Dimensions Of Heterogeneity Research Articles

The Effects of Macroeconomic Shocks: Household Financial Distress Matters

Abstract When a macroeconomic shock arrives, variation in household balance sheet health (captured by the presence of financial distress, or “FD”) leads to differential access to credit and hence a distribution in consumption responses. As we document, though, over the past two recessions, households in prior FD also experienced macroeconomic shocks more intensely than others, leading to a distribution of shock severity. Quantifying the importance of each dimension of heterogeneity (FD or shock severity) for consumption requires a structural model. We find that heterogeneity in FD matters more for shaping the responses of individual and aggregate consumption to shocks. (JEL D31, E21, E44, G11, G12, G21)

Identification of a Triangular Random Coefficient Model using a Correction Function

Abstract Previously, identification of triangular random coefficient models required a restriction on the dimension of the first stage heterogeneity or independence assumptions across the different sources of the heterogeneity. This note proposes a new identification strategy that does not rely on either of these restrictions but rather assumes conditional means have a conditional linear projection representation in order to construct “correction functions” to address endogeneity and gain identification of the average partial effect. This identification strategy allows for both continuous and discrete instruments. Finally, the proposed identification method is illustrated in estimating the returns to education.

Social Dimensions of Housing Heterogeneity: A Pathway to Understanding Market Mechanics and Valuation

Social Dimensions of Housing Heterogeneity: A Pathway to Understanding Market Mechanics and Valuation

Disentangling brain atrophy heterogeneity in Alzheimer's disease: A deep self-supervised approach with interpretable latent space

Alzheimer's disease (AD) is heterogeneous, but existing methods for capturing this heterogeneity through dimensionality reduction and unsupervised clustering have limitations when it comes to extracting intricate atrophy patterns. In this study, we propose a deep learning based self-supervised framework that characterizes complex atrophy features using latent space representation. It integrates feature engineering, classification, and clustering to synergistically disentangle heterogeneity in Alzheimer's disease. Through this representation learning, we trained a clustered latent space with distinct atrophy patterns and clinical characteristics in AD, and replicated the findings in prodromal Alzheimer's disease. Moreover, we discovered that these clusters are not solely attributed to subtypes but also reflect disease progression in the latent space, representing the core dimensions of heterogeneity, namely progression and subtypes. Furthermore, longitudinal latent space analysis revealed two distinct disease progression pathways: medial temporal and parietotemporal pathways. The proposed approach enables effective latent representations that can be integrated with individual-level cognitive profiles, thereby facilitating a comprehensive understanding of AD heterogeneity.

Conserving ecosystem integrity: Ecological theory as a guide for marine protected area monitoring.

Global policies increasingly focus on the importance of maintaining or improving the integrity of ecosystems, but defining, assessing, and monitoring integrity in marine protected areas (MPAs) remains a challenge. In this paper, we conceptualized ecological integrity along dimensions of heterogeneity and stability containing seven components: physical structure, diversity, function, persistence, resistance, resilience, and natural variability. Through a structured literature search, we identified indicators and metrics used for quantifying ecosystem status components in the marine environment, then reviewed MPA management plans worldwide for inclusion of these components. We evaluated 202 papers applying 83 ecological indicators built from 72 metrics. Ecosystem components were most comprehensively addressed by metrics of taxa presence, organisms count, and area occupied by benthic organisms, and community structure, biomass, and percent cover indicators. Of the 557 MPA management plans we reviewed globally, 93% used at least one ecosystem status term or its synonym in an ecologically relevant context, but 39% did not address any components of stability. In particular, resistance was mentioned in only 1% of management plans, but in some cases it may be inferred from indicators and metrics used to track the best addressed component in management plans, diversity. Plans for MPAs with both an ecological/biological purpose and a research and education purpose contained ecosystem status terms more frequently than other plans, suggesting that engagement with the scientific community may have improved the application of these terms. An improved understanding of how to operationalize and measure ecological integrity can help MPA monitoring and management.

Stock Market Participation, Inequality, and Monetary Policy

Abstract Recent literature has shown that the fraction of liquidity-constrained households in the population critically determines the mix of transmission channels of monetary policy. In this article, we bring a different but important dimension of heterogeneity to the forefront: stock market participation. We show that the stock market participation rate not only shapes the mix of policy channels but also heavily affects the aggregate responses. This happens as direct rebalancing effects and indirect equilibrium effects into investment are both increasing in the number of stock market participants, reinforcing each other. We show this in a quantitative New Keynesian model designed to account for the population share of stock market participants, their position in the income and wealth distribution, and their saving rates. The model implies that, as stock market participation has increased since the 1980s, the power of monetary policy on the real economy has strengthened considerably.

