Aggregator

Allocation of discretionary funds from Q2 2018

6 years 10 months ago

In April to June 2018, we received $1.2 million in funding for making grants at our discretion. In addition, GiveWell’s Board of Directors voted to allocate $2.9 million in unrestricted funds to making grants to recommended charities. In this post we discuss:

  • The decision to allocate the $4.1 million to the Against Malaria Foundation (AMF) (70 percent) and the Schistosomiasis Control Initiative (SCI) (30 percent).
  • Our recommendation that donors give to GiveWell for granting to top charities at our discretion so that we can direct the funding to the top charity or charities with the most pressing funding need. For donors who prefer to give directly to our top charities, we continue to recommend giving 70 percent of your donation to AMF and 30 percent to SCI to maximize your impact.
  • Why we have allocated unrestricted funds to making grants to recommended charities.

Allocation of discretionary funds

The allocation of 70 percent of the funds to AMF and 30 percent to SCI follows the recommendation we have made, and continue to make, to donors. For more discussion on this allocation, see our blog post about allocating discretionary funds from the fourth quarter of 2017.

We ask each top charity to provide details of how they will use additional funding each year, as part of our process to update our “room for more funding” summary for each top charity. This year, we have asked for this information by the end of July. We also ask each of our top charities to let us know if they encounter unexpected funding gaps at other times of year. We have not learned of new funding gaps in the last quarter.

What is our recommendation to donors?

We continue to recommend that donors give to GiveWell for granting to top charities at our discretion so that we can direct the funding to the top charity or charities with the most pressing funding need. For donors who prefer to give directly to our top charities, we are continuing to recommend giving 70 percent of your donation to AMF and 30 percent to SCI to maximize your impact. The reasons for this recommendation are the same as in our Q4 2017 post on allocating discretionary funding.

We will complete a full analysis of our top charities’ funding gaps and cost-effectiveness by November and expect to update our recommendation to donors at that time.

Why we have allocated unrestricted funds to making grants to recommended charities

In June, GiveWell’s Board of Directors voted to allocate $2.9 million in unrestricted funds to making grants to recommended charities. We generally use unrestricted funds to support GiveWell’s operating costs. The decision was made to grant out some of the unrestricted funds we hold in accordance with two policies:

  • Our “excess assets” policy specifies that once we surpass a certain level of unrestricted assets, we grant out the excess rather than continue to hold it ourselves. We reviewed our unrestricted asset holdings and projected revenue and expenses for 2018-2020 and concluded that we held $1.8 million more than was required to give us a stable, predictable financial situation (details of how this rule is applied are at the previous link). The Board voted to irrevocably restrict this amount to making grants to recommended charities. Note that we continue to need ongoing donor support for our operations. This decision incorporates our projections for future donations.
  • In order to limit the risks of relying too heavily on any single source of revenue, we cap the amount of funding that we will use from one source to support our operating costs at 20% of our projected annual expenses. In early 2018, we received a donation of $2.1 million in unrestricted funds. Our operating expense budget for 2018 is $4.9 million. Therefore, the Board voted to retain $1.0 million to support operating costs in 2018 and irrevocably restrict $1.1 million to making grants to recommended charities.

The post Allocation of discretionary funds from Q2 2018 appeared first on The GiveWell Blog.

Natalie Crispin

Allocation of discretionary funds from Q2 2018

6 years 10 months ago

In April to June 2018, we received $1.2 million in funding for making grants at our discretion. In addition, GiveWell’s Board of Directors voted to allocate $2.9 million in unrestricted funds to making grants to recommended charities. In this post we discuss:

  • The decision to allocate the $4.1 million to the Against Malaria Foundation (AMF) (70 percent) and the Schistosomiasis Control Initiative (SCI) (30 percent).
  • Our recommendation that donors give to GiveWell for granting to top charities at our discretion so that we can direct the funding to the top charity or charities with the most pressing funding need. For donors who prefer to give directly to our top charities, we continue to recommend giving 70 percent of your donation to AMF and 30 percent to SCI to maximize your impact.
  • Why we have allocated unrestricted funds to making grants to recommended charities.

Allocation of discretionary funds

The allocation of 70 percent of the funds to AMF and 30 percent to SCI follows the recommendation we have made, and continue to make, to donors. For more discussion on this allocation, see our blog post about allocating discretionary funds from the fourth quarter of 2017.

We ask each top charity to provide details of how they will use additional funding each year, as part of our process to update our “room for more funding” summary for each top charity. This year, we have asked for this information by the end of July. We also ask each of our top charities to let us know if they encounter unexpected funding gaps at other times of year. We have not learned of new funding gaps in the last quarter.

What is our recommendation to donors?

We continue to recommend that donors give to GiveWell for granting to top charities at our discretion so that we can direct the funding to the top charity or charities with the most pressing funding need. For donors who prefer to give directly to our top charities, we are continuing to recommend giving 70 percent of your donation to AMF and 30 percent to SCI to maximize your impact. The reasons for this recommendation are the same as in our Q4 2017 post on allocating discretionary funding.

We will complete a full analysis of our top charities’ funding gaps and cost-effectiveness by November and expect to update our recommendation to donors at that time.

Why we have allocated unrestricted funds to making grants to recommended charities

In June, GiveWell’s Board of Directors voted to allocate $2.9 million in unrestricted funds to making grants to recommended charities. We generally use unrestricted funds to support GiveWell’s operating costs. The decision was made to grant out some of the unrestricted funds we hold in accordance with two policies:

  • Our “excess assets” policy specifies that once we surpass a certain level of unrestricted assets, we grant out the excess rather than continue to hold it ourselves. We reviewed our unrestricted asset holdings and projected revenue and expenses for 2018-2020 and concluded that we held $1.8 million more than was required to give us a stable, predictable financial situation (details of how this rule is applied are at the previous link). The Board voted to irrevocably restrict this amount to making grants to recommended charities. Note that we continue to need ongoing donor support for our operations. This decision incorporates our projections for future donations.
  • In order to limit the risks of relying too heavily on any single source of revenue, we cap the amount of funding that we will use from one source to support our operating costs at 20% of our projected annual expenses. In early 2018, we received a donation of $2.1 million in unrestricted funds. Our operating expense budget for 2018 is $4.9 million. Therefore, the Board voted to retain $1.0 million to support operating costs in 2018 and irrevocably restrict $1.1 million to making grants to recommended charities.

The post Allocation of discretionary funds from Q2 2018 appeared first on The GiveWell Blog.

Natalie Crispin

Why we don’t use subnational malaria mortality estimates in our cost-effectiveness models

6 years 10 months ago
Summary

We recently completed a small project to determine whether using subnational baseline malaria mortality estimates would make a difference to our estimates of the cost-effectiveness of two of our top charities, the Against Malaria Foundation and Malaria Consortium. We ultimately decided not to include these adjustments because they added complexity to our models and would require frequent updating, while only making a small difference (a 3-4% improvement) to our bottom line.

Though this post is on a fairly narrow topic, we believe this example illustrates the principles we use to make decisions about what to include in our cost-effectiveness model.

Background

Two of our top charities—the Against Malaria Foundation (AMF) and Malaria Consortium’s seasonal malaria chemoprevention program—implement programs to prevent malaria, a leading killer of people in low- and middle-income countries.

One of the core reasons we recommend AMF and Malaria Consortium is their cost-effectiveness: how much impact they have (e.g., cases of malaria prevented, malaria deaths averted) with the funds they receive. Our estimates of charities’ cost-effectiveness isn’t just helpful to us in determining which charities should be GiveWell top charities; we also rely on these estimates to guide our decisions about how to allocate funding between our top charities.

Our cost-effectiveness estimates for AMF and Malaria Consortium use country-wide data on malaria mortality and malaria incidence in the places that both organizations work.1In both cases, we rely on reports by Cochrane, an organization that produces systematic reviews and other synthesized research to inform decision-makers. For AMF, we use a decline in all-cause mortality, because the Cochrane review of anti-malarial bed net distributions reports the effect in terms of a reduction in all-cause mortality. For Malaria Consortium, we use a decline in malaria mortality (proxied by a decline in malaria incidence), as the Cochrane review of seasonal malaria chemoprevention reports the effect in terms of a reduction in malaria incidence, but not all-cause mortality. See our cost-effectiveness analysis for more details. jQuery("#footnote_plugin_tooltip_1").tooltip({ tip: "#footnote_plugin_tooltip_text_1", tipClass: "footnote_tooltip", effect: "fade", fadeOutSpeed: 100, predelay: 400, position: "top right", relative: true, offset: [10, 10] }); However, neither organization serves a whole country—rather, they operate in sub-national regions—so the use of country-level estimates could cause us to either underestimate or overestimate their cost-effectiveness. If, for example, these programs are focused in the areas of the country with the highest malaria burden, using the average burden for the country would lead us to underestimate their cost-effectiveness. So, we completed a project to determine how much of an impact using subnational estimates would have, to consider whether we ought to incorporate this information into our cost-effectiveness analysis.

How we estimated the impact of subnational malaria incidence

AMF distributes insecticide-treated nets to prevent malaria; Malaria Consortium’s seasonal malaria chemoprevention (SMC) program provides preventive anti-malarial drugs. We used estimates of subnational malaria incidence from the Malaria Atlas Project (MAP) to see if regions covered by nets or eligible for SMC had higher or lower incidence than the average in the country in which they are located.2We assume that the regional distribution of malaria incidence is a reasonable proxy for the regional distribution of malaria mortality. jQuery("#footnote_plugin_tooltip_2").tooltip({ tip: "#footnote_plugin_tooltip_text_2", tipClass: "footnote_tooltip", effect: "fade", fadeOutSpeed: 100, predelay: 400, position: "top right", relative: true, offset: [10, 10] });

We focused on all areas covered by nets or eligible for SMC (rather than those covered by our top charities, specifically) for two reasons:

  1. Our understanding is that when our top charities contribute resources to a country’s net distribution or SMC programs, the marginal region covered by these additional resources is not necessarily the same as the region to which these resources are assigned (because these resources are fungible with other resources within the national programs).3A limitation of this analysis is it does not account for the possibility that AMF and Malaria Consortium are causing locations that are higher priority or lower priority than the average location already covered by nets or eligible for SMC to be covered on the margin. We do not explicitly include estimates of the marginal region funded in our cost-effectiveness analysis because we often have limited information about which regions would be covered with marginal additional funds. jQuery("#footnote_plugin_tooltip_3").tooltip({ tip: "#footnote_plugin_tooltip_text_3", tipClass: "footnote_tooltip", effect: "fade", fadeOutSpeed: 100, predelay: 400, position: "top right", relative: true, offset: [10, 10] });
  2. Our aim is to estimate the cost-effectiveness of funds donated to these organizations in the future. The subnational region where AMF has worked in the past has not historically been a good indicator of the region where it will work in future.
Results for net distributions in countries where AMF works

We looked at geographical variation in malaria incidence in countries where AMF works, weighting each region by the number of nets it currently receives.4We assume that where nets have been delivered in the past is a good proxy for where new nets will be delivered in the future. The data and calculations are in this spreadsheet. jQuery("#footnote_plugin_tooltip_4").tooltip({ tip: "#footnote_plugin_tooltip_text_4", tipClass: "footnote_tooltip", effect: "fade", fadeOutSpeed: 100, predelay: 400, position: "top right", relative: true, offset: [10, 10] });

The average net delivered in the countries in which AMF works is hung in an area with 0-9% higher malaria incidence than the average in that country, and the weighted average adjustment to AMF’s cost-effectiveness would be 3% (in other words, AMF becomes 3% more cost-effective if we incorporate subnational estimates).5See Cell J114. We did not include Papua New Guinea (where AMF funds some nets) in this analysis, as MAP only covers countries in Africa. jQuery("#footnote_plugin_tooltip_5").tooltip({ tip: "#footnote_plugin_tooltip_text_5", tipClass: "footnote_tooltip", effect: "fade", fadeOutSpeed: 100, predelay: 400, position: "top right", relative: true, offset: [10, 10] });

Country Adjustment Zambia +9% Uganda +4% Ghana +4% Democratic Republic of the Congo +1% Togo +1% Malawi +0% Results for SMC in countries where Malaria Consortium works

We looked at six countries comprising >95% of Malaria Consortium’s SMC spending and compared malaria incidence in districts eligible for SMC with the country-wide average.6“The suitability of an area for SMC is determined by the seasonal pattern of rainfall, malaria transmission and the burden of malaria. SMC is recommended for deployment in areas: (i) where more than 60% of the annual incidence of malaria occurs within 4 months (ii) where there are measures of disease burden consistent with a high burden of malaria in children (incidence ≥ 10 cases of malaria among every 100 children during the transmission season) (iii) where SP and AQ [the drugs used to treat children] retain their antimalarial efficacy.” WHO SMC field guide (2013), Pg 8. jQuery("#footnote_plugin_tooltip_6").tooltip({ tip: "#footnote_plugin_tooltip_text_6", tipClass: "footnote_tooltip", effect: "fade", fadeOutSpeed: 100, predelay: 400, position: "top right", relative: true, offset: [10, 10] });7The data and calculations are in this spreadsheet. jQuery("#footnote_plugin_tooltip_7").tooltip({ tip: "#footnote_plugin_tooltip_text_7", tipClass: "footnote_tooltip", effect: "fade", fadeOutSpeed: 100, predelay: 400, position: "top right", relative: true, offset: [10, 10] });

The average region eligible for SMC in countries where Malaria Consortium works has -2% to 17% higher malaria incidence than the average in that country. The weighted average adjustment to Malaria Consortium’s cost-effectiveness would be 4%.8See Cell C126. jQuery("#footnote_plugin_tooltip_8").tooltip({ tip: "#footnote_plugin_tooltip_text_8", tipClass: "footnote_tooltip", effect: "fade", fadeOutSpeed: 100, predelay: 400, position: "top right", relative: true, offset: [10, 10] });

Country Adjustment Commentary Guinea +17% Conakry, the capital, is ineligible for SMC and has low incidence. Nigeria +12% SMC appears to be targeted in the north, where malaria incidence is slightly higher. Niger +2% The majority of the population is either covered or planned to be covered from 2019. Burkina Faso 0% All districts are eligible. Mali 0% All districts are eligible. Chad -2% The four regions with very low malaria incidence (Borkou, Tibesti, Ennedi Est and Ouest) aren’t eligible for SMC, but are sparsely populated. What we concluded

We decided not to include these adjustments in our cost-effectiveness analysis because they increased complexity, without substantially affecting the bottom line.

