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on Network Economics |
| By: | Mauleon, Ana (Université catholique de Louvain, LIDAM/CORE, Belgium); Nanumyan, Mariam (Bielefeld University); Vannetelbosch, Vincent (Université catholique de Louvain, LIDAM/CORE, Belgium) |
| Abstract: | We study a network game on a fixed multi-layer network of two types of relationships. The social interactions in the first layer carries a pressure to conform with the social norm within the layer. The second layer provides additional strategic complementarities from players’ interaction. Players are endowed with personal ideal efforts and are heterogeneous in their ideal efforts and productivity. Each player repeatedly chooses her effort level in the network game and updates her ideal effort based on the new effort choice. Each player suffers disutility when her effort differs from her neighbors’ efforts or is inconsistent with her ideal effort. We find the pure Nash equilibrium of the game in each period and provide conditions for the convergence of efforts and ideals to a steady state. Furthermore, we provide conditions for emerging long-run consensus about ideals in groups of players and the entire network. |
| Keywords: | Multi-layer networks ; network games ; personal norms ; social norms ; strategic complementarities |
| JEL: | A14 C72 D85 |
| Date: | 2024–09–25 |
| URL: | https://d.repec.org/n?u=RePEc:cor:louvco:2024023 |
| By: | Cyril Chambefort (Université Jean Monnet Saint-Etienne, CNRS, Université Lumière Lyon 2, emlyon business school, GATE, 69007, Lyon, France); Magali Chaudey (Université Jean Monnet Saint-Etienne, CNRS, Université Lumière Lyon 2, emlyon business school, GATE, 69007, Lyon, France) |
| Abstract: | The paper studies DAOs (Decentralized Autonomous Organizations), which are based on blockchain technology, emphasizing that they rely on a complex, multi-level trust framework that extends beyond purely technological trust. We define DAOs as digital blockchain-based organizations powered by open virtual networks of contributors. Their coordination and management are decentralized, without any central control. This structure allows peers to work autonomously on a token-based system of on-chain coordination, where rules are self-executed using smart-contracts and off-chain coordination mechanisms. The study of DAOs reveals the emergence of a particular form of trust, “trust in code”. Our contribution is threefold: First, we provide an empirical study of Uniswap DAO, the largest decentralized finance network. Secondly, we demonstrate that a complementarity exists in the notion of trust production in such networks, which includes trust in technology, but also personal trust developed outside the blockchain. Finally, the study of this particular network, combining multi-level trust, leads us to explore approaches in terms of social capital. Our description of the Uniswap network suggests that it both requires and enables the accumulation of social capital, in its relational, structural, and cognitive dimensions. |
| Keywords: | Blockchain, Smart-contract, DAO (Decentralised Autonomous Organisation), Uniswap, Trust |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:gat:wpaper:2416 |
| By: | García-Lembergman, Ezequiel; Hajdini, Ina; Leer, John; Pedemonte, Mathieu; Schoenle, Raphael |
| Abstract: | Using a novel dataset that integrates inflation expectations with information on social network connections, we show that inflation expectations within one's social network have a positive, causal relationship with individual inflation expectations. This relationship is stronger for groups that share common demographic characteristics such as gender, income, or political affiliation and when salient information disseminates through the network. In a monetary union New-Keynesian model, socially determined inflation expectations induce imperfect risk-sharing and can affect the inflation and real output propagation of local and aggregate shocks. To reduce welfare losses due to socially determined expectations, monetary policy should optimally put more weight on the inflation rate of socially more connected regions. |
| Keywords: | Inflation expectations;Social network;Monetary union |
| JEL: | E31 E71 C83 |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:idb:brikps:13787 |
| By: | Osman Do\u{g}an; Raffaele Mattera; Philipp Otto; S\"uleyman Ta\c{s}p{\i}nar |
| Abstract: | We introduce a dynamic spatiotemporal volatility model that extends traditional approaches by incorporating spatial, temporal, and spatiotemporal spillover effects, along with volatility-specific observed and latent factors. The model offers a more general network interpretation, making it applicable for studying various types of network spillovers. The primary innovation lies in incorporating volatility-specific latent factors into the dynamic spatiotemporal volatility model. Using Bayesian estimation via the Markov Chain Monte Carlo (MCMC) method, the model offers a robust framework for analyzing the spatial, temporal, and spatiotemporal effects of a log-squared outcome variable on its volatility. We recommend using the deviance information criterion (DIC) and a regularized Bayesian MCMC method to select the number of relevant factors in the model. The model's flexibility is demonstrated through two applications: a spatiotemporal model applied to the U.S. housing market and another applied to financial stock market networks, both highlighting the model's ability to capture varying degrees of interconnectedness. In both applications, we find strong spatial/network interactions with relatively stronger spillover effects in the stock market. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.16526 |
| By: | Alvaro Silva |
| Abstract: | This paper studies inflation in small open economies with production networks. I show that production networks alter the elasticity of the consumer price index (CPI) to changes in sectoral technology, factor prices, and import prices. Sectors can import and export directly but also indirectly through domestic intermediate inputs. Indirect exporting dampens the inflationary pressure from domestic forces, while indirect importing increases the inflation sensitivity to import price changes. Computing these CPI elasticities requires knowledge of the production network structure because these do not coincide with typical sufficient statistics used in the literature such as sectoral sales-to-GDP ratios, factor shares, or imported consumption shares. Using input-output tables, I provide empirical evidence that adjusting CPI elasticities for indirect exports and imports matters quantitatively for small open economies. I use the model to illustrate the importance of production networks during the COVID-19–related inflation in Chile and the United Kingdom. |
