GiveDirectly (Malawi)

Same model as GiveDirectly (Kenya), with GiveWell's Malawi inputs. Recipients are poorer, so the same transfer counts for more. Shows how a model inherits everything from a parent and overrides only what differs.

Organisation: GiveDirectly
Based on: GiveWell, GiveDirectly's Cash for Poverty Relief Program (November 2024) (spreadsheet)
Published: November 2024, retrieved October 2026
Licence: CC BY-NC-SA 3.0 US
Extends: GiveDirectly (Kenya): inherits every row and overrides a few
Model file: givedirectly-malawi.json · change history

Our model: GiveWell's analysis plus your assumptions and our accounting adjustments (salary sacrifice, credit for shared outcomes).

1.5× as much good per dollar as GiveDirectly · rank 2 of 3

Joint low 0.46×, joint high 4.7×. In its own unit: 1.26 × 10⁻² GiveWell units of value per dollar. Change your assumptions

69% of the uncertainty comes from judgment calls and shared assumptions.
17 inputs on the result's path have no range, so they add no uncertainty here.
Source analysis is 23 months old.

The evaluation

Summary

The same GiveDirectly program, delivered in Malawi. GiveWell estimates it at 0.0128 units of value per dollar, 3.8 times its older cash benchmark and about 1.5 times GiveDirectly’s Kenya program on this site. Our adjustments change it as little as they change Kenya.

Why Malawi comes out higher

The model is identical to the Kenya one; only the inputs that describe Malawi differ.

Recipients are poorer: baseline consumption is about $1.29 a day against $1.79 in Kenya. On a log scale the same transfer is worth more to them.
The transfer is larger ($1,000 against $865) and buys more, at 2.9 PPP dollars per US dollar against 2.4.
Gains last longer relative to year one (136% against 123%): the invested share is larger relative to baseline consumption.
Spillovers are discounted more (external validity 70% against 80%), because Malawi is further from the Western Kenya setting where they were measured.

Child mortality is slightly lower, so the mortality benefit is a smaller share of the total (9% against 12%).

How this page is built

This model inherits every row, explanation and source from GiveDirectly (Kenya) and overrides only the eight inputs above. Malawi’s purchasing-power and child mortality figures come from the shared Malawi facts. If GiveWell revises a part of the model common to both countries, both pages update from a single change.

Biggest uncertainties

The same as Kenya. In addition, the spillover estimate travels further from its evidence base here. GiveWell’s own range on it runs from 28% to 112% of the Kenya-based effect.

Sources

GiveWell, GiveDirectly’s Cash for Poverty Relief Program (November 2024), Malawi column of the cost-effectiveness spreadsheet. CC BY-NC-SA 3.0 US.

What drives this result

Each bar moves one input from the low to the high end of its range, holding the rest at their central values. Log scale, as a multiple of GiveDirectly. Dashed line: central estimate.

0.50×1.0×2.0×5.0×

Value of raising ln(consumption) by 1 for one person for one year

1.1×1.8×

Value of year 2–10 gains, relative to year 1

1.2×1.8×

External validity adjustment (spillovers)

1.2×1.8×

Internal validity adjustment (spillovers)

1.2×1.7×

Baseline annual consumption per person (PPP)

1.3×1.6×

Distributional adjustment (richer non-recipients)

1.3×1.6×

Transfers as a share of total cost

1.4×1.6×

Internal validity adjustment (mortality)

1.4×1.5×

observed estimate judgment or shared assumption

Shows the 8 inputs that move the result most. Ranges come from the source where it gives them; explainers say when a range is ours. Source: charityevals model of GiveWell, GiveDirectly's Cash for Poverty Relief Program (November 2024).

The model, row by row

Click any row for its explanation, source and formula. Formulas link to the rows they use. Values update with your assumptions.

