Nicolas Gérard Vaillant
email: nicolas.vaillant@icl-lille.fr

François-Charles Wolff

Understanding how experts rate cigars: A "havanometric" analysis

Keywords: JEL Classification: L15, L66, Z1 Cigars, expert opinion, quality

Drawing on data from an annual French guidebook published in 2004, this paper focuses on the determinants of experts' ratings on Cuban cigars. We find that high quality cigars are more likely to be recommended to more experienced cigar lovers. Both the selfassessed quality and recommendation depend on the length and diameter of the cigars, but also on more subjective characteristics like number and type of aromas. Results from a Fields' decomposition show that the quality of cigars is much more sensitive to the presence of a defect and number of aromas than the experts' recommendation, which is more influenced by the rarity of cigars. Finally, it is essentially the cigars' objective characteristics that have an influence on their prices, meaning that consumers truly benefit from additional qualitative information when reading experts' advice and ratings.

1/ Introduction

Opinions and ratings from experts matter in many types of markets. A few recent studies have shown the significant impact of rankings in guidebooks on market outcomes. In the gastronomy sector, Gergaud et alii (2007) found that a star from the Michelin Guide Rouge is associated with a high price premium, independent of the quality of foods 1 . In addition, [START_REF] Chossat | Expert opinion and gastronomy: The recipe for success[END_REF] have shown that guide evaluations are not exclusively related to food quality. Restaurants have an incentive to invest in their setting (through the choice of their wines in their cellars among other things) to achieve higher marks from guides.

Hadj Ali et alii (2008) looked at the influence of Robert Parker oenological grades on primeur wine prices using a natural experiment, the wine grades having been attributed after the determination of prices in 2003. Their estimate of the Parker effect is about 2.8 Euros per bottle of wine. Using data from the Queen Elisabeth piano competition over the period 1952-1991[START_REF] Ginsburgh | Expert opinion and compensation : Evidence from a musical competition[END_REF] have shown that better rankings lead to more success, measured by the presence of recodes in different catalogues of music and by ratings from Belgian music critics 2 . This is puzzling as the order of appearance, which is randomly assigned, should have no influence on the judges' ranking.

By definition, experts are expected to provide useful information on product quality to consumers, at a relatively low cost. However, one difficulty with the ratings they provide is that they sometimes mislead consumers and this affects the structure of the market itself as shown above. These results, along with those of [START_REF] Ashenfelter | The demand for expert opinions: Bordeaux Wine[END_REF] on the relationship between experts' rating of wines and their prices, cast doubt on the ability of experts to make fully objective judgments. But do consumers really seek only objective information when reading a guidebook?

If one assumes that most of the more objective attributes of products are reflected in their prices, then it could be argued that consumers expect something else from experts, with more qualitative (and hence subsequently more subjective) information. Given the increasing availability of guidebooks and other consumer reports, which have a large audience, it is important to understand how experts form their opinions. In particular, how are they influenced by the intrinsic and extrinsic characteristics of products when giving their recommendation?

1 Results from a hedonic price equation show that being featured in the Michelin guide is associated to a price premium of 9%. The premium increases by 27% when a restaurant is awarded its first star. 2 Expert opinions also matter in art markets [START_REF] Ginsburgh | Awards, success and aesthetic quality in the arts[END_REF], sport betting markets [START_REF] Avery | Identifying investor sentiment from price paths: the case of football betting[END_REF] or in the stallion vaulting semen market (Vaillant et alii, 2010).

Many papers have relied on hedonic methods to measure both price and quality of wines (see among others Combris et alii, 1997, 2000[START_REF] Landon | The use of quality and reputation indicators by consumers: the case of Bordeaux wine[END_REF][START_REF] Lecocq | What determines wine prices: Objective vs. sensory characteristics[END_REF]. In this research, we focus on the complementary, albeit less explored, market of cigars. After a period of low demand during the second part of the 20 th century, during recent years the cigar market has become much more attractive around the world . The global cigar and cigarillo market in 2006 was around 18 millions units, with a robust growth of premium cigars. The United States and four Western European countries (namely France, Germany, Spain and United Kingdom) account for about 96 percent of the global demand3 .

An important feature that wines and cigars have in common is that their intrinsic quality cannot really be appreciated before purchase [START_REF] Shapiro | Premiums for high quality products as returns to reputations[END_REF][START_REF] Allen | Reputation and product quality[END_REF] 4 . Experts' ratings should then be of the highest importance for the cigar market. To the best of our knowledge, so far only three studies have dealt with the estimation of hedonic price and quality equations of cigars [START_REF] Stover | An hedonic price index for premium cigars[END_REF], Freccia et alii, 2003[START_REF] Livat | Expert opinion and brand reputation : An analysis from a French Cuban cigars Guidebook[END_REF]. Our contribution then adds empirical evidence to the role of experts on cigar markets. Specifically, we attempt to measure the added-value of experts' opinions on cigars with respect to the information included in price. Do consumers benefit from fresh information not reflected in the cigar's price when reading the experts' opinion?

To provide an answer to this question, we use data on Cuban cigars published in a French guidebook to study the determinants of experts' ratings through an econometric analysis. Specifically, we investigate the quality grade measured on a five-point scale and the recommendation of the cigar to a particular group of smokers (all cigar smokers, experienced smokers, etc). We estimate hedonic rating functions, cigars being regressed on a set of characteristics attributes that are objective (diameter, length, handmade cigars) and on more subjective characteristics like strength, lighting, combustion or aromas among others.

Drawing on decomposition techniques [START_REF] Fields | Accounting for income inequality and its change: A new method, with application to the distribution of earnings in the United States[END_REF], we calculate the exact contribution of each explanatory variable when explaining variation in rating. We also perform a similar analysis with information on prices and study whether more subjective attributes have more influence on experts' ratings than on prices.

