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on Network Economics |
| By: | Choi, S.; Goyal, S.; Guo, F.; Moisan, F. |
| Abstract: | This paper presents experimental evidence on games where individuals can unilaterally decide on their links with each other. Linking decisions give rise to directed graphs. We consider two classes of situations: one, benefits flow along the direction of the network paths (one-way flow), and two, when the benefits flow on network paths without regard to the direction of links (two-way flow). Our experiments reveal that in the one-way flow model subjects create sparse networks whose distance grows and efficiency falls as group size grows; by contrast, in the two-way flow model subjects create sparse and small world networks whose efficiency remains high in both small and large groups. We show that a bounded rational model that combines myopic best response with targeting a most connected individual provides a coherent account of our experimental data. |
| Keywords: | Data, Group Size, Network Formation, Network Games, Networks |
| Date: | 2024–03–26 |
| URL: | http://d.repec.org/n?u=RePEc:cam:camdae:2417&r=net |
| By: | Luis E. Candelaria; Yichong Zhang |
| Abstract: | This paper introduces a methodology to conduct robust inference in bipartite networks under local misspecification. We focus on a class of dyadic network models with misspecified conditional moment restrictions. The framework of misspecification is local, as the effect of misspecification varies with the sample size. We utilize this local asymptotic approach to construct a robust estimator that is minimax optimal for the mean square error within a neighborhood of misspecification. Additionally, we introduce bias-aware confidence intervals that account for the effect of the local misspecification. These confidence intervals have the correct asymptotic coverage for the true parameter of interest under sparse network asymptotics. Monte Carlo experiments demonstrate that the robust estimator performs well in finite samples and sparse networks. As an empirical illustration, we study the formation of a scientific collaboration network among economists. |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2403.13725&r=net |
| By: | Emre Oral; Simon Rabaté; Arthur Seibold |
| Abstract: | We study the influence of family members, neighbors and coworkers on retirement behavior. To estimate causal retirement spillovers between individuals, we exploit a pension reform in the Netherlands that creates exogenous variation in peers’ retirement ages, and we use administrative data on the full Dutch population. We find large spillovers in couples, primarily due to women reacting to their husband’s retirement choices. Consistent with homophily in social interactions, the influence of the average sibling, neighbor and coworker is modest, but sizable spillovers emerge between similar individuals in these groups. Additional evidence suggests both leisure complementarities and the transmission of social norms as mechanisms behind retirement spillovers. Our findings imply that pension reforms have a large social multiplier, amplifying their overall impact on retirement behavior by 40%. |
| Keywords: | retirement, pension reform, social networks, spillover, peer effects |
| JEL: | D91 H55 J26 |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_10999&r=net |
| By: | Bramoulle, Y.; Goyal, S.; Morelli, M. |
| Abstract: | Most societies in the world contain strong group identities and the culture supporting these groups is highly persistent. This persistence in turn gives rise to a practical problem: how do and should societies with strong group identities organize themselves for exchange and public good provision? In this paper, we develop a theoretical framework – with social structure characterized by number and size of groups as well as quality of ties between them – that allows us to study, normatively and positively, the relationship between social structure, state capacity, and economic activity. |
| Keywords: | Economic Activity, Networks, Social Networks |
| Date: | 2024–03–24 |
| URL: | http://d.repec.org/n?u=RePEc:cam:camdae:2416&r=net |
| By: | Muzi Chen; Nan Li; Lifen Zheng; Difang Huang; Boyao Wu |
| Abstract: | The connectivity of stock markets reflects the information efficiency of capital markets and contributes to interior risk contagion and spillover effects. We compare Shanghai Stock Exchange A-shares (SSE A-shares) during tranquil periods, with high leverage periods associated with the 2015 subprime mortgage crisis. We use Pearson correlations of returns, the maximum strongly connected subgraph, and $3\sigma$ principle to iteratively determine the threshold value for building a dynamic correlation network of SSE A-shares. Analyses are carried out based on the networking structure, intra-sector connectivity, and node status, identifying several contributions. First, compared with tranquil periods, the SSE A-shares network experiences a more significant small-world and connective effect during the subprime mortgage crisis and the high leverage period in 2015. Second, the finance, energy and utilities sectors have a stronger intra-industry connectivity than other sectors. Third, HUB nodes drive the growth of the SSE A-shares market during bull periods, while stocks have a think-tail degree distribution in bear periods and show distinct characteristics in terms of market value and finance. Granger linear and non-linear causality networks are also considered for the comparison purpose. Studies on the evolution of inter-cycle connectivity in the SSE A-share market may help investors improve portfolios and develop more robust risk management policies. |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2403.19363&r=net |
| By: | Elliott, M.; Galeotti, A.; Koh, A.; Li, W. |
