Théo Naccache

Slow oil shocks and the "weakening of the oil pricemacroeconomy relationship"

Keywords: JEL Classification: Q43, C22 Oil prices, Gross Domestic Product, recursive causality tests

 to restore the stability of the relationship fail to Granger cause output or unemployment in post-1980 data. By using the concept of accelerations of the oil price, we show that the weakening of this relationship corresponds to the appearance of slow oil price increases, which have less impact on the economy. When filtering out these slow oil price variations from the sample, we manage to rehabilitate the causality running from the oil price to the macroeconomy and show that far from weakening, the oil price accelerations -GDP relationship has even been growing stronger since the early eighties.

Introduction

Ever since [START_REF] Hamilton | Oil and the Macroeconomy since World War II[END_REF] reported the existence of a statistical relationship between oil price variations and GDP, many were the authors [START_REF] Mork | Oil and the Macroeconomy When Prices Go Up and Down: An Extension of Hamilton's Results[END_REF], [START_REF] Lee | Oil Shocks and the Macroeconomy: The Role of Price Variability[END_REF], Hooker (1996), [START_REF] Hamilton | This is What Happened to the Oil Price-Macroeconomy Relationship[END_REF], [START_REF] Hooker | Are Oil Shocks Inflationary? Asymmetric and Nonlinear Specifications versus Changes in Regime[END_REF]) who showed that this relationship was suffering from breakpoints, and worse, that it was globally weakening after 1980. Efforts were therefore devoted to restore a stable relationship between the oil price and the economic activity. The most notable attempts were aimed at transforming the oil price variable, by ways of filters and other non linear transformations, so that the alternative models would be able to restore causality running from the oil price to GDP. [START_REF] Mork | Oil and the Macroeconomy When Prices Go Up and Down: An Extension of Hamilton's Results[END_REF], [START_REF] Lee | Oil Shocks and the Macroeconomy: The Role of Price Variability[END_REF] and [START_REF] Hamilton | This is What Happened to the Oil Price-Macroeconomy Relationship[END_REF], offered alternative oil price specifications, which are able to restore a globally stable relationship between their variables and GDP. However, Hooker (2000) showed that none of these asymmetric and nonlinear transformations of the oil price were satisfying as they were not able to Granger cause output or unemployment in post-1980 data.

In this paper, we offer a simple and intuitive alternative to these specifications, by separating oil price increases into two components, simple increases and accelerating increases. While the simple increase corresponds to [START_REF] Mork | Oil and the Macroeconomy When Prices Go Up and Down: An Extension of Hamilton's Results[END_REF]'s oil price increase, the accelerating increase is an original specification, easily interpretable, and with excellent fitting properties. Without having to switch from quarterly to annual data as [START_REF] Hooker | Are Oil Shocks Inflationary? Asymmetric and Nonlinear Specifications versus Changes in Regime[END_REF] did to regain significance, this new specification enables us to show that while other oil price specifications do not Granger cause US GDP after 1980, oil price accelerations do. Our findings lead to the conclusion that the non accelerating oil price variations (the slow oil shocks probably caused by demand shocks rather than by oil production disruptions), that appeared after the early eighties are causing the weakening of the oil pricemacroeconomy relationship.

The paper is organized as follows. Section 2 is reviewing the arguments explaining the asymmetry and weakening of this relationship. Section 3 presents and compares the perfomances of some of the oil price specifications aiming at restoring the stability of the relationship, by carrying out recursive causality tests in a VAR model inspired by [START_REF] Hamilton | Oil and the Macroeconomy since World War II[END_REF] and [START_REF] Mork | Oil and the Macroeconomy When Prices Go Up and Down: An Extension of Hamilton's Results[END_REF]. Section 4 presents our proposed specification, based on the concept of oil price accelerations, and shows how it out-performs the other specifications in Granger causing US GDP. Section 5 concludes.

An asymmetric and weakening oil price -GDP relationship

Ever since the first oil shocks in the seventies, the oil pricemacroeconomy relationship has become a major center of interest for economists. Many transmission routes have been identified to explain how oil price increases work their way up to affect economic activity, and may be found in the exhaustive reviews of the large literature on the subject made by [START_REF] Jones | The Macroeconomic Impacts of Oil Price Shocks: a Review of Literature and Issues[END_REF], [START_REF] Jones | Oil Price Shocks and the Macroeconomy: What has been learned since 1996[END_REF] and Brown and Yücel (2002).

Yet, no sooner had [START_REF] Hamilton | Oil and the Macroeconomy since World War II[END_REF] established that oil price variations Granger caused US GDP variations, that [START_REF] Mork | Oil and the Macroeconomy When Prices Go Up and Down: An Extension of Hamilton's Results[END_REF] showed that this did not hold after the 1986 collapse of the oil price. Indeed, it became obvious that while oil price increases induced recessions, oil price decreases did not stimulate economic activity in a symmetric manner. To account for this asymmetry, [START_REF] Hamilton | A Neo-classical Model of Unemployment and the Business Cycle[END_REF] suggested that some attention be given to adjustment costs. Indeed, these costs appear whether the oil price goes up or goes down. When the oil price goes up, the adjustment costs merely represent additional costs to the direct oil shock impact, but when it goes down, they may offset the benefits of low energy prices. A review of these adjustment costs can be found in Jones et al. (2002) and Brown and Yücel (2002). They include costs related to sectoral shifts, changes in the capital stock, coordination problems between firms and finally costs related to the uncertainty of future oil prices.

The sectoral shifts costs were initially identified by [START_REF] Lilien | Sectoral Shifts and Cyclical Unemployment[END_REF], who established that inter-sectoral reallocations of labour following exogenous shocks were a major cause of increases of the unemployment rate. Oil price increases or decreases cause labor reallocations between energy intensive sectors and energy efficient ones, which in the short term lead to unemployment and underutilisation of resources. Using Lilien's findings in an empirical study, [START_REF] Davis | Sectoral Job Creation And Destruction Responses to Oil Price Changes[END_REF] identified two transmission mechanisms through which oil price variations affect economic activity and which provide an explanation for the asymmetry. The first one is the aggregate channel, which works through potential output, income transfer and sticky wages effects, as well as imperfect competition effects (evidenced by [START_REF] Rotemberg | Imperfect Competition and the Effects of Energy Price Increases on Economic Activity[END_REF]). The second one is the allocative channel, which reflects the mismatch between a firm's desired and actual levels of labor and capital.

The authors observe that oil price increases have a negative impact on unemployment through both channels, while oil price decreases have a negative impact on the allocative channel, which cancels out the positive impact on unemployment obtained through the aggregate one.

To explain the costs induced by changes in the capital stock following energy price variations, [START_REF] Atkeson | Models of Energy Use: Putty-Putty versus Putty-Clay[END_REF] use KLEM production functions (relating output to capital, labor, energy and non energy material). The authors assume that technology is following a putty-clay model, which allows for ex-ante substitutions of factors but not ex-post ones, which means that industries can take into account the trends of energy and of other factors to make their investment, but once the technology is adopted, there is no turning back. In the short term, a variation of energy which necessitates a change in the capital stock therefore induces a costly reorganisation.

Due to their experience of prior oil price shocks, it is also safe to assume that firms know what their reaction will be to oil price variations. However, they do not know how other firms, especially in different sectors, will react to these shocks, which may induce economic disruptions due to wrong expectations and inadequate coordination, as explained in [START_REF] Huntington | Oil Security as a Macroeconomic Externality[END_REF]. Finally, Federer (1996) and [START_REF] Lee | Oil Shocks and the Macroeconomy: The Role of Price Variability[END_REF] have showed that both oil price variations and oil price volatility have a negative impact on output growth, through the postponing of irreversible investment.

In addition to the asymmetry induced by the above mentioned adjustment costs, it was also noticed by some authors [START_REF] Hooker | Are Oil Shocks Inflationary? Asymmetric and Nonlinear Specifications versus Changes in Regime[END_REF], Loungany and Yücel (2000)) that the oil priceeconomic activity relationship was weakening. Main factors to explain this weakening are the changes that have transformed the US monetary policy and its response to oil price shocks in the early eighties, the decline of the energy consumption to GDP ratio, the growing endogeneity of energy prices with regards to the economic activity which makes oil price increases less disruptive, the gains in productivity which may enable firms to suffer less from high energy prices and finally, the so-called "experience effect" which leads to better policy responses, better coverage against high energy prices and lower adjustment costs, based on prior experience of oil price shocks. [START_REF] Blanchard | The Macroeconomic Effects of Oil Price Shocks: Why are the 2000s so different from the 1970s?[END_REF] have also identified the decrease in real wage rigidities to account for the weakening of the oil price -GDP relationship. In a rigid labour market in which real wages adjust slowly, an adverse supply shock will induce an increase of inflation and a decrease of output. The increased flexibility that has characterized the US labour market ever since the early eighties may therefore account for the mild influence of oil price variations on GDP.

Given the asymmetry and the weakening of the oil pricemacroeconomy relationship, many efforts were then devoted to restore its stability. Below are presented some of these attempts, followed by our own proposition.