Our results show that high quality cigars are, on average, recommended to more experienced cigar lovers. Grades on quality and recommendations depend on both the length and diameter of the cigar, but also on more subjective characteristics like the presence of a defect or the number of aromas. Conversely, it is essentially the more objective characteristics of the cigars that influence their prices. Thus, our findings suggest that consumers truly benefit from additional qualitative information when reading experts' advice and ratings.

The reminder of this paper is organized as follows. In the next Section, we briefly review the few economic papers dealing with cigars. The data that we use for our empirical analysis are described in Section 3. We comment on the results of our econometric analysis in Section 4. Finally, Section 5 concludes.

2/ Literature review

When turning to the specific field of cigar consumption, literature remains scarce.

Most of the published contributions so far deal with health considerations. For instance, Vlachopulos et alii (2004) show that cigar smoking increases stiffness of large arteries. Using data from 1977 to 1984, Higgins et alii (1988) study the effect of pipe and cigar smoking on the risk of lung cancer, and find that cigar and pipe smokers experienced much lower lung cancer risks than cigarette smokers. However, Refs et alii (1982) highlight the irritant effects of cigars and note that cigar smoke produces similar responses to an unfiltered cigarette at the top of the tar and nicotine range. Cigars are not then a safe alternative to cigarettes. This is of interest as Delnevo et alii (2004) have recently shown that a rise in the cigarette excise tax was leading to some product substitution between cigarettes and cigars5 .

To the best of our knowledge, only three papers have focused on hedonic equations related to either prices or quality of cigars. Using a data set of 380 cigars, [START_REF] Stover | An hedonic price index for premium cigars[END_REF] estimates a hedonic price index for premium cigars using OLS regressions. On average, cigars are more costly when they are longer, thicker, of higher quality and also when they are made in Cuba. Using an unbalanced sample of the cigars of the 32 Cuban brands commercialized in [START_REF] Livat | Expert opinion and brand reputation : An analysis from a French Cuban cigars Guidebook[END_REF] estimate experts' rating hedonic equations. They show that the quality of cigars is independent of their prices and is linked to their physical attributes. Furthermore, these objective characteristics play an increasing role over time and some brands are preferred by experts. Freccia et alii (2003) account both for price and quality of cigars using data from the leading cigar rater (the Cigar Aficionado Magazine) over the 1992-1999 period. Using a sample of 689 cigars restricted to two cigar sizes (Coronas and Double Coronas), they show that there is a positive correlation between price and quality rating. However, rating explains only a small proportion of price variation and the overall correlation between price and quality tends to decrease over time. Finally, results from a hedonic price regression indicate that flavor dummies influence price less than objective characteristics like origin country6 .

When considering the determinants of ratings, a reverse pattern holds true since sensory characteristics are more significant than more objective features, with additional evidence of a bias of the judges against mild cigars.

3/ Description of the data

To study the determinants of cigars, we use a cross-sectional dataset collected from an annual guidebook published in France, the Havanoscope. The experts from Havanoscope tasted the 261 Cuban cigars commercialized in France in 2004.

The editor of the guidebook is Jean-Paul Kaufmann, a famous journalist in France. He explicitly takes on the subjectivity of experts and the fact that the evaluation is not conducted in blind tasting. In other words, because the experts know the cigar, it is possible that quality and recommendation reflect some sort of brand reputation. Unfortunately, the exact number and the personality of experts remain unknown. For each cigar, the Havanoscope provides three sets of information related to both objective and subjective characteristics respectively and to evaluations giving by the experts of the Havanoscope.

Let us first describe the more objective features of the cigars. According to the descriptive statistics provided in Table 1, the mean diameter of the cigar is about 1.64 centimeters and the mean length is 13.96 centimeters. There are large variations in cigar length, since the shortest and the longest ones respectively measure 9.8 and 23.5 centimeters.

Among the Cuban cigars under consideration, 16.6% of them are machine-rolled. Most of them are thus handmade, a feature usually associated to the highest level of quality.

Concerning the date of creation of cigars brands, about 65% have been launched before 1900.

Only 15% of them have been produced after the Second World War. Finally, the mean price of a cigar is 6.56 Euros, with a median price of 5.5 Euros7 .

Insert Table 1 here

The dataset includes several subjective characteristics related to the consumption of cigars. According to the experts of the Havanoscope, 54.4% of the cigars are considered as strong. A similar proportion of cigars (54.4%) is concerned with remarks about lighting, while remarks about consumption are less frequently reported (23.2%). Two other characteristics, which are likely to influence the final rating given by the experts, are respectively any remarks about a defect linked to consumption (in 39% of cases) and remarks about the scarcity of the cigars, which remains infrequent (5.4%)8 .

The Havanoscope includes a detailed description of the aromas perceived by the experts (see Table 1). We choose to categorize these aromas using Houvenaghel's classification (2005) and make a distinction between 10 aromas. The mean number of perceived aromas in a cigar is 3.2. About 6% of the cigars are characterized by only one aroma, while 35% of them have at least four aromas. Woody, humus, spicy, and roasted are the most frequently reported aromas in our database, respectively 59.85%, 53.67%, 52.90% and 43.24%. A few cigars (10.81%) are perceived as having an unpleasant aroma.

We now turn to the quality evaluation and recommendations formulated by the experts of the cigar guidebook. The Havanoscope provides the two following indicators. The first one is a mark given to the cigar, which is a measure of its quality. The experts' rating ranges from 1 "ring" (low-quality) to 5 "rings" (high-quality), the mean mark related to quality being 2.62.

