Since I reviewed, back in April, the paper by Ashraf, Field, and Lee on the effect of providing vouchers for injectable contraceptives to women in reducing unwanted pregnancies in Lusaka, Zambia, I had been worrying about the use of these modern, convenient, and reliable technologies in those parts of the world in which HIV is highly prevalent.
On my return from a long work trip in Thailand and the Philippines, I stopped at the University of Southern California to attend the 4th global health supply chain summit. I typically enjoy attending meetings outside my immediate discipline since I get to hear about new ideas in fields far from my own. This conference was no exception.
At least not in Benin. This week, I take a look at interesting paper by Leonard Wantchekon documenting an experiment he did in Benin with this year’s presidential election. In this paper, Leonard compares the results from a deliberative sharing of a candidate’s platform in a local town hall against a one-way communication of the candidate (or his broker) with a big rally.
This is a joint post with Miriam Bruhn.
I thought I’d kick-start what I hope will be a somewhat regular feature on this blog, which is some insights, observations, and general glimpses of the real world encountered as we work on implementing new impact evaluations. I know some of our readers take umbrage with the term “the field” but I’m sure it is preferred to “Mission musings” , although maybe “Random rambling” might be appropriate.
If the data and related metadata collected for impact evaluations was more readily discoverable, searchable, and made available, the world would be a better place. Well, at least the research would be better. It would be easier to replicate studies and, in the process, to expand them by for example: trying other outcome indicators; checking robustness; and looking for heterogeneity effects (e.g.
Yesterday, in Part I of this post, we argued the extant empirical evidence suggests that the conditions cause a substantial amount of the desired behavior change intended by CCT programs. In other words: the “substitution effect” due to the condition may well be larger than the “income effect” of the transfers. For example, in the case of the Malawi experiment, the income effect was responsible for less than half of the total impact on school enrollment.
One of the questions discussed at the recent World Bank workshop on the "Second Generation of CCT Evaluations" (website, complete with at least some of the presentations, here) was the role of the first C in the performance of the CCT: how important is the condition in accounting for the outcomes of conditional cash transfer programs?