About

Shawn Mankad is an Associate Professor of Analytics and the Stephen P. Zelnak Jr. Scholar at NC State's Poole College of Management. His research centers on the development and application of statistical methods to address critical issues in business, economics, and public policy. He specializes in utilizing text data to explain and predict economic variables, as well as modeling networks to assess the risk of systemic failure. His interdisciplinary work has been supported by NSF funding and published in leading outlets across the fields of Statistics, Operations Management, Information Systems, and Finance. Before joining NC State, Professor Mankad held faculty positions at Cornell University and the University of Maryland and was a visiting scholar at the Federal Reserve Bank of Philadelphia. He earned a bachelor’s degree in mathematics from Carnegie Mellon University and a PhD in statistics from the University of Michigan.

Research topics

• Computer Science
• Data Mining
• Artificial Intelligence
• Computer Security
• Mathematics
• Biology
• Econometrics
• Data science
• Statistics
• Business
• Engineering
• Marketing
• Database
• Demography

Selected publications

• Data from: Social vulnerability, capacity, and economic outcomes in North Carolina populations

  DRYAD · 2026-02-18

  datasetOpen access1st authorCorresponding

  This dataset contains census-tract-level indicators of social vulnerability and community resources across North Carolina, used to study their impact on economic outcomes following the COVID-19 pandemic. The data includes a comparison of variables from 2018 and 2022 to measure changes in unemployment and poverty rates across 1,698 census tracts . Data Sources and Variables: Social and Economic Vulnerability: Derived from the 2020 American Community Survey (ACS) and the CDC's Social Vulnerability Index (SVI), including metrics for education level, disability, minority status, and housing cost burden . Infrastructure and Institutional Capacity: Includes variables such as broadband access (FCC), hospital capacity (NC OneMap), public and non-public school proximity, and annual vehicle hours of public transit service (U.S. DOT) . Environmental Context: Includes the EPA Walkability Index and Census Bureau diversity indices . The data were processed using ArcGIS to map geographical overlaps between census tract definitions across different time periods. Summary statistics and correlation matrices are provided to describe the relationship between these vulnerability factors and economic resilience in rural and non-rural areas. This data supports an empirical analysis using linear regression, logistic regression, and random forest models to identify how community resources moderate the effects of social vulnerability on economic recovery.

  PublisherDOI

• Working with Generative AI vs Humans: The Impact on Well-being

  SSRN Electronic Journal · 2026-01-01

  preprintOpen access
  PublisherDOI

• The Best of Both Worlds: Machine Learning and Behavioral Science in Operations Management

  SSRN Electronic Journal · 2024-01-01

  articleOpen access
  PublisherDOI

• Nonstandard Errors

  The Journal of Finance · 2024 · 85 citations

  • Computer Science
  • Econometrics
  • Statistics

  ABSTRACT In statistics, samples are drawn from a population in a data‐generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence‐generating process (EGP). We claim that EGP variation across researchers adds uncertainty—nonstandard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for more reproducible or higher rated research. Adding peer‐review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants.

  DOI

• OM Forum—The Best of Both Worlds: Machine Learning and Behavioral Science in Operations Management

  Manufacturing & Service Operations Management · 2024-07-25 · 19 citations

  article

  Problem definition: Two disciplines increasingly applied in operations management (OM) are machine learning (ML) and behavioral science (BSci). Rather than treating these as mutually exclusive fields, we discuss how they can work as complements to solve important OM problems. Methodology/results: We illustrate how ML and BSci enhance one another in non-OM domains before detailing how each step of their respective research processes can benefit the other in OM settings. We then conclude by proposing a framework to help identify how ML and BSci can jointly contribute to OM problems. Managerial implications: Overall, we aim to explore how the integration of ML and BSci can enable researchers to solve a wide range of problems within OM, allowing future research to generate valuable insights for managers, companies, and society.

  PublisherDOI

• A Structural Topic and Sentiment-Discourse Model for Text Analysis

  Management Science · 2024-10-16 · 8 citations

  articleSenior author

  We consider the common setting where one observes a large number of opinionated text documents and related covariates, such as the text of online reviews along with the date of the review and the author demographic information. In this setting it can be of interest to understand how the covariates determine the text composition, as well as the prevalence, sentiment, and/or discourse of various discussion themes. Yet, most topic modeling methods in the machine learning literature are designed to summarize the text for the purpose of exploratory analysis and not to perform this type of formal statistical inference. Further, topic modeling methods generally do not try to estimate the sentiment or discourse of discussion along separate topics that can be critical in business applications (e.g., for summarizing service or product quality). We develop a topic model called the structural topic and sentiment-discourse (STS) model that introduces a new document-level latent variable that captures the sentiment and/or discourse (termed as “sentiment-discourse”) for each topic, which modulates the word frequency within a topic. These latent topic sentiment-discourse variables are controlled by document-level covariates to allow for experimental control and regression analysis. We also introduce new computational methods to resolve scalability issues that have forced previous models to restrict to a small number of categorical covariates. We benchmark the STS model on three real-world data sets from surveys, blogs, and Yelp restaurant reviews around the COVID-19 pandemic. Our model recovers meaningful results including rich insights about how COVID-19 affects online reviews, demonstrating that the STS model can be useful for regression analysis with text data in addition to topic modeling’s traditional use of descriptive analysis. This paper was accepted by Anindya Ghose, information systems. Supplemental Material: The online appendices and data files are available at https://doi.org/10.1287/mnsc.2022.00261 . An updated version of the R package implementing the STS model is available at https://CRAN.R-project.org/package=sts .

