Job Tasks
From Crisis to Norm: Remote Work Trends and Employee Engagement Across Industries, Occupations, and Geography
Christos Makridis & Jason Schloetzer
NBER Working Paper, December 2024
Abstract:
We use a survey of nearly 360,000 workers conducted from May 2020 through December 2023 to characterize shifts in remote work across time, industry, occupation, and geography, and examine the evolving relationship between remote work and employee engagement. We find remarkable stability in the incidence of remote work since mid-2021 with roughly one-half of workers reporting always working remotely or in a hybrid arrangement. While remote work arrangements across industries remain broad-based, at the occupation level, they are conspicuously concentrated in certain job classifications. Remote work continues to evolve across the U.S., with 13 (14) states experiencing reported increases (decreases) in remote work rates since 2022 with the most populous states experiencing remote working rates exceeding 40% of workers. Empirical evidence shows that while working remotely correlates with higher job satisfaction and lower intentions to quit, these correlations disappear when other workplace characteristics such as pay practices, human resources policies, and managerial relationships are considered. If remote work remains the norm, our results suggest it may not directly influence employee engagement -- the workplace still matters.
The Hidden Costs of Networking and How it Affects Mutual Fund Managers
Elias Ohneberg
University of Cambridge Working Paper, January 2025
Abstract:
This paper examines the impact of within-firm connections on mutual fund performance. Using mutual fund managers' connections, I find that well-connected managers within fund families exhibit poorer performance and add less value than worse-connected ones. Using departures of managers' connections to establish a causal relationship, I find that the loss of one connection increases the managers' annual three-factor (four-factor) performance by 1.14% (1.08%). The negative relationship between within-firm connectedness and performance can be explained by the influence that social connections have on managerial behavior. Well-connected managers face less pressure to perform since their job security is less affected by poor performance, leading them to manage their funds less actively. Within-firm connectedness also hampers the efficient allocation of mutual fund assets to managers. These findings highlight potential downsides of internal networks in asset management firms.
AI Adoption and the Demand for Managerial Expertise
Liudmila Alekseeva et al.
IESE Business School Working Paper, October 2024
Abstract:
This paper investigates how AI adoption influences the demand for managers and their required skills. Using a skills-based measure of AI adoption derived from Lightcast job postings data, we show that when firms intensify their AI adoption they experience a significant increase in the number and share of managerial vacancies, indicating that AI implementation drives a greater demand for managerial roles. Furthermore, AI adoption shifts the skill requirements for managers, increasing the demand for cognitive and interpersonal skills such as collaboration, creativity, stakeholder management, and data analysis, while reducing the need for routine administrative skills like scheduling and budgeting. These findings suggest that AI reshapes organizations by increasing the importance of managerial expertise and by changing managerial roles and responsibilities within firms.
Combining for Unconventionality: When Resource Constraints May Promote Innovation Capabilities
Harsh Ketkar & Maria Roche
Harvard Working Paper, December 2024
Abstract:
The availability of financial resources significantly shapes firm innovation outcomes, especially for early-stage, innovation-focused technology startups. However, prior research has provided conflicting findings about this relationship: on the one hand, resource availability may boost innovation outcomes, but on the other hand, resource constraints may also do so. We hypothesize that the timing and size of resource availability in the early life stages of a startup may predict its propensity to generate more unique innovation compared to their competitors in terms of technological recombinations. Using a novel data set constructed from PitchBook and BuiltWith consisting of nearly 800,000 observations covering 11,853 firms founded and funded within 2010-2019, we find that early-stage startups that take longer to obtain first funding are more likely to use unique technological combinations to build their product after the funding event. However, startups that receive larger first funding experience a decline in uniqueness of their technology stack following the funding event. Thus, by focusing on timing and size of first funding, we offer a way to reconcile conflicting findings from prior literature.
Hedging First Offers Permits Assertiveness While Lowering Risk a Partner Walks
Alice Lee, Malia Mason & Claire Malcomb
Social Psychological and Personality Science, forthcoming
Abstract:
Negotiators often open with assertive offers to anchor discussions in their favor, yet this approach risks offending potential partners and foreclosing negotiations. Across four experiments, we demonstrate that hedged language softens proposals, allowing negotiators to remain assertive while reducing the risk of offending deal partners and preventing their early exit. Experiment 1 established this effect in a transactional sale context without post-deal interaction. Experiment 2 generalized the effect to a setting in which parties have an ongoing relationship and ruled out a confounding effect of message length. Experiment 3 revealed hedging signals flexibility, but this perception alone does not fully explain the effect. Experiment 4 found that hedged offers did not invite more assertive counteroffers and, after accounting for its reduced effect on impasses, led to better overall performance than directly stated ones. Hedging allows negotiators to introduce self-favorable starting points while minimizing offense and missed deal opportunities.
Generative AI and the Nature of Work
Manuel Hoffmann et al.
Harvard Working Paper, October 2024
Abstract:
Recent advances in artificial intelligence (AI) technology demonstrate considerable potential to complement human capital intensive activities. While an emerging literature documents wide-ranging productivity effects of AI, relatively little attention has been paid to how AI might change the nature of work itself. How do individuals, especially those in the knowledge economy, adjust how they work when they start using AI? Using the setting of open source software, we study individual level effects that AI has on task allocation. We exploit a natural experiment arising from the deployment of GitHub Copilot, a generative AI code completion tool for software developers. Leveraging millions of work activities over a two year period, we use a program eligibility threshold to investigate the impact of AI technology on the task allocation of software developers within a quasi-experimental regression discontinuity design. We find that having access to Copilot induces such individuals to shift task allocation towards their core work of coding activities and away from non-core project management activities. We identify two underlying mechanisms driving this shift -- an increase in autonomous rather than collaborative work, and an increase in exploration activities rather than exploitation. The main effects are greater for individuals with relatively lower ability. Overall, our estimates point towards a large potential for AI to transform work processes and to potentially flatten organizational hierarchies in the knowledge economy.
Revenue-Sharing Teams with Remote Workers
Glenn Dutcher & Krista Saral
NBER Working Paper, December 2024
Abstract:
Remote work policies remain controversial because of the perceived opportunity for increased shirking outside of the traditional office; a problem that is potentially exacerbated if employees work in a revenue-sharing team environment. Using a controlled experiment, where individuals are randomized to different work locations (remote or an office-like setting), we examine how remote work impacts effort choices under individual pay schemes and in revenue sharing teams. Treatments vary the number of remote workers on a team. Our results suggest that work location alone does not lead to productivity differences. However, the location of partners does impact an individual's effort levels in revenue-sharing teams. Non-remote workers reduce effort as the number of remote partners increases, and remote workers increase effort as the number of remote workers increases. These results are driven predominantly by those who are relatively less productive as individuals. Post-experiment incentivized survey evidence points to expectations of partner productivity as a contributing factor.