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on Cultural Economics |
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Issue of 2026–06–08
three papers chosen by Roberto Zanola, Università degli Studi del Piemonte Orientale |
| By: | Ioannis Laliotis; Christos A. Makridis |
| Abstract: | This paper documents the labour market position of artists across Europe and examines how public cultural spending is related to artists' outcomes. Using EU-LFS micro-data for 2009--2023, we compare artists to non-artists using harmonised measures of wage rank, employment, and non-standard work. Using within-country variation and controlling for demographic factors, artists rank substantially lower in the wage distribution: in the pooled sample, the estimated penalty is about 0.46 wage deciles relative to other salaried workers and about 0.28 deciles relative to other professionals. These earnings gaps coexist with higher exposure to non-standard employment, including part-time work, temporary contracts, and multiple job holding, with patterns that persist over the life cycle and appear in most countries. We then link individual outcomes to a country-year panel of real per capita public expenditure on cultural services. Higher cultural spending is associated with modest improvements in aggregate employment and a lower incidence of part-time work, but these associations do not systematically differ for artists or arts graduates. The only consistent differential correlation is an increase in multiple job holding among arts graduates in higher-spending environments. Thus, changes in aggregate cultural spending are not sufficient on their own to narrow wage or job-quality gaps for artists, motivating a rethinking of cultural policy instruments toward mechanisms that more directly address labour market risks faced by cultural workers. |
| Keywords: | artists, cultural employment, public cultural expenditure, Europe |
| JEL: | Z11 J44 J31 J68 H52 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12699 |
| By: | Kostiantyn Okhrimenko (University of Warsaw, Faculty of Economic Sciences) |
| Abstract: | This paper investigates the feasibility of predicting art prices using machine learning methods applied to a dataset of 20, 905 paintings and drawings scraped from the Artsper online marketplace. We test tree-based ensemble models (Decision Tree, Random Forest, XGboost) and deep learning architectures (MLP, CNN, Fusion) on both tabular metadata and hand-crafted image features. Results consistently show poor predictive performance across all model types and feature sets. We argue that this outcome is not a methodological failure but a substantive finding: it constitutes a diagnosis of the market structure of contemporary art. Drawing on hedonic pricing theory (Rosen 1974), the sociology of cultural fields (Bourdieu 1993), and the economics of valuation (Velthuis 2005; Beckert and Rössel 2013), we propose a three-layer model of art price determinants: physical attributes (observable and partially captured by models), visual-aesthetic features (observable but poorly quantifiable), and narrative-reputational capital (largely unobservable in cross-sectional platform data). |
| Keywords: | art market, machine learning, hedonic pricing, art valuation, cultural economics, XGboost, CNN |
| JEL: | Z11 C45 C53 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:war:wpaper:2026-18 |
| By: | Steinhoff, James (University College Dublin) |
| Abstract: | Synthetic data has recently been proposed as an alternate means of procuring data for training AI which dispenses with data work. However the labour required to produce it has not been studied. This paper does so by looking at the technical artist: a hybrid programmer and 3D artist recently brought into the AI industry from the games and film industry. I argue that technical art, in the synthetic data context, is data work but of an unfamiliar kind. I demonstrate this through a labour process analysis of procedural asset creation. I show that in the synthetic data context, technical art is governed by the goal of forcing generalization. I suggest that the concept of data work should not ossify to capture only its present state of collection and cleaning, but that a more mutable concept is necessary to track changes in the AI industry. While claims of data work’s coming disappearance are implausible, it seems unwise to overstate its permanency in its present state. |
| Date: | 2026–05–23 |
| URL: | https://d.repec.org/n?u=RePEc:osf:mediar:t7kvz_v1 |