| A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | LEEP | Overall | Madagascar | Zimbabwe | Sierra Leone | Bolivia | Niger | Angola | Ghana | Rwanda | Uganda | Nigeria | Burundi | Benin | Senegal | Côte d’Ivoire | Uzbekistan | Malawi | Pakistan | Botswana | Source | Notes | |
2 | Active Country | This row indicates which countries are being included in the final cost-effectiveness number. Malawi and Pakistan are excluded since they have already been successes, whilst Botswana is excluded since the program has been shut down | |||||||||||||||||||||
3 | User inputs | ||||||||||||||||||||||
4 | Duration of benefits (years) | 40 | 40 | 40 | 40 | 40 | 40 | 40 | 40 | 40 | 40 | 40 | 40 | 40 | 40 | 40 | 40 | 40 | 40 | 40 | User input | We use 40 years, the same gap as GiveWell do for the income benefits of malaria/deworming prevention. Deworming effects children at a similar age, so seems appropriate here. | |
5 | Discount rate | 4% | 4% | 4% | 4% | 4% | 4% | 4% | 4% | 4% | 4% | 4% | 4% | 4% | 4% | 4% | 4% | 4% | 4% | 4% | User input | ||
6 | Income doublings per DALY averted | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | User input | ||
7 | # Children poisoned / year | ||||||||||||||||||||||
8 | Children (0-19) with lead poisoning (BLL > 5 μg/dL) | 4,850,537 | 5,709,835 | 2,297,191 | 3,231,154 | 12,500,267 | 4,738,129 | 1,731,786 | 1,327,355 | 5,243,550 | 43,178,214 | 3,053,133 | 3,148,829 | 1,896,621 | 5,252,219 | 1,642,279 | 3,431,433 | 41,121,401 | 216,886 | The Toxic Truth Report (UNICEF) | Original source is from The Toxic Truth (pages 67 - 72) | ||
9 | Annual additional children with lead poisoning (BLL > 5 μg/dL) | 242,527 | 285,492 | 114,860 | 161,558 | 625,013 | 236,906 | 86,589 | 66,368 | 262,178 | 2,158,911 | 152,657 | 157,441 | 94,831 | 262,611 | 82,114 | 171,572 | 2,056,070 | 10,844 | We assume that if x 0-19 year olds have a BLL > 5 μg/dL, then x/20 will be additionally have a BLL > 5 μg/dL each year | |||
10 | Proportion of lead poisoning attributable to paint | 20% | 20% | 20% | 20% | 20% | 20% | 20% | 20% | 20% | 20% | 20% | 20% | 20% | 20% | 20% | 20% | 20% | 20% | 20% | LEEP Malawi CEA | We assume that paint accounts for approximately 20% of lead exposure in most low- and middle- income countries. This is based on a subjective estimate from LEEP's cost-effectiveness analysis in Malawi However, is a highly uncertain estimate, with limited data available. A report from Rethink Priorities states: "Our impression from conversations and the gray literature is that lead paint and unsafe recycling of lead acid batteries are the largest sources of exposure in LMICs". Unfortunately, it is challenging to directly estimate this percentage. | |
11 | Annual additional children with lead poisoning due to paint (BLL > 5 μg/dL) | 48,505 | 57,098 | 22,972 | 32,312 | 125,003 | 47,381 | 17,318 | 13,274 | 52,436 | 431,782 | 30,531 | 31,488 | 18,966 | 52,522 | 16,423 | 34,314 | 411,214 | 2,169 | ||||
12 | Health Burden | ||||||||||||||||||||||
13 | Annual DALYs due to lead poisoning (<20 year olds) | 4,062 | 2,498 | 1,556 | 911 | 6,322 | 2,089 | 2,062 | 1,657 | 6,134 | 20,610 | 3,059 | 1,816 | 1,430 | 2,436 | 2,622 | 3,675 | 72,455 | 86 | GBD Results | Data from Global Burden of Disease The 0-19 age range is selected to be consistent with data from the Toxic Truth Report | ||