When we decide whether to include adjustments in our model in general, we use a framework that first takes our best guess of the likely effect size and then rates each of the remaining question on a three-point scale.

Score9We use these scores as a qualitative guide to help us think through what to include in our cost-effectiveness analysis. You can see the rubric we use to assign scores in this spreadsheet. jQuery("#footnote_plugin_tooltip_9").tooltip({ tip: "#footnote_plugin_tooltip_text_9", tipClass: "footnote_tooltip", effect: "fade", fadeOutSpeed: 100, predelay: 400, position: "top right", relative: true, offset: [10, 10] }); Commentary Best guess of effect size 3-4% Can it be objectively justified? 3/3 While we have not investigated the MAP data in detail, we would guess that after further investigation, we would conclude it provides a reasonable approximation of subnational malaria incidence.10You can read more about MAP’s methodology in this paper. jQuery("#footnote_plugin_tooltip_10").tooltip({ tip: "#footnote_plugin_tooltip_text_10", tipClass: "footnote_tooltip", effect: "fade", fadeOutSpeed: 100, predelay: 400, position: "top right", relative: true, offset: [10, 10] }); How easily can it be modelled? 3/3 The methodology is clear and simple. Is it consistent with our other cost-effectiveness analyses? 2/3 We could include subnational adjustments for both of our top charities that implement malaria-prevention programs, but we believe it is unlikely there would be sufficient data to do the same for prevalence of worms or vitamin A deficiency (the focus of five of our other seven top charities).

Even though these adjustments can be objectively justified and are fairly easy to model, the bottom-line difference they make to our cost-effectiveness estimates is insufficient to warrant the (moderate) increase in the complexity of our models. These adjustments would also introduce an inconsistency between our methodologies for top charities. As a result, we are not planning to incorporate subnational adjustments at this time.

When would we revisit this conclusion?

We will revisit using subnational malaria mortality estimates if AMF or Malaria Consortium start working in countries where it would make a large difference to the bottom line. We would include subnational adjustments if AMF contributed nets in any of these countries: Djibouti (+500% adjustment), South Africa (+259%), and Swaziland (+126%), where malaria is endemic in some parts of the country but not others. We would also consider subnational adjustments if AMF contributed nets in Namibia (+25%), Kenya (+23%), Madagascar (+14%), or Rwanda (+10%).11The data and calculations are in this spreadsheet. jQuery("#footnote_plugin_tooltip_11").tooltip({ tip: "#footnote_plugin_tooltip_text_11", tipClass: "footnote_tooltip", effect: "fade", fadeOutSpeed: 100, predelay: 400, position: "top right", relative: true, offset: [10, 10] });

We will investigate whether subnational adjustments would make a substantial difference if Malaria Consortium enters additional countries; at this time, we do not have details on which regions are eligible for SMC in countries in which Malaria Consortium is not currently operating.12We have not yet prioritized getting details on which regions are eligible for SMC in countries in which Malaria Consortium does not currently work, as this would likely impose a substantial time cost on Malaria Consortium. jQuery("#footnote_plugin_tooltip_12").tooltip({ tip: "#footnote_plugin_tooltip_text_12", tipClass: "footnote_tooltip", effect: "fade", fadeOutSpeed: 100, predelay: 400, position: "top right", relative: true, offset: [10, 10] });

You can read the internal emails discussing our decision process here.

Notes   [ + ]

1. ↑ In both cases, we rely on reports by Cochrane, an organization that produces systematic reviews and other synthesized research to inform decision-makers. For AMF, we use a decline in all-cause mortality, because the Cochrane review of anti-malarial bed net distributions reports the effect in terms of a reduction in all-cause mortality. For Malaria Consortium, we use a decline in malaria mortality (proxied by a decline in malaria incidence), as the Cochrane review of seasonal malaria chemoprevention reports the effect in terms of a reduction in malaria incidence, but not all-cause mortality. See our cost-effectiveness analysis for more details. 2. ↑ We assume that the regional distribution of malaria incidence is a reasonable proxy for the regional distribution of malaria mortality. 3. ↑ A limitation of this analysis is it does not account for the possibility that AMF and Malaria Consortium are causing locations that are higher priority or lower priority than the average location already covered by nets or eligible for SMC to be covered on the margin. We do not explicitly include estimates of the marginal region funded in our cost-effectiveness analysis because we often have limited information about which regions would be covered with marginal additional funds. 4. ↑ We assume that where nets have been delivered in the past is a good proxy for where new nets will be delivered in the future. The data and calculations are in this spreadsheet. 5. ↑ See Cell J114. We did not include Papua New Guinea (where AMF funds some nets) in this analysis, as MAP only covers countries in Africa. 6. ↑ “The suitability of an area for SMC is determined by the seasonal pattern of rainfall, malaria transmission and the burden of malaria. SMC is recommended for deployment in areas: (i) where more than 60% of the annual incidence of malaria occurs within 4 months (ii) where there are measures of disease burden consistent with a high burden of malaria in children (incidence ≥ 10 cases of malaria among every 100 children during the transmission season) (iii) where SP and AQ [the drugs used to treat children] retain their antimalarial efficacy.” WHO SMC field guide (2013), Pg 8. 7, 11. ↑ The data and calculations are in this spreadsheet. 8. ↑ See Cell C126. 9. ↑ We use these scores as a qualitative guide to help us think through what to include in our cost-effectiveness analysis. You can see the rubric we use to assign scores in this spreadsheet. 10. ↑ You can read more about MAP’s methodology in this paper. 12. ↑ We have not yet prioritized getting details on which regions are eligible for SMC in countries in which Malaria Consortium does not currently work, as this would likely impose a substantial time cost on Malaria Consortium. function footnote_expand_reference_container() { jQuery("#footnote_references_container").show(); jQuery("#footnote_reference_container_collapse_button").text("-"); } function footnote_collapse_reference_container() { jQuery("#footnote_references_container").hide(); jQuery("#footnote_reference_container_collapse_button").text("+"); } function footnote_expand_collapse_reference_container() { if (jQuery("#footnote_references_container").is(":hidden")) { footnote_expand_reference_container(); } else { footnote_collapse_reference_container(); } } function footnote_moveToAnchor(p_str_TargetID) { footnote_expand_reference_container(); var l_obj_Target = jQuery("#" + p_str_TargetID); if(l_obj_Target.length) { jQuery('html, body').animate({ scrollTop: l_obj_Target.offset().top - window.innerHeight/2 }, 1000); } }

The post Why we don’t use subnational malaria mortality estimates in our cost-effectiveness models appeared first on The GiveWell Blog.

James Snowden (GiveWell)

Why we don’t use subnational malaria mortality estimates in our cost-effectiveness models

6 years 10 months ago
Summary

We recently completed a small project to determine whether using subnational baseline malaria mortality estimates would make a difference to our estimates of the cost-effectiveness of two of our top charities, the Against Malaria Foundation and Malaria Consortium. We ultimately decided not to include these adjustments because they added complexity to our models and would require frequent updating, while only making a small difference (a 3-4% improvement) to our bottom line.

Though this post is on a fairly narrow topic, we believe this example illustrates the principles we use to make decisions about what to include in our cost-effectiveness model.

Background

Two of our top charities—the Against Malaria Foundation (AMF) and Malaria Consortium’s seasonal malaria chemoprevention program—implement programs to prevent malaria, a leading killer of people in low- and middle-income countries.

One of the core reasons we recommend AMF and Malaria Consortium is their cost-effectiveness: how much impact they have (e.g., cases of malaria prevented, malaria deaths averted) with the funds they receive. Our estimates of charities’ cost-effectiveness isn’t just helpful to us in determining which charities should be GiveWell top charities; we also rely on these estimates to guide our decisions about how to allocate funding between our top charities.

Our cost-effectiveness estimates for AMF and Malaria Consortium use country-wide data on malaria mortality and malaria incidence in the places that both organizations work.1In both cases, we rely on reports by Cochrane, an organization that produces systematic reviews and other synthesized research to inform decision-makers. For AMF, we use a decline in all-cause mortality, because the Cochrane review of anti-malarial bed net distributions reports the effect in terms of a reduction in all-cause mortality. For Malaria Consortium, we use a decline in malaria mortality (proxied by a decline in malaria incidence), as the Cochrane review of seasonal malaria chemoprevention reports the effect in terms of a reduction in malaria incidence, but not all-cause mortality. See our cost-effectiveness analysis for more details. jQuery("#footnote_plugin_tooltip_1").tooltip({ tip: "#footnote_plugin_tooltip_text_1", tipClass: "footnote_tooltip", effect: "fade", fadeOutSpeed: 100, predelay: 400, position: "top right", relative: true, offset: [10, 10] }); However, neither organization serves a whole country—rather, they operate in sub-national regions—so the use of country-level estimates could cause us to either underestimate or overestimate their cost-effectiveness. If, for example, these programs are focused in the areas of the country with the highest malaria burden, using the average burden for the country would lead us to underestimate their cost-effectiveness. So, we completed a project to determine how much of an impact using subnational estimates would have, to consider whether we ought to incorporate this information into our cost-effectiveness analysis.

How we estimated the impact of subnational malaria incidence

AMF distributes insecticide-treated nets to prevent malaria; Malaria Consortium’s seasonal malaria chemoprevention (SMC) program provides preventive anti-malarial drugs. We used estimates of subnational malaria incidence from the Malaria Atlas Project (MAP) to see if regions covered by nets or eligible for SMC had higher or lower incidence than the average in the country in which they are located.2We assume that the regional distribution of malaria incidence is a reasonable proxy for the regional distribution of malaria mortality. jQuery("#footnote_plugin_tooltip_2").tooltip({ tip: "#footnote_plugin_tooltip_text_2", tipClass: "footnote_tooltip", effect: "fade", fadeOutSpeed: 100, predelay: 400, position: "top right", relative: true, offset: [10, 10] });

We focused on all areas covered by nets or eligible for SMC (rather than those covered by our top charities, specifically) for two reasons:

  1. Our understanding is that when our top charities contribute resources to a country’s net distribution or SMC programs, the marginal region covered by these additional resources is not necessarily the same as the region to which these resources are assigned (because these resources are fungible with other resources within the national programs).3A limitation of this analysis is it does not account for the possibility that AMF and Malaria Consortium are causing locations that are higher priority or lower priority than the average location already covered by nets or eligible for SMC to be covered on the margin. We do not explicitly include estimates of the marginal region funded in our cost-effectiveness analysis because we often have limited information about which regions would be covered with marginal additional funds. jQuery("#footnote_plugin_tooltip_3").tooltip({ tip: "#footnote_plugin_tooltip_text_3", tipClass: "footnote_tooltip", effect: "fade", fadeOutSpeed: 100, predelay: 400, position: "top right", relative: true, offset: [10, 10] });
  2. Our aim is to estimate the cost-effectiveness of funds donated to these organizations in the future. The subnational region where AMF has worked in the past has not historically been a good indicator of the region where it will work in future.
Results for net distributions in countries where AMF works

We looked at geographical variation in malaria incidence in countries where AMF works, weighting each region by the number of nets it currently receives.4We assume that where nets have been delivered in the past is a good proxy for where new nets will be delivered in the future. The data and calculations are in this spreadsheet. jQuery("#footnote_plugin_tooltip_4").tooltip({ tip: "#footnote_plugin_tooltip_text_4", tipClass: "footnote_tooltip", effect: "fade", fadeOutSpeed: 100, predelay: 400, position: "top right", relative: true, offset: [10, 10] });

The average net delivered in the countries in which AMF works is hung in an area with 0-9% higher malaria incidence than the average in that country, and the weighted average adjustment to AMF’s cost-effectiveness would be 3% (in other words, AMF becomes 3% more cost-effective if we incorporate subnational estimates).5See Cell J114. We did not include Papua New Guinea (where AMF funds some nets) in this analysis, as MAP only covers countries in Africa. jQuery("#footnote_plugin_tooltip_5").tooltip({ tip: "#footnote_plugin_tooltip_text_5", tipClass: "footnote_tooltip", effect: "fade", fadeOutSpeed: 100, predelay: 400, position: "top right", relative: true, offset: [10, 10] });

Country Adjustment Zambia +9% Uganda +4% Ghana +4% Democratic Republic of the Congo +1% Togo +1% Malawi +0% Results for SMC in countries where Malaria Consortium works

We looked at six countries comprising >95% of Malaria Consortium’s SMC spending and compared malaria incidence in districts eligible for SMC with the country-wide average.6“The suitability of an area for SMC is determined by the seasonal pattern of rainfall, malaria transmission and the burden of malaria. SMC is recommended for deployment in areas: (i) where more than 60% of the annual incidence of malaria occurs within 4 months (ii) where there are measures of disease burden consistent with a high burden of malaria in children (incidence ≥ 10 cases of malaria among every 100 children during the transmission season) (iii) where SP and AQ [the drugs used to treat children] retain their antimalarial efficacy.” WHO SMC field guide (2013), Pg 8. jQuery("#footnote_plugin_tooltip_6").tooltip({ tip: "#footnote_plugin_tooltip_text_6", tipClass: "footnote_tooltip", effect: "fade", fadeOutSpeed: 100, predelay: 400, position: "top right", relative: true, offset: [10, 10] });7The data and calculations are in this spreadsheet. jQuery("#footnote_plugin_tooltip_7").tooltip({ tip: "#footnote_plugin_tooltip_text_7", tipClass: "footnote_tooltip", effect: "fade", fadeOutSpeed: 100, predelay: 400, position: "top right", relative: true, offset: [10, 10] });

The average region eligible for SMC in countries where Malaria Consortium works has -2% to 17% higher malaria incidence than the average in that country. The weighted average adjustment to Malaria Consortium’s cost-effectiveness would be 4%.8See Cell C126. jQuery("#footnote_plugin_tooltip_8").tooltip({ tip: "#footnote_plugin_tooltip_text_8", tipClass: "footnote_tooltip", effect: "fade", fadeOutSpeed: 100, predelay: 400, position: "top right", relative: true, offset: [10, 10] });

Country Adjustment Commentary Guinea +17% Conakry, the capital, is ineligible for SMC and has low incidence. Nigeria +12% SMC appears to be targeted in the north, where malaria incidence is slightly higher. Niger +2% The majority of the population is either covered or planned to be covered from 2019. Burkina Faso 0% All districts are eligible. Mali 0% All districts are eligible. Chad -2% The four regions with very low malaria incidence (Borkou, Tibesti, Ennedi Est and Ouest) aren’t eligible for SMC, but are sparsely populated. What we concluded

We decided not to include these adjustments in our cost-effectiveness analysis because they increased complexity, without substantially affecting the bottom line.

