Simona Mateut, Spiros Bougheas and Paul Mizen,
. "Trade credit, bank lending and monetary policy transmission," SSRN.
Dirk Engelmann, Hans Peter Grüner, Alex Possajennikov and Timo Hoffmann,
. "Minority Protection in Voting Mechanisms – Experimental Evidence," SSRN.
Under simple majority voting an absolute majority of voters may choose policies that are harmful to minorities. It is the purpose of sub- and super-majority rules to protect legitimate minority interests. We study how voting rules are chosen under the veil of ignorance. In our experiment, individuals choose voting rules for given distributions of gains and losses that can arise from a policy, but before learning their own valuation of the policy. We find that subjects on average adjust the voting rule in line with the skewness of the distribution. As a result, a higher share of the achievable surplus can be extracted with the suggested rules than with exogenously given simple majority voting. While the rule choices are not significantly biased towards underor overprotection of the minority, towards majority voting or towards status-quo preserving rules, they only imperfectly reflect the distributions of benefits and costs.
Most products are produced and sold by supply chain networks, where an interconnected network of producers and intermediaries set prices to maximize their profits. I show that there exists a unique equilibrium in a price-setting game on a network. The key distortion reducing both total profits and social welfare is multiple-marginalization, which is magnified by strategic interactions. Individual profits are proportional to influentiality, a new measure of network centrality defined by the equilibrium characterization. The results emphasize the importance of the network structure when considering policy questions such as mergers or trade policies.
Spiros Bougheas, Pasquale Commendatore, Laura Gardini and Ingrid Kubin,
. "Financial Development, Cycles and Income Inequality in a Model with Good and Bad Projects," SSRN.
We introduce a banking sector and heterogeneous agents in the Matsuyama et al. (2016) dynamic over-lapping generations neoclassical model with good and bad projects. The model captures the benefits and costs of an advanced banking system which can facilitate economic development when allocates resources to productive activities but can also hamper progress when invests in projects that do not contribute to capital formation. When the economy achieves higher stages of development it becomes prone to cycles. We show how the disparity of incomes across agents de-pends on changes in both the prices of the factors of production and the reallocation of agents across occupations.
We introduce endogenous fire sales into a simple network model. For any given initial distribution of shocks across the network, we develop a clearing algorithm to solve for the financial equilibrium. We then utilise the results to perform ex ante risk assessment and derive risk premia for every balance sheet item where liabilities are differentiated according to priority rights. We find that risk premia reflect both idiosyncratic risk and risk of contagion (network risk). Moreover, we show that network risk magnifies the gap between the risk premia of equity and debt. We also perform comparative statics, showing that changes to the distribution of shocks and network structure can have substantial effects on the level of systemic losses.
Jacopo Bizzotto, Toomas Hinnosaar and Adrien Vigier,
. "The Limits of Commitment," SSRN.
We study partial commitment in leader-follower games. A collection of subsets covering the leader's action space determines her commitment opportunities. We characterize the outcomes resulting from all possible commitment structures of this kind. If the commitment structure is an interval partition, then the leader's payoff is bounded by the payoffs she obtains under the full and no-commitment benchmarks. We apply our results to study new design problems.
This paper investigates self-serving belief distortion about dominant norms of honesty. We consider an environment where the subject can earn a monetary reward by lying. In contrast to the existing literature on motivated beliefs, we do not focus on distortion in one dimension alone, but instead consider beliefs in two dimensions, both of which have been shown to affect individual behavior: empirical (what other people do) and normative (what other people approve of). Our experimental findings are consistent with the predictions of a dual-self model in which conditional norm-followers strategically distort their beliefs to justify self-serving behavior. We argue that the asymmetry between what we infer from empirical as opposed to normative information is a key ingredient of belief distortion in our context: widespread honest behavior is a strong indicator of disapproval of lying (and thus that a norm of honesty is followed), but the opposite does not hold. Taken together, we show why, when, and which norm-relevant beliefs are strategically distorted.
Marit Hinnosaar and Toomas Hinnosaar,
. "Influencer Cartels," SSRN.
Influencer marketing is a large and growing but mostly unregulated industry. The majority of influencers are not paid based on their marketing campaigns’ success. Instead, their prices are based on engagement (number of likes and comments). This gives incentives for fraudulent behavior—for inflating engagement. We study influencer cartels, where groups of influencers collude to increase engagement to improve their market outcomes. Our theoretical model shows that such cartels mitigate the free-rider problem and may increase or decrease welfare, depending on the quality of induced engagement. We use a novel dataset of Instagram influencer cartels and confirm that the cartels increase engagement as intended. Importantly, we show that engagement from non-specific cartels is of lower quality, whereas engagement from topic-specific cartels may be as good as natural engagement. Therefore topic-specific cartels may sometimes be welfare-improving, whereas typical non-specific cartels hurt everyone.
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