Should we create an EA index?

Perhaps just randomly: the Trade for Sustainable Development scoring of the International Trade Centre includes a list of companies implementing 14 certifiable voluntary sustainability standards. According to some trade experts, the cost of certification is often the bigger hindrance the smaller the company is. Also, the profits of a sustainable enterprise may go to the middle-income managers as opposed to the low-skilled workers (one research).

Also, Resonance works on impact investing. I do not believe that they focus on reporting/scoring but could be a valuable resource to inquire about the landscape and perhaps criteria.

Do you know of the T100 project of the Toniic impact investing community and the IRIS+ metrics of the GIIN impact investing network?

Should social return on investment (increase in everyone's profits/investment) be considered (One Acre Fund, Babban Gona, ImpactMatters top list)? Should the idea that the value of life may be ~proportional to the GDP/capita of an area be considered?

Informational Lobbying: Theory and Effectiveness

Hello. I studied lobbying in DC during my MA in International Trade and Economic Diplomacy. I also tried to estimate the cost-effectiveness of lobbying per QALY - and came up with $280 per quality life (or about $4.7/QALY) (p. 3) or the first draft on the EA Forum.

I think that EA Brazil and some others are working on starting an EA lobbying group. I dm'd you regarding this.

Investing to Save Lives

(reference to a Nigerian social impact bond here) "Investors can opt to purchase Babban Gona’s Raise Out of Poverty bond (ROPO). ROPO is Nigeria’s first social impact bond that enables investors attain a reasonable return on their investment" I wonder if social impact bonds have been considered by EA - or, if these are not where EA has comparative advantage, at least static.

Perhaps social impact bonds create additional impact for every bond purchased - room for investment is limited only by the capacity of persons to increase their income. This contrasts with impact investment to specific companies (e. g. those that are competitive at gaining capital) where additional investment may displace someone else's investment - research by Founders Pledge.

Also, check out this comment.

Sample size and clustering advice needed

Hello Sindy,

Thank you so much. This answers my question. Yes, there will be a before and after qualitative survey asking about own and others' behavior - which may need to be truncated to speak with more different groups. Then, the face covering data can be used to complement the survey information.

Sample size and clustering advice needed

Thank you. I was not able to get (a pdf of) Field Experiments, but downloaded the "Field Experimental Designs for the Study of Media Effects," also co-authored by Green. They point out "robust cluster standard errors" to estimate "individual-level average treatment effect" (172).

To answer your points:

  • The smallest effect size you would hope to observe
    • 20%. From 5/10 to 6/10 or equivalent % increase
  • Your available resources
    • Researchers in all of the campaign clusters and some of the non-campaign ones. They can count whether e. g. few hundreds of individuals wear face covering
  • The population within each cluster
    • Different, average of 180,000/6 = 30,000.
  • The total population
    • Since we are just looking to estimate the impact of the 180,000-person campaign and not to generalize it, this should be 180,000x2 (180,000 participating and an equal number of non-participants who are the nearest geographically and in characteristics).
  • Your analysis methodology
    • Probit, logit or simple linear regression, but open to suggestions

I meant 6 groups in the intervention area, and some number of groups (e. g. 3 or 6) in the non-intervention area.

OK. So 3 intervention clusters and 3 non-intervention clusters are better than 6 intervention clusters and 3 non-intervention clusters but 6+6 may be necessary? Would the answer depend on the intra-cluster correlation coefficient (ρ)? Perhaps, the texts that generally talk about clustering assume relatively significant between cluster variability and low within cluster variability (so high ρ). However, in this study, how people respond to the messaging may not depend much on their 'cluster assignment,' but much more on their individual characteristics that, on average, may be comparable across the clusters and the studied population.

I should ask EA Cameroon about the possibility of different average responses in different villages.

Do you know of any online sample size calculator that includes clusters?

Is region-level cause prioritization research valuable to spot promising long-term priority causes worldwide?

What about the Cameroonian Civil War that (or at least of which effects) can be mitigated by a combination of EA and local knowledge? This can be a potentially high-impact problem/intervention that has not been covered by other EA research, perhaps due to its localized nature.

Is region-level cause prioritization research valuable to spot promising long-term priority causes worldwide?

Research of the most cost-effective causes, interpreted as means to create additional impact can inform long-term priorities – in regions of any levels of development. E. g. in Lokoja in Northern Nigeria, that means may be very different from that in Bangkok or Washington, D. C. Maybe in Lokoja that is informing mothers on the available prenatal and early childhood healthcare incentives (that in the long term gives rise to institutions perpetuating increased wellbeing), in Bangkok supporting regional norms on migrant work, and in Washington, D. C. lobbying for trade policy favorable to LMICs.

Different locally-identified measures can be globally compared in their cost-effectiveness, complementarities potentially concluded, and individual EAs may decide, based on their expertise and the extent of the fulfillment of care of more inner moral circles, whether they wish to focus on a local or more distant measure, or even relocate.

For this global cost-effectiveness comparison and insights into complementarities, knowledge of the entire field of possible impact, as well as the global structure within which the intervention extends and cascades impact, is needed.

Research of the most cost-effective local causes, interpreted as means to help locals, may also inform long-term priorities – also in regions of any levels of development. First, comparison can show where a local should allocate their focus to help most effectively (e. g. a person in Washington, D. C. can conclude that supporting migrant laborers in Southeast Asia is more cost-effective than supporting local homeless persons). Second, complementarities can be also drawn - e. g. a DC-based person may be able to benefit from focusing on a positive measure (e. g. migrant labor laws in Southeast Asia) as opposed to negative-emotions based advertisement - and person in Bangkok can benefit from increased ability to institutionalize positive change. Third, the identification of cost-effective means to help locals enables persons to fulfill their need to care for more inner moral circles more cost-effectively, so that further funds are left for more outer moral circles.

EA Cameroon - COVID-19 Awareness and Prevention in the Santa Division of Cameroon Project Proposal

OK, thank you. Added that better fitting masks made of denser material work better.

EA Cameroon - COVID-19 Awareness and Prevention in the Santa Division of Cameroon Project Proposal

Yes. In terms of percentage, how less effective are 4 layers of woven fabric in preventing the spread of coronavirus than 4 layers of knitted fabric? Than 2 layers of woven fabric? The idea is to have at least 4 layers of the sock (after folding) or at least 2 layers of other fabric. In preventing breathing in the virus?

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