When we decide whether to include adjustments in our model in general, we use a framework that first takes our best guess of the likely effect size and then rates each of the remaining question on a three-point scale.

Score9We use these scores as a qualitative guide to help us think through what to include in our cost-effectiveness analysis. You can see the rubric we use to assign scores in this spreadsheet. jQuery("#footnote_plugin_tooltip_9").tooltip({ tip: "#footnote_plugin_tooltip_text_9", tipClass: "footnote_tooltip", effect: "fade", fadeOutSpeed: 100, predelay: 400, position: "top right", relative: true, offset: [10, 10] }); Commentary Best guess of effect size 3-4% Can it be objectively justified? 3/3 While we have not investigated the MAP data in detail, we would guess that after further investigation, we would conclude it provides a reasonable approximation of subnational malaria incidence.10You can read more about MAP’s methodology in this paper. jQuery("#footnote_plugin_tooltip_10").tooltip({ tip: "#footnote_plugin_tooltip_text_10", tipClass: "footnote_tooltip", effect: "fade", fadeOutSpeed: 100, predelay: 400, position: "top right", relative: true, offset: [10, 10] }); How easily can it be modelled? 3/3 The methodology is clear and simple. Is it consistent with our other cost-effectiveness analyses? 2/3 We could include subnational adjustments for both of our top charities that implement malaria-prevention programs, but we believe it is unlikely there would be sufficient data to do the same for prevalence of worms or vitamin A deficiency (the focus of five of our other seven top charities).

Even though these adjustments can be objectively justified and are fairly easy to model, the bottom-line difference they make to our cost-effectiveness estimates is insufficient to warrant the (moderate) increase in the complexity of our models. These adjustments would also introduce an inconsistency between our methodologies for top charities. As a result, we are not planning to incorporate subnational adjustments at this time.

When would we revisit this conclusion?

We will revisit using subnational malaria mortality estimates if AMF or Malaria Consortium start working in countries where it would make a large difference to the bottom line. We would include subnational adjustments if AMF contributed nets in any of these countries: Djibouti (+500% adjustment), South Africa (+259%), and Swaziland (+126%), where malaria is endemic in some parts of the country but not others. We would also consider subnational adjustments if AMF contributed nets in Namibia (+25%), Kenya (+23%), Madagascar (+14%), or Rwanda (+10%).11The data and calculations are in this spreadsheet. jQuery("#footnote_plugin_tooltip_11").tooltip({ tip: "#footnote_plugin_tooltip_text_11", tipClass: "footnote_tooltip", effect: "fade", fadeOutSpeed: 100, predelay: 400, position: "top right", relative: true, offset: [10, 10] });

We will investigate whether subnational adjustments would make a substantial difference if Malaria Consortium enters additional countries; at this time, we do not have details on which regions are eligible for SMC in countries in which Malaria Consortium is not currently operating.12We have not yet prioritized getting details on which regions are eligible for SMC in countries in which Malaria Consortium does not currently work, as this would likely impose a substantial time cost on Malaria Consortium. jQuery("#footnote_plugin_tooltip_12").tooltip({ tip: "#footnote_plugin_tooltip_text_12", tipClass: "footnote_tooltip", effect: "fade", fadeOutSpeed: 100, predelay: 400, position: "top right", relative: true, offset: [10, 10] });

You can read the internal emails discussing our decision process here.

Notes   [ + ]

1. ↑ In both cases, we rely on reports by Cochrane, an organization that produces systematic reviews and other synthesized research to inform decision-makers. For AMF, we use a decline in all-cause mortality, because the Cochrane review of anti-malarial bed net distributions reports the effect in terms of a reduction in all-cause mortality. For Malaria Consortium, we use a decline in malaria mortality (proxied by a decline in malaria incidence), as the Cochrane review of seasonal malaria chemoprevention reports the effect in terms of a reduction in malaria incidence, but not all-cause mortality. See our cost-effectiveness analysis for more details. 2. ↑ We assume that the regional distribution of malaria incidence is a reasonable proxy for the regional distribution of malaria mortality. 3. ↑ A limitation of this analysis is it does not account for the possibility that AMF and Malaria Consortium are causing locations that are higher priority or lower priority than the average location already covered by nets or eligible for SMC to be covered on the margin. We do not explicitly include estimates of the marginal region funded in our cost-effectiveness analysis because we often have limited information about which regions would be covered with marginal additional funds. 4. ↑ We assume that where nets have been delivered in the past is a good proxy for where new nets will be delivered in the future. The data and calculations are in this spreadsheet. 5. ↑ See Cell J114. We did not include Papua New Guinea (where AMF funds some nets) in this analysis, as MAP only covers countries in Africa. 6. ↑ “The suitability of an area for SMC is determined by the seasonal pattern of rainfall, malaria transmission and the burden of malaria. SMC is recommended for deployment in areas: (i) where more than 60% of the annual incidence of malaria occurs within 4 months (ii) where there are measures of disease burden consistent with a high burden of malaria in children (incidence ≥ 10 cases of malaria among every 100 children during the transmission season) (iii) where SP and AQ [the drugs used to treat children] retain their antimalarial efficacy.” WHO SMC field guide (2013), Pg 8. 7, 11. ↑ The data and calculations are in this spreadsheet. 8. ↑ See Cell C126. 9. ↑ We use these scores as a qualitative guide to help us think through what to include in our cost-effectiveness analysis. You can see the rubric we use to assign scores in this spreadsheet. 10. ↑ You can read more about MAP’s methodology in this paper. 12. ↑ We have not yet prioritized getting details on which regions are eligible for SMC in countries in which Malaria Consortium does not currently work, as this would likely impose a substantial time cost on Malaria Consortium. function footnote_expand_reference_container() { jQuery("#footnote_references_container").show(); jQuery("#footnote_reference_container_collapse_button").text("-"); } function footnote_collapse_reference_container() { jQuery("#footnote_references_container").hide(); jQuery("#footnote_reference_container_collapse_button").text("+"); } function footnote_expand_collapse_reference_container() { if (jQuery("#footnote_references_container").is(":hidden")) { footnote_expand_reference_container(); } else { footnote_collapse_reference_container(); } } function footnote_moveToAnchor(p_str_TargetID) { footnote_expand_reference_container(); var l_obj_Target = jQuery("#" + p_str_TargetID); if(l_obj_Target.length) { jQuery('html, body').animate({ scrollTop: l_obj_Target.offset().top - window.innerHeight/2 }, 1000); } }

The post Why we don’t use subnational malaria mortality estimates in our cost-effectiveness models appeared first on The GiveWell Blog.

James Snowden (GiveWell)

Why we don’t use subnational malaria mortality estimates in our cost-effectiveness models

6 years 10 months ago
Summary

We recently completed a small project to determine whether using subnational baseline malaria mortality estimates would make a difference to our estimates of the cost-effectiveness of two of our top charities, the Against Malaria Foundation and Malaria Consortium. We ultimately decided not to include these adjustments because they added complexity to our models and would require frequent updating, while only making a small difference (a 3-4% improvement) to our bottom line.

Though this post is on a fairly narrow topic, we believe this example illustrates the principles we use to make decisions about what to include in our cost-effectiveness model.

Background

Two of our top charities—the Against Malaria Foundation (AMF) and Malaria Consortium’s seasonal malaria chemoprevention program—implement programs to prevent malaria, a leading killer of people in low- and middle-income countries.

One of the core reasons we recommend AMF and Malaria Consortium is their cost-effectiveness: how much impact they have (e.g., cases of malaria prevented, malaria deaths averted) with the funds they receive. Our estimates of charities’ cost-effectiveness isn’t just helpful to us in determining which charities should be GiveWell top charities; we also rely on these estimates to guide our decisions about how to allocate funding between our top charities.

Our cost-effectiveness estimates for AMF and Malaria Consortium use country-wide data on malaria mortality and malaria incidence in the places that both organizations work.1In both cases, we rely on reports by Cochrane, an organization that produces systematic reviews and other synthesized research to inform decision-makers. For AMF, we use a decline in all-cause mortality, because the Cochrane review of anti-malarial bed net distributions reports the effect in terms of a reduction in all-cause mortality. For Malaria Consortium, we use a decline in malaria mortality (proxied by a decline in malaria incidence), as the Cochrane review of seasonal malaria chemoprevention reports the effect in terms of a reduction in malaria incidence, but not all-cause mortality. See our cost-effectiveness analysis for more details. jQuery("#footnote_plugin_tooltip_1").tooltip({ tip: "#footnote_plugin_tooltip_text_1", tipClass: "footnote_tooltip", effect: "fade", fadeOutSpeed: 100, predelay: 400, position: "top right", relative: true, offset: [10, 10] }); However, neither organization serves a whole country—rather, they operate in sub-national regions—so the use of country-level estimates could cause us to either underestimate or overestimate their cost-effectiveness. If, for example, these programs are focused in the areas of the country with the highest malaria burden, using the average burden for the country would lead us to underestimate their cost-effectiveness. So, we completed a project to determine how much of an impact using subnational estimates would have, to consider whether we ought to incorporate this information into our cost-effectiveness analysis.

How we estimated the impact of subnational malaria incidence

AMF distributes insecticide-treated nets to prevent malaria; Malaria Consortium’s seasonal malaria chemoprevention (SMC) program provides preventive anti-malarial drugs. We used estimates of subnational malaria incidence from the Malaria Atlas Project (MAP) to see if regions covered by nets or eligible for SMC had higher or lower incidence than the average in the country in which they are located.2We assume that the regional distribution of malaria incidence is a reasonable proxy for the regional distribution of malaria mortality. jQuery("#footnote_plugin_tooltip_2").tooltip({ tip: "#footnote_plugin_tooltip_text_2", tipClass: "footnote_tooltip", effect: "fade", fadeOutSpeed: 100, predelay: 400, position: "top right", relative: true, offset: [10, 10] });

We focused on all areas covered by nets or eligible for SMC (rather than those covered by our top charities, specifically) for two reasons:

  1. Our understanding is that when our top charities contribute resources to a country’s net distribution or SMC programs, the marginal region covered by these additional resources is not necessarily the same as the region to which these resources are assigned (because these resources are fungible with other resources within the national programs).3A limitation of this analysis is it does not account for the possibility that AMF and Malaria Consortium are causing locations that are higher priority or lower priority than the average location already covered by nets or eligible for SMC to be covered on the margin. We do not explicitly include estimates of the marginal region funded in our cost-effectiveness analysis because we often have limited information about which regions would be covered with marginal additional funds. jQuery("#footnote_plugin_tooltip_3").tooltip({ tip: "#footnote_plugin_tooltip_text_3", tipClass: "footnote_tooltip", effect: "fade", fadeOutSpeed: 100, predelay: 400, position: "top right", relative: true, offset: [10, 10] });
  2. Our aim is to estimate the cost-effectiveness of funds donated to these organizations in the future. The subnational region where AMF has worked in the past has not historically been a good indicator of the region where it will work in future.
Results for net distributions in countries where AMF works

We looked at geographical variation in malaria incidence in countries where AMF works, weighting each region by the number of nets it currently receives.4We assume that where nets have been delivered in the past is a good proxy for where new nets will be delivered in the future. The data and calculations are in this spreadsheet. jQuery("#footnote_plugin_tooltip_4").tooltip({ tip: "#footnote_plugin_tooltip_text_4", tipClass: "footnote_tooltip", effect: "fade", fadeOutSpeed: 100, predelay: 400, position: "top right", relative: true, offset: [10, 10] });

The average net delivered in the countries in which AMF works is hung in an area with 0-9% higher malaria incidence than the average in that country, and the weighted average adjustment to AMF’s cost-effectiveness would be 3% (in other words, AMF becomes 3% more cost-effective if we incorporate subnational estimates).5See Cell J114. We did not include Papua New Guinea (where AMF funds some nets) in this analysis, as MAP only covers countries in Africa. jQuery("#footnote_plugin_tooltip_5").tooltip({ tip: "#footnote_plugin_tooltip_text_5", tipClass: "footnote_tooltip", effect: "fade", fadeOutSpeed: 100, predelay: 400, position: "top right", relative: true, offset: [10, 10] });

Country Adjustment Zambia +9% Uganda +4% Ghana +4% Democratic Republic of the Congo +1% Togo +1% Malawi +0% Results for SMC in countries where Malaria Consortium works

We looked at six countries comprising >95% of Malaria Consortium’s SMC spending and compared malaria incidence in districts eligible for SMC with the country-wide average.6“The suitability of an area for SMC is determined by the seasonal pattern of rainfall, malaria transmission and the burden of malaria. SMC is recommended for deployment in areas: (i) where more than 60% of the annual incidence of malaria occurs within 4 months (ii) where there are measures of disease burden consistent with a high burden of malaria in children (incidence ≥ 10 cases of malaria among every 100 children during the transmission season) (iii) where SP and AQ [the drugs used to treat children] retain their antimalarial efficacy.” WHO SMC field guide (2013), Pg 8. jQuery("#footnote_plugin_tooltip_6").tooltip({ tip: "#footnote_plugin_tooltip_text_6", tipClass: "footnote_tooltip", effect: "fade", fadeOutSpeed: 100, predelay: 400, position: "top right", relative: true, offset: [10, 10] });7The data and calculations are in this spreadsheet. jQuery("#footnote_plugin_tooltip_7").tooltip({ tip: "#footnote_plugin_tooltip_text_7", tipClass: "footnote_tooltip", effect: "fade", fadeOutSpeed: 100, predelay: 400, position: "top right", relative: true, offset: [10, 10] });

The average region eligible for SMC in countries where Malaria Consortium works has -2% to 17% higher malaria incidence than the average in that country. The weighted average adjustment to Malaria Consortium’s cost-effectiveness would be 4%.8See Cell C126. jQuery("#footnote_plugin_tooltip_8").tooltip({ tip: "#footnote_plugin_tooltip_text_8", tipClass: "footnote_tooltip", effect: "fade", fadeOutSpeed: 100, predelay: 400, position: "top right", relative: true, offset: [10, 10] });

Country Adjustment Commentary Guinea +17% Conakry, the capital, is ineligible for SMC and has low incidence. Nigeria +12% SMC appears to be targeted in the north, where malaria incidence is slightly higher. Niger +2% The majority of the population is either covered or planned to be covered from 2019. Burkina Faso 0% All districts are eligible. Mali 0% All districts are eligible. Chad -2% The four regions with very low malaria incidence (Borkou, Tibesti, Ennedi Est and Ouest) aren’t eligible for SMC, but are sparsely populated. What we concluded

We decided not to include these adjustments in our cost-effectiveness analysis because they increased complexity, without substantially affecting the bottom line.

