Jiaen Wu

Jiaen Wu

JIA-en, like “giant” without the “t.”

Ph.D. Candidate in Public Affairs
Indiana University Bloomington

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On the 2026–27 academic job market

I am a Ph.D. candidate at the O’Neill School of Public and Environmental Affairs, Indiana University Bloomington. My research focuses on public management and AI governance. I use causal inference, machine learning, and large-scale administrative data to study how public organizations make decisions and manage their workforce.

I study how organizational arrangements shape professional judgment, workforce behavior, and the allocation of public resources. Specifically, I examine how governments’ use of AI reshapes bureaucratic discretion and accountability for public decisions.

I am the principal investigator of a $200,000 project funded by Schmidt Sciences’ AI at Work program, examining how AI training and work design affect public officials’ analytical capacity.

Research Interests

  • Public Management
  • AI and Governance
  • Bureaucratic Behavior
  • Public Workforce Management
Job Market Paper (PDF) CV (PDF)

Job Market Paper

Routinized Deference: Predictive Screening and Bureaucratic Capacity in Child Protective Services

Jiaen Wu

Journal of Public Administration Research and Theory (2026, early view)

I develop the concept of routinized deference to explain how organizational pressures can make following algorithmic advice more defensible than exercising independent judgment. Using a stacked difference-in-differences design in U.S. Child Protective Services, I find that predictive screening adoption was associated with a 15% decline in substantiation rates, longer investigations, and increasing alignment between frontline decisions and model-derived risk scores.

Key takeaway: AI’s contribution to bureaucratic capacity depends on how agencies incorporate it into everyday work and support professional judgment.

Read paper (PDF) Research details

Published Papers

All papers

Working Papers

Different Ways of Doing Good: Public Service Motivation and Ethical Judgment in Public Administration

Karl Santiago, Kyuwoong Kyeong, Jiaen Wu

Under review

Using the Taiwan Government Bureaucrats Survey, we examine public service motivation across seven ethical dilemmas evaluated against a common Do No Harm benchmark. Overall motivation is associated with judgments that avoid unjustified harm in six scenarios, while commitment to public interest, compassion, and self-sacrifice differ in their relevance across dilemmas.

Research details

Buprenorphine Prescribing Trends and Practice Environment Associations with Clinician Adoption

Jiaen Wu, Nicole Siegal, Christopher Whaley, Jay Shrok, Stephen Crystal, Jennifer Miles, Hillary Samples, Kosali Simon, Sumedha Gupta

Working paper

Using national prescribing and practice-affiliation data from 2013–2024, we examine how clinicians’ buprenorphine prescribing changes when they move between group practices. Moving into a practice with greater peer prescribing is associated with a higher probability of prescribing buprenorphine. The findings suggest a role for organizational context in treatment adoption, while selection into practices limits a causal interpretation.

Research details

Grants

AI at Work Grant

2026 · Principal investigator · US$200,000 · Schmidt Sciences

Supported by Schmidt Sciences’ AI at Work program, I am conducting a field experiment with the Pakistan government. The project examines how AI training and work design affect officers’ analytical capacity and whether these changes shift enforcement from small-taxpayer audits toward large-firm investigations.

Top Grant

2026 · Principal investigator · US$30,000 · National Science and Technology Council (Taiwan)

Teaching

Public management,
program evaluation,
and quantitative methods

My teaching prepares future public servants to understand public organizations and make informed, accountable decisions. Through public management cases, policy simulations, and program evaluation, students weigh competing values, consider the perspectives of affected communities, and explain the reasoning behind their choices.