the decreasing informational value of credit ratings.”8 Because of the regulation-based demand for ratings, recent studies that document market reaction to ratings changes or other real economic effects (e.g., Kisgen 2012; Almeida et al. 2017; and Begley 2015) cannot conclude that investors rely on ratings for information. Results from these studies may instead reflect changes in regulatory compliance costs. In fact, Almeida et al. (2017) specifically focus on ratings-based Basel II capital requirements in their study of the real effects of sovereign debt downgrades. One benefit of our setting is that the retail investors who dominate the muni market are subject to none of the aforementioned regulations. Any reaction among retail investors must therefore reflect an information effect. We are the first, to our knowledge, to directly test the extent to which market participants rely on ratings to assess credit risk apart from their need to manage regulatory capital charges and comply with other ratings-based regulations. 1.3 Modern relevance of CRAs as information intermediaries It is not obvious that modern investors rely on credit ratings to assess risk. For one thing, ratings are too coarse to fully reflect differences in credit quality across all rated securities; see Goel and Thakor (2015). Second, traditional ratings are designed to be stable over time and not to reflect real-time changes in credit quality (Moody’s 2006). Third, the conflicts of interest in the issuer-pays CRA compensation structure are well known.9 Fourth, improvements in information technology provide investors more-granular and more-timely credit risk metrics than traditional credit ratings (e.g., Cornaggia and Cornaggia 2013). Finally, the CRAs lost significant reputation capital due to their role in the subprime crisis. Finally, although Kliger and Sarig (2000) demonstrate a causal impact of ratings on security prices, these authors use an event from 1982. In 1982, investors lacked internet access, paid for long-distance telephone service, and received hard copies of Moody’s investment manuals in the mail. As such, it is unclear whether traditional CRAs matter as much as they did in 1982. We are the first, to our knowledge, to disentangle both the regulatory and endogeneity problems faced by existing literature in the modern information era. 1.4 Muni market information environment Another novel feature of our paper is the ability to exploit cross-sectional variation in the quality of information available to investors in municipal bonds. Other papers focus primarily on corporate securities that trade in relatively liquid, transparent markets and therefore may not be generalized to markets with less transparent issuers. Although the municipal finance market is large ( $\$$ 3.77 trillion in March 2014), this market is opaque and decentralized. Unlike corporations, state and local governments are not subject to the registration and reporting requirements of the SEC. Therefore, financial disclosure by municipalities is often less reliable, less comparable, and less timely than information released by corporations; however, the information quality varies widely across municipal issuers (see Ingram, Brooks, and Copeland 1983; Gore 2004; and Cuny 2016). This cross-sectional variation in the information environment across muni markets allows for additional tests of investor reliance on ratings to assess credit risk. 2. Data Collection and Sample Description 2.1 The recalibration event Our data consist of ratings from Moody’s and S&P, bond market transaction prices and volume from the Municipal Securities Rulemaking Board (MSRB), and issue/issuer characteristics from Ipreo. From Moody’s, we collect ratings data on every bond issue by a state or local government that had a “Change in Scale” rating action on April 16, April 23, May 1, or May 7 in 2010, as well as the ratings on all past and future issues by the same issuers. Because market perception of insured bonds reflects the credit quality of the monolines, we focus on uninsured bonds in our empirical analyses. Table 1 presents the number of issues and cumulative par value of recalibrated investment-grade munis.10 Panel A contains all bonds with a “Change in Scale” rating action. Panel B contains the uninsured bonds from which we draw our