Economic Valuation of Chemicals‑Related Health Effects

Annex B. Recommended values for 38 OECD Member countries and South Africa

Table B.1. Recommended values for all 38 OECD Member countries and South Africa for 9 health effects

For non-surveyed countries, the recommended values are obtained using a benefit transfer function and are presented below with a white background. Values for surveyed countries are shown with a blue background.

Countries	Asthma severity in adult*	Asthma severity in childhood*	Infertility	Very low birth weight	IQ loss	Chronic kidney disease	Miscarriage	Hyper-tension	Non-fatal cancer	Under-active thyroid
	Mean WTP	Mean WTP	Mean VSC	Mean VSC	Mean WTP	Mean VSC	Mean VSC	Mean VSC	Mean VSC	Mean VSC
Australia	516	1 179	100 000	843 000	419	1 112 000	444 000	68 000	989 000	241 000
Austria	514	1 223	122 000	825 000	499	1 141 000	428 000	45 000	923 000	212 000
Belgium	497	1 155	113 000	791 000	502	1 097 000	310 000	35 000	908 000	227 000
Canada	496	973	102 000	990 000	1 010	819 000	439 000	56 000	1 212 000	149 000
Chile	399	933	107 000	662 000	365	1 180 000	381 000	51 000	645 000	187 000
Colombia	416	972	102 000	682 000	377	847 000	365 000	50 000	612 000	188 000
Costa Rica	332	771	91 000	596 000	315	691 000	380 000	47 000	652 000	181 000
Czechia	870	1 372	107 000	1 212 000	369	1 028 000	356 000	57 000	704 000	199 000
Denmark	444	1 035	109 000	734 000	619	869 000	507 000	54 000	870 000	135 000
Estonia	399	940	98 000	649 000	342	983 000	322 000	48 000	577 000	313 000
Finland	479	1 117	114 000	774 000	452	1 089 000	398 000	44 000	883 000	191 000
France	575	1 018	118 000	804 000	482	1 084 000	325 000	66 000	905 000	219 000
Germany	501	1 204	89 000	781 000	454	895 000	413 000	67 000	895 000	223 000
Greece	428	1 031	110 000	714 000	375	1 013 000	362 000	85 000	735 000	236 000
Hungary	387	944	96 000	623 000	317	920 000	310 000	48 000	613 000	193 000
Iceland	454	1 034	110 000	757 000	482	1 004 000	405 000	57 000	846 000	221 000
Ireland	557	1 277	117 000	862 000	639	1 118 000	501 000	62 000	1 110 000	255 000
Israel	536	1 212	118 000	852 000	600	1 079 000	460 000	70 000	1 037 000	244 000
Italy	486	1 210	123 000	2 086 000	422	1 316 000	579 000	61 000	842 000	209 000
Japan	475	1 095	72 000	777 000	465	1 058 000	387 000	29 000	754 000	76 000
Korea	463	1 060	109 000	757 000	830	972 000	630 000	60 000	868 000	218 000
Latvia	407	972	100 000	661 000	338	985 000	332 000	47 000	683 000	191 000
Lithuania	418	979	94 000	644 000	371	967 000	335 000	52 000	738 000	206 000
Luxembourg	491	1 122	113 000	818 000	579	1 053 000	476 000	64 000	1 065 000	247 000
Mexico	430	1 017	105 000	1 635 000	365	843 000	403 000	57 000	559 000	192 000
Netherlands	476	1 107	113 000	1 136 000	749	1 083 000	319 000	62 000	898 000	212 000
New Zealand	460	1 069	107 000	727 000	424	983 000	371 000	67 000	778 000	212 000
Norway	467	1 076	113 000	783 000	495	917 000	428 000	56 000	697 000	223 000
Poland	746	1 334	85 000	681 000	988	983 000	360 000	56 000	713 000	209 000
Portugal	466	1 132	122 000	766 000	595	1 071 000	410 000	74 000	826 000	276 000
Slovak Republic	490	1 181	113 000	747 000	394	1 085 000	388 000	56 000	815 000	199 000
Slovenia	458	1 081	111 000	742 000	420	1 035 000	385 000	48 000	699 000	209 000
Spain	478	1 133	115 000	782 000	449	1 063 000	424 000	60 000	1 049 000	212 000
Sweden	564	906	102 000	769 000	684	1 031 000	386 000	54 000	851 000	210 000
Switzerland	545	1 277	125 000	1 342 000	584	1 146 000	490 000	66 000	1 092 000	239 000
Türkiye	214	505	45 000	572 000	174	940 000	166 000	26 000	203 000	206 000
United Kingdom	517	863	80 000	898 000	365	671 000	426 000	40 000	1 146 000	141 000
United States	611	1 372	105 000	1 447 000	686	737 000	569 000	74 000	1 266 000	159 000
South Africa	271	623	90 000	576 000	383	679 000	300 000	40 000	469 000	160 000

Note: All values have been converted and are expressed in 2023 USD PPP, using PPP for actual individual consumption data for all OECD countries. As PPP for actual individual consumption is not available for South Africa, PPP for household final consumption expenditure is used instead. These values represent mean WTP/VSC estimates. The values in blue represent those from surveyed countries and report the recommended values estimated in the working papers. The other values represent those from non‑surveyed countries and come from the benefit function transfer.