Attempts to restore a stable GDPmacroeconomy relationship

Data and Methodology

In his 1983 article, Hamilton used a multivariate reduced-form VAR approximation to macroeconomic reality inspired by [START_REF] Sims | Macroeconomics and Reality[END_REF]. To represent the oil price variable, Hamilton used the log change of the Producer Price Index for crude oil. Since this index does not take into account international movements of the oil price prior to 1974 (due to oil price controls in the US in the 1970s), we decided to follow [START_REF] Mork | Oil and the Macroeconomy When Prices Go Up and Down: An Extension of Hamilton's Results[END_REF] by using the composite refiner acquisition cost (RAC), a weighted average of domestic and imported crude oil costs, including transportation and other fees paid by the refiners in the US. This index is available from the US Energy Information Agency at a quarterly rate from 1974Q2 until today, and at an annual rate from 1968 until today. It is therefore necessary to extend it so that our sample spans the whole 1948-2009 period at a quarterly rate. To do so, we constructed the following oil price variable:

-For 1974Q2 until today, we use the log change of the refiner acquisition cost for crude oil (source: US Energy Information Agency).

-For 1971Q3 to 1974Q1, we use the log change of the PPI for crude oil (source: US Bureau of Labour Statistics), which annual variations are corrected to correspond to the RAC's annual variations (source: US Energy Information Agency).

-For 1948Q1 to 1971Q2, we use the log change of the PPI for crude oil (source: US Bureau of Labour Statistics).

The sample [START_REF] Hamilton | Oil and the Macroeconomy since World War II[END_REF] used to estimate his model, 1949Q2-1972Q4, did not include major oil price decreases, as [START_REF] Mork | Oil and the Macroeconomy When Prices Go Up and Down: An Extension of Hamilton's Results[END_REF] noted, and thus was not likely to hold after the large drop of the oil price in 1986. To investigate the validity of this assumption, we estimate Hamilton's VAR model and apply recursive exclusion tests, which are explained below, for all oil price coefficients in the GDP equation. This model includes four lags and the following variables: the log change in the real GDP, the log change of the GDP implicit price deflator, the log change of the average hourly earnings in the manufacturing sector, the log change of the oil price specification, the level of the US civilian unemployment rate and the level of the three months Treasury bill rate (the secondary market one).

The data is provided by the Federal reserve Bank of St Louis, taken at a quarterly rate (a simple average is performed for the monthly data), and spans the period starting in the 1st quarter of 1948 and ending in the 2 nd quarter of 2009, which offers 246 observations.

To determine the order of integration of these variables, augmented Dickey Fuller tests were carried out. GDP, the oil price and the treasury bill rate were found to be integrated of the 1 st order. The unemployment rate was found to be stationary. However, earnings and inflation were found to be integrated of the 2 nd order. The first difference of these two variables were used nevertheless, in order to be able to compare the results with Mork's and Hamilton's who had used the first difference as well1 . The recursive exclusion test consists in estimating the Fisher statistics for the joint significance of oil price coefficients in the GDP equation, in sub-samples in which we add one observation at each iteration. The first sample's size must be strictly superior to the number of parameters (25 including the constant), and the last sample's size corresponds to the whole sample. As in [START_REF] Andrews | Tests for Parameter Instability and Structural Change with Unknown Change Points[END_REF], it is likely that the Fisher statistics at the beginning of the sample is degenerate. A trimming of 13% of the observations was therefore carried out at the beginning of the sample. These recursive exclusion tests help us understand how the oil price -GDP relationship has been evolving along time, and at which dates breakpoints may have occurred. 1976 during which the 4 oil price lags were only significant at the 5% statistical level (and not at 1%), we observe that the relationship is mostly significant on the first part of the sample, between the early 1950s until the early 1980s. It then starts to weaken around 1982Q2 when a first breakpoint occurs, and sees the oil price variable's significance fall below the 1% curve. This breakpoint is then followed by a second sharp breakpoint in 1986Q2 when the curve drops below the 5% significance level. Ever since this period, we note that the simple oil price variations never became significant again in the model. A reason which might explain this breakpoint has been put forward by [START_REF] Hooker | Are Oil Shocks Inflationary? Asymmetric and Nonlinear Specifications versus Changes in Regime[END_REF] who suggested that it may be caused by systematic changes in the US monetary policy from the end of the 1970s on. With regards to the second breakpoint, it simply coincides with the 1986 negative oil shock, and had been evidenced by [START_REF] Mork | Oil and the Macroeconomy When Prices Go Up and Down: An Extension of Hamilton's Results[END_REF]. F for dlOilp 1% 5%

Recursive causality tests

Recursive exclusion test for dlOilp

In order to restore a significant relationship, [START_REF] Mork | Oil and the Macroeconomy When Prices Go Up and Down: An Extension of Hamilton's Results[END_REF] offered to separate the oil price variable into two components, oil price increases and oil price decreases. His positive specification was defined as follows:

t t dlOilp dlOilp   1 if 0  t dlOilp
, else 0. In the rest of the paper, we will refer to this specification as dlOilp1, as the negative counterpart of Mork's specification is not significant in any of our estimations. As can be seen on Figure 2, the use of dlOilp1 somehow enables to rehabilitate the oil price -GDP relationship. While Mork's specification loses in significance in the first half of the sample compared with dlOilp, we note that it clearly gains in significance in the second half. It does not provide a very significant relationship with the real GDP as the curve rarely reaches above the 1% confidence curve, but it does provide a more stable one as the F curve is above the 5% confidence curve on most of the sample.

Following Mork, Hamilton (1996) offered a new specification, the Net Oil Price Increase (NOPI), defined as the percentage of increase of the oil price compared with its values at the 4 previous quarters (resp. 36 quarters for another version), and zero if there is no increase. We will refer to this measure as NOPI 12 when the number of quarters used for the comparison is 12, and as NOPI 36 when this number is 36 . The idea behind such measures is to filter out oil price increases that merely correspond to corrections of former oil price decreases, and hence to capture a "surprise effect". Figure 3 represents the recursive exclusion tests for NOPI 12 and NOPI 36. Overall, these specifications offer a more stable relationship between the oil price and GDP, as they all manage to regain some significance in the second half of the sample. But they do not manage to reach often above the 1% confidence curve, and mostly hover between the 5% and 1% lines. This means that

Slow and accelerating oil price increases

Methodology

Following [START_REF] Huntington | The Economic Consequences of Higher Crude Oil Prices[END_REF][START_REF] Huntington | Oil Shocks and Real US Income[END_REF] who stresses the distinction between sudden and gradual oil price increments, we offer a simple and intuitive alternative to the above mentioned specifications, by separating oil price increases into two components, simple increases and accelerating increases. The classical way in economics to deal with a non stationary variable is to take its first difference (or its log first difference) in order to eliminate the secular time trend. Which is why in most papers dedicated to the oil pricemacroeconomy relationship, the common approach is to consider the first difference of the oil price. This basically means that these studies choose to focus on the speed of the oil price (the ratio of the first difference of a variable on a time unit). However, in many other academic disciplines, physics and mechanics for instance, the sole speed does not suffice to explain all, and higher differencing is often needed. The second derivative, i.e. the acceleration, is a fundamental variable in mechanics. The third derivative, called "jerk"2 , which corresponds to a variation of the acceleration, can also be used in many applications (to measure a train's comfort for example). Based on these concepts, we decide to consider a new specification, which we call an accelerating increase. It should be noted that our variable is not an acceleration per se, since we do not want to over-differentiate the series. We keep the first difference of the oil price, but we filter out oil price variations whose accelerations are not positive: While dlOilp2 offers a poor fit for the first half of the sample, it exhibits excellent F statistics for the second half of the sample. After 1980, its F value is always above the 1% confidence curve, and clearly outperforms the previously shown specifications. Far from weakening, the oil price accelerationmacroeconomy relationship seems to grow even stronger after 1980.

t t dlOilp dlOilp  2 if 0  t dlOilp and if 0 1    t t dlOilp dlOilp else 0.

Interpretation

There is a very simple explanation to this phenomenon, which can be found on figure 5, representing the difference between dlOilp1 and dlOilp2. On the figure are represented all the oil price variations which are not accelerating ones. Prior to 1975, we note that all oil price variations were also accelerating ones, since the difference is almost always null. Then during the first and the second oil shocks, slow oil price variations events started to appear, represented by the two remaining peaks around 1974 and 1980. After the second half of the eighties, slow oil price variations become much more common, including the periods around the first Gulf war and the 2000's oil price increases. These slow oil price increases, which can be more easily anticipated by economic actors, are likely to have less impact on the economy than accelerating oil price variations, which carry a much larger surprise effect. When using other oil price specifications, which do not separate slow and accelerating oil price variations, previous papers were carrying their econometric estimations with both kinds of shocks combined, therefore averaging the impact on the economy and concluding with a weakening of the oil pricemacroeconomy relationship. But it is the apparition of slow oil price variations, which do less harm to the economy, that seems to be the cause of this weakening.

Two questions therefore arise: why did these slow oil shocks start to appear as off the early eighties?

And why would they cause less harm to the economy?

An answer to the first question may be found in [START_REF] Kilian | Not All Oil Prices Shocks Are Alike: Disentangling Demand and Supply Shocks on the Crude Oil Market[END_REF], which is addressing the nature of oil price shocks. Using an SVAR, the author decomposes the real oil price into four components: oil supply shocks driven by political events in OPEC countries, other oil supply shocks, aggregate shocks to the demand for industrial commodities, and demand shocks that are specific to the crude oil market.

.00

.

04

.

