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Marketing matters: Increasing microinsurance coverage beyond lowering prices: Guest Post by Jonathan Bauchet

Poor households in developing countries face large and varied risks. Many agriculture-dependent households, for example, are at risk of drought- or flood-induced crop failures or livestock deaths. The death of a family member often implies having to fund expensive burial ceremonies, and if the deceased was the household’s primary earner, replacing her/his stream of income is an even bigger problem.

Tools of the Trade: Intra-cluster correlations

David McKenzie's picture

In clustered randomized experiments, random assignment occurs at the group level, with multiple units observed within each group. For example, education interventions might be assigned at the school level, with outcomes measured at the student level, or microfinance interventions might be assigned at the savings group level, with outcomes measured for individual clients.

You think randomized controlled trials are great? Actually, they are even better than that-Guest post by Clement de Chaisemartin

My job market paper brings some good news to the impact evaluation community. First, it shows that causal inference in randomized controlled trials (RCTs) relies on weaker assumptions than was previously thought. Second, it shows that RCTs capture local treatment effects that are less local than we previously believed.

Sorting through heterogeneity of impact to enhance policy learning

Jed Friedman's picture

The demand and expectation for concrete policy learning from impact evaluation are high. Quite often we don’t want to know only the basic question that IE addresses: “what is the impact of intervention X on outcome Y in setting Z”. We also want to know the why and the how behind these observed impacts. But these why and how questions, for various reasons often not explicitly incorporated in the IE design, can be particularly challenging.