CategoriesBlog PostAs we all grapple with the immediate impacts of the COVID-19 virus on our lives and the lives of those in our families and communities, it can be difficult to zoom out and think about the possible impacts beyond our town, city, and/or nation. It is nearly impossible to wrap our heads around the uncertainties the pandemic has presented for our own future, much less the future of 7 billion others.
Causal Design has been awarded a blanket purchase agreement (BPA) by the Millennium Challenge Corporation (MCC), distinguishing the firm as a trusted and valued provider of independent monitoring and evaluation services in the international development sector.
Between Greta Thunberg’s Time ‘Person of the Year’ awards, and almost concurrent end to a do-nothing UN climate talks in Madrid (to borrow from our US Congress lexicon), two things were reinforced, again, these past weeks: the urgency of climate change, and our inability to address it, at least from on-high.
Remarkably, however, for an industry that is rooted in evidence, which examines disparities in health and well being across time and space (and especially among groups and sub-groups), and which holds dear an ethical mantra of ‘do no harm,’ Evaluation has done little to either mainstream the role of climate in our own practice, nor to ameliorate the industry’s impact (because, while disparate and far flung, we are an industry).
The OECD-DAC recently added to its list of evaluation criteria—the de facto norm through which organizations like Causal Design frequently organize evaluations and reporting. Specifically, after a multi-year process of considering how to best adapt its existing criteria, OECD added Coherence: How well does the intervention fit? to the existing and remaining five criteria.
Reactions around our proverbial dinner table were appropriately mixed: How does this further a wider learning agenda? How does this differ from the existing Relevance? (which at times already overlaps with Sustainability) What does “fit” actually mean, and how do we use it, and meaningfully?
https://eship.georgetown.edu/2019/10/30/ventureforward-season-two-ep-2-causal-design/
Our partners at Mercy Corps referenced some research that ODI recently did on real-time resilience measurement and analysis (see paper here) in Myanmar. It’s not everyday that you can track resilience measurement every two months for a panel of 1,200 households.
Good resilience measures can sometimes be a moving target, so any research that can be used to reinforce good thinking and expand beyond static binary is certainly welcome.
I’m really excited to be a part of the development community that uses WeWork for office space in the US. Next week, WeWork K Street (DC) and Cooper/Smith are hosting a panel discussion on data collection, analysis, and use in international development. The (large) panel includes staff from 8 different wework-based firms all working in data for development.
Come check out the panel, ask some hard questions, and enjoy the free drinks.
This week’s blog will feature a set of Stata tricks we used to addresses a particular issue that we encountered in our dataset. Many of the variables were in string form and were not useable for Stata analysis. Furthermore, the values of the variables were not in the correct order for our purposes. A couple of commands came in handy here. Loops are useful for many different repetitive commands. They allowed us to quickly recode the values of a set of variables that have similar categorical values and also enabled us to destring sets of variables, setting them to numeric values. These numeric values were in turn reordered to fit a desired pattern. Finally, the labels for the numeric values were recoded to appear as the original text instead of just “1, 2, 3, etc”.
Building scalable and sustainable food systems presents many challenges along farming value chains – not least of which is the point where our small holder famers interact with agricultural inputs. For an isolated farmer with limited education, making wise choices about farming can be challenging, meaning that many farmers fall short of the mark when it comes to running successful commercial enterprises.
The coefplot command allows you to plot results from estimation commands. It is a user package, so you will have to find and install it by inputting into the Stata command line:
findit coefplot