08

.12 .16

.20

.24 50 55 60 65 70 75 80 85 90 95 00 05 dlOilp1-dlOilp2

13 Thanks to this orthogonal decomposition of shocks affecting the oil market, Kilian manages to show that shocks to the world aggregate demand and fears about future supplies of oil have much more influence on oil price variations than shocks to the crude oil production do. Do the slow oil price shocks identified in figure 5 correspond to Kilian's aggregate demand and oil market specific demand shocks? It seems at least consistent with the intuitive idea that oil price variations should be driven more slowly by progressive demand shocks than by brutal oil production disruptions, and makes it plausible to conclude that the appearance of the slow oil price shocks may have been caused by the growing importance of oil consuming emerging countries like China and India ever since the early eighties. However, it would require another study to prove this theory, along with another methodology that would enable to separate exogenous and endogenous components of oil price variations, as was suggested in Barsky and Kilian (2002) and implemented in [START_REF] Kilian | Exogenous oil supply Shocks, how big are they and how much do they matter for the US economy?[END_REF][START_REF] Kilian | Not All Oil Prices Shocks Are Alike: Disentangling Demand and Supply Shocks on the Crude Oil Market[END_REF].

As to why these non accelerating oil price increases do less harm to the economy than oil price accelerations, [START_REF] Huntington | The Economic Consequences of Higher Crude Oil Prices[END_REF] showed how sudden oil price increases scare households and firms, and induce brutal adjustments with regards to investment, production, consumption and to the negotiations of wages and prices. These brutal adjustments can temporarily leave plants and equipments idle, and hence workers unemployed. While non accelerating oil price increases, by allowing more progressive adjustments, enable firms and households to adopt more efficient strategies. This issue of adjustment was also addressed by [START_REF] Blanchard | The Macroeconomic Effects of Oil Price Shocks: Why are the 2000s so different from the 1970s?[END_REF], who showed how the increased flexibility of the labour market may have also played a crucial role in explaining the milder impact of oil price shocks on inflation and output. Combined together, these two arguments support the theory according to which slow oil price shocks may have favoured progressive and harmless adjustments for firms and households, helped in that by the increased flexibility of the labour market.

Conclusion

The aim of this paper is to investigate the oil pricemacroeconomy relationship using a new specification for the oil price variable, based on the concept of acceleration. Using a VAR model inspired by [START_REF] Hamilton | Oil and the Macroeconomy since World War II[END_REF] and [START_REF] Mork | Oil and the Macroeconomy When Prices Go Up and Down: An Extension of Hamilton's Results[END_REF] and carrying out recursive exclusion tests, we are able to show that the so-called "weakening of the oil price -macroeconomy relationship" observed by many authors seems to be due to the appearance of slow oil price shocks as off the seventies and eighties. These slow oil price shocks, by allowing firms and households to adjust more progressively, have less impact on the economy. Factors causing the appearance of these slow oil price shocks remain to be identified, but are more likely to be found in the increasing importance played by demand shocks rather than oil supply disruptions. By filtering out these slow oil price increases from the sample, and by focusing only on accelerating oil price increases, we show that far from weakening, the oil price accelerations -GDP relationship has been steadily growing stronger ever since the early eighties.

Figure 1

 1 Figure 1 reports the value of the F statistics for recursive exclusion tests for all oil price coefficients in

FIGURE 1 .

 1 FIGURE 1 Recursive exclusion tests for dlOilp coefficients in the dlGDP equation of the VAR, where   1

FIGURE 2

 2 FIGURE 2 Recursive exclusion tests for dlOilp1 coefficients in the dlGDP equation of the VAR. Sample beginning in 1956Q2.

FIGURE 3

 3 FIGURE 3 (a) Recursive exclusion tests for NOPI12 coefficients in the dlGDP equation of the VAR. (b) Recursive exclusion tests for NOPI36 coefficients in the dlGDP equation of the VAR. Sample beginning in 1956Q2 for (a) and in 1958Q2 for (b).

  for NOPI 36 they probably do not properly reflect what has been happening to this relationship ever since the early 1980s.

FIGURE 4

 4 FIGURE 4 (a) Recursive exclusion tests for dlOilp2 coefficients in the dlGDP equation of the VAR. Sample beginning in 1956Q2.

FIGURE 5

 5 FIGURE 5Representation of dlOilp1-dlOilp2.

The same regressions with the second differences of earnings and inflation do not produce significantly different results. All results are available upon request to the author.

The tests performed with oil price accelerations were also performed with oil price jerks, and did not come up with significantly different results regarding the impact on GDP. However, at the sectoral level, when considering the impact on sectoral output, we did find that some sectors were much more responsive to oil price jerks than to oil price accelerations. This observation is to be further developed in a coming paper.


Ludovic Moreau
email: lumoreau@u-paris10.fr

M Aglietta

E Altman

V Bignon

I Bilon

R Breton

G Capelle-Blancard

J B Chatelain

J Z Chen

O Darné

M Geix

Moody's Investor Services V Mignon

F Moraux

P Prakash

S&P A Rébérioux

J Simonoff

Regulatory versus Informational Value of Bond Ratings: Hints from History… *

Keywords: bond ratings, bond yields, financial regulation JEL codes: G12, G18, G24

A multivariate analysis can be used in order to investigate the relationship between bond yields, ratings and standard control variables. Replicating such a test on a number of cross-sections may evidence a possible impact of financial regulations relying on ratings. Datasets for American corporate bond issues allow a focus on two key events of the development of rating driven regulations: the valuation of bank and life insurance portfolios introduced in the 1930's and the net capital requirements for broker dealers introduced in the 1970's. The "value" of bond ratings does show some improvement once these regulations have been passed.

Résumé

En se donnant pour cadre la relation entre rendements de marché, notations et variables de contrôle, on peut répliquer un test économétrique de manière à étudier une influence possible de l'utilisation des notations par la réglementation financière. Des échantillons de données sur les obligations d'entreprises américaines permettent de s'intéresser à deux grandes étapes: la réglementation des portefeuilles d'investissement des banques et des compagnies d'assurance dans les années 1930 et celle des marges des courtiers en bourse dans les années 1970. La "valeur" des notations évolue avec la mise en place de ces réglementations.

Mots clés: notations, rendements d'obligations, réglementation financière.

Codes JEL: G12, G18, G24

This paper investigates the concern driving these policy moves. How is it that ratings are recognized by investors? The usual explanations are information specialization by the bond rating firm and information equalizing among investors. If these natural market forces were not at work, regulators would not have considered ratings as straightforward inputs… What made these inputs uncontroversial was the fact that ratings were recognized by investors and this could be evidenced by the relation between bond ratings and yields. Once regulations have been enacted, can the very relationship that motivated regulators' choice still be interpreted the same way? In other words: did the relationship between bond yields, ratings and standard control measures change significantly?

One may take advantage of History by performing identical tests before and after the enactment of these regulations. While this may be done for any relevant regulation, there are two reasons making the study of the early American regulations interesting. First, the impact of yet another regulatory use of rating may be harder to detect given a long track record of similar rulings. The first rules relying on ratings are likely to cause greater effects on the market place. Secondly, on a global scale, the use of ratings by bond markets has been quite contemporaneous to the one by their financial authorities (see [START_REF] Packer | Credit Ratings and the Japanese Bond Market[END_REF] about Japan). The American bond markets provide a more paced sequence of events.

With this in mind, this paper provides a thorough discussion of West (1973), which remains widely quoted for pointing out a straightforward effect of rating driven regulations enacted in the 1930's. I have built over the original datasets and I criticize the way they have been dealt with thanks to modern standardized econometric techniques. This first brings a negative result: I do not find the lasting inflation of non-investment grade bond yields that has been interpreted as an impact of the regulations of the 1930's. By sticking to a multivariate analysis, I go beyond this negative result and show that the explanatory power of ratings reaches a climax on the wake of the most controversial rating driven regulation. To further investigate such a finding, I introduce a similar empirical setting around the regulation of broker dealers at the beginning of the 1970's. Again, the "informational value" of bond ratings increases on the wake of the regulatory move by which the SEC revived rating as key regulatory tools. These two findings are hints of a regulatory value fitting into a theory of ratings as coordination variables: the value of ratings as "focal points" rises once authorities officially endorse them (see Boot et al (2006, p. 112)).

The rest of the paper is organized as follows. Section 1 gives background information on bond ratings as regulatory inputs. Section 2 reviews the existing literature on bond ratings and yields.

Section 3 introduces the chosen empirical framework. Section 4 gives the results and the way they are interpreted.

Background Information: Ratings as Financial Regulation Tools

An interesting feature of bond markets is that some firms deal with the established business of rating bonds on the basis of their relative financial quality. These bond ratings are meant to proxy for the "expected reliability in meeting future financial requirements" and have become a quite shared measure of default risk. These proxies are meant to be relative and organized in ordinal scales.3 While bond ratings were born at the turn of the twentieth century, regulatory agencies started to use these privately issued opinions in the 1930's. The primary goal of this section is to introduce in full details these first rulings by American insurance and banking regulators. This section also sketches how the reliance on ratings evolved through time and across financial regulation fields.

Insurance

In 1910, the National Convention of Insurance Commissioners began publications of uniform price lists in order to push forward the use of market prices to value securities in insurance company portfolios. Yet this use of market prices proved problematic during the crises of 1907 and 1914. Over the 1920's, the insurance industry evolved a doctrine holding that "ample secured" bonds should be valued at cost modified by accrued amortization of discount or premium. The use of market prices for "non-amortizable" securities however remained broad.