* Use of a readjustment factor based on the total population, assuming that the ratio of asthmatic adults and asthmatic children’s socio-economic characteristics relative to the general population observed in surveyed countries also apply to non-surveyed countries.

Source: Values for non-surveyed countries are compiled by the OECD (OECD, 2026[1]). Values from surveyed countries come from (Dockins et al., 2023[2]; Dussaux et al., 2023[3]; Herrera-Araujo et al., 2026[4]; Hunt et al., 2026[5]; Mourato et al., 2023[6]; Ščasný, Zvěřinová and Dussaux, 2023[7]; Ahtiainen et al., 2026[8]; Dockins et al., 2026[9])

Table B.2. Recommended values for all 38 OECD Member countries and South Africa for asthma risk and skin sensitisation

For the non-surveyed countries, recommended values are derived from income-adjusted transfers and are presented below with a white background. Values for surveyed countries are shown with a blue background.

Countries	Asthma risk among adults*	Asthma risk among children*	Skin sensitisation among skin-sensitised**				Skin sensitisation among non-skin-sensitised**
	Mean VSC		Mean WTP over one year				Mean VSC
			One extra day of flare-up	Severity attribute	Face attribute	Other visible areas attribute	One extra day of flare-up	Severity attribute	Face attribute	Other visible areas attribute
Australia	253 000	475 000	1.2	21.0	58.7	14.5	80	804	2 439	1 424
Austria	270 000	507 000	1.3	22.4	62.6	15.4	85	858	2 602	1 519
Belgium	260 000	488 000	1.2	22.0	61.6	15.2	84	845	2 561	1 495
Canada	265 000	349 000	1.4	0	66.0	17.0	89	729	2 832	1 604
Chile	127 000	239 000	0.6	10.0	28.0	6.9	38	383	1 162	679
Colombia	81 000	153 000	0.3	6.1	17.0	4.2	23	233	705	412
Costa Rica	96 000	181 000	0.5	8.8	24.7	6.1	33	338	1 025	599
Czechia	215 000	403 000	1.0	17.0	47.6	11.7	65	652	1 979	1 155
Denmark	286 000	538 000	1.3	0	65.0	17.0	89	847	2 430	1 599
Estonia	214 000	401 000	0.9	15.8	44.1	10.8	60	604	1 830	1 069
Finland	237 000	446 000	1.2	20.9	58.4	14.4	79	800	2 425	1 416
France	300 000	407 000	1.1	19.0	53.3	13.1	72	730	2 213	1 292
Germany	270 000	507 000	1.3	22.4	62.8	15.5	85	860	2 608	1 523
Greece	144 000	271 000	0.7	12.0	33.6	8.3	46	461	1 397	816
Hungary	191 000	359 000	0.7	13.2	36.9	9.1	50	505	1 532	895
Iceland	269 000	506 000	1.2	21.6	60.3	14.8	82	826	2 505	1 463
Ireland	504 000	947 000	2.4	42.0	117.5	28.9	159	1 611	4 884	2 852
Israel	193 000	362 000	0.9	16.2	45.3	11.1	61	620	1 881	1 098
Italy	208 000	391 000	1.0	18.1	50.6	12.5	69	693	2 102	1 227
Japan	176 000	331 000	1.6	40.0	70.0	16.0	86	774	2 736	1 615
Korea	191 000	360 000	1.0	17.2	48.2	11.9	65	660	2 001	1 168
Latvia	170 000	320 000	0.8	13.4	37.4	9.2	51	513	1 556	908
Lithuania	221 000	416 000	0.9	16.5	46.1	11.3	62	631	1 914	1 117
Luxembourg	585 000	1 100 000	2.7	48.0	134.1	33.0	182	1 838	5 573	3 254
Mexico	88 000	166 000	0.4	7.3	20.5	5.0	28	281	852	498
Netherlands	294 000	553 000	1.3	24.0	67.0	17.0	101	920	2 790	1 630
New Zealand	201 000	378 000	1.0	17.4	48.6	12.0	66	666	2 019	1 179
Norway	386 000	726 000	1.9	33.0	92.3	22.7	125	1 265	3 836	2 240
Poland	381 000	482 000	1.2	19.0	65.0	17.0	82	577	2 700	1 587
Portugal	166 000	313 000	0.8	13.7	38.2	9.4	52	523	1 586	926
Slovak Republic	172 000	324 000	0.8	13.7	38.4	9.5	52	526	1 596	932
Slovenia	204 000	384 000	0.9	16.4	45.9	11.3	62	629	1 909	1 114
Spain	187 000	352 000	1.3	23.0	64.0	17.0	91	928	2 536	1 552
Sweden	276 000	435 000	1.3	22.5	63.0	15.5	85	864	2 619	1 529
Switzerland	319 000	601 000	1.2	29.0	68.0	17.0	89	815	2 725	1 547
Türkiye	392 000	737 000	0.4	7.3	20.4	5.0	28	279	846	494
United Kingdom	195 000	375 000	1.3	20.0	66.0	17.0	91	859	2 784	1 660
United States	301 000	548 000	1.3	21.0	69.0	17.0	88	686	2 529	1 646
South Africa	57 000	108 000	0.3	4.9	13.7	3.4	19	187	568	331

