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What methodology do the authors use and why? Are there any implications of the finding for policy makers or business leaders?

What methodology do the authors use and why? Are there any implications of the finding for policy makers or business leaders?

ENGR.ECONOMIC ANALYSIS
ENGR.ECONOMIC ANALYSIS
14th Edition
ISBN:9780190931919
Author:NEWNAN
Publisher:NEWNAN
Chapter1: Making Economics Decisions
Section: Chapter Questions
Problem 1QTC
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Explain with clear brief points with correct answer - please do not attempt to answer if you are not confident please do not waste my chance to ask questions as they are not for free and please do not copy. What methodology do the authors use and why? Are there any implications of the finding for policy makers or business leaders?
46 Table 1 Descriptive statistics and sample distribution. Panel A: Pooled sample statistics Variable Price Price+bp Scrap Weight Spread Age Order Q1 02 prov arm assay_off 2009 2010 2011 2012 2013 2014 Quarter Mean 1st 2nd 3rd 4th 880.74 1073.17 295.16 25.93 0.39 166.06 Obs. 551 667 671 539 447 332 0.67 0.08 489 1134 424 1160 0.19 0.02 0.40 0.62 Std. dev, 1906.84 2311.57 441.80 37.03 0.13 77.75 0.25 0.26 0.39 0.15 0.49 0.48 Price Scrap 708.85 220.07 698.21 278.90 899.24 538.90 1089.38 645.23 1062.86 417.61 1386.42 485.82 P. Draper et al. / Economics Letters 162 (2018) 45-48 892.68 391.53 928.36 424.30 895.67 393.99 959.65 452.56 Min. 20.00 24.40 0.89 Panel B: Sample distribution of lots sold, average lot hammer price (£), and average lot scrap value (£) Year Bonhams Knightsbridge 0.10 0.05 1.00 0.00 Max. 85,00000 101,5700 6,211.80 553.00 1.00 510.00 1.00 1.00 1.00 1.00 Knowle Obs. Price Scrap 179.87 402 382.44 476 458.25 298.34 472 596.60 459.47 1.00 1.00 291 465.10 275.94 394 488.48 296 487.53 324.64 306.71 358.25 369 491.45 the bullion dealer at the prices quoted and with speedy or even immediate payment. The possibility, therefore, of selling for scrap provides a minimum guaranteed price, allowing sellers to realise cash with a minimum of restrictions. Both sales for scrap, and to antique dealers provide immediacy to the seller. They also provide certainty and low transaction costs. egener. They also provide They do not however provide exposure to a wide range of buyers. The significant transactions costs associated with selling by auction suggests that sellers of silver goods that are of low quality and plentiful supply should sell direct to bullion or antique dealers to sential supplying fras antig maximise their receipts from sale. Despite this, low quality silver in cold at aucti Collion for is sold at auction. Selling for scrap removes any chance of receiving is sold at auction sering for a premium for artistic merit or scarcity and there may be some sellers of low quality items who prefer, perhaps through ignorance, to sell by auction. High quality items should not be scrapped but sold to dealers or by auction. Mani Many silver goods have some worth relating to their scarcity, usefulness, artistic merit and quality of workmanship. A number of items, however, have no such merit. They may have been mass produced and of poor quality originally, or may be battered and broken. The demand for such items is slight. They are of limited interest to collectors or dealers. They do however retain value, a value that depends on their silver content. They may be easily melted and reduced to pure silver. In an efficient market, we would expect any silver item to have a minimum value directly related to its scrap value. 