In an answer to the 1931 crisis, the New York State Insurance Department ruled in 1932 that bonds rated in the first five rating grades by one of the rating agencies would be considered eligible for amortization on a cost basis. The decision was criticized over the 1930's: extensive use of amortization led to dubious valuations in front of quite low market prices. In 1940, the NAIC stated that amortization could be given to bond rated: i) in the first four grades by two rating agencies, ii) in the first five grades by three agencies or iii) in the first five grades by two agencies plus a pricing requirement4 . In 1953, the NAIC reformulated the eligibility criteria in two tests. "Test 1" was a rating from the first four rating grades of one of the accredited agencies or a number of balance sheet requirements 5 . "Test 2" mainly dealt with earnings requirements 6 .

While criteria may have changed, the reliance on privately issued bond ratings has not been open to question. The modern National Association of Insurance Commissioners (NAIC) bond classification system adopted in the early 1990's is equating "top quality" with the first 3 rating grades and "high quality" with the fourth one (Moody's (2004, 2 p. 3)).

Banking

By 1930, the Federal Reserve had begun using bond ratings in their examination of member bank portfolios7 . This use could be considered "informal"; in 1931, the Comptroller of the Currency officially adopted ratings as proper measures of the quality of the national banks' bond accounts: bonds rated Baa/BBB or above would be carried at cost; bonds with lower ratings would however be marked to market with the help of fractional write-offs8 . This ruling was in tune with previous insurance practices introduced above and then was well received at the time (see WSJ (1931a &b); Harold (1938, p. 27) quotes J. Moody's comments). During the following years, many State banking superintendents adopted the Comptroller's plan (see Harold (1938, pp. 27-28)).

In 1935, Amendments to the Federal Banking Act specified that all national banks were subject to the orders of the Comptroller's Office as for the securities they might purchase for their own accounts. On February, 15 th 1936, the Comptroller issued a new ruling stating that "the purchase of investment securities in which the investment characteristics are distinctly and predominantly speculative, or investment in securities of a lower designated standard than those which are distinctly and predominantly speculative, is prohibited". A footnote added that "the terms applied herein may be found in recognized rating manuals" (see Harold, (1938 p. 30)). This more radical decision spurred unprecedented hostility about the use of bond ratings as tools to influence the structure of commercial banks portfolios (see WSJ (1936a &b)). It also created confusion about what the footnote exactly meant because it was relying on an unsettled market convention: Moody's kept interpreting the ruling as pointing to Baa/BBB as a cutoff but the American Banker considered A/A (see Moody's (2004, pp. 1-2)). The Comptroller refused to make this point clear and then stated that ratings were not "the sole criterion, or even a necessary criterion, for judging whether or not a particular bond was eligible for purchase by a national bank"; nonetheless, controversies did not quiet down (see WSJ (1936c, d &e)).

adjusted earnings equal to mandatory principal payments and sinking fund requirements (excluding final maturities), or working capital equal to 100% of long-term debt. Further modifications were made for new enterprises or special obligations (see Atkinson (1967, 3 p. 37 -1 p. 39)). bank's entire portfolio based on credit ratings, so that the portfolio's "safety" or "desirability" could be expressed in a single number, referred to as a "desirability weighting." Harold (1938, 3p.25) mentions the use Harold (1938, 3p.25) mentions the use of systems similar to this one by several branches of the Federal Reserve.

The footnote was deleted only but a few days after all federal banking authorities had published an agreement more in tune with the original 1931 ruling 9 . A joint statement by the Secretary of the Treasury, the Board of Governors of the Federal Reserve System, the Directors of the Federal Deposit Insurance Corporation, and the Comptroller of the Currency was made on June, 27 th 1938.

Following this 1938 Agreement, bonds would be divided into groups and the first four rating grades would provide a privileged status (by being valued at their purchase price or at par and by being therefore insulated from day-to-day price fluctuations) 10 . The use of ratings by all federal banking authorities was now clearly set. For individual banks, this meant that informational requirements and uncertainties would be minimized for investments in the top four rating categories while lower rated or even unrated bonds would require an added burden of justification. In 1949, the Executive Committee of the National Association of Supervisors of State Banks joined the Federal authorities in reaffirming the process outlined by the 1938 statement (see Federal Reserve (1949)). This process has remained at the heart of American banking regulations to date. For example, White (2009, p. 3) mentions that its application to saving institutions in 1989 brought selling pressures to the junk bond market.

The regulation of insurance investment however took some years to adjust its own system to this new convention. Remember from section 1. 1 that, up until 1953, insurance companies could consider bonds that were rated one notch below the "cut-off" required by banking authorities. This precision aside, the use of ratings by financial regulators was now affirmed more than ever. It would then be left unquestioned and find new applications.

Securities Law

Post-world war II decades brought a standstill ended by the SEC adopting Rule 15c3-1 on broker-dealers as an answer to the credit crises of the early 1970's. This rule set forth "haircut" requirements based on the credit ratings assigned to the asset. A "haircut" is the percentage of a financial asset's market value a broker-dealer is required to deduct for the purpose of calculating its net capital requirement 11 . This ruling was nothing more than another use of ratings in checking on the 9 The deletion became effective on July 1 st 1938. 10 Book value for bonds of Group I (Aaa to Baa inclusive); current market value plus any unrealized 50 cent depreciation on them should be charged against net bank capital for Group II (Ba or below or unrated securities of equivalent value), Group III referred to securities in default while group IV was for equities (see Federal Reserve (1938)). Note also that, as in the original 1931 rule, American sovereign and sub-sovereign issues are not concerned by this process.

11 Partnoy (1999, footnote 344 p. 690) refers to Notice of Revision Proposed Amendments to Rule 15c3-1 under the Securities Exchange Act of 1934, Release No. 34-10, 525, 1973SEC LEXIS 2309(Nov. 29, 1973): "The Commission to a limited extent has also recognized the usefulness of the nationally recognized statistical rating organizations as a basis for establishing a dividing line for securities with a greater or lesser degree of market volatility." SEC (2003, note 9 p.6), as most other sources, points to the final enactment: Adoption of Amendments "safety and soundness" of a financial intermediary investments. SEC (2003, note 9 p.6) mentions two historical precedents: (i) certain securities exchanges, including the New York Stock Exchange (NYSE), already utilized ratings to calculate haircuts following their respective net capital rules (NYSE Rule 325(c)( 5) and (c)(6)); (ii) a number of states also used the concept of ratings to limit the investment discretion of certain fiduciaries and relied only on ratings provided by firms designated as reliable by the state. However, for the first time it included the creation of a Nationally Recognized Statistical Rating Organization (NRSRO) status.

The existence of this NRSRO status paved the way for numerous uses of ratings by the SEC and by other regulatory bodies. There have then been credit-rating dependent rules and regulations promulgated under the Securities Act of 1933, the Securities Exchange Act of 1934, the Investment Company Act of 1940, and various banking, insurance, pension, and real estate regulations (for a tentative overall picture see SEC (2003a, pp. 6-8)). SEC (2008b, c, &d) give details on all the uses of ratings by the SEC. Yet another example of safety and soundness regulation is the use of ratings to check on money market fund portfolios. In the 1980's, the Agency somewhat innovated by using ratings to deal with the very process of security offerings on which the Securities Exchange Act had focused: good ratings would now screen high quality offerings for which the issuance requirements would be eased12 .

Collateral policy

In a detailed discussion of the 1930's banking regulations introduced in section 1.2, Palyi (1938, 3 p. 75) noted that a "carrot" came with the "stick" of the new rules for valuing bank's portfolio (relevant in a context of mandatory examinations by comptrollers). This carrot was the fact that once investment grade securities were officially recognized as safe investments, little argument could prevent them from being posted as collateral (see Federal Reserve (1937)).

While there has been much debate on the possible shortcomings of using ratings to guide bank's portfolio evaluations, it must be said that ratings have always been thought as guides.

Examinators could end up departing from a straightforward use. Although to a limited extent, this point could be made about the 1930's regulations, it holds even more for the modern Basel II framework thanks to the alternatives to third party ratings (pilars 2 and 3). On the contrary, once collateral policy starts using ratings, these privately issued opinions serve as the basis for numerous to Rule 15c3-1 and Adoption of Alternative Net Capital Requirement for Certain Brokers andDealers, Release No. 34-11497 (June 26, 1975), 40 FR 29795 (July 16, 1975).

transactions between the central bank and the market place: they are unequivocally endorsed as screens for quality.

The attitude of the European Central Bank (ECB) makes this point clear. The European Operational Framework came out of a compromise between numerous central bank practices and emerged as "collateral intensive" (see IMF (2008, figure 1 p.16 andAppendix 3 p. 55)). Although the European Central Bank (ECB) always pointed out that third party rating providers were only one source of information out of four in the European Credit Assessment Framework (ECAF), it felt required to create its own "ECAF relevant" designation for third party ratings. Note that the implementation of the BASEL II framework in Europe had reached a point to which most of the concerned rating providers had already been registered by banking regulators (as External Credit Assessment Institutions (ECAI)). Nonetheless, the use of ratings in collateral policy proved so critical that it led the ECB to create both a designation process and a monitoring framework (see ECB ( 2006&2007)).