14 | Annual DALYs from lead poisoning for 1 child | 0.0008 | 0.0004 | 0.0007 | 0.0003 | 0.0005 | 0.0004 | 0.0012 | 0.0012 | 0.0012 | 0.0005 | 0.0010 | 0.0006 | 0.0008 | 0.0005 | 0.0016 | 0.0011 | 0.0018 | 0.0004 | DALYs each year, for an average child poisoned by lead | |||
15 | Present value lifetime DALYs from lead poisoning for 1 child | 0.017 | 0.009 | 0.014 | 0.006 | 0.010 | 0.009 | 0.025 | 0.026 | 0.024 | 0.010 | 0.021 | 0.012 | 0.016 | 0.010 | 0.033 | 0.022 | 0.036 | 0.008 | We estimate how many DALYs the average child will experience over their lifetime. We use a present value formula, discounting future years. This is the same method that GiveWell use to estimate longterm health benefits [see this cell for an example] | |||
16 | Income Burden | ||||||||||||||||||||||
17 | Average Blood Lead Levels (BLL) | 4.8 | 9.2 | 6.3 | 6.5 | 12.1 | 4.2 | 2.9 | 3.7 | 3.8 | 4.9 | 5.6 | 5.4 | 3.9 | 5.1 | 2.4 | 4.7 | 4.9 | 3.9 | Data from Leadpollution.org | We take a simple mean average of blood lead levels (BLL) in each country. | ||
18 | IQ loss for average child | 1.23 | 3.49 | 2.00 | 2.10 | 4.30 | 0.92 | 0.26 | 0.67 | 0.72 | 1.28 | 1.64 | 1.54 | 0.77 | 1.39 | 0.00 | 1.18 | 1.28 | 0.77 | Rethink Priorities - relationship between Blood lead level (μg/dl) and IQ loss | We use a function mapping BLL to IQ, estimated by Rethink Priorities' report on lead exposure. This function comes from estimates in Lanphear et al. (2005) Copied below is a table illustrating IQ loss from a 1 unit increase in BLL (μg/dl), at different BLLs. Blood lead level (μg/dl) | IQ loss per μg/dl [95% confidence interval] ------------------------------------------------------------------------------------------------------ 0-2.4 | - 2.4-10 | 0.51 [0.32-0.70] 10-20 | 0.19 [0.12-0.26] 20+ | 0.11 [0.07-0.15] | ||
19 | % income loss from 1 drop in IQ | 0.0022 | 0.0022 | 0.0022 | 0.0022 | 0.0022 | 0.0022 | 0.0022 | 0.0022 | 0.0022 | 0.0022 | 0.0022 | 0.0022 | 0.0022 | 0.0022 | 0.0022 | 0.0022 | 0.0022 | 0.0022 | 0.0022 | Income benefits of Education | We estimate this based on our analysis about the income returns to improved education - see here | |
20 | % income loss from lead poisoning for 1 child | 0.27% | 0.77% | 0.44% | 0.46% | 0.95% | 0.20% | 0.06% | 0.15% | 0.16% | 0.28% | 0.36% | 0.34% | 0.17% | 0.30% | 0.00% | 0.26% | 0.28% | 0.17% | ||||
21 | Ln(income) lost each year, for an average child poisoned by lead | 0.003 | 0.008 | 0.004 | 0.005 | 0.009 | 0.002 | 0.001 | 0.001 | 0.002 | 0.003 | 0.004 | 0.003 | 0.002 | 0.003 | 0.000 | 0.003 | 0.003 | 0.002 | Converts income to ln(income) | |||
22 | Present value lifetime ln(income) cost from lead poisoning for 1 child | 0.08 | 0.21 | 0.12 | 0.13 | 0.26 | 0.06 | 0.02 | 0.04 | 0.04 | 0.08 | 0.10 | 0.09 | 0.05 | 0.08 | 0.00 | 0.07 | 0.08 | 0.05 | We assume the average child poisoned by lead will earn income approximately 10 years after poisoning, and then continue for 40 years. This is the same method that GiveWell use to estimate longterm economic benefits [see this cell for an example]. We multiply by 2 to account for the same household multiplier as deworming. | |||
