Work in Progress
Green Pioneers to Green Hands: Is China’s Green Financial Regulations Efficient? with Qi Pan and Xiaoming Xie
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Abstract: The growing concerns about climate change have prompted numerous countries to implement green financial instruments aimed at promoting green innovations and ensuring long-term environmental sustainability. Nevertheless, conditional financial support may inadvertently incentivize companies to prioritize high-return green innovations or engage in greenwashing practices, compromising the overall quality of their innovations. This research investigates the impact of China's constraint-based green credit policy on firms' green patenting, utilizing comprehensive firm-level performance and innovation data from 2004 to 2014. Employing a quasi-experimental approach and analyzing firms' innovation activities over an 11-year period, we find that regulated companies experienced a 2.2% increase in their annual green innovation ratio compared to that of the unregulated companies. However, while incentivizing green innovations, this policy resulted in an inefficient allocation of green credits and a deterioration in the quality of green innovations in regulated firms. A notable shift in green innovations was observed from ``green pioneer" enterprises to ``green hand" enterprises, characterized by a 4.5 reduction in the number of green patents and a 1% decline in the green innovation ratios of the former. The policy had pronounced adverse effects on ``green pioneers", resulting in underperformance across both green and non-green sectors. Further analysis indicates that the shift in qualities and innovators is primarily attributable to the policy's vague evaluation criteria for green innovations, with effects varying across distinct industrial sectors. While regulated labor-intensive industries witnessed a substantial rise in green innovations at the expense of patent quality, regulated capital-intensive sectors were able to maintain the quality of their green innovations.
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Presented at Southern Economic Association (SEA) Annual Conference, NAREA EJ Circle, HKUST brownbag
The Recovery of Forest and its Impacts on Left-Behind Children in Western China, with Christopher Timmins
(Draft available upon request)
Abstract: This paper explores the effectiveness of these policies on afforestation on rural families (including adultsand children) considering policy-induced land transition and labor migration. Embedding changes in land use and labor allocation in the triple difference approach and the instrumental variable approach, we extend afforestation studies in three dimensions: characterizing its short- and long-run effects on income and off-farm migration, estimating its spillover effects on Left Behind Children, and evaluating its distributional welfare effects. The results provide evidence of negative effects on vulnerable groups and left-behind children, showing that lower-income rural adults are significantly more likely to out-migrate for urban jobs corresponding to afforestation induced land transition. Though children are less likely to quit schools due to compensation from afforestation and remittance, left-behind children’s education performance and life attainments including marriage, health, and income in adulthood are adversely affected. Afforestation policies have regressive impacts and favor wealthy families and families with less arable land. Provided that the PES policy was designed to protect forest resources as well as alleviate poverty in rural areas, the differential welfare
results raise the concern of environmental justice in policy design and evaluations.
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Presented at Western Economic Association (WEAI) Annual Conference, Southern Economic Association (SEA) Annual Conference, AARES Conference, NAREA Scholar Circle
Costly Price Adjustment and Automated Pricing: The Case of Airbnb, with Qi Pan
(Draft available upon request)
Abstract: On many e-commerce platforms such as Airbnb, StubHub and TURO, where each seller sells a fixed inventory over a finite horizon, the pricing problems are intrinsically dynamic. However, many sellers on these platforms do not update prices frequently. In this paper, I develop a dynamic pricing model to study the revenue and welfare implication of automated pricing which allows sellers to update their prices without manual interference. The model focuses on three factors through which automated pricing influences sellers: price adjustment cost, buyer's varying willingness to pay and inventory structure. In the model, I also take into account competition among sellers. Utilizing a unique data set of detailed Airbnb rental history and price trajectory in New York City, I find that the price rigidity observed in the data can be rationalized by a price adjustment cost ranging from 0.9% to 2.2% of the listed price. Moreover, automated pricing can increase the platform's revenue by 4.8% and the hosts' (sellers') by 3.9%. The renters (buyers) could be either better off or worse off depending on the length of their stays.
Impacts of Transportation Infrastructure on Labor Market and Residential Mobility: the Effects of New Metro Lines in Los Angeles, with Chris Severen
High-speed Rail and Migration in China, with Deyu Rao, Lin Yang and Yatang Lin
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