Treatment and Spillover Effects Under Network Interference

2020 ◽  
Vol 102 (2) ◽  
pp. 368-380 ◽  
Author(s):  
Michael P. Leung
Keyword(s):  
Degree Distribution ◽  
Network Inference ◽  
Network Formation ◽  
Spillover Effects ◽  
Asymptotically Normal ◽  
Variance Estimators ◽  
Strategic Model ◽  
Regression Estimators ◽  
Network Interference ◽  
Using Data

We study nonparametric and regression estimators of treatment and spillover effects when interference is mediated by a network. Inference is nonstandard due to dependence induced by treatment spillovers and network-correlated effects. We derive restrictions on the network degree distribution under which the estimators are consistent and asymptotically normal and show they can be verified under a strategic model of network formation. We also construct consistent variance estimators robust to heteroskedasticity and network dependence. Our results allow for the estimation of spillover effects using data from only a single, possibly sampled, network.

Models of network formation

2018 ◽  
Author(s):  
Mark Newman
Keyword(s):  
Degree Distribution ◽  
Network Formation ◽  
Preferential Attachment ◽  
Network Size ◽  
Large Network ◽  
Citation Networks ◽  
Airline Networks ◽  
Attachment Model ◽  
Delivery Networks

This chapter describes models of the growth or formation of networks, with a particular focus on preferential attachment models. It starts with a discussion of the classic preferential attachment model for citation networks introduced by Price, including a complete derivation of the degree distribution in the limit of large network size. Subsequent sections introduce the Barabasi-Albert model and various generalized preferential attachment models, including models with addition or removal of extra nodes or edges and models with nonlinear preferential attachment. Also discussed are node copying models and models in which networks are formed by optimization processes, such as delivery networks or airline networks.

scholarly journals The effects of physical restructuring on the socioeconomic status of neighbourhoods: Selective migration and upgrading

Urban Studies ◽  
2018 ◽  
Vol 56 (8) ◽  
pp. 1647-1663
Author(s):  
Merle Zwiers ◽  
Maarten van Ham ◽  
Reinout Kleinhans
Keyword(s):  
Large Scale ◽  
Spillover Effects ◽  
High Income ◽  
Direct Effects ◽  
Urban Restructuring ◽  
Deprived Neighbourhoods ◽  
New Construction ◽  
Using Data ◽  
Main Instrument ◽  
Dutch Cities

In the last few decades, many governments have implemented urban restructuring programmes with the main goal of combating a variety of socioeconomic problems in deprived neighbourhoods. The main instrument of restructuring has been housing diversification and tenure mixing. The demolition of low-quality (social) housing and the construction of owner-occupied or private rented dwellings was expected to change the population composition of deprived neighbourhoods through the in-migration of middle- and high-income households. Many studies have been critical with regard to the success of such policies in actually upgrading neighbourhoods. Using data from the 31 largest Dutch cities for the 1999 to 2013 period, this study contributes to the literature by investigating the effects of large-scale demolition and new construction on neighbourhood income developments on a low spatial scale. We use propensity score matching to isolate the direct effects of policy by comparing restructured neighbourhoods with a set of control neighbourhoods with low demolition rates, but with similar socioeconomic characteristics. The results indicate that large-scale demolition leads to socioeconomic upgrading of deprived neighbourhoods as a result of attracting and maintaining middle- and high-income households. We find no evidence of spillover effects to nearby neighbourhoods, suggesting that physical restructuring only has very local effects.

scholarly journals Carbon Productivity and Mitigation: Evidence from Industrial Development and Urbanization in the Central and Western Regions of China

2021 ◽  
Vol 13 (16) ◽  
pp. 9014
Author(s):  
Yongjiao Wu ◽  
Huazhu Zheng ◽  
Yu Li ◽  
Claudio O. Delang ◽  
Jiao Qian
Keyword(s):  
Carbon Emissions ◽  
Industrial Development ◽  
Industrial Structure ◽  
Spillover Effects ◽  
Spatial Lag Model ◽  
Workforce Allocation ◽  
Lag Model ◽  
Using Data ◽  
Shape Curve ◽  
Carbon Productivity

