↳ Great+depression

May 26th, 2020

↳ Great+depression

Radio

STATE CAPACITY IN THE US

Analyses of variation in state-level responses to the coronavirus tend to focus on party determination: On the whole, states led by Democrats have been found to undertake more rapid and extensive responses to the crisis. The focus on immediate political factors, however, masks the broader history of America's uneven and disaggregated bureaucratic capacity.

A 1982 book by STEPHEN SKOWRONEK presents one of the most comprehensive accounts of the origins of the US administrative state. Focusing on reforms in civil administration, the army, and national railroad regulation from 1870-1920, the book demonstrates how regional differences contributed to the particular character of American state development.

"Unravelling the state-building problem in modern American political development places the apparent statelessness of early America in a new light. The governmental forms and procedures necessary for securing order in industrial America emerged through a labored exercise in creative destruction. Modernization of national administrative controls did not entail making the established state more efficient; it entailed building a qualitatively different kind of state.

The Civil War brought national military conscription, a national welfare agency for former slaves, a national income tax, national monetary controls, and citizenship. Yet, this was a state grounded in only half the nation. As the South returned, national electoral politics changed, and these institutional achievements began to be undone. Here, then, was a state only in the sense of the word imputed to it by the interests and strategies of the mass electoral organizations controlling its offices. No institution stood beyond the reach of party concerns. The fate of the wartime governmental apparatus suggests that if new institutional forms are to constitute a new state, they must alter the procedural bonds that tie governmental institutions together and define their relationship to society."

Link to the publisher's page.

  • Theda Skocpol and Kenneth Finegold expand Skowronek's research into the New Deal era. Link.
  • "In societies where social status is a cleavage, elites can use the threat of desegregation to unite wealthy and poor members of high-status groups against taxation and the bureaucratic capacity required to collect taxes." Pavithra Suryanarayan and Steven White on "Slavery, Reconstruction, and Bureaucratic Capacity in the American South." Link. In another article, Roberto Stefan Foa and Anna Nemirovskaya analyze the development of state capacity on the frontier. Link.
  • Daniel Berliner, Anne Greenleaf, Milli Lake, and Jennifer Noveck present "systematic study of relationship between state capacity and labor rights." Link.
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March 2nd, 2020

Honeysuckle

CLEAR MEANS

Evaluating evidence-based policy

Over the past two decades, "evidence-based policy" has come to define the common sense of research and policymakers around the world. But while attempts have been made to create formalization schemes for the ranking of evidence for policy, a gulf remains between rhetoric about evidence-based policy and applied theories for its development.

In a 2011 paper, philosophers of science NANCY CARTWRIGHT and JACOB STEGENGA lay out a "theory of evidence for use," discussing the role of causal counterfactuals, INUS conditions, and mechanisms in producing evidence—and how all this matters for its evaluators.

From the paper:

"Truth is a good thing. But it doesn’t take one very far. Suppose we have at our disposal the entire encyclopaedia of unified science containing all the true claims there are. Which facts from the encyclopaedia do we bring to the table for policy deliberation? Among all the true facts, we want on the table as evidence only those that are relevant to the policy. And given a collection of relevant true facts we want to know how to assess whether the policy will be effective in light of them. How are we supposed to make these decisions? That is the problem from the user’s point of view and that is the problem of focus here.

We propose three principles. First, policy effectiveness claims are really causal counterfactuals and the proper evaluation of a causal counterfactual requires a causal model that (i) lays out the causes that will operate and (ii) tells what they produce in combination. Second, causes are INUS conditions, so it is important to review both the different causal complexes that will affect the result (the different pies) and the different components (slices) that are necessary to act together within each complex (or pie) if the targeted result is to be achieved. Third, a good answer to the question ‘How will the policy variable produce the effect’ can help elicit the set of auxiliary factors that must be in place along with the policy variable if the policy variable is to operate successfully."

Link to the paper.

  • Cartwright has written extensively on evidence and its uses. See: her 2012 book Evidence Based Policy: A Practical Guide to Doing it Better; her 2011 paper in The Lancet on RCTs and effectiveness; and her 2016 co-authored monograph on child safety, featuring applications of the above reasoning.
  • For further introduction to the philosophical underpinnings of Cartwright's applied work, and the relationship between theories of causality and evidence, see her 2015 paper "Single Case Causes: What is Evidence and Why." Link. And also: "Causal claims: warranting them and using them." Link.
  • Obliquely related, see this illuminating discussion of causality in the context of reasoning about discrimination in machine learning and the law, by JFI fellow and Harvard PhD Candidate Lily Hu and Yale Law School Professor Issa Kohler-Hausmann: "What's Sex Got To Do With Machine Learning?" Link.
  • A 2017 paper by Abhijit Banerjee et al: "A Theory of Experimenters," which models "experimenters as ambiguity-averse decision-makers, who make trade-offs between subjective expected performance and robustness. This framework accounts for experimenters' preference for randomization, and clarifies the circumstances in which randomization is optimal: when the available sample size is large enough or robustness is an important concern." Link.
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