When we decide whether to include adjustments in our model in general, we use a framework that first takes our best guess of the likely effect size and then rates each of the remaining question on a three-point scale.

Score9We use these scores as a qualitative guide to help us think through what to include in our cost-effectiveness analysis. You can see the rubric we use to assign scores in this spreadsheet. jQuery("#footnote_plugin_tooltip_9").tooltip({ tip: "#footnote_plugin_tooltip_text_9", tipClass: "footnote_tooltip", effect: "fade", fadeOutSpeed: 100, predelay: 400, position: "top right", relative: true, offset: [10, 10] }); Commentary Best guess of effect size 3-4% Can it be objectively justified? 3/3 While we have not investigated the MAP data in detail, we would guess that after further investigation, we would conclude it provides a reasonable approximation of subnational malaria incidence.10You can read more about MAP’s methodology in this paper. jQuery("#footnote_plugin_tooltip_10").tooltip({ tip: "#footnote_plugin_tooltip_text_10", tipClass: "footnote_tooltip", effect: "fade", fadeOutSpeed: 100, predelay: 400, position: "top right", relative: true, offset: [10, 10] }); How easily can it be modelled? 3/3 The methodology is clear and simple. Is it consistent with our other cost-effectiveness analyses? 2/3 We could include subnational adjustments for both of our top charities that implement malaria-prevention programs, but we believe it is unlikely there would be sufficient data to do the same for prevalence of worms or vitamin A deficiency (the focus of five of our other seven top charities).

Even though these adjustments can be objectively justified and are fairly easy to model, the bottom-line difference they make to our cost-effectiveness estimates is insufficient to warrant the (moderate) increase in the complexity of our models. These adjustments would also introduce an inconsistency between our methodologies for top charities. As a result, we are not planning to incorporate subnational adjustments at this time.

When would we revisit this conclusion?

We will revisit using subnational malaria mortality estimates if AMF or Malaria Consortium start working in countries where it would make a large difference to the bottom line. We would include subnational adjustments if AMF contributed nets in any of these countries: Djibouti (+500% adjustment), South Africa (+259%), and Swaziland (+126%), where malaria is endemic in some parts of the country but not others. We would also consider subnational adjustments if AMF contributed nets in Namibia (+25%), Kenya (+23%), Madagascar (+14%), or Rwanda (+10%).11The data and calculations are in this spreadsheet. jQuery("#footnote_plugin_tooltip_11").tooltip({ tip: "#footnote_plugin_tooltip_text_11", tipClass: "footnote_tooltip", effect: "fade", fadeOutSpeed: 100, predelay: 400, position: "top right", relative: true, offset: [10, 10] });

We will investigate whether subnational adjustments would make a substantial difference if Malaria Consortium enters additional countries; at this time, we do not have details on which regions are eligible for SMC in countries in which Malaria Consortium is not currently operating.12We have not yet prioritized getting details on which regions are eligible for SMC in countries in which Malaria Consortium does not currently work, as this would likely impose a substantial time cost on Malaria Consortium. jQuery("#footnote_plugin_tooltip_12").tooltip({ tip: "#footnote_plugin_tooltip_text_12", tipClass: "footnote_tooltip", effect: "fade", fadeOutSpeed: 100, predelay: 400, position: "top right", relative: true, offset: [10, 10] });

You can read the internal emails discussing our decision process here.

Notes   [ + ]

1. ↑ In both cases, we rely on reports by Cochrane, an organization that produces systematic reviews and other synthesized research to inform decision-makers. For AMF, we use a decline in all-cause mortality, because the Cochrane review of anti-malarial bed net distributions reports the effect in terms of a reduction in all-cause mortality. For Malaria Consortium, we use a decline in malaria mortality (proxied by a decline in malaria incidence), as the Cochrane review of seasonal malaria chemoprevention reports the effect in terms of a reduction in malaria incidence, but not all-cause mortality. See our cost-effectiveness analysis for more details. 2. ↑ We assume that the regional distribution of malaria incidence is a reasonable proxy for the regional distribution of malaria mortality. 3. ↑ A limitation of this analysis is it does not account for the possibility that AMF and Malaria Consortium are causing locations that are higher priority or lower priority than the average location already covered by nets or eligible for SMC to be covered on the margin. We do not explicitly include estimates of the marginal region funded in our cost-effectiveness analysis because we often have limited information about which regions would be covered with marginal additional funds. 4. ↑ We assume that where nets have been delivered in the past is a good proxy for where new nets will be delivered in the future. The data and calculations are in this spreadsheet. 5. ↑ See Cell J114. We did not include Papua New Guinea (where AMF funds some nets) in this analysis, as MAP only covers countries in Africa. 6. ↑ “The suitability of an area for SMC is determined by the seasonal pattern of rainfall, malaria transmission and the burden of malaria. SMC is recommended for deployment in areas: (i) where more than 60% of the annual incidence of malaria occurs within 4 months (ii) where there are measures of disease burden consistent with a high burden of malaria in children (incidence ≥ 10 cases of malaria among every 100 children during the transmission season) (iii) where SP and AQ [the drugs used to treat children] retain their antimalarial efficacy.” WHO SMC field guide (2013), Pg 8. 7, 11. ↑ The data and calculations are in this spreadsheet. 8. ↑ See Cell C126. 9. ↑ We use these scores as a qualitative guide to help us think through what to include in our cost-effectiveness analysis. You can see the rubric we use to assign scores in this spreadsheet. 10. ↑ You can read more about MAP’s methodology in this paper. 12. ↑ We have not yet prioritized getting details on which regions are eligible for SMC in countries in which Malaria Consortium does not currently work, as this would likely impose a substantial time cost on Malaria Consortium. function footnote_expand_reference_container() { jQuery("#footnote_references_container").show(); jQuery("#footnote_reference_container_collapse_button").text("-"); } function footnote_collapse_reference_container() { jQuery("#footnote_references_container").hide(); jQuery("#footnote_reference_container_collapse_button").text("+"); } function footnote_expand_collapse_reference_container() { if (jQuery("#footnote_references_container").is(":hidden")) { footnote_expand_reference_container(); } else { footnote_collapse_reference_container(); } } function footnote_moveToAnchor(p_str_TargetID) { footnote_expand_reference_container(); var l_obj_Target = jQuery("#" + p_str_TargetID); if(l_obj_Target.length) { jQuery('html, body').animate({ scrollTop: l_obj_Target.offset().top - window.innerHeight/2 }, 1000); } }

The post Why we don’t use subnational malaria mortality estimates in our cost-effectiveness models appeared first on The GiveWell Blog.

James Snowden (GiveWell)

GiveWell’s money moved and web traffic in 2017

7 years ago

GiveWell is dedicated to finding outstanding giving opportunities and publishing the full details of our analysis. In addition to evaluations of other charities, we publish substantial evaluation of our own work. This post lays out highlights from our 2017 metrics report, which reviews what we know about how our research impacted donors. Please note:

  • We report on “metrics years” that run from February through January; for example, our 2017 data cover February 1, 2017 through January 31, 2018.
  • We differentiate between our traditional charity recommendations and the work of the Open Philanthropy Project, which became a separate organization in 2017 and whose work we exclude from this report.
  • More context on the relationships between GiveWell, Good Ventures, and the Open Philanthropy Project can be found here.

Summary of influence: In 2017, GiveWell influenced charitable giving in several ways. The following table summarizes our understanding of this influence.

Headline money moved: In 2017, we tracked $117.5 million in money moved to our recommended charities. Our money moved only includes donations that we are confident were influenced by our recommendations.

Money moved by charity: Our nine top charities received the majority of our money moved. Our seven standout charities received a total of $1.8 million.

Money moved by size of donor: In 2017, the number of donors and amount donated increased across each donor size category, with the notable exception of donations from donors giving $1,000,000 or more. In 2017, 90% of our money moved (excluding Good Ventures) came from 20% of our donors, who gave $1,000 or more.

Donor retention: The total number of donors who gave to our recommended charities or to GiveWell unrestricted increased about 29% year-over-year to 23,049 in 2017. This included 14,653 donors who gave for the first time. Among all donors who gave in the previous year, about 42% gave again in 2017, up from about 35% who gave again in 2016.

Our retention was stronger among donors who gave larger amounts or who first gave to our recommendations prior to 2015. Of larger donors (those who gave $10,000 or more in either of the last two years), about 73% who gave in 2016 gave again in 2017.

GiveWell’s expenses: GiveWell’s total operating expenses in 2017 were $4.6 million. Our expenses decreased from about $5.5 million in 2016 due to the Open Philanthropy Project becoming a separate organization in June 2017. We estimate that 67% of our total expenses ($3.1 million) supported our traditional top charity work and about 33% supported the Open Philanthropy Project. In 2016, we estimated that expenses for our traditional top charity work were about $2.0 million.

Donations supporting GiveWell’s operations: GiveWell raised $5.7 million in unrestricted funding (which we use to support our operations) in 2017, compared to $5.6 million in 2016. Our major institutional supporters and the six largest individual donors contributed about 49% of GiveWell’s operational funding in 2017.

Web traffic: The number of unique visitors to our website remained flat in 2017 compared to 2016 (when excluding visitors driven by AdWords, Google’s online advertising product).

For more detail, see our full metrics report (PDF).

The post GiveWell’s money moved and web traffic in 2017 appeared first on The GiveWell Blog.

Maryana Pinchuk

GiveWell’s money moved and web traffic in 2017

7 years ago

GiveWell is dedicated to finding outstanding giving opportunities and publishing the full details of our analysis. In addition to evaluations of other charities, we publish substantial evaluation of our own work. This post lays out highlights from our 2017 metrics report, which reviews what we know about how our research impacted donors. Please note:

  • We report on “metrics years” that run from February through January; for example, our 2017 data cover February 1, 2017 through January 31, 2018.
  • We differentiate between our traditional charity recommendations and the work of the Open Philanthropy Project, which became a separate organization in 2017 and whose work we exclude from this report.
  • More context on the relationships between GiveWell, Good Ventures, and the Open Philanthropy Project can be found here.

Summary of influence: In 2017, GiveWell influenced charitable giving in several ways. The following table summarizes our understanding of this influence.

Headline money moved: In 2017, we tracked $117.5 million in money moved to our recommended charities. Our money moved only includes donations that we are confident were influenced by our recommendations.

Money moved by charity: Our nine top charities received the majority of our money moved. Our seven standout charities received a total of $1.8 million.

Money moved by size of donor: In 2017, the number of donors and amount donated increased across each donor size category, with the notable exception of donations from donors giving $1,000,000 or more. In 2017, 90% of our money moved (excluding Good Ventures) came from 20% of our donors, who gave $1,000 or more.

Donor retention: The total number of donors who gave to our recommended charities or to GiveWell unrestricted increased about 29% year-over-year to 23,049 in 2017. This included 14,653 donors who gave for the first time. Among all donors who gave in the previous year, about 42% gave again in 2017, up from about 35% who gave again in 2016.

Our retention was stronger among donors who gave larger amounts or who first gave to our recommendations prior to 2015. Of larger donors (those who gave $10,000 or more in either of the last two years), about 73% who gave in 2016 gave again in 2017.

GiveWell’s expenses: GiveWell’s total operating expenses in 2017 were $4.6 million. Our expenses decreased from about $5.5 million in 2016 due to the Open Philanthropy Project becoming a separate organization in June 2017. We estimate that 67% of our total expenses ($3.1 million) supported our traditional top charity work and about 33% supported the Open Philanthropy Project. In 2016, we estimated that expenses for our traditional top charity work were about $2.0 million.

Donations supporting GiveWell’s operations: GiveWell raised $5.7 million in unrestricted funding (which we use to support our operations) in 2017, compared to $5.6 million in 2016. Our major institutional supporters and the six largest individual donors contributed about 49% of GiveWell’s operational funding in 2017.

Web traffic: The number of unique visitors to our website remained flat in 2017 compared to 2016 (when excluding visitors driven by AdWords, Google’s online advertising product).

For more detail, see our full metrics report (PDF).

