From MercyCorps.org: We are excited to release Mercy Corps’ newest research, in partnership with Causal Design, bringing more evidence on how households and communities can be resilient in the face of crisis. What Next for Nepal? Evidence of What Matters for Building Resilience After the Gorkha Earthquake When a 7.8 magnitude earthquake struck Nepal in April […]
While using hashtags is simpler than generating the interaction term as a new variable, there is a necessary rule to remember: use the variable prefixes. In Stata, -i.[variable]- indicates that the variable is categorical, and -c.[variable]- indicates a continuous variable. Because the hashtag code assumes the variables in the interaction term are categorical, it is necessary to define numerical variables as numerical with the -c.- prefix.
Wage = β0 + β1Education + β2Minority + β3Education*Minority + ε
With the steady rise of the number of impact evaluations (IEs) per year, it should come as no surprise that not every single IE will show a positive impact. The authors of “no impact” evaluations will understandably be worried that their work will not be academically published nor be used for public policy. There is, however, still value in such information. Evidence that a particular program does not work paves the way for alternative interventions to happen. Licona (2017) provides several examples where null results in Mexican education programs encouraged the tweaking of aspects such as selection criteria, consolidation of redundant programs, and budget optimization.