The crisis of structured finance also brought this point to limelight. Other major central banks answered to financial difficulties by working on their collateral policy. In so doing, their increasing straightforward reliance on rating providers caught public attention. While the Bank of England rulings explicitly named the three leaders of the global rating business (S&P, Moody's and Fitch)), the Board of the Federal Reserve used a sentence implicitly doing the same thing (see (see IMF (2008, box 5 p. 63) and Bluementhal (2009)). To detractors deeply convinced that these firms had fuelled market disruptions with structured finance securities, this reliance was to say the least puzzling (see (Bluementhal (2009)).

Although discussing on collateral policy brought a global point of view, overall, this section focused on how American financial authorities came to use ratings in an increasing number of rules.

The regulatory use of ratings has first been mirrored in Japan in the early 1980's and ratings as regulatory tools have been promoted internationally through the advancement of the Basel II scheme for the global standardization of bank regulations. Providing a global picture of the official uses of ratings is a difficult task (see IMF (1999, Table A6.2 p.156) and BIS (2000) for tentative tables; JFRAC (2009) gives a thorough listing). Beyond such a task, remember that outside the United States most bond markets previously worked without privately issued bond ratings. On a global scale, the adoption of ratings by economic agents has thus been quite contemporaneous to the one by their financial authorities. With this in mind, the United States provides an interesting historical experiment.

From publishers of opinions on creditworthiness, rating firms ended up providing information as to the future treatment by financial regulations. A theory of rating agencies as "regulatory licenses providers" has even been formulated (see Partnoy (1999, p. 681)). This generalizing effort aside, numerous observers have wondered about the potential impacts of the use of ratings by financial regulation. With such a concern in mind, a straightforward field of investigation is the relation between ratings and yields.

Literature Review: Bond Ratings and Yields

Bond ratings are ultimately valued because they are recognized as a shared measure of bond default risk by investors. Investigating their relationship with bond yields then makes sense in order to elaborate on the rationality of their use13 . Bond yields can be offering yields at issuance on the primary market, actual yields as quoted on the second market or realized yields once the bond came to expire.

Looking at realized yields is an ex post analysis. For example, considering bond issues over 1900-1943, Hickman (1958, table 1 p. 10, table 1 p. 10) came to the conclusion that, on average, actual loss rates did not completely eliminate the higher yields that had been accorded to lower rated bonds. This finding was then restudied and contested by [START_REF] Fraine | Effects of Defaults and Credit Deterioration on Yields of Corporate Bonds[END_REF]. It however remained a piece of evidence that could be interpreted as a claim for a more active trading of high yield debt securities14 .

Along with the rise of the high yield (or "junk") bond market from 1977, further investigations focused on whether investors in speculative bonds could be more than satisfactorily compensated for default risk (see, for example, [START_REF] Fitzpatrick | Hickman Revisited: the Case for Junk Bonds[END_REF]). Producing evidence on the overcompensation for default risk by high yield debt securities would usually go along with noting that demand for these securities had been constrained by legal restrictions for a number of institutional investors (see, for example, Altman (1989, 4 p. 921)) 15 .

Focusing on realized yields, the use of ratings by safety and soundness regulations has then been used to justify evidence going against the efficiency of bond markets. Proving a superiority of high yield investment is however not a straightforward exercise. The early literature dealt with averages of lifespan realized returns and results proved sensible to variations in risk premia, to changes in the level of interest rates and to early redemptions (see [START_REF] Fraine | Superiority of High-Yield bonds Not Substantiated by 1927-1936 Performance, The Analyst[END_REF] versus Dewing (1926, p. 1,192 -1,195) and [START_REF] Fraine | Effects of Defaults and Credit Deterioration on Yields of Corporate Bonds[END_REF] versus [START_REF] Hickman | Corporate Bond Quality and Investor Experience[END_REF]). Modern contributions introduced annualized returns and more refined analysis building on the risk/reward trade-off (see, for example, [START_REF] Fons | The Default Premium and Corporate Bond Experience[END_REF], [START_REF] Altman | Measuring Corporate Bond Mortality and Performance[END_REF] and Blume et al. (1991)). Yet the computation of default rates remained quite controversial. For example, [START_REF] Asquith | Original Issue High Yield Bonds: Aging Analyses of Defaults, Exchanges, and Calls[END_REF] criticized earlier studies for not taking into account exchanges and for poorly dealing with the aging effect on bonds. Precisely, building on data from [START_REF] Fraine | Effects of Defaults and Credit Deterioration on Yields of Corporate Bonds[END_REF]), Fridson (1994, pp. 49-50) shows that the "odd" finding in [START_REF] Hickman | Corporate Bond Quality and Investor Experience[END_REF] was driven by a small share of "irregulars offerings" (in other words, offered through contract modifications in already outstanding issues and through exchanges related to corporate reorganizations).

When evidencing a superiority of high yield investment has been recognized as a challenging task, focusing on historical yields appears less welcome. For example, in a follow-up study to the famous work of [START_REF] Stigler | Public Regulation of Securities Markets[END_REF] on registered securities and the 1933 Securities Act, Jarell (1981, pp. 654) acknowledges that using data from the Hickman studies brings little results and advocates an analysis of yields and market variability. Turning to an ex ante analysis may indeed prove a more straightforward way to deal with a possible impact of relevant regulations. This means looking either at offering yields or at actual yields and the relevant literature can be introduced with the help of the following questions: a) Do bond yields react to a change in bond ratings? b) Are bond ratings relevant to explain bond yields? Dealing with a) means introducing a temporal analysis and then requires continuous data from the second market (see, for example, [START_REF] Weinstein | The Effect of Rating Change Announcement on Bond Price[END_REF]). This can become quite a challenge since bond markets do not always prove liquid. Researchers have then naturally turned to the stock market for real time quotations (see, for example, Hand et al. (1992)). [START_REF] Jorion | Informational effects of regulation FD: evidence from rating agencies[END_REF] used investigations of this kind to show an impact of the first regulation clearly giving an informational advantage to rating firms over equity investors 16 .

Given data limitations with bond market quotations, a focus on b) makes sense for an historical investigation. This involves a cross examination of bond ratings, control variables and yields. This kind of analysis can be fed by data either on offering yields at issuance or on actual yields computed from the prices on the second market17 . A review of literature may be: West (1973), Liu &Thakor (1984), [START_REF] Ederington | The informational content of bond ratings[END_REF][START_REF] Reiter | Bond Yields, Ratings, and Financial Information: Evidence from Public Utility Issues[END_REF], [START_REF] Brister | The Regulation Effect of Credit Ratings on Bond Interest Yield: The Case of Junk Bonds[END_REF], Levingston et al. (2003). The focus is on evidencing an informational value of ratings, in other words on testing the following null hypothesis: yields per rating category over the weeks following rating driven regulation enactments in 1931 and in 1936 and concludes to a limited impact.

Empirical Framework

As in Merton (1974, p. 449), let us start with the assumption that:

Y i = f (C i , X i , YREF i , ε 1i )
(1) where, Y i : yield to maturity on the issue i C i : issuer's creditworthiness X i : issue i "other characteristics" YREF i : yield on the chosen risk free issue ε 1i : random error A first step is to change the target variable in order to focus on the spread between the yield on the issue i and the yield on the chosen risk free issue19 :

YSprd i = Y i -YREF i = f (C i , X i , ε 2i ) (2)
A second step is to raise the issue of ratings' relevance. West (1973) did it in a way that can be interpreted as a special case of the following Levene's test:

ε 2i = f (C i , R i , X i , ε 3i ) (3)
There is however no reason against including ratings in equation ( 2) and the starting overall specification should be:

YSprd i = f (C i , R i , X i , ε 4i ) (4)
where, YSprd i : yield spread on the issue i C i : issuer's creditworthiness R i : bond rating of the issue i X i : issue i other characteristics ε 4i : random error

The empirical analysis will run two specifications of this general model, respectively on five cross sections surrounding the 1930's rulings and on three cross sections surrounding the early 1970's enactment. Data issues are introduced before turning to the choices guiding the specification of this overall model.

Data

In order to deal with 1930's regulations, building datasets starts by computing data from the Appendix of L. Fisher's PhD dissertation thesis (see Fisher (1959, appendix D p.66)). These original datasets are several samples of average yield spreads according to prices on bond issues outstanding on the New York Stock Exchange (NYSE) on December 31 st . These "risk premia" are given per issuer and along with financial ratios. Replicating R. West's work, these datasets have been matched with the relevant issues of the Moody's manuals 20 .

In order to deal with the 1970's ruling, the starting point has been data communications by S&P and Moody's according to their archiving of American corporate bond rating histories. These datasets provide bond ratings outstanding on December 31 st 1971, 1973 and 1975. At the end of 1973, the use of ratings by the SEC has been recently proposed and at the end of 1975, the rule 15c3-1 has been on for 6 months (see, supra, note 8). When information on the bond issue bearing the rating was missing, it has been found in the Mergent Fixed Income Securities Database (FISD). In order to get information on the issuing company, the resulting datasets were merged with the Compustat North America Industrial Annually database. The next step is to compute actual yields. To be able to do so, bond prices have been hand-computed following the New York Stock Exchange (NYSE) quotations as reported by the Bank and Quotation Report. Last but not least, the outcome was a number of datasets plotting ratings, prices, etc., per bond issue. To ensure comparability with the tests for the 1930's, weighted average yield spreads per bond issuer had to be computed. The respective outstanding amounts were found in the relevant issues of the Moody's Industrials and Public Utilities manuals 21 .