Row	Value	Kind
Program reach How many people a fixed donation reaches. The $1M donation is arbitrary; it cancels out in the per-dollar result.
	$1.00M	Judgment
Arbitrary scale for the calculation. Any amount gives the same result per dollar. Source: cell `New CEA!B4` in the original sheet Defined in GiveDirectly (Kenya).
overridden	$1,000	Observed
Average one-time transfer in Kenya, from GiveDirectly's program data. Source: GiveDirectly program data via GiveWell, cell `New CEA!C5` in the original sheet Defined in GiveDirectly (Kenya); this model changes its value, source.
	84%79% – 89%	Observed
The rest pays for targeting, enrolment, delivery and overhead. Range is GiveWell's own ±6% Monte Carlo bound. Source: GiveDirectly financials via GiveWell, cell `New CEA!B6; Inputs!D5:F5` in the original sheet Defined in GiveDirectly (Kenya).
	840	Calculated
Formula`Donation * Transfers as a share of total cost / Transfer per household (nominal USD)` The source reports 840; this model reproduces it. Defined in GiveDirectly (Kenya).
overriddenshared fact	2.93	Calculated
Formula`Malawi: PPP conversion factor (2017 international $ per US $)` From the shared Malawi facts. A dollar buys about 2.9 times as much in Malawi as in the US. Shared fact from Malawi: every model that needs it reads the same value. Source: World Bank, via GiveWell's GiveDirectly CEA. The source reports 2.93; this model reproduces it. Defined in GiveDirectly (Kenya); this model changes its formula, explainer.
	$2,932	Calculated
Formula`PPP adjustment (2017 prices) * Transfer per household (nominal USD)` Defined in GiveDirectly (Kenya).
overridden	4.5	Observed
Everyone in the household is assumed to share the transfer equally. Source: GiveDirectly data via GiveWell, cell `Baseline!C4` in the original sheet Defined in GiveDirectly (Kenya); this model changes its value, source.
	$651	Calculated
Formula`Transfer per household (PPP) / Average household size` Defined in GiveDirectly (Kenya).
	3,780	Calculated
Formula`Average household size * Households receiving a transfer` Defined in GiveDirectly (Kenya).
Consumption gains to recipients Recipients spend part of the transfer in year one and invest the rest, which pays off over the next nine years. Gains are valued on a log scale: a dollar is worth more to someone poorer.
overridden	$470$399 – $540	Observed
About $1.29 a day, well below Kenya. Range applies GiveWell's ±15% to the Malawi figure. Source: GiveWell baseline consumption estimate, cell `Baseline!C36` in the original sheet Defined in GiveDirectly (Kenya); this model changes its value, source, explainer.
	43%	Estimate
Spent on assets such as livestock, roofs and businesses rather than consumed in year one. Returns on it show up in years 2–10. Source: GiveWell, from GiveDirectly RCTs, cell `New CEA!B15` in the original sheet Defined in GiveDirectly (Kenya).
	57%	Calculated
Formula`1 - Share of the transfer invested` Defined in GiveDirectly (Kenya).
	$371	Calculated
Formula`Share consumed in year 1 * Transfer per person (PPP)` Defined in GiveDirectly (Kenya).
	79%	Calculated
Formula`Extra consumption per person in year 1 (PPP) / Baseline annual consumption per person (PPP)` Defined in GiveDirectly (Kenya).
	0.582	Calculated
Formula`ln(1 + Increase over baseline in year 1)` Defined in GiveDirectly (Kenya).
	0.84	Calculated
Formula`Increase in ln(consumption) in year 1 * Value of raising ln(consumption) by 1 for one person for one year` Defined in GiveDirectly (Kenya).
	3,175	Calculated
Formula`Units of value per person, year 1 * People reached` Defined in GiveDirectly (Kenya).
overridden	136%81.6% – 190%	Estimate
Higher than Kenya because the invested share is larger relative to baseline consumption. Range applies GiveWell's ±40% to the Malawi figure. Source: GiveWell Consumption tab, cell `Consumption!C23` in the original sheet Defined in GiveDirectly (Kenya); this model changes its value, source, explainer.
	7,496	Calculated
Formula`Units of value from year 1 consumption * (1 + Value of year 2–10 gains, relative to year 1)` Defined in GiveDirectly (Kenya).
	90%	Judgment
Gains are uneven: some go to recipients above the average baseline, who value them less on a log scale. GiveWell knocks 10% off. Source: cell `New CEA!B25` in the original sheet Defined in GiveDirectly (Kenya).
