- Email: [email protected]
Dining out: Are seniors today different from seniors a decade ago?
International Journal of Hospitality Management 83 (2019) 128–131
Contents lists available at ScienceDirect
International Journal of Hospitality Management journal homepage: www.elsevier.com/locate/ijhm
Dining out: Are seniors today different from seniors a decade ago? a,⁎
Renuka Mahadevan , Leonora Risse a b
T
b
School of Economics, The University of Queensland, Brisbane, Queensland, 4072, Australia School of Economics, Finance & Marketing, Royal Melbourne Institute of Technology University, Melbourne, Victoria, 3001, Australia
A R T I C LE I N FO
A B S T R A C T
Keywords: Seniors Food-away-from-home Income elasticity Life cycle
This is the first intertemporal analysis of seniors’ income elasticity of expenditure on food-away-from-home (FAFH) across age cohorts. While the expenditure behaviour of Australian seniors today is not too different from those a decade ago, today’s seniors are more homogenous in terms of the sensitivity of their FAFH spending to a change in income. The finding that seniors do not treat FAFH as a luxury good can serve as a buffer against potential declines in demand for food services in times of economic crisis and uncertainty, especially as seniors’ share of the overall population continues to grow in future years.
1. Introduction The food service industry is a large and important one that has been evolving to cater to different consumer tastes. This paper focuses on the impact of income on food-away-from-home (FAFH) expenditure of the seniors who are an exponentially growing age cohort especially in developed countries. The proportion of people aged 60 years and over in the developed countries has risen from about 16% in 1980 to 25% in 2017 and is projected to reach 33% in 2050 (United Nations, 2017). While Hung and Lu (2016) note a lack of ageing studies in the hospitality industry, Nielsen (2014) and Chen and Shoemaker (2014) argue that it should not be automatically assumed that today’s seniors want exactly what seniors did 10 years ago. Most studies on seniors’ spending on FAFH have used a cross-sectional approach with data collected at a single point in time. But without incorporating a temporal dimension, researchers cannot gain a full picture of consumer behaviour (Chen and Shoemaker, 2014). The need to consider expenditure patterns in the context of demographic changes over time has also been highlighted by Bernini and Cracolici (2015). Furthermore, the need to consider a broad cross-section of the senior population, in order to capture heterogeneity within this age cohort, has been emphasised (Sun and Morrison, 2007). This paper examines the impact of seniors’ income on their expenditure on FAFH in Australia across 2005 and 2015. The availability of longitudinal data in the Household, Income and Labour Dynamics in Australia (HILDA) Survey means that the same cohort of individuals can be tracked across different years, to detect whether their spending changes as they grow older. Apart from Burzig and Herrmann (2012), none of the existing studies on seniors’ FAFH (Jang et al., 2007, 2011;
⁎
Lee, 2016; Velarde and Herrmann, 2014) have focused on income elasticity. However, the sensitivity of expenditure to income provides information as to whether FAFH is a normal or luxury good. This important distinction is theoretically founded on the Engel curve relating income to food consumption, which has its origins in the economic theory of household production developed by Becker (1965) and Lancaster (1971). There have however been a number of studies on the income elasticity of FAFH expenditure among the general population. A summary by Cupak et al. (2016) shows that FAFH expenditure is found to be a luxury good (elasticity value > 1) in the Czech Republic, Russia, Slovakia and Spain, but a normal good (elasticity value < 1) in the USA, Ireland and Malaysia, while studies of China generate mixed results. Among the senior age cohort, Burzig and Herrmann (2012) find that, in Germany, the elasticity for seniors above 50 years of age is low at 0.14. However, for a narrower age sample (65 years and older), Jang et al. (2007) detected no significant relationship between income and FAFH expenditure. Given that previous studies have highlighted heterogeneity among seniors with regards to age (Lee, 2016; Patterson, 2018), this study considers two separate age cohorts of seniors defined as ‘55–64 years’ and ‘65 years or over’ and examines if FAFH expenditure changes for seniors over their life cycle. Hence this study examines the following: 1) What is the income elasticity of seniors’ FAFH expenditure in 2005? Has this changed a decade later in 2015? 2) Is there a cohort effect among the seniors? Are these effects similar across time? 3) Is there an age effect over the life cycle indicating a change in the
Corresponding author. E-mail addresses: [email protected] (R. Mahadevan), [email protected] (L. Risse).
https://doi.org/10.1016/j.ijhm.2019.03.020 Received 23 May 2018; Received in revised form 8 March 2019; Accepted 18 March 2019 0278-4319/ © 2019 Elsevier Ltd. All rights reserved.
