Title: The European Social Model as the key for competitive growth: the role of social investment in the era of poly-crisis

Abstract: The seminar will examine the intersection of the European Social Model and the EU’s deepening competitiveness crisis and will discuss how the social investment is a key ingredient for competitive growth. The presentation will start revisiting the European Union project as convergence machine for the economic and social performance of its Member States and will discuss the impact of the recent crisis on that. It will refer to the urgent call for competitiveness as per the 2024 Mario Draghi report and will argue the requirement of a complementary "social investment" strategy to succeed. It will show the economic cost of social inaction and will conclude discussing that the "competitiveness" Europe seeks is unattainable without the capacitating functions of a modernized welfare state.

Title: Intersecting Shocks: The Combined Labor Market Impacts of Automation and Immigration
(Patrick Bennett, Julian Vedeler Johnsen)

Abstract: We study how the labor market shocks of automation and immigration interact to shape workers’ outcomes. Using matched employer–employee data from Norwegian administrative registers, we combine an immigration shock triggered by the European Union’s 2004 enlargement with an automation shock based on the adoption of industrial robots across Europe. Although these shocks largely occur in separate industries, we show that automation reduces earnings not only in manufacturing but also in construction, where tasks overlap with robot-exposed sectors. Importantly, workers jointly exposed to automation and immigration suffer earnings losses greater than those facing either shock in isolation. These losses are driven by downward occupational mobility into low-wage services and re-sorting into lower-premium firms. Even within the Norwegian welfare system, the ability of social insurance to offset these long-run earnings declines is limited. Our findings underscore the importance of analyzing labor market shocks jointly, rather than in isolation, to fully understand their distributional consequences.

Title: Data, Power and Emissions: The Environmental Cost of AI
(Alessandra Bonfiglioli, Rosario Crinò, Mattia Filomena, Gino Gancia)

Abstract: We study the environmental impact of artificial intelligence (AI) using a novel dataset that links measures of AI penetration, the location of data centers and power plants, and CO2 emissions across US commuting zones between 2002 and 2022. Our analysis yields four main findings. First, exploiting a shift–share identification strategy, we show that localities more exposed to AI experience relatively faster emissions growth. Second, decomposition results indicate that scale effects dominate, while changes in industrial composition exert at most a weak mitigating effect; at the same time, electricity generation becomes more carbon intensive. Third, AI penetration raises dependence on non-renewable electricity. Fourth, proximity to data centers is a key driver of this effect, as nearby power plants shift toward greater fossil fuel use. These findings suggest that, absent a rapid decarbonization of power generation, the diffusion of AI is likely to exacerbate environmental externalities through the energy demand of data centers.

Title: When Teachers Break the Rules: Imitation and Reciprocity in the Transmission of Ethical Behavior and the Role of Community Structure
(Victor Lavy, Moses Shayo) 

Abstract: We study how teachers' rule violations in grading affect students' ethical behavior, using administrative data that track teacher violations and subsequent student cheating on high-stakes exams. Exploiting within-student variation in exposure to different teachers, we find that students are significantly more likely to cheat when teachers break the rules to their detriment (giving exceptionally low internal grades). However, when teachers break the rules in their favor (e.g., by inflating internal grades), the response varies across community contexts. In homogeneous communities, students respond to favorable teacher violations by cheating less, consistent with reciprocal norms. In heterogeneous communities, both types of teacher violations increase student cheating. This pattern holds across multiple measures of community homogeneity, including surname concentration and residential clustering. Survey measures of mutual support, trust, and reciprocity between students and teachers support this pattern.

This presentation will cover the material of two related papers on organizational sabotage and unintended consequences of policy tools.

Title: Optimal Deterrence of Workplace Sabotage: The Unintended Consequence of Intermediate Fines
(Subhasish M. Chowdhury, Iryna Topolyan) 

Abstract: We investigate the effects of punitive fines on sabotage behavior in organizations. We consider a two-stage Tullock contest in which agents exert productive effort in the first stage. In the second stage, they can diminish the effort of the opponent by incurring a sabotage, which is costly due to a possible fine. This structure is common in the field, but new to the literature. We fully characterize the equilibria and show that sabotage decreases with an increase in the fine level. However, the effect of fine on effort as well as on payoff are non-monotonic. As an unintended consequence, for an intermediate level of fine the total effort and total payoff may decrease substantially.

Title: Organization, Sabotage, and the Unintended Consequences of Nudges on Non-targeted Behavior
(Subhasish M. Chowdhury, Joo Young Jeon, Maroš Servátka, Jiří Špalek)

Abstract: In organizational settings where relative performance is the key to success, employees often resort to sabotaging co-worker’s productive efforts. Since nudges have the potential to change people’s behavior as a cost-effective tool, we investigate experimentally the effectiveness of such a nudge – the presence of a social cue in the form of a pair of eyes – on sabotage behavior. We also examine whether the nudge effect can spill over to other domains of behavior. We find that the costless nudge indeed significantly reduces sabotage behavior. However, as an unintended consequence, it also results in a decreased productive effort. Furthermore, we find significant gender differences in the effect of the nudge. Implications for behavioral science and managerial practice are discussed.

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Title: Profit Shifting and Real Investment Activity
(Tobias Hahn, Dirk Schindler, Georg Wamser)

Abstract: This paper investigates the relationship between profit shifting behavior and real investment activity of multinational corporations (MNCs). We first model the three main channels of profit shifting and show that two of those affect investment behavior through their effect on the user cost of capital. For all channels, we identify conditions under which the minimum tax rate in the MNC affects investment in other affiliates. We test the theoretical predictions by using rich micro-level data on foreign affiliates of MNCs. Based on instrumental variable regressions and event study estimates, we find that the incentives to shift profits to low-tax locations reflect in the user cost of capital in high-tax countries. While the tax semi-elasticity of investment with respect to a one percentage point increase in the local statutory tax rate is about 0.45%, the responsiveness to the MNC-specific minimum tax is substantially smaller, however. Independent of user costs of capital, transfer pricing via intermediate goods also affects investment via the minimum tax rate. In sum, our results are informative for evaluations of measures such as the Global Minimum Tax.

Title: A Bayesian Dynamic Latent Space Model for Weighted Networks
(Matteo Iacopini, Roberto Casarin, Antonio Peruzzi)

Abstract: A new dynamic latent-space (LS) eigenmodel is proposed for time-series of weighted networks. The model accounts for integer-valued weights with an excess of zeroes, and for time-varying node positions (features), and time-varying sparsity probability. The latent positions evolve according to a vector autoregressive process that accounts for possible lagged and contemporaneous dependence across nodes and features, which is neglected in the LS literature. A Bayesian approach is used to address two of the primary sources of inference intractability in dynamic LS models: latent feature estimation and the choice of latent space dimension. Regarding the first task, we employ an efficient auxiliary-mixture sampler that performs data augmentation and supports conditionally conjugate prior distributions. A point-process representation of the network weights and the finite-dimensional distribution of the latent processes are used to derive a multi-move sampler in which each feature trajectory is drawn in a single block, without recursions. This sampling strategy is new to the network literature and can significantly reduce computing time while enhancing the mixing of the chain. To avoid trans-dimensional samplers, a Laplace approximation to a partial marginal likelihood is used. Overall, our partially collapsed Gibbs sampler is general, as it can be easily extended to static and dynamic settings and to discrete or continuous network weights.

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