As shown in Table 2, only a few Cuban cigars are considered as being of high quality, since only 8.5% have the highest mark and 16.6% have the grade of 4. About 47.5% of the cigars are of low quality, with a mark of either 1 or 2.

Insert Table 2 here

Experts complete their mark with a recommendation since they indicate to which category of consumers each cigar seems the most appropriate. Again, the recommendations are distributed according to an ordered scale with five categories. The worst category is related to cigars that are not recommended to smokers. Then, four kinds of consumers are distinguished, on the basis of their experience in cigars smoking. According to the data, 11.2% of the cigars are not recommended, while 47.5% are recommended to all cigar smokers. In fact, very high recommendations are not frequent since the top two categories (respectively experienced cigar lovers and very experienced cigar lovers) concern only 17% of the consumers as shown in Table 2.

On a priori grounds, there is no reason for experts to recommend better cigars to more experienced consumers. Simple calculations show that this is absolutely not the case. Treating both the experts' rating and recommendation as continuous outcomes, we find a very high value for the corresponding correlation coefficient (0.741), which is significant at the one percent level. Therefore, a high mark awarded to a cigar is more often associated with a high recommendation from the experts, as clearly shown in Table 2. For instance, cigars not recommended by the experts are all in the lowest quality category. Among cigars recommended to cigar lovers, 75% of them are at least of medium quality (with a mark of 3 and above) and about 90% of the cigars recommended to experienced or very experienced cigar lovers are characterized by a quality of four or five. This first interesting result suggests that experts explicitly consider cigars as experience goods, i.e. goods whose true quality cannot be known before consumption. As a consequence, the utility derived from the consumption of the cigar cannot perfectly be known before smoking. According to the experts from Havanoscope, one defective product on 25 is an acceptable frequency. Furthermore, from the expert point of view, an inexperienced cigar smoker should not be able to know what a good cigar is. This finding is somewhat puzzling as one expects that information provided in guidebook concerns not only experienced consumers (defined as consumers who already benefit from their own experience).

The data finally allow us to understand how the recommendations formulated by experts depend on the characteristics of the cigar. For the sake of illustration, in what follows we focus on the role of aromas on both the self-reported quality and the recommendation.

Descriptive statistics reported in Table 3 suggest that experts have strong preferences for specific aromas. A first expected finding is that cigars featuring an unpleasant taste are much more likely to be not recommended (39.3%) or recommended to every cigar smoker (46.4%). The same pattern holds when considering the self-rated quality, since 71.4% of the cigars characterized by an unpleasant taste are evaluated as being of the lowest quality. A second result is that both recommendation and quality are positively influenced by the same aromas. On average, flower-, animal-, sweet-and spicy-flavored cigars benefit from a higher evaluation from experts. Conversely, woody-(which is the most common aroma) and herbaceous-flavored cigars are more often recommended to every cigars smoker and are assessed as being of low or medium quality.

Insert Table 3 here

To further investigate the role of both the objective and subjective cigar characteristics on the experts' opinions, we now turn to an econometric analysis. We are not only interested in knowing which factors significantly influence the levels of quality and recommendation provided by the cigar guidebook, but we also attempt to quantify the influence of each cigars characteristic on the experts' ratings. Finally, we compare these contributions to those obtained from a hedonic cigar price equation.

4/ An econometric analysis of experts' rating

4.1/ A bivariate ordered Probit model of recommendation and quality

The first step of our analysis is to understand the determinants of both the levels of recommendation and quality formulated by experts in the guidebook. Let * m Y be a latent, unobserved variable corresponding to a mark given by an expert to a cigar. The mark may be related either to recommendation ( r m

) or to quality ( q m

). Each opinion from the expert is expected to depend on a set of exogenous characteristics X , assumed to be the same for both recommendation and quality. By definition, we only observe the ordered indicators r Y

( 5 ,..., 1 r Y

) and q Y ( 5 ,..., 1 q Y

) in the database.

We use a bivariate ordered Probit model, which is made up of two equations that are jointly estimated. The first one refers to recommendation, while the second one corresponds to the quality level. The bivariate model may be expressed as:

q q q r r r X Y X Y ' ' * * (1) with j Y r when 1 , * , j r r j r Y and 5 ,..., 1 j , k Y q when 1 , * , k q q k q Y and 5 ,..., 1 k . We assume that 1 , r , 6 , r , 1 , q and 6 , q
. The various j r, and j q, are a set of threshold parameters that have to be estimated jointly with the parameters r and q . Finally, we assume that the random disturbances r and q follow a bivariate normal distribution with variances equal to one and an unknown coefficient correlation to be estimated.

For a given observation i , the log likelihood may be expressed as: 4.

j k qi ri qi ri i k Y j Y k Y j Y D ) , Pr( ) , ( ln 1 (2) with ) , Pr( ) , Pr( 1 , * , 1 , * , k q qi k q j r ri j r i q ri Y Y k Y j Y

Insert Table 4 here

A few objective and subjective characteristics (respectively age of the cigar's brand and remarks about lighting and combustion) have no significant effect on quality, nor on recommendation, except in the recommendation equation with controls for the aromas dummies. In that case, experts are more likely to recommend cigars with good combustion.

The fact that cigars are machine-made only affects quality, but the negative relationship is only significant at the 10 percent level. This is surprising, insofar as a handmade cigar consists of the filler, the binder to hold the filler, and the outer wrapper, all consisting of 100% tobacco. Conversely, the filler in machine-made cigars is not 100% tobacco and such cigars frequently have defects in their appearance (diameter or length are not constant).

