Working papers results

We study in a theoretical and experimental setting the interaction between belief-dependent preferences and reputation building in a finitely repeated trust game. We focus mainly on the effect of guilt aversion. In a simple two-types model, we analyze the effect of reputation building in presence of guilt-averse players and derive behavioral predictions. In the experiment, we elicit information on trustees' belief-dependent preferences and disclose it to the paired trustor before the repeated game. Our experimental results show that disclosing information on the trustee's belief-dependent preferences and thus letting players play the repeated trust game in presence of almost complete information leads to higher trust and cooperation than in the corresponding incomplete information game setting. In particular, disclosure of information on preferences of guilt-averse trustees also enhances the trustors'cooperation. Disclosure of information on belief-dependent preferences of reciprocity-concerned trustees, instead, does not lead to higher trust and cooperation. We show that this is theoretically consistent with subjects featuring low reciprocity concerns.

In social dilemmas, choices may depend on belief-dependent motivations enhancing the credibility of promises or threats at odds with personal gain maximization. We address this issue theoretically and experimentally in the context of the Ultimatum Minigame, assuming that the choice of accepting or rejecting an unfair proposal is affected by a combination of frustration, due to unfulfilled expectations, and inequity aversion. We increase the responder's payoff from the default allocation (the proposer's outside option) with the purpose of increasing the responder's frustration due to the unfair proposal, and thus his willingness to reject it. In addition, we manipulate the method of play, with the purpose of switching on (direct response method) and off (strategy method) the responder's experience of anger. Our behavioral predictions across and within treatments are derived from the theoretical model complemented by explicit auxiliary assumptions, without relying on equilibrium analysis.

We show that a probability measure on a metric space X has full support if and only if the set of all probability measures that are absolutely continuous with respect to it is dense in P (X). We illustrate the result through a general version of Laplace's method, which in turn leads to a general stochastic convergence result to global maxima.

Firm-level productivity shocks can help understand sector- and macroeconomic-level outcomes. Capturing the market power of these firms is important: it determines how productivity gains translate into prices and markups. In existing models, firms do not internalize the impact of their systemic size. This paper explores the alternative oligopolistic market structure. To this end, I build a tractablemulti-sector heterogeneous-firmgeneral equilibriummodel featuring oligopolistic competition and an input-output (I-O) network. By affecting price and markup, firm-level productivity shocks propagate both to the downstream and upstream sectors. Sector-level competition intensity affects the strength of these new propagation mechanisms. The structural importance of a firm is determined by the interaction of (i) the sector-level competition intensity, (ii) the firm's sector position in the I-O network, and (iii) the firm size. In a calibration exercise, the aggregate volatility arising from independent firm-level shock is 34% of the one observed in the data.

by Nicola Fontana, Tommaso Nannicini, Guido Tabellini

The Italian civil war and the Nazi occupation of Italy occurred at a critical juncture, just before the birth of a new democracy. We study the impact of these traumatic events by exploiting geographic heterogeneity in the duration and intensity of civil war, and the persistence of the battlefront along the "Gothic line" cutting through Northern-Central Italy. We find that the Communist Party gained votes in postwar elections where the Nazi occupation lasted longer, mainly at the expense of centrist parties. This effect persists until the late 1980s and appears to be driven by equally persistent changes in political attitudes.

In this work we propose a definition of comonotonicity for elements of B (H)sa, i.e., bounded self-adjoint operators defined over a complex Hilbert space H. We show that this notion of comonotonicity coincides with a form of commutativity. Intuitively, comonotonicity is to commutativity as monotonicity is to bounded variation. We also define a notion of Choquet expectation for elements of B (H)sa that generalizes quantum expectations. We characterize Choquet expectations as the real-valued functionals over B (H)sa which are comonotonic additive, c- monotone, and normalized.

We review recent models of choices under uncertainty that have been proposed in the economic literature. The framework that we propose is general and may be applied in many different fields of environmental economics. To illustrate, we provide a simple application in the context of an optimal mitigation policy. Our objective is to offer guidance to policy makers who face uncertainty when designing climate policy.

We provide both an axiomatic and a neuropsychological characterization of the dependence of choice probabilities on time in the softmax (or Multinomial Logit Process) form (see below picture) MLP is the most widely used model of preference discovery in all fields of decision making, from Quantal Response Equilibrium to Discrete Choice Analysis, from Psychophysics and Neuroscience to Combinatorial Optimization. Our axiomatic characterization of softmax permits to empirically test its descriptive validity and to better understand its conceptual underpinnings as a theory of agents'rationality. Our neuropsychological foundation provides a computational model that may explain softmax emergence in human behavior and that naturally extends to multialternative choice the classical Diffusion Model paradigm of binary choice. These complementary approaches provide a complete perspective on softmaximization as a model of preference discovery, both in terms of internal (neuropsychological) causes and external (behavioral) effects.

We develop new likelihood-based methods to estimate factor-based Stochastic Discount Factors (SDF) that may accommodate Hidden Markov dynamics in the factor loadings. We use these methods to investigate whether it is possible to find a SDF that jointly prices the cross-section of eight U.S. portfolios of stocks, Treasuries, corporate bonds, and commodities. In particular, we test a range of possible different specification of the SDF, including single-state and Hidden Markov models and compare their statistical and pricing performances. In addition, we assess whether and to which extent a selection of these models replicates the observed moments of the return series, and especially correlations. We report that regime-switching models clearly outperform single-state ones both in term of statistical and pricing accuracy. However, while a four-state model is selected by the information criteria, a two-state three-factor full Vector Autoregression model outperforms the others as far as the pricing accuracy is concerned.

Deposit volatility and costly bank liquidity increase the long-term lending rates offered by banks, which reduce loan maturities, long-term investment and output. We formalise this mechanism in a banking model and analyse exogenous variation in deposit volatility induced by a Sharia levy in Pakistan. Data from the credit registry and a firm-level survey show that deposit volatility and liquidity cost: 1) reduce loan maturities and lending rates; 2) leave loan amounts and total investment unchanged; 3) redirect investment from fixed assets towards working capital. A targeted liquidity program is quantified to generate yearly output gains between 0.042% and 0.205%.