We examine how a government-initiated e-commerce platform (GEP) affects sales of a local specialty in China’s Pu’er tea market. Using a unique dataset from field experiments and surveys of 983 farmers, we examine changes in online and offline sales over time. We employ two-way fixed effects (TWFE) models to identify the causal impact of GEP access. The results reveal significant substitution effects: access to the GEP increases online sales by 16.649% and decreases offline sales by 15.549%, indicating an overall shift from offline to online sales. On the extensive margin, households that previously sold only offline become more likely to sell online. On the intensive margin, adopters expand their online channels and offer a wider range of tea qualities. The mediation analysis suggests that the increase in online sales channels and product variety accounts for the impact of GEP access on the shift to online transactions.

The COVID-19 pandemic triggered a global shift from face-to-face instruction to online learning, presenting new operational challenges for business schools, particularly in equipping students with quantitative skills essential for the labor market. Leveraging China’s abrupt lockdown policies as a natural experiment, we examine the heterogeneous effects of online education on student academic performance. Using a panel dataset of 15,329 observations from 7,867 undergraduate students across nine Chinese universities, covering four semesters (fall 2018 to spring 2020), we compare student outcomes before and during the transition to online learning. We find that online education led to an average increase of 8 to 11 points in math scores on a 100-point scale during the pandemic. Applying principal component analysis, we identify four key policy measures that capture lockdown stringency: stay-at-home orders, workplace closures, public transportation suspension, and public information campaigns. Stricter stay-at-home orders issued by the government reduce the effectiveness of online learning; however, these negative effects are partially offset by increased parental supervision and fewer external distractions resulting from workplace closures and the suspension of public transportation. Further, we show that online learning is more effective for reasoning-focused courses (e.g., mathematics) than interpretation-focused courses (e.g., English), and the academic benefits of face-to-face peer interactions diminish significantly in online settings relative to offline environments. Our findings offer actionable insights for managing educational delivery during operational disruptions, emphasizing the importance of tailoring online curriculum design and support systems to discipline-specific needs and student mobility constraints.

Modern business practices frequently employ a blend of pricing strategies to segment markets effectively. As a result, consumers may encounter pricing schedules that are non-linear and multidimensional. This paper presents a structural approach for estimating multidimensional non-linear pricing models involving multiple decision variables in an energy market. Using a unique, rich panel dataset of Chinese household electricity consumption, we structurally estimate consumer preferences under the influence of an Increasing Block Price (IBP) and a Time-of-Use (ToU) system. Our structural approach allows us to distinguish and evaluate household-level price elasticities of demand, presenting a novel explanation for consumers’ feedback on marginal price changes. Through model-based simulations, we demonstrate that a 1% increase in price corresponds to a 0.7% reduction in total electricity demand. However, our analysis indicates that practical opportunities for optimization within multi-dimensional pricing systems are limited. Our findings offer distinct insights into the complex interplay between intricate pricing structures and energy consumption behavior, thereby providing valuable guidance for policymakers and regulators.

In this study, we explore the effect of peer recognition on content creation within a prominent Chinese Question-and-Answer (Q&A) platform, specifically focusing on whether votes from peers encourage influencers to engage in providing more answers. Using panel regression models with instrumental variables, our analysis reveals that peer votes have a substantial positive effect on content production. Additionally, we investigate the consequences of two distinct badge policies, the “self-authentication” and the “best-answerer” badge, on content production. Our results demonstrate that while badges aid users in recognizing the quality of an influencer, badges with strong connotations may constrain content creation due to concerns about reputation management and privacy. As such, strategies that enhance platform traffic by promoting voting could be counterproductive if they exacerbate privacy and reputation worries. Our findings provide valuable insights into the role of peer recognition and badge policies in shaping content contribution, bearing crucial policy implications for the design of Q&A platforms.

The objective of this study is to examine the impact of discriminatory platform commissions on creators’ pricing decisions and content quality. Our identification strategy relies on the dynamic changes in commission policy on a well-established creator platform. In August 2019, the platform increased its commission from 5% to 20% for all creators and later allowed some creators to revert to the original 5% commission. A difference-in-differences approach is employed to examine the consequences of implementing discriminatory commissions on price and content quality. The results indicate that implementing discriminatory commissions results in a significant price increase. Further mechanism checks demonstrate that the initial commission increase altered market concentration, leading to a pooling equilibrium: some creators increase prices while others adjust quality. The subsequent reduction in commission could shift prices and qualities again, as high-quality creators choose to increase their prices to avoid being considered low-quality. This study suggests that changes in commission should be approached with caution due to their irreversible effects. An increase in commission while discriminating against a segment of creators has the potential to shift content and price distribution, potentially disadvantaging subscribers and reducing their welfare.

This paper studies how network structure can affect the speed of adoption. In particular, we model the decisions to adopt Python 3 by software packages. Python 3 provides advanced features but is not backward compatible with Python 2, which implies adoption costs. Moreover, packages form input-output networks through dependency relationships with other packages, and they face additional adoption costs if the dependency packages lack Python 3 support. We build a dynamic model of technology adoption that incorporates such a network and estimate it using a complete dataset of Python packages. Estimation results show that the average cost of one incompatible dependency is roughly three times the cost to update one’s code. We conduct counterfactual policies of community-level targeted cost subsidy and show network structure is crucial to determine an optimal policy of cost subsidy.