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Interdependent policy instrument preferences: a two-mode network approach

Published online by Cambridge University Press:  30 July 2018

Florence Metz*
Affiliation:
Institute of Environmental Decisions, ETH Zürich, Switzerland Institute of Political Science, University of Bern, Switzerland
Philip Leifeld
Affiliation:
School of Social and Political Sciences, University of Glasgow, UK
Karin Ingold
Affiliation:
Institute of Political Science, University of Bern, Switzerland Department of Environmental Social Sciences, Swiss Federal Institute of Aquatic Science and Technology (Eawag), Switzerland Oeschger Centre for Climate Change Research, University of Bern, Switzerland
*
*Corresponding author. Email: florence.metz@usys.ethz.ch

Abstract

In policymaking, actors are likely to take the preferences of others into account when strategically positioning themselves. However, there is a lack of research that conceives of policy preferences as an interdependent system. In order to analyse interdependencies, we link actors to their policy preferences in water protection, which results in an actor-instrument network. As actors exhibit multiple preferences, a complex two-mode network between actors and policies emerges. We analyse whether actors exhibit interdependent preference profiles given shared policy objectives or social interactions among them. By fitting an exponential random graph model to the actor-instrument network, we find considerable clustering, meaning that actors tend to exhibit preferences for multiple policy instruments in common. Actors tend to exhibit interdependent policy preferences when they are interconnected, that is, they collaborate with each other. By contrast, actors are less likely to share policy preferences when a conflict line divides them.

Type
Research Article
Copyright
© Cambridge University Press, 2018 

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Footnotes

Cite this article: Metz F, Leifeld P, Ingold K. 2019. Interdependent policy instrument preferences: a two-mode network approach. Journal of Public Policy 39: 609–636,, doi:10.1017/S0143814X18000181

