Back in October, I wrote a long post about the seeming trend toward admitting failure (and learning from failure) among nonprofits. In that post, I made the point that admitting failure was the not-for-profit world equivalent of corporate social responsibility in the for-profit world.
The post generated quite a bit of buzz, and Valerie Bauman, a Seattle-based reporter, got in touch with me to discuss the idea of nonprofits admitting failure — and its relationship with corporate social responsibility.
Here is an excerpt from the article Valerie wrote at the time for the Puget Sound Business Journal:
Marc Bellemare, a development economist who teaches public policy and economics at Duke University, views admitting failure as a public relations move to enhance credibility and reputation, similar to touting corporate social responsibility efforts in the for-profit world.
“When I started hearing about admitting failure, it is very nice, but there’s nothing that prevents you from learning from your own failures without having to admit them,” Bellemare said. “For me, it really is a marketing tool more than anything.”
However, he said the move toward disclosure could eventually have a positive effect overall, when it reaches a tipping point and every nonprofit has to be more forthcoming about failure.
“We may soon be moving toward a new equilibrium where everyone has to admit failure, and say ‘where did we go wrong?'” Bellemare said. “Everyone has to look contrite in a way — or else they start looking suspicious.”
Still, disclosing the failure of a project or cost overruns is less scary for nonprofits than disclosing financial mismanagement or fraud, Bellemare said.
“That’s a whole different ball game,” he said. “I think it’s much more likely to scare away donors than failure of projects.”
There’s No Free (Causality) Lunch
First of all, causality requires identification. Vector autoregressions (VARs) do not provide any automatic or free identification. To do policy analysis with a VAR (as opposed to agnostic forecasting) one has to make the same type of untestable identifying assumptions here as one does in the older, explicitly simultaneous equation, Cowles commission approach.
The most common way of identifying a VAR (ordering the variables and performing a Cholesky decomposition) is EXACTLY the same as using exclusion restrictions to identify a system of equations. Other structural VARS do NOT remove the need for identifying assumptions. VARS are not a free lunch.
That is Kevin Grier, in a post over at Kids Prefer Cheese.
I was looking forward to someone finally saying it, as many of the media discussions of the 2011 Nobel prize for economics somehow made it sound as though the work of Sargent and Sims allowed us to estimate causal relationships.
Not so. The estimation of causal relationships is difficult in the social sciences for the simple reason that we almost never observe the counterfactual. This is especially true in macroeconomics, where the opportunities to run an experiment are few and far between. And even in more micro contexts, we’re not completely sure about much.