The Value of Preventing a Farm Fatality in Northern Ireland

1. The Value of Preventing a Farm Fatality in Northern Ireland C. Cockerill & Prof G. Hutchinson (QUB) Prof S. Chilton (University of Newcastle upon Tyne)

2. Background/Rationale • Agriculture is a significant component of NI economy • High occupational fatality rate: 24.1 fatalities/10,000 workers ie. 6/21 occupational fatalities in one year between 2002/3 (HSENI) • Valuation is important to ensure costs of policy to reduce risk of accidents are outweighed by the benefits • Valuation techniques are well developed eg. DETR (UK) adopted value for the prevention of a road fatality from Carthy et al (1999) • Previous efforts (Monk et al, 1986 (UK); Tormoehlen & Field, 1995 (US); Low & Griffith, 1996 (Australia)) to calculate a value for farm safety are general deficient.

3. Aims • To generate a value for reducing the risk of a fatal farm accident to farmers in Northern Ireland • To investigate if this estimate is transferable to other accident contexts or if a context premium exists This study will provide a prototype that can be applied for all farm accidents types as a means of obtaining a more accurate value for a prevented farm fatality

4. Methods • Stated preference techniques: • Contingent Valuation - Modified Standard Gamble • Matching • CV-MSG: • CV elicits a value for the pain, grief and suffering associated with a non-fatal farm injury • MSG elicits a risk-risk trade-off between the pain, grief and suffering associated with a non-fatal injury and chance of recovery of death • These results are “chained” to estimate the value of preventing a farm fatality • Matching: • elicits preference based values for preventing farm fatalities caused by one accident context relative to another accident context • asks respondent to state the number of deaths prevented from one type of accident that is equivalent to x deaths prevented from another accident

5. Advantages of Adopted Methods • Captures the costs of pain, grief and suffering • Does not rely on wage rates (human capital approach) or the purchase of a safety product (revealed preference approach) • Avoids difficulty of directly trading off risk of death for wealth, e.g. • lack of comprehension of small probabilities associated with fatal risk (Beattie et al, 1998) • Provides estimates for different accident contexts without the need to repeat the CV-MSG task, which would be time consuming and potentially result in respondent fatigue.

6. Survey • Face to face interviews • 2 versions of the questionnaire to allow checks for scope insensitivity, sequencing effects, internal consistency Table 1: Summary Descriptions of severity associated with non fatal injuries (Jones Lee et al, 1985)

7. Sample • 293 Farms in Northern Ireland • Representative of full time, commercial, specialised farms, where farmer, spouse and at least one child under 18 years of age resided on the farm site • Farms selected using a 3 stage approach: • Geographical clusters of 3-4 electoral wards in each of 12 rural districts (i.e. 2 per county) • Farms selected by systematic sampling with proportional allocation in relation to six strata (3 types: Dairy; Cattle & Sheep; Cereal and 2 sizes: 16-40 ESU and 40+ ESU) • Farms were then contacted by telephone to ensure that a spouse and child under 18 years resided on the farm site

8. Results: Value for Preventing a Fatality Table 4: Estimates for Value of Preventing a Farm Machinery Fatality Table 5: Lower and Upper bounds for Value of Preventing a Farm Machinery Fatality

10. Results: Context Effects Table 6: Mean relativities (VPF1/VPF2) M = Machinery Accident F = Fall L = Livestock Accident D = Drowning in Slurry

11. Conclusions • Conservative estimate of Machinery VPF is £1.5 – 2.5 million (€2.2 - 3.6 million) • Total cost requires addition of direct costs, e.g. medical costs • VPF from a road accident was £1 – 1.6 million (Carthy et al, 1999). • Why is road fatality valued less than farm fatality? • Farmers are less constrained by budget • Farmers place a higher value on life and good health • Farmers are self-employed • Farmers sampled were parents • Inflation • No significant context premium, therefore the Machinery VPF could be applied to other types of fatal accidents

12. Questions?

13. Calculating a Value for Preventing a Fatality • VPF = pop. mean of marginal rates of substitution of wealth for risk of death (md) • CV elicits: • WTA compensation for sustaining non-fatal injury (upper bound) • WTP for treatment which provides a quick recovery (lower bound) • mi marginal rate of substitution of wealth for risk of injury, mi is calculated from WTA and WTP for 4 functional forms of utility (Carthy et al, 1999) • MSG elicits a trade off between risk of death for risk of injury • Offers a choice between 2 treatments with chances of success or failure • Treatment 1: Probability of injury i v. Given probability of Death • Treatment 2: Probability of normal health v. Probability of Death • What is the maximum acceptable probability of death for Treatment 2? • This elicits a ratio md/mi (Carthy et al, 1999) • These results are chained: md = md/mi * mi (Direct Approach) • Indirect Approach: md = md/mX * mX/mF * mF