The two other objective characteristics, diameter and length, have a positive and significant impact on recommendation and quality. As expected, experts give a higher rating to both larger and longer cigars, but the diameter coefficient is much higher than the length coefficient. In other words, a "chubby" cigar is viewed as a better cigar than a long one.

Nevertheless, in some circumstances, it may present the advantage of being less time consuming, especially for beginner smokers. This could explain why the difference in the diameter and length coefficients is smaller for recommendation than for quality.

According to the data, the presence of an explicit defect in the cigar strongly reduces its quality level evaluation. A similar effect, albeit of a slightly smaller magnitude, is found with the recommendation level. It is interesting to note that the difficulty of finding a cigar is assessed both in the recommendation and quality equations. An interpretation could deal with snobbism [START_REF] Corneo | Conspicuous consumption, snobbism and conformism[END_REF] if experts tend to favor cigars that are more difficult to find in a cigar shop. At the same time, the remark on scarcity may simply be due to the fact that these cigars are relatively more in demand, which should be the case if they are of high quality. The strength of the cigar has a positive effect only in the recommendation equation.

Strong cigars are not systematically better in terms of quality, but according to experts, only experienced smokers are able to enjoy this characteristic.

Finally, there is a positive relationship between the number of aromas and quality and aromatic cigars are more likely to be recommended by experts. Results in Table 4 show that herbaceous and fruity cigars are not significantly higher rated, nor recommended. While the woody, roasted and humus dummies have no effect on recommendation, the woody and roasted flavors improve quality. Conversely, humus flavor (which is the most common aroma among low quality cigars) sounds more like a negative characteristic. Unpleasant cigars are logically penalized and not recommended, whereas sweet, flowered, spicy and animal features are positively related to both quality and recommendation.

Interestingly, our results on the impact of the aroma statements are very similar to those described in Freccia et alii (2003). With cigars from different countries of origin, these authors show that sensory characteristics strongly influence ratings. We get very similar results with our subsample of Cuban cigars, with the same positive correlation between ratings and spicy, sweet and woody flavors. A more original result, specific to our study, is that the various flavors do not have exactly the same influence on ratings and on recommendation. This is the case for the woody and humus aromas, with respectively positive and negative effects on ratings, but insignificant impact on recommendation.

4.2/ Assessing the contributions of cigars' characteristics to ratings

We now attempt to assess the respective influence of the various cigars' characteristics on the experts' ratings. The idea is to know, for instance, whether the diameter and length of the cigars are more influential when explaining either recommendation or quality. In the context of linear models estimated through OLS, the decomposition methodology proposed in [START_REF] Fields | Accounting for income inequality and its change: A new method, with application to the distribution of earnings in the United States[END_REF] provides a simple way of measuring the role of each covariate when explaining a given outcome. Let us briefly present the methodology.

For that purpose, we consider the following linear model

m m m X Y
. Suppose that there are N exogenous regressors in X , indexed by n (with N n ,..., 1 ). Then, the variance of the dependent variable m Y may be decomposed in the following way:

) , cov( ) , cov( ) var( m m n m n m m Y Y X Y (3)
We then deduce the relative contribution of each covariate and the residual from:

% 100 ) ( ) ( m n n s X s (4) with ) var( / ) , cov( ) ( m m n m n Y Y X X s and ) var( / ) , cov( ) ( m m m m Y Y s
. The first term on the left-hand side of ( 4) is equal to the R², meaning that ) ( n X s indicates the weight of each regressor on the variable of interest m Y .

A difficulty in our context is that both recommendation and quality are ordered indicators, so that they cannot be treated as continuous outcomes. We thus turn to the technique of simulated residuals described in Gouriéroux et alii (1987) to get continuous outcomes c r Y and c q Y respectively from r Y and q Y . We proceed in the following way. First, using two separate ordered Probit regressions, we get consistent estimates for both the parameters of interest r ˆ and q ˆ and the threshold values j r , ˆ and j q, ˆ9. Then, for each cigar i , we draw random terms ri and qi in the normal density function respectively for recommendation and quality till the following conditions hold:

1 , , ' ˆj r ri i ri j r X (5) 1 , , ' ˆj q qi i qi j q X (6)
For each cigar, the continuous proxy of the latent variables measuring recommendation and quality are respectively given by 5.

Insert Table 5 here

According to the dataset, the most influential factor both in the recommendation and quality equations is the presence of a defect. When the cigar has a defect, this explains respectively 31.8% and 45.8% of the variation in recommendation and in quality. The role of diameter and length is also important when explaining both outcomes. The contribution of these physical characteristics is equal to 31.4% for recommendation and 25.8% for quality.

While experts put more emphasis on diameter than on length for quality (14.6% instead of 11.2%), the reverse pattern holds for quality.

The influence of the other objective characteristics (machine made cigar, age of the cigar's brand) is more modest, as well as lighting, combustion and, to a lower extent, strength in the recommendation equation (about 5%). An interesting result of Table 5 is that the difficulty of finding a cigar has a much higher weight in the recommendation equation (12.5%) than in the quality equation (4.2%), reinforcing the hypothesis of experts' snobbism.

Finally, the number of aromas account for about 19.6% of the variation in quality, and 12.6% of the variation in recommendation. For both ratings, the most influential aromas in quality are spicy and animal.

4.3/ Insights from a hedonic price equation

We now turn to a hedonic price equation. Our concern here is to know whether the various cigars' characteristics have the same influence on both the experts' rating and price.

Considering the log of the cigar price as dependent variable, we estimate OLS regressions to explain the price level. The corresponding estimates are shown in Table 6.