International Journal of Hospitality Management 83 (2019) 128–131
R. Mahadevan and L. Risse
significance of the model is verified by the F-values. Seniors’ income elasticity of FAFH remains low between 0.176 and 0.259 for both time periods. This indicates that FAFH is income inelastic and is thus a normal good rather than a luxury good among seniors in Australia. In 2005, seniors aged 55–64 years showed higher income sensitivity than those aged 65 years and above, with an elasticity value of 0.259 compared to 0.18. However, by the time the 55–64 age cohort became the older cohort a decade later, their elasticity value had decreased to 0.195, though they demonstrated relatively higher income sensitivity than their younger counterparts at this time who had an elasticity value of 0.176. These changes in seniors’ income sensitivity over age suggest the existence of an age effect, which draws on the theory of continuity (Chen and Shoemaker, 2014). That is, expenditure patterns differed depending on the stages of one’s life cycle, as purported by the life cycle theory (ibid). The cohort effect (You and O’Leary, 2000; Chen and Shoemaker, 2014), on the other hand is revealed by comparing the same age cohort across two different time periods. It can be seen that the cohort effects differed between the two senior age groups. For instance, relative to 2005, in 2015, FAFH expenditure of seniors aged 55–64 was less responsive (0.176% compared to 0.259%) to a 1% increase in income, while that of those aged 65 years and older was more responsive (0.195% compared to 0.18%). Table 2 shows that the income elasticities between the two senior groups (55–64 years and 65 years and over) in 2015 has narrowed compared to 2005. Using data on seniors’ population and their weekly income, the equivalent dollar values on FAFH expenditure resulting from a 1% increase in income can be meaningfully compared across time. For example, Table 2 shows that, in aggregate for the 65 and older age group, a given 1% increase in income has double the monetary effect on weekly FAFH expenditure (about A$63 000 compared to about A$130 000) over the decade. Conversely, in aggregate among seniors aged 55–64 years, this same change in income would see a marginal decline of A$9000 in weekly FAFH expenditure over the same decade. The estimation as seen in Table 1 provides no evidence that seniors substitute FAFH for food consumed at home, as a positive and significant relationship is detected between these two food types. In this instance, seniors’ expenditure on food could serve as a signal of food quality, and seniors who prefer high quality food at home also value
FAFH expenditure of seniors in 2005 and 2015? 2. Data and method The HILDA Survey is a longitudinal dataset collected annually since 2001 and is funded by the Australian Government Department of Social Services and managed by the Melbourne Institute of Applied Economic and Social Research. This paper uses unit-record data from the HILDA Survey which is a nationally representative survey of individuals drawn from a stratified sample of Australian households (Summerfield et al., 2016; Wilkins, 2015). The HILDA Survey collects information on respondents’ weekly expenditure on meals consumed outside of their home (that is, restaurants, take-aways, and bought lunches and snacks) which serves as a proxy for FAFH. The exact questions related to FAFH in the survey are provided in the Appendix. Given the importance of sociodemographic characteristics in determining dining behaviour (Olsen et al., 2000), a set of control variables are included in the empirical model. These include whether or not the individual receives a pension, was born in Australia or arrived as a migrant, lives by themselves or as a couple, resides in an urban or rural location, participates in social events, their general and mental health, value of their home, socioeconomic status of their area, and their expenditure on food consumed at home. These measures control for the individual’s spending capacity, the cost of living in their area, and other lifestyle factors that could affect their predisposition to dine out. The years 2005 and 2015 were chosen as no macro-level economic shocks or adverse events occurred during these time periods that could have otherwise caused a deviation from normal consumption patterns. A double-log function of income and FAFH expenditure is estimated using ordinary least square regression because the coefficients directly provide elasticity values. To control for variations in expenditure patterns that can arise due to household size and composition, the sample only considers lone-person or two-person couple households. 3. Results and discussion Table 1 reports the estimation results, accompanied by a summary of income elasticity values and marginal effects in Table 2. The overall Table 1 Results of Linear Regression on Log of FAFH Weekly Expenditure Per Capita. 2005
Sample size Constant Log of weekly per capita income Log of weekly per capita expenditure on food at home Vocational education Tertiary education General health Mental health Social participation Pensioner Migrant (if individual arrived in Australia) Log of home value Socioeconomic disadvantage Urban area (dummy variable with rural area = 0) Couple (dummy variable with lone person = 0) F-value R2
2015
55–64 years
65+ years
55–64 years
65+ years
860 −4.160*** 0.259*** 0.264*** −0.081 0.077 −0.001 0.000 0.071** −0.453*** 0.022 0.243*** 0.075*** 0.354*** 0.009 14.07*** 0.25
