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In the shadow of sunshine regulation: considering disclosure biases
BOLOGNESI, Thomas
Abstract
Information asymmetries and principal-agent relations are crucial issues in network industries regulation. Therefore, besides incentive-based regulations, regulators developed tools dedicated to reveal hidden information and make “regulation smart”. In their very essence, these tools consist in benchmarking services and differ according to the use of benchmarking outputs. In this contribution, we consider performance measurement and sunshine regulation because of a discrepancy between literature and practices. Most of the empirical assessments conclude impacts of sunshine regulation on service performance are not significant while in policies the use of benchmarking increase. Instead of focussing on impacts, we look at the process of disclosure in sunshine regulation arguing that this process is subject to biases avoiding robust analysis of impacts. We assume there are three types of behaviour that cause these biases: opportunism, transaction costs minimisation and pro-social motivations. Our dataset combines 795 observations and results confirm impacts of opportunism and pro-social motivations while we find no [...]
BOLOGNESI, Thomas. In the shadow of sunshine regulation: considering disclosure biases. In:
Florence School of Regulation 6th Conference on the Regulation of Infrastructure
, Florence (Italy), 16th, 2017
Available at:
http://archive-ouverte.unige.ch/unige:95182
Disclaimer: layout of this document may differ from the published version.
In the Shadow of Sunshine Regulation
Considering disclosure biases
Thomas Bolognesi
University of Geneva
6th Conference on the Regulation of Infrastructure Florence School of Regulation
16 June 2017
Outline
1 Introduction
2 Empirical strategy
3 Results
4 Discussion
Introduction
The puzzle
Sunshine regulation (Baldwin and Black, 2008; Baldwin et al., 2010; Laffont and Tirole, 1993)
proliferation of performance indicators
But...
empirical assessments: small average effects (Gerrish, 2016; Witte and Saal, 2010)
a performance paradox (Pollitt, 2013; Thiel and Leeuw, 2002)
But...
limitations of disclosure assessments (Leuz and Wysocki, 2016)
existence of disclosure biases(Bolognesi et al., 2016)
⇒ explaining these biases
Introduction
The puzzle
Sunshine regulation (Baldwin and Black, 2008; Baldwin et al., 2010; Laffont and Tirole, 1993)
proliferation of performance indicators But...
empirical assessments: small average effects (Gerrish, 2016; Witte and Saal, 2010)
a performance paradox (Pollitt, 2013; Thiel and Leeuw, 2002)
But...
limitations of disclosure assessments (Leuz and Wysocki, 2016)
existence of disclosure biases(Bolognesi et al., 2016)
⇒ explaining these biases
Introduction
The puzzle
Sunshine regulation (Baldwin and Black, 2008; Baldwin et al., 2010; Laffont and Tirole, 1993)
proliferation of performance indicators But...
empirical assessments: small average effects (Gerrish, 2016; Witte and Saal, 2010)
a performance paradox (Pollitt, 2013; Thiel and Leeuw, 2002)
But...
limitations of disclosure assessments (Leuz and Wysocki, 2016)
existence of disclosure biases(Bolognesi et al., 2016)
⇒ explaining these biases
Introduction
The puzzle
Sunshine regulation (Baldwin and Black, 2008; Baldwin et al., 2010; Laffont and Tirole, 1993)
proliferation of performance indicators But...
empirical assessments: small average effects (Gerrish, 2016; Witte and Saal, 2010)
a performance paradox (Pollitt, 2013; Thiel and Leeuw, 2002)
But...
limitations of disclosure assessments (Leuz and Wysocki, 2016)
existence of disclosure biases(Bolognesi et al., 2016)
⇒ explaining these biases
Introduction
Research Question and Assumptions
Research question
Which behavioural mechanisms could explain information biases in sunshine regulation?
Assumptions
Causes Behaviour Literature
Opportunism (H1) Cream-skimming (Heckman et al., 1997)
Over-estimation (Laffont and Martimort, 2009)
Free-riding (Laffont and Martimort, 2009)
Implementation (H2) Transaction costs (Williamson, 2005)
Non feasibility (Dosi and Egidi, 1991)
Political Economy (H3) Symbolic (Perry et al., 2010)
Introduction
Research Question and Assumptions
Research question
Which behavioural mechanisms could explain information biases in sunshine regulation?
Assumptions
Causes Behaviour Literature
Opportunism (H1) Cream-skimming (Heckman et al., 1997)
Over-estimation (Laffont and Martimort, 2009)
Free-riding (Laffont and Martimort, 2009)
Implementation (H2) Transaction costs (Williamson, 2005)
Non feasibility (Dosi and Egidi, 1991)
Political Economy (H3) Symbolic (Perry et al., 2010)
Empirical strategy
The case: Water services of the Grenoble area (France)
Performance indicators in France(Canneva and Guérin-Schneider, 2011a,b)
17 indicators since 2007
reporting to ONEMA through SISPEA not compulsory
Grenoble area(Brochet, 2015; Brochet et al., 2016)
53 water services: very diverse very good quality of water 48% direct public management
Empirical strategy
Data
Dependent variable: quality of performance disclosure (Leuz and Wysocki, 2016)
795 observational data: disclosed VS own calculation
4 outputs: dist (28%), NR (41%), NA (15%), correct (16%) Independent variables:
services organisation: contracts, supply chain indicators: theme, complexity, nb of inputs Controls
density of pipes, volume, users density and size(Chong and Huet, 2009; Marques et al., 2015)
service
Results
3 Logit models
NR DIST NA
Implementation
nbinput -0.543∗∗∗ 0.139 -0.270∗ meth_f -1.859∗∗ 0.512 -1.830∗∗∗
meth_report -5.254∗∗∗ 0.366 0
Oppportunism
price . 5.149∗∗∗ .
th_cust 4.474∗∗∗ -0.933∗ . th_finance 3.298∗∗∗ -0.143 . th_network 3.803∗∗∗ 1.247∗ .
