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on Cultural Economics |
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Issue of 2021‒12‒13
two papers chosen by Roberto Zanola Università degli Studi del Piemonte Orientale |
| By: | Renée B Adams; Roman Kräussl; Marco Navone (Finance Discipline Group, University of Technology Sydney); Patrick Verwijmeren |
| Abstract: | We provide evidence that culture is a source of pricing bias. In a sample of 1.9 million auction transactions in 49 countries, paintings by female artists sell at an unconditional discount of 42.1%. The gender discount increases with measures of country-level gender inequality—even in artist fixed effects regressions. Our results are robust to accounting for potential gender differences in art characteristics and their liquidity. Evidence from two experiments supports the argument that women’s art may sell for less because it is made by women. However, the gender discount reduces over time as gender equality increases. |
| JEL: | D44 J16 Z11 |
| Date: | 2021–01–01 |
| URL: | http://d.repec.org/n?u=RePEc:uts:ppaper:2021-4&r= |
| By: | Leogrande, Angelo; Costantiello, Alberto; Laureti, Lucio; Leogrande, Domenico |
| Abstract: | We estimate the Landscape and Cultural Heritage among Italian regions in the period 2004-2019 using data from ISTAT-BES. We use Panel Data with Fixed Effects, Panel Data with Random Effects, Pooled OLS, WLS, Dynamic Panel. We found that the Landscape and Cultural Heritage is negatively associated with “Dissatisfaction with the landscape of the place of life”, “Illegal building”, “Density and relevance of the museum heritage”, “Internal material consumption”, “Erosion of the rural space due to abandonment”, “Availability of urban green”, and positively associated with “Pressure from mining activities”, “Erosion of the rural space by urban dispersion”, “Concern about the deterioration of the landscape”, “Diffusion of agritourism farms”, “Current expenditure of the Municipalities for culture”. Secondly, we have realized a cluster analysis with the k-Means algorithm optimized with the Silhouette Coefficient and we found two clusters in the sense of “Concern about the deterioration of the landscape”. Finally, we use eight different machine learning algorithms to predict the level of “Concern about the deterioration of the landscape” and we found that the Tree Ensemble Regression is the best predictor. |
| Keywords: | Environmental Economics; Valuation of Environmental Effects; Pollution Control Adoption and Costs; Sustainability; Government Policy. |
| JEL: | Q50 Q51 Q52 Q56 Q58 |
| Date: | 2021–11–24 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:110814&r= |