The Effect of Tax Credit Policy on Electric Vehicle Sales: A Synthetic Control Approach Using Bayesian Structural Time Series

What is it about?

The government is intervening in automobile markets to reduce greenhouse gas emissions in many countries. Several tax incentive programs, at both federal and state level, are being effective for the adoption of environment-friendly vehicles over the past few years. Previous studies in this field focused on discrete choice models analyzing such policies. In this study, however, I employed a Bayesian structural forecasting model to construct a synthetic control to test the effects of state-level tax credit policy in Maryland using a unique time-series data set of vehicle sales records. I observed a significant positive policy effect on electric vehicle sales.

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Photo by CHUTTERSNAP on Unsplash

Photo by CHUTTERSNAP on Unsplash

Why is it important?

This research specifically worked on Maryland's electric vehicle (EV) incentive policy, where State Policymakers hoped to boost EV adoption for environmental benefits by incentivizing consumers. But the research found that the goal and the adopted policy were not well-synchronized. The state's goal was to achieve 300,000 EVs on the road by 2025, while according to the research, only around thirty thousand EVs will be adopted by 2025. The research further highlighted that this would lead to the premature depletion of the allocated six million dollars in state funds. And evidently, in 2020, the state's website confirmed this fund exhaustion.

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