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 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 4.5 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 such as local coverage scope, topic diversity, and political slant remain stable post-merger. Structural demand estimation further shows that consumers are tolerant of advertising, so the merger reduces consumer welfare only modestly. Together, these findings suggest that efficiency-driven consolidation can generate meaningful revenue gains and cost savings for newspaper chains without substantially 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.

We examine how unanticipated income shocks shape taxi drivers’ labor supply within a shift, focusing jointly on break-taking and shift-ending decisions. Using high-frequency GPS data from 3.4 million shifts and more than 21,000 drivers in Singapore, we distinguish active work from non-work spells while drivers remain on shift, providing a direct measure of within-shift effort adjustment. Consistent with a dynamic model of reference-dependent labor supply, income shocks generate substantial responses along both margins, but the operative margin depends systematically on a driver’s position in the shift. Shocks primarily affect break-taking over a broad middle stretch of the shift and affect quitting only as drivers approach their typical end-of-shift time. Break adjustments account for a considerable portion of the total change in labor supply. Focusing on stopping behavior alone, therefore, understates the magnitude of reference-dependent labor supply responses and makes them appear more concentrated near the end of the shift than they are. The findings show that workers partially absorb income shocks through within-shift effort adjustments well before quitting becomes the relevant margin, with implications for measuring labor supply and designing incentives in goal-directed work settings.

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