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1 Conference Presentation 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 [...] Reference 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: Disclaimer: layout of this document may differ from the published version.

2 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 Sunshine regulation biases Florence / 9

3 Outline 1 Introduction 2 Empirical strategy 3 Results 4 Discussion thomas.bolognesi@unige.ch Sunshine regulation biases Florence / 9

4 Introduction The puzzle Sunshine regulation (Baldwin and Black, 2008; Baldwin et al., 2010; Laffont and Tirole, 1993) proliferation of performance indicators Sunshine regulation biases Florence / 9

5 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) thomas.bolognesi@unige.ch Sunshine regulation biases Florence / 9

6 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) thomas.bolognesi@unige.ch Sunshine regulation biases Florence / 9

7 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 thomas.bolognesi@unige.ch Sunshine regulation biases Florence / 9

8 Introduction Research Question and Assumptions Research question Which behavioural mechanisms could explain information biases in sunshine regulation? Sunshine regulation biases Florence / 9

9 Introduction Research Question and Assumptions Research question Which behavioural mechanisms could explain information biases in sunshine regulation? Assumptions Causes Behaviour Literature Opportunism (H 1 ) Cream-skimming (Heckman et al., 1997) Over-estimation (Laffont and Martimort, 2009) Free-riding (Laffont and Martimort, 2009) Implementation (H 2 ) Transaction costs (Williamson, 2005) Non feasibility (Dosi and Egidi, 1991) Political Economy (H 3 ) Symbolic (Perry et al., 2010) thomas.bolognesi@unige.ch Sunshine regulation biases Florence / 9

10 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 Sunshine regulation biases Florence / 9

11 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 thomas.bolognesi@unige.ch Sunshine regulation biases Florence / 9

12 Results 3 Logit models NR DIST NA Implementation nbinput meth_f meth_report Oppportunism price th_cust th_finance th_network Pol-Eco public cons p < 0.05, p < 0.01, p < thomas.bolognesi@unige.ch Sunshine regulation biases Florence / 9

13 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 Implementation reject reject reject Political-economy Table 1: Decision on assumptions thomas.bolognesi@unige.ch Sunshine regulation biases Florence / 9

14 Discussion Thank You! 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 Sunshine regulation biases Florence / 9

15 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 H 0 no bias Correct Table 2: Behavioural causes of biases: expected values thomas.bolognesi@unige.ch Sunshine regulation biases Florence / 6

16 Appendix Independent variable Disclosure biases distribution (Bolognesi et al., 2016) Sunshine regulation biases Florence / 6

17 Appendix Model specifications Bias i,s = α + β 1 opportunism i,s + β 2 implementation i,s + β 3 pol i,s + ε i,s (1) NR i,s = β 0 + β 1.theme i + β 2.nbinput i + β 3.meth i + β 4.regie s + control s + ε i,s dist i,s = β 0 + β 1.theme i + β 2.price s + β 3.nbinput i + β 4.meth i + control s + ε i,s NA i,s = β 0 + β 1.nbinput i + β 2.meth i + control s + ε i,s (2) (3) (4) thomas.bolognesi@unige.ch Sunshine regulation biases Florence / 6

18 Appendix Model dist Implementation Opportunism Full nbinput (7.09) (1.02) (1.54) meth_f (3.44) (0.94) (0.82) meth_report (2.01) (0.46) (0.45) price (5.32) (4.57) th_cust (-2.29) (-2.12) th_finance (-0.72) (-0.35) th_network (2.17) (2.53) _cons (-2.60) (-3.20) (-2.15) Control service yes no yes Control operation no no yes N pseudo R AIC BIC t statistics in parentheses p < 0.05, p < 0.01, p < thomas.bolognesi@unige.ch Sunshine regulation biases Florence / 6

19 Appendix Model NR Opportunism Implementation Pol-eco Full nr th_cust (6.94) (9.37) (7.59) (8.82) th_finance (5.12) (7.16) (5.51) (6.83) th_network (4.26) (7.17) (5.39) (6.85) nbinput (-6.25) (-4.50) (-6.07) meth_f (-2.85) (-2.10) (-2.92) meth_report (-6.09) (-4.75) (-6.05) regie (-0.50) (2.91) _cons (-4.59) (-1.65) (0.10) (0.42) Control service yes yes no yes Control operation no no no yes N pseudo R AIC BIC t statistics in parentheses p < 0.05, p < 0.01, p < thomas.bolognesi@unige.ch Sunshine regulation biases Florence / 6