	6,746	Calculated
Formula`Units of value from years 1–10 * Distributional adjustment (richer recipients)` The source reports 6,746; this model reproduces it. Defined in GiveDirectly (Kenya).
Spillovers to non-recipients Cash spent locally raises neighbours' incomes too. Based on Egger et al. (2022), heavily discounted for doubts about how well it replicates.
	2.5×	Observed
Each $1 transferred in Western Kenya produced about $2.50 of local economic activity. Only significant at the 10% level. Source: Egger et al. (2022), Econometrica Defined in GiveDirectly (Kenya).
	70.3%	Estimate
Part of the 2.5× is the recipients' own consumption, already counted above. This keeps only the share accruing to others. Source: GiveWell GE tab, cell `GE!B7` in the original sheet Defined in GiveDirectly (Kenya).
	60%27% – 93%	Judgment
Regresses a single, marginally significant study towards GiveWell's prior that spillovers are smaller. Range is GiveWell's own, and very wide. Source: cell `New CEA!B33; Inputs!D33:F33` in the original sheet Defined in GiveDirectly (Kenya).
overridden	70%28% – 112%	Judgment
Lower than Kenya: Malawi is further from the Egger et al. setting. Range applies GiveWell's ±60%. Source: cell `New CEA!C34` in the original sheet Defined in GiveDirectly (Kenya); this model changes its value, source, explainer.
	80%60% – 100%	Judgment
Spillovers partly reach better-off traders and business owners, who value a dollar less. Source: cell `New CEA!B35; Inputs!D35:F35` in the original sheet Defined in GiveDirectly (Kenya).
	59.1%	Calculated
Formula`Egger et al. economic multiplier * Distributional adjustment (richer non-recipients) * Share of the multiplier that isn't recipients' own gain * Internal validity adjustment (spillovers) * External validity adjustment (spillovers)` Defined in GiveDirectly (Kenya).
	3,985	Calculated
Formula`Recipient consumption benefits * Spillover benefits as a share of recipient benefits` The source reports 3,985; this model reproduces it. Defined in GiveDirectly (Kenya).
Mortality Preliminary evidence that cash reduces child deaths, halved for doubts about the study.
overridden	15.1%	Observed
From GiveDirectly's household rosters. Source: GiveWell Equivalence tab, cell `Equivalence!C20/C41` in the original sheet Defined in GiveDirectly (Kenya); this model changes its value, source.
	572	Calculated
Formula`Share of people reached who are under five * People reached` Defined in GiveDirectly (Kenya).
overriddenshared fact	4.01%	Calculated
Formula`Malawi: Under-five mortality rate (2023)` National average, from the shared Malawi facts. Shared fact from Malawi: every model that needs it reads the same value. Source: UN IGME via GiveWell. The source reports 4.01%; this model reproduces it. Defined in GiveDirectly (Kenya); this model changes its label, formula, explainer.
	5.7%	Observed
57 per 1,000. Compared with the national rate for the same years to estimate how much riskier GiveDirectly's target population is. Source: cell `New CEA!B44` in the original sheet Defined in GiveDirectly (Kenya).
shared fact	4.63%	Calculated
Formula`Kenya: Under-five mortality rate (2016)` National rate in the Egger et al. study period, from the shared Kenya facts. Shared fact from Kenya: every model that needs it reads the same value. Source: UNICEF via GiveWell. Defined in GiveDirectly (Kenya).
	1.23×	Calculated
Formula`Under-five mortality in the Egger et al. sample (2015–2017) / Under-five mortality, national (2016)` Defined in GiveDirectly (Kenya).
	4.94%	Calculated
Formula`Under-five mortality rate, Malawi (2023) * Extra risk in GiveDirectly's target population` Defined in GiveDirectly (Kenya).
	45%	Observed
A 45% drop in all-cause child mortality. Preliminary and unpublished at the time. Source: Preliminary results from the Egger et al. Kenya trial, cell `New CEA!B48` in the original sheet Defined in GiveDirectly (Kenya).
	50%25% – 75%	Judgment
Halves the effect because one preliminary result this large is likely an overestimate. Range is GiveWell's own. Source: cell `New CEA!B49; Inputs!D49:F49` in the original sheet Defined in GiveDirectly (Kenya).
	22.5%	Calculated
Formula`Reduction in under-five mortality from the transfer (unadjusted) * Internal validity adjustment (mortality)` Defined in GiveDirectly (Kenya).