Predicting the pathways of string-like motions in metallic glasses via path-featurizing graph neural networks.

String-like motions (SLMs)-cooperative, "snake"-like movements of particles-are crucial for dynamics in diverse glass formers. Despite their ubiquity, questions persist: Do SLMs prefer specific paths? If so, can we predict these paths? Here, in Al-Sm glasses, our isoconfigurational ensemble simulations reveal that SLMs do follow certain paths. By designing a graph neural network (GNN) to featurize the environment around directional paths, we achieve a high-fidelity prediction of likely SLM pathways, solely based on the static structure. GNN gauges a structural measure to assess each path's propensity to engage in SLMs, akin to a "softness" metric, but for paths rather than for atoms. Our GNN interpretation reveals the critical role of the bottleneck zone along a path in steering SLMs. By monitoring "path softness," we elucidate that SLM-favored paths transit from fragmented to interconnected upon glass transition. Our findings reveal that, beyond atoms or clusters, glasses have another dimension of structural heterogeneity: "paths."

Patterns of regulatory heterogeneity in international trade: Intensity, coverage, and structure

AbstractWith falling tariffs the role of regulatory heterogeneity in international trade has become central in recent debates about regional integration and trade costs. In describing the NTM incidence few studies explicitly take into account the specific nature of underlying regulatory differences. We propose distinguishing regulatory heterogeneity with respect to the intensity, coverage, and structure of regulations, and present indicators reflecting each one of these dimensions. Enabled by detailed product‐level regulatory data based on coded reviews of national legislation, we illustrate the different channels of regulatory heterogeneity on the country‐ and sector‐level. The findings motivate a separate treatment of the different heterogeneity dimensions in the assessment of non‐tariff measures in international trade.

Toward Cooperative Federated Learning Over Heterogeneous Edge/Fog Networks

Federated learning (FL) has been promoted as a popular technique for training machine learning (ML) models over edge/fog networks. Traditional implementations of FL have largely neglected the potential for inter-network cooperation, treating edge/fog devices and other infrastructure participating in ML as separate processing elements. Consequently, FL has been vulnerable to several dimensions of network heterogeneity, such as varying computation capabilities, communication resources, data qualities, and privacy demands. We advocate for cooperative federated learning (CFL), a cooperative edge/fog ML paradigm built on device-to-device (D2D) and device-toserver (D2S) interactions. Through D2D and D2S cooperation, CFL counteracts network heterogeneity in edge/fog networks through enabling a model/data/resource pooling mechanism, which will yield substantial improvements in ML model training quality and network resource consumption. We propose a set of core methodologies that form the foundation of D2D and D2S cooperation and present preliminary experiments that demonstrate their benefits. We also discuss new FL functionalities enabled by this cooperative framework such as the integration of unlabeled data and heterogeneous device privacy into ML model training. Finally, we describe some open research directions at the intersection of cooperative edge/fog and FL.

Effect of Digital Silk Road and innovation heterogeneity on digital economy growth across 29 countries: New evidence from PSM-DID

Digital Silk Road (DSR) initiated in 2015 aimed to foster industry collaboration within high-tech sectors. We compare different effects of the Digital Silk Road and innovation heterogeneity on digital economy growth between 9 treatment and 20 control countries via propensity score matching (PSM)-DID (difference-in-differences) methodology. The PSM-DID approach have the merits to address the differences biases between treatment and controlled groups, while embracing the complex dimensions of individual heterogeneity. Furthermore, we also explore the different effects of institutions innovation (INS), human capital and research innovation (HCR), infrastructure innovation (INF), market sophistication innovation (MAS), and business sophistication innovation (BUS) on the digital economy. Notably, the investigation also probes the interactions that transpire between human capital & research innovation and different innovation on the digital economy. The DID regression revealed that policy shock of DSR has exerted a discernibly positive effect on the digital economy. Infrastructure innovation improved digital economy. Human capital and research innovation also enhanced digital economy. Business sophistication innovation promotes the increase of the digital economy.

Heterogeneous Intermediary Asset Pricing in Iran’s Stock Market: Privately-Owned vs. State-Owned

ABSTRACT In Iran’s stock market, this paper examines a new dimension of heterogeneity: ownership type, using an intermediary asset pricing model. When only state-owned intermediaries are considered, the price for exposure to capital ratio shocks is negative; thus, the group is not a marginal investor. Considering only privately-owned intermediaries has greater explanatory power than considering both types, and the price for capital risk is positive in both cases. We propose a new criterion to represent the sector with even greater explanatory power: a group of privately-owned intermediaries with positive capital risk prices when tested individually.