20 Appendix Model NA Implementation Full na nbinput (-2.29) (-2.29) meth_f (-0.02) (-4.83) meth_report (1.26) (.) _cons (-1.51) (0.02) Control service no yes Control operation no yes N pseudo R AIC BIC t statistics in parentheses p < 0.05, p < 0.01, p < thomas.bolognesi@unige.ch Sunshine regulation biases Florence / 6

21 References Baldwin, R. and Black, J. (2008). Really Responsive Regulation. The Modern Law Review, 71(1): Baldwin, R., Cave, M., and Lodge, M., editors (2010). The Oxford Handbook of Regulation. Oxford University Press, 1 edition. DOI: /oxfordhb/ Bolognesi, T., Brochet, A., and Renou, Y. (2016). Assessing resistance to public policy tools: insights from water performance indicators in the Grenoble area (France). SSRN Working Paper. Brochet, A. (2015). Petite histoire grenobloise du grand réseau, des origines de la cité à l aube du XXe siècle. Urbanités, 6. Brochet, A., Bolognesi, T., and Renou, Y. (2016). Caractériser l étendue des résistances locales aux indicateurs de performance des services d eau. Le cas de l agglomération grenobloise. Développement durable et territoires. Économie, géographie, politique, droit, sociologie, 7(3). Canneva, G. and Guérin-Schneider, L. (2011a). La construction des indicateurs de performance des services d eau en France : mesurer le développement durable? Natures Sciences Sociétés, 19(3): Canneva, G. and Guérin-Schneider, L. (2011b). National monitoring of water utility performance in France. Water Science & Technology: Water Supply, 11(6):745. thomas.bolognesi@unige.ch Sunshine regulation biases Florence / 6

22 References Chong, E. and Huet, F. (2009). Yardstick Competition, Franchise Bidding and Firms Incentives to Collude. Review of Industrial Organization, 35(1-2): Dosi, G. and Egidi, M. (1991). Substantive and procedural uncertainty. Journal of Evolutionary Economics, 1(2): Gerrish, E. (2016). The Impact of Performance Management on Performance in Public Organizations: A Meta-Analysis. Public Administration Review, 76(1): Heckman, J., Heinrich, C., and Smith, J. (1997). Assessing the Performance of Performance Standards in Public Bureaucracies. The American Economic Review, 87(2): Laffont, J.-J. and Martimort, D. (2009). The theory of incentives: the principal-agent model. Princeton University Press, Princeton. Laffont, J.-J. and Tirole, J. (1993). A theory of incentives in procurement and regulation. MIT press, Cambridge. Leuz, C. and Wysocki, P. D. (2016). The Economics of Disclosure and Financial Reporting Regulation: Evidence and Suggestions for Future Research. Journal of Accounting Research, 54(2): Marques, R. C., da Cruz, N. F., and Pires, J. (2015). Measuring the sustainability of urban water services. Environmental Science & Policy, 54: thomas.bolognesi@unige.ch Sunshine regulation biases Florence / 6

23 Appendix Model NA Perry, J. L., Hondeghem, A., and Wise, L. R. (2010). Revisiting the Motivational Bases of Public Service: Twenty Years of Research and an Agenda for the Future. Public Administration Review, 70(5): Pollitt, C. (2013). The logics of performance management. Evaluation, 19(4): Thiel, S. v. and Leeuw, F. L. (2002). The Performance Paradox in the Public Sector. Public Performance & Management Review, 25(3): Williamson, O. E. (2005). The Economics of Governance. The American Economic Review, 95(2):1 18. Witte, K. D. and Saal, D. S. (2010). Is a little sunshine all we need? On the impact of sunshine regulation on profits, productivity and prices in the Dutch drinking water sector. Journal of Regulatory Economics, 37(3): thomas.bolognesi@unige.ch Sunshine regulation biases Florence / 6