References

Berardo, R (2014) Bridging and Bonding Capital in Two-Mode Collaboration Networks. Policy Studies Journal 42(2): 197225.CrossRefGoogle Scholar
Berardo, R Scholz, J (2010) Self-Organizing Policy Networks: Risk, Partner Selection, and Cooperation in Estuaries. American Journal of Political Science 54(3): 632649.CrossRefGoogle Scholar
Bodin, Ö Crona, B (2009) The Role of Social Networks in Natural Resource Governance: What Relational Patterns Make a Difference? Global Environmental Change 19, 366374.CrossRefGoogle Scholar
Bodin, Ö Prell, C (2011) Social Networks and Natural Resource Management. Uncovering the Social Fabric of Environmental Governance. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Börzel, T (1998) Organizing Babylon. On the Diffierent Conceptions of Policy Networks. Public Administration 76, 253273.CrossRefGoogle Scholar
Bressers, H, Huitema, D Kuks, S (1995) Policy Networks in Dutch Water Policy. In Bressers H, O’Toole L and Richardson J (eds.), Networks for Water Policy. A Comparative Perspective. London: Frank Cass, 2451.Google Scholar
Bressers, H O’Toole, L (1998) The Selection of Policy Instruments: A Network-Based Perspective. Journal of Public Policy 18(3): 213239.CrossRefGoogle Scholar
Bressers, H O’Toole, L (2005) Instrument Selection and Implementation in a Networked Context. In Eliadis P, Hill M and Howlett M (eds.), Designing Government: From Instruments to Governance. Montreal, Kingston: McGill-Queen’s University Press, 132153.Google Scholar
Bressers, H, O’Toole, L Richardson, J (1995) Networks for Water Policy: A Comparative Perspective. London: Frank Cass.Google Scholar
Briatte, F (2016) Network Patterns of Legislative Collaboration in Twenty Parliaments. Network Science 4(2): 266271.CrossRefGoogle Scholar
Calanni, J, Siddiki, S Weible Cand Leach, W (2014) Explaining Coordination in Collaborative Partnerships and Clarifying the Scope of the Belief Homophily Hypothesis. Journal of Public Administration Research and Theory 25(3): 901927.CrossRefGoogle Scholar
Cranmer, S, Leifeld, P, McClurg, S Rolfe, M (2017) Navigating the Range of Statistical Tools for Inferential Network Analysis. American Journal of Political Science 61(1): 237251.CrossRefGoogle Scholar
Crona, B John, P (2012) Learning in Support of Governance: Theories, Methods, and a Framework to Assess How Bridging Organizations Contribute to Adaptive Resource Governance. Ecology and Society 17(1): 3249.CrossRefGoogle Scholar
Duxbury, S (2017) Diagnosing Multicollinearity in Exponential Random Graph Models. Master’s Thesis, The Ohio State University, http://rave.ohiolink.edu/etdc/view?acc_num=osu1491393848069144 (accessed 16 July 2018).Google Scholar
Fischer, M Leifeld, P (2015) Policy Forums: Why Do they Exist and What are they Used for? Policy Sciences 48(3): 363382.CrossRefGoogle Scholar
Fischer, M Sciarini, P (2016) Drivers of Collaboration in Political Decision Making: A Cross-Sector Perspective. The Journal of Politics 78(1): 6374.CrossRefGoogle Scholar
Gerber, E, Henry, A Lubell, M (2013) Political Homophily and Collaboration in Regional Planning Networks. American Journal of Political Science 57(3): 598610.CrossRefGoogle Scholar
Gilardi, F (2016) Four Ways We Can Improve Policy Diffusion Research. State Politics & Policy Quarterly 16(1): 821.CrossRefGoogle Scholar
Granovetter, M (1985) Economic Action and Social Structure: The Problem of Embeddedness. American Journal of Sociology 91, 481510.CrossRefGoogle Scholar
Granovetter, M (1992) Economic Institutions as Social Construction: A Framework of Analysis. Acta Sociologica 35, 311.CrossRefGoogle Scholar
Harward, B Moffett, K (2010) The Calculus of Cosponsorship in the U.S. Senate. Legislative Studies Quarterly 35(1): 117143.CrossRefGoogle Scholar
Henry, A (2011) Ideology, Power, and the Structure of Policy Networks. Policy Studies Journal 39(3): 361383.CrossRefGoogle Scholar
Hollender, J, Singer, H McArdell, C (2008) Polar Organic Micropollutants in The Water Cycle. In Hlavinek P, Bonacci O, Marsalek J and Mahrikova I (eds.), Dangerous Pollutants (Xenobiotics) in Urban Water Cycle. Dodrecht: Springer, 103116.CrossRefGoogle Scholar
Howlett, M (2009) Governance modes, policy regimes and operational plans: A multi-level nested model of policy instrument choice and policy design. Policy Science 42, 7389.CrossRefGoogle Scholar
Howlett, M Ramesh, M (1995) Studying Public Policy: Policy Cycles and Policy Subsystems . Toronto, New York: Oxford University Press.Google Scholar
Hunter, D (2007) Curved Exponential Family Models for Social Networks. Social Networks 29(2): 216230.CrossRefGoogle ScholarPubMed