	6.36	Calculated
Formula`Children under five reached * Under-five mortality in the target population * Reduction in under-five mortality (adjusted)` Defined in GiveDirectly (Kenya).
	737	Calculated
Formula`Under-five deaths averted * Value of averting the death of a child under five` Defined in GiveDirectly (Kenya).
	50%	Judgment
Cash plausibly reduces adult deaths too, but there is little data. GiveWell adds half the child benefit. Source: cell `New CEA!B54` in the original sheet Defined in GiveDirectly (Kenya).
	1,106	Calculated
Formula`Units of value from child deaths averted * (1 + Adult mortality benefits, as a share of child benefits)` The source reports 1,106; this model reproduces it. Defined in GiveDirectly (Kenya).
Supplementary adjustments Effects GiveWell doesn't model explicitly, each as a % of the modelled benefits.
	10%	Judgment
Fewer illnesses short of death. Source: cell `New CEA!B59` in the original sheet Defined in GiveDirectly (Kenya).
	5%	Judgment
Long-run gains for children, e.g. from better nutrition. Source: cell `New CEA!B60` in the original sheet Defined in GiveDirectly (Kenya).
	2%	Judgment
Net effect of the donation drawing in, or displacing, other funders. Source: cell `New CEA!B61` in the original sheet Defined in GiveDirectly (Kenya).
	-2%	Judgment
Transfers that are diverted or lost. Source: cell `New CEA!B62` in the original sheet Defined in GiveDirectly (Kenya).
	-5%	Judgment
Money given for one country may free up GiveDirectly money for less cost-effective work. Source: cell `New CEA!B63` in the original sheet Defined in GiveDirectly (Kenya).
	-2%	Judgment
Non-recipients may feel worse off by comparison. Source: cell `New CEA!B64` in the original sheet Defined in GiveDirectly (Kenya).
	108%	Calculated
Formula`1 + Reduced morbidity + Developmental effects + Leverage and funging + Risk of wastage or fraud + Within-organisation fungibility + Psychological spillovers` Defined in GiveDirectly (Kenya).
Bottom line
	11,837	Calculated
Formula`Recipient consumption benefits + Spillover benefits + Mortality benefits` Defined in GiveDirectly (Kenya).
	12,784	Calculated
Formula`Modelled benefits (consumption, spillovers, mortality) * Total adjustment factor` Defined in GiveDirectly (Kenya).
	1.28 × 10⁻²	Calculated
Formula`Total benefits / Donation` The source reports 1.28 × 10⁻²; this model reproduces it. Defined in GiveDirectly (Kenya).
	3.35 × 10⁻³	Estimate
GiveWell still expresses everything as a multiple of this older figure ("× cash"). Kept so this model reproduces GiveWell's headline numbers. Source: cell `New CEA!B28` in the original sheet Defined in GiveDirectly (Kenya). Context only: the bottom line doesn't use this row.
	3.81×	Calculated
Formula`Units of value per dollar / GiveWell's previous estimate for cash (units of value / $)` The source reports 3.81×; this model reproduces it. Defined in GiveDirectly (Kenya). Context only: the bottom line doesn't use this row.
Your dollar's share charityevals adjustments on top of GiveWell's model. With the default assumptions they change little for GiveDirectly, because most of its cost is the transfers themselves.
charityevals	8%4% – 12%	Judgment
About 16% of GiveDirectly's costs are not transfers (see the cost share above). We guess half of that is staff pay. GiveDirectly publishes audited financials that could pin this down; we haven't extracted them yet. Range is ours. Defined in GiveDirectly (Kenya).
charityevals	15%0% – 35%	Judgment
How much less staff earn than they could elsewhere, as a share of what they'd earn. Field staff in Kenya are paid at or near local market rates; headquarters staff in tech, finance and operations likely take a larger cut. Range is ours. Defined in GiveDirectly (Kenya).
	$14,118	Calculated
Formula`Donation * Staff pay as a share of total cost * Pay gap versus comparable for-profit jobs / (1 - Pay gap versus comparable for-profit jobs)` Defined in GiveDirectly (Kenya).
	98.6%	Calculated
Formula`Donation / (Donation + if(Count staff salary sacrifice as a donation, Salary forgone by staff (in-kind donation), 0))` Defined in GiveDirectly (Kenya).
	1.26 × 10⁻²	Calculated
Formula`Units of value per dollar * Share of the full cost paid by cash donations` Defined in GiveDirectly (Kenya).

