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A Rant on Estimation with Binary Dependent Variables (Technical)

Suppose you are trying to explain some outcome [math]y[/math], where [math]y[/math] is equal to 0 or 1 (e.g., whether someone is a nonsmoker or a smoker). You also have data on a vector of explanatory variables [math]x[/math] (e.g., someone’s age, their gender, their level of education, etc.) and on a treatment variable [math]D[/math], which we will also assume is binary, so that [math]D[/math] is equal to 0 or 1 (e.g., whether someone has attended an information session on the negative effects of smoking).

If you were interested in knowing what the effect of attending the information session on the likelihood that someone is a smoker, i.e., the impact of [math]D[/math] on [math]y[/math] The equation of interest in this case is

The State of Development Economics

The Institute for International Economic Studies (IIES) at Stockholm University is celebrating its 50th anniversary this month.

To celebrate, the IIES held a symposium last week that featured presentations by (all links open .pdf documents):

  1. Daron Acemoglu on institutions,
  2. Esther Duflo on policy evaluation,
  3. Michael Kremer on health,
  4. Mark Rosenzweig on education,
  5. Nancy Stokey on health,
  6. Robert Townsend on credit and insurance, and
  7. Chris Udry on financial market imperfections,

among others.