This paper investigates carbon productivity (CP) from the perspectives of industrial development and urbanization to mitigate carbon emissions. We propose a hybrid model that includes a spatial lag model (SLM) and a fixed regional panel model using data from the 17 provinces in the central and western regions of China from 2000 to 2018. The results show that the slowly increasing CP has significant spatial spillover effects, with High–High (H–H) and Low–Low (L–L) spatial distributions in the central and western regions of China. In addition, industrial development and urbanization in the study area play different roles in CP, while economic urbanization and industrial fixed investment negatively affect CP, and population urbanization affects CP along a U-shape curve. Importantly, the results show that the patterns of industrial development and urbanization that influence CP are homogenous and mutually imitated in the 17 studied provinces. Furthermore, disparities in CP between regions are due to industrial workforce allocation (TL), but TL has been inefficient; industrial structure upgrades are slowly improving conditions. Therefore, the findings suggest that, in the short term, policymakers in China should implement industrial development policies that reduce carbon emissions in the western and central regions by focusing on improving industrial workforce allocation.

Bias in retrospective analyses of biomarker effect using data from an outcome-adaptive randomized trial

2019 ◽  
Vol 16 (6) ◽  
pp. 599-609 ◽  
Author(s):  
Lingyun Ji ◽  
Lisa M McShane ◽  
Mark Krailo ◽  
Richard Sposto
Keyword(s):  
Simulation Studies ◽  
Adaptive Randomization ◽  
Prognostic Effect ◽  
Variance Estimators ◽  
Oncology Research ◽  
Previously Treated Patients ◽  
Previously Treated ◽  
Effect Of Treatment ◽  
Using Data ◽  
Biased Estimates

Background/Aims Biomarker-stratified outcome-adaptive randomization trials, in which randomization probabilities depend on both biomarker value and outcomes of previously treated patients, are receiving increased attention in oncology research. Data from these trials can also form the basis of investigation of additional biomarkers that may not have been incorporated into the original trial design. In this article, we investigate the validity of a standard analytical method that utilizes data from a biomarker-stratified outcome-adaptive randomization trial to assess the effect of a newly identified biomarker on patient outcomes. Methods In the context of an ancillary biomarker study for a two-arm phase II trial with a response endpoint, we conduct analytic and simulation studies to investigate bias in estimated biomarker effects under outcome-adaptive randomization. Conditions under which bias arises and magnitude of the bias are examined in several settings. We then propose unbiased estimators of biomarker effects with appropriate variance estimators. Results We demonstrate that use of biomarker-stratified outcome-adaptive randomization perturbs the patient population and treatment assignments. Consequently, application of standard analysis methods to data from an outcome-adaptive randomization trial either to estimate prognostic effect of a new biomarker in uniformly treated patients or to estimate effect of treatment in relation to the new biomarker can lead to substantially biased estimates. The proposed adjusted estimators are asymptotically unbiased, and the proposed variance estimators correctly reflect the sample variability in the estimators. Conclusion This article demonstrates existence of bias when standard, naïve statistical methods are utilized to assess biomarker effects using data from a biomarker-stratified outcome-adaptive randomization trial, and hence that results from naïve analyses must be interpreted with great caution. These findings highlight that, in an era where data and specimens are increasingly being shared for biomarker studies, care must be taken to document and understand implications of the study design under which specimens or data have been obtained.

scholarly journals Perceived Consumer Effectiveness as A Trigger of Behavioral Spillover Effects: A path towards Recycling