Research on the influence of social network on young people’s capacity of innovation and entrepreneurship based on stochastic forest model

Abstract In this paper, firstly, the social network theory and features are studied in detail, and the key to improving the model prediction performance using the Bagging algorithm is to use the CART regression tree algorithm to make the variance as small as possible while ensuring that the average performance of the model does not drop significantly. Next, the random forest model was improved by using the feature importance measure algorithm and the F-measure weighting algorithm, which improved the overall performance of the random forest model. Then the elements of innovation and entrepreneurship ability of college students were identified, and the basic characteristics of the research subjects were obtained using the scale, and the influence of social networks on innovation and entrepreneurship ability based on the random forest model was studied empirically. The results show that the coefficients of the two dimensions of association strength and heterogeneity in the dimensions of social networks of college students are larger, 0.251 and 0.222, respectively, indicating that it is the network association strength and heterogeneity that have a greater impact on the improvement of innovative thinking ability. This study promotes innovation and entrepreneurship education in colleges and universities, which is important for the improvement of students’ innovation and entrepreneurship ability.

Geopolitical risk and stock market development

While previous literature has documented adverse impacts on financial markets in the days following individual acts of terrorism or conflict-related events, we consider the more general, ongoing uncertainty those events contribute to: geopolitical risk (GPR). Using a sample of 37 countries spanning 1975–2019, we provide evidence of a strong, negative relationship between GPR and stock market development. To support our main results we run a series of robustness and endogeneity checks, in the process demonstrating that the impact of GPR is distinct from other macroeconomic risks such as economic policy uncertainty. We also consider several dimensions of heterogeneity among countries that cause the effects of GPR on stock market development to vary, providing more insight into those effects. In particular, we find that GPR’s negative impact is stronger for those in North America and Europe as compared to Asia. We also find that GPR’s effect is stronger for economies with higher levels of investment, which is consistent with our theory of investment as a mechanism for GPR’s influence on stock market development.

Preventing reforming unequally

Population aging has forced policy makers in most developed countries to reform pension systems with the aim of maintaining or re-establishing financial sustainability. This usually involves cost-cutting measures like later pension eligibility ages and lower replacement rates. Such reforms face harsh trade-offs with the objective of providing adequate pensions. Social welfare and inequality have emerged as crucial concerns about recent pension reforms, stressing that the lack of “social sustainability” may undermine financial sustainability. This paper analyzes such trade-offs and may explain why support for pension reform has dwindled in Europe. The paper evaluates reform effects on financial sustainability, social welfare, and intra- and inter-generational equality in a rich unified framework with several dimensions of heterogeneity and various behavioral reactions. Our simulations shed light on the complex distributional effects of pension reform on different cohorts and societal groups. They show where policy tends to reform unequally and why reforms may fail to find voters’ approval.

Exploring New Dimensions of Tumor Heterogeneity: The Application of Single Cell Analysis to Organoid-Based 3D In Vitro Models.

Modeling the heterogeneity of the tumor microenvironment (TME) in vitro is essential to investigating fundamental cancer biology and developing novel treatment strategies that holistically address the factors affecting tumor progression and therapeutic response. Thus, the development of new tools for both in vitro modeling, such as patient-derived organoids (PDOs) and complex 3D in vitro models, and single cell omics analysis, such as single-cell RNA-sequencing, represents a new frontier for investigating tumor heterogeneity. Specifically, the integration of PDO-based 3D in vitro models and single cell analysis offers a unique opportunity to explore the intersecting effects of interpatient, microenvironmental, and tumor cell heterogeneity on cell phenotypes in the TME. In this review, the current use of PDOs in complex 3D in vitro models of the TME is discussed and the emerging directions in the development of these models are highlighted. Next, work that has successfully applied single cell analysis to PDO-based models is examined and important experimental considerations are identified for this approach. Finally, open questions are highlighted that may be amenable to exploration using the integration of PDO-based models and single cell analysis. Ultimately, such investigations may facilitate the identification of novel therapeutic targets for cancer that address the significant influence of tumor-TME interactions.

Risk classification with on‐demand insurance

AbstractOn‐demand insurance is an innovative business model from the InsurTech space, which provides coverage for episodic risks. It makes use of a simple fact in a practical way: People differ in their frequency of exposure as well as the probability of loss. The extra dimension of heterogeneity can be used to screen the insured and shifts the utility‐possibility frontier outward. We provide a sufficient condition under which type‐specific full insurance at the actuarially fair price is incentive compatible. We also show that our results hold for various real‐world implementations of on‐demand insurance.

