Three essays on peer effects and applications in environmental economics and family economics

Abstract

This dissertation focuses on analyzing peer effects in household decisions and the diffusion of renewable energy. The first chapter investigates peer effects in two family planning decisions among Chinese households – having a second child and having a son. The second chapter focuses on evaluating environmental policies (tax credit and Corporate Average Fuel Economy standard) on the diffusion of electric vehicles in the US. And the third chapter analyzes peer effects in residential solar panels adoption with a geographic focus on California. In summary, the first and third chapter adopt two structural peer effects models to analyze household behavior under two distinct decision-making context – family planning and solar panels adoption. And the second and third chapter focuses the diffusion of two renewable energy powered products – electric vehicle and solar panels.Peer effect measures how much the decision made by an agent (usually refers to an house-hold in this dissertation) is influenced by peers’ decisions under the same decision context. Manski (1993) summarizes the obstacles in identifying peer effects. The first is to separate peer effects with contextual effects, or how much the observed similarity in decision making among peer group members are attributed to similar backgrounds between peers due to endogenous group formation. The second is to separate peer effects with correlated effects, which refers to unobserved household characteristics and are believed to be correlated with each other. We use static and dynamic structural peer effects models to analyze family planning decisions and solar panels adoption decision separately, and these models are capable of disentangling the difficulties mentioned above. For the demand estimation of electric vehicle, we use a random coefficient model which has been broadly used in industrial organization.The first chapter is motivated by the increasingly unbalanced sex ratio in China. This phenomenon and associated social challenges have been widely documented, though few studies have rigorously investigated the role that peer effects have played in this unbalanced sex ratio. This paper fills this gap by focusing on peer effects in the decision to have a second child, and to have a son. The data we use comes from the 2016 data of China Family Panel Studies, and is a ten-year cohort of women aged 45-54 by 2016; we use a structural discrete choice model to estimate the peer effects. We find that peer choices significantly influence the probability that a family has a second child, but not the probability of having a son. Instead, having a son is largely driven by contextual effects, and in particular, by the education level of one’s peer group.The second chapter uses the random coefficient model with post-estimation counterfactual analysis to answer two research questions: (1) How much the tax credit has facilitated the diffusion of EV; (2) How much the CAFE standard and penalty level have facilitated the diffusion of EV. We obtain the data from Wards Auto with a years range from 2012 to 2019.We find that the EV market share will decrease by 35.82% if there is no tax credit. CAFE marks down the price of EV in average by 3.4 percent but marks up the price of other types of vehicles by 3.26 percent, whose absolute value far exceeds the CAFE penalty itself. We also find that increasing the penalty level from$55 to$140 per vehicle per mpg below the standard will only increase the EV market share by 0.23% and decrease the non-EV market share by 0.12%.The third chapter applies a utility-based structural optimal stopping time model developed by de Paula (2009) to analyze solar PV adoption. We use both econometrics model and nonparametric test to support the evidence of peer effects, using public solar PV data obstained from CaliforniaDGStats. And we apply the optimal stopping time model with a confidential data set obtained from PG&E. We find significant peer effects and correlated effects in a case study which contains 20 non-adjacent communities in the suburb of San Jose. And we predicted the adoption rate in this area will increase from 19.77% in 2019 to39.65% in 2029.