1092 −3.511*** 0.180*** 0.187** 0.063 0.279** 0.003 −0.002 0.124*** −0.327*** −0.045 0.242*** 0.034** 0.159 0.000 9.93*** 0.16
1151 −2.640*** 0.176*** 0.217*** 0.025 −0.017 0.000 0.002 0.073*** −0.342*** −0.056 0.194*** 0.049*** 0.337*** −0.198** 11.73*** 0.17
1951 −3.855*** 0.195*** 0.308*** 0.088 0.122 0.002 −0.002 0.123*** −0.193*** 0.036 0.255*** 0.022* 0.160* −0.210 *** 14.07 *** 0.25
Notes: *, ** and *** denote significance at 10%, 5% and 1% level. State and territory dummies were included in the estimations but not reported for brevity. Education is relative to secondary school education. General health and mental health ratings are obtained from the SF-36 Health Survey items, taking a value from 0 (worst) to 100 (best) (see Summerfield et al., 2016). Social participation refers to individual’s frequency of attendance at community events, taking a value of 1 (never) to 6 (very often). Socioeconomic disadvantage is measured by the Socio-Economic Indexes for Areas, taking a value of 1 (lowest decile) to 10 (highest decile) as explained by the Australian Bureau of Statistics (2018). 129
International Journal of Hospitality Management 83 (2019) 128–131
R. Mahadevan and L. Risse
Table 2 Income Elasticity and its Marginal Effect on FAFH Expenditure. 55–64 years
2005 2015
65+ years
Elasticity
Population count
Population-aggregated increase in weekly FAFH expenditure for 1% increase in income
Elasticity
Population count
Population-aggregated increase in weekly FAFH expenditure for 1% increase in income
0.259 0.176
2,161,731 2,727,797
A$122 236 A$112 952
0.180 0.195
2,611,879 3,554,304
A$63 253 A$129 600
Notes: 2015 values are deflated to 2005 values for comparison. Population numbers are drawn from Australian Bureau of Statistics (ABS) Cat. no. 3101.0 Australian Demographic Statistics. This includes all individuals in these age categories, including those who live in households other than couple-only or lone persons. Due to consumption economies of scale in larger households, these figures are upper estimates. The monetary values are computed by multiplying the population count by the increase in weekly FAFH expenditure per capita for a 1% increase in mean income per capita using the elasticity value.
changed over time. The finding that Australian senior’s expenditure on FAFH is income-inelastic, and remains so as they grow older and across different cohorts, suggests that seniors’ expenditure on FAFH is likely to remain stable even during an economic recession. In times of economic crisis and uncertainty, the stability of seniors’ expenditure behaviour can thus serve as a buffer against potential declines in demand for food services from among other more volatile sectors of the consumer market. This behaviour of seniors is especially significant given that in 2015, the share of seniors aged 55 and older in the total population is about 26% and is set to increase in the future (Australian Bureau of Statistics, 2017). The evidence on seniors’ FAFH expenditure in this paper supports the cohort effect and life cycle hypothesis. However, there are some limitations of this study. First, income elasticities of FAFH may vary for different wealth quantiles. Second, seniors living with children and other household members could have different considerations affecting their food consumption. Third, a disaggregation of FAFH into different types of meals (such as restaurant meals, fast-food or take-away, or home-cooking replacement meals) may shed light on seniors’ consumption patterns in relation to health and diet consciousness. Such an analysis can also indicate if seniors trade up to more premium food products.
high quality FAFH. This result is similar to Burzig and Herrmann (2012) and Jang et al. (2007). However, unlike our results, these studies find depression and poor health to affect FAFH. Differences in the sample and data collection could contribute to these differences in findings, as these previous studies included individuals from the age of 50 years and households with children, and collected data on the mental and general heath using a simpler method than the comprehensive set of survey items used in this study. Social participation is found to significantly affect FAFH expenditure, while tertiary education matters only for the 65 years and over cohort in 2015. As expected, pensioners have lesser means to spend on FAFH, while those with a higher house value are likely to have less financial constraint and therefore spend more on FAFH. Living as a couple as opposed to living alone decreases FAFH expenditure in 2015, indicating the presence of scale effects. Whether the individual was born in Australia or arrived as a migrant has no significant effect on FAFH expenditure, but those residing in urban areas spend more on FAFH. Seniors living in socio-economically disadvantaged areas spend relatively more on FAFH. This is consistent with other studies’ finding that low socioeconomic groups tend to consume more fast-food and less healthy food in general (Thornton et al., 2010; VicHealth, 2016). 4. Conclusion
Acknowledgements It is economically important to consider the expenditure patterns of seniors, given their growing share of the total consumer market. However, there is little recent research into this segment of the market, or any understanding of whether seniors’ spending on FAFH has
The analysis and views presented in this paper are those of the authors only and cannot be attributed to the Australian Government or the Melbourne Institute.
