Modes of Policy Learning As Causal Mechanisms: Coming up with a “Policy Learning Measuring Instrument” for Qualitative Research

Draft - Please do not quote or circulate without permission Modes of policy learning as causal mechanisms: coming up with a “policy learning measuring instrument” for qualitative research Jonathan c. Kamkhaji University of Exeter Abstract Drawing on the recent systematisation of the field of Dunlop and Radaelli (2013), this contribution seeks to achieve a number of conceptual and operational goals related to the use of modes of policy learning as causal mechanisms in the context of qualitative, case-study research, providing hence a practical application of the arguments advanced by Felleti and Lynch (2009) and Heikkila and Gerlak (2013). At the outset, in line with the seminal work of Hugh Heclo (1974), the paper puts forth a provocative conceptualisation of learning as one of the ontologies of policy making – complementary to, but distinct from, a more classical “powering” ontology. Under the ontological account of learning, modes of policy learning work as epistemological lenses that are to be applied empirically to the policy process in general, and to policy interactions in particular. In fact, the paper argues that the empirical locus whereby policy learning occurs and needs to be typified is the policy-specific set of interactions taking place among actors. But how to apply these lenses in practice? How to distinguish between one mode of learning and another in the context of a given interactive policy process and how to assess explanatory leverage? To answer these questions the paper extends the dimensions of variation of policy learning detected by Dunlop and Radaelli (actor certification and problem tractability) by adding the time-frame that characterises policy interactions and the institutional venues where they take place. Equipped with these four dimensions of variation, the paper proceeds to develop a “policy learning measuring instrument”. In more detail, through the review of a broad corpus of literature, the paper identifies mode-specific empirical scope conditions for each dimension of variation. The final step to come up with the measuring instrument involves systematising a series of observable implications related to each mode of learning. Importantly, these mode-specific expected outcomes are not skewed toward a value-laden view of learning as an invariably positive mechanism for the sake of the policy process, but also include instances of dysfunctional learning. The resulting measuring instrument allows policy learning scholars to tackle case study research with a systematic analytical device that suits extremely well process-tracing informed methodologies based on causal mechanisms. In fact, by pinning down the observed scope conditions for each of the dimensions of variation of policy learning, the researcher is able to precisely characterise a case (i.e. a set of policy-specific interactions) as belonging to one or another mode of learning and then to test whether the observed outcomes fit with the implications theorised for that mode. This operationalisation arguably allows for claiming solid explanatory leverage for modes of policy learning as causal mechanisms. 1 Draft - Please do not quote or circulate without permission 1. Introduction Over the last decades, learning has been one of the most employed concepts in the social sciences. Almost no academic discipline has been spared by the kind but robust offensive of learning-oriented approaches. Political and policy sciences have not differed in embracing, more or less explicitly, learning-oriented perspectives to analysis, in line with the behavioural turn of social sciences and its search for more solid micro foundations to explain individual and collective action. As a result, the body of literature dealing with learning in policy making is at the present time mature, vast and varied, including both theoretical and empirical contributions (Heclo 1974; Argyris and Schön 1978; Etheredge 1985; Sabatier 1988; Rose 1991; Haas 1992; May 1992; Hall 1993; Radaelli 1995 and 2009; Checkel 2001; Weyland 2005; Sabel and Zeitlin 2008; Meseguer 2009; Gilardi 2010), along with review pieces that try to systematize extant theories and findings in order to produce cumulative knowledge (Bennet and Howlett 1992; Dolowitz and Marsh 1996; Freeman 2006; Grin and Loeber 2007; Zito and Schout 2009; Gilardi and Radaelli 2012; Dunlop and Radaelli 2013 and 2017; Heikkila and Gerlak 2013). As is often the case, debates, controversies and challenges abound within this relatively young scholarship - see Lodge and James (2003) for a well-known critique of the policy transfer and lesson drawing scholarships, a critique that can be extended to account for the limitations and issues, conceptual and practical, of the policy learning literature at large. The aim of this article is strongly pragmatic. It does not aim to set out all of the scholarly disagreements and intellectual controversies that have progressively emerged in the literature but rather to propose a novel way of thinking about the phenomenon of learning in policy making and most of all, thanks to the review of selected contributions, construct a reliable and theoretically valid tool for empirical analysis. The review articles referenced above already provide, in fact, a complete overview of the policy learning scholarship and coming up with a new one would be redundant. The present article, therefore, restrains from telling a story already repeatedly told in the field, a story of increasing engagement with and use of the concept, but also of definitional elusiveness, weak empirical measurability and debatable explanatory leverage (James and Lodge 2003; Dunlop and Radaelli 2017). What this article aims to do instead is to review the literature with a theory-building aim. This is to be achieved firstly through the resort to a new ontological approach to learning in policy making, and secondly through the creation of an informed tool for empirical analysis, a “policy learning measuring instrument” constructed around different typologies/modes of learning, their analytical components and expected empirical manifestations. The present article, hence, engages in a theory-driven literature review ultimately aimed at deriving a tool for empirical typological analysis. The answers provided in this article are informed by a pragmatic call for improved and clearer explanatory leverage of learning-based causal research and are meant to be employed operationally in empirical research. In the remainder of this article, I first discuss definitional issues related to of policy learning (Section 2). Then, I put forth a conceptual argument whereby learning represents one of the ontologies of policy making and modes of policy learning can be seen as epistemological instruments of that ontology (Section 3). After having expanded the property space of the prevalent modes of learning detected in the literature (Section 4), I proceed to discuss how and why they can be considered as distinct causal mechanisms for the sake of process-based qualitative research (Section 5). A long part of the article (Section 6) is then devoted to review the literature in order to pin down empirical scope conditions attached to each dimension of variation of each of the four modes of learning. 2 Draft - Please do not quote or circulate without permission Section presents and discusses the resulting “policy learning measuring instrument”, while Section 8 concludes. Section 2. Reviewing policy learning: controversies in the literature and possible conceptual solutions One of the key puzzles arising out of the specialized literature is that the notion, and hence the definition, of policy learning are conceptually contested (Bennett and Howlett 1992), generating ambiguity regarding its empirical measurability and independent explanatory value (Lodge and James 2003). As a matter of fact, it is quite difficult to find two different contributions belonging to the policy learning literature whereby the latter is defined univocally. This definitional ambiguity is clearly acknowledged in both early review articles on policy learning (Bennett and Howlett 1992) as well as in subsequent attempts at systematization (Freeman 2006; Dunlop and Radaelli 2013 and 2017). Arguably, the main conceptual problem that makes the notion and definition of learning in policy making contentious is represented by the fact that learning is eminently an individual, micro-level process, whereas the kind of phenomena that inspire political science scholars are, generally, collective ones (Zito and Schout 2009; Heikkila and Gerlak 2013). This is termed somewhere as the problem of aggregation (Levy 1994) or the problem of who the learning actors - i.e. the agents and units of analysis of policy learning processes - are (May 1992; Hall 1993; Stern 1997). In other words, can learning as an analytical concept be profitably applied to collective actors and decision making? And, if so, how and where are we to find evidence of the occurrence of learning within groups of individuals or institutions? To cope with these problems, I maintain that if the interest devoted to learning as an explanatory construct of policy making outcomes is to be confined to the individual cognitive processes taking place within the black box of individual decision makers’ minds, learning would be both of little interest for political scientists (i.e. merely subsumed by individual agency) and hardly measurable. Instead, the kind of phenomena that spur the conceptualisation of the distinct category of “policy learning”

Load more