Promoting Performance Information Use in Government Analytics

By IndraStra Global Editorial Team

Cover Image Attribute: This image is computer-generated using AI and is intended solely for illustrative purposes./ INDRASTRA-CREATIVES-AI202403001

Governmental bodies have the potential to enhance their service quality and internal operations through the utilization of government analytics. However, the mere presence of government analytics does not guarantee its utilization. This article summarizes a book chapter titled "Measuring and Encouraging Performance Information Use in Government" by Donald Moynihan, who holds the McCourt Chair at the McCourt School of Public Policy, Georgetown University. The chapter published in "The Government Analytics Handbook: Leveraging Data to Strengthen Public Administration" (Pages 121-130) explores methods for governments to evaluate the effectiveness of government analytics, essentially analyzing the use of such analytics within the government. The focus is mainly on one crucial aspect of government analytics: performance management systems.
To begin with, Moynihan compared various forms of performance data utilization, distinguishing between political and functional purposes. Subsequently, he evaluated diverse methodologies for gauging the utilization of performance data, encompassing approaches based on surveys, administrative records, and qualitative investigations. Lastly, he illustrates how the assessment of performance data utilization can evolve over time and be harnessed to enhance government analytics, drawing from the example of the US Government Accountability Office (GAO). The key takeaway from this article is the significance of robustly measuring the utilization of government analytics to enable governments to optimize their use of performance information.

Introduction

Government analytics is a tool for governments to assess and enhance public management across various public administration sectors. However, measuring the government's internal mechanisms only automatically translates into the practical utilization of government analytics to enhance public administration. Overall the emphasis on the necessity of assessing whether and how government analytics is employed by public sector organizations to enhance their operations, emphasizes the need for analyzing the usage of government analytics.
In this book chapter, Moynihan delves into the methodologies for achieving this, focusing on performance management systems. These systems are integral to government operations, comprising coordinated processes aimed at measuring, validating, disseminating, and utilizing performance data within the government. While early government performance systems primarily concentrated on measurement, they have gradually evolved towards performance management, albeit with varying degrees of success. Performance management systems typically prioritize data collection routines, including establishing strategic goals and short-term targets, and evaluate a combination of outputs and outcomes. However, more than merely performance data collection is needed to guarantee effective utilization.
Also, he tries to offer guidance to monitor the progress in utilizing performance measurements. Initially, it provides a conceptual foundation by discussing different categories of performance data utilization, encompassing political and functional purposes. Subsequently, it addresses measurement techniques, including various approaches such as engagement metrics derived from administrative data, survey-based tools, and qualitative investigations. Finally, using the US Government Accountability Office (GAO) as a case study, he exemplifies how to develop comprehensive governmentwide measures of performance data utilization and underscores their significance in enhancing performance management systems.

Performance Management System

Performance management systems often need help to prove their effectiveness, leading to a scarcity of evidence regarding how they directly impact government performance (Gerrish 2016). However, one measurable outcome of these systems is the utilization of performance information, which refers to how data influences the decisions and actions of public officials (Kroll 2015).
The effectiveness of performance management systems can be partially gauged by the extent to which performance information is utilized. Simply having performance measures in place does not automatically lead to improved performance; instead, their effectiveness hinges on influencing the decision-making processes of individuals. With effective utilization of data by management, it is possible to enhance public sector outcomes through changes in civil servants' behavior.

Types of Performance Information Use

There are four distinct types of performance information use: purposeful, passive, tactical, and symbolic, each with implications (Kroll 2015; Moynihan 2009). Purposeful use, in particular, involves aligning performance indicators with organizational goals to enhance resource allocation and strategic decision-making, thereby improving administrative efficiency.
Public servants are the primary users of performance data, as they possess the necessary expertise and knowledge to interpret and utilize detailed performance information effectively. While the public may express interest in performance-driven governance, their active involvement in utilizing performance data is limited, except in cases where there is market-based choice in service provision or when services directly impact them, such as education or healthcare selection.
Elected officials may seek performance data in specific areas of interest but are often hesitant to let it influence their decision-making, particularly when it conflicts with their ideological preferences (Christensen and Moynihan 2020; Moynihan 2008).
Ultimately, designers of performance management systems have more influence over the environment of public servants, thus shaping their usage of performance data. The effectiveness of performance management systems can be enhanced by creating an environment that promotes active engagement with performance information among public servants.

