Local newspaper mergers have become increasingly common as chains seek efficiency gains in advertising sales and content production. However, policymakers worry that such mergers harm consumers through higher advertising loads, increased subscription fees, and more centralized, less locally tailored content. This paper examines these concerns using the largest merger in recent U.S. newspaper history. Drawing on a novel dataset of advertising revenue, circulation records, and 5.4 million news articles analyzed with large language models and machine learning methods, I find that the merger substantially increases newspapers' advertising revenue and the share of centrally produced content, consistent with supply-side synergies. The advertising revenue gain is driven by higher demand rather than price increases, suggesting advertisers also benefit from the merger. On the consumer side, content characteristics on local coverage scope, topic diversity, and political slant remain stable post merger. Structural demand estimation further shows that consumers are tolerant of advertising and consumer welfare is unchanged. Together, these findings suggest that efficiency-driven consolidation can generate meaningful revenue gains and cost savings for newspaper chains without significantly harming consumer welfare.

A growing number of professionals, such as physicians, use live streaming to attract clients and promote their services. Yet whether live streaming generates demand in professional service settings, and which features predict its effectiveness, remains unclear. We examine these questions using data from one of China’s largest online healthcare platforms, combining consultation records for more than 7,000 physicians with detailed information on their live streaming sessions. Using a generalized synthetic control approach, we show that live streaming adoption causally increases service demand. The effect persists for several months and is heterogeneous, with larger gains for more established physicians, including chief and associate chief physicians and those with higher baseline consultation volumes. To analyze live streaming effectiveness, we conduct a multimodal analysis of live streaming videos, extracting visual, auditory, and textual features using machine learning and large language models. Audience interaction, content, linguistic features, and emotional expression emerge as the strongest predictors of effectiveness. Interactive sessions, clear and concrete language, and a neutral emotional display are associated with stronger demand responses, whereas greater emotional variability is associated with weaker responses. Extending the analysis to investment fund managers reveals similar patterns, suggesting that live streaming is an effective demand-generation tool across professional service domains.

Analyzing the transaction data of a retail chain selling storable products and targeting upper-middle-class customers, we find that, besides making larger expenditures on a payday, even on a non-payday, customers make larger expenditures as long as it is their first trip to the retail chain since apayday. Thereafter, the per-trip expenditures decrease over trips within the monthly paycheck cycle until an upward jump on the first trip since the next payday. This pattern suggests that consumers without facing monthly liquidity constraints may self-impose a monthly mental budget. Their daily expenditure decisions follow a rule of thumb trying not to overspend beyond the mental budget. They renew the mental budget on paydays and the salience of paydays also causes overshoots in expenditures

Workers pursuing earnings targets can adjust both when to stop working and how intensively to work. To understand how goal pursuit operates across both margins, we develop a dynamic model in which agents choose among working, resting, and quitting, with cumulative fatigue and goal-based payoffs that render the marginal value of income state-dependent. The model predicts that target dependence activates different margins at different points in the shift: income shortfalls early in a shift compress breaks, while shortfalls near the expected quitting time extend shift length. We test these predictions jointly using high-frequency GPS data from 3.4 million Singapore taxi shifts, which distinguish working time from breaks. Exploiting quasi-exogenous variation from passenger cancellations and no-shows, we find that intensive-margin adjustments are economically significant and concentrated during the middle of the shift, partially offsetting lost income before the quitting decision is reached. Analyses based only on quitting understate both the prevalence and the magnitude of reference-dependent labor supply. Our findings demonstrate that reference-dependent behavior is margin-specific and timing-dependent, and that measuring its full extent requires observing all margins of adjustment.