Table 1 displays the respective populations per rating categories. 21 These are averages given all variables in our model. Doing so, ratings from Moody's and S&P are treated as equivalent. Observations do remain distinct in case of (a) a different level of proxies for bond covenants or (b) a difference between S&P and Moody's ratings (a split rating; a limited access to historical data makes it very rare in the present setting).

Variable selection

Table 2 summarizes data definitions and sources. With equation (4) in mind, the main concern is to account for credit risk (C i ). A first and conventional step is to focus on financial ratios. This can lead to a broad discussion and to a quite extensive set of relevant predictors (see, for example, Chan &Jengadesh (2001, Appendix p. 23)). Note however that the exercise is neither about finding the best approximation for credit risk nor about aiming at the best ratings determinants. The goal is to find a set of control variables that can be viewed as a potential standard for a typical investor: a balance has to be stricken between accuracy and simplicity. A minimal requirement is to pick financial ratios from at least each of the following broad categories: i) liquidity, ii) profitability and iii) capital structure. An example is: i) liquidity: the volume of bond outstanding22 , ii) profitability: the 9 years net income variation coefficient and iii) capital structure: the ratio of equity market value on par value of debt.

These variables and a proxy for financial reliability make the [START_REF] Fisher | Report on Enhancing Market and Institutional Resilience[END_REF] model.

The building of new datasets for the early 1970's brought the opportunity to depart from a straight use of this [START_REF] Fisher | Report on Enhancing Market and Institutional Resilience[END_REF] model. Previous studies could be interpreted as pointing out the choice of: i) liquidity: firm size, ii) profitability: interest coverage or operating margin and return on assets, iii) any measure of leverage (see Livingston et al. (2008, p. 17 and table 1 p. 39)). This said, looking for a standard way to analyze default risk, the success and common use of the Z score models must be outlined (see [START_REF] Altman | Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy[END_REF] and [START_REF] Altman | Predicting Financial Distress Of Companies: Revisiting The Z-Score And Zeta® Models[END_REF]). It has indeed been quite common to plot Z scores against ratings. [START_REF] Brister | The Regulation Effect of Credit Ratings on Bond Interest Yield: The Case of Junk Bonds[END_REF] replicated a Z score methodology in order to use the computed scores as default risk proxies in a cross examination of ratings and yields. This two stepped process started with a Multi Discriminant Analysis, which went along with several hypotheses and computational complexity. Rather than focusing on the output of a Z score model, the inputs make an interesting set of predictors for credit risk. For instance, Altman &Rijken ( 2004) uses these Z score determinants to build an "agency rating prediction model" and to run ordinal logit regressions. This model was also including the number of years since a company was first rated by a company (see [START_REF] Altman | How rating agencies achieve rating stability[END_REF], p. 2686)). This can be considered as adding a proxy for financial reliability to a set of widely acknowledged financial ratios. This "agency ratings prediction model" is then particularly interesting in an investigation parallel to one dealing with the Fisher (1959) model.

Still with equation ( 4) in mind, dealing with issue characteristics other than default risk (X i ) is a rather difficult task. The bond prospectus may include numerous features and their relevance for the bond pricing process is open to discussion (see Kose et al. (2008)). A cautious strategy can then be to gather a sample of bond issues with similar features and hence focus on ratings and default risk variables (see Livingston et al. (2003, 2 p. 22)). In a similar manner, [START_REF] Fisher | Report on Enhancing Market and Institutional Resilience[END_REF] took care of these characteristics during the computation of yield spreads. Building the datasets for the 1970's, information on the subordination and security level of bond issues could be gathered and is summarized thanks to two dummies variables (SUB and SEC). There is unfortunately no account of other common features such as the presence of a call and/or the one of a sinking fund.

Last but not least, [START_REF] Fisher | Report on Enhancing Market and Institutional Resilience[END_REF] focused on industrial bond issues. Gathering data on the early 1970's gave the opportunity to get a broader view: original datasets covered corporate bond issues and then mixed industrials and utilities issues, which are usually considered as two different realms (especially when dealing with financial ratio analysis). Instead of splitting the datasets, what is proposed here is the other option of including a dummy variable coding for public utilities (UTILITY)

to the model. Table 2 summarizes variable definitions and data sources.

Model specification

Using the previous remarks and building on equation (4):

YSprd i, = f (LIQUw i , PROFx i , LEVy i , BCKGRNDz i , R j , X j , ε 4ai ) (4a)
Where, YSprd i : yield spread on issue i LIQUw i : Liquidity proxy "w" for issuer of i PROFx i : Profitability proxy "x" for issuer of i LEVy i : Leverage proxy "y" for issuer of i BCKGRNDz i : financial background proxy "z" for issuer of i R i : bond rating level for issue i X i : other characteristics of issue i ε 4ai : random error Furthermore, following [START_REF] Fisher | Report on Enhancing Market and Institutional Resilience[END_REF], a new target variable is defined as follows:

If there is any k ≥ 1, such as

LIQUw i = LIQUw i+1 = (…) = LIQUw i+k AND PROFx i = PROFx i+1 = (…) = PROFx i+k (…) AND X i = X i+k
Then, AYSPRD i = ω j YSprd j, / ω j , where ω j : outstanding volume on issue j

Otherwise, AYSPRD i = YSprd i,

For datasets intended to deal with 1930's regulation enactment, the first task is to provide a close discussion of West (1973) by running regressions following equations (2a) and (3a):

Log (AYSprd i ) = α 2a + β 2a1 Log(1/PROFa i ) + β 2a2 Log(LEVa i ) + β 2a3 Log(LIQUIa i ) + β 2a4 Log(BCKGRNDa i ) + ε 2a i (2a) ε 2ai = α 3a + β 3a1 Log(1/PROFa i ) + β 3a2 Log(LEVa i ) + β 3a3 Log(LIQUIa i ) + β 3a4 Log(BCKGRNDa i ) + RATING i + ε 3ai (3a)
where, α, β : constants ε : random error Equation (2a) is a standard ordinary least square (OLS) regression case that allows a check on how the present empirical setting replicates the one of [START_REF] Fisher | Report on Enhancing Market and Institutional Resilience[END_REF]. Controlling for the variability in regression residuals, equation (3a) is a Levene's test handled thanks to Generalized Linear Model statistical routines. Equation (3a) checks on the relevancy of the tailored "two stage" analysis found in i+k ∑

j =i i+k ∑ j =i
West (1973). Furthermore, the results of this close discussion motivate a straightforward multivariate setting using the following ANCOVA equation:

Log (AYSprd i ) = α 4b + β 4b1 Log(1/PROFa i ) + β 4b2 Log(LEVa i ) + β 4b3 Log(LIQUIa i ) + β 4b4 Log(BCKGRNDa i ) + RATING i + ε 4bi (4b)
To deal with the regulation of broker dealers in the early 1970's, similar multivariate estimations are run on genuine datasets. This time they are based on the Altman &Rijken ( 2004) rating prediction model:

Log (AYSprd i ) = α 4c + β 4c1 Log(1-PROFb i ) + β 4c2 Log(1-PROFc i ) + β 4c3 Log(LEVb i ) + β 4c4 Log(LIQUIb i ) + β 4c5 Log (1-LIQUIc i ) + β 4c6 Log(BCKGRNDb i ) + β 4c7 SUB i + β 5c8 SEC i + β 4c9 SPLIT i + β 4c10 UTILITY i + RATING i + ε 4ci (4c)
where, α, β : constants ε : random error

Results

For each of the cross-sections surrounding rating driven regulations of the 1930's, table 3 provides a summary of statistical outputs. The first set of columns gives results for the [START_REF] Fisher | Report on Enhancing Market and Institutional Resilience[END_REF] model alone (equation 2a). It can be seen that this model performs well and in line with previously reported results given in annex A. However, in the second set of columns (equation 3a), the study of residuals shows that these regressions are not perfect: non constant variance remains in all years except 1927. A further result is that ratings help explaining this non constant variance in 1932, 1937 and 1953. Yet note that the coefficients for non-investment grades are not statistically significant. This finding provides a critique of the analysis found in West (1973).

Going beyond such a critique means focusing on the fact that ratings can help reduce the nonconstant variance in the residuals of the regressions using the [START_REF] Fisher | Report on Enhancing Market and Institutional Resilience[END_REF] model. Ratings could be treated as an originally omitted variable. But, as pointed out before writing equation ( 4), there is no reason to omit ratings in a straightforward multivariate analysis. For this reason, the first columns of table 4 give the results of an ANCOVA analysis with ratings as a categorical variable and with covariates relying on [START_REF] Fisher | Report on Enhancing Market and Institutional Resilience[END_REF] (equation 4b). When residuals did exhibit non-constant variance, a weighted analysis has been performed. In straight line with the results displayed in table 3, this analysis was not needed for 1927. In all other cases, non constant variance has been addressed as far as can be judged by using the predictors in the original regressions 23 .

Given this weighted analysis, the R² displayed in Table 4 are not straightforward goodness of fit measures24 . With this disclaimer in mind, the overall performance seems to have benefited from introducing rating along with the Fisher (1959) model. Furthermore, this overall performance increases to reach a peak in 1937 and then decreases so that the level in 1953 is similar to the one in 1927. West (1973, note 22 p.165) stated that including ratings with the help of dummies and running a multivariate analysis led to an unfortunate perturbation in the estimation of coefficients for predictors.