Appendix Data and method The questions in the survey pertaining to FAFH are: 1) How much does this household spend on all groceries in a normal week? And of this amount, about how much of the weekly grocery bill goes on food and drink (but not alcohol)? 2) Approximately, how much would this household usually spend per week on meals outside the home; that is, restaurants, take-aways, bought lunches and snacks? Do not include anything spent on alcohol. Summary Statistics of the variables
Variable
2005
2015
55-64 years
Logged weekly per capita income Logged weekly per capita FAFH expenditure Logged weekly per capita expenditure on food at home Secondary education Vocational education Tertiary education
65+ years
55-64 years
65+ years
Mean
Std. dev.
Mean
Std. dev.
Mean
Std. dev.
Mean
Std. dev.
6.30 2.41 4.03 0.51 0.30 0.19
(0.96) (1.33) (0.47) (0.50) (0.46) (0.39)
5.78 1.94 3.91 0.60 0.28 0.12
(0.83) (1.29) (0.52) (0.49) (0.45) (0.32)
6.55 2.67 4.56 0.34 0.38 0.27
(0.92) (1.17) (0.49) (0.48) (0.49) (0.45)
6.07 2.32 4.47 0.49 0.31 0.20
(0.82) (1.27) (0.56) (0.50) (0.46) (0.40)
130
International Journal of Hospitality Management 83 (2019) 128–131
R. Mahadevan and L. Risse General health Mental health Social participation Pensioner Migrant Logged home value Socioeconomic disadvantage Urban area versus rural area Couple versus lone person Sample size
68.21 77.80 3.31 0.28 0.27 12.83 5.49 0.85 0.80 860
(21.34) (15.93) (1.22) (0.45) (0.45) (0.61) (2.82) (0.36) (0.40)
62.68 78.63 3.24 0.77 0.24 12.69 5.33 0.86 0.69 1092
(21.59) (15.49) (1.33) (0.42) (0.43) (0.60) (2.94) (0.35) (0.46)
64.33 76.07 3.26 0.19 0.22 12.93 5.74 0.85 0.79 1151
(21.95) (16.98) (1.19) (0.40) (0.41) (0.61) (2.86) (0.36) 0.41
62.70 78.46 3.42 0.72 0.29 12.88 5.55 0.86 0.71 1951
(21.73) (16.05) (1.25) (0.45) (0.45) (0.62) (2.89) (0.35) (0.45)
Note: 2015 dollar values are deflated to 2005 values. The method used was to first estimate the linear equation below and then compute the values in Table 2.
Eit = β0 + β1 Yit + β′Xit + et where E denotes logged expenditure on FAFH, Y denotes logged income, X denotes a set of socioeconomic control variables, and e denotes the random error term. Subscript i represents the individual and t denotes the year 2005 or 2015. As the above equation is double logged, β1 gives the direct estimate of the income elasticity.
home. J. Tour. Hosp. Manag. 4, 22–36. Nielsen, K., 2014. ‘Approaches to seniors’ tourist behaviour. Tour. Rev. 69, 111–121. Olsen, W.K., Warde, A., Martens, L., 2000. Social differentiation and the market for eating out in the UK. Int. J. Hosp. Manag. 19, 173–190. Patterson, I., 2018. Tourism and Leisure Behaviour in an Ageing World. CABI Publishing, Wallingford, United Kingdom. Summerfield, M., Freidin, S., Hahn, M., La, N., Li, N., Macalalad, N., O’Shea, M., Watson, N., Wilkins, R., Wooden, M., 2016. HILDA User Manual – Release 15. Melbourne Institute of Applied Economic and Social Research, University of Melbourne. Sun, Y.C., Morrison, A.M., 2007. Senior citizens and their dining-out traits: implications for restaurants. Int. J. Hosp. Manag. 26, 376–394. Thornton, L.E., Crawford, D.A., Kylie Ball, K., 2010. Who is eating where? Findings from the socio economic status and activity in women study. Public Health Nutr. 14, 523–531. United Nations, 2017. World Population Ageing 2017. Department of Economic and Social Affairs, Population Division. Velarde, M., Herrmann, R., 2014. How retirement changes consumption and household production of food: lessons from German time-use data. J. Econ. Ageing 3, 1–10. VicHealth, 2016. Obesity and Healthy Eating in Australia: Evidence Summary, Victorian Health Promotion Foundation. Victorian Government. Wilkins, R., 2015. The Household, Income and Labour Dynamics in Australia Survey: Selected Findings From Waves 1 to 12. Melbourne Institute of Applied Economic and Social Research, Melbourne. You, X., O’Leary, J., 2000. Age and cohort effects: an examination of older Japanese travelers. J. Travel Tour. Mark. 9, 21–42.