The post GiveWell’s money moved and web traffic in 2017 appeared first on The GiveWell Blog.

Maryana Pinchuk

GiveWell’s money moved and web traffic in 2017

7 years ago

GiveWell is dedicated to finding outstanding giving opportunities and publishing the full details of our analysis. In addition to evaluations of other charities, we publish substantial evaluation of our own work. This post lays out highlights from our 2017 metrics report, which reviews what we know about how our research impacted donors. Please note:

  • We report on “metrics years” that run from February through January; for example, our 2017 data cover February 1, 2017 through January 31, 2018.
  • We differentiate between our traditional charity recommendations and the work of the Open Philanthropy Project, which became a separate organization in 2017 and whose work we exclude from this report.
  • More context on the relationships between GiveWell, Good Ventures, and the Open Philanthropy Project can be found here.

Summary of influence: In 2017, GiveWell influenced charitable giving in several ways. The following table summarizes our understanding of this influence.

Headline money moved: In 2017, we tracked $117.5 million in money moved to our recommended charities. Our money moved only includes donations that we are confident were influenced by our recommendations.

Money moved by charity: Our nine top charities received the majority of our money moved. Our seven standout charities received a total of $1.8 million.

Money moved by size of donor: In 2017, the number of donors and amount donated increased across each donor size category, with the notable exception of donations from donors giving $1,000,000 or more. In 2017, 90% of our money moved (excluding Good Ventures) came from 20% of our donors, who gave $1,000 or more.

Donor retention: The total number of donors who gave to our recommended charities or to GiveWell unrestricted increased about 29% year-over-year to 23,049 in 2017. This included 14,653 donors who gave for the first time. Among all donors who gave in the previous year, about 42% gave again in 2017, up from about 35% who gave again in 2016.

Our retention was stronger among donors who gave larger amounts or who first gave to our recommendations prior to 2015. Of larger donors (those who gave $10,000 or more in either of the last two years), about 73% who gave in 2016 gave again in 2017.

GiveWell’s expenses: GiveWell’s total operating expenses in 2017 were $4.6 million. Our expenses decreased from about $5.5 million in 2016 due to the Open Philanthropy Project becoming a separate organization in June 2017. We estimate that 67% of our total expenses ($3.1 million) supported our traditional top charity work and about 33% supported the Open Philanthropy Project. In 2016, we estimated that expenses for our traditional top charity work were about $2.0 million.

Donations supporting GiveWell’s operations: GiveWell raised $5.7 million in unrestricted funding (which we use to support our operations) in 2017, compared to $5.6 million in 2016. Our major institutional supporters and the six largest individual donors contributed about 49% of GiveWell’s operational funding in 2017.

Web traffic: The number of unique visitors to our website remained flat in 2017 compared to 2016 (when excluding visitors driven by AdWords, Google’s online advertising product).

For more detail, see our full metrics report (PDF).

The post GiveWell’s money moved and web traffic in 2017 appeared first on The GiveWell Blog.

Maryana Pinchuk

Announcing Zusha! as a standout charity

7 years ago

We’ve added the Georgetown University Initiative on Innovation, Development, and Evaluation (gui2de)'s Zusha! Road Safety Campaign (from here on, "Zusha!") as a standout charity; see our full review here. Standout charities do not meet all of our criteria to be a GiveWell top charity, but we believe they stand out from the vast majority of organizations we have considered. See more information about our standout charities here.

Read More

The post Announcing Zusha! as a standout charity appeared first on The GiveWell Blog.

Josh Rosenberg

Announcing Zusha! as a standout charity

7 years ago

We’ve added the Georgetown University Initiative on Innovation, Development, and Evaluation gui2de‘s Zusha! Road Safety Campaign (from here on, “Zusha!”) as a standout charity; see our full review here. Standout charities do not meet all of our criteria to be a GiveWell top charity, but we believe they stand out from the vast majority of organizations we have considered. See more information about our standout charities here.

Zusha! is a campaign intended to reduce road accidents. Zusha! supports distribution of stickers to public service vehicles encouraging passengers to speak up and urge drivers to drive more safely. We provided a GiveWell Incubation Grant to Zusha! in January 2017 and discussed it in a February 2017 blog post.

For more information, see our full review. Interested donors can give to Zusha! by clicking “Donate” on that page.

The post Announcing Zusha! as a standout charity appeared first on The GiveWell Blog.

Josh (GiveWell)

Announcing Zusha! as a standout charity

7 years ago

We’ve added the Georgetown University Initiative on Innovation, Development, and Evaluation gui2de‘s Zusha! Road Safety Campaign (from here on, “Zusha!”) as a standout charity; see our full review here. Standout charities do not meet all of our criteria to be a GiveWell top charity, but we believe they stand out from the vast majority of organizations we have considered. See more information about our standout charities here.

Zusha! is a campaign intended to reduce road accidents. Zusha! supports distribution of stickers to public service vehicles encouraging passengers to speak up and urge drivers to drive more safely. We provided a GiveWell Incubation Grant to Zusha! in January 2017 and discussed it in a February 2017 blog post.

For more information, see our full review. Interested donors can give to Zusha! by clicking “Donate” on that page.

The post Announcing Zusha! as a standout charity appeared first on The GiveWell Blog.

Josh (GiveWell)

June 2018 open thread

7 years ago

Our goal with hosting quarterly open threads is to give blog readers an opportunity to publicly raise comments or questions about GiveWell or related topics (in the comments section below). As always, you’re also welcome to email us at info@givewell.org or to request a call with GiveWell staff if you have feedback or questions you’d prefer to discuss privately. We’ll try to respond promptly to questions or comments.

You can view our March 2018 open thread here.

The post June 2018 open thread appeared first on The GiveWell Blog.

Catherine

June 2018 open thread

7 years ago

Our goal with hosting quarterly open threads is to give blog readers an opportunity to publicly raise comments or questions about GiveWell or related topics (in the comments section below). As always, you’re also welcome to email us at info@givewell.org or to request a call with GiveWell staff if you have feedback or questions you’d prefer to discuss privately. We’ll try to respond promptly to questions or comments.

You can view our March 2018 open thread here.

The post June 2018 open thread appeared first on The GiveWell Blog.

Catherine

Allocation of discretionary funds from Q1 2018

7 years ago

In the first quarter of 2018, we received $2.96 million in funding for making grants at our discretion. In this post we discuss:

  • The decision to allocate the $2.96 million to the Against Malaria Foundation (AMF) (70 percent) and the Schistosomiasis Control Initiative (SCI) (30 percent).
  • Our recommendation that donors give to GiveWell for granting to top charities at our discretion so that we can direct the funding to the top charity or charities with the most pressing funding need. For donors who prefer to give directly to our top charities, we continue to recommend giving 70 percent of your donation to AMF and 30 percent to SCI to maximize your impact.

Allocation of discretionary funds

The allocation of 70 percent of the funds to AMF and 30 percent to SCI follows the recommendation we have made, and continue to make, to donors. For more discussion on this allocation, see our blog post about allocating discretionary funds from the previous quarter.

We also considered the following possibilities for this quarter:

Helen Keller International (HKI) for stopgap funding in one additional country

We discussed this possibility in our blog post about allocating discretionary funds from the previous quarter. After further discussing this possibility with HKI, our understanding is that (a) the amount of funding needed to fill this gap will likely be small relative to the amount of GiveWell-directed funding that HKI currently holds, and (b) we will have limited additional information in time for this decision round that we could use to compare this new use of funding to HKI’s other planned uses of funding. We will continue discussing this opportunity with HKI and may allocate funding to it in the future. Our current expectation is that we will ask HKI to make the tradeoff between allocating the GiveWell-directed funding it holds to this new opportunity and continuing to hold the funds. Holding the funds gives the current programs more runway (originally designed to fund three years) and gives HKI more flexibility to fund highly cost-effective, unanticipated opportunities in the future. We believe that HKI is currently in a better position to assess cost-effectiveness of the opportunities it has than we are, while we will seek to maximize cost-effectiveness in the longer run by assessing HKI’s track record of cost-effectiveness and comparing that to the cost-effectiveness of other top charities.

We remain open to the possibility that HKI will share information with us that will lead us to conclude that this new opportunity is a better use of funds than our current recommendation of 70 percent to AMF and 30 percent to SCI. In that case, we would allocate funds from the next quarter to fill this funding gap (and could accelerate the timeline on that decision if it were helpful to HKI).

Evidence Action’s Deworm the World Initiative for funding gaps in India and Nigeria

We spoke with Deworm the World about two new funding gaps it has due to unexpected costs in its existing programs in India and Nigeria.

In India, the cost overruns total $166,000. Deworm the World has the option of drawing down a reserve of $5.5 million (from funds donated on GiveWell’s recommendation). The reserve was intended to backstop funds that were expected but not fully confirmed from another funder. Given the small size of the gap relative to the available reserves, our preference is for Deworm the World to use that funding and for us to consider recommending further reserves as part of our end-of-year review of our top charities’ room for more funding.

In Nigeria, there is a funding gap of $1.7 million in the states that Deworm the World is currently operating in. Previous budgets assumed annual treatment for all children, and Deworm the World has since become aware of the existence of areas where worm prevalence is high enough that twice per year treatment is recommended. Our best guess is that AMF and SCI are more cost-effective than Deworm the World’s Nigeria program (see discussion in this post). It is possible that because additional funding would go to support additional treatments in states where programs already operate, the cost to deliver these marginal treatments would be lower. We don’t currently have enough data to analyze whether that would significantly change the cost-effectiveness in this case.

Deworm the World also continues to have a funding gap for expansion to other states in Nigeria. We wrote about this opportunity in our previous post on allocating discretionary funding.

Malaria Consortium for seasonal malaria chemoprevention (SMC)

We continue to see a case for directing additional funding to Malaria Consortium for SMC, as we did last quarter. Our views on this program have not changed. For further discussion, see our previous post on allocating discretionary funding.

What is our recommendation to donors?

We continue to recommend that donors give to GiveWell for granting to top charities at our discretion so that we can direct the funding to the top charity or charities with the most pressing funding need. For donors who prefer to give directly to our top charities, we are continuing to recommend giving 70 percent of your donation to AMF and 30 percent to SCI to maximize your impact. The reasons for this recommendation are the same as in our previous post on allocating discretionary funding.

The post Allocation of discretionary funds from Q1 2018 appeared first on The GiveWell Blog.

Natalie Crispin

Allocation of discretionary funds from Q1 2018

7 years ago

In the first quarter of 2018, we received $2.96 million in funding for making grants at our discretion. In this post we discuss:

  • The decision to allocate the $2.96 million to the Against Malaria Foundation (AMF) (70 percent) and the Schistosomiasis Control Initiative (SCI) (30 percent).
  • Our recommendation that donors give to GiveWell for granting to top charities at our discretion so that we can direct the funding to the top charity or charities with the most pressing funding need. For donors who prefer to give directly to our top charities, we continue to recommend giving 70 percent of your donation to AMF and 30 percent to SCI to maximize your impact.

Read More

The post Allocation of discretionary funds from Q1 2018 appeared first on The GiveWell Blog.

Natalie Crispin

Allocation of discretionary funds from Q1 2018

7 years ago

In the first quarter of 2018, we received $2.96 million in funding for making grants at our discretion. In this post we discuss:

  • The decision to allocate the $2.96 million to the Against Malaria Foundation (AMF) (70 percent) and the Schistosomiasis Control Initiative (SCI) (30 percent).
  • Our recommendation that donors give to GiveWell for granting to top charities at our discretion so that we can direct the funding to the top charity or charities with the most pressing funding need. For donors who prefer to give directly to our top charities, we continue to recommend giving 70 percent of your donation to AMF and 30 percent to SCI to maximize your impact.

Allocation of discretionary funds

The allocation of 70 percent of the funds to AMF and 30 percent to SCI follows the recommendation we have made, and continue to make, to donors. For more discussion on this allocation, see our blog post about allocating discretionary funds from the previous quarter.

We also considered the following possibilities for this quarter:

Helen Keller International (HKI) for stopgap funding in one additional country

We discussed this possibility in our blog post about allocating discretionary funds from the previous quarter. After further discussing this possibility with HKI, our understanding is that (a) the amount of funding needed to fill this gap will likely be small relative to the amount of GiveWell-directed funding that HKI currently holds, and (b) we will have limited additional information in time for this decision round that we could use to compare this new use of funding to HKI’s other planned uses of funding. We will continue discussing this opportunity with HKI and may allocate funding to it in the future. Our current expectation is that we will ask HKI to make the tradeoff between allocating the GiveWell-directed funding it holds to this new opportunity and continuing to hold the funds. Holding the funds gives the current programs more runway (originally designed to fund three years) and gives HKI more flexibility to fund highly cost-effective, unanticipated opportunities in the future. We believe that HKI is currently in a better position to assess cost-effectiveness of the opportunities it has than we are, while we will seek to maximize cost-effectiveness in the longer run by assessing HKI’s track record of cost-effectiveness and comparing that to the cost-effectiveness of other top charities.

We remain open to the possibility that HKI will share information with us that will lead us to conclude that this new opportunity is a better use of funds than our current recommendation of 70 percent to AMF and 30 percent to SCI. In that case, we would allocate funds from the next quarter to fill this funding gap (and could accelerate the timeline on that decision if it were helpful to HKI).

Evidence Action’s Deworm the World Initiative for funding gaps in India and Nigeria

We spoke with Deworm the World about two new funding gaps it has due to unexpected costs in its existing programs in India and Nigeria.

In India, the cost overruns total $166,000. Deworm the World has the option of drawing down a reserve of $5.5 million (from funds donated on GiveWell’s recommendation). The reserve was intended to backstop funds that were expected but not fully confirmed from another funder. Given the small size of the gap relative to the available reserves, our preference is for Deworm the World to use that funding and for us to consider recommending further reserves as part of our end-of-year review of our top charities’ room for more funding.