Introducing a RATING variable in the multivariate analysis has certainly put BCKGRNDa to a test.

However, the three other covariates perform well and when coefficients are significant their sign is similar to the one in the Ordinary Least Square setting. Last but not least, the statistical significance of RATING is always validated.

21

The second set of columns on UTILITY is significant only once. 1-PROFc, SEC and SPLIT never have an explanatory power.

By looking at the results of these two multivariate settings, one has to reject the first null hypothesis for every investigated year and admit: (ha) = "ratings have an explanatory power". But "an explanatory power" may not be enough for a categorical variable. Since the chosen ANCOVA models do not include interactions, the significance of RATING can be further assessed with the help of Tukey multiple comparison tests. Table 5 gives a summary of the produced results. The process is iterative: after picking the top rating category as a reference, the relevance of sorting this category (Aaa/ AAA) from the next ones is tested; then the relevance of the second one (Aa/ AA) against the remaining categories; and so on. These tests control for the fact that a categorical variable may reach statistical significance by chance.

Overall, the rating scale is often poorly validated. Note however that what is tested is the significance of the rating categories given all other variables in the model. This overall result is then hardly surprising given the pertinence of both the [START_REF] Fisher | Report on Enhancing Market and Institutional Resilience[END_REF] and the Altman &Rijken (2004) models. A further disclaimer is that small samples are not well suited to study the significance of rating categories.

On the left-hand side of Table 5, as soon as 1927, the only rating category to exhibit statistical significance is the first of the non-investment grades (Ba/ BB). Well before the passing of any financial regulation, one may conclude to an investment grade effect, which would then be validated for the next 4 years. This investment-grade effect would predate any rating-driven regulation. This said, the aim of these tests is to look at the pertinence of the whole rating scale. by all federal banking authorities in 1938 (see, supra, section 1). On the right hand side of Table 5, there is no sign of a particular relevance for the first of non-investment grades. In 1971 and 1973, the only rating categories that prove significant are either one rank above (Baa /BBB) or one rank below (B /B). Once again, the main result is that most of the rating scale proved significant beyond the 5% level in 1975 as opposed to the two previous years.

These results can be interpreted as invalidating (H o ). Beyond acknowledging that the explanatory power of ratings does change over the selected years, the increase of this explanatory power both in 1937and in 1975 provide two hints of a regulatory value. After introducing a theory of bond rating as coordination variables, Boot et al (2006, p. 112) mentions an exogenous key point: the fact that institutional investors face restrictions linked to ratings. Along this line of thoughts, the two increases may be interpreted as pointing respectively to the 1936 US Comptroller ruling on bank investment and to the 1975 SEC rule on broker-dealers, which would have increased the value of ratings as "focal points".

Concluding remarks

Echoing a casual reading of [START_REF] Hickman | Corporate Bond Quality and Investor Experience[END_REF] (1973) and others focused. Of course, one answer may be that these unconstrained investors did not have a sufficient market power to influence a structurally altered market place. [START_REF] Glenn | Super Premium Security Prices and Optimal Corporate Financing Decisions[END_REF] then introduces a theoretical discussion focusing on the prohibition of short-sales. This burden of proof aside, this paper came back on West (1973) with the help of modern regression routines and did not find evidence of a "regulatory premium" for non-investment grade bond. This paper then criticizes an alleged straightforward effect of rating driven regulations introduced in the 1930's. This contribution is similar to Fridson (1994, pp. 49-50) showing how readers inferring a similar effect from [START_REF] Hickman | Corporate Bond Quality and Investor Experience[END_REF] were misled (see, supra, section 2).

Going beyond such a critique, the focus on non-investment grade bond yields may be removed. Another effect of the ruling could be an increase in the reliance on bond ratings for the pricing of all bond issues. Looking at the overall pertinence of ratings for investors in the 1930's and 1970's with no spectacular effect as a guide provides a discussion of the standard literature on the structural relation between ratings and yields. This literature usually interprets the statistical significance of ratings as a proof of their informational value. This paper then looks at how this informational value evolves with the passing of financial regulation using ratings. When the explanatory power of ratings changes over the selected cross-sections, the interesting results are that there is a climax in 1937 and a striking improvement in 1975. These findings deserve the following comments.

Why do yields gravitate more around ratings in these two years? or what may have caused an increased reliance on ratings for these particular years? An appealing idea is that once authorities officially endorse ratings, their value as "focal points" rises (see [START_REF] Boot | Credit Ratings as Coordination Mechanisms[END_REF]). However, the question does remain open. First, difficulties in building first-hand datasets led to an unfortunate lack of cross sections for years after the passing of the 1975 regulation by the SEC. They would have helped to assess whether the 1975 striking improvement lasted or proved a temporary climax as 1937 did. Secondly, this paper was meant to be a discussion of West (1973) and then relied on data from [START_REF] Fisher | Report on Enhancing Market and Institutional Resilience[END_REF]. This brought a number of methodological choices, which applied to the 1970's extension for the sake of comparability. Overall, the present investigation may then appear disconnected from the modern finance literature on the pricing of debt issues (see, for example, [START_REF] Elton | Explaining the Rate Spread on Corporate Bonds[END_REF]). The overall empirical approach is not fundamentally obsolete but there would be a lot to gain in importing more sophisticated credit risk pricing techniques. Thirdly, this finding needs to be further investigated by looking at the dynamics of the relationship between ratings and yields.

Given the liquidity of the corporate bond market up to the 1940's (see Biais &Green ( 2007)), a more promising strategy would focus on how yields react to rating changes before and after the enactments of the 1930's (see [START_REF] Jorion | Informational effects of regulation FD: evidence from rating agencies[END_REF] and [START_REF] Boot | Credit Ratings as Coordination Mechanisms[END_REF]).

These improvements may bring a better view on whether the use of rating in regulations had an impact on their value for investors. Yet an "informational" framework may be restrictive for studying the impact of these financial regulations. For example, recent contributions suggest paying more attention to the capital structure of corporations (see Faulkender &Peterson (2006), [START_REF] Kisgen | Credit Ratings and Capital Structure[END_REF], [START_REF] Sufi | The Real Effects of Debt Certification: Evidence from the Introduction of Bank Loan Ratings[END_REF] and [START_REF] Kisgen | Credit Ratings and Capital Structure[END_REF]. A look at the balance sheet of the regulated financial intermediaries is also well deserved. Musing on the choice of 1930's regulators, Flandreau et al. (2009, p. 23) pioneered this perspective by computing the nominal benefit from booking at face value. Last but not least, in an early comment of the regulations enacted in the 1930's, Harold (1938, pp. 33-34) mentioned a first "practical effect" on non-investment grade yields but also "more far reaching effects" such as the development of other more yielding avenues of investment. About 70 years later, comments on the recent credit crisis often noted that regulatory arbitrage had been a rationale for the boom of structured finance.

  (h o ) :"Bond ratings do not have an explanatory power"For example,[START_REF] Ederington | The informational content of bond ratings[END_REF] "explores the information content" of Moody's and S&P ratings beyond publicly available accounting variables by relating them to the yield to maturity. The authors used a non-linear least square procedure on data concerning bonds traded on February, 28 th1979[START_REF] Jarell | The Economic Effects of Federal Regulation of the Market for New Securities Issues[END_REF]. Also, Levingston et al. (2003, pp. 4-6) uses a latent variable methodology and yields on new industrial bond issues to focus on whether bond ratings contain non-publicly available information.

Table 1

 1

					-Sample Size and Rating Categories
				RATING	1927	1932	1937	1949	1953	1971	1973	1975
	Investment	grades	Aaa/ AAA Aa/ AA A/ A Baa/ BBB	10 9 15 18	3 4 4 14	4 5 8 27	1 12 15 16	2 16 29 21	9 24 50 15	11 31 57 14	15 32 79 18
	Non-investment	grades	Ba/ BB B/ B Caa/CCC	14 ----	10 10 --	19 10 2	13 2 --	8 4 --	3 4 --	4 6 --	3 5 --
				Total (N)	66	45	75	59	80	105	123	152
											--: unrelevant
	20 Unfortunately, this did not lead to a perfect replication… the differences are however sufficiently small to be
	overlooked. Compare datasets size in Table 1 to the following ones inferred from West (1973, table 2 p. 166): 67
	(1927); 44 (1932); 84 (1937); 63 (1949); 81 (1953).

