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on Microeconomics |
| By: | Nicolas Schutz |
| Abstract: | I study a model in which two upstream firms compete to supply a homogeneous input to two downstream firms selling differentiated products. Upstream firms offer exclusive, discriminatory, public, two-part tariff contracts to the downstream firms. I show that, under very general conditions, this game does not have a pure-strategy subgame-perfect equilibrium. The intuition is that variable parts in such an equilibrium would have to be pairwise-stable; however, with pairwise-stable variable parts, downstream competitive externalities are not internalized, implying that upstream firms can profitably deviate. I contrast this non-existence result with earlier papers that found equilibria in related models. |
| Keywords: | vertical relations, exclusive dealing, two-part tariffs, slotting fees. |
| JEL: | L13 L14 L42 |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:bon:boncrc:crctr224_2024_591 |
| By: | Prabal Roy Chowdhury (Indian Statistical Institute, Delhi) |
| Abstract: | In the presence of individuals who think `categorically', we demonstrate that profit-maximizing social media platforms indulge in identity-based censoring, with nature of bias depending on the efficiency of the other epistemic institutions (EIs) of the society. In general, when the EIs are neither very efficient, nor very inefficient, the optimal strategy involves a mixture of confirmatory and (a minimal amount of) contrarian reporting, with the contrarian elements being used to create a false perception that no news is good news, which in turn allows the platform to camouflage the fact that it is suppressing all but one unwelcome signals. Further, public good implementation is inefficient if and only if the other epistemic institutions are inefficient. |
| Keywords: | social media, confirmation bias, contrarianism, identity-based censoring, categorical thinking |
| JEL: | L82 D91 |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:alo:isipdp:24-03 |
| By: | Yasunori Okumura |
| Abstract: | This study examines strategic issues in fair rank-minimizing mechanisms, which choose an assignment that minimizes the average rank of object types to which agents are assigned and satisfy a fairness property called equal treatment of equals. As one of these fair mechanisms, the uniform rank-minimizing mechanism is considered. We particularly focus on the case where agents can refuse their assignment and obtain the outside option instead. Without the refusal option, if the uniform rank-minimizing mechanism is used, truth-telling is not strategically dominated by any strategies. However, if agents have the option, then an outside option demotion strategy -- revealing all object types as acceptable without changing the ranking of truly acceptable ones -- strategically dominates truth-telling. Moreover, we show that adopting this strategy may lead to inefficient assignments. To counter this, we propose the modified uniform rank-minimizing mechanism, though it may lead agents to strategically reduce the number of acceptable types. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2408.01673 |
| By: | Gabriele Camera; Alessandro Gioffré |
| Abstract: | The literature on cooperation in infinitely repeated Prisoner’s Dilemmas covers the extreme opposites of the matching spectrum: partners, a player’s opponent never changes, and strangers, a player’s opponent randomly changes in every period. Here, we extend the analysis to settings where the opponent changes, but not in every period. In these temporary partnerships, players can deter some deviations by directly sanctioning their partner. Hence, relaxing the extreme assumption of one-period matchings can support some cooperation also off equilibrium because a class of strategies emerges that are less extreme than the typical “grim†strategy. We establish conditions supporting full cooperation as a subgame perfect equilibrium under a social norm that complements direct sanctions with a cyclical community sanction. Though this strategy less effectively incentivizes cooperation, it more effectively incentivizes punishment after a deviation, hence, can be preferable to the grim strategy under certain conditions. |
| Keywords: | prisoner's dilemma, random matching, social norms. |
| JEL: | E4 E5 C7 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:frz:wpaper:wp2024_15.rdf |
| By: | Jean-Michel Benkert; Armin Schmutzler |
| Abstract: | This paper investigates the value of recommendations for disseminating economic information, with a focus on frictions resulting from preference heterogeneity. We consider Bayesian expected-payoff maximizers who receive non-strategic recommendations by other consumers. The paper provides conditions under which different consumer types accept these recommendations. Moreover, we assess the overall value of a recommendation system and the determinants of that value. Our analysis highlights the importance of disentangling objective information from subjective preferences when designing value-maximizing recommendation systems. |
| Keywords: | Recommendations, preference heterogeneity, optimal design |
| JEL: | D02 D47 D83 |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:zur:econwp:448 |
| By: | Masuyama, Ryo |
| Abstract: | Targeted pricing is an aggressive strategy that steals demand from rivals. Previous studies have shown that a firm prefers targeted pricing to uniform pricing when another supply chain is vertically integrated and thus its downstream firm purchases an input at a constant price. This study relaxes the assumption that supply chains are vertically integrated. When supply chains are vertically separated, downstream firms face increasing input-supply function. Then, targeted pricing reduces the rival's demand and hence its input price, which intensifies competition. This negative effect is so severe in our Hotelling model that a firm prefers uniform pricing to targeted pricing when another supply chain is vertically separated. |
| Keywords: | targeted pricing, uniform pricing, vertical structure, supply chain management, Hotelling model. |
| JEL: | D43 L10 L13 |
| Date: | 2024–08–10 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:121680 |
| By: | Jens Gudmundsson; Jens Leth Hougaard; Jay Sethuraman |