In Nigeria, there is a funding gap of $1.7 million in the states that Deworm the World is currently operating in. Previous budgets assumed annual treatment for all children, and Deworm the World has since become aware of the existence of areas where worm prevalence is high enough that twice per year treatment is recommended. Our best guess is that AMF and SCI are more cost-effective than Deworm the World’s Nigeria program (see discussion in this post). It is possible that because additional funding would go to support additional treatments in states where programs already operate, the cost to deliver these marginal treatments would be lower. We don’t currently have enough data to analyze whether that would significantly change the cost-effectiveness in this case.

Deworm the World also continues to have a funding gap for expansion to other states in Nigeria. We wrote about this opportunity in our previous post on allocating discretionary funding.

Malaria Consortium for seasonal malaria chemoprevention (SMC)

We continue to see a case for directing additional funding to Malaria Consortium for SMC, as we did last quarter. Our views on this program have not changed. For further discussion, see our previous post on allocating discretionary funding.

What is our recommendation to donors?

We continue to recommend that donors give to GiveWell for granting to top charities at our discretion so that we can direct the funding to the top charity or charities with the most pressing funding need. For donors who prefer to give directly to our top charities, we are continuing to recommend giving 70 percent of your donation to AMF and 30 percent to SCI to maximize your impact. The reasons for this recommendation are the same as in our previous post on allocating discretionary funding.

The post Allocation of discretionary funds from Q1 2018 appeared first on The GiveWell Blog.

Natalie Crispin

Allocation of discretionary funds from Q1 2018

7 years ago

In the first quarter of 2018, we received $2.96 million in funding for making grants at our discretion. In this post we discuss:

  • The decision to allocate the $2.96 million to the Against Malaria Foundation (AMF) (70 percent) and the Schistosomiasis Control Initiative (SCI) (30 percent).
  • Our recommendation that donors give to GiveWell for granting to top charities at our discretion so that we can direct the funding to the top charity or charities with the most pressing funding need. For donors who prefer to give directly to our top charities, we continue to recommend giving 70 percent of your donation to AMF and 30 percent to SCI to maximize your impact.

Read More

The post Allocation of discretionary funds from Q1 2018 appeared first on The GiveWell Blog.

Natalie Crispin

Allocation of discretionary funds from Q1 2018

7 years ago

In the first quarter of 2018, we received $2.96 million in funding for making grants at our discretion. In this post we discuss:

  • The decision to allocate the $2.96 million to the Against Malaria Foundation (AMF) (70 percent) and the Schistosomiasis Control Initiative (SCI) (30 percent).
  • Our recommendation that donors give to GiveWell for granting to top charities at our discretion so that we can direct the funding to the top charity or charities with the most pressing funding need. For donors who prefer to give directly to our top charities, we continue to recommend giving 70 percent of your donation to AMF and 30 percent to SCI to maximize your impact.

Allocation of discretionary funds

The allocation of 70 percent of the funds to AMF and 30 percent to SCI follows the recommendation we have made, and continue to make, to donors. For more discussion on this allocation, see our blog post about allocating discretionary funds from the previous quarter.

We also considered the following possibilities for this quarter:

Helen Keller International (HKI) for stopgap funding in one additional country

We discussed this possibility in our blog post about allocating discretionary funds from the previous quarter. After further discussing this possibility with HKI, our understanding is that (a) the amount of funding needed to fill this gap will likely be small relative to the amount of GiveWell-directed funding that HKI currently holds, and (b) we will have limited additional information in time for this decision round that we could use to compare this new use of funding to HKI’s other planned uses of funding. We will continue discussing this opportunity with HKI and may allocate funding to it in the future. Our current expectation is that we will ask HKI to make the tradeoff between allocating the GiveWell-directed funding it holds to this new opportunity and continuing to hold the funds. Holding the funds gives the current programs more runway (originally designed to fund three years) and gives HKI more flexibility to fund highly cost-effective, unanticipated opportunities in the future. We believe that HKI is currently in a better position to assess cost-effectiveness of the opportunities it has than we are, while we will seek to maximize cost-effectiveness in the longer run by assessing HKI’s track record of cost-effectiveness and comparing that to the cost-effectiveness of other top charities.

We remain open to the possibility that HKI will share information with us that will lead us to conclude that this new opportunity is a better use of funds than our current recommendation of 70 percent to AMF and 30 percent to SCI. In that case, we would allocate funds from the next quarter to fill this funding gap (and could accelerate the timeline on that decision if it were helpful to HKI).

Evidence Action’s Deworm the World Initiative for funding gaps in India and Nigeria

We spoke with Deworm the World about two new funding gaps it has due to unexpected costs in its existing programs in India and Nigeria.

In India, the cost overruns total $166,000. Deworm the World has the option of drawing down a reserve of $5.5 million (from funds donated on GiveWell’s recommendation). The reserve was intended to backstop funds that were expected but not fully confirmed from another funder. Given the small size of the gap relative to the available reserves, our preference is for Deworm the World to use that funding and for us to consider recommending further reserves as part of our end-of-year review of our top charities’ room for more funding.

In Nigeria, there is a funding gap of $1.7 million in the states that Deworm the World is currently operating in. Previous budgets assumed annual treatment for all children, and Deworm the World has since become aware of the existence of areas where worm prevalence is high enough that twice per year treatment is recommended. Our best guess is that AMF and SCI are more cost-effective than Deworm the World’s Nigeria program (see discussion in this post). It is possible that because additional funding would go to support additional treatments in states where programs already operate, the cost to deliver these marginal treatments would be lower. We don’t currently have enough data to analyze whether that would significantly change the cost-effectiveness in this case.

Deworm the World also continues to have a funding gap for expansion to other states in Nigeria. We wrote about this opportunity in our previous post on allocating discretionary funding.

Malaria Consortium for seasonal malaria chemoprevention (SMC)

We continue to see a case for directing additional funding to Malaria Consortium for SMC, as we did last quarter. Our views on this program have not changed. For further discussion, see our previous post on allocating discretionary funding.

What is our recommendation to donors?

We continue to recommend that donors give to GiveWell for granting to top charities at our discretion so that we can direct the funding to the top charity or charities with the most pressing funding need. For donors who prefer to give directly to our top charities, we are continuing to recommend giving 70 percent of your donation to AMF and 30 percent to SCI to maximize your impact. The reasons for this recommendation are the same as in our previous post on allocating discretionary funding.

The post Allocation of discretionary funds from Q1 2018 appeared first on The GiveWell Blog.

Natalie Crispin

Allocation of discretionary funds from Q1 2018

7 years ago

In the first quarter of 2018, we received $2.96 million in funding for making grants at our discretion. In this post we discuss:

  • The decision to allocate the $2.96 million to the Against Malaria Foundation (AMF) (70 percent) and the Schistosomiasis Control Initiative (SCI) (30 percent).
  • Our recommendation that donors give to GiveWell for granting to top charities at our discretion so that we can direct the funding to the top charity or charities with the most pressing funding need. For donors who prefer to give directly to our top charities, we continue to recommend giving 70 percent of your donation to AMF and 30 percent to SCI to maximize your impact.

Allocation of discretionary funds

The allocation of 70 percent of the funds to AMF and 30 percent to SCI follows the recommendation we have made, and continue to make, to donors. For more discussion on this allocation, see our blog post about allocating discretionary funds from the previous quarter.

We also considered the following possibilities for this quarter:

Helen Keller International (HKI) for stopgap funding in one additional country

We discussed this possibility in our blog post about allocating discretionary funds from the previous quarter. After further discussing this possibility with HKI, our understanding is that (a) the amount of funding needed to fill this gap will likely be small relative to the amount of GiveWell-directed funding that HKI currently holds, and (b) we will have limited additional information in time for this decision round that we could use to compare this new use of funding to HKI’s other planned uses of funding. We will continue discussing this opportunity with HKI and may allocate funding to it in the future. Our current expectation is that we will ask HKI to make the tradeoff between allocating the GiveWell-directed funding it holds to this new opportunity and continuing to hold the funds. Holding the funds gives the current programs more runway (originally designed to fund three years) and gives HKI more flexibility to fund highly cost-effective, unanticipated opportunities in the future. We believe that HKI is currently in a better position to assess cost-effectiveness of the opportunities it has than we are, while we will seek to maximize cost-effectiveness in the longer run by assessing HKI’s track record of cost-effectiveness and comparing that to the cost-effectiveness of other top charities.

We remain open to the possibility that HKI will share information with us that will lead us to conclude that this new opportunity is a better use of funds than our current recommendation of 70 percent to AMF and 30 percent to SCI. In that case, we would allocate funds from the next quarter to fill this funding gap (and could accelerate the timeline on that decision if it were helpful to HKI).

Evidence Action’s Deworm the World Initiative for funding gaps in India and Nigeria

We spoke with Deworm the World about two new funding gaps it has due to unexpected costs in its existing programs in India and Nigeria.

In India, the cost overruns total $166,000. Deworm the World has the option of drawing down a reserve of $5.5 million (from funds donated on GiveWell’s recommendation). The reserve was intended to backstop funds that were expected but not fully confirmed from another funder. Given the small size of the gap relative to the available reserves, our preference is for Deworm the World to use that funding and for us to consider recommending further reserves as part of our end-of-year review of our top charities’ room for more funding.

In Nigeria, there is a funding gap of $1.7 million in the states that Deworm the World is currently operating in. Previous budgets assumed annual treatment for all children, and Deworm the World has since become aware of the existence of areas where worm prevalence is high enough that twice per year treatment is recommended. Our best guess is that AMF and SCI are more cost-effective than Deworm the World’s Nigeria program (see discussion in this post). It is possible that because additional funding would go to support additional treatments in states where programs already operate, the cost to deliver these marginal treatments would be lower. We don’t currently have enough data to analyze whether that would significantly change the cost-effectiveness in this case.

Deworm the World also continues to have a funding gap for expansion to other states in Nigeria. We wrote about this opportunity in our previous post on allocating discretionary funding.

Malaria Consortium for seasonal malaria chemoprevention (SMC)

We continue to see a case for directing additional funding to Malaria Consortium for SMC, as we did last quarter. Our views on this program have not changed. For further discussion, see our previous post on allocating discretionary funding.

What is our recommendation to donors?

We continue to recommend that donors give to GiveWell for granting to top charities at our discretion so that we can direct the funding to the top charity or charities with the most pressing funding need. For donors who prefer to give directly to our top charities, we are continuing to recommend giving 70 percent of your donation to AMF and 30 percent to SCI to maximize your impact. The reasons for this recommendation are the same as in our previous post on allocating discretionary funding.

The post Allocation of discretionary funds from Q1 2018 appeared first on The GiveWell Blog.

Natalie Crispin

New research on cash transfers

7 years 1 month ago
Summary
  • There has been a good deal of discussion recently about new research on the effects of cash transfers, beginning with a post by economist Berk Özler on the World Bank’s Development Impact blog. We have not yet fully reviewed the new research, but wanted to provide a preliminary update for our followers about our plans for reviewing this research and how it might affect our views of cash transfers, a program implemented by one of our top charities, GiveDirectly.
  • In brief, the new research suggests that cash transfers may be less effective than we previously believed in two ways. First, cash transfers may have substantial negative effects on non-recipients who live near recipients (“negative spillovers”). Second, the benefits of cash transfers may fade quickly.
  • We plan to reassess the cash transfer evidence base and provide our updated conclusions in the next several months (by November 2018 at the latest). One reason that we do not plan to provide a comprehensive update sooner is that we expect upcoming midline results from GiveDirectly’s “general equilibrium” study, a large and high-quality study explicitly designed to estimate spillover effects, will play a major role in our conclusions. Results from this study are expected to be released in the next few months.
  • Our best guess is that we will reduce our estimate of the cost-effectiveness of cash transfers to some extent, but will likely continue to recommend GiveDirectly. However, major updates to our current views, either in the negative or positive direction, seem possible.

More detail below.

Background

GiveDirectly, one of our top charities, provides unconditional cash transfers to very poor households in Kenya, Uganda, and Rwanda.

Several new studies have recently been released that assess the impact of unconditional cash transfers, including a three-year follow-up study (Haushofer and Shapiro 2018, henceforth referred to as “HS 2018”) on the impact of transfers that were provided by GiveDirectly. Berk Özler, a senior economist at the World Bank, summarized some of this research in two posts on the World Bank Development Impact blog (here and here), noting that the results imply that cash transfers may be less effective than proponents previously believed. In particular, Özler raises the concerns that cash may:

  1. Have negative “spillovers”: i.e., negative effects on households that did not receive transfers but that live near recipient households.
  2. Have quickly-fading benefits: i.e., the standard of living for recipient households may converge to be similar to non-recipient households within a few years of receiving transfers.

Below, we discuss the topics of spillover effects and the duration of benefits of cash transfers in more detail, as well as some other considerations relevant to the effectiveness of cash transfers. In brief:

  • If substantial spillover effects exist, they have the potential to significantly affect our cost-effectiveness estimates for cash transfers. We are uncertain what we will conclude about spillover effects of cash transfers after deeply reviewing all relevant new literature, but we expect that upcoming midline results from GiveDirectly’s “general equilibrium” study will play a major role in our conclusions. Our best guess is that the general equilibrium study and other literature will not imply that GiveDirectly’s program has large negative spillovers, but we remain open to the possibility that we should substantially negatively update our views after reviewing the relevant literature.
  • Several new studies seem to find that cash may have little effect on recipients’ standard of living beyond the first year after receiving a transfer. Our best guess is that after reviewing the relevant research in more detail we will decrease our estimate of the cost-effectiveness of cash transfers to some extent. In the worst (unlikely) case, this factor could lead us to believe that cash is about 1.5-2x less cost-effective than we currently do.
Spillovers

Negative spillovers of cash transfers have the potential to lead us to majorly revise our estimates of the effects of cash; we currently assume that cash does not have major negative or positive spillover effects. At this point, we are uncertain what we will conclude about the likely spillover effects of cash after reviewing all relevant new literature, including GiveDirectly’s forthcoming “general equilibrium” study. Our best guess is that GiveDirectly’s current program does not have large spillover effects, but it seems plausible that we could ultimately conclude that cash either has meaningful negative spillovers or positive spillovers.

