Complexity, errors, and administrative burdens
Bibliographic Data
| ID | 21723738 |
|---|---|
| Authors | Matthew Young (0000-0002-9262-2346, Leiden University, corresponding author), Mallory E Compton (0000-0002-5124-238X, Texas A&M University), Justin B Bullock (0000-0001-6263-2343, Evans School of Public Policy and Governance, Seattle, WA, USA), Rowan A Greer (0000-0002-9458-6414), Robert Greer (Texas A&M University) |
| Year | 2024 |
| Volume | 26 |
| Issue | 10 |
| Pages | 2847-2867 |
| Publication date | 2024-10-02 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Public Management Review (JOURNAL) |
| Journal identifiers | ISSN: 1471-9037 • E-ISSN: 1471-9045 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/14719037.2023.2288247 |
| OpenAlex | W4390112400 |
| Language | EN |
| Citations received | 12 |
| References cited | 43 |
Errors in administrative processes cost clientele and organizations, yet are understudied. Beyond efficiency losses, errors impose administrative burdens on clientele. Automation is a common tool for reducing errors. Little is known, however, about the factors that may augment automation’s effectiveness. We theorize that administrative errors are a function of program complexity. We expect automation to improve accuracy in less complex programs but worsen with increased complexity. With U.S. Unemployment Insurance program audit data, we use longitudinal Poisson analysis to test our expectations. Complexity is associated with greater incidences of administrative errors. As expected, automation’s effects vary with level of complexity
Actuarial science · Audit · Automation · Business · Economics · Macroeconomics · Poisson distribution · Public economics · Statistics · Unemployment · Auction Theory and Applications · Auditing, Earnings Management, Governance · Computer Science · Engineering · Imbalanced Data Classification Techniques · Accounting
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Reputation signals, individual characteristics and citizens’ coproduction willingness
Reducing administrative burdens to increase the take-up of public services
The role of non-state actors in administrative burden reduction (ABR)
Examining the drivers of citizen-to-citizen co-production amidst administrative burdens
Organizational adaptation, task complexity, and effective administration of unemployment programs in the American states
Administrative Burden Revisited
Playing the Game”
How Do Algorithmic Decision‐Making Systems Used in Public Benefits Determinations Fail? Insights From Legal Challenges
Automating administrative burden in algorithmic criminal record expungement
Debunking the Digital Myth
Intent vs impact
Organizational Culture as a Source of High Reliability
The Theory Of Social And Economic Organization
Keeping Time
Measuring Citizen and Government Ideology in the U.S. States
The digital cage
When Claimant Characteristics and Prior Performance Predict Bureaucratic Error
Red Tape and Democracy
Bureaucracy
The Political Economy of Administrative Burdens
Bureaucratic Disentitlement in Social Welfare Programs
The Effect of Administrative Burden on Bureaucratic Perception of Policies
Decreasing Improper Payments in a Complex Federal Program
Approaching Public Administration from a Complexity Perspective
How the Complexity Sciences Can Inform Public Administration
Administrative Capital and Citizens’ Responses to Administrative Burden
The Measurement and Stability of State Citizen Ideology
Content and Complexity in Policy Reinvention and Diffusion
Coping with Policy Complexity in the Globalized World
Administrative exclusion in the infrastructure‐level bureaucracy
Ordo Ab Chao? Complexity and Its Implications
Understanding Interaction Models
Administrative Burden
Administrative Errors and Race
At the Discretion of Rogue Agents
Why Do Policymakers Support Administrative Burdens? The Roles of Deservingness, Political Ideology, and Personal Experience
Measuring Citizen and Government Ideology in the American States, 1960-93
Automating Inequality
Social Construction of Target Populations
| Citations per year | 6 |
|---|---|
| Citation span | 2024 - 2026 (3) |
| Highly cited | No |