| Abstract: | There are many markets that are networked in these sense that not all consumers have access to (or are aware of) all products, while, at the same time, firms have some information about consumers and can distinguish some consumers from some others (for example, in online markets through cookies). With unit demand and price-setting firms we give a complete characterization of all welfare outcomes achievable in equilibrium (for arbitrary buyer-seller networks and arbitrary information structures), as well as the designs (networks and information structures) which implement them. |
| Date: | 2023–02–04 |
| URL: | http://d.repec.org/n?u=RePEc:cam:camjip:2304&r=net |
| By: | Taejin Park |
| Abstract: | This paper introduces a Large Language Model (LLM)-based multi-agent framework designed to enhance anomaly detection within financial market data, tackling the longstanding challenge of manually verifying system-generated anomaly alerts. The framework harnesses a collaborative network of AI agents, each specialised in distinct functions including data conversion, expert analysis via web research, institutional knowledge utilization or cross-checking and report consolidation and management roles. By coordinating these agents towards a common objective, the framework provides a comprehensive and automated approach for validating and interpreting financial data anomalies. I analyse the S&P 500 index to demonstrate the framework's proficiency in enhancing the efficiency, accuracy and reduction of human intervention in financial market monitoring. The integration of AI's autonomous functionalities with established analytical methods not only underscores the framework's effectiveness in anomaly detection but also signals its broader applicability in supporting financial market monitoring. |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2403.19735&r=net |
| By: | Supriya Tiwari; Pallavi Basu |
| Abstract: | Many classical inferential approaches fail to hold when interference exists among the population units. This amounts to the treatment status of one unit affecting the potential outcome of other units in the population. Testing for such spillover effects in this setting makes the null hypothesis non-sharp. An interesting approach to tackling the non-sharp nature of the null hypothesis in this setup is constructing conditional randomization tests such that the null is sharp on the restricted population. In randomized experiments, conditional randomized tests hold finite sample validity. Such approaches can pose computational challenges as finding these appropriate sub-populations based on experimental design can involve solving an NP-hard problem. In this paper, we view the network amongst the population as a random variable instead of being fixed. We propose a new approach that builds a conditional quasi-randomization test. Our main idea is to build the (non-sharp) null distribution of no spillover effects using random graph null models. We show that our method is exactly valid in finite-samples under mild assumptions. Our method displays enhanced power over other methods, with substantial improvement in complex experimental designs. We highlight that the method reduces to a simple permutation test, making it easy to implement in practice. We conduct a simulation study to verify the finite-sample validity of our approach and illustrate our methodology to test for interference in a weather insurance adoption experiment run in rural China. |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2403.16673&r=net |
| By: | Enzo Brox; Michael Lechner |
| Abstract: | This article shows how coworker performance affects individual performance evaluation in a teamwork setting at the workplace. We use high-quality data on football matches to measure an important component of individual performance, shooting performance, isolated from collaborative effects. Employing causal machine learning methods, we address the assortative matching of workers and estimate both average and heterogeneous effects. There is substantial evidence for spillover effects in performance evaluations. Coworker shooting performance, meaningfully impacts both, manager decisions and third-party expert evaluations of individual performance. Our results underscore the significant role coworkers play in shaping career advancements and highlight a complementary channel, to productivity gains and learning effects, how coworkers impact career advancement. We characterize the groups of workers that are most and least affected by spillover effects and show that spillover effects are reference point dependent. While positive deviations from a reference point create positive spillover effects, negative deviations are not harmful for coworkers. |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2403.15200&r=net |
| By: | Alexander Berry; Elizabeth M. Maloney; David Neumark |
| Abstract: | Stronger enforcement of discrimination laws can help to reduce disparities in economic outcomes with respect to race, ethnicity, and gender in the United States. However, the data necessary to detect possible discrimination and to act to counter it is not publicly available – in particular, data on racial, ethnic, and gender disparities within specific companies. In this paper, we explore and develop methods to use information extracted from publicly available LinkedIn data to measure the racial, ethnic, and gender composition of company workforces. We use predictive tools based on both names and pictures to identify race, ethnicity, and gender. We show that one can use LinkedIn data to obtain reasonably reliable measures of workforce demographic composition by race, ethnicity, and gender, based on validation exercises comparing estimates from scraped LinkedIn data to two sources – ACS data, and company diversity or EEO-1 reports. Next, we apply our methods to study the race, ethnic, and gender composition of workers who were hired and those who experienced mass layoffs at two large companies. Finally, we explore using LinkedIn data to measure race, ethnic, and gender differences in promotion. |
| JEL: | J15 J16 J7 |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:32294&r=net |