23 | Present value income doublings lost from lead poisoning for 1 child | 0.11 | 0.31 | 0.18 | 0.19 | 0.38 | 0.08 | 0.02 | 0.06 | 0.06 | 0.11 | 0.14 | 0.14 | 0.07 | 0.12 | 0.00 | 0.10 | 0.11 | 0.07 | Converts ln(income) to income doublings | |||
24 | Time inputs | ||||||||||||||||||||||
25 | Paint market growth rate | 5% | 5% | 5% | 5% | 5% | 5% | 5% | 5% | 5% | 5% | 5% | 5% | 5% | 5% | 5% | 5% | 5% | 5% | 5% | Paint market growth rate and market share of solvent vs water-based.xlsx | We assume a prior of 5% growth in the paint market, based on LEEP's anlaysis | |
26 | Frequency of repainting or replacement | 3.5% | 3.5% | 3.5% | 3.5% | 3.5% | 3.5% | 3.5% | 3.5% | 3.5% | 3.5% | 3.5% | 3.5% | 3.5% | 3.5% | 3.5% | 3.5% | 3.5% | 3.5% | 4% | Assumption | Once anti-lead regulations are put in place, people replace leaded paint with non-lead paint. There is little empirical evidence on how frequently people repaint their homes, but a rate of 3.5% per year implies half of houses are repainted every 20 years. This lines up with anecodetal evidence from LEEP | |
27 | Compliance with regulation (after passing law) | 50% | 50% | 50% | 50% | 50% | 50% | 50% | 50% | 50% | 50% | 50% | 50% | 50% | 50% | 50% | 50% | 50% | 50% | 50% | We assume that approximately 50% of paint manufacturers (by market share) will comply with new lead paint regulations. We have high uncertainty over this number, as no follow-up study has yet been conducted to see if the sale of leaded paints does fall following government regulation. Follow-up studies conducted by LEEP should better inform this number. | ||
28 | Change in children poisoned before regulation (%) | 1.00% | 1.00% | 1.00% | 1.00% | 1.00% | 1.00% | 1.00% | 1.00% | 1.00% | 1.00% | 1.00% | 1.00% | 1.00% | 1.00% | 1.00% | 1.00% | 1.00% | 1.00% | Calculation | The number of children poisoned grows proportionally to the growth of the paint market. This is then discounted back by 4% for each future year | ||
29 | Change in children poisoned after regulation (%) | -3.25% | -3.25% | -3.25% | -3.25% | -3.25% | -3.25% | -3.25% | -3.25% | -3.25% | -3.25% | -3.25% | -3.25% | -3.25% | -3.25% | -3.25% | -3.25% | -3.25% | -3.25% | Calculation | The number of children poisoned declines as lead-painted houses are slowly replaced with lead-free painted houses. This is then discounted back by 4% for each future year | ||
30 | Scenario 1: LEEP launches a program in country | ||||||||||||||||||||||
31 | Year in which regulations are implemented | 2023 | 2024 | 2024 | 2024 | 2024 | 2025 | 2025 | 2025 | 2025 | 2025 | 2025 | 2025 | 2025 | 2025 | 2025 | 2022 | 2024 | 2023 | Assumption | An input, based upon when we expect regulations to start taking effect. Inputs are based on how much time LEEP has spent in the country | ||
32 | Children poisoned before regulation | 48,505 | 114,768 | 46,174 | 64,946 | 251,255 | 143,570 | 52,475 | 40,220 | 158,885 | 1,308,343 | 92,513 | 95,413 | 57,470 | 159,147 | 49,763 | 0 | 826,540 | 2,169 | ||||
33 | Children poisoned after regulation | 1,116,414 | 1,311,117 | 527,491 | 741,951 | 2,870,365 | 1,084,821 | 396,502 | 303,905 | 1,200,540 | 9,885,891 | 699,032 | 720,942 | 434,242 | 1,202,525 | 376,009 | 791,212 | 9,442,472 | 49,919 | ||||