Hunter, D, Handcock, M, Butts, C, Goodreau, S Morris, M (2008) ergm: A Package to Fit, Simulate and Diagnose Exponential-Family Models for Networks. Journal of Statistical Software 24(3): nihpa54860.CrossRefGoogle ScholarPubMed
Ingold, K (2014) How Involved are they Really? A Comparative Network Analysis of the Institutional Drivers of Local Actor Inclusion. Land Use Policy 39, 376387.CrossRefGoogle Scholar
Ingold, K Fischer, M (2014) Drivers of Collaboration to Mitigate Climate Change: An Illustration of Swiss Climate Policy Over 15 Years. Global Environmental Change 24, 8898.CrossRefGoogle Scholar
Ingold, K, Varone, F Stokman, F (2013) A Social Network-Based Approach to Assess De Facto Independence of Regulatory Agencies. Journal of European Public Policy 20(10): 14641481.CrossRefGoogle Scholar
Jasny, L (2012) Baseline Models for Two‐Mode Social Network Data. Policy Studies Journal 40(3): 458491.CrossRefGoogle Scholar
Jasny, L Mark, L (2015) Two-Mode Brokerage in Policy Networks. Social Networks 41, 3647.CrossRefGoogle Scholar
Jenkins‐Smith, H, Silva, C, Gupta, K Ripberger, J (2014) Belief System Continuity and Change in Policy Advocacy Coalitions: Using Cultural Theory to Specify Belief Systems, Coalitions, and Sources of Change. Policy Studies Journal 42(4): 484508.CrossRefGoogle Scholar
Jones, C, Hesterley, W Borgatti, S (1997) A General Theory of Network Governance: Exchange Conditions and Social Mechanisms. Academy of Management Review 22(4): 911945.CrossRefGoogle Scholar
Kammermann, L Dermont, C (2018) How Beliefs of the Political Elite and Citizens on Climate Change Influence Support for Swiss Energy Transition Policy. Energy Research & Social Science, https://doi.org/10.1016/j.erss.2018.05.010 available online 22 May, 2018.CrossRefGoogle Scholar
Keohane, N, Revesz, R Stavins, R (1998) The Choice of Regulatory Instruments in Environmental Policy. Harvard Environmental Law Review 22(2): 313367.Google Scholar
Knill, C Tosun, J (2012) Public Policy: A New Introduction. New York: Palgrave Macmillan.CrossRefGoogle Scholar
Knoke, D (1994) Networks of Elite Structure and Decision Making. In Wasserman S and Galaskiewicz J (eds.), Advances in Social Network Analysis: Research in the Social and Behavioral Sciences. Thousand Oaks, CA: Sage, 274295.CrossRefGoogle Scholar
Landry, R Varone, F (2005) The Choice of Policy Instruments: Confronting the Deductive and the Interactive Approaches. In Eliadis F, Hill M and Howlett M (eds.), Designing Government. From Instruments to Governance. Montreal, Kingston: McGill-Queen’s University Press, 106131.Google Scholar
Lasswell, H (1958) Politics: who gets what, when, how. With postscript. New York: Meridian Books.Google Scholar
Laumann, E, Marsden, P Prensky, D (1983) The Boundary Specification Problem in Network Analysis. In Burt R and Minor M (eds.), Applied Network Analysis: A Methodological Introduction. Beverly Hills, CA: Sage, 6188.Google Scholar
Leifeld, P (2013) texreg: Conversion of Statistical Model Output in R to LaTeX and HTML Tables. Journal of Statistical Software 55(8): 1–24.Google Scholar
Leifeld, P, Cranmer, S Desmarais, B (2018) Temporal Exponential Random Graph Models with btergm: Estimation and Bootstrap Confidence Intervals. Journal of Statistical Software 83(6): 136.CrossRefGoogle Scholar
Leifeld, P Schneider, V (2012) Information Exchange in Policy Networks. American Journal of Political Science 56(3): 731744.CrossRefGoogle Scholar
Linder, S Peters, G (1989) Instruments of Government: Perceptions and Contexts. Journal of Public Policy 9(1): 3558.CrossRefGoogle Scholar
Lindstäd, R, Vander Wielen, R Green, M (2017) Diffusion in Congress: Measuring the Social Dynamics of Legislative Behavior. Political Science Research and Methods 5(3): 511527.CrossRefGoogle Scholar
Lubell, M Fulton, A (2007) Local Diffusion Networks as Pathways to Sustainable Agriculture. California Agriculture 61(3): 131137.CrossRefGoogle Scholar
Lubell, M, Scholz, J, Berardo, R Robins, G (2012) Testing Policy Theory with Statistical Models of Networks. Policy Studies Journal 40(3): 351374.CrossRefGoogle Scholar
Majone, G (1976) Choice Among Policy Instruments for Pollution Control. Policy Analysis 2(3): 589613.Google Scholar
Malang, T, Brandenberger, L Leifeld, P (2017) Networks and social influence in European legislative politics. British Journal of Political Science, 1–24.Google Scholar
March, J Olsen, J (1989) Rediscovering Institutions: The Organizational Basis of Politics. New York: Free Press.Google Scholar
Marsden, P (1981) Introducing Influence Processes into a System of Collective Decisions. American Journal of Sociology 86(6): 12031235.CrossRefGoogle Scholar
