Means, Motive, and Opportunity – Local Government Data Distortion in a High‐Stakes Environment
Bibliographic Data
| ID | 11960959 |
|---|---|
| Authors | Joseph Drew (0000-0003-3579-5758, University of Technology Sydney, corresponding author), Bligh Grant (0000-0002-8055-6046, University of Technology Sydney) |
| Year | 2016 |
| Volume | 76 |
| Issue | 2 |
| Pages | 237-250 |
| Publication date | 2016-10-20 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Australian Journal of Public Administration (JOURNAL) |
| Journal identifiers | ISSN: 0313-6647 • E-ISSN: 1467-8500 |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1111/1467-8500.12225 |
| OpenAlex | W2543789053 |
| Language | EN |
| Citations received | 2 |
| References cited | 17 |
Regulatory authorities are increasingly relying upon performance data for developing public policy. However, this reliance necessarily assumes that the data are free from material distortion. This paper provides a conceptual framework for understanding the ‘means’, ‘motive’, and ‘opportunity’ for distorting data employed in high‐stakes performance‐management programmes. We present empirical evidence which suggests that the use of data drawn entirely from financial statements by no means guarantees a distortion‐free depiction of performance. In addition, we provide econometric evidence of some important determinants of performance data distortion. Taken as a whole, the following analysis provides a comprehensive picture of the salient matters which must be addressed to ensure accurate data for public policy‐making purposes
Business · Conceptual framework · Depiction · Distortion (music · Economics · Empirical evidence · Government (linguistics · Public economics · Public policy · Salient · Sociology · Computer Science · Corruption and Economic Development · Fiscal Policies and Political Economy · Public Policy and Administration Research · Artificial Intelligence
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| Citations per year | 0.29 |
|---|---|
| Citation span | 2019 - 2024 (6) |
| Highly cited | No |