References Australian Bureau of Statistics, 2017. Australian Demographic Statistics. Catalogue no. 3101.0. Australian Government, Canberra. Australian Bureau of Statistics, 2018. Census of Population and Housing: Socio-Economic Indexes for Areas, Australia, 2016. Catalogue no. 2033.0.55.001. . Becker, G., 1965. A theory of allocation of time. Econ. J. 75, 493–517. Bernini, C., Cracolici, M., 2015. Demographic change, tourism expenditure and life cycle behaviour. Tour. Manag. 47, 191–205. Burzig, J., Herrmann, R., 2012. Food expenditure patterns of the generation 50+: an Engel-curve analysis for Germany. Br. Food Policy 114, 1380–1393. Chen, S., Shoemaker, S., 2014. Age and cohort effects: the American senior tourism market. Ann. Tour. Res. 48, 58–75. Cupak, A., Pokrivcak, J., Rizov, M., 2016. Demand for food away from home in Slovakia. Czech J. Econ. Finan. 66, 354–369. Hung, K., Lu, J., 2016. Active living in later life: an overview of ageing studies in hospitality and tourism journals. Int. J. Hosp. Manage. 53, 133–144. Jang, S., Ham, S., Hong, G., 2007. Food-away-from-home expenditure of senior households in the United States: a double-hurdle approach. J. Hosp. Tour. Res. 32, 147–167. Jang, Y., Kim, W., Yang, I., 2011. ‘Mature consumers’ patronage motives and the importance of attributes regarding HMR based on the food-related lifestyles of the upper middle class. Int. J. Hosp. Manage. 30, 55–63. Lancaster, D., 1971. Consumer Demand. Columbia University Press, New York. Lee, K., 2016. Understanding Taiwan seniors’ motivation to consume food-away-from-
131
Contents lists available at ScienceDirect
International Journal of Hospitality Management journal homepage: www.elsevier.com/locate/ijhm
Dining out: Are seniors today different from seniors a decade ago? a,⁎
Renuka Mahadevan , Leonora Risse a b
T
b
School of Economics, The University of Queensland, Brisbane, Queensland, 4072, Australia School of Economics, Finance & Marketing, Royal Melbourne Institute of Technology University, Melbourne, Victoria, 3001, Australia
A R T I C LE I N FO
A B S T R A C T
Keywords: Seniors Food-away-from-home Income elasticity Life cycle
This is the first intertemporal analysis of seniors’ income elasticity of expenditure on food-away-from-home (FAFH) across age cohorts. While the expenditure behaviour of Australian seniors today is not too different from those a decade ago, today’s seniors are more homogenous in terms of the sensitivity of their FAFH spending to a change in income. The finding that seniors do not treat FAFH as a luxury good can serve as a buffer against potential declines in demand for food services in times of economic crisis and uncertainty, especially as seniors’ share of the overall population continues to grow in future years.
1. Introduction The food service industry is a large and important one that has been evolving to cater to different consumer tastes. This paper focuses on the impact of income on food-away-from-home (FAFH) expenditure of the seniors who are an exponentially growing age cohort especially in developed countries. The proportion of people aged 60 years and over in the developed countries has risen from about 16% in 1980 to 25% in 2017 and is projected to reach 33% in 2050 (United Nations, 2017). While Hung and Lu (2016) note a lack of ageing studies in the hospitality industry, Nielsen (2014) and Chen and Shoemaker (2014) argue that it should not be automatically assumed that today’s seniors want exactly what seniors did 10 years ago. Most studies on seniors’ spending on FAFH have used a cross-sectional approach with data collected at a single point in time. But without incorporating a temporal dimension, researchers cannot gain a full picture of consumer behaviour (Chen and Shoemaker, 2014). The need to consider expenditure patterns in the context of demographic changes over time has also been highlighted by Bernini and Cracolici (2015). Furthermore, the need to consider a broad cross-section of the senior population, in order to capture heterogeneity within this age cohort, has been emphasised (Sun and Morrison, 2007). This paper examines the impact of seniors’ income on their expenditure on FAFH in Australia across 2005 and 2015. The availability of longitudinal data in the Household, Income and Labour Dynamics in Australia (HILDA) Survey means that the same cohort of individuals can be tracked across different years, to detect whether their spending changes as they grow older. Apart from Burzig and Herrmann (2012), none of the existing studies on seniors’ FAFH (Jang et al., 2007, 2011;
⁎