2020 ◽  
Vol 12 (11) ◽  
pp. 4348 ◽  
Author(s):  
Claudia Arias ◽  
Carlos A. Trujillo
Keyword(s):  
Environmental Behavior ◽  
Sustainable Consumption ◽  
Spillover Effects ◽  
Situational Factors ◽  
Mediation Model ◽  
Starting Point ◽  
Perceived Consumer Effectiveness ◽  
Using Data ◽  
The Relationship ◽  
Level Of Effort

Increasing and promoting recycling is crucial to achieving sustainable consumption. However, this is a complex task that involves the interplay of beliefs, knowledge and situational factors in ways not yet understood. This study examines a spill-over model in which perceived consumer effectiveness influences the adoption of an easy task (carrying reusable shopping bags) and that, in turn, influences recycling. Using data from a national survey with a representative sample of 1286 respondents in Colombia, we test a hypothesized path using a mediation model. Our results suggest that the relationship between perceived consumer effectiveness and recycling is mediated by the use of reusable shopping bags. Thus, once the adoption of simple pro-environmental behavior is triggered by pro-environmental beliefs, spillover effects may ensue to favor the adoption of recycling behavior. This suggests that individuals may adopt pro-environmental behavior in stages or levels. Therefore, focusing on behaviors that require less effort (e.g., reducing/reusing) could be a starting point when it comes to encouraging the adoption of other behaviors that demand a greater level of effort such as recycling. This study suggests that attitudinal variables can be the starting point of spill-over effects.

scholarly journals The Spillover Effects of Spousal Chronic Diseases on Married Couples’ Labour Supply: Evidence from China

2019 ◽  
Vol 16 (21) ◽  
pp. 4214 ◽  
Author(s):  
Shen ◽  
Zheng ◽  
Tan
Keyword(s):  
Chronic Diseases ◽  
Labour Supply ◽  
Labour Force ◽  
Working Hours ◽  
Married Couples ◽  
Spillover Effects ◽  
Average Treatment Effect ◽  
Full Time ◽  
Using Data ◽  
High Level

The objective of this study is to examine the spillover effects of chronic diseases experienced by spouses on their wives or husbands’ labour supply. Using data from 2010 and 2012 of the China Family Panel Studies (CFPS), this study employed a difference-in-difference (DD) strategy to investigate the average treatment effect of affected adults on their spouses’ working hours. The results show that, after their spouses were diagnosed with chronic diseases, the average weekly working hours of wives and husbands would be significantly reduced by 3.7–4.2 h and 3.8–4.4 h, respectively. Specially, the average weekly hours of full-time work would be reduced by 2.1–3.3 h for wives and 3.6–3.8 h for husbands. The effect was stronger for those married couples with lower socioeconomic status (SES), such as low-level education, family asset, non-labour income, while the effect was insignificant for high-level SES households. Therefore, as a result of the adverse spillover effects on household labour supply, chronic diseases could cause a greater loss of labour force productivity. Additionally, households in low levels of SES may suffer more losses from reduced labour supply when spousal chronic diseases take place.

scholarly journals A strategic model for network formation

2015 ◽  
Vol 2 (1) ◽  
Author(s):  
Omid Atabati ◽  
Babak Farzad
Keyword(s):  
Network Formation ◽  
Strategic Model

Shadow and Spillover Effects of Competition in NBA Playoffs

2017 ◽  
Vol 19 (8) ◽  
pp. 1067-1092 ◽  
Author(s):  
Brian Hill
Keyword(s):  
Spillover Effect ◽  
Spillover Effects ◽  
National Basketball Association ◽  
Shadow Effect ◽  
Previous Effort ◽  
The Future ◽  
Using Data

The National Basketball Association (NBA) playoffs are structured as an elimination tournament where reseeding does not occur after each round. This structure leads to situations where future competitors (the shadow effect) and previous effort (the spillover effect) affect current performance. Using data from the 2009-2014 NBA playoffs, results here find that, when a future opponent is known, a series favorite is significantly more likely to win a game when the future opponent is weaker than expected. Estimates also provide evidence that greater previous effort by teams increases the probability the series favorite wins a game.

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