2. Sample and data To examine the role that the scrap value plays in determining the price of silver items sold at auction, along with any possible opportunities for riskless arbitrage, we construct a unique database from the catalogues of 88 silver auctions from two major En- glish auction houses, Bonhams, and Woolley and Wallis, between Description Hammer price of lot (£) Hammer price of lot including the buyer's premium (£) Scrap value of lot (£) Weight of lot (ounces) Auctioneer's spread (High estimate - Low estimate)/Low estimate) Age of lot (years) Order of lot in the auction Dummy (= 1 if notable 20th century maker; 0 otherwise) Dummy (= 1 if notable pre-20th century makers; 0 otherwise) Dummy (= 1 if provenance; 0 otherwise) Dummy (= 1 if armorial; 0 otherwise) Dummy (= 1 if London assay office; 0 otherwise) New Bond Street Obs. Price 148 3245.54 132 146 61 93 42 Scrap 283.95 3614.77 377.16 3987.33 741.78 3179.48 674.79 4074.73 798.47 5035.71 685.61 274 3748.17 635.91 72 2750.69 285.49 276 3981.84 541.78 Woolley and Wallis Salisbury Obs. Price 567 478.66 920.42 684.69 828 986 1018 580.38 1080 559.84 956 506.69 1180.00 549.56 1376.00 691.92 1415.00 607.24 1464.00 634.33 Scrap 81.93 192.16 288.25 211.68 162.78 116.80 170.31 186.19 186.37 185.68 January 2009 and December 2014. Although no statistics are available for the proportion of antique silver sold by dealers in the UK, informed sources have suggested that, while dealers are responsible for about half of annual sales, auction houses have become more dominant in the market. Bonhams is the third major international UK auction house and the largest seller of UK antique silver by volume over our sample period, and Woolley and Wallis is the UK's largest regional auction house and the second largest seller of UK antique silver by volume. 112 Since it is common for auctioneers to exclude some of the com available pre-sale information from the online data (Campos and Barbosa, 2009), copies of the physical catalogues are used. Whilst attempting to be as comprehensive as possible we exclude unsold lots, mixed collections (since it is impossible to assign detailed information to the individual pieces), and lots without a pre- sale estimate. Our final sample contains information on 10,614 sold lots spread across three Bonhams locations, Knightsbridge (30%), Knowle (13%), New Bond Street (6%), and Woolley and Wallis' house in Salisbury (51%). In terms of the timing of the silver auctions, Woolley and Wallis hold auctions quarterly, while Bonhams have departed from a fixed schedule in recent years, and closed their Knowle salesroom in 2011. Table 1 reports the main descriptive statistics and sample distribution of the dataset described above. 3. A hedonic price model for silver goods To examine the relation between the hammer price and the scrap value of the auction lot, we use the following hedonic pricing 1 Unlike other studies, we do not concentrate on the international auction houses, Christies and Sotheby's, since both rarely hold specialist silver auctions in London over the sample period.
Transcribed Image Text:46 Table 1 Descriptive statistics and sample distribution. Panel A: Pooled sample statistics Variable Price Price+bp Scrap Weight Spread Age Order Q1 02 prov arm assay_off 2009 2010 2011 2012 2013 2014 Quarter Mean 1st 2nd 3rd 4th 880.74 1073.17 295.16 25.93 0.39 166.06 Obs. 551 667 671 539 447 332 0.67 0.08 489 1134 424 1160 0.19 0.02 0.40 0.62 Std. dev, 1906.84 2311.57 441.80 37.03 0.13 77.75 0.25 0.26 0.39 0.15 0.49 0.48 Price Scrap 708.85 220.07 698.21 278.90 899.24 538.90 1089.38 645.23 1062.86 417.61 1386.42 485.82 P. Draper et al. / Economics Letters 162 (2018) 45-48 892.68 391.53 928.36 424.30 895.67 393.99 959.65 452.56 Min. 20.00 24.40 0.89 Panel B: Sample distribution of lots sold, average lot hammer price (£), and average lot scrap value (£) Year Bonhams Knightsbridge 0.10 0.05 1.00 0.00 Max. 85,00000 101,5700 6,211.80 553.00 1.00 510.00 1.00 1.00 1.00 1.00 Knowle Obs. Price Scrap 179.87 402 382.44 476 458.25 298.34 472 596.60 459.47 1.00 1.00 291 465.10 275.94 394 488.48 296 487.53 324.64 306.71 358.25 369 491.45 the bullion dealer at the prices quoted and with speedy or even immediate payment. The possibility, therefore, of selling for scrap provides a minimum guaranteed price, allowing sellers to realise cash with a minimum of restrictions. Both sales for scrap, and to antique dealers provide immediacy to the seller. They also provide certainty and low transaction costs. egener. They also provide They do not however provide exposure to a wide range of buyers. The significant transactions costs associated with selling by auction suggests that sellers of silver goods that are of low quality and plentiful supply