We will not rehash the methodological details and estimated effect sizes of HS 2018 in this post. For a basic understanding of the findings and methodological issues, we recommend reading Özler’s posts, the Center for Global Development’s Justin Sandefur’s post, GiveDirectly’s latest post, or Haushofer and Shapiro’s response to Özler’s posts. The basic conclusions that we draw from this research are:

  • Under one interpretation of its findings, HS 2018 measures negative spillover effects that could outweigh the positive effects of cash transfers.1From Sandefur’s post: “Households who had been randomly selected to receive cash were much better off than their neighbors who didn’t. They had $400 more assets—roughly the size of the original transfer, with all figures from here on out in PPP terms—and about $47 higher consumption each month. It looked like an amazing success.
     
    “But when Haushofer and Shapiro compared the whole sample in these villages—half of whom had gotten cash, half of whom hadn’t—they looked no different than a random sample of households in control villages. In fact, their consumption was about $6 per month less ($211 versus $217 a month).
     
    “There are basically two ways to resolve this paradox:
     
    “1) Good data, bad news. Cash left recipients only modestly better off after three years (lifting them from $217 to $235 in monthly consumption), and instead hurt their neighbors (dragging them down from $217 to $188 in monthly consumption). Taking the data at face value, this is the most straightforward interpretation of the results.
     
    “2) Bad data, good news. Alternatively, the $47 gap in consumption between recipients and their neighbors is driven by gains to the former not losses to the latter. The estimates of negative side-effects on neighbors are driven by comparisons with control villages where—if you get into the weeds of the paper—it appears sampling was done differently than in treatment villages. (In short, the $217 isn’t reliable.)” jQuery("#footnote_plugin_tooltip_1").tooltip({ tip: "#footnote_plugin_tooltip_text_1", tipClass: "footnote_tooltip", effect: "fade", fadeOutSpeed: 100, predelay: 400, position: "top right", relative: true, offset: [10, 10] });
  • We do not yet have a strong view on how likely it is that the negative interpretation of HS 2018’s findings is correct. This would require having a deeper understanding of what we should believe about a number of key methodological issues in HS 2018 (see following footnote for two examples).2One methodological issue is how to deal with attrition, as discussed in Haushofer and Shapiro 2018, Pg. 9: “However, there is a statistically significant difference in attrition levels for households in control villages relative to households in treatment villages from endline 1 to endline 2: 6 percentage points more pure control households were not found at endline 2 relative to either group of households in treatment villages. In the analysis of across-village treatment effects and spillover effects we use Lee bounds to deal with this differential attrition; details are given below.”
     
    Another potential issue as described by Özler’s post: “The short-term impacts in Haushofer and Shapiro (2016) were calculated using within-village comparisons, which was a big problem for an intervention with possibility of spillovers, on which the authors had to do a lot of work earlier (see section IV.B in that paper) and in the recent paper. They got around this problem by arguing that spillover effects were small and insignificant. Of course, then came the working paper on negative spillovers on psychological wellbeing mentioned above and now, the spillover effects look sustained and large and unfortunately negative on multiple domains three years post transfers.
     
    “The authors estimated program impacts by comparing T [treatment group] to S [spillover group], instead of the standard comparison of T to C [control group], in the 2016 paper because of a study design complication: researchers randomly selected control villages, but did not collect baseline data in these villages. The lack of baseline data in the control group is not just a harmless omission, as in ‘we lose some power, no big deal.’ Because there were eligibility criteria for receiving cash, but households were sampled a year later, no one can say for certain if the households sampled in the pure control villages at follow-up are representative of the would-be eligible households at baseline.
     
    “So, quite distressingly, we now have two choices to interpret the most recent findings:
     
    “1) We either believe the integrity of the counterfactual group in the pure control villages, in which case the negative spillover effects are real, implying that total causal effects comparing treated and control villages are zero at best. Furthermore, there are no ITT [intention to treat] effects on longer-term welfare of the beneficiaries themselves – other than an increase in the level of assets owned. In this scenario, it is harder to retain confidence in the earlier published impact findings that were based on within-village comparisons – although it is possible to believe that the negative spillovers are a longer-term phenomenon that truly did not exist at the nine-month follow-up.
     
    “2) Or, we find the pure control sample suspect, in which case we have an individually randomized intervention and need to assume away spillover effects to believe the ITT estimates.” jQuery("#footnote_plugin_tooltip_2").tooltip({ tip: "#footnote_plugin_tooltip_text_2", tipClass: "footnote_tooltip", effect: "fade", fadeOutSpeed: 100, predelay: 400, position: "top right", relative: true, offset: [10, 10] }); HS 2018 reports that the potential bias introduced by methodological issues may be able to explain much of the estimated spillover effects.3Haushofer and Shapiro 2018, Pgs. 24-25: “These results appear to differ from those found in the initial endline, where we found positive spillover effects on female empowerment, but no spillover effects on other dimensions. However, the present estimates are potentially affected by differential attrition from endline 1 to endline 2: as described above, the pure control group showed significantly greater attrition than both treatment and spillover households between these endlines. To assess the potential impact of attrition, we bound the spillover effects using Lee bounds (Table 8). This analysis suggests that differential attrition may account for several of these spillover effects. Specifically, for health, education, psychological well-being, and female empowerment, the Lee bounds confidence intervals include zero for all sample definitions. For asset holdings, revenue, and food security, they include zero in two of the three sample definitions. Only for expenditure do the Lee bounds confidence intervals exclude zero across all sample definitions. Thus, we find some evidence for spillover effects when using Lee bounds, although most of them are not significantly different from zero after bounding for differential attrition across treatment groups.” jQuery("#footnote_plugin_tooltip_3").tooltip({ tip: "#footnote_plugin_tooltip_text_3", tipClass: "footnote_tooltip", effect: "fade", fadeOutSpeed: 100, predelay: 400, position: "top right", relative: true, offset: [10, 10] });
  • The mechanism for what may have caused large negative spillovers (if they exist) in HS 2018 is uncertain, though the authors provide some speculation (see footnote).4Haushofer and Shapiro 2018, Pg. 3: “We do not have conclusive evidence of the mechanism behind spillovers, but speculate it could be due to the sale of productive assets by spillover households to treatment households, which in turn reduces consumption among the spillover group. Though not always statistically different from zero, we do see suggestive evidence of negative spillover effects on the value of productive assets such as livestock, bicycles, motorbikes and appliances. We note that GiveDirectly’s current operating model is to provide transfers to all eligible recipients in each village (within village randomization was conducted only for the purpose of research), which may mitigate any negative spillover effects.” jQuery("#footnote_plugin_tooltip_4").tooltip({ tip: "#footnote_plugin_tooltip_text_4", tipClass: "footnote_tooltip", effect: "fade", fadeOutSpeed: 100, predelay: 400, position: "top right", relative: true, offset: [10, 10] }); We would increase our credence in the existence of negative spillover effects if there were strong evidence for a particular mechanism.

One further factor that complicates application of HS 2018’s estimate of spillover effects is that GiveDirectly’s current program is substantially different from the version of its program that was studied in HS 2018. GiveDirectly now provides $1,000 transfers to almost all households in its target villages in Uganda and Kenya; the intervention studied by HS 2018 predominantly involved providing ~$287 transfers to about half of eligible (i.e., very poor) households within treatment villages, and HS 2018 measured spillover effects on eligible households that did not receive transfers.5See this section of our cash transfers intervention report. jQuery("#footnote_plugin_tooltip_5").tooltip({ tip: "#footnote_plugin_tooltip_text_5", tipClass: "footnote_tooltip", effect: "fade", fadeOutSpeed: 100, predelay: 400, position: "top right", relative: true, offset: [10, 10] }); GiveDirectly asked us to note that it now defaults to village-level (instead of within-village) randomization for the studies it participates in, barring exceptional circumstances. Since GiveDirectly’s current program provides transfers to almost all households in its target villages, spillovers of its program may largely operate across villages rather than within villages. These changes to the program and the spillover population of interest may lead to substantial differences in estimated spillover effects.

Fortunately, GiveDirectly is running a large (~650 villages) randomized controlled trial of an intervention similar to its current program that is explicitly designed to estimate the spillover (or “general equilibrium”) effects of GiveDirectly’s program.6From the registration for “General Equilibrium Effects of Cash Transfers in Kenya”: “The study will take place across 653 villages in Western Kenya. Villages are randomly allocated to treatment or control status. In treatment villages, GiveDirectly enrolls and distributes cash transfers to households that meet its eligibility criteria. In order to generate additional spatial variation in treatment density, groups of villages are assigned to high or low saturation. In high saturation zones, 2/3 of villages are targeted for treatment, while in low saturation zones, 1/3 of villages are targeted for treatment. The randomized assignment to treatment status and the spatial variation in treatment intensity will be used to identify direct and spillover effects of cash transfers.”
 
Note that this study will evaluate a variant of GiveDirectly’s program that is different from its current program in that it will not provide transfers to almost all households in target villages. The study will estimate the spillover effects of cash transfers on ineligible (i.e., slightly wealthier) households in treatment villages, among other populations. Since GiveDirectly’s standard program now provides transfers to almost all households in its target villages, estimates of effects on ineligible households may need to be extrapolated to other populations of interest (e.g., households in non-target villages) to be most relevant to GiveDirectly’s current program. jQuery("#footnote_plugin_tooltip_6").tooltip({ tip: "#footnote_plugin_tooltip_text_6", tipClass: "footnote_tooltip", effect: "fade", fadeOutSpeed: 100, predelay: 400, position: "top right", relative: true, offset: [10, 10] }); Midline results from this study are expected to be released in the next few months.

Since we expect GiveDirectly’s general equilibrium study to play a large role in our view of spillovers, we expect that we will not publish an overview of the cash spillovers literature until we’ve had a chance to review its results. However, we see the potential for negative spillover effects of cash as very concerning and it is a high-priority research question for us; we plan to publish a detailed update that incorporates HS 2018, previous evidence for negative spillovers (such as studies on inflation and happiness), the general equilibrium study, and any other relevant literature in time for our November 2018 top charity recommendations at the latest.

Duration of benefits

Several new studies seem to find that cash may have little effect on recipients’ standard of living beyond the first year after receiving a transfer. Our best guess is that after reviewing the relevant research in more detail we will decrease our estimate of the cost-effectiveness of cash to some extent. In the worst (unlikely) case, this could lead us to believe that cash is about 1.5-2x less cost-effective than we currently do.

In our current cost-effectiveness analysis for cash transfers, we mainly consider two types of benefits that households experience due to receiving a transfer:

  1. Increases in short-term consumption (i.e., immediately after receiving the transfer, very poor households are able to spend money on goods such as food).
  2. Increases in medium-term consumption (i.e., recipients may invest some of their cash transfer in ways that lead them to have a higher standard of living in the 1-20 years after first receiving the transfer).

Potential spillover effects aside, our cost-effectiveness estimate for cash has a fairly stable lower bound because we place substantial value on increasing short-term consumption for very poor people, and providing cash allows for more short-term consumption almost by definition. In particular:

  • Our current estimates are consistent with assuming little medium-term benefit of cash transfers. We estimate that about 60% of a typical transfer is spent on short-term goods such as eating more food, and count this as about 40-60% of the benefits of the program.7For our estimate of the proportion of the benefits of cash transfers that come from short-term consumption increases, see row 30 of the “Cash” sheet in our 2018 cost-effectiveness model.
     
    For our estimate of the proportion of transfers that is spent on short-term consumption, we rely on results from GiveDirectly’s randomized controlled trial, which shows investments of $505.94 (USD PPP) (within villages, or $601.88 across villages) on a transfer of $1,525 USD PPP, or about one-third of the total. See Pg. 117 here and Pg. 1 here for total transfer size. jQuery("#footnote_plugin_tooltip_7").tooltip({ tip: "#footnote_plugin_tooltip_text_7", tipClass: "footnote_tooltip", effect: "fade", fadeOutSpeed: 100, predelay: 400, position: "top right", relative: true, offset: [10, 10] }); If we were to instead assume that 100% of the transfer was spent on short-term consumption (i.e., none of it was invested), our estimate of the cost-effectiveness of cash would become about 10-30% worse.8See a version of our cost-effectiveness analysis in which we made this assumption here. The calculations in row 35 of the “Cash” tab show how assuming that 0% of the transfer is invested would affect staff members’ bottom line estimates. jQuery("#footnote_plugin_tooltip_8").tooltip({ tip: "#footnote_plugin_tooltip_text_8", tipClass: "footnote_tooltip", effect: "fade", fadeOutSpeed: 100, predelay: 400, position: "top right", relative: true, offset: [10, 10] }); We think using the 100% short-term consumption estimate may be a reasonable and robust way to model the lower bound of effects of cash given various measurement challenges (discussed below).
  • Nevertheless, our previous estimates of the medium-term benefits of cash transfers may have been too optimistic. Based partially on a speculative model of the investment returns of iron roofs (a commonly-purchased asset for GiveDirectly recipients), most staff assumed that about 40% of a transfer will be invested, and that those investments will lead to roughly 10% greater consumption for 10-15 years.9See rows 5, 8, and 14, “Cash” sheet, 2018 Cost-Effectiveness Analysis – Version 1. jQuery("#footnote_plugin_tooltip_9").tooltip({ tip: "#footnote_plugin_tooltip_text_9", tipClass: "footnote_tooltip", effect: "fade", fadeOutSpeed: 100, predelay: 400, position: "top right", relative: true, offset: [10, 10] }); Some new research discussed in Özler’s first post suggests that there may be little return on investment from cash transfers within 2-4 years after the transfer, though the new evidence is somewhat mixed (see footnote).10See this section of Özler’s post: “This new paper and Blattman’s (forthcoming) work mentioned above join a growing list of papers finding short-term impacts of unconditional cash transfers that fade away over time: Hicks et al. (2017), Brudevold et al. (2017), Baird et al. (2018, supplemental online materials). In fact, the final slide in Hicks et al. states: ‘Cash effects dissipate quickly, similar to Brudevold et al. (2017), but different to Blattman et al. (2014).’ If only they were presenting a couple of months later…”
     
    See also two other recent papers that find positive effects of cash transfers beyond the first year: Handa et al. 2018 and Parker and Vogl 2018. The latter finds intergenerational effects of a conditional cash transfer program in Mexico, so may be less relevant to GiveDirectly’s program. jQuery("#footnote_plugin_tooltip_10").tooltip({ tip: "#footnote_plugin_tooltip_text_10", tipClass: "footnote_tooltip", effect: "fade", fadeOutSpeed: 100, predelay: 400, position: "top right", relative: true, offset: [10, 10] }); Additionally, under the negative interpretation of HS 2018’s results, it finds that cash transfers did not have positive consumption effects for recipients three years post-transfer, though it finds a ~40% increase in assets for treatment households (even in the negative interpretation).11Haushofer and Shapiro 2018, Abstract: “Comparing recipient households to non-recipients in distant villages, we find that transfer recipients have 40% more assets (USD 422 PPP) than control households three years after the transfer, equivalent to 60% of the initial transfer (USD 709 PPP).”
     
