An Econometric Analysis of Irish Households FAFH Expenditure Patterns: 1994-2000. Conor Keelan

1. An Econometric Analysis of Irish Households FAFH Expenditure Patterns: 1994-2000. Conor Keelan Dr. Maeve Henchion, AFRC, and Dr. Carol Newman, TCD.

2. Outline • Motivation and Objectives • Drivers of Demand • Data and Variables • Methodology • Results • Future work • Summation.

3. Motivation and Objectives • To analyse the factors influencing Irish household’s expenditure patterns on Food-Away-From-Home (FAFH) • Dramatic increase in FAFH expenditure in recent times • Much European research has not used disaggregated data • Analysis is important given the diverse nature of the FAFH market

4. FAFH Definition • Food is defined as “at home” or “away from home” based on where the food was prepared or obtained, not where it was consumed (Lin et al., 2001) • Includes takeaways eaten at home • Further define FAFH as food eaten away from home at commercial facilities (McCracken and Brandt, 1987) • Excludes school and work canteen meals • Quick-service and Full-service

5. Food-Away-From-Home Source: Adapted from the 1987,1994/5 and 1999/2000 HBS.

6. Drivers of Demand • Rising incomes • Labour force participation • Demand for convenience • Health knowledge • Ageing of population • Household composition

7. Data • Household Budget Survey (HBS) data from 1994/5 and 1999/2000 collected by the CSO • Reflects the Celtic Tiger years • 2 week expenditure diary • Representative sample of households in the state • Head of household is not the meal planner • Household manager

8. Variables • Income • Household size and composition • Age, education and social class • Urbanisation • Opportunity cost of time • Commuter variable • Health awareness • Tenancy

9. Methodology • Cross-sectional data • Complicated by zero observations • Conventional regression analysis is unsuitable • Limited Dependent variable models or semiparametric estimators

10. Methodology cont’d • Tobit adjusted for mis-specification outperforms both the CLAD and SCLS • Use tobit and double hurdle models • Test and adjust for mis-specification • Double hurdle model adjusted for mis-specification outperforms tobit. • Use double hurdle model with the Box-Cox transformation

11. Participation Results

12. Participation cont’d

13. Expenditure Results

14. Expenditure cont’d

15. Summation and Future Work • Most results are significant and have the expected sign • The benefits of disaggregating the data are clearly emphasised • Marginal Effects and Elasticities • Developing policy recommendations

16. Questions?