Table 2 -

 2 Variables Definitions and Sources

		Name	Definition	Source
			Weighted averages (given all variables in	Fisher (1959, appendix D p.66)
			the models and using outstanding	Bank and Quotation record
		AYSprd	volumes on 31 st ) of absolute yield spread	CRSP Monthly Treasury fixed term
			(Yi -YREFi)	indices and Fama risk free rate
				Moody's manuals
			Net income after all charges and taxes:
		PROFa	9 years variation coefficient	Fisher (1959, appendix D p.66, x1)
			(=standard deviation / arithmetic mean )
		PROFb	Retained earnings / Total assets	Compustat Industrials Annually (data 36 / data 6)
	Numerical	PROFc LEVa LEVb	Earnings before interest and taxes / Total assets Market value of equity / par value of debt Fisher (1959, appendix D p.66, x3) Compustat Industrials Annually ( (data 170 + data 15) / data 6)) Market value of equity / book value of Compustat Industrials Annually total liabilities ((data 24 * data 25) / data 181)
		LIQUIa	Bond outstanding volume	Fisher (1959, appendix D p.66, x4)
		LIQUIb	Book value of total liabilities / US equity market capitalization	Compustat Industrials Annually (data 181) and CRSP database
		LIQUIc	Working capital / Total assets	Compustat Industrials Annually (data 179 / data 6)
		BCKGRNDa	Period of solvency since creation or last default episode	Fisher (1959, appendix D p.66, x2)
		BCKGRNDb	Years since a firm was first rated by an agency	S&P and Moody's communications
	Categorical	RATING SPLIT SUB SEC	Effect codings for bearing Moody's and/or S&P's ratings Dummy coding for: "two different ratings levels given all other predictors" Dummy coding for subordination Dummy coding for security	Moody's manuals S&P and Moody's communications Moody's manuals S&P and Moody's communications S&P dataset and Mergent FISD S&P dataset and Mergent FISD
		UTILITY	Dummy coding for public utilities	Compustat issuer codes

  table 4 deals with genuine cross-sections at the beginning of the 1970's (equation 4c). Considering that the detailed discussion ofWest (1973) has shown that the multivariate analysis was appropriate, here are the results of an ANCOVA setting involving ratings and the Altman &Rijken (2004) model. Again, a weighted analysis has been found helpful. To justify this analysis, non constant variance in residuals has first been evidenced by using all the predictors in the model and the Standard & Poor's Industry Classification (SIC) codes. Using weights has been successful in correcting non constant variance as far as can be judged by this limited set of control variables 25 . The goodness of fit measures indicate fair results. The performance remains similar in1971 and 1973 and rises in 1975. Only RATING and two covariates (LIQUIb and 1-PROFb) have an explanatory power for every investigated year. LEVb, LIQUIb and SUB proved significant twice;

Table 5 -A Closer Look at the Significance of the Rating Scale

 5

  that has had some success,West (1973) exhibits an over-inflation of non-investment grade corporate bond yields in 1949 and 1953 as a lasting and straightforward effect of the 1938 regulation of bank investment. It is then tempting to conclude that the public use of ratings would have altered how investors value bonds. Yet, how come other developments did not challenge such a straightforward effect? Proponents of a "regulatory induced" premium need to investigate why arbitrage by unconstrained investors has not taken place. For example, up to 1953 and under certain conditions, insurers could invest in bonds below the regulatory threshold on which West

A convention has eventually emerged among rating providers. This paper builds on this convention by using the most widely known name of a rating category preceded by its Moody's transcription. From the highest grade downward, this means using the following scale: Aaa/AAA; Aa/AA; A/ A; Baa/BBB; Ba/BB; B/B; Caa/CCC;… The scale goes down to default grades but this paper will focus on the top grades. Note also that the historical discussion introduced here only deals with broad rating categories (which were refined in the early 1980's with the help of "modifiers" (for example: AA  (AA-, AA, AA+) and Aa  (Aa3, Aa2, Aa1))).

Priced at

or better in September, October, and November. This was later changed to a 3.9% yield spread over US government bonds, which was then reduced to 1.5% in 1950.5 Depending of industry, debt ratio of 50 to 75% of total capitalization; plus a 1.5 average of before tax earnings coverage over five preceding years and similar 1.5 coverage in either of the last two years.6 An earnings on fixed charges ratio equal to 1 on average over 5 year and in either of the last 2 years. For railroad bonds, current assets equal to 125% of current liabilities. For public utilities and industrials, each year

InOsterhus (1931), a member of the Federal Reserve Bank of New York introduced a system for weighting a

Mimeographed ruling issued by J.W. Pole, then Comptroller of the Currency, not dated, although was made onSeptember 11, 1931 according to The Commercial and Financial Chronicle (No.133, 09/12/1931, p. 1672).

This means allowing "shelf registration" and using short form Securities Act registration statements (forms S-3 and F-3). In 1982, this was first allowed for investment grade non convertible domestic bond issues but was later transposed for foreign issues and then for Asset Backed Securities (ABS). Note also that, independently from these regulations and starting in 1984, rule 415 of the Securities Act gave this eligibility to every mortgage related security rated in the two top notches of the rating scale by a registered rating firm (NRSRO).

In order to study the behavior of bond market agents, it has been a convention to focus on the annual rates of return implied by bond prices or yields as they are referred to.

Fridson (1994, p. 43) notes that even severe critics of M. Milken and Drexel Burnham conceded this interpretation to junk bond market makers. The legend also says that reading[START_REF] Hickman | Corporate Bond Quality and Investor Experience[END_REF] in business school led M. Milken to start his now famous career.

Harold (1938, p. v) provides an early statement of the basis of this kind of argument: "Following the Comptroller of the Currency statement on 02/15/1936, it became common knowledge in bond circles that bond rated below that of "a business man investment "(BBB, Baa, B**, B1+) could almost never be sold to a bank."

Implemented on October 23, 2000, regulation Full-Disclosure (F-D) prohibits American public companies from doing selective disclosures to a broad category of "investment professionals". Rating firms were granted an exclusion from this list and these authors investigates whether this brought a strategic advantage by looking for a greater impact of rating change announcements on stock prices.

While mixing studies using actual yields and offering yields has been common (seeLiu &Thakor (1984, footnote 4 p. 348) criticizing this point), studies focusing on the impact of multiple ratings on yields at issuance have usually been set aside (see, for example, Liu &Moore (1987),Billingsley et al. (1985),[START_REF] Hsueh | Bond Ratings: Are Two Better than One?[END_REF][START_REF] Thompson | Dual Bond Ratings: A Test of the Certification Function of Rating Agencies[END_REF]).

The spread is absolute as opposed to relative (Y i -YREF i )/ YREF i ).The basis of YREF i is the yield on US Treasury bonds, which of course are not exempt from risk but have extensively been used as a pure rate approximation. The methodology used while computing the spread is aimed at being a replication of the one displayed inFisher (1959, appendix A p.52) with the slight change of building the Treasury yield curve thanks to CRSP fixed term indices (as opposed to using yields from the board of the Federal Reserve).

Original purpose of the volume of bond outstanding was to account for marketability but has then traditionally been interpreted as a proxy for liquidity.

The reported value are not R² = 1 -(Residual Sum of Square/Total Sum of Square) but approximations defined by R² = (pF) / (pF + np -1) where p is the number of predictors in the model.

New York University Stern Business School Economics Department thanks to the sponsorship and the kind research guidance by R. Sylla. It also benefited from a funding by the Georges Lurcy Board of Trustees.

An early contribution to this body of literature did something else than focusing on an informational content of ratings. [START_REF] Fisher | Report on Enhancing Market and Institutional Resilience[END_REF] produced classical a study of corporate bond yields using a log/log transformation of the common Ordinary Least Square (OLS) regression analysis. West (1973) picked up on that study by looking at the relationship between the regression residuals and Moody's ratings. As opposed to 1927, 1932and 1937, the behavior of these residuals could be linked to the investment grade status in 1949 and 1953. This result could be interpreted as an impact of the regulations enacted in the 1930's (see, supra, section 1.2).

When this result has often been mentioned or commented, the issue of sorting the investigated informational value from a regulatory value has then been poorly faced. To my knowledge, such a concern can only be found in [START_REF] Brister | The Regulation Effect of Credit Ratings on Bond Interest Yield: The Case of Junk Bonds[END_REF]. Echoing the literature dealing with realized yields, the authors focus on a straightforward reading of existing regulations and proceed with several tests on offering yields over 1982-1987. The goal is to find an ex post piece of evidence by showing how noninvestment grade bond yields are above the levels that could be expected by judging on default risk.

While the discussion in West (1973) did share this focus on the investment grade distinction, the methodology had a more neutral perspective taking advantage of the spacing of cross-sections in [START_REF] Fisher | Report on Enhancing Market and Institutional Resilience[END_REF]. This more neutral perspective means studying the relationship between yields and ratings before and after the enactment of regulations. It can be formulated by investigating the following "meta-null hypothesis": (H o ): "The explanatory power of ratings does not systematically change over years" Departing from a focus on the over-inflation of non-investment grade bond yields, this concern about the robustness of the relationship between ratings and yields makes two tasks interesting. First, the "two stage" methodology of West (1973) is somewhat peculiar and an open question is whether a more conventional multivariate design would bring the same findings. Secondly, a similar empirical setting can be worked out in order to check whether these findings hold for crosssections surrounding the 1975 regulation of broker dealers by the SEC.

The following empirical analysis undertakes these two tasks. This goal is of course constraining. I have mentioned above the dynamics of the relationship between rating and yields as an alternative. Another one is to build tailored tests to check the impact of an exogenous event on the value of ratings 18 . Kisgen &Strahan (2009) discusses on cumulative yields per rating category over the weeks following the official designation of the Canadian DBRS by the SEC in February 2003. These authors evidence an impact driven by cases where DBRS was less conservative than already designated rating firms and larger around the investment grade cut-off. They interpret their findings as pointing to an effect of rating based regulations on firm's cost of capital. Sharing an historical perspective with this paper, Gaillard (2008, pp. 93-111) introduces a broad discussion on cumulative 18 This was first done for checking whether the unannounced refinement of Moody's rating scale in April 1982 had any impact (see [START_REF] Kliger | The Information Value of Bond Ratings[END_REF] and Tang ( 2009)).

ANNEX -A

Source: Pandini (1969, p 221)