should sell direct to bullion or antique dealers to sential supplying fras antig maximise their receipts from sale. Despite this, low quality silver in cold at aucti Collion for is sold at auction. Selling for scrap removes any chance of receiving is sold at auction sering for a premium for artistic merit or scarcity and there may be some sellers of low quality items who prefer, perhaps through ignorance, to sell by auction. High quality items should not be scrapped but sold to dealers or by auction. Mani Many silver goods have some worth relating to their scarcity, usefulness, artistic merit and quality of workmanship. A number of items, however, have no such merit. They may have been mass produced and of poor quality originally, or may be battered and broken. The demand for such items is slight. They are of limited interest to collectors or dealers. They do however retain value, a value that depends on their silver content. They may be easily melted and reduced to pure silver. In an efficient market, we would expect any silver item to have a minimum value directly related to its scrap value. 2. Sample and data To examine the role that the scrap value plays in determining the price of silver items sold at auction, along with any possible opportunities for riskless arbitrage, we construct a unique database from the catalogues of 88 silver auctions from two major En- glish auction houses, Bonhams, and Woolley and Wallis, between Description Hammer price of lot (£) Hammer price of lot including the buyer's premium (£) Scrap value of lot (£) Weight of lot (ounces) Auctioneer's spread (High estimate - Low estimate)/Low estimate) Age of lot (years) Order of lot in the auction Dummy (= 1 if notable 20th century maker; 0 otherwise) Dummy (= 1 if notable pre-20th century makers; 0 otherwise) Dummy (= 1 if provenance; 0 otherwise) Dummy (= 1 if armorial; 0 otherwise) Dummy (= 1 if London assay office; 0 otherwise) New Bond Street Obs. Price 148 3245.54 132 146 61 93 42 Scrap 283.95 3614.77 377.16 3987.33 741.78 3179.48 674.79 4074.73 798.47 5035.71 685.61 274 3748.17 635.91 72 2750.69 285.49 276 3981.84 541.78 Woolley and Wallis Salisbury Obs. Price 567 478.66 920.42 684.69 828 986 1018 580.38 1080 559.84 956 506.69 1180.00 549.56 1376.00 691.92 1415.00 607.24 1464.00 634.33 Scrap 81.93 192.16 288.25 211.68 162.78 116.80 170.31 186.19 186.37 185.68 January 2009 and December 2014. Although no statistics are available for the proportion of antique silver sold by dealers in the UK, informed sources have suggested that, while dealers are responsible for about half of annual sales, auction houses have become more dominant in the market. Bonhams is the third major international UK auction house and the largest seller of UK antique silver by volume over our sample period, and Woolley and Wallis is the UK's largest regional auction house and the second largest seller of UK antique silver by volume. 112 Since it is common for auctioneers to exclude some of the com available pre-sale information from the online data (Campos and Barbosa, 2009), copies of the physical catalogues are used. Whilst attempting to be as comprehensive as possible we exclude unsold lots, mixed collections (since it is impossible to assign detailed information to the individual pieces), and lots without a pre- sale estimate. Our final sample contains information on 10,614 sold lots spread across three Bonhams locations, Knightsbridge (30%), Knowle (13%), New Bond Street (6%), and Woolley and Wallis' house in Salisbury (51%). In terms of the timing of the silver auctions, Woolley and Wallis hold auctions quarterly, while Bonhams have departed from a fixed schedule in recent years, and closed their Knowle salesroom in 2011. Table 1 reports the main descriptive statistics and sample distribution of the dataset described above. 3. A hedonic price model for silver goods To examine the relation between the hammer price and the scrap value of the auction lot, we use the following hedonic pricing 1 Unlike other studies, we do not concentrate on the international auction houses, Christies and Sotheby's, since both rarely hold specialist silver auctions in London over the sample period.
