Bibliographic record
Abstract
Chapter 1 investigates the effects of two types of policies on consumer demand for EVs, subsidy programs and driving restrictions on gasoline cars, using rich consumer-level data from a major Chinese city on all car purchases. I estimate a structural demand model for differentiated products that incorporates these policies. I find both policies show their success in stimulating consumer demand for EVs and the positive effect of driving restrictions on the demand for EVs is stronger for consumers who already own a car than for those who do not.Chapter 2 studies the contribution to the dramatic decline of the market share of BYD Company in the Electric Vehicle (EV) market: from above 95% in 2014 to close to 10% in 2020 in a changing environment characterized by growing demand, rapid technological progress, disruptive market entry, and frequent changes in government subsidies to EVs. Our results are based on the estimation of a structural model of demand and price competition, and on a large set of counterfactual experiments that identify the contribution of different factors to the observed decline. We find that most of the decline in BYD’s market share can be explained by product proliferation and product design from early competitors. A second important factor was the rapid technological improvement in the industry during this period that made BYD to lose its initial cost advantage. Despite the decline in its market share, BYD increased its average price cost margin during this period. Our counterfactuals show that the decline in BYD’s market share and the increase in its markup are both closely related to changes in product design by BYD and its competitors. Chapter 3 compares the performance of estimation methods using market level data with a maximum likelihood–control function estimator to deal with the issue of zero market shares in demand estimation. We apply these methods to study consumer behavior during the COVID-19 pandemic using data from a leading department store in China. We find very substantial biases in the methods using market level data while estimates using consumer level data show a more plausible result.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".