Insert Table 6 here

A first finding is that the explanatory variables playing a role in the price equation are not systematically the same than those previously having an influence in the quality and recommendation equations. In other words, information revealed in the hedonic price function is adequately completed with information revealed by experts. From Table 6, we note that subjective characteristics like strength, lighting and combustion do have a significant influence on the price level. This is also the case for the various aromas, except for the animal and sweet flavors whose presence in a cigar increases its price by 17.4% and 15.7%. In the same way, the most aromatic cigars are more costly, but the effect of each further aroma on price remains somewhat low (+4.5%).

We also include the contribution of each covariate from a Fields decomposition in Table 6. The contribution of the number of aromas is equal to 4.5% in the price equation, but the corresponding weight was much higher in the recommendation (12.6%) and quality (19.6%) equations. This suggests that experts devote more importance to the subjective characteristics of the cigar compared to the market, which is precisely what consumers expect from a guidebook. This result is reinforced by the fact that both diameter and length of the cigars have a much higher weight in the price equations. These two factors explain more than 45% of the variation in price.

Another influent objective characteristic is whether the cigar is handmade or not.

When the cigar is machine-rolled, its price is reduced by 83.6% and this is the most influential factor (about 34%) when explaining price. The presence of a defect and the difficulty of finding a cigar have a significant impact on the price level. On the one hand, the price is reduced by 21.6% when the cigar has a defect. On the other hand, scarce cigars are more costly, with a 35% increase in price. Nevertheless, it should be noted that the weight of these two covariates is much smaller in the price equation (respectively 7.2% for defects and 4.1%

for scarcity) than in the experts' rating equations.

A last comment about our price regressions is that we get high values of R². According to Table 6, the selected covariates explain a large part of the variation of the dependent variable (around 75%). This seems higher than the other empirical estimates in the cigar market. For instance, estimates from hedonic real price equations reported in Freccia et alii (2003, p. 182) lead to a R² equal to 0.583. An explanation of the gap could be that we consider a much more homogeneous sample since we only consider cigars from Cuban.

Conversely, Freccia et alii (2003) draw on data from the Cigar Aficionado Magazine and they include cigars from 14 different countries, 68.9% of them coming from Dominican Republic and Honduras.

5/ Concluding comments

The purpose of our empirical study was to provide insights on the determinants of both quality and price of cigars using data from the Havanoscope, an annual French guidebook.

Our main results are twofold.

First, the experts' ratings on quality and recommendation depend on both objective and subjective characteristics of the cigars like length, diameter, or number and types of aromas. As previously shown in [START_REF] Chossat | Expert opinion and gastronomy: The recipe for success[END_REF] on the gastronomy market and in Freccia et alii (2003) and [START_REF] Livat | Expert opinion and brand reputation : An analysis from a French Cuban cigars Guidebook[END_REF] on the cigar market, experts facilitate the discovering and choice of this experience good. Furthermore, consumers truly benefit from additional qualitative information when reading experts' advice and ratings. Secondly, experts are likely to recommend high quality cigars to more experienced cigar lovers. This proves that experts are not substitutes to experience for experience goods, and they are thus useful to consumers that need less information.

A limitation of our study is that we do not investigate the relationship between cigar's price and experts' rating, either for recommendation or quality. However, since our sample is a cross-section of cigars, we would only be able to document the correlation between price and rating, while it is certainly important to comment on causality issues. On the one hand, the ratings formulated by experts may lead to an increase in price if better-evaluated cigars are in higher demand (an expert premium). On the other hand, experts may be tempted to interpret the price of a cigar as a sign of its quality, thereby systematically giving higher marks to more costly cigars.

Finally, several extensions of our work may come to mind. Firstly, it would be worthwhile to collect longitudinal data in order to estimate hedonic price and quality equations net of unobserved heterogeneity. For instance, this would give the opportunity to study the persistence of quality over time and the possible influence of adverse climatic shocks on the production of cigars. Secondly, it would be of interest to extend our analysis to the whole market of cigars in order to include cigars from other major countries producing cigars like Jamaica, Bahamas, Dominican Republic, Honduras, Nicaragua, Costa Rica, Ecuador or Brazil. We leave all these issues for future research.

France

  covering the years 2000 (270 observations), 2001 (275 observations), 2002 (261 observations) and 2003 (237 observations),

  function of the selected covariates and apply the basic Fields decomposition to get the relative weight of each covariate on the experts' rating. The corresponding results are in Table

Table 4 . Bivariate ordered Probit estimates of recommendation and quality

 4 Maximum likelihood estimates from bivariate ordered Probit models. Absolute values of t-statistics are in parentheses, significance levels being respectively 1% ( *** ), 5% ( ** ) and 10% ( * ).