1Sinergia Animal157×
2GiveDirectly (Malawi)1.5×
3GiveDirectly (Kenya)1.0×

Your assumptions

Moral weights and exchange rates are judgment calls. Drag them and every result on the site recomputes.

How impact is counted

Count staff salary sacrifice as a donation

What this means

Many nonprofit staff earn less than they could in comparable for-profit jobs. That gap is a donation in kind: the staff pay part of the cost of the impact. When this is on, the cost of the work is cash spending plus that forgone salary, so your dollar is credited with only its share of the impact.

When it's off, as in GiveWell's and ACE's models, all of the impact is credited to cash donations. Each model estimates its own staff costs and pay gap, and says how.

Give full credit for outcomes shared with other organisations

What this means

Most outcomes need several organisations: a corporate pledge follows campaigns by several groups. Off (the default, and ACE's approach) credits each charity with its estimated share. On credits each with the whole outcome, on the view that removing any necessary link would have stopped it.

Shares never add up across charities under full credit, so comparisons between charities that share outcomes get harder. Use it to see how much a ranking depends on the choice.

Exchange rates between causes

Units of value per human DALY averted2

Why this value

Bridge from health to GiveWell's units. A rough derivation: GiveWell values an under-five death at 116 units, and a child death in sub-Saharan Africa costs roughly 55–65 years of life, so one year of healthy life is worth about 2 units.

This ignores GiveWell's age weighting and treats years lost to disability like years lost to death. Ours, not GiveWell's.

Human DALYs per suffering-adjusted day1.10 × 10⁻³

2.70 × 10⁻⁴2.70 × 10⁻³

Why this value

Suffering-adjusted days (SADs), from Ambitious Impact, count days of animal suffering weighted by pain intensity and by a species' welfare range relative to humans. ACE uses them.

We read one SAD as one human-equivalent day of disabling-level pain and give that day a disability weight of 0.4 (0.1–1.0), so one SAD is 0.4 / 365 DALY. AIM doesn't publish an official SAD-to-DALY rate, so this is the least grounded number on the site and often decides whether animal charities rank above or below global health.

Extra weight on animal suffering, beyond welfare ranges1×

0.05×1×

Why this value

SADs already scale suffering by each species' welfare range. At 1 you take those welfare ranges at face value. Below 1 you discount animal suffering further, for example because you're unsure animals are sentient or because you give more weight to humans for other reasons.

Moral weights

Value of raising ln(consumption) by 1 for one person for one year1.44

1.081.8

Why this value

GiveWell defines one unit of value as doubling one person's consumption for one year. Doubling raises ln(consumption) by ln 2, so one unit of ln(consumption) is worth 1/ln 2 ≈ 1.44 units.

The low and high ends are the ±25% uniform range GiveWell itself uses in its Monte Carlo inputs. Moving this moves the value of consumption gains relative to deaths averted.

Value of averting the death of a child under five116

70160

Why this value

GiveWell's moral weight: averting one under-five death is worth as much as doubling consumption for 116 person-years. It comes from a mix of donor surveys, a survey of people in Ghana and Kenya, and staff judgment.

GiveWell doesn't vary this in its own uncertainty analysis. The 70–160 range is ours, to show how much the ranking leans on it.

Economics

Annual discount rate4%

0%8%

Why this value

How much less a benefit a year from now counts than one today, compounding. GiveWell and ACE both use 4%.

Used where a model discounts explicitly (ACE's cage-free campaigns). GiveWell's GiveDirectly model has the 4% built into its year 2–10 consumption ratio, so changing this doesn't affect it yet.

1.5× as much good per dollar as GiveDirectly · rank 2 of 3

The evaluation

Summary

Why Malawi comes out higher

How this page is built

Biggest uncertainties

Sources

What drives this result

The model, row by row

Program reach

Consumption gains to recipients

Spillovers to non-recipients

Mortality

Supplementary adjustments

Bottom line

Your dollar's share