Lee, 2016; Velarde and Herrmann, 2014) have focused on income elasticity. However, the sensitivity of expenditure to income provides information as to whether FAFH is a normal or luxury good. This important distinction is theoretically founded on the Engel curve relating income to food consumption, which has its origins in the economic theory of household production developed by Becker (1965) and Lancaster (1971). There have however been a number of studies on the income elasticity of FAFH expenditure among the general population. A summary by Cupak et al. (2016) shows that FAFH expenditure is found to be a luxury good (elasticity value > 1) in the Czech Republic, Russia, Slovakia and Spain, but a normal good (elasticity value < 1) in the USA, Ireland and Malaysia, while studies of China generate mixed results. Among the senior age cohort, Burzig and Herrmann (2012) find that, in Germany, the elasticity for seniors above 50 years of age is low at 0.14. However, for a narrower age sample (65 years and older), Jang et al. (2007) detected no significant relationship between income and FAFH expenditure. Given that previous studies have highlighted heterogeneity among seniors with regards to age (Lee, 2016; Patterson, 2018), this study considers two separate age cohorts of seniors defined as ‘55–64 years’ and ‘65 years or over’ and examines if FAFH expenditure changes for seniors over their life cycle. Hence this study examines the following: 1) What is the income elasticity of seniors’ FAFH expenditure in 2005? Has this changed a decade later in 2015? 2) Is there a cohort effect among the seniors? Are these effects similar across time? 3) Is there an age effect over the life cycle indicating a change in the
Corresponding author. E-mail addresses: [email protected] (R. Mahadevan), [email protected] (L. Risse).
https://doi.org/10.1016/j.ijhm.2019.03.020 Received 23 May 2018; Received in revised form 8 March 2019; Accepted 18 March 2019 0278-4319/ © 2019 Elsevier Ltd. All rights reserved.
International Journal of Hospitality Management 83 (2019) 128–131
R. Mahadevan and L. Risse
significance of the model is verified by the F-values. Seniors’ income elasticity of FAFH remains low between 0.176 and 0.259 for both time periods. This indicates that FAFH is income inelastic and is thus a normal good rather than a luxury good among seniors in Australia. In 2005, seniors aged 55–64 years showed higher income sensitivity than those aged 65 years and above, with an elasticity value of 0.259 compared to 0.18. However, by the time the 55–64 age cohort became the older cohort a decade later, their elasticity value had decreased to 0.195, though they demonstrated relatively higher income sensitivity than their younger counterparts at this time who had an elasticity value of 0.176. These changes in seniors’ income sensitivity over age suggest the existence of an age effect, which draws on the theory of continuity (Chen and Shoemaker, 2014). That is, expenditure patterns differed depending on the stages of one’s life cycle, as purported by the life cycle theory (ibid). The cohort effect (You and O’Leary, 2000; Chen and Shoemaker, 2014), on the other hand is revealed by comparing the same age cohort across two different time periods. It can be seen that the cohort effects differed between the two senior age groups. For instance, relative to 2005, in 2015, FAFH expenditure of seniors aged 55–64 was less responsive (0.176% compared to 0.259%) to a 1% increase in income, while that of those aged 65 years and older was more responsive (0.195% compared to 0.18%). Table 2 shows that the income elasticities between the two senior groups (55–64 years and 65 years and over) in 2015 has narrowed compared to 2005. Using data on seniors’ population and their weekly income, the equivalent dollar values on FAFH expenditure resulting from a 1% increase in income can be meaningfully compared across time. For example, Table 2 shows that, in aggregate for the 65 and older age group, a given 1% increase in income has double the monetary effect on weekly FAFH expenditure (about A$63 000 compared to about A$130 000) over the decade. Conversely, in aggregate among seniors aged 55–64 years, this same change in income would see a marginal decline of A$9000 in weekly FAFH expenditure over the same decade. The estimation as seen in Table 1 provides no evidence that seniors substitute FAFH for food consumed at home, as a positive and significant relationship is detected between these two food types. In this instance, seniors’ expenditure on food could serve as a signal of food quality, and seniors who prefer high quality food at home also value
FAFH expenditure of seniors in 2005 and 2015? 2. Data and method The HILDA Survey is a longitudinal dataset collected annually since 2001 and is funded by the Australian Government Department of Social Services and managed by the Melbourne Institute of Applied Economic and Social Research. This paper uses unit-record data from the HILDA Survey which is a nationally representative survey of individuals drawn from a stratified sample of Australian households (Summerfield et al., 2016; Wilkins, 2015). The HILDA Survey collects information on respondents’ weekly expenditure on meals consumed outside of their home (that is, restaurants, take-aways, and bought lunches and snacks) which serves as a proxy for FAFH. The exact questions related to FAFH in the survey are provided in the Appendix. Given the importance of sociodemographic characteristics in determining dining behaviour (Olsen et al., 2000), a set of control variables are included in the empirical model. These include whether or not the individual receives a pension, was born in Australia or arrived as a migrant, lives by themselves or as a couple, resides in an urban or rural location, participates in social events, their general and mental health, value of their home, socioeconomic status of their area, and their expenditure on food consumed at home. These measures control for the individual’s spending capacity, the cost of living in their area, and other lifestyle factors that could affect their predisposition to dine out. The years 2005 and 2015 were chosen as no macro-level economic shocks or adverse events occurred during these time periods that could have otherwise caused a deviation from normal consumption patterns. A double-log function of income and FAFH expenditure is estimated using ordinary least square regression because the coefficients directly provide elasticity values. To control for variations in expenditure patterns that can arise due to household size and composition, the sample only considers lone-person or two-person couple households. 