An empirical examination of risk equalisation in a regulated community rated health insurance market

Despite universal access entitlements to the public healthcare system in Ireland, over half the population is covered by voluntary private health insurance. The market operates on the basis of community rating, open enrolment and lifetime cover. A set of minimum benefits also exists, and two risk equalisation schemes have been put in place but neither was implemented. These schemes have proved highly controversial. To date, the debate has primarily consisted of qualitative arguments. This study adds a quantitative element by analysing a number of pertinent issues. A model of a community rated insurance market is developed, which shows that community rating can only be maintained in a competitive market if all insurers in the market have the same risk profile as the market overall. This has relevance to the Irish market in the aftermath of a Supreme Court decision to set aside risk equalisation. Two reasons why insurers’ risk profiles might differ are adverse selection and risk selection. Evidence is found of the existence of both forms of selection in the Irish market. A move from single rate community rating to lifetime community rating in Australia had significant consequences for take-up rates and the age profile of the insured population. A similar move has been proposed in Ireland. It is found that, although this might improve the stability of community rating in the short term, it would not negate the need for risk equalisation. If community rating were to collapse then risk rating might result. A comparison of the Irish, Australian and UK health insurance markets suggests that community rating encourages higher take-up among older consumers than risk rating. Analysis of Irish hospital discharge figures suggests that this yields significant savings for the Irish public healthcare system. This thesis has implications for government policy towards private health insurance in Ireland.

Risk management and the potential of cattle insurance in Tigray, Northern Ethiopia

This study explores the role of livestock insurance to complement existing risk management strategies adopted by smallholder farmers. Using survey data, first, it provides insights into farmers’ risk perception of livestock farming, in terms of likelihood and severity of risk, attitude to risk and their determinants. Second, it examines farmers’ risk management strategies and their determinants. Third, it investigates farmers’ potential engagement with a hypothetical cattle insurance decision and their intensity of participation. Factor analysis is used to analyse risk sources and risk management, multiple regressions are used to identify the determinants; a Heckman model was used to investigate cattle insurance participation and intensity of participation. The findings show different groups of farmers display different risk attitude in their decision-making related to livestock farming. Production risk (especially livestock diseases) was perceived as the most likely and severe source of risk. Disease control was perceived as the best strategy to manage risk overall. Disease control and feed management were important strategies to mitigate the production risks. Disease control and participation on safety net program were found to be important to counter households’ financial risks. With regard to the hypothetical cattle insurance scheme, 94.38% of households were interested to participate in cattle insurance. Of those households that accepted cattle insurance, 77.38% of the households were willing to pay the benchmark annual premium of 4% of the animal value while for the remaining households this was not affordable. The average number of cattle that farmers were willing to insure was 2.71 at this benchmark. Results revealed that income (log income) and education levels influenced positively and significantly farmers’ participation in cattle insurance and the number of cattle to insure. The findings prompt policy makers to consider livestock insurance as a complement to existing risk management strategies to reduce poverty in the long-run.

The airEXODUS evacuation model and its application to evacuation certification, crew training and accident investigation.

Computer based mathematical models describing the aircraft evacuation process have a vital role to play in the design and development of safer aircraft, in the implementation of safer and more rigorous certification criteria, cabin crew training and in post mortuum accident investigation. As the risk of personal injury and costs involved in performing large-scale evacuation experiments for the next generation 'Ultra High Capacity Aircraft' (UHCA) are expected to be high, the development and use of these evacuation modelling tools may become essential if these aircraft are to prove a viable reality. In this paper the capabilities and limitations of the airEXODUS evacuation model are described. Its successful application to the prediction of a recent certification trial, prior to the actual trial taking place, is described. Also described is a newly defined parameter known as OPS which can be used as a measure of evacuation trial optimality. In addition, sample evacuation simulations in the presence of fire atmospheres are described. Finally, the data requiremnets of the airEXODUS evacuation model is discussed along with several projects currently underway at the the Univesity of Greenwich designed to obtain this data. Included in this discussion is a description of the AASK - Aircraft Accident Statistics and Knowledge - data base which contains detailed information from aircraft accident survivors.