  PublisherDOI

• sts: Estimation of the Structural Topic and Sentiment-Discourse Model for Text Analysis

  2024-09-17 · 1 citations

  datasetOpen access1st authorCorresponding

  The Structural Topic and Sentiment-Discourse (STS) model allows researchers to estimate topic models with document-level metadata that determines both topic prevalence and sentiment-discourse. The sentiment-discourse is modeled as a document-level latent variable for each topic that modulates the word frequency within a topic. These latent topic sentiment-discourse variables are controlled by the document-level metadata. The STS model can be useful for regression analysis with text data in addition to topic modeling’s traditional use of descriptive analysis. The method was developed in Chen and Mankad (2024) &lt;<a href="https://doi.org/10.1287%2Fmnsc.2022.00261" target="_top">doi:10.1287/mnsc.2022.00261</a>&gt;.

  PublisherOA PDFDOI

• Evidence of the Unintended Labor Scheduling Implications of the Minimum Wage

  Manufacturing & Service Operations Management · 2023-06-07 · 11 citations

  article

  Problem definition: The effect of the minimum wage is an important yet controversial topic that has received attention for decades. Our study is the first to take an operational lens and empirically study the impact of the minimum wage on firms’ scheduling practices. Methodology/results: Using a highly granular data set from a chain of fashion retail stores, we estimate that a $1 increase in the minimum wage, although having a negligible impact on the total labor hours used by the stores, leads to a 27.7% increase in the number of workers scheduled per week, but a 19.4% reduction in weekly hours per worker. For an average store in California, these changes translate into four extra workers and five fewer hours per worker per week. Such scheduling adjustment not only reduces the total wage compensation per worker but also reduces workers’ eligibility for benefits. We also show that the minimum wage increase reduces the consistency of weekly and daily schedules for workers. For example, the absolute (relative) deviation in weekly hours worked by each worker increases by up to 32.9% (6.6%) and by up to 9.7% (2.1%) in daily hours, as the minimum wage increases by $1. Managerial implications: Our study empirically identifies and highlights a new operational mechanism through which increasing the minimum wage may negatively impact worker welfare. Our further analysis suggests that the combination of the reduced hours, lower eligibility for benefits, and less consistent schedules (that resulted from the minimum wage increase) may substantially hurt worker welfare, even when the overall employment at the stores stay unchanged. By better understanding the intrinsic tradeoff of firms’ scheduling decisions, policy makers can better design minimum wage policies that will truly benefit workers. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.1212 .

  PublisherDOI

• Replication Data for: Assessing Undiversified Holding and Contagion Risks in the Banking Sector

  Harvard Dataverse · 2023-05-23

  datasetOpen access1st authorCorresponding

  The data and R scripts implement the matrix factorization model and also replicate the main tables and figures from "Assessing Undiversified Holding and Contagion Risks in the Banking Sector" by Mankad, Brunetti, and Harris.

  PublisherDOI

• Networks, interconnectedness, and interbank information asymmetry

  Journal of Financial Stability · 2023-07-28 · 7 citations

  articleSenior authorCorresponding
  PublisherDOI

Recent grants

• BIGDATA: Collaborative Research: IA: F: Too Interconnected to Fail? Network Analytics on Complex Economic Data Streams for Monitoring Financial Stability

  NSF · $525k · 2016–2021

Frequent coauthors

• George Michailidis

  University of Florida

  29 shared

• Celso Brunetti

  Federal Reserve

  25 shared

• Keith Baker

  Massachusetts General Hospital

  16 shared

• Jeffrey H. Harris

  16 shared

• Bishr Haydar

  Michigan Medicine

  16 shared

• Anandasivam Gopal

  Nanyang Technological University

  8 shared

• Donggeng Xia

  7 shared

• Qiuping Yu

  Georgetown University

  6 shared

Education

• Ph.D., Operations Research and Industrial Engineering

  University of North Carolina at Chapel Hill

  2011

• M.S., Operations Research and Industrial Engineering

  University of North Carolina at Chapel Hill

  2007

• B.S., Electrical Engineering

  University of Pune

  2005