| Abstract: | Interconnected agents such as firms in a supply chain make simultaneous preparatory investments to increase chances of honouring their respective bilateral agreements. Failures cascade: if one fails their agreement, then so do all who follow in the chain. Thus, later agents' investments turn out to be pointless when there is an earlier failure. How losses are shared affects how agents invest to avoid the losses in the first place. In this way, a solution sets agent liabilities depending on the point of disruption and induces a supermodular investment game. We characterize all efficient solutions. These have the form that later agents -- who are not directly liable for the disruption -- still shoulder some of the losses, justified on the premise that they might have failed anyway. Importantly, we find that such indirect liabilities are necessary to avoid unbounded inefficiencies. Finally, we pinpoint one efficient solution with several desirable properties. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2408.07361 |
| By: | Hans Gersbach; Manvir Schneider; Parnian Shahkar |
| Abstract: | This paper investigates which alternative benefits from vote delegation in binary collective decisions within blockchains. We begin by examining two extreme cases of voting weight distributions: Equal-Weight (EW), where each voter has equal voting weight, and Dominant-Weight (DW), where a single voter holds a majority of the voting weights before any delegation occurs. We show that vote delegation tends to benefit the ex-ante minority under EW, i.e., the alternative with a lower initial probability of winning. The converse holds under DW distribution. Through numerical simulations, we extend our findings to arbitrary voting weight distributions, showing that vote delegation benefits the ex-ante majority when it leads to a more balanced distribution of voting weights. Finally, in large communities where all agents have equal voting weight, vote delegation has a negligible impact on the outcome. These insights provide practical guidance for governance decisions in blockchains. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2408.05410 |
| By: | Guido Menzio |
| Abstract: | I derive a formula for the equilibrium distribution of markups in the search- theoretic model of imperfect competition of Butters (1977), Varian (1980), and Burdett and Judd (1983). The level of markups and the sign of the relationship between a seller’s markup and its size depends on the extent of search frictions, as well as on other deep parameters. Markups are efficient. Markups are positive even though the varieties produced by sellers are perfect substitutes. Markups are heterogeneous even when all sellers operate the same production technology. Markups depend on size, even though the substitutability between a variety and the others does not depend on how much of that variety is consumed. Interpreting these markups through the lens of the monopolistic competition model of Dixit and Stiglitz (1977) would lead one to recover incorrect and unstable buyers’ preferences. Interpreting these markups through the lens of the Dixit-Stiglitz model would also leads to incorrect policy recommendations. These results are a cautionary note on recent work in macroeconomics. |
| JEL: | D43 D83 L16 |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:32888 |
| By: | Tejas Pagare; Avishek Ghosh |
| Abstract: | Online learning in a decentralized two-sided matching markets, where the demand-side (players) compete to match with the supply-side (arms), has received substantial interest because it abstracts out the complex interactions in matching platforms (e.g. UpWork, TaskRabbit). However, past works assume that each arm knows their preference ranking over the players (one-sided learning), and each player aim to learn the preference over arms through successive interactions. Moreover, several (impractical) assumptions on the problem are usually made for theoretical tractability such as broadcast player-arm match Liu et al. (2020; 2021); Kong & Li (2023) or serial dictatorship Sankararaman et al. (2021); Basu et al. (2021); Ghosh et al. (2022). In this paper, we study a decentralized two-sided matching market, where we do not assume that the preference ranking over players are known to the arms apriori. Furthermore, we do not have any structural assumptions on the problem. We propose a multi-phase explore-then-commit type algorithm namely epoch-based CA-ETC (collision avoidance explore then commit) (\texttt{CA-ETC} in short) for this problem that does not require any communication across agents (players and arms) and hence decentralized. We show that for the initial epoch length of $T_{\circ}$ and subsequent epoch-lengths of $2^{l/\gamma} T_{\circ}$ (for the $l-$th epoch with $\gamma \in (0, 1)$ as an input parameter to the algorithm), \texttt{CA-ETC} yields a player optimal expected regret of $\mathcal{O}\left(T_{\circ} (\frac{K \log T}{T_{\circ} \Delta^2})^{1/\gamma} + T_{\circ} (\frac{T}{T_{\circ}})^\gamma\right)$ for the $i$-th player, where $T$ is the learning horizon, $K$ is the number of arms and $\Delta$ is an appropriately defined problem gap. Furthermore, we propose a blackboard communication based baseline achieving logarithmic regret in $T$. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2408.08690 |
| By: | Melissa Dell |
| Abstract: | Deep learning provides powerful methods to impute structured information from large-scale, unstructured text and image datasets. For example, economists might wish to detect the presence of economic activity in satellite images, or to measure the topics or entities mentioned in social media, the congressional record, or firm filings. This review introduces deep neural networks, covering methods such as classifiers, regression models, generative AI, and embedding models. Applications include classification, document digitization, record linkage, and methods for data exploration in massive scale text and image corpora. When suitable methods are used, deep learning models can be cheap to tune and can scale affordably to problems involving millions or billions of data points.. The review is accompanied by a companion website, EconDL, with user-friendly demo notebooks, software resources, and a knowledge base that provides technical details and additional applications. |
| JEL: | C0 |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:32768 |