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 effort within a work period and when to stop. We study both margins jointly using high-frequency GPS data from 3.4 million Singapore taxi shifts. A dynamic model of work, rest, and quitting under income targeting predicts that negative earnings shocks reduce break-taking and delay quitting, with the responsive margin depending on the worker’s position relative to her planned endpoint. Exploiting quasi-exogenous shocks from trip cancellations, we confirm these predictions. Because reduced break-taking after a shortfall partially offsets lost income before the quitting decision, intensive-margin adjustment absorbs part of the shock, attenuating its effect on shift length. This behavioral absorption offers a complementary channel–alongside adaptive reference points for the declining influence of earlier shocks on stopping. Our findings establish that intensive margin is first-order, both in terms of its impact on total labor supply and its response to income shocks, and highlight that understanding reference-dependent behavior requires observing all adjustment margins