Big data and the measurement of public organizations’ performance and efficiency: The state-of-the-art
| Date | 01 October 2017 |
| Author | Nicky Rogge,Tommaso Agasisti,Kristof De Witte |
| Published date | 01 October 2017 |
| DOI | 10.1177/0952076716687355 |
| Subject Matter | Symposium: Big data analytics and its use in the measurement of public organizations' performance and efficiencySymposium articles |
Public Policy and Administration
2017, Vol. 32(4) 263–281
! The Author(s) 2017
Big data and the
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measurement of
DOI: 10.1177/0952076716687355
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public organizations’
performance
and efficiency:
The state-of-the-art
Nicky Rogge
KU Leuven, Belgium
Tommaso Agasisti
School of Management, Politecnico di Milano, Italy
Kristof De Witte
Leuven Economics of Education Research, KU Leuven Top
Institute for Evidence Based Education Research, University
of Maastricht, Netherlands
Abstract
The increasing availability of statistical data raises opportunities for ‘big’ data and learn-
ing analytics. Here, we review the academic literature and research relating to the use of
big data analytics in the public sector, and its contribution to public organizations’
performance and efficiency. We outline the advantages as well as the limitations of
using big data in public sector organizations and identify research gaps in recent studies
and interesting areas for future research.
Keywords
Big data, efficiency analysis, learning analytics, performance analysis, public sector
performance
Corresponding author:
Tommaso Agasisti, Via Lambruschini 4b, Milan 20156, Italy.
Email: tommaso.agasisti@polimi.it
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Public Policy and Administration 32(4)
Introduction1
The persistent and global financial crisis, and attendant strategies for fiscal con-
solidation have accentuated the problem of maximizing public administration (PA)
efficiency. In many areas of public activities – especially in the social welfare
domain (e.g. health care, education, elderly care) – securing increased efficiency
and productivity ensure resources are focused on improving the quality of activities
and outputs, guaranteeing the satisfaction of citizens who receive the services, and
assuring effectiveness and equity in the public sector as a whole.
The literature addressing the empirical measurement of public sector efficiency
employs a wide range of techniques and focuses on various units of analysis: local
governments (Asatryan and De Witte, 2015; De Borger and Kerstens, 1996; De
Witte and Geys, 2011; De Witte and Moesen, 2010; Revelli and Tovmo, 2007),
public hospitals (Hollingsworth, 2008), public agencies (Neshkova and Guo, 2012),
public transportation services (Pina and Torres, 2001; Sampaio et al., 2008), and,
education (Cherchye et al., 2010; De Witte and Lopez-Torres, 2017).2,3
The application of empirical models for assessing the efficiency of public entities
can also open the door to the study of its determinants, and consequently have
interesting implications for policy, administration and management of the public
services. In this context, for instance, it can be tested whether particular managerial
tools, different roles for the regulations, or stimulating policies and interventions
(for instance, favoring competition, or facilitating strategic management processes)
have a positive or negative impact on the efficiency of public spending (e.g. see De
Witte and Geys, 2011, for a discussion on the preferences of voters in local gov-
ernment efficiency).
The new opportunities offered by big data can help the efficiency analysis of
public entities make a further step.4 More specifically, nowadays, administrative
datasets are ‘big’ in the sense that the individual organizations, in many sectors,
periodically produce very detailed questionnaires and databases that include struc-
tural or ‘hard’ information and soft data about managerial practices, quality of
outputs and inputs, etc. In addition, a huge amount of information is released by
individual public organizations, and can be collected as open data. Finally, the
diffusion of e-government practices implies the production of huge amount of data
through, for instance, the social networks, open data platforms, and public agen-
cies websites.
Yet, the way in which governments, policy makers, and public organizations use
big data and learning analytics is still an under-investigated topic, as is the possi-
bility of using big data for enriching efficiency analyses and performance measure-
ments. This review and the symposium of papers in this journal issue, examine both
the use of big data for efficiency purposes and the effectiveness of public and wel-
fare services, and for measuring performance in a broader sense.
We are guided by four research foci:
1. From a theoretical perspective, what are the advantages of using big data in the
understanding of public sector organizations? Can new available datasets help the
Rogge et al.
265
organizations in designing new services, better evaluating their activities, meet-
ing new and more articulated needs, improving the efficiency of operations?
2. How do public administrations use big data for their internal performance man-
agement procedures? Is big data employed for comparing outputs, practices
(processes) and resources invested, with an explicit aim of benchmarking with
similar organizations? How can using big data improve such benchmarking?
3. Can big data help the development of new indicators for outputs and inputs, thus
allowing innovative efficiency analyses, which can be used to challenge the existing
evidence about the efficiency of public administrations? How do these new studies
change the implications that derive from existing literature in the field?
4. Are public policy-makers using big data for designing policies and/or adjusting
them, for example following the judgments of citizens that can be processed
via adequate analytics?
The main objective of this review is to give an overview of the academic litera-
ture and research related to the theme of big data analytics for public organiza-
tions’ performance and efficiency measurement, with specific attention to our four
themes. To conduct this research, we focus on the most recent studies in leading
journals that publish on the relationships between information technology, (public)
policy making, PA and government (predominantly the journals Public Policy and
Administration and Government Information Quarterly). We also look at recent
research report by important political and consultancy institutions (European
Commission, McKinsey Global Institute) and recent books on the topic. For the
big data applications, we take a broader look at the literature. It is important to
note that the overview is not intended to be comprehensive.
This article is the first within a symposium in ‘Public Policy and Administration’
on ‘Big data analytics and its use in the measurement of public organizations’
performance and efficiency’. The next two papers of the symposium deal with
innovative applications. The paper by Johnes and Ruggiero (2017) focuses on
revenue efficiency, in particular ascertaining the extent to which, given output
prices, producers choose the revenue maximizing vector of outputs. They evaluate
efficiencies for English institutions of higher education for the academic year
2012–13 and find considerable variation across institutions in revenue efficiency.
The relaxation of the price-taking assumption leads to relatively small changes, in
either direction, to the estimated revenue efficiency scores. A number of issues
surrounding the modeling process are raised and discussed, including the determin-
ation of the demand function for each type of output and the selection of inputs
and outputs to be used in the model.
The third paper of the symposium is by Agostino and Arnaboldi (2017). They
show how social media data represent a potential powerful tool in the hands of
public authorities to support the evaluation of public service performance. By
relying on an action research project in the higher education field, this study
explores how social media data can contribute to measure service effectiveness
by focusing specifically on Twitter in the higher education field. The aim of the
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Public Policy and Administration 32(4)
paper is to develop a set of measures, derived from Twitter data, to quantify the
effectiveness of higher education services. This investigation supports a broader
discussion about the extent to which social media data can contribute to perform-
ance measurement in the public sector.
The article at hand has five main goals. First, it provides readers with a general
introduction to the topic area: in particular, it aims to give a clear understanding of
the most prescient insights, opinions, results, and big data applications for the
public sector that have been described in the literature. Second, a special focus
in the review will be on the advantages as well as the limitations of using big data in
public sector organizations. Third, the review briefly describes what past studies
have written about the use of big data by governments and PAs for internal per-
formance management. In this regard, a particularly interesting research question
is whether managers and heads of department have used big data to develop new or
better versions of performance indicators (inputs, outputs, and/or outcomes), and/
or have used information generated through big data for improving policy making
and managerial practices. Fourth, the review considers the potential benefits and
applications of big data for commerce and industry (in a context of providing
services to the government or not) – this insight is helpful in detecting factors
that are growingly important also for the public sector. Finally, the review identi-
fies research gaps in recent...
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