scholarly journals Lock-In and Team Effects

2015 ◽  
Vol 18 (4) ◽  
pp. 376-387
Author(s):  
Trey Dronyk-Trosper ◽  
Brandli Stitzel
Keyword(s):  
Set Covering ◽  
Future Research ◽  
Prior Work ◽  
Omitted Variable Bias ◽  
Specific Effects ◽  
Heterogeneous Effects ◽  
Team Success ◽  
Variable Bias ◽  
Lock In ◽  
Least Squares Approach

How important is recruiting to a football program’s success? While prior research has attempted to answer this question, we utilize an extensive panel set covering 13 years of games along with a two-stage least squares approach to investigate the effects of recruiting on team success. This article also includes new control variables to account for omitted variable bias that prior work may have missed. We also split our sample to investigate whether recruiting displays heterogeneous effects across schools. Additionally, we find evidence that the benefits of recruiting are driven by team-specific effects, indicating that team success may be more heavily derived from the ability of teams to harness and improve their recruits than their ability to utilize each athlete’s raw abilities. This leads to important revelations regarding future research into both the value of recruits and what drives a football team’s success.

scholarly journals A Reassessment of the Relationship Between Inequality and Growth

2000 ◽  
Vol 90 (4) ◽  
pp. 869-887 ◽  
Author(s):  
Kristin J Forbes
Keyword(s):  
Economic Growth ◽  
Income Inequality ◽  
Negative Relationship ◽  
Data Set ◽  
Omitted Variable Bias ◽  
Specific Effects ◽  
Variable Bias ◽  
Time Invariant ◽  
The Relationship ◽  
Country Specific

This paper challenges the current belief that income inequality has a negative relationship with economic growth. It uses an improved data set on income inequality which not only reduces measurement error, but also allows estimation via a panel technique. Panel estimation makes it possible to control for time-invariant country-specific effects, therefore eliminating a potential source of omitted-variable bias. Results suggest that in the short and medium term, an increase in a country's level of income inequality has a significant positive relationship with subsequent economic growth. This relationship is highly robust across samples, variable definitions, and model specifications. (JEL O40, O15, E25)

Taking Innovation to the Streets: Microgeography, Physical Structure, and Innovation

2020 ◽  
Vol 102 (5) ◽  
pp. 912-928 ◽  
Author(s):  
Maria P. Roche
Keyword(s):  
Fixed Effects ◽  
City Planning ◽  
Knowledge Exchange ◽  
Physical Structure ◽  
Set Covering ◽  
Regional Innovation ◽  
Data Set ◽  
Omitted Variable Bias ◽  
Variable Bias ◽  
Block Groups

In this paper, we analyze how the physical layout of cities affects innovation by influencing the organization of knowledge exchange. We exploit a novel data set covering all census block groups in the contiguous United States with information on innovation outcomes, street infrastructure, as well as population and workforce characteristics. To deal with concerns of omitted variable bias, we apply commuting zone fixed effects and construct instruments based on historic city planning. The results suggest that variation in street network density may explain regional innovation differentials beyond the traditional location externalities found in the literature.

scholarly journals When less conditioning provides better estimates: overcontrol and endogenous selection biases in research on intergenerational mobility

2022 ◽  
Author(s):  
Michael Grätz
Keyword(s):  
Intergenerational Mobility ◽  
Panel Study ◽  
Social Science Research ◽  
Science Research ◽  
Future Research ◽  
Omitted Variable Bias ◽  
Confounding Variables ◽  
Endogenous Selection ◽  
Variable Bias ◽  
Counterfactual Approach

AbstractThe counterfactual approach to causality has become the dominant approach to understand causality in contemporary social science research. Whilst most sociologists are aware that unobserved, confounding variables may bias the estimates of causal effects (omitted variable bias), the threats of overcontrol and endogenous selection biases are less well known. In particular, widely used practices in research on intergenerational mobility are affected by these biases. I review four of these practices from the viewpoint of the counterfactual approach to causality and show why overcontrol and endogenous selection biases arise when these practices are implemented. I use data from the German Socio-Economic Panel Study (SOEP) to demonstrate the practical consequences of these biases for conclusions about intergenerational mobility. I conclude that future research on intergenerational mobility should reflect more upon the possibilities of bias introduced by conditioning on variables.

Mines in the End Zone: Are there Downsides to Team Performance?

2020 ◽  
Vol 23 ◽  
Author(s):  
Troy V. Mumford ◽  
M. Travis Maynard
Keyword(s):  
Team Performance ◽  
Role Overload ◽  
Future Research ◽  
Negative Consequences ◽  
Regression To The Mean ◽  
Occupational Psychology ◽  
Team Success ◽  
The Mean ◽  
Overconfidence Bias

Abstract Research on teams in organizations tends to focus on understanding the causes of team performance with a focus on how to enjoy the benefits of team success and avoid the negative consequences of team failure. This paper instead asks the question, ‘what are some of the negative consequences of team success?’ A review of the literature on teams is augmented with research from cognitive science, sociology, occupational psychology, and psychology to explore the potential negative long-term consequences of teamwork success. The general topics of groupthink, overconfidence bias, regression to the mean, role overload, and strategy calcification are reviewed while discussing the implications for future research streams and practical team management.

scholarly journals Omitted Variable Bias in GLMs of Neural Spiking Activity

2018 ◽  
Vol 30 (12) ◽  
pp. 3227-3258 ◽  
Author(s):  
Ian H. Stevenson
Keyword(s):  
Neural Activity ◽  
Single Neuron ◽  
Linear Models ◽  
Theory And Practice ◽  
Parameter Estimates ◽  
Omitted Variables ◽  
History Effects ◽  
Omitted Variable Bias ◽  
Wide Range ◽  
Variable Bias

Generalized linear models (GLMs) have a wide range of applications in systems neuroscience describing the encoding of stimulus and behavioral variables, as well as the dynamics of single neurons. However, in any given experiment, many variables that have an impact on neural activity are not observed or not modeled. Here we demonstrate, in both theory and practice, how these omitted variables can result in biased parameter estimates for the effects that are included. In three case studies, we estimate tuning functions for common experiments in motor cortex, hippocampus, and visual cortex. We find that including traditionally omitted variables changes estimates of the original parameters and that modulation originally attributed to one variable is reduced after new variables are included. In GLMs describing single-neuron dynamics, we then demonstrate how postspike history effects can also be biased by omitted variables. Here we find that omitted variable bias can lead to mistaken conclusions about the stability of single-neuron firing. Omitted variable bias can appear in any model with confounders—where omitted variables modulate neural activity and the effects of the omitted variables covary with the included effects. Understanding how and to what extent omitted variable bias affects parameter estimates is likely to be important for interpreting the parameters and predictions of many neural encoding models.

The Impact of ICT Investment on Establishment Productivity

2003 ◽  
Vol 184 ◽  
pp. 99-110 ◽  
Author(s):  
Thomas Zwick
Keyword(s):  
Panel Data ◽  
Unobserved Heterogeneity ◽  
Data Set ◽  
Omitted Variable Bias ◽  
Lagged Effects ◽  
Variable Bias ◽  
Employee Characteristics ◽  
The Impact ◽  
Establishment Panel Data

This paper finds substantial effects of ICT investments on productivity for a large and representative German establishment panel data set. In contrast to the bulk of the literature also establishments without ICT capital are included and lagged effects of ICT investments are analysed. In addition, a broad range of establishment and employee characteristics are taken account of in order to avoid omitted variable bias. It is shown that taking into account unobserved heterogeneity of the establishments and endogeneity of ICT investments increases the estimated lagged productivity impact of ICT investments.

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