Persuading Older Adults to Eat Less Meat
Appraising the Potential Dietary Supplements Market for the Asian Aging Population
Borui Fang (30328934), Tain Foung (31396445)
26-OCT-2021
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library(gtsummary)
library(modelsummary)1 Introduction
1.1 Background
Lack of intake of nutrition and micronutrient affect the health situation (Rautiainen, Manson, Lichtenstein, & Sesso, 2016). People can improve the food quality to increase daily nutrient intake (Rautiainen et al., 2016), such as consuming more meat, fruits, and vegetables to diversify their diets. Alternatively, adopting dietary supplements can be another option. However, consuming meat to get daily nutrition increases the risk of developing obesity, diabetes, heart disease, and other serious illnesses (Campbell, 2021). Compared to improving food quality, dietary supplements provide the vital substance to ensure the body gets enough support, such as vitamins, minerals, herbs, etc. (FDA, 2017), and more targeted reduction of the individuals at risk of undernutrition (Rautiainen et al., 2016). For example, the older generations above 65 years old can intentionally select the dietary supplements with calcium and vitamin because of the concern in bone health (Rautiainen et al., 2016). Another example is that pregnant women and reproductive age can adopt folic acid dietary supplements to sustain their intake more efficiently (Rautiainen et al., 2016).
The global dietary supplements market will increase its value to USD 252.1 Billion by 2025 (Dietary Supplements Market, 2021). In particular, the Asia Pacific region accounted for the majority market share, which was 31.01% (Market Analysis Report, 2020) and almost USD 353 billion in 2019 (Lordan, 2021). The promising Asian dietary supplements markets were due to increased disposable income, shifting preference into moderate healthy nutrition (Market Analysis Report, 2020). Because more and more people accept dietary supplements, the markets are competitive and tend to saturate, where around 1,000 new nutritional supplements are introduced to the public annually (Fragakis, 2008). However, these markets’ highest dietary supplements usage age group is from 35-44, around 81% (Ridley, 2019). For those older generations above 65 years old, only 38.8% of them adopt the dietary supplements (Lee, Son, & Short, 2017), which means there is still a vast potential market in the dietary supplements for older adults.
1.2 Research Motivation
The GDP per Asian has increased by 82.46% from 2000 to 2018 (GDP per Capita, 2020). The increased disposable income leads to positive consequences such as higher living standards, better education, reduced poverty, and longer life expectancy (Pettinger, 2019). However, it also increased carbon dioxide emissions (Mardani, Streimikiene, Cavallaro, Loganathan, & Khoshnoudi, 2019), which is a negative effect. The dramatic increase is mainly due to escalating consumption in burning fossil energy, deforestation, and meat production (Sanglimsuwan, 2011). From the existing literature, people are encouraged to use nuclear and renewable technologies to reduce carbon dioxide emissions (Ritcie & Roser, 2020) and take a few actions toward reducing meat consumption. Thus, this paper will demonstrate the unfriendly environment of consuming meat and persuading older adults to adopt alternative dietary supplements to match their protein and nutrition supply.
The younger and middle-aged dietary supplements markets are fierce competition (Ridley, 2019), a challenge to enter this market, yet only 38.8% of older adults above 65 years old have adopted the dietary supplements (Lee et al., 2017). Thus, there is a massive gap in the dietary supplements market for older generations. Furthermore, the statistic shows that the majority of Asian countries are facing the aging trend. The aging population across 47 Asian countries increased by 1.812% between 2000 and 2018 (Population ages 65 and above, 2019). The aging trend is more advanced in developed countries (Menon & Melendez, 2009), such as Japan increased by 10.59%, 7.23% in South Korea, and Hong Kong increased by 5.86% (Population ages 65 and above, 2019). And many developing countries in Asia are on the same demographic path (Menon & Melendez, 2009). In other words, East Asia is transitioning to an aging population country, and the rest of Asia will adjust its demographic structure at a different pace (Chomik & Piggott, 2015).
Figure 1. Aging growth rate (%) from 2000 to 2018
Additionally, Sustainable Develop Goals (SDG) four and five have actively promoted gender and educational equality to promote women’s rights in society. As a result, women obtained empowerment as a benefit of achieving these SDG goals by 2030 (“THE 17 GOALS,” 2016). Kim (2021) stated that women’s educational opportunity negatively correlates with the fertility rate because they can improve outstanding performance in the workplace to achieve their dream instead of raising children as their whole life. Therefore, the decreased fertility rate will hugely increase the percentage of older adults in the future.
