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Power rangers Drivers of information exchange in South African tree plantation policy

This study explores the drivers of information exchange in South African tree plantation policy, including the role of power, networks, and contextual factors. The findings shed light on how actors in the policy process shape the success or failure of policies.

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Power rangers Drivers of information exchange in South African tree plantation policy

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  1. Power rangers Drivers of information exchange in South African tree plantation policy University of Helsinki Arttu Malkamäki, TuomasYlä-Anttila, Maria Brockhaus, Anne Toppinen

  2. Policy networks Patterns of interactionamongstate and non-stateactorsworkingon policy, in reference to certainbehavioursuch as communication. Shapethecontext of policysuccessorfailure. Couldalso help solvingdysfunctionalstructures. Self-organisingtendencies. Exogenousconstraints (rules). Endogenousdrivers (norms). Henry et al., 2011; Rhodes, 1996

  3. Information exchange Reduceuncertainty. Acquireknowledge. Avoidconflicts. Exertpower. Buildcoalitions. Policyactorsintend to influence policy outcomes in their favour. Powerful actors could have more control over what is shared, how and where. With whom, and why so, dopolicyactorschoose to establishcontact and exchangeinformation? Balaand Goyal, 2000; Berardo, 2014

  4. Institutionalopportunitystructures Commoncommittees Relationalopportunitystructures Reciprocity Socialopportunitystructures Commonpartners Brokerage Power attribution Reputationalinfluence Formalauthority Ideologicalproximity Buildscoalitions and mayconditioninfluenceattribution Lubell et al., 2016; Leifeldand Schneider, 2012; Pfefferand Salancik, 1978; Putnam, 1995; Weible et al., 2011

  5. Institutionalopportunitystructures H1: The more common policy-relevant forums, venues or committees two actors participate, the more likely they are to exchange information Relational opportunity structures H2: If an actor receives information from another actor, the more likely it is to send information to this actor Social opportunity structures H3a: The more common partners two actors share, the more likely they are to exchange information H3b: Policy brokers encourage information exchange embedded in transitive triads Power attribution H4a: The greater an actor perceives the influence of another actor, the more likely the actor is to establish an information tie to this organisation H4b: Governmental actors have disproportionately many incoming information exchange ties

  6. Contextual factors which may alter rules and norms Politicaluncertainty Technical specificity Institutionalstrength History and culture Possible logic for frequent contacting Learn and updateexpectations Gaininfluence as a trustedpartner Detect and sanctiondefectivebehaviour Berardoand Lubell, 2016; Centolaand Macy, 2007; Emirbayerand Goodwin, 1994; Fischer et al., 2017

  7. SouthAfricantreeplantationpolicy Firstcommercialplantation of exotictreespecies to Cape Colony in 1876 Regulations on streamflowreduction and alieninvasivespecies in 1972, 1984 and 1998 Landclaimsdue to (past) raciallydiscriminatorypracticesbetween 1913 and 1994 Economicwoes, unemployment, ruralpoverty, and a ”looming” woodshortage Kruger and Bennett, 2013

  8. SouthAfricantreeplantationpolicy ”…implement a proactive approach to forestry development in areas that have substantial opportunities for afforestation, namely a co-operative government initiative to authorise swift afforestation licensing in areas that have been identified and demarcated as being suitable for afforestationin the Eastern Cape and KwaZulu-Natal.” AmendedForestSector Code, 2017

  9. Data An a priori roster with 57 identified policy actors 55 interviewed in South Africa in 2017 Frequency of information exchange (binary, directed) Perceived influence (binary, directed) Committee participation (valued, undirected) Disagreement over policy preferences and beliefs to be extracted from qualitative data using Discourse Network Analyzer(valued, undirected) Social trust (valued, directed)

  10. Modelling Exponential random graph modelling Tests the effects of a set of dependent factors on an observed network structure Probability of information exchange (tie) between two actors (nodes) could depend on structure Actor variables (node cov.), dyadic variables (tie cov.) and structural effects on the network itself Iterative MCMC-MLE optimisation Markov Chain Monte Carlo Maximum Likelihood Estimation Cranmer et al., 2017; Robins et al., 2007

  11. Business 13 Government 10 Civil society 7 Industry associations 7 Labour unions 3 Media 2 Research 13

  12. Signif. codes: 0 '***' 0,001 '**' 0,01 '*' 0.05 ‘.' 0,1

  13. Signif. codes: 0 '***' 0,001 '**' 0,01 '*' 0.05 ‘.' 0,1

  14. Signif. codes: 0 '***' 0,001 '**' 0,01 '*' 0.05 ‘.' 0,1

  15. Signif. codes: 0 '***' 0,001 '**' 0,01 '*' 0.05 ‘.' 0,1

  16. Discussion Policy actors rely on institutional (H1) and relational (H2) opportunity structures to reduce transaction costs of information exchange. However, perceived influence (H4a) matters the most, although the effect is not as strong for frequent information exchange. Social opportunity structures are harnessed only in terms of frequent contacting (H3: two actors are more likely to share multiple transitive partners than expected by chance). Zuma’s cabinet, not so relevant. Model fit could be further improved and policy preferences and beliefs should be controlled for. Additional terms (e.g. social trust) could be added. Coalitionbuilding (Weible et al., 2011)? Brokeragepositions to becomemorepowerfulare in use, butwithminorcoefficients and fitimprovements. Brokers seem to work passively. Friend of my friend is my friend. Difficulties in policy implementation? Industry domination?