Metz, F Ingold, K (2014) Sustainable Wastewater Management: Is It Possible to Regulate Micropollution in the Future by Learning from the Past? A Policy Analysis. Sustainability 6(4): 19922012.CrossRefGoogle Scholar
McPherson, M, Smith-Lovin, L Cook, J (2001) Birds of a Feather: Homophily in Social Networks. Annual Review of Sociology 27, 415444.CrossRefGoogle Scholar
Munro, G Ditto, P (1997) Biased Assimilation, Attitude Polarization, and Affect in Reactions to Stereotype-Relevant Scientific Information. Personality and Social Psychology Bulletin 23(6): 636653.CrossRefGoogle Scholar
Munro, G, Ditto, P, Lockhart, L, Fagerlin, A, Gready, M Peterson, E (2002) Biased Assimilation of Sociopolitical Arguments: Evaluating the 1996 U.S. Presidential Debate. Basic and Applied Social Psychology 24(1): 1526.CrossRefGoogle Scholar
Newig, J Fritsch, O (2009) Environmental Governance: Participatory, Multi-Level – and Effective? Environmental Policy and Governance 19(3): 197214.CrossRefGoogle Scholar
Newman, M (2002) Assortative Mixing in Networks. Physical Review Letters 89(20): 14.CrossRefGoogle ScholarPubMed
Olson, M (1965) The Logic of Collective Action. Cambridge, MA: Harvard University Press.Google Scholar
Ostrom, E, Cox, M Schlager, E (2014) An Assessment of the Institutional Analysis and Development Framework and Introduction of the Social-Ecological Systems Framework. In Sabatier P and Weible C (eds.), Theories of the Policy Process. Boulder, CO: Westview Press, 267306.Google Scholar
Pahl-Wostl, C (2007) The Implications of Complexity for Integrated Resources Management. Environmental Modelling & Software 22(5): 561569.CrossRefGoogle Scholar
Robins, G, Bates, L Pattison, P (2011) Network Governance and Environmental Management: Conflict and Cooperation. Public Administration 89(4): 12931313.CrossRefGoogle Scholar
Robins, G, Pattison, P, Kalish, Y Lusher, D (2007a) An Introduction to Exponential Random Graph (p*) Models for Social Networks. Social Networks 29(2): 173191.CrossRefGoogle Scholar
Robins, G, Snijders, T, Wang, P, Handcock, M Pattison, P (2007b) Recent Developments in Exponential Random Graph (p*) Models for Social Networks. Social Networks 29(2): 192215.CrossRefGoogle Scholar
Sabatier, P (1999) Theories of the Policy Process. Boulder, CO: Westview Press.Google Scholar
Sabatier, P Jenkins-Smith, H (1993) Policy Change and Learning: An Advocacy Coalition Approach. Boulder, CO: Westview Press.Google Scholar
Sager, F (2009) Governance and Coercion. Political Studies 57(3): 537558.CrossRefGoogle Scholar
Sandström, A Carlsson, L (2008) The Performance of Policy Networks: The Relation Between Network Structure and Network Performance. The Policy Studies Journal 36(4): 497524.CrossRefGoogle Scholar
Schneider, V (2014) Akteurskonstellationen und Netzwerke in der Politikentwicklung. In Schubert D and Bandelow N (eds.), Lehrbuch der Politikfeldanalyse. München: Oldenbourg, 259288.Google Scholar
Schwarzenbach, R, Escher, B, Fenner, K, Hofstetter, T, Johnson, A, Von Gunten, U Wehrli, B (2006) The Challenge of Micropollutants in Aquatic Systems. Science 313(5790): 10721077.CrossRefGoogle ScholarPubMed
Shalizi, C Thomas, A (2011) Homophily and Contagion Are Generically Confounded in Observational Social Network Studies. Sociological Methods & Research 40(2): 211239.CrossRefGoogle ScholarPubMed
Varone, F (1998) Le choix des instruments des politiques publiques. Une analyse comparée des politiques d’efficience énergétique du Canada, du Danemark, des Etats-Unis, de la Suède et de la Suisse. Bern: Paul Haupt Verlag.Google Scholar
Wang, P, Sharpe, K, Robins, G Pattison, P (2009) Exponential Random Graph (p∗) Models for Affiliation Networks. Social Networks. 31(1): 1225.CrossRefGoogle Scholar
Wasserman, S Pattison, P (1996) Logit Models and Logistic Regressions for Social Networks: I. An Introduction to Markov Graphs and p*. Psychometrika 61, 401425.CrossRefGoogle Scholar
Weible, C, Pattison, A Sabatier, P (2010) Harnessing Expert-Based Information for Learning and the Sustainable Management of Complex Socio-Ecological Systems. Environmental Science & Policy 13(6): 522534.CrossRefGoogle Scholar
Weible, C Sabatier, P (2005) Comparing Policy Networks: Marine Protected Areas in California. Policy Studies Journal 33(2): 181201.CrossRefGoogle Scholar
Zafonte, M Sabatier, P (1998) Shared Beliefs and Imposed Interdependencies as Determinants of Ally Networks in Overlapping Subsystems. Journal of Theoretical Politics 10(4): 473505.CrossRefGoogle Scholar
Zhang, Y, Friend, A, Traud, A, Porter, M, Fowler, J Mucha, P (2008) Community Structure in Congressional Cosponsorship Networks. Physica A: Statistical Mechanics and its Applications 387(7): 17051712.CrossRefGoogle Scholar
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