Table 2 Hedonic regression of natural log of hammer price on the natural log of scrap value and other characteristics. Variable Predicted sign Full sample Bonhams Coeff. (3) 0.485*** -0.239*** 0.317*** -0.100** In (scrap) Spread In (age) Order Q1 Q2 prov arm assay_off Constant Observations Description dummies +ve -ve +ve ? +ve +ve +ve +ve -ve N.A P. Draper et al. / Economics Letters 162 (2018) 45-48 Table 3 Identifying arbitrage opportunities. Arbitrage category Price > Scrap Price < Scrap Price & buyer's premium < Scrap No. lots sold 9924 690 56 Coeff. (1) 0.457*** -0.248*** 0.411*** 0.129*** 0.240*** 0.056*** 0.602*** 0.050*** -0.071*** (11) Place dummies (3) Year dummies (5) Quarter dummies (3) Adj. R-squared 0.547 See Table 1 for variable definitions. *** p < 0.01, ** p < 0.05, * p < 0.1. 1.850*** 10,614 Std. err. (2) (0.008) (0.046) (0.018) (0.033) (0.029) (0.016) (0.061) (0.014) (0.015) (0.115) Total weight (oz.) 245,311 29,877 1,608 regression: In(Price); ao + a,In(Scrap); + Characteristics; + Controls + e₁ (1) Where In(Price); is the natural log of the auction lot's hammer price, and In(Scrap), is our main variable of interest, the natural log of the lot's scrap value, for which we expect a significant positive coefficient if buyers' consider the scrap value of a lot an important feature when bidding? Characteristics; include characteristics found in the auction catalogues that may explain the auction price. Specifically, we control for uncertainty as the spread between the high and low pre-sale estimate, age as the natural log of the lot age, the lot position in the auction, two binary quality variables to indicate if the lot was made by a notable silversmith working in the twentieth century (Q1) or earlier (Q2), and three binary variables to indicate provenance, the presence of an armorial or other inscription, and whether the assay office was other than London. Finally, we include binary variables ( Controls) to control for the lot description (e.g. flatware), place of sale, year of sale, and quarter within the year in which the lot was sold. 4. Empirical analysis and results Table 2 reports the hedonic regression results of Eq. (1) for the full sample, lots sold by Bonhams, and lots sold by Woolley and Wallis. In all three regressions, we use unreported dummy variables to control for the lot description, place of sale, year of sale and quarter within the year in which the lot was sold. We report robust standard errors using the White heteroskedasticity-robust procedure. For all three regressions, the scrap value of silver has a 2 The lot's scrap value is calculated as the product of the lot's weight in ounces and the corresponding non-trade scrap price of silver on the auction day from Cookson Precious Metals Ltd at http://www.cooksongold.com/. This represents the price at which they are prepared to trade on that day. Over our sample period the non-trade scrap price of silver per ounce ranges from £5.26 on 15 January 2009 to £21.38 on 28 April 2011, has a mean (standard deviation) of £11.03 (£3.53), and an average (standard deviation) daily change of £0.002 (£0.295). 0.187*** 0.058*** 0.268*** 0.047*** 0.018 2.255*** 5,179 (11) (2) (5) (3) 0.638 Total price (£) 8,976,321 371,875 14.490 Std.err. (4) (0.010) (0.055) (0.022) (0.043) (0.034) (0.020) (0.092) (0.018) (0.019) (0.134) 5. Conclusions Woolley and Wallis Coeff. (5) 0.399*** -0.061 0.512*** 0.421*** 0.240*** 0.052** 0.772*** 0.061*** -0.104*** 1.147*** 5,435 (11) N.A (5) (3) 0.389 Total price+bp (£) 10,900,000 454,229 17.679 47 Std. err. (6) (0.011) (0.074) (0.029) (0.052) (0.046) (0.025) (0.074) (0.021) (0.021) (0.180) Total scrap (£) 2,734,583 398,286 20,792 significant positive effect on the hammer price and the remaining variables, where significant, are all of the predicted sign. In an auction environment, this implies that bidders are partly setting their bids based on the lot's scrap value. The evidence does not, however, confirm whether