Defining Success In Industrial Policy
==Success in industrial policy is defined by whether the policy actually delivers the intended structural transformation in pursuit of a public goal==.[:cite[1]{ln=1}], [:cite[1]{ln=2}] More concretely, the paper ...
==Success in industrial policy is defined by whether the policy actually delivers the intended structural transformation in pursuit of a public goal==.[:cite[1]{ln=1}], [:cite[1]{ln=2}] More concretely, the paper highlights three layers you need to see to call an industrial policy “successful”: 1) It accomplishes the targeted structural change (effectiveness) Most empirical evaluations start by checking whether the policy “moved the needle” on measurable outcomes like investment, exports, or total factor productivity (TFP) .[:cite[2]{ln=2}] But the authors stress that this is mainly a test of effectiveness and only a partial view of success.[:cite[2]{ln=3}] 2) It solves the right problem and doesn’t cause too much collateral damage (efficiency + low distortions) The paper says successful industrial policy must not only achieve the targeted structural change, but do so (a) by truly alleviating the underlying market failures and (b) without causing too many distortions elsewhere in the economy .[:cite[2]{ln=4}] They illustrate the difference between effectiveness and efficiency: if a policy boosts investment in one sector while the “real” market failures are elsewhere, the policy can look effective but still be inefficient .[:cite[2]{ln=5}] They also note that this full assessment is hard because market failures are rarely directly observable , even after the fact.[:cite[2]{ln=6}] 3) It has governance that can learn and correct (benchmarks, monitoring, and stopping failures) Because governments face uncertainty and knowledge limits, the paper argues the “ultimate test” isn’t reliably picking winners; it’s whether the state can let losers go .[:cite[3]{ln=1}], [:cite[3]{ln=2}] They connect “letting losers go” to practical institutional features: clear benchmarks, close monitoring, and explicit mechanisms for reversing course .[:cite[4]{ln=4}] Relatedly, industrial policy often involves conditionality , including explicit, quantitative performance criteria or “softer, iterative” monitoring to check compliance.[:cite[5]{ln=1}], [:cite[5]{ln=4}], [:cite[5]{ln=5}] And in their framing of “new” industrial policy, they emphasize that knowledge about market failures is widely dispersed and government operates under substantial uncertainty , which further pushes success toward an iterative, learning oriented approach.[:cite[6]{ln=1}], [:cite[7]{ln=1}], [:cite[7]{ln=2}]