AASK - Aircraft accident statistics and knowledge: A database of human experience in evacuation, derived from aviation accident reports

Computer based mathematical models describing the aircraft evacuation process have a vital role to play in aviation safety. However such models have a heavy dependency on real evacuation data in order to (a) identify the key processes and factors associated with evacuation, (b) quantify variables and parameters associated with the identified factors/processes and finally (c) validate the models. The Fire Safety Engineering Group of the University of Greenwich is undertaking a large data extraction exercise from three major data sources in order to address these issues. This paper describes the extraction and application of data from one of these sources - aviation accident reports. To aid in the storage and analysis of the raw data, a computer database known as AASK (aircraft accident statistics and knowledge) is under development. AASK is being developed to store human observational and anecdotal data contained in accident reports and interview transcripts. AASK comprises four component sub-databases. These consist of the ACCIDENT (crash details), FLIGHT ATTENDANT (observations and actions of the flight attendants), FATALS (details concerning passenger fatalities) and PAX (observations and accounts from individual passengers) databases. AASK currently contains information from 25 survivable aviation accidents covering the period 4 April 1977 to 6 August 1995, involving some 2415 passengers, 2210 survivors, 205 fatalities and accounts from 669 people. In addition to aiding the development of aircraft evacuation models, AASK is also being used to challenge some of the myths which proliferate in the aviation safety industry such as, passenger exit selection during evacuation, nature and frequency of seat jumping, speed of passenger response and group dynamics. AASK can also be used to aid in the development of a more comprehensive approach to conducting post accident interviews, and will eventually be used to store the data directly.

An aircraft accident database of human experience in evacuation derived from aviation accident reports

Computer based mathematical models describing the aircraft evacuation process have a vital role to play in aviation safety. However, such models have a heavy dependency on real evacuation data. The Fire Safety Engineering Group of the University of Greenwich is undertaking a large data extraction exercise in order to address this issue. This paper describes the extraction and application of data from aviation accident reports. To aid in the storage and analysis of the raw data, a computer database known as AASK (Aircraft Accident Statistics and Knowledge) is under development. AASK is being developed to store human observational and anecdotal data contained in accident reports and interview transcripts. AASK currently contains information from 25 survivable aviation accidents covering the period 04/04/77 to 06/08/95, involving some 2415 passengers, 2210 survivors, 205 fatalities and accounts from 669 people. Copyright © 1999 John Wiley & Sons, Ltd.

The AASK database V4.0: aircraft accident statistics and knowledge. A database to record human experience of evacuation in aviation accidents

This paper describes the AASK database. The AASK database is unique as it is a record of human behaviour during survivable aviation accidents. The AASK database is compiled from interview data compiled by agencies such as the NTSB and the AAIB. The database can be found on the website http://fseg.gre.ac.uk

Aircraft accident statistics and knowledge database: analyzing passenger behavior in aviation accidents

The Aircraft Accident Statistics and Knowledge (AASK) database is a repository of passenger accounts from survivable aviation accidents/incidents compiled from interview data collected by agencies such as the US NTSB. Its main purpose is to store observational and anecdotal data from the actual interviews of the occupants involved in aircraft accidents. The database has wide application to aviation safety analysis, being a source of factual data regarding the evacuation process. It also plays a significant role in the development of the airEXODUS aircraft evacuation model, where insight into how people actually behave during evacuation from survivable aircraft crashes is required. This paper describes the latest version of the database (Version 4.0) and includes some analysis of passenger behavior during actual accidents/incidents.

CAA paper 2006/01: a database to record human experience of evacuation in aviation accidents: the aircraft accident statistics and knowledge database (AASK)

This report concerns the development of the AASK V4.0 database (CAA Project 560/SRG/R+AD). AASK is the Aircraft Accident Statistics and Knowledge database, which is a repository of survivor accounts from aviation accidents. Its main purpose is to store observational and anecdotal data from interviews of the occupants involved in aircraft accidents. The AASK database has wide application to aviation safety analysis, being a source of factual data regarding the evacuation process. It is also key to the development of aircraft evacuation models such as airEXODUS, where insight into how people actually behave during evacuation from survivable aircraft crashes is required. With support from the UK CAA (Project 277/SRG/R&AD), AASK V3.0 was developed. This was an on-line prototype system available over the internet to selected users and included a significantly increased number of passenger accounts compared with earlier versions, the introduction of cabin crew accounts, the introduction of fatality information and improved functionality through the seat plan viewer utility. The most recently completed AASK project (Project 560/SRG/R+AD) involved four main components: a) analysis of the data collected in V3.0; b) continued collection and entry of data into AASK; c) maintenance and functional development of the AASK database; and d) user feedback survey. All four components have been pursued and completed in this two-year project. The current version developed in the last year of the project is referred to as AASK V4.0. This report provides summaries of the work done and the results obtained in relation to the project deliverables.