The reason dietary supplements companies cannot exclude older adults as their target customers is that the purchasing power of older adults is predicted to spend $15 trillion every year by 2020 (Arensberg, 2018). Besides, the senior customers are more loyal than the young customers once they select one brand. Moreover, these older adults are also more concerning their health and desire to contribute to society from the socioemotional selectivity theory (Carstensen, 2006). Therefore, educating senior customers about the negative effect of overeating meat (Yip, Crane, & Karnon, 2013) to persuade the aging population to adopt dietary supplements as the alternative daily protein, energy, vitamin intake. Thus, this is meaningful research towards the potential dietary supplements market for the aging population in Asia.
1.3 Research Objective
The main objective is to appraise the potential dietary supplements market for the aging population in Asia by persuading older adults to eat less meat. To achieve this primary objective, it will demonstrate the effect of consuming meat on carbon dioxide emissions. In addition, it will examine the relationship between women’s empowerment and the fertility rate.
1.4 Research Questions
In this research, we come up with three research questions.
Will people consume more meat when they are wealthy?
Will consuming and producing meat increases carbon dioxide emissions?
Will women with empowerment reduce the fertility rate?
Firstly, finding the correlation between the GDP per capita and meat supply per person can illustrate the increased purchasing power. Secondly, understanding the negative effect of producing meat will encourage the old to adopt dietary supplements as an alternative nutrition intake supply. Thirdly, the Asian women in power encourage them to live independently and access education so that the low birth rate will exacerbate the percentage of aging in Asia.
Figure 2. The logic diagram of the report
2 Data and Methodology
2.1 Data
To achieve the main research objective and answer three research questions. The hypothsis is stated as below:
Hypothesis 1: The women empowerment will be positively related to the percentage of the aging population.
Hypothesis 2a: The GDP per capita will be positively related to carbon dioxide emission per capita.
Hypothesis 2b: The meat consumption will mediate the relationship between GDP and carbon dioxide emission per capita.
| Types of Variable | Terms |
|---|---|
| Independent Variable 1 | Women empowerment |
| Independent Variable 2 | GDP per capita |
| Dependent Variable 1 | Percentage of aging population |
| Dependent Variable 2 | Carbon dioxide emission per capita |
| Mediator Variable 2 | Meat Consumption |
The conceptual model is demonstrated as below:
Figure 3. The Conceptual Model
| Information | Description | Source | Scale | Collection Method | URL |
|---|---|---|---|---|---|
| 1. Percentage of Older Adults | The worldwide population ages 65 and above, from 1960-2020 | World Bank | Continuous | Download csv | https://data.worldbank.org/indicator/SP.POP.65UP.TO.ZS |
| 2. Gross Domestic Products | Worldwide GDP per capita, Measured in 2011 U.S dollars price, from 1961-2018 | Our World in Data | Continuous | Download csv | https://ourworldindata.org/economic-growth |
| 3. Carbon Dioxide Emissions | CO2 per caipta worldwide, investment profile risk, from 1961-2017 | Our World in Data | Continuous | Download csv | https://ourworldindata.org/grapher/co-emissions-per-capita?tab=chart |
| 4. Animal Products Consumption | Daily protein supply from animal and plant-based foods, from 1961-2017 | Our World in Data | Continuous | Download csv | https://ourworldindata.org/grapher/daily-protein-supply-from-animal-and-plant-based-foods?country=USA~OWID_WRL~CHN~GBR~JPN~IND~ZAF~TCD~NGA~BRA~FRA~ESP |
| 5. Fertility Rate | Fertility rate vs. contraceptive prevalence worldwide, from 1961-2020 | Our World in Data | Continuous | Download csv | https://ourworldindata.org/grapher/fertility-vs-contraception |
| 6. Female Labor Force | Female labor force participation worldwide, from 1961-2020 | Our World in Data | Continuous | Download csv | https://ourworldindata.org/grapher/recent-ilo-lfp |
From the original dataset, we realized this is worldwide data and covered for several decades. Therefore, to achieve our primary research objective, we first narrow to the Asia countries at first. Secondly, considering the resources are not equally distributed among Asia, we categorize them into five regions: Central, East, South, Southeast, and Western Asia, covering 47 Asia countries. Lastly, we set the year from 2000 to 2017 due to the limited data.