	Variables		(1)				(2)
		Recommendation	Quality	Recommendation	Quality
	Threshold levels
	Cutoff 1	1.931 ***	(2.77)	2.507 ***	(3.62)	1.294 *	(1.72)	2.122 ***	(2.84)
	Cutoff 2	3.997 ***	(5.66)	3.774 ***	(5.41)	3.580 ***	(4.71)	3.623 ***	(4.83)
	Cutoff 3	5.208 ***	(6.99)	5.109 ***	(6.97)	4.946 ***	(6.17)	5.092 ***	(6.46)
	Cutoff 4	5.859 ***	(7.70)	6.313 ***	(8.15)	5.696 ***	(6.95)	6.448 ***	(7.72)
	Objective characteristics
	Diameter	0.107 ***	(2.88)	0.155 ***	(4.17)	0.082 **	(2.03)	0.149 ***	(3.69)
	Length	0.010 ***	(2.94)	0.010 ***	(3.00)	0.010 ***	(2.91)	0.009 ***	(2.73)
	Machine	-0.090	(0.38)	-0.458 *	(1.88)	-0.014	(0.06)	-0.258	(1.00)
	Seniority	0.001	(0.58)	-0.003 *	(1.81)	0.001	(0.49)	-0.002	(1.43)
	Subjective characteristics
	Strong	0.317 **	(2.09)	-0.047	(0.31)	0.395 **	(2.50)	-0.020	(0.13)
	Lighting	0.079	(0.54)	-0.001	(0.01)	0.027	(0.18)	-0.083	(0.55)
	Combustion	0.246	(1.43)	0.116	(0.68)	0.391 **	(2.11)	0.157	(0.85)
	Defect	-1.060 ***	(5.97)	-1.653 ***	(9.16)	-0.853 ***	(4.53)	-1.562 ***	(8.10)
	Scarcity	1.090 ***	(3.21)	0.686 **	(2.07)	1.193 ***	(3.20)	0.981 ***	(2.72)
	Number of aromas	0.210 ***	(3.00)	0.295 ***	(4.20)
	Herbaceous					-0.253	(1.53)	-0.045	(0.28)
	Woody					-0.130	(0.81)	0.392 **	(2.41)
	Humus					-0.046	(0.28)	-0.360 **	(2.18)
	Spicy					0.530 ***	(3.10)	0.641 ***	(3.84)
	Fruity					-0.719	(1.56)	-0.214	(0.49)
	Flower					0.318 *	(1.69)	0.417 **	(2.23)
	Animal					0.890 ***	(4.50)	0.735 ***	(3.74)
	Sweet					0.431 **	(2.21)	0.346 *	(1.80)
	Roasted					0.220	(1.32)	0.490 ***	(2.99)
	Unpleasant					-0.458 *	(1.69)	-0.683 **	(2.22)
	Coefficient of correlation (t-test)		0.678 ** (7.85)			0.605 *** (2.84)
	Log likelihood		-498.3			-457.6
	Source: Havanoscope, 2004.
	Note:

Table 5 . Fields decomposition of recommendation and quality

 5

	Variables	Recommendation	Quality	Recommendation	Quality
	Objective characteristics
	Diameter	14.1%	14.6%	9.2%	11.1%
	Length	17.3%	11.2%	13.6%	8.9%
	Machine	1.1%	3.3%	0.0%	0.7%
	Seniority	-0.2%	1.6%	-0.1%	1.0%
	Subjective characteristics
	Strong	5.6%	-0.3%	5.0%	-0.2%
	Lighting	3.0%	-0.5%	2.2%	-0.7%
	Combustion	2.2%	0.6%	2.5%	0.6%
	Defect	31.8%	45.8%	20.6%	34.1%
	Scarcity	12.5%	4.2%	10.7%	4.6%
	Number of aromas	12.6%	19.6%
	Herbaceous			2.4%	0.0%
	Woody			-0.3%	2.8%
	Humus			-0.2%	1.3%
	Spicy			8.3%	10.3%
	Fruity			0.6%	0.0%
	Flower			1.9%	5.1%
	Animal			13.7%	9.6%
	Sweet			5.1%	3.0%
	Roasted			2.6%	4.4%
	Unpleasant			2.1%	3.3%
	Total	100.0%	100.0%	100.0%	100.0%
	Source: Havanoscope, 2004.

Table 6 . Prices estimates of cigars

 6 OLS estimates. Absolute values of t-statistics are in parentheses, significance levels being respectively 1% ( *** ), 5% ( ** ) and 10% (

	Variables		(1)			(2)
		coef	t-test	Fields %	coef	t-test	Fields %
	Constant	-1.119***	(4.31)		-1.055***	(3.91)
	Objective characteristics
	Diameter	0.101***	(7.06)	23.7%	0.101***	(6.72)	23.2%
	Length	0.009***	(7.00)	23.4%	0.009***	(6.80)	22.0%
	Machine	-0.836***	(9.43)	34.0%	-0.785***	(8.73)	31.2%
	Seniority	-0.001**	(2.55)	2.5%	-0.001**	(2.29)	2.2%
	Subjective characteristics
	Strong	0.039	(0.67)	0.6%	0.042	(0.73)	0.6%
	Lighting	-0.041	(0.72)	-0.4%	-0.048	(0.84)	-0.5%
	Combustion	0.058	(0.88)	0.5%	0.040	(0.59)	0.3%
	Defect	-0.216***	(3.42)	7.2%	-0.162**	(2.44)	5.3%
	Scarcity	0.350***	(2.66)	4.1%	0.398***	(2.89)	4.5%
	Number of aromas	0.045*	(1.68)	4.5%
	Herbaceous				-0.002	(0.03)	0.0%
	Woody				0.086	(1.44)	1.2%
	Humus				-0.100	(1.65)	0.5%
	Spicy				0.042	(0.67)	1.1%
	Fruity				-0.033	(0.20)	0.0%
	Flower				-0.021	(0.30)	-0.4%
	Animal				0.174**	(2.37)	3.8%
	Sweet				0.157**	(2.13)	2.5%
	Roasted				0.034	(0.55)	0.7%
	Unpleasant				-0.139	(1.46)	1.9%
	R²		0.74			0.76
	Note:

* ).

The per capita consumption is about 19 cigars in North America, while it is 17.7 in Western Europe. The per capita consumption is much lower (less than 1) in Latin America and Asia.

Both Landon and Smith (1998) in the area of wine and[START_REF] Livat | Expert opinion and brand reputation : An analysis from a French Cuban cigars Guidebook[END_REF] in the area of cigars show that the reputation of products plays a substantial role in consumer decisions.

Delnevo et alii (2004) have found such product substitution in New Jersey using data from 2001 and 2002. The proportion of cigar smokers especially increases among males, whites, all cigarette smokers and those who have gone through higher education.

