“Access over Ownership – Considering Alternative Means for Improving Sanitation Outcomes” While browsing for WASH financial interventions in India the other day, I happened to stumble across Chen Guangzhe’s recent blog post on the benefits of constructing clean, well-maintained, shared toilets in communities with low levels of latrine ownership. This post struck a chord, […]
Managing multiple editors in STATA: For this week’s blog post, it seems useful to cover some code I’ve learned for Stata. This first useful trick allows multiple users who are going to use the same dofile to run that file without having to first customize the filepath code. By copying the individual usernames and filepaths […]
We work with clients who are running poverty interventions in the field. These are not experiments – where the program is designed around an experiment – these are impact evaluations that have to be designed around a development program that has already been designed or is in progress. As such, often times the sample size […]
Introduction It is often the case in Impact Evaluation that we have a need to analyze binary, qualitative variables such as savings behavior (saves vs. does not save), voting behavior (votes vs. does not vote), or gender (male vs. female). In general, we are interested in whether a condition exists or does not exist, rather […]
Causal Design & Mercy Corps Partner to Investigate Cash Transfers in Humanitarian Response Last year Causal Design partnered with Mercy Corps to investigate the impact of various operational models for dispersing cash grants after a natural disaster. Specifically, we wanted to see if the frequency of cash disbursements, offering of financial literacy training, or sending reinforcing voice messages had […]
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
According to a 2015 report by the UN’s International Telecommunication Union, 2/3rd of global internet users come from the developing world. In some countries, like India, mobile phone use is as high as 75%. These trends are generating large amounts of data, which present a new opportunity for tech-savvy development practitioners. Governments and NGOs can utilize the data to make well-informed decisions and provide more effective services at a cheaper price. The question however, is how to do this.
With support from the William and Flora Hewlett Foundation, IDinsight published a 3ie Working Paper on the “Future of Impact Evaluation.” We really like the team over at IDinsight and think they are spot-on with this work. Causal Design’s primary focus is on providing decision-focused evaluations like they describe in the paper. Their main arguments are listed […]
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 […]
It looks like the folks over at CDG have been digging into the effectiveness of cell-phone based surveys in poor countries. We have used a couple of these platforms including the one used in their study (Voto). Bottom Line Up Front: It looks like this works better than most of us thought. Read their full […]