profitable arbitrage opportunities exist betw between the silver auctions and scrap value of the lots sold and purchased. If arbitrage opportunities exist, then there is prima facie ev- dan idence of market inefficiency. While this is a weak-form test of market offi market efficiency, since it only uses price information, the absence of arbitrage is a very important characteristic of financial markets, which we test for in the physical silver market. Purchasers silver goods are faced with a variety of costs, notably the buyer's premium. Therefore, we expect profitable arbitrage may be pos- sible before allowing for the buyer's premium but is most likely impossible once it is added into the price of the good. To test this, we identify and report summary statistics in Table 3 of all lots sold above and below their scrap value based on the hammer price paid to the seller, and all lots sold below the scrap value based on the hammer price and buyer's premium paid by the bidder. Our findings in Table 3 show that arbitrage opportunities are available if the only cost imposed on buyers is the hammer price. However, once the buyer's premium is included, arbitrage oppor- tunities almost completely disappear. Specifically, less than one percent of our sample would yield a profit of, on average, less than £60 per transaction, ignoring any physical costs of collection and resale. The absence of arbitrage opportunities after costs, particularly the buyer's premium, suggests that bidders are sensitive to the total price. Moreover, although scrap silver can be sold for cash at 3 The buyer's premium is a fee set by the auction house, and paid to the auction- eer by the buyer. This is constant for each auction, but varies between houses and over the sample period from 19.5-25% of the hammer price. 4 Analysis of the 56 lots for which arbitrage is possible indicates that 91% were sold by Bonhams and more than half in the fourth quarter.
Transcribed Image Text:Table 2 Hedonic regression of natural log of hammer price on the natural log of scrap value and other characteristics. Variable Predicted sign Full sample Bonhams Coeff. (3) 0.485*** -0.239*** 0.317*** -0.100** In (scrap) Spread In (age) Order Q1 Q2 prov arm assay_off Constant Observations Description dummies +ve -ve +ve ? +ve +ve +ve +ve -ve N.A P. Draper et al. / Economics Letters 162 (2018) 45-48 Table 3 Identifying arbitrage opportunities. Arbitrage category Price > Scrap Price < Scrap Price & buyer's premium < Scrap No. lots sold 9924 690 56 Coeff. (1) 0.457*** -0.248*** 0.411*** 0.129*** 0.240*** 0.056*** 0.602*** 0.050*** -0.071*** (11) Place dummies (3) Year dummies (5) Quarter dummies (3) Adj. R-squared 0.547 See Table 1 for variable definitions. *** p < 0.01, ** p < 0.05, * p < 0.1. 1.850*** 10,614 Std. err. (2) (0.008) (0.046) (0.018) (0.033) (0.029) (0.016) (0.061) (0.014) (0.015) (0.115) Total weight (oz.) 245,311 29,877 1,608 regression: In(Price); ao + a,In(Scrap); + Characteristics; + Controls + e₁ (1) Where In(Price); is the natural log of the auction lot's hammer price, and In(Scrap), is our main variable of interest, the natural log of the lot's scrap value, for which we expect a significant positive coefficient if buyers' consider the scrap value of a lot an important feature when bidding? Characteristics; include characteristics found in the auction catalogues that may explain the auction price. Specifically, we control for uncertainty as the spread between the high and low pre-sale estimate, age as the natural log of the lot age, the lot position in the auction, two binary quality variables to indicate if the lot was made by a notable silversmith working in the twentieth century (Q1) or earlier (Q2), and three binary variables to indicate provenance, the presence of an armorial or other inscription, and whether the assay office was other than London. Finally, we include binary variables ( Controls) to control for the lot description (e.g. flatware), place