In particular, the mean price of a cigar is much higher when the Cigar is made in Cuba, with a

dollar increase(Freccia et alii, 2003).

About 20 percent of the cigars exceed 10 Euros, the highest price in our database being 28 Euros (the most expensive cigar being the Monte Cristo A).

Presence of a defect refers to some difficulties in lighting the cigar, the fact it frequently goes out, etc.

We neglect here the correlation between the residuals of the two outcomes. Estimates from the independent ordered regressions are very similar to those from the bivariate model. Results are available upon request.


Michel Legrand
email: legrand@loa.univ-lille1.fr

Isabelle Chiapello

Philippe François

Mohamed T Nakes

The Infrared Difference Dust Index: Past, Present and Future

Introduction

The Infrared Difference Dust Index (IDDI) [Legrand et al, 2001] is a Meteosat-derived thermalinfrared (TIR) index dedicated to remote sensing of desert aerosol over land (based on the former Meteosat First Generation (MFG) series). The IDDI product proves to be effective in studies of dust source location and seasonal activity [START_REF] Brooks | Linking climate change to landsurface change[END_REF]Léon and Legrand, 2003;Deepshikha et al, 2003a;b], in the physics of dust emission with respect to the surface properties [Chomette et al, 1999;Marticorena et al, 1997;1999;2004], for description of dust transport [Petit et al, 2005], of dust mineral composition [Caquineau et al, 2002] and for dust (meteorological) forecasting [Hu et al, 2008].

An improved algorithm and a new cloud masking technique applied to full resolution MFG IR images has given rise to an upgraded dust index. These improvements are described in the next section and resulting IDDI images are displayed and compared with images of the UV Aerosol Index (AI) from the Ozone Monitoring Instrument (OMI) aboard EOS-AURA. They are part of a database (to be completed), over the period 1981-2006 covered by MFG. Obviously, the 1-channel IDDI cannot match with the multi-channel data provided by the new spatial instruments (such as MSG/SEVIRI), but it has the advantage of covering a quarter century and thus it is relevant for climate studies over this period (including the severe Sahelian drought of the 80s) as well as for investigating on the nature of the link between dust occurrence and epidemics of meningococcal meningitis in West Africa, throughout the 25 past years [Martiny et al, 2007].

In the last section of this abstract, the physical bases of dust remote sensing in the TIR during daylight are briefly presented. They allow to understand the fundamental differences with the more usual detection using channels in the solar spectrum (VIS and NIR) and they bear on the favorable or adverse conditions to produce a dust index of quality. Lastly, it is possible from such results to outline an approach for the determination of the TIR dust optical depth (or a dust index closely related to it), using the TIR channels IR8.7, IR10.8 and IR 12.0 of MSG/SEVIRI, as well as its median infrared (MIR) channel IR3.9.

Upgraded IDDI with MFG data

The original IR images of Meteosat, are processed according to an algorithm based on the simple assumptions for the Earth-atmosphere system of an unchanging ground surface, in the presence of an atmosphere varying with respect to cloudiness and dust. The presence of clouds or/and dust plumes is accompanied by a decrease of radiance outgoing to space, during the daytime, maximum around midday. The processing is performed daily at a fixed time (12 UTC), to avoid the effect of the diurnal cycle. The technique consists of separating in these images, the contribution of the unchanging surface, from the variable atmospheric contribution. The surface contribution is approximated by the maximum radiance of every pixel during a 15-day period long enough to ensure the occurrence of at least one reasonably cloudless and dustless day for each pixel. Hence a clear-and-clean (hot ) composite reference image (RI) is created with the collection of these hottest pixel values. Then, a difference image (DI) is created by subtracting every original image from its corresponding RI. The information content of the DI relates to clouds and dust. The last step consists of identifying and masking clouds in the DIs. After elimination of the permanent surface pattern in the DIs, clouds are observed over continental regions as over a smooth ocean-like background -so they are more conveniently identified in DIs than in original images.

The differences between the new IDDI processing and the former one are summarized in Table 1. The main change is the new cloud mask obtained through cloud identification analysing both MFG IR and VIS DIs using a structure function [START_REF] Holben | [END_REF]. This change results in an improved discrimination between clouds and dust (less clouds confused with dust, less dust confused with clouds). The dust AI is derived from the deficit of the UV radiance backscattered to space in the presence of (absorbing) dust. The downwelling UV radiance decreases rapidly when penetrating into the atmosphere, so that the UV radiance deficit is stronger for dust being in altitude than close to the surface. As dust is at higher levels in June than in March (this can be verified from the profiles of the space lidar CALIPSO/CALIOP, e.g. in ICARE archive www.icare.univ-lille1.fr/calipso/browse/), it is more efficient in backscattering the UV, which explains the difference between the AI/AOD equivalence in March and in June (see equations in Figs 1 and 2 captions). This explains the difference of intensity (color) in the AI images in Figs 1 and2 (the equations show that the AODs are not so different between both cases).

Both presented cases show a general agreement between IDDI and OMI dust distributions (the grids can help to locate the dust plumes). Note that there is no AI cloud mask so that the presence of clouds appears generally as the absence of dust (however in Figure 2 the IDDI image shows an extensive cloudiness and the AI shows clearly a significant presence of dust over clouds). The compared differences of AI to IDDI intensities for the various plumes which can be identified both in AI and IDDI images are mainly explained according to the dust altitude. So in Figure 1, the Bodele dust plume, north of the Chad Lake is certainly lifted in altitude, while on the contrary the dust plume centered (18°N, 5°E) is certainly quite low. Another feature is that the shape and structure of the corresponding AI and IDDI dust plumes are different (IDDI is more structured), due to (i) the difference between spatial resolutions and (ii) the fact that the AI dust pattern describes more dust in altitude while the IDDI dust pattern can be strong for example in case of dust emission, due to a maximum atmospheric dust loading confined near the surface.