34 | Total children poisoned | 1,164,919 | 1,425,884 | 573,664 | 806,898 | 3,121,620 | 1,228,391 | 448,977 | 344,125 | 1,359,425 | 11,194,234 | 791,545 | 816,355 | 491,711 | 1,361,672 | 425,772 | 791,212 | 10,269,012 | 52,088 | ||||
35 | Present value DALYs due to lead paint | 20,081 | 12,841 | 7,999 | 4,683 | 32,498 | 11,148 | 11,004 | 8,843 | 32,735 | 109,989 | 16,325 | 9,691 | 7,631 | 13,000 | 13,993 | 17,443 | 372,451 | 425 | ||||
36 | Present value Income doublings lost due to lead paint | 126,436 | 437,405 | 101,093 | 149,469 | 1,178,732 | 100,028 | 10,163 | 20,244 | 86,118 | 1,265,533 | 114,497 | 110,718 | 33,372 | 166,237 | 0 | 82,302 | 1,160,935 | 3,535 | ||||
37 | Income doubling-equivalents lost | 166,598 | 463,086 | 117,090 | 158,835 | 1,243,728 | 122,324 | 32,171 | 37,930 | 151,588 | 1,485,511 | 147,147 | 130,100 | 48,635 | 192,237 | 27,985 | 117,187 | 1,905,837 | 4,385 | Describes both health and economic impacts in terms of income-doublings lost | |||
38 | Scenario 2: Government intervenes several years later than LEEP | ||||||||||||||||||||||
39 | Years before government would intervene without LEEP | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | This is how many years we estimate LEEP will bring forward regulations by. We are highly uncertain when to expect the government to intervene, in absence of LEEP's action. LEEP very roughly estimates a time lag of (at least) 5 years, although they are also highly uncertain of this. | ||
40 | Year of regulation without LEEP intervention | 2028 | 2029 | 2029 | 2029 | 2029 | 2030 | 2030 | 2030 | 2030 | 2030 | 2030 | 2030 | 2030 | 2030 | 2030 | 2027 | 2029 | 2028 | ||||
41 | Children poisoned before regulation | 298,406 | 411,881 | 165,709 | 233,080 | 901,711 | 392,586 | 143,490 | 109,980 | 434,463 | 3,577,605 | 252,973 | 260,902 | 157,148 | 435,182 | 136,074 | 175,038 | 2,966,307 | 13,343 | ||||
42 | Children poisoned after regulation | 1,069,100 | 1,250,505 | 503,106 | 707,652 | 2,737,671 | 1,030,246 | 376,555 | 288,616 | 1,140,143 | 9,388,553 | 663,865 | 684,673 | 412,396 | 1,142,028 | 357,093 | 760,557 | 9,005,958 | 47,804 | ||||
43 | Total children poisoned | 1,367,505 | 1,662,386 | 668,814 | 940,732 | 3,639,383 | 1,422,832 | 520,045 | 398,597 | 1,574,606 | 12,966,157 | 916,837 | 945,574 | 569,544 | 1,577,210 | 493,166 | 935,594 | 11,972,265 | 61,146 | ||||
44 | Present value DALYs due to lead paint | 23,573 | 14,971 | 9,325 | 5,460 | 37,888 | 12,913 | 12,746 | 10,243 | 37,917 | 127,399 | 18,909 | 11,225 | 8,839 | 15,058 | 16,208 | 20,626 | 434,227 | 499 | ||||
45 | Present value Income doublings lost due to lead paint | 148,424 | 509,954 | 117,860 | 174,260 | 1,374,240 | 115,861 | 11,772 | 23,448 | 99,749 | 1,465,853 | 132,620 | 128,243 | 38,655 | 192,550 | 0 | 97,320 | 1,353,492 | 4,150 | ||||
46 | Income doubling-equivalents lost | 195,570 | 539,895 | 136,511 | 185,180 | 1,450,016 | 141,687 | 37,264 | 43,933 | 175,582 | 1,720,651 | 170,438 | 150,694 | 56,333 | 222,666 | 32,415 | 138,572 | 2,221,946 | 5,148 | Describes both health and economic impacts in terms of income-doublings lost | |||
47 | Expected value of LEEP | ||||||||||||||||||||||
48 | Additional income doublings attributable to LEEP's intervention (conditional on successful program) | 28,972 | 76,809 | 19,421 | 26,345 | 206,289 | 19,363 | 5,092 | 6,004 | 23,995 | 235,140 | 23,292 | 20,593 | 7,698 | 30,429 | 4,430 | 21,385 | 316,108 | 763 | ||||