3. Results and discussion Table 1 reports the estimation results, accompanied by a summary of income elasticity values and marginal effects in Table 2. The overall Table 1 Results of Linear Regression on Log of FAFH Weekly Expenditure Per Capita. 2005
Sample size Constant Log of weekly per capita income Log of weekly per capita expenditure on food at home Vocational education Tertiary education General health Mental health Social participation Pensioner Migrant (if individual arrived in Australia) Log of home value Socioeconomic disadvantage Urban area (dummy variable with rural area = 0) Couple (dummy variable with lone person = 0) F-value R2
2015
55–64 years
65+ years
55–64 years
65+ years
860 −4.160*** 0.259*** 0.264*** −0.081 0.077 −0.001 0.000 0.071** −0.453*** 0.022 0.243*** 0.075*** 0.354*** 0.009 14.07*** 0.25
1092 −3.511*** 0.180*** 0.187** 0.063 0.279** 0.003 −0.002 0.124*** −0.327*** −0.045 0.242*** 0.034** 0.159 0.000 9.93*** 0.16
1151 −2.640*** 0.176*** 0.217*** 0.025 −0.017 0.000 0.002 0.073*** −0.342*** −0.056 0.194*** 0.049*** 0.337*** −0.198** 11.73*** 0.17
1951 −3.855*** 0.195*** 0.308*** 0.088 0.122 0.002 −0.002 0.123*** −0.193*** 0.036 0.255*** 0.022* 0.160* −0.210 *** 14.07 *** 0.25
Notes: *, ** and *** denote significance at 10%, 5% and 1% level. State and territory dummies were included in the estimations but not reported for brevity. Education is relative to secondary school education. General health and mental health ratings are obtained from the SF-36 Health Survey items, taking a value from 0 (worst) to 100 (best) (see Summerfield et al., 2016). Social participation refers to individual’s frequency of attendance at community events, taking a value of 1 (never) to 6 (very often). Socioeconomic disadvantage is measured by the Socio-Economic Indexes for Areas, taking a value of 1 (lowest decile) to 10 (highest decile) as explained by the Australian Bureau of Statistics (2018). 129
International Journal of Hospitality Management 83 (2019) 128–131
R. Mahadevan and L. Risse
Table 2 Income Elasticity and its Marginal Effect on FAFH Expenditure. 55–64 years
2005 2015
65+ years
Elasticity
Population count
Population-aggregated increase in weekly FAFH expenditure for 1% increase in income
Elasticity
Population count
Population-aggregated increase in weekly FAFH expenditure for 1% increase in income
0.259 0.176
2,161,731 2,727,797
A$122 236 A$112 952
0.180 0.195
2,611,879 3,554,304
A$63 253 A$129 600
Notes: 2015 values are deflated to 2005 values for comparison. Population numbers are drawn from Australian Bureau of Statistics (ABS) Cat. no. 3101.0 Australian Demographic Statistics. This includes all individuals in these age categories, including those who live in households other than couple-only or lone persons. Due to consumption economies of scale in larger households, these figures are upper estimates. The monetary values are computed by multiplying the population count by the increase in weekly FAFH expenditure per capita for a 1% increase in mean income per capita using the elasticity value.
changed over time. The finding that Australian senior’s expenditure on FAFH is income-inelastic, and remains so as they grow older and across different cohorts, suggests that seniors’ expenditure on FAFH is likely to remain stable even during an economic recession. In times of economic crisis and uncertainty, the stability of seniors’ expenditure behaviour can thus serve as a buffer against potential declines in demand for food services from among other more volatile sectors of the consumer market. This behaviour of seniors is especially significant given that in 2015, the share of seniors aged 55 and older in the total population is about 26% and is set to increase in the future (Australian Bureau of Statistics, 2017). The evidence on seniors’ FAFH expenditure in this paper supports the cohort effect and life cycle hypothesis. However, there are some limitations of this study. First, income elasticities of FAFH may vary for different wealth quantiles. Second, seniors living with children and other household members could have different considerations affecting their food consumption. Third, a disaggregation of FAFH into different types of meals (such as restaurant meals, fast-food or take-away, or home-cooking replacement meals) may shed light on seniors’ consumption patterns in relation to health and diet consciousness. Such an analysis can also indicate if seniors trade up to more premium food products.
