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Publication analysis
NBER Working Paper 31161
5 selected findings · 9 of 9 quotations matched
Source passages
9Returned quotations from the admitted text projection. Select a field citation to locate its referenced passage.
Quotation: matched
Proposed locator: p. 1
GENERATIVE AI AT WORK Erik Brynjolfsson Danielle Li Lindsey R. Raymond Working Paper 31161 http://www.nber.org/papers/w31161 NATIONAL BUREAU OF ECONOMIC RESEARCH 1050 Massachusetts Avenue Cambridge, MA 02138 April 2023 , revised November 2023
Proposed printed page: 1
Quotation: matched
Proposed locator: p. 2
Generative AI at Work Erik Brynjolfsson, Danielle Li, and Lindsey R. Raymond NBER Working Paper No. 31161 April 2023 , revised November 2023 JEL No. D8,J24,M15,M51,O33 ABSTRACT New AI tools have the potential to change the way workers perform and learn, but little is known about their impacts on the job. In this paper, we study the staggered introduction of a generative AI-based conversational assistant using data from 5,179 customer support agents. Access to the tool increases productivity, as measured by issues resolved per hour, by 14% on average
Proposed printed page: 2
Quotation: matched · Referenced by selected finding
Proposed locator: p. 14, Main Results
In Column 1, we show that, controlling for time and location fixed effects, access to AI recommendations increases resolu- tions per hour by 0.47 chats, up 22.2% from an average of 2.12. In Column 2, we include fixed effects for individual agents to account for potential differences between treated and untreated agents. In Column 3, we include additional controls for the time-varying agent tenure. As we add controls, our effects fall slightly, so that, with agent and tenure fixed effects, we find that the deployment of AI increases RPH by 0.30 chats or 13.8%. Columns 4 through 6 produce these same patterns and magnitudes for the log of RPH.
Proposed printed page: 14
Quotation: matched
Proposed locator: p. 14, Main Results
In Table 3, we report additional results using our preferred specification with year-month, agent, and agent tenure fixed effects. Column 1 documents a 3.8 minute decrease in the average duration of customer chats, a 9% decline from the baseline mean (shorter handle times are generally considered better). Next, Column 2 indicates a 0.37 unit increase in the number of chats that an agent can handle per hour. Relative to a baseline mean of 2.6, this represents an increase of roughly 14%.
Proposed printed page: 14
Quotation: matched · Referenced by selected finding
Proposed locator: p. 12, Summary Statistics
To examine the impacts of this deployment, we construct several key variables, all aggregated to the agent-month level, which is our primary level of analysis. Our primary measure of productivity is resolutions per hour (RPH), the number of chats a worker is able to successfully resolve per hour.
Proposed printed page: 12
Quotation: matched · Referenced by selected finding
Proposed locator: p. 13, Empirical Strategy
y it “ δ t ` α i ` βAI it ` γX it ` ϵ it (1) Our outcome variables y it capture various measures of productivity for agent i in year-month t , as outlined earlier. Because workers often work only for a portion of the year, we include only year-month observations for an agent who is actively employed (e.g. assigned to chats). Our main variable of interest is AI it , an indicator equal to one if agent i has access to AI recommendations at time t . All regressions include year-month fixed effects δ t to control for common, time-varying factors such as tax season or the end of the business quarter. In our preferred specification, we also include controls for time-invariant agent-level fixed effects α i and time-varying agent tenure. Standard errors are clustered at the agent level.
Proposed printed page: 13
Quotation: matched · Referenced by selected finding
Proposed locator: Table 2, p. 52
Post AI X Ever Treated 0.468*** 0.371*** 0.301*** 0.221*** 0.180*** 0.138*** (0.0542) (0.0520) (0.0498) (0.0211) (0.0188) (0.0199)
Proposed printed page: 52
Quotation: matched
Proposed locator: Table 3, p. 53
Post AI X Ever Treated -3.750*** 0.366*** 0.0128* -0.128 -0.0851*** 0.149*** 0.00973* -0.000406 (0.476) (0.0363) (0.00717) (0.660) (0.0110) (0.0142) (0.00529) (0.00915)
Proposed printed page: 53
Quotation: matched
Proposed locator: Table 4, p. 54
Post AI X Ever Treated 0.177*** 0.0198*** (0.0133) (0.00315)
Proposed printed page: 54
Economic context
1 / 5research estimate · proposed mapping
resolutions per hour
Reported expression: 0.301. Assignment: reported.
Baseline: 2.174 · Intervention: Post AI X Ever Treated
Original generated description
Impact of Generative AI Deployment on Resolutions Per Hour (Levels)
Deployment of the generative AI conversational assistant increased resolutions per hour by 0.301 chats per hour (std err 0.0498) in the preferred specification with agent and tenure fixed effects, relative to a baseline mean of 2.174.
Structured-field presentation; field attribution and economic meaning are not independently validated.
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- Sample limited to subset of agents where call quality and resolution data are tracked by subcontracted firms.
AI-generated interpretation. Matched quotations establish text locations; economic validity and publisher attribution remain unverified.
Source and processing receipt: 4 passed · 0 partial
Source, document identity and inspected coverage
- Supplied document
- R01.pdf
- Input
- Uploaded file
- Inspected surface
- application/pdf; 67 physical PDF pages
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- Publisher
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- Selected findings are not an exhaustive inventory. Publisher authority and semantic mappings are not established by this analysis.
- Physical PDF page indices differ from printed page labels. Text order and table header/note association are not independently validated.
- The native PDF is supplied to Gemini. Figure, scan and layout interpretations without matched text anchors remain unverified.
APPLICATION SCHEMA: passed
Tool arguments match the versioned draft schema, field scopes and knowledge-state invariants.
SOURCE ANCHORS: passed
9 of 9 quotations uniquely match the admitted text projection. This checks location, not meaning.
FIELD SUPPORT: passed
0 populated finding fields lack matched literal/source support. Field attribution, table association and entailment are not assessed.
EXPRESSION PLACEMENT: passed
Numeric prose beside an unassigned expression is disclosed in the structured view. This is a completeness control, not numeric extraction or semantic validation.
openucem.publication-record/0.3.0 · Gemini · gemini-3.8-flash
2026-11-02T04:33:57.598Z · 1 model request
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- The native PDF is supplied to Gemini. Figure, scan and layout interpretations without matched text anchors remain unverified.
- Findings are drawn from a single Fortune 500 firm and its subcontractors providing enterprise business process software technical support.
- Results may not generalize to settings where products or workflows change rapidly or non-textual interaction channels dominate.
- All assignments are proposed. Matching quotations and tokens do not certify economic meaning, causal claims, independent support or compatibility for calculation.
- Source bytes and inspected text are archived with immutable generations and hashes. Retention does not verify publisher authority or economic meaning.
- Findings and repeated specifications from one publication are potentially dependent. No coefficient conversion, pooling or statistical inference was performed.
Inspect the structured record
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