  17. Acknowledgements Corefunding Doctoral Programme in Sustainable Use of Renewable Natural Resources, University of Helsinki, Finland Travel support Finnish Forest Foundation, Finland Foundation for Promotion of International Exchange in Forestry, Finland MetsämiestenSäätiö Foundation, Finland

  18. Literature cited Bala, V., and Goyal, S. (2000). A Noncooperative Model of Network Formation. Econometrica, 68(5), 1181–1229. https://doi.org/10.1111/1468-0262.00155 Berardo, R. (2014). The evolution of self-organizing communication networks in high-risk social-ecological systems. International Journal of the Commons, 8(1), 236–258. https://doi.org/10.18352/ijc.463 Berardo, R., and Lubell, M. (2016). Understanding What Shapes a Polycentric Governance System. Public Administration Review, 76(5), 738–751. https://doi.org/10.1111/puar.12532 Centola, D., and Macy, M. (2007). Complex Contagions and the Weakness of Long Ties. American Journal of Sociology, 113(3), 702–734. https://doi.org/10.1086/521848 Cranmer, S. J., Leifeld, P., McClurg, S. D., and Rolfe, M. (2017). Navigating the Range of Statistical Tools for Inferential Network Analysis. American Journal of Political Science, 61(1), 237–251. https://doi.org/10.1111/ajps.12263 Emirbayer, M., and Goodwin, J. (1994). Network Analysis, Culture, and the Problem of Agency. American Journal of Sociology, 99(6), 1411–1454. https://doi.org/10.1086/230450 Fischer, M., Ingold, K., and Ivanova, S. (2017). Information exchange under uncertainty: The case of unconventional gas development in the United Kingdom. Land Use Policy, 67, 200–211. https://doi.org/10.1016/j.landusepol.2017.05.003

  19. Literature cited Granovetter, M. (2005). The Impact of Social Structure on Economic Outcomes. Journal of Economic Perspectives, 19(1), 33–50. https://doi.org/10.1257/0895330053147958 Henry, A. D., Lubell, M., and McCoy, M. (2011). Belief systems and social capital as drivers of policy network structure: The case of California regional planning. Journal of Public Administration Research and Theory, 21(3), 419–444. https://doi.org/10.1093/jopart/muq042 Kruger, F. J., and Bennett, B. M. (2013). Wood and water: an historical assessment of South Africa’s past and present forestry policies as they relate to water conservation. Transactions of the Royal Society of South Africa, 68(3), 163–174. https://doi.org/10.1080/0035919X.2013.833144 Leifeld, P., and Schneider, V. (2012). Information Exchange in Policy Networks. American Journal of Political Science, 56(3), 731–744. https://doi.org/10.1111/j.1540-5907.2011.00580.x Lubell, M. N., Mewhirter, J. M., Berardo, R., and Scholz, J. T. (2016). Transaction Costs and the Perceived Effectiveness of Complex Institutional Systems. Public Administration Review, 77(5), 668–680. https://doi.org/10.1111/puar.12622 Pfeffer, J., and Salancik, G. R. (1978). The External Control of Organizations: A Resource Dependence Perspective. Stanford: Stanford Business Books.

  20. Literature cited Putnam, R. D. (1995). Bowling Alone: Americas´s Declining Social Capital. Journal of Democracy, 6(11), 65–78. https://doi.org/10.1353/jod.1995.0002 Rhodes, R. A. W. (1996). The New Governance: Governing without Government. Political Studies, 44(4), 652–667. https://doi.org/10.1111/j.1467-9248.1996.tb01747.x Robins, G., Pattison, P., Kalish, Y., and Lusher, D. (2007). An introduction to exponential random graph (p*) models for social networks. Social Networks, 29(2), 173–191. https://doi.org/10.1016/j.socnet.2006.08.002 Weible, C. M., Sabatier, P., Jenkins-Smith, H. C., Nohrsted, D., Henry, A. D., and deLeon, P. (2011). A Quarter Century of the Advocacy Coalition Framework: An Introduction to the Special Issue. Policy Studies Journal, 39(3), 349–360. https://doi.org/10.1111/j.1541-0072.2011.00412.x

  21. Relational and social effects and network configurations

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