sample. Table 1 Number and par values of recalibrated bonds Panel A: All bonds All “Change in Scale” rating actions “Change in Scale” results in an upgrade “Change in Scale” results in no change in rating Recalibration date $$N$$ bonds Total par $$N$$ bonds Total par $$N$$ bonds Total par April 16, 2010 213,260 $\$$932.8 billion 190,144 $\$$812.5 billion 23,116 $\$$120.4 billion April 23, 2010 201,962 $\$$312.9 billion 186,946 $\$$281.2 billion 15,016 $\$$31.7 billion May 1, 2010 124,053 $\$$249.9 billion 108,046 $\$$199.4 billion 16,007 $\$$50.6 billion May 7, 2010 105,855 $\$$715.2 billion 24,221 $\$$67.4 billion 81,634 $\$$647.8 billion Sum 645,130 $\$$2,210.8 billion 509,357 $\$$1,360.5 billion 135,773 $\$$850.5 billion Panel B: Uninsured bonds April 16, 2010 90,621 $\$$566.3 billion 72,213 $\$$466.0 billion 18,408 $\$$100.3 billion April 23, 2010 55,891 $\$$96.8 billion 42,769 $\$$70.5 billion 13,122 $\$$26.4 billion May 1, 2010 54,021 $\$$117.2 billion 40,550 $\$$72.3 billion 13,471 $\$$44.9 billion May 7, 2010 65,510 $\$$461.2 billion 8,944 $\$$31.5 billion 56,566 $\$$429.6 billion Sum 266,043 $\$$1,241.5 billion 164,476 $\$$640.3 billion 101,567 $\$$601.2 billion Panel A: All bonds All “Change in Scale” rating actions “Change in Scale” results in an upgrade “Change in Scale” results in no change in rating Recalibration date $$N$$ bonds Total par $$N$$ bonds Total par $$N$$ bonds Total par April 16, 2010 213,260 $\$$932.8 billion 190,144 $\$$812.5 billion 23,116 $\$$120.4 billion April 23, 2010 201,962 $\$$312.9 billion 186,946 $\$$281.2 billion 15,016 $\$$31.7 billion May 1, 2010 124,053 $\$$249.9 billion 108,046 $\$$199.4 billion 16,007 $\$$50.6 billion May 7, 2010 105,855 $\$$715.2 billion 24,221 $\$$67.4 billion 81,634 $\$$647.8 billion Sum 645,130 $\$$2,210.8 billion 509,357 $\$$1,360.5 billion 135,773 $\$$850.5 billion Panel B: Uninsured bonds April 16, 2010 90,621 $\$$566.3 billion 72,213 $\$$466.0 billion 18,408 $\$$100.3 billion April 23, 2010 55,891 $\$$96.8 billion 42,769 $\$$70.5 billion 13,122 $\$$26.4 billion May 1, 2010 54,021 $\$$117.2 billion 40,550 $\$$72.3 billion 13,471 $\$$44.9 billion May 7, 2010 65,510 $\$$461.2 billion 8,944 $\$$31.5 billion 56,566 $\$$429.6 billion Sum 266,043 $\$$1,241.5 billion 164,476 $\$$640.3 billion 101,567 $\$$601.2 billion This table displays the number and total par value of municipal bonds for which Moody’s issued a “Change in Scale” rating action between April 16, 2010, and May 7, 2010. Panel A includes all bonds rated by Moody’s. Panel B restricts the sample in Panel A to uninsured bonds. We collect ratings data on bonds issued by state or local governments from Moody’s. Table 1 Number and par values of recalibrated bonds Panel A: All bonds All “Change in Scale” rating actions “Change in Scale” results in an upgrade “Change in Scale” results in no change in rating Recalibration date $$N$$ bonds Total par $$N$$ bonds Total par $$N$$ bonds Total par April 16, 2010 213,260 $\$$932.8 billion 190,144 $\$$812.5 billion 23,116 $\$$120.4 billion April 23, 2010 201,962 $\$$312.9 billion 186,946 $\$$281.2 billion 15,016 $\$$31.7 billion May 1, 2010 124,053 $\$$249.9 billion 108,046 $\$$199.4 billion 16,007 $\$$50.6 billion May 7, 2010 105,855 $\$$715.2 billion 24,221 $\$$67.4 billion 81,634 $\$$647.8 billion Sum 645,130 $\$$2,210.8 billion 509,357 $\$$1,360.5 billion 135,773 $\$$850.5 billion Panel B: Uninsured bonds April 16, 2010 90,621 $\$$566.3 billion 72,213 $\$$466.0 billion 18,408 $\$$100.3 billion April 23, 2010 55,891 $\$$96.8 billion 42,769 $\$$70.5 billion 13,122 $\$$26.4 billion May 1, 2010 54,021 $\$$117.2 billion 40,550 $\$$72.3 billion 13,471 $\$$44.9 billion May 7, 2010 65,510 $\$$461.2 billion 8,944 $\$$31.5 billion 56,566 $\$$429.6 billion Sum 266,043 $\$$1,241.5 billion 164,476 $\$$640.3 billion 101,567 $\$$601.2 billion Panel A: All bonds All “Change in Scale” rating actions “Change in Scale” results in an upgrade “Change in Scale” results in no change in rating Recalibration date $$N$$ bonds Total par $$N$$ bonds Total par $$N$$ bonds Total par April 16, 2010 213,260 $\$$932.8 billion 190,144 $\$$812.5 billion 23,116 $\$$120.4 billion April 23, 2010 201,962 $\$$312.9 billion 186,946 $\$$281.2 billion 15,016 $\$$31.7 billion May 1, 2010 124,053 $\$$249.9 billion 108,046 $\$$199.4 billion 16,007 $\$$50.