of sale, year of sale, and quarter within the year in which the lot was sold. 4. Empirical analysis and results Table 2 reports the hedonic regression results of Eq. (1) for the full sample, lots sold by Bonhams, and lots sold by Woolley and Wallis. In all three regressions, we use unreported dummy variables to control for the lot description, place of sale, year of sale and quarter within the year in which the lot was sold. We report robust standard errors using the White heteroskedasticity-robust procedure. For all three regressions, the scrap value of silver has a 2 The lot's scrap value is calculated as the product of the lot's weight in ounces and the corresponding non-trade scrap price of silver on the auction day from Cookson Precious Metals Ltd at http://www.cooksongold.com/. This represents the price at which they are prepared to trade on that day. Over our sample period the non-trade scrap price of silver per ounce ranges from £5.26 on 15 January 2009 to £21.38 on 28 April 2011, has a mean (standard deviation) of £11.03 (£3.53), and an average (standard deviation) daily change of £0.002 (£0.295). 0.187*** 0.058*** 0.268*** 0.047*** 0.018 2.255*** 5,179 (11) (2) (5) (3) 0.638 Total price (£) 8,976,321 371,875 14.490 Std.err. (4) (0.010) (0.055) (0.022) (0.043) (0.034) (0.020) (0.092) (0.018) (0.019) (0.134) 5. Conclusions Woolley and Wallis Coeff. (5) 0.399*** -0.061 0.512*** 0.421*** 0.240*** 0.052** 0.772*** 0.061*** -0.104*** 1.147*** 5,435 (11) N.A (5) (3) 0.389 Total price+bp (£) 10,900,000 454,229 17.679 47 Std. err. (6) (0.011) (0.074) (0.029) (0.052) (0.046) (0.025) (0.074) (0.021) (0.021) (0.180) Total scrap (£) 2,734,583 398,286 20,792 significant positive effect on the hammer price and the remaining variables, where significant, are all of the predicted sign. In an auction environment, this implies that bidders are partly setting their bids based on the lot's scrap value. The evidence does not, however, confirm whether profitable arbitrage opportunities exist betw between the silver auctions and scrap value of the lots sold and purchased. If arbitrage opportunities exist, then there is prima facie ev- dan idence of market inefficiency. While this is a weak-form test of market offi market efficiency, since it only uses price information, the absence of arbitrage is a very important characteristic of financial markets, which we test for in the physical silver market. Purchasers silver goods are faced with a variety of costs, notably the buyer's premium. Therefore, we expect profitable arbitrage may be pos- sible before allowing for the buyer's premium but is most likely impossible once it is added into the price of the good. To test this, we identify and report summary statistics in Table 3 of all lots sold above and below their scrap value based on the hammer price paid to the seller, and all lots sold below the scrap value based on the hammer price and buyer's premium paid by the bidder. Our findings in Table 3 show that arbitrage opportunities are available if the only cost imposed on buyers is the hammer price. However, once the buyer's premium is included, arbitrage oppor- tunities almost completely disappear. Specifically, less than one percent of our sample would yield a profit of, on average, less than £60 per transaction, ignoring any physical costs of collection and resale. The absence of arbitrage opportunities after costs, particularly the buyer's premium, suggests that bidders are sensitive to the total price. Moreover, although scrap silver can be sold for cash at 3 The buyer's premium is a fee set by the auction house, and paid to the auction- eer by the buyer. This is constant for each auction, but varies between houses and over the sample period from 19.5-25% of the hammer price. 4 Analysis of the 56 lots for which arbitrage is possible indicates that 91% were sold by Bonhams and more than half in the fourth quarter.
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