Environmental heterogeneity promotes coexistence among plant life-history strategies through stabilizing mechanisms in space and time

Spatiotemporal environmental heterogeneity can promote species coexistence through stabilizing mechanisms such as the storage effect. These mechanisms require low density advantages and species-specific responses to heterogeneity. Theory based on organisms, which are short-lived and capitalize beneficial patches instantaneously, suggests that in the presence of spatiotemporal heterogeneity, stabilizing mechanisms should arise more commonly in space than in time.We used an individual-based model to investigate if spatiotemporal soil water heterogeneity promotes stable coexistence among annual and perennial plant life-history strategies, and which functional traits explain the requirements. Scenarios explicitly differed in the resource benefits offered by the range of soil water used to simulate heterogeneity.Our results show that spatiotemporal heterogeneity promoted stable coexistence solely among perennial life-history strategies. Perennial life form delayed population responses to heterogeneity, which resulted in population decoupling among life-history strategies in time as well as in space. Recovery from low density could be explained by a combination of one classical trait which stores the effects of favorable environmental conditions (longevity) and, unexpectedly, a fast resource acquisition strategy evidenced by high adult growth rate and short-term dormancy. Life-history strategy-specific responses emerged from functional differences in seedling competitive ability and seed dispersal distance alone or, additionally, from differences in the hydrological niche. Interestingly, life-history strategies which functionally differed only in dispersal distance coexisted irrespective of whether they experienced a difference in resource benefits themselves or coexisted with life-history strategies experiencing this difference.We show that spatiotemporal heterogeneity in a single abiotic factor promoted stable coexistence through stabilizing mechanisms. Our study highlights the need to study stabilizing mechanisms across different life-history strategies to better understand the effectiveness and dimensions of spatiotemporal heterogeneity in promoting coexistence.

Selection Bias in Voluntary Random Testing: Evidence from a COVID-19 Antibody Study

We use data from a serological study that experimentally varied financial incentives for participation to detect and characterize selection bias. Participants are from neighborhoods with substantially lower COVID-19 risks. Existing methods to account for the resulting selection bias produce wide bounds or estimates that are inconsistent with the population. One explanation for these inconsistent estimates is that the underlying methods presume a single dimension of unobserved heterogeneity. The data suggest that there are two types of nonparticipants with opposing selection patterns. Allowing for these different types may lead to better accounting for selection bias.

Unpacking the Potential Influence of Life Satisfaction on Network Heterogeneity, Emotional Exhaustion and Mobile App Fatigue: A Stressor-Strain-Outcome Approach.

The ramifications of mobile apps' detrimental aspect on users' life satisfaction have garnered increased attention from academics. To probe the underlying association between life satisfaction and mobile app fatigue, this article builds a research model based on a stressor-strain-outcome approach. In addition, the study investigates the relationships between different dimensions of network heterogeneity, emotional exhaustion, and mobile app fatigue among users. Furthermore, the study uncovers the moderating influence of upward comparison, self-presentation, and privacy invasion on the association between life satisfaction and emotional exhaustion in the mobile app context. The study collected data in mainland China using a cross-sectional approach and analyzed the data using structural equation modeling. The findings demonstrate that life satisfaction is positively associated with self-presentation and negatively associated with upward comparison. Moreover, privacy invasion and upward comparison are positively correlated with emotional exhaustion, whilst self-presentation exerts no correlation with emotional exhaustion. Furthermore, upward comparison could mediate the association between life satisfaction and emotional exhaustion. The results provide fresh light on the mechanisms through which the life satisfaction of mobile app users and network heterogeneity might lead to emotional exhaustion and mobile app fatigue, highlighting important theoretical and practical implications.

Competitive targeted online advertising

This paper examines how an online publisher utilizes its information about consumer preference to target advertising. In our model, two firms first bid for a prominent ad position in a publisher-organized position auction, and then compete on price in the subsequent product marketplace. We consider two dimensions of consumer heterogeneity. First, consumers are heterogeneous in product preference. Based on their tastes, some consumers prefer one product over the other, whereas other consumers may rank the products in an opposite order. Second, consumers differ in search preference, i.e., “nonshoppers” only consider the advertised product, while “shoppers” always search both firms’ products before buying. We show that targeted advertising based on product preference will mitigate price competition in product markets as well as competition in position auctions, the latter to the detriment of the publisher. In contrast, targeted advertising based on search preference always benefits the publisher, as the winning firm can charge monopoly prices to nonshoppers. We show that the publisher’s optimal choice is to utilize only the information about consumer search preference. We also explore the welfare implications of targeted advertising based on different types of consumer preference.