2.2 Methodology
The secondary data collected from Our World in Data and World Bank will measure the construct. First, the horizontal bar chart will demonstrate the countries whose aging increased rate ranked in the top five and last five, showing which part of Asia faces the strongly aging trend and which region is relatively young. This bar chart can also give us the perception of which region we should focus on as our target market. Secondly, the scatter plot will show the correlations between GDP per capita and meat consumption, meat consumption and CO2 emission per capita, women empowerment towards the fertility rate, etc. The above scatter plot can analyze bivariable simultaneously, such as positive, negative relationship or no effect between them.
The relevant index should be stated here. Once the p-value is less than 0.05, we will regard this dataset as statistically significant, confirming the validity. Besides, the beta will use in regression. If the beta is a positive number, we would say the dependent variable has a significant positive effect. Conversely, the negative beta will have a significant negative effect on the dependent variable. However, if the beta is zero, which represents there is no significant effect on the dependent variable.
In practice, Microsoft Excel will filter data to make it more relevant to our research questions. Then, the tableau will prepare the diagram to show the visualization. Lastly, the research writing work, summary data, calculate the p-value, and beta will be conducted on R Studio.
3 Visualization
The research paper aims to evaluate the dietary supplements market for the aging population. Thus figure 4 can demonstrate the location of the “olderest” (East Asia) and “youngest”(Central Asia) citizens, which gives a quick insight that East Asia has the most potential target customers.
Figure 4. Aging population growth rate from 2000 to 2018.
Figure 5 shows no relationship between GDP per capita and the Female labor force, which means the number of women to work will not be affected by their economic situation. In other words, no matter whether women were wealthy or poor, the attractive factor to work is not for money only.
| Term | Value | StdErr | t-value | p-value |
|---|---|---|---|---|
| GDP | 7.4e-05 | 0.0001484 | 0.498667 | 0.620889 |
| intercept | 498.134 | 79.0933 | 6.29805 | < 0.0001 |
| P-Value | Equation |
|---|---|
| 0.620889 | Labor Fource Rate = 7.39969e-05*GDP + 498.134 |
Figure 5. The relationship between GDP per capita and the number of female labour fource.
The Figure 6 presents a negative relationship between the number of the female workforce and the fertility rate. The trend means that when women participate in the workplace, they are less willing to give birth to a baby and feed the next generation. In other words, women empowerment will result in fewer baby birth, which can imply that the aging trend will be exaggerated.
| Term | Value | StdErr | t-value | p-value |
|---|---|---|---|---|
| Labor Fource Rate | -0.0251451 | 0.0066134 | -3.80212 | 0.0004477 |
| intercept | 58.6996 | 4.22322 | 13.8993 | < 0.0001 |
| P-Value | Equation |
|---|---|
| 0.0004477 | Labor Fertility Rate = -0.0251451*Labor Fource Rate + 58.6996 |
Figure 6. The correlation between the number of female labor force and fertility rate.
The below diagram present the correlation between the GDP per Capita and meat consumption per capita per day. Again, the upward reference line means a positive relationship between the GDP per capita and daily meat consumption per capita. Thus, the diagram can explain that when there is a higher GDP per capita, those people have better purchasing power and are willing to consume more meat to improve their living standards.
| Term | Value | StdErr | t-value | p-value |
|---|---|---|---|---|
| GDP | 0.0007495 | 0.0001271 | 5.89703 | < 0.0001 |
| intercept | 352.213 | 51.3739 | 6.85587 | < 0.0001 |
| P-Value | Equation |
|---|---|
| < 0.0001 | Animal Products Consumption = 0.000749535*GDP + 352.213 |
Figure 7. The correlation between GDP per capita and daily meat consumption per person.
Figure 8 illustrates the positive relationship between meat consumption and CO2 emission, which means that when people consume more meat, there will emit more CO2. This relationship can be explained that the animals produce methane through digestive processes and deforestation to establish pasture to increase carbon dioxide emissions, as human demand for meat has led to increased meat production. Therefore, to achieve the supply and demand of meat equilibrium point, the environment will be affected by consuming more meat than before.
| Term | Value | StdErr | t-value | p-value |
|---|---|---|---|---|
| Animal Products Consumption | 0.183867 | 0.0504524 | 3.64436 | 0.0007629 |
| intercept | -4.10693 | 33.7537 | -0.121673 | 0.903767 |
| P-Value | Equation |
|---|---|
| 0.0007629 | CO2 = 0.183867*Animal Products Consumption + -4.10693 |
Figure 8. The correlation between daily meat consumption per capita and CO2 emission per person.