Pol-Eco public 3.038∗∗ . .
∗∗
Discussion
What explanation of disclosure biases in sunshine regulation?
1 Political-economy: the need for aligning formal and informal institutions
2 Opportunism is diverse and significant
3 Themes are more influential than indicators design
4 Transaction costs at system level
Model dist Model NR Model NA
Opportunism X X
Implementation reject reject reject
Political-economy X X
Table 1:Decision on assumptions
Discussion
Thank You!
In the Shadow of Sunshine Regulation
Considering disclosure biases
Thomas Bolognesi
University of Geneva
6th Conference on the Regulation of Infrastructure
Appendix Expected values
Causes Behaviour Disclosure bias
Opportunism cream-skimming NR
over-estimation dist free-riding dist Implementation transaction costs NR or dist
non feasibility NA
Political Economy Symbolic NR
H0 no bias Correct
Table 2:Behavioural causes of biases: expected values
Appendix Independent variable
Disclosure biases distribution
(Bolognesi et al., 2016)Appendix Model specifications
Biasi,s =α+β1opportunismi,s+β2implementationi,s+β3poli,s+εi,s (1)
NRi,s =β0+β1.themei +β2.nbinputi +β3.methi +β4.regies
+controls+εi,s
(2) disti,s =β0+β1.themei +β2.prices +β3.nbinputi +β4.methi
+controls+εi,s
(3) NAi,s =β0+β1.nbinputi+β2.methi
+controls+εi,s
(4)
Appendix Model dist
Implementation Opportunism Full
nbinput 0.429∗∗∗ 0.0617 0.139
(7.09) (1.02) (1.54)
meth_f 1.903∗∗∗ 0.376 0.512
(3.44) (0.94) (0.82)
meth_report 1.429∗ 0.258 0.366
(2.01) (0.46) (0.45)
price 2.194∗∗∗ 5.149∗∗∗
(5.32) (4.57)
th_cust -0.808∗ -0.933∗
(-2.29) (-2.12)
th_finance -0.235 -0.143
(-0.72) (-0.35)
th_network 0.769∗ 1.247∗
(2.17) (2.53)
_cons -2.364∗∗ -1.728∗∗ -2.263∗∗
(-2.60) (-3.20) (-2.15)
Control service yes no yes
Control operation no no yes
N 510 795 450
pseudoR2 0.231 0.106 0.316
AIC 611.5 859.6 496.7
BIC 768.1 897.0 648.7
tstatistics in parentheses
∗p<0.05,∗∗p<0.01,∗∗∗p<0.001
Appendix Model NR
Opportunism Implementation Pol-eco Full nr
th_cust 2.662∗∗∗ 4.383∗∗∗ 2.329∗∗∗ 4.474∗∗∗
(6.94) (9.37) (7.59) (8.82)
th_finance 2.050∗∗∗ 3.225∗∗∗ 1.718∗∗∗ 3.298∗∗∗
(5.12) (7.16) (5.51) (6.83)
th_network 1.598∗∗∗ 3.626∗∗∗ 1.946∗∗∗ 3.803∗∗∗
(4.26) (7.17) (5.39) (6.85)
nbinput -0.489∗∗∗ -0.263∗∗∗ -0.543∗∗∗
(-6.25) (-4.50) (-6.07)
meth_f -1.661∗∗ -0.882∗ -1.859∗∗
(-2.85) (-2.10) (-2.92)
meth_report -4.742∗∗∗ -2.623∗∗∗ -5.254∗∗∗
(-6.09) (-4.75) (-6.05)
regie -0.0782 3.038∗∗
(-0.50) (2.91)
_cons -3.915∗∗∗ -1.844 0.0592 0.441
(-4.59) (-1.65) (0.10) (0.42)
Control service yes yes no yes
Control operation no no no yes
N 720 720 750 600
pseudoR2 0.309 0.405 0.087 0.423
AIC 782.6 694.3 948.8 570.5
BIC 1016.1 941.6 985.7 772.7
t statistics in parentheses
∗p<0.05,∗∗p<0.01,∗∗∗p<0.001
Appendix Model NA
Implementation Full na
nbinput -0.168∗ -0.270∗
(-2.29) (-2.29)
meth_f -0.0159 -1.830∗∗∗
(-0.02) (-4.83)
meth_report 1.251 0
(1.26) (.)
_cons -1.557 0.0209
(-1.51) (0.02)
Control service no yes
Control operation no yes
N 795 532
pseudoR2 0.110 0.255
AIC 617.3 407.9
BIC 636.0 579.0
t statistics in parentheses
∗p<0.05,∗∗p<0.01,∗∗∗p<0.001
References
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