Shiva Chakravarti Sharma, MPP, Staff Writer, Brief Policy Perspectives

Disagreement is a vital locus of any policy environment. Hours are spent in academic town halls, on prime time shows, and in parliaments around the world identifying policy resolutions.

Disagreement in policy analysis can be a function of several factors: existing evidence, interpretation of that evidence, value judgments, or results from alternative methodologies. Often, these disagreements stem from the generalizability of the results. Policy students navigate these questions structurally through methods and analysis courses. Yet, formal discourse over what is the best way to address disagreement is sparse in the policy sphere. For instance, we often hear that two policy researchers disagree on policies like corporate taxes or tariffs, but the discourse rarely systematically identifies the sources of these disagreements.

Policy resolution and subsequent policy prescription can significantly benefit from exploring disagreements in depth. Professors Christopher Robert and Richard Zeckhauser at the Harvard Kennedy School of Government offer a model to understand potential sources of disagreement in their paper, The Methodology of Normative Policy Analysis. They classify disagreements into positive and value disagreements, which follows the taxonomy of positive and normative economics. Positive analysis pertains to fact-based research (what is?); whereas, normative analysis relates to in value-based research (how should?).

In positive analysis, Professors Robert and Zeckhauser argue that disagreements can be classified on the basis of scope, model, and estimate. “Scope” is defined as elements of the policy that the researchers try to understand, which includes unit of analysis, actors, and costs and benefits. “Model” refers to the mechanism that the researcher is explaining. For example, researchers accounting for behavioral changes argue that “nudges” can be an affective policy to increase the amount that people save for retirement. On the other hand, researchers relying on neo-classical economic insights argue that “nudges” are not an effective model because researchers that support nudges make assumptions about rationality. “Estimate” indicates a model’s parameters in particular contexts. Disagreements based on estimates stem from methodology. For instance, researchers might disagree on the basis of sampling methodology, or how a sample population being studied is selected.

In value analysis, according to the aforementioned model, disagreements can be categorized into standing, criteria, and weights. “Standing” is used to determine “who counts?” For instance, in a cost-benefit analysis, lists of stakeholders, or persons who have power over or interest in a program, can differ between researchers. “Criteria” refers to “what counts?” For example, justice could be an important criterion in one research project, but omitted altogether in another. Finally, “weights” refer to the significance of different criterion. For instance, several world organizations use different poverty measures based on their choice of weights. Organizations interested in intensity of poverty use cubed measures as opposed to linear cutoffs, which are used by most governments.

This model with both types of analysis functions as a structured analytical tool to identify disagreements. Identifying disagreements paves the way for resolution. Moreover, this identification will make policy debates more constructive. If two policy researchers spell out their assumptions, the seemingly disagreeable conversation begins to show roots of discrepancy. The discourse can then focus on these discrepancies, ranging from ideas of fairness to appropriate methodology, morality, and ethics, in the hopes of resolving them for a policy solution.

While screaming matches in the policy realm will continue because of embedded political and strategic incentives, focusing on disagreements in a structured way can reduce the noise between analysis and prescription within policy circles.