Thus, figure 9 also showed a positive relationship between GDP per capita and CO2 emission per person. The logic is as similar as mentioned above. When people are more prosperous than before, they are willing to consume more meat to improve living standards. However, to fulfill the demand, the farmer has to exploit the environment, such as deforestation to graze. Therefore, Hypothesis 2a and Hypothesis 2b are accepted.
| Term | Value | StdErr | t-value | p-value |
|---|---|---|---|---|
| GDP | 0.0003981 | 2.616e-05 | 15.2203 | < 0.0001 |
| intercept | -7.13944 | 13.6427 | -0.523316 | 0.603642 |
| P-Value | Equation |
|---|---|
| < 0.0001 | CO2 = 0.000398112*GDP + -7.13944 |
Figure 9. The correlation between GDP per capita and CO2 emission per person.
4 Analysis Results
4.1 Descriptive Summary Table
#Import dataset
our.data <- read.csv('data/all.csv', header= TRUE, sep =",")
#glimpse(our.data)
#Summary statistics table--Overall
tb1_desc <- our.data %>%
select(GDP,CO2,Region, Animal_Products_Consumption,Percentage_Aging,Fertility_Rate, Labor_Fource_Rate) %>%
tbl_summary (
by= Region,
missing = "no",
type = list(c(CO2,GDP,Animal_Products_Consumption, Percentage_Aging,Fertility_Rate, Labor_Fource_Rate) ~ "continuous2", Region ~ "categorical"),
statistic = list(all_continuous() ~ "{mean} ({sd})", all_categorical() ~ "{n} ({p}%)")
)%>%
bold_labels %>%
add_n() %>%
add_p() %>%
bold_p(t=0.05)%>%
modify_header(label="**Variable**") %>%
bold_labels() %>%
modify_spanning_header(update = starts_with("stat_")~ "**Region**") %>%
modify_caption("Table 1: Overall summary table") %>%
as_hux_table()
tb1_descRegion | |||||||
Variable | N | Central Asia, N = 90 | East Asia, N = 126 | South Asia, N = 144 | Southeast Asia, N = 162 | Western Asia, N = 324 | p-value |
| GDP | 756 | <0.001 | |||||
| Mean (SD) | 9,146 (7,070) | 20,810 (15,733) | 2,886 (1,384) | 14,956 (15,997) | 27,768 (29,214) | ||
| CO2 | 846 | <0.001 | |||||
| Mean (SD) | 6 (6) | 6 (3) | 1 (1) | 5 (6) | 11 (13) | ||
| Animal_Products_Consumption | 752 | <0.001 | |||||
| Mean (SD) | 33 (13) | 47 (22) | 21 (20) | 28 (11) | 35 (16) | ||
| Percentage_Aging | 846 | <0.001 | |||||
| Mean (SD) | 4.9 (1.3) | 10.4 (5.6) | 4.7 (1.5) | 5.4 (1.9) | 5.4 (3.9) | ||
| Fertility_Rate | 846 | <0.001 | |||||
| Mean (SD) | 2.85 (0.48) | 1.54 (0.51) | 3.17 (1.37) | 2.26 (0.64) | 2.65 (0.99) | ||
| Labor_Fource_Rate | 505 | <0.001 | |||||
| Mean (SD) | 54 (12) | 54 (6) | 39 (18) | 59 (10) | 39 (19) | ||
| Kruskal-Wallis rank sum test | |||||||
Notes: The data covered from 2000 to 2017 (18years), so the N should be dived 18 to get the number of countries in each region.
4.2 Predictive Summary Table
model1 <- lm( Percentage_Aging ~ Labor_Fource_Rate+ Fertility_Rate, #first is Y to answer Hypothesis 1
data=our.data)
model1##
## Call:
## lm(formula = Percentage_Aging ~ Labor_Fource_Rate + Fertility_Rate,
## data = our.data)
##
## Coefