Survey explores what workers value beyond their pay

Work can offer purpose, status and social connections alongside a paycheck. An arXiv preprint by Julian Jacobs examines how workers value those benefits and what their answers might mean for a future shaped by AI.

What the study found

Jacobs reports results from an original survey of 8,234 working-age adults in the United Kingdom, United States, Denmark and India. According to the abstract, 81% of respondents reported being satisfied with their job, and a majority in each country were strongly motivated by non-monetary aspects of employment.

Using an original Work Values Index, Jacobs reports that one third of working respondents valued their job intrinsically, or for its own sake. Nearly one quarter were motivated by social and community aspects, 17% by social status, and just over one quarter by financial benefits.

A majority said they would leave their role if offered their current salary as guaranteed income. But one fifth of workers said they would stay even if offered up to 120% of their salary. Those motivated intrinsically were disproportionately likely to refuse a basic income at any substitution level considered.

Why it matters

Jacobs argues that, if AI leads to a future labor shock, guaranteed income might not compensate for the loss of purpose or community that some people associate with work. Respondents attached to their jobs for nonfinancial reasons were also more likely to say they would pursue volunteering or work-like activities if given guaranteed income but unable to continue their role.

Limits and unknowns

The abstract does not explain how participants were recruited, how representative they were, how the index was constructed or precisely how the hypothetical income questions were worded. It also does not provide country-by-country breakdowns or uncertainty estimates. Stated choices in hypothetical scenarios do not establish what workers would actually do, and the survey does not demonstrate that AI has caused job losses or predict their scale.

Read the research

Authors: Julian Jacobs

Published: 2026-10-29

Source basis: Based on the abstract only

Original abstract

This paper investigates the role of work in providing meaning, purpose, community, and other non-monetary benefits. Using an original cross-national survey of 8,234 working-age adults across the UK, US, Denmark, and India, I study the factors that motivate people in their work, with an eye toward the future AI economic transformation. I find that 81% of respondents report being satisfied with their job, and a majority across all four countries are strongly motivated by the non-monetary components of their employment. Using an original "Work Values" Index, I show that one third of working respondents are motivated intrinsically by their job--valuing their role for its own sake. Nearly one quarter are driven by social and community-related aspects of their employment, 17% by social status, and just over one quarter by the transactional financial benefits of their job. Although a majority of people would leave their role if offered their current salary as a guaranteed income, one fifth of workers would stay even if offered up to 120% of their salary. Intrinsically motivated workers were disproportionately likely to refuse a basic income at any substitution level. Groups attached to their job for non-monetary reasons were also more likely to pursue volunteering or work-like activities if they received guaranteed income substitution but could no longer perform their role. Crucially, these non-monetary work motivations are evenly distributed across the income distribution and between white- and blue-collar workers. I argue that these findings have implications for managing the AI transition, understanding attitudes to AI, and making sense of policy preferences. Guaranteeing a basic income may indeed be necessary for managing any future AI labor shock, yet it could ultimately fail to compensate workers for any loss of the non-monetary benefits they derive from work.

This article is an AI-generated adaptation, not a substitute for the original research. Preprints may not have been peer reviewed; AI can make mistakes.