high quality FAFH. This result is similar to Burzig and Herrmann (2012) and Jang et al. (2007). However, unlike our results, these studies find depression and poor health to affect FAFH. Differences in the sample and data collection could contribute to these differences in findings, as these previous studies included individuals from the age of 50 years and households with children, and collected data on the mental and general heath using a simpler method than the comprehensive set of survey items used in this study. Social participation is found to significantly affect FAFH expenditure, while tertiary education matters only for the 65 years and over cohort in 2015. As expected, pensioners have lesser means to spend on FAFH, while those with a higher house value are likely to have less financial constraint and therefore spend more on FAFH. Living as a couple as opposed to living alone decreases FAFH expenditure in 2015, indicating the presence of scale effects. Whether the individual was born in Australia or arrived as a migrant has no significant effect on FAFH expenditure, but those residing in urban areas spend more on FAFH. Seniors living in socio-economically disadvantaged areas spend relatively more on FAFH. This is consistent with other studies’ finding that low socioeconomic groups tend to consume more fast-food and less healthy food in general (Thornton et al., 2010; VicHealth, 2016). 4. Conclusion
Acknowledgements It is economically important to consider the expenditure patterns of seniors, given their growing share of the total consumer market. However, there is little recent research into this segment of the market, or any understanding of whether seniors’ spending on FAFH has
The analysis and views presented in this paper are those of the authors only and cannot be attributed to the Australian Government or the Melbourne Institute.
Appendix Data and method The questions in the survey pertaining to FAFH are: 1) How much does this household spend on all groceries in a normal week? And of this amount, about how much of the weekly grocery bill goes on food and drink (but not alcohol)? 2) Approximately, how much would this household usually spend per week on meals outside the home; that is, restaurants, take-aways, bought lunches and snacks? Do not include anything spent on alcohol. Summary Statistics of the variables
Variable
2005
2015
55-64 years
Logged weekly per capita income Logged weekly per capita FAFH expenditure Logged weekly per capita expenditure on food at home Secondary education Vocational education Tertiary education
65+ years
55-64 years
65+ years
Mean
Std. dev.
Mean
Std. dev.
Mean
Std. dev.
Mean
Std. dev.
6.30 2.41 4.03 0.51 0.30 0.19
(0.96) (1.33) (0.47) (0.50) (0.46) (0.39)
5.78 1.94 3.91 0.60 0.28 0.12
(0.83) (1.29) (0.52) (0.49) (0.45) (0.32)
6.55 2.67 4.56 0.34 0.38 0.27
(0.92) (1.17) (0.49) (0.48) (0.49) (0.45)
6.07 2.32 4.47 0.49 0.31 0.20
(0.82) (1.27) (0.56) (0.50) (0.46) (0.40)
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R. Mahadevan and L. Risse General health Mental health Social participation Pensioner Migrant Logged home value Socioeconomic disadvantage Urban area versus rural area Couple versus lone person Sample size
68.21 77.80 3.31 0.28 0.27 12.83 5.49 0.85 0.80 860
(21.34) (15.93) (1.22) (0.45) (0.45) (0.61) (2.82) (0.36) (0.40)
62.68 78.63 3.24 0.77 0.24 12.69 5.33 0.86 0.69 1092
(21.59) (15.49) (1.33) (0.42) (0.43) (0.60) (2.94) (0.35) (0.46)
64.33 76.07 3.26 0.19 0.22 12.93 5.74 0.85 0.79 1151
(21.95) (16.98) (1.19) (0.40) (0.41) (0.61) (2.86) (0.36) 0.41
62.70 78.46 3.42 0.72 0.29 12.88 5.55 0.86 0.71 1951
(21.73) (16.05) (1.25) (0.45) (0.45) (0.62) (2.89) (0.35) (0.45)
Note: 2015 dollar values are deflated to 2005 values. The method used was to first estimate the linear equation below and then compute the values in Table 2.
Eit = β0 + β1 Yit + β′Xit + et where E denotes logged expenditure on FAFH, Y denotes logged income, X denotes a set of socioeconomic control variables, and e denotes the random error term. Subscript i represents the individual and t denotes the year 2005 or 2015. As the above equation is double logged, β1 gives the direct estimate of the income elasticity.
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