49 | Prior on successful execution of a LEEP program | 67% | 83% | 67% | 67% | 60% | 60% | 60% | 50% | 48% | 50% | 50% | 50% | 50% | 50% | 50% | 100% | 100% | 0% | [redacted] | [redacted] | ||
50 | Expected children saved from lead poisoning | 135,733 | 196,297 | 63,750 | 89,669 | 310,657 | 116,664 | 42,641 | 27,236 | 103,287 | 885,961 | 62,646 | 64,610 | 38,916 | 107,769 | 33,697 | 144,382 | 1,703,253 | 0 | ||||
51 | Expected Income doubling-equivalents gained (total program) | 19,412 | 63,751 | 13,012 | 17,651 | 123,773 | 11,618 | 3,055 | 3,002 | 11,517 | 117,570 | 11,646 | 10,297 | 3,849 | 15,214 | 2,215 | 21,385 | 316,108 | 0 | ||||
52 | Program Cost | ||||||||||||||||||||||
53 | Rough % costs by country for 2023 (adjusted) | 7% | 7% | 0% | 4% | 7% | 7% | 6% | 4% | 6% | 13% | 4% | 4% | 6% | 6% | 4% | 3% | 13% | [redacted] | LEEP provided data on the % of funding that went to different countries in 2023. This helps us estimate approximate cost per country per year, going forward | |||
54 | Cost per country per year | $900,000 | $64,585 | $64,585 | $3,355 | $31,873 | $64,585 | $64,585 | $52,842 | $31,873 | $52,842 | $120,783 | $31,873 | $31,873 | $52,842 | $52,842 | $31,873 | $24,324 | $120,783 | NA | Estimate based on data from LEEP | ||
55 | Years in country | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | NA | We assume it takes 5 years for LEEP to fully execute an intervention in each country | ||
56 | Total program cost | $3,766,076 | $322,926 | $322,926 | $16,775 | $159,366 | $322,926 | $322,926 | $264,212 | $159,366 | $264,212 | $603,914 | $159,366 | $159,366 | $264,212 | $264,212 | $159,366 | $121,622 | $603,914 | NA | |||
57 | Initial Cost-effectiveness | ||||||||||||||||||||||
58 | Income doubling-equivalents (in active countries only) | 427,583 | |||||||||||||||||||||
59 | Program Cost (in active countries) | $3,766,076 | |||||||||||||||||||||
60 | $s/Income doubling-equivalents (active countries) | $8.81 | $16.64 | $5.07 | $1.29 | $9.03 | $2.61 | $27.80 | $86.47 | $53.09 | $22.94 | $5.14 | $13.68 | $15.48 | $68.64 | $17.37 | $71.95 | $5.69 | $1.91 | NA | |||
61 | Initial GD estimate (active countries) | 37x | 19x | 64x | 251x | 36x | 124x | 12x | 4x | 6x | 14x | 63x | 24x | 21x | 5x | 19x | 5x | 57x | 170x | ||||
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63 | Adjustments | Adjustments based on external factors (outside the CEA) | |||||||||||||||||||||
64 | Government counterfactual adjustment | We deflate the estimate because regulating lead paint requires government to spend money - money that might otherwise have been put to something valuable | |||||||||||||||||||||
65 | Approximate salary for government official | $505 | $1,267 | $461 | $3,523 | $533 | $2,999 | $2,176 | $966 | $964 | $2,184 | $238 | $1,303 | $1,599 | $2,486 | $2,255 | $645 | $1,597 | $7,738 | We assume a goverment official's salary is approximately the same as the countries' GDP per capita | |||