Measuring Performance Information Utilization

Assessing the extent of performance information use poses challenges as it involves observing a cognitive behavior, judgment and decision-making, often not readily observable. Various methodological approaches exist for measuring the utilization of performance systems, each with its trade-offs. Surveys emerge as the most viable option for governments to monitor performance management system use, although administrative data and qualitative methods offer valuable supplementary insights.
Observing Engagement with Administrative Data
A straightforward method directly observes whether public officials interact with existing performance management systems. Metrics of engagement, such as downloading performance data or incorporating them into public discussions or reports, offer tangible indicators. While this method avoids reliance on subjective reports and can be nonintrusive, it requires real-time tracking of public officials' engagement, typically facilitated through centralized platforms. However, such measures might only partially capture how performance information influences decision-making and could be biased toward frequent but inconsequential use.
Survey-Based Evaluation
Another prevalent approach involves surveying public servants to assess performance data utilization. For instance, the Government Accountability Office (GAO) conducts periodic surveys to gauge the effectiveness of the performance management system in the US federal government. These surveys provide consistent indicators over time, facilitating cross-agency comparisons. While survey-based assessments offer valuable insights, they may need more depth provided by qualitative methods.
Qualitative Insights
Qualitative assessments, such as focus groups and structured interviews, offer a contextual understanding of performance information utilization in government. By allowing public servants to share their experiences, these assessments provide a holistic view of how performance information integrates into organizational routines and decision-making processes. While qualitative methods enrich understanding, they may not offer quantitative precision.
Decision-Based Analysis
Lastly, assessing whether providing decision-makers with performance information influences behavior offers another avenue. However, such studies often rely on hypothetical scenarios, limiting generalizability. Despite this constraint, experiments in this realm have yielded valuable insights into how performance indicators shape decision-making.
Consideration of Biases
Recognizing biases that may influence decision-makers when consulting performance information is essential. Negativity bias, for example, leads individuals to prioritize harmful data over positive ones. Decision-makers may also exhibit ideological or political biases, selecting data that align with their beliefs while discounting contradictory information. Addressing these biases is crucial for accurately interpreting performance information and informed decision-making.

Case Summary: US Government Accountability Office (GAO)

The current performance management system utilized by the US federal government was established under the Government Performance and Results Act Modernization Act (GPRAMA) of 2010. Oversight of the system falls under the purview of the Office of Management and Budget (OMB) within the Executive Office of the President, with monitoring responsibilities entrusted to the Government Accountability Office (GAO) within the legislative branch. From this ongoing endeavor, which stands as one of the most sustained efforts globally to measure performance information use across government, several critical lessons regarding systematic measurement can be gleaned.
Lesson One emphasizes the importance of an independent agency employing consistent measurement tools to bolster credibility and accumulate knowledge. The GAO issues reports to Congress, evaluating progress in performance management and the use of performance information. These reports, often based on qualitative case analyses or employee surveys, have contributed significantly to the credibility of the data. However, please note that the GAO opted for a random sampling approach targeting mid- and senior-level federal managers instead of surveying every employee. Initially, in 1997 and 2000, mail surveys were employed, but by 2003, they shifted to email-based surveys connected to a web questionnaire, with subsequent phone follow-ups for those who did not respond.
Lesson Two underscores the value of stability in performance information for evaluating progress over time. The consistency in the performance management system has facilitated longitudinal comparisons. Despite incorporating changes over time, core survey questions have remained constant, allowing for analysis of trends and gradual improvements.
Lesson Three highlights the benefits of data-sharing agreements with external researchers in enriching insights into performance information use. The GAO has fostered additional analyses beyond government reports by collaborating with external researchers and sharing data. This approach has led to a deeper understanding of the factors influencing performance information use and has informed policy and planning documents.
Moreover, the GPRAMA offers broader lessons for promoting performance information use, reinforcing the importance of sharing data with researchers. A robust evidence base has emerged through repeated measurement, shedding light on factors encouraging performance information use. Findings indicate that organizational routines, leadership engagement, employee discretion, training, and program evaluations significantly influence performance information use.

Conclusion

Moynihan's book chapter demonstrates the diverse ways government analytics data can be employed. While it can enhance management practices, it can also be politically manipulated or distorted, leading to skewed organizational incentives.
Therefore, it is imperative for government analytics not only to assess the frequency and manner of data usage but also to scrutinize its intended purpose. Like the broader domain of government analytics, various data sources such as administrative data (e.g., tracking dashboard users), survey data (e.g., surveys of public employees and managers), and qualitative inquiry are available for such analysis.
Drawing from the United States' extensive experience in measuring performance information use across government, it becomes evident that such analytics are most effective with specific design features. Stability in performance information use measures, and regular data collection contribute to its efficacy. Additionally, a community of practice involving executive branch officials overseeing performance issues, GAO officials conducting analyses, and external researchers sharing insights plays a vital role in refining and incorporating lessons into practice over time.
Analyzing the utilization of government analytics can enhance and institutionalize how performance data are leveraged to achieve better outcomes. However, the transformative potential of government analytics can only be realized if public officials engage meaningfully with analytical insights and translate them into operational strategies. Hence, robust measurement of government analytics usage is crucial for enabling governments to optimize the tools available to them effectively. 

IndraStra Global is now available on
Apple News, Google News, Feedly, Flipboard, and  WhatsApp Channel

COPYRIGHT: This article is published under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. https://creativecommons.org/licenses/by-nc-nd/4.0/

REPUBLISH: Republish our articles online or in print for free if you follow these guidelines. https://www.indrastra.com/p/republish-us.html