    Haushofer and Shapiro 2018, Pg. 28: “Since we have outcome data measured in the short run (~9 months after the beginning of the transfers) and in the long-run (˜3 years after the beginning of transfers), we test equality between short and long-run effects…Results are reported in Table 9. Focusing on the within-village treatment effects, we find no evidence for differential effects at endline 2 compared to endline 1, with the exception of assets, which show a significantly larger treatment effect at endline 2 than endline 1. However, this effect is largely driven by spillovers; for across-village treatment effects, we cannot reject equality of the endline 1 and endline 2 outcomes. This is true for all variables in the across-village treatment effects except for food security and psychological well-being, which show a smaller treatment effect at endline 2 compared to endline 1. Thus, we find some evidence for decreasing treatment effects over time, but for most outcome variables, the endline 1 and 2 outcomes are similar.” jQuery("#footnote_plugin_tooltip_11").tooltip({ tip: "#footnote_plugin_tooltip_text_11", tipClass: "footnote_tooltip", effect: "fade", fadeOutSpeed: 100, predelay: 400, position: "top right", relative: true, offset: [10, 10] }); Note that any benefits from owning iron roofs were not factored in to the consumption estimates in HS 2018.12Haushofer and Shapiro 2018, pgs. 32-33: “Total consumption…Omitted: Durables expenditure, house expenditure (omission not pre-specified for endline 1 analysis)” jQuery("#footnote_plugin_tooltip_12").tooltip({ tip: "#footnote_plugin_tooltip_text_12", tipClass: "footnote_tooltip", effect: "fade", fadeOutSpeed: 100, predelay: 400, position: "top right", relative: true, offset: [10, 10] }); If we imagine the potential worst case scenario implied by these results and assume that the ~40% of a cash transfer that is invested has zero benefits, our cost-effectiveness estimate would get about 2x worse.

Our best guess is that we’ll decrease our estimate for the medium-term effects of cash to some extent, though we’re unsure by how much. Challenging questions we’ll need to consider in order to arrive at a final estimate include:

  • If we continue to assume that about 40% of transfers are invested, and that those investments do not lead to any future gains in consumption, then we are effectively assuming that money spent on investments is wasted. Is this an accurate reflection of reality, i.e. are recipients failing to invest transfers in a beneficial manner?
  • Is our cost-effectiveness model using a reasonable framework for estimating recipients’ standard of living over time? Currently, we only estimate cash’s effects on consumption. However, assets such as iron roofs may provide an increase in standard of living for multiple years even if they do not raise consumption. How, if at all, should we factor this into our estimates?
  • GiveDirectly’s cash transfer program differs in many ways from other programs that have been the subject of impact evaluations. For example, GiveDirectly provides large, one-time transfers whereas many government cash transfers provide smaller ongoing support to poor families. How should we apply new literature on other kinds of cash programs to our estimates of the effects of GiveDirectly?
Next steps

We plan to assess all literature relevant to the impact of cash transfers and provide an update on our view on the nature of spillover effects, duration of benefits, and other relevant issues for our understanding of cash transfers and their cost-effectiveness in time for our November 2018 top charity recommendations at the latest.

Notes   [ + ]

1. ↑ From Sandefur’s post: “Households who had been randomly selected to receive cash were much better off than their neighbors who didn’t. They had $400 more assets—roughly the size of the original transfer, with all figures from here on out in PPP terms—and about $47 higher consumption each month. It looked like an amazing success.
 
“But when Haushofer and Shapiro compared the whole sample in these villages—half of whom had gotten cash, half of whom hadn’t—they looked no different than a random sample of households in control villages. In fact, their consumption was about $6 per month less ($211 versus $217 a month).
 
“There are basically two ways to resolve this paradox:
 
“1) Good data, bad news. Cash left recipients only modestly better off after three years (lifting them from $217 to $235 in monthly consumption), and instead hurt their neighbors (dragging them down from $217 to $188 in monthly consumption). Taking the data at face value, this is the most straightforward interpretation of the results.
 
“2) Bad data, good news. Alternatively, the $47 gap in consumption between recipients and their neighbors is driven by gains to the former not losses to the latter. The estimates of negative side-effects on neighbors are driven by comparisons with control villages where—if you get into the weeds of the paper—it appears sampling was done differently than in treatment villages. (In short, the $217 isn’t reliable.)” 2. ↑ One methodological issue is how to deal with attrition, as discussed in Haushofer and Shapiro 2018, Pg. 9: “However, there is a statistically significant difference in attrition levels for households in control villages relative to households in treatment villages from endline 1 to endline 2: 6 percentage points more pure control households were not found at endline 2 relative to either group of households in treatment villages. In the analysis of across-village treatment effects and spillover effects we use Lee bounds to deal with this differential attrition; details are given below.”
 
Another potential issue as described by Özler’s post: “The short-term impacts in Haushofer and Shapiro (2016) were calculated using within-village comparisons, which was a big problem for an intervention with possibility of spillovers, on which the authors had to do a lot of work earlier (see section IV.B in that paper) and in the recent paper. They got around this problem by arguing that spillover effects were small and insignificant. Of course, then came the working paper on negative spillovers on psychological wellbeing mentioned above and now, the spillover effects look sustained and large and unfortunately negative on multiple domains three years post transfers.
 
“The authors estimated program impacts by comparing T [treatment group] to S [spillover group], instead of the standard comparison of T to C [control group], in the 2016 paper because of a study design complication: researchers randomly selected control villages, but did not collect baseline data in these villages. The lack of baseline data in the control group is not just a harmless omission, as in ‘we lose some power, no big deal.’ Because there were eligibility criteria for receiving cash, but households were sampled a year later, no one can say for certain if the households sampled in the pure control villages at follow-up are representative of the would-be eligible households at baseline.
 
“So, quite distressingly, we now have two choices to interpret the most recent findings:
 
“1) We either believe the integrity of the counterfactual group in the pure control villages, in which case the negative spillover effects are real, implying that total causal effects comparing treated and control villages are zero at best. Furthermore, there are no ITT [intention to treat] effects on longer-term welfare of the beneficiaries themselves – other than an increase in the level of assets owned. In this scenario, it is harder to retain confidence in the earlier published impact findings that were based on within-village comparisons – although it is possible to believe that the negative spillovers are a longer-term phenomenon that truly did not exist at the nine-month follow-up.
 
“2) Or, we find the pure control sample suspect, in which case we have an individually randomized intervention and need to assume away spillover effects to believe the ITT estimates.” 3. ↑ Haushofer and Shapiro 2018, Pgs. 24-25: “These results appear to differ from those found in the initial endline, where we found positive spillover effects on female empowerment, but no spillover effects on other dimensions. However, the present estimates are potentially affected by differential attrition from endline 1 to endline 2: as described above, the pure control group showed significantly greater attrition than both treatment and spillover households between these endlines. To assess the potential impact of attrition, we bound the spillover effects using Lee bounds (Table 8). This analysis suggests that differential attrition may account for several of these spillover effects. Specifically, for health, education, psychological well-being, and female empowerment, the Lee bounds confidence intervals include zero for all sample definitions. For asset holdings, revenue, and food security, they include zero in two of the three sample definitions. Only for expenditure do the Lee bounds confidence intervals exclude zero across all sample definitions. Thus, we find some evidence for spillover effects when using Lee bounds, although most of them are not significantly different from zero after bounding for differential attrition across treatment groups.” 4. ↑ Haushofer and Shapiro 2018, Pg. 3: “We do not have conclusive evidence of the mechanism behind spillovers, but speculate it could be due to the sale of productive assets by spillover households to treatment households, which in turn reduces consumption among the spillover group. Though not always statistically different from zero, we do see suggestive evidence of negative spillover effects on the value of productive assets such as livestock, bicycles, motorbikes and appliances. We note that GiveDirectly’s current operating model is to provide transfers to all eligible recipients in each village (within village randomization was conducted only for the purpose of research), which may mitigate any negative spillover effects.” 5. ↑ See this section of our cash transfers intervention report. 6. ↑ From the registration for “General Equilibrium Effects of Cash Transfers in Kenya”: “The study will take place across 653 villages in Western Kenya. Villages are randomly allocated to treatment or control status. In treatment villages, GiveDirectly enrolls and distributes cash transfers to households that meet its eligibility criteria. In order to generate additional spatial variation in treatment density, groups of villages are assigned to high or low saturation. In high saturation zones, 2/3 of villages are targeted for treatment, while in low saturation zones, 1/3 of villages are targeted for treatment. The randomized assignment to treatment status and the spatial variation in treatment intensity will be used to identify direct and spillover effects of cash transfers.”
 
Note that this study will evaluate a variant of GiveDirectly’s program that is different from its current program in that it will not provide transfers to almost all households in target villages. The study will estimate the spillover effects of cash transfers on ineligible (i.e., slightly wealthier) households in treatment villages, among other populations. Since GiveDirectly’s standard program now provides transfers to almost all households in its target villages, estimates of effects on ineligible households may need to be extrapolated to other populations of interest (e.g., households in non-target villages) to be most relevant to GiveDirectly’s current program. 7. ↑ For our estimate of the proportion of the benefits of cash transfers that come from short-term consumption increases, see row 30 of the “Cash” sheet in our 2018 cost-effectiveness model.
 
For our estimate of the proportion of transfers that is spent on short-term consumption, we rely on results from GiveDirectly’s randomized controlled trial, which shows investments of $505.94 (USD PPP) (within villages, or $601.88 across villages) on a transfer of $1,525 USD PPP, or about one-third of the total. See Pg. 117 here and Pg. 1 here for total transfer size. 8. ↑ See a version of our cost-effectiveness analysis in which we made this assumption here. The calculations in row 35 of the “Cash” tab show how assuming that 0% of the transfer is invested would affect staff members’ bottom line estimates. 9. ↑ See rows 5, 8, and 14, “Cash” sheet, 2018 Cost-Effectiveness Analysis – Version 1. 10. ↑ See this section of Özler’s post: “This new paper and Blattman’s (forthcoming) work mentioned above join a growing list of papers finding short-term impacts of unconditional cash transfers that fade away over time: Hicks et al. (2017), Brudevold et al. (2017), Baird et al. (2018, supplemental online materials). In fact, the final slide in Hicks et al. states: ‘Cash effects dissipate quickly, similar to Brudevold et al. (2017), but different to Blattman et al. (2014).’ If only they were presenting a couple of months later…”
 
See also two other recent papers that find positive effects of cash transfers beyond the first year: Handa et al. 2018 and Parker and Vogl 2018. The latter finds intergenerational effects of a conditional cash transfer program in Mexico, so may be less relevant to GiveDirectly’s program. 11. ↑ Haushofer and Shapiro 2018, Abstract: “Comparing recipient households to non-recipients in distant villages, we find that transfer recipients have 40% more assets (USD 422 PPP) than control households three years after the transfer, equivalent to 60% of the initial transfer (USD 709 PPP).”
 
Haushofer and Shapiro 2018, Pg. 28: “Since we have outcome data measured in the short run (~9 months after the beginning of the transfers) and in the long-run (˜3 years after the beginning of transfers), we test equality between short and long-run effects…Results are reported in Table 9. Focusing on the within-village treatment effects, we find no evidence for differential effects at endline 2 compared to endline 1, with the exception of assets, which show a significantly larger treatment effect at endline 2 than endline 1. However, this effect is largely driven by spillovers; for across-village treatment effects, we cannot reject equality of the endline 1 and endline 2 outcomes. This is true for all variables in the across-village treatment effects except for food security and psychological well-being, which show a smaller treatment effect at endline 2 compared to endline 1. Thus, we find some evidence for decreasing treatment effects over time, but for most outcome variables, the endline 1 and 2 outcomes are similar.” 12. ↑ Haushofer and Shapiro 2018, pgs. 32-33: “Total consumption…Omitted: Durables expenditure, house expenditure (omission not pre-specified for endline 1 analysis)” function footnote_expand_reference_container() { jQuery("#footnote_references_container").show(); jQuery("#footnote_reference_container_collapse_button").text("-"); } function footnote_collapse_reference_container() { jQuery("#footnote_references_container").hide(); jQuery("#footnote_reference_container_collapse_button").text("+"); } function footnote_expand_collapse_reference_container() { if (jQuery("#footnote_references_container").is(":hidden")) { footnote_expand_reference_container(); } else { footnote_collapse_reference_container(); } } function footnote_moveToAnchor(p_str_TargetID) { footnote_expand_reference_container(); var l_obj_Target = jQuery("#" + p_str_TargetID); if(l_obj_Target.length) { jQuery('html, body').animate({ scrollTop: l_obj_Target.offset().top - window.innerHeight/2 }, 1000); } }

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Josh