66 | FTE required in each country | 0.6 | 0.3 | 0.2 | 0.2 | 0.5 | 0.7 | 0.7 | 0.3 | 0.9 | 4.4 | 0.3 | 0.3 | 0.3 | 0.6 | 0.7 | 0.4 | 4.7 | 0.1 | We (very roughly) guess that it takes about 1 FTE of government work, for every 50 million people. These numbers pass a basic sanity check, being in the same order of magnitude as LEEP's guesses for how much resourcing lead exposure regulations require | |||
67 | Annual cost to government | $20,148 | $303 | $380 | $92 | $705 | $267 | $2,099 | $1,523 | $290 | $868 | $9,610 | $71 | $391 | $480 | $1,492 | $1,579 | $258 | $7,506 | $774 | |||
68 | Value of government health spending (xGD) | 2.18 | 2.18 | 2.18 | 2.18 | 2.18 | 2.18 | 2.18 | 2.18 | 2.18 | 2.18 | 2.18 | 2.18 | 2.18 | 2.18 | 2.18 | 2.18 | 2.18 | 2.18 | 2.18 | We assume lead exposure regulation is paid for out of health spending. GiveWell estimate low- and middle income health spending is very roughly 2x as cost-effective as GiveDirectly. GiveWell assumes government health spend is valued at roughly 12.1 WELLBYs per $1000, whereas $1000 given to GiveDirectly generates 5.9 WELLBYs [according to our GiveDirectly analysis]. | ||
69 | Government health cost to double income | $148.62 | $148.62 | $148.62 | $148.62 | $148.62 | $148.62 | $148.62 | $148.62 | $148.62 | $148.62 | $148.62 | $148.62 | $148.62 | $148.62 | $148.62 | $148.62 | $148.62 | $148.62 | $148.62 | This converts the value of government spending in terms of multiples of GiveDirectly, into a value in terms of $s/ income doubling | ||
70 | Lost income doublings | 136 | 2 | 3 | 1 | 5 | 2 | 14 | 10 | 2 | 6 | 65 | 0 | 3 | 3 | 10 | 11 | 2 | 51 | 5 | |||
71 | Income doubling-equivalents gained (after government spending adjustment) | 427,447 | 19,409 | 63,749 | 13,011 | 17,646 | 123,771 | 11,603 | 3,045 | 3,000 | 11,512 | 117,505 | 11,645 | 10,294 | 3,846 | 15,204 | 2,204 | 21,383 | 316,058 | NA | |||
72 | FP adjustments | ||||||||||||||||||||||
73 | Quality of analysis discount | 20% | We adjust our analysis for the fact that, relative to GiveWell's cost-effectiveness analysis, we am more likely to have made an error (since GiveWell dedicate more resources to their cost-effectiveness estimates. As such, they rely less heavily on a starting sceptical prior about any charities' cost-effectiveness [see this blogpost on the rationale for this] | ||||||||||||||||||||
74 | Final cost-effectiveness | ||||||||||||||||||||||
75 | Income doubling-equivalents gained (after all adjustments) | 341,958 | 15,528 | 50,999 | 10,409 | 14,117 | 99,017 | 9,283 | 2,436 | 2,400 | 9,209 | 94,004 | 9,316 | 8,235 | 3,077 | 12,164 | 1,763 | 17,106 | 252,846 | NA | |||
76 | $/ Income doubling | $11.01 | $20.8 | $6.3 | $1.6 | $11.3 | $3.3 | $34.8 | $108.5 | $66.4 | $28.7 | $6.4 | $17.1 | $19.4 | $85.9 | $21.7 | $90.4 | $7.1 | $2.4 | NA | |||
77 | GD Multiple | 29.4x | 15.6x | 51.1x | 200.9x | 28.7x | 99.3x | 9.3x | 3.0x | 4.9x | 11.3x | 50.4x | 18.9x | 16.7x | 3.8x | 14.9x | 3.6x | 45.5x | 135.6x | NA | |||
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83 | Expected Impact stats | ||||||||||||||||||||||
84 | Cost | $3,766,076 | |||||||||||||||||||||
85 | Expected children prevented from lead poisoning | 2,279,535 | |||||||||||||||||||||
86 | Cost to prevent one child from lead poisoning (in expectation) | $1.65 | |||||||||||||||||||||
87 | Equivalent iq increase per child | 0.105 | |||||||||||||||||||||
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