Abstract
Despite the increase of specialised law enforcement and commercial art crime databases concerning the registration of luxury products, it remains an often-overlooked category in art crime research. This chapter analyses the market for luxury products, focusing specifically on watches, jewellery, and designer clothing, on defunct anonymous marketplace Evolution, which was active between January 2014 and March 2015. We argue that this marketplace works as a way to buy exclusivity through the purchase of both original and counterfeited luxury goods, here called ‘conspicuous goods’. The goods we focus on in our analysis endow cultural value, and their possession allows consumers to display a higher level of distinction. However, rather than looking at consumers who desire to differentiate themselves by purchasing these objects, we were more interested in how the market is structured to best sell these products. Therefore, we have implemented a series of statistical analyses on the market supply, focusing on the type of traded object, their brand, and the average prices in Bitcoin, finding that a brand effect on price is at work both in counterfeited and original conspicuous goods. This signals that the market is aware of the dynamics of conspicuous goods and its sellers behave accordingly.
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Notes
- 1.
At the time of writing, May 2021.
- 2.
Angelini and Castellani (2019) present a critical review on the link between these two values, with a focus on (potential) causal effects.
- 3.
The dates of the data dumps were not equally distanced, in particular in the first two months, when the download dates were very sparse. In our final data, the distance between one dump and the other goes between two and nine days.
- 4.
Between the start of Evolution as a marketplace until roughly the end of April 2014, the reputation system did not consist of a numerical value, but only of levels (Freshman, Junior, Senior, Expert, tentatively ordered by us since there is no way to check for this). From May 2014 onwards, the reputation system changed to a numerical value, starting from 0, as in most online marketplace in the legal market, that was associated with a reputation level based on these numbers (Level 1, Level 2, etc.).
- 5.
Notice that each unique id is associated with a certain object that was up on the marketplace in a certain day.
- 6.
The figures are computed considering the lowest BTC to EUR exchange rate, so they should be taken as the lower boundaries of the ranges of these prices over time, since the exchange rate is highly volatile in the considered period.
- 7.
Notice that the number of dropped observations in this case would have been higher if we did not drop the N/A reputation number in the previous step, since all the dates before May 2014 presented very few observations with respect to the rest of the period. This might be due to the fact that the marketplace did not start ‘at full power’ from the beginning, but we have no means to test this hypothesis.
- 8.
Combining the categories necklace (6.61 percent), bracelet (5.10 percent), and earrings (2.45 percent). Other less represented sub-categories which can be considered as jewels are ring (1.40 percent), cufflinks (0.34 percent), ring and necklace (0.18 percent), keyring (0.07 percent), and money clips (0.01 percent). Some sellers also sold branded boxes of necklaces and bracelets, and these accounted for 0.23 percent of total observations, and branded boxes of watches, accounting for 1.53 percent of all observations.
- 9.
We cannot know how many of these objects were sold, since we only observed the ads of the sellers and information of consumers is not available. What we can observe is the reputation number as a noisy measure of completed transactions. Inferring remains complex because we cannot assert how many points of the reputation number comes from our examined categories, and how many are from other categories. However, this information is not reproducible from the original dataset.
- 10.
The median is computed over a moving window of 10 periods, the signal level is estimated at the end of each time window. The computation was implemented using the R package by Fried et al. (2019).
- 11.
We reported these values in logarithmic form since the high variability of these values (see Table 1) would impede a graphical comparison otherwise.
- 12.
In our analysis, we pool the data at cross-sectional level, meaning that we do not consider time dynamics. This is because our data is irregularly spaced with respect to time and an approach to consider time dynamics with this type of data would be too complex.
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We would like to thank Michelangelo Puliga for his assistance with the database.
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Oosterman, N., Angelini, F. (2021). One Flew Over the Cuckoo’s Clock. In: Oosterman, N., Yates, D. (eds) Crime and Art. Studies in Art, Heritage, Law and the Market, vol 1. Springer, Cham. https://doi.org/10.1007/978-3-030-84856-9_16
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