Physical bases of dust detection in the TIR

Remote sensing of desert dust in the TIR over the continent is a thermal contrast method [King et al]. During daylight, Earth's surface absorbs Sun radiation so that it is hotter than the overlaying atmosphere. If this atmosphere is dusty, it can be detected because it is colder than the surface. The main source of TIR radiation is the hot surface which emits radiation to space. If dust is present, it partly absorbs the radiation from the ground, then it emits its own radiation to space, but there is a radiation deficit because it is colder than surface. This process can be expressed by a simple equation provided the following simplistic assumptions are applied: (i) absorbing gases are not considered, (ii) surface is assumed black at temperature T s , (iii) dust is assumed non scattering and in an isothermal layer at temperature T d with transmittance Tr (at the satellite wavelength). The quantitative result is expected to be realistic (but with a non negligible bias). The radiance ongoing to space is then: For an arid surface, a large amount of solar energy is absorbed during daylight and mostly confined within a thin layer containing the surface. This enhances the skin surface temperature T S and creates a strong superadiabatic lapse rate in the first meters over the surface of up to 60°C/m interpreted as a temperature discontinuity of 15-20°C at the surface, from measurements near Niamey [Frangi et al, 1992].

The layer transmittance is related to the TIR AOD  IR according to Tr = exp(- IR ). It is related with the "visible" photometric AOD according to  IR = k. VIS , k being a parameter depending on the dust model, defined from the particle size distribution and mineral composition. From AERONET results near Niamey, a typical value is k = 0.36 (for AOD at 0.5µm and 11.5µm) which means Tr = 0.70 with a unit AOD value at 0.5µm. The value of ∆L sat which can be derived from these numerical values is in good agreement with the empirical linear relation between photometric AOD and IDDI near Niamey [Vergé-Dépré et al, 2006], confirming the validity of this simple approach.

A conclusion is that the dust impact (and the IDDI) will be high where the surface discontinuity of temperature is strong, i.e. over the dry bare grounds of the arid and semi-arid regions (Sahara, Namibian desert, Arabian peninsula, as well as Sahel, Somalia and Kalahari desert during the dry season). On the contrary, wet vegetated surfaces and water bodies will experience little or no discontinuity, resulting in a weak sensitivity to dust. A second point is that only mineral dust, characterized by large particles and a significant absorption in the TIR, is able to involve a large impact on the TIR radiance ongoing to space, at the exclusion of all other aerosol species. Last but not least, this simple analytical approach lets imagine a method for deriving the dust TIR AOD from measurements of the MSG/SEVIRI channels considered in introduction, through intermediate determinations of surface temperature T S and dust temperature T d .

  FORMER VERSION with C. Deroo, N. Brooks, L. Shipman (2002) NEW VERSION with A. Alias, P. François, M. Nakes (in progress, 2010) 1983-1998 -B2-format Meteosat IR channel, resolution 30 km at nadir 1981-2006 -full resolution Meteosat IR channel, 5 km at nadir -fixed reference period (1-15, 16-end of each month) -15-day centered running reference period -unique radiometric scale for all instruments (M2 to M7) -IR cloud mask using thresholds for mean and s.d. of 3X3pixel arrays (derived from the "spatial coherence method") -IR/VIS cloud mask using thresholds for structure functions (means and s.d. marginally used) (in progress)-quality flag added to the product Table1. Upgrade of the IDDI product bearing on (i) spatial resolution, (ii) reference image processing, (iii) a single radiometric scale for the Meteosat series, (iv) cloud mask processing and (v) quality flag.

Figure 1 .

 1 Figure 1. Comparison between AI OMI (left) and IDDI Meteosat (right) of a strong dust outbreak on 2006/03/07 during Sahelian dry season. Resolution is 5 km at nadir for IDDI and 0.25° for AI. Grids are 10°X10° for both images. AI color scale [0-5], IDDI color scale [0-36K] in brightness temperature deficit. IDDI/AOD equivalence: 8±4 K for a unit VIS AOD at 0.5µm (5 K near Niamey). AI/AOD equivalence during March :  440 = 1.1.AI -1.3.

Figure 2 .

 2 Figure 2. Comparison between AI OMI (left) and IDDI Meteosat (right) of a winding dust plume on 2006/06/10 during the monsoon onset in Sahel.The characteristics of IDDI and AI images are the same as in Figure 1, except the AI/AOD equivalence during June :  440 = 0.357.AI +0.04, revealing a more sensitive AI to dust in June than in March.

  L sat = Tr.B(T S )+(1-Tr).B(T d ) with B the blackbody function of the channel. The dust impact measured from space (i.e. the IDDI) is: ∆L sat = (1-Tr).[B(T d )-B(T S )]


Paul Hamalainen

Adrian Pop

Max Hall

Barry Howcroft

Did the Market Signal Impending Problems at Northern Rock? An Analysis of Four Financial Instruments

whether they are published or not. The documents may come   L'archive ouverte pluridisciplinaire

EA 4272 Did the market signal impending problems at Northern Rock? An analysis of four financial instruments Paul Hamalainen (*) Adrian Pop (**) Max Hall (***) Barry Howcroft (****) 2010/39 (*) Essex Business School, University of Essex, Colchester, Essex (**) LEMNA -Université de Nantes -France (***) Department of Economics, Loughborough University, Loughborough, Leicestershire (****) The Business School, Loughborough University, Loughborough, Leicestershire