Note: All values have been converted and are expressed in 2023 USD PPP, using PPP for actual individual consumption data for all OECD countries. As PPP for actual individual consumption is not available for South Africa, PPP for household final consumption expenditure is used instead. The values in blue represent those from surveyed countries and report the recommended values estimated in the working papers.

* For asthma risk, values in white cells are derived from an income-adjusted transfer from the United States for asthma risk among adults and from France for asthma risk among children.

** For skin sensitisation, values in white cells are derived from an income-adjusted transfer from the Netherlands, which is the best donor country to minimise on average transfer errors between primary and transferred WTP to avoid one extra day of flare up among skin sensitised. Values in blue represent those from surveyed countries and report the recommended values estimated in the working papers.

Source: Values for non-surveyed countries are compiled by the OECD (OECD, 2026[1]). Values from surveyed countries come from (Appéré et al., 2023[10]; Contu et al., 2026[11])

References

[8] Ahtiainen, H. et al. (2026), “Valuing a reduction in the risk of an underactive thyroid: A large-scale multi-country stated preference approach”, OECD Environment Working Papers, No. 272, OECD Publishing, Paris, https://doi.org/10.1787/4af8f3aa-en.

[10] Appéré, G. et al. (2023), “Valuing a reduction in the risk and severity of asthma: A large scale multi-country stated preference approach”, OECD Environment Working Papers, No. 218, OECD Publishing, Paris, https://doi.org/10.1787/f289d29e-en.

[11] Contu, D. et al. (2026), “Valuing a reduction in the risk of skin sensitisation: A large-scale multi-country stated preference approach”, OECD Environment Working Papers, No. 277, OECD Publishing, Paris, https://doi.org/10.1787/fc305de9-en.

[9] Dockins, C. et al. (2026), “Valuing a reduction in the risk of miscarriage: A large-scale multi-country stated preference approach”, OECD Environment Working Papers, No. 271, OECD Publishing, Paris, https://doi.org/10.1787/23bb6643-en.

[2] Dockins, C. et al. (2023), “Valuing a reduction in the risk of chronic kidney disease: A large scale multi-country stated preference approach”, OECD Environment Working Papers, No. 216, OECD Publishing, Paris, https://doi.org/10.1787/9c93138f-en.

[3] Dussaux, D. et al. (2023), “Valuing a reduction in the risk of infertility: A large scale multi-country stated preference approach”, OECD Environment Working Papers, No. 215, OECD Publishing, Paris, https://doi.org/10.1787/7242509f-en.

[4] Herrera-Araujo, D. et al. (2026), “Valuing a reduction in the risk of non-fatal cancer: A large-scale multi-country stated preference approach”, OECD Environment Working Papers, No. 274, OECD Publishing, Paris, https://doi.org/10.1787/0345660e-en.

[5] Hunt, A. et al. (2026), “Valuing a reduction in the risk of hypertension: A large-scale multi-country stated preference approach”, OECD Environment Working Papers, No. 270, OECD Publishing, Paris, https://doi.org/10.1787/9da8057a-en.

[6] Mourato, S. et al. (2023), “Valuing the avoidance of IQ losses in children: A large scale multi-country stated preference approach”, OECD Environment Working Papers, No. 219, OECD Publishing, Paris, https://doi.org/10.1787/71574eb4-en.

[1] OECD (2026), Benefit transfer of willingness-to-pay estimates for avoiding negative chemicals-related health effects, OECD Publishing, Paris, https://doi.org/10.1787/f11379a0-en.

[7] Ščasný, M., I. Zvěřinová and D. Dussaux (2023), “Valuing a reduction in the risk of very low birth weight: A large scale multi-country stated preference approach”, OECD Environment Working Papers, No. 217, OECD Publishing, Paris, https://doi.org/10.1787/dfd159a1-en.