| Keywords: | inflation; Small open economies; networks; input-output tables; COVID-19 |
| JEL: | C67 D57 E31 F14 F41 L16 |
| Date: | 2024–10–01 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedbwp:99025 |
| By: | K.P. Prabheesh (Indian Institute of Technology Hyderabad, India); C.T. Vidya (Center for Economic and Social Studies, Hyderabad, India.) |
| Abstract: | This paper examines the global semiconductor industry trade network and the place of the Association of Southeast Asian Nations (ASEAN) in it. The network analysis parameters (e.g. degree centrality, eigenvector centrality, and closeness) are calculated for two semiconductor product classifications (i.e. Harmonized System (HS) 8541 and HS 8542) as well as key inputs for the manufacturing process and testing, packaging, and distribution. The study finds that the coronavirus disease (COVID-19) pandemic has a significant adverse effect on the semiconductor trade network. It finds that the central position in the network is determined by Germany, the United States, China, Belgium, India, Italy, Spain, the Netherlands, France, and the United Kingdom. It is also found that Singapore, an ASEAN Member State, occupies the central position in the trade network. Other ASEAN Member States, such as Viet Nam, Malaysia, Thailand, the Philippines, and Indonesia, have been well integrated in the trade network in recent years. However, countries such as Myanmar, Cambodia, Brunei, and the Lao People’s Democratic Republic (Lao PDR) are still in the peripheral area of the network. Nonetheless, over the years, these countries have improved their participation in semiconductor trade.The COVID-19 pandemic has impacted trade in key inputs and manufacturing parts of semiconductor production. It has drastically reduced the trade flows, connectivity, and dependence of many countries in the network. |
| Keywords: | Semiconductor trade; COVID-19; ASEAN; trade network; regional trade |
| JEL: | D85 F14 F15 L63 |
| Date: | 2024–02–23 |
| URL: | https://d.repec.org/n?u=RePEc:era:wpaper:dp-2023-32 |
| By: | Bocquet, L. |
| Abstract: | How fast do labor markets adjust to technology shocks? This paper introduces a novel network-based framework to model skill frictions between occupations. Using expert data on skills, I construct a network of occupations and find it is sparse, divided in clusters of similar occupations with 'bridge occupations' linking distinct clusters. Leveraging French administrative data, I show that workers transitioning through these 'bridges' move to occupations with higher wages and lower unemployment. Next, I build a tractable model of job search with networked labor markets, and demonstrate that bridge occupations significantly affect reallocation speed, with slow reallocation creating large adjustment costs. I then augment the model with quantitative extensions, leveraging hat-algebra methods to solve counterfactuals without having to estimate large numbers of parameters. Calibrated to French data, the model predicts that robot adoption induces slow reallocation, around 40 quarters, and that this sluggish reallocation reduces welfare gains by approximately 40%- an order of magnitude higher than previous estimates. However, policies targeting bridge occupations can speed-up reallocation, and much more so than policies targeting tight occupations directly. These findings highlight the crucial role of the occupation network in shaping reallocation dynamics and provide new insights for the design of labor market policies. |
| Date: | 2024–10–28 |
| URL: | https://d.repec.org/n?u=RePEc:cam:camjip:2427 |
| By: | C.T. Vidya (Centre for Economic and Social Studies (CESS), Hyderabad, India) |
| Abstract: | This paper analyses the trade characteristics, competition networks, and fragility of global trade in goods in the Association of Southeast Asian Nations (ASEAN) economies, particularly in the context of the coronavirus disease (COVID-19). The study covers the 10 ASEAN Member States and 110 trade partners, using the Harmonized System (HS) 6-digit product classification from 2010 to 2021. The findings reveal that ASEAN dominates with trade complementarity. Dense and intense competition networks are found. The electrical and machinery imports from central players are highly sensitive to shocks, with electronics also becoming susceptible to shocks after the pandemic. The study also shows that liquefied natural gas products and countries such as Singapore, Indonesia, Brunei, and Myanmar experienced increased shocks. The research underscores the importance of policymakers prioritising their understanding of trade linkages and potential spillover effects when formulating policies to mitigate the impact of shocks. The findings have implications for policymakers, highlighting the need for them to take a holistic approach when devising policies and strategies to mitigate the adverse effects of global shocks. |
| Keywords: | Export similarity, trade complementarity, competition trade networks, trade fragility, ASEAN |
| JEL: | F14 F15 L14 |
| Date: | 2024–02–16 |
| URL: | https://d.repec.org/n?u=RePEc:era:wpaper:dp-2023-27 |
| By: | Mengyu Wang; Shay B. Cohen; Tiejun Ma |
| Abstract: | The diffusion of financial news into market prices is a complex process, making it challenging to evaluate the connections between news events and market movements. This paper introduces FININ (Financial Interconnected News Influence Network), a novel market prediction model that captures not only the links between news and prices but also the interactions among news items themselves. FININ effectively integrates multi-modal information from both market data and news articles. We conduct extensive experiments on two datasets, encompassing the S&P 500 and NASDAQ 100 indices over a 15-year period and over 2.7 million news articles. The results demonstrate FININ's effectiveness, outperforming advanced market prediction models with an improvement of 0.429 and 0.341 in the daily Sharpe ratio for the two markets respectively. Moreover, our results reveal insights into the financial news, including the delayed market pricing of news, the long memory effect of news, and the limitations of financial sentiment analysis in fully extracting predictive power from news data. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.10614 |