Spillover bias in multigenerational income regressions

Intergenerational persistence estimates are susceptible to several well-documented biases arising from income measurement, and it has become standard practice to construct income measures to mitigate these. However, remaining bias can lead to a spurious grandparent coefficient estimate in multigenerational regressions, a recent focus of the mobility literature. We show with theory and simulations that even using a 30-year income average can result in a small positive spurious grandfather coefficient estimate. We further propose an IV approach, showing that it is not susceptible to this spillover bias in simplified settings and that it can provide bounds on the parameters in a more general scenario. With administrative data from Norway, we reveal a positive spillover bias in the grandfather coefficient estimates, and the combined evidence from our OLS and IV approaches suggest the preferred small positive OLS estimate could still be upward biased

Spillover bias in multigenerational income regressions

Intergenerational persistence estimates are susceptible to several well-documented biases arising from income measurement, and it has become standard practice to construct income measures to mitigate these. However, remaining bias can lead to a spurious grandparent coefficient estimate in multigenerational regressions, a recent focus of the mobility literature. We show with theory and simulations that even using a 30-year income average can result in a small positive spurious grandfather coefficient estimate. We further propose an IV approach, showing that it is not susceptible to this spillover bias in simplified settings and that it can provide bounds on the parameters in a more general scenario. With administrative data from Norway, we reveal a positive spillover bias in the grandfather coefficient estimates, and the combined evidence from our OLS and IV approaches suggest the preferred small positive OLS estimate could still be upward biased

Distributed control of plug-in hybrid electric vehicles on a smart grid

This paper explores the effect of a distributed control system for the charging of plug-in hybrid electric vehicles utilizing an agent-based approach in a smart grid. The vehicles are regarded as additional loads in addition to a primary forecasted load and use information transfer with the grid to make their charging decisions. MATLAB was used as the simulation tool to design the control strategy and simulate its effect on a power grid. The findings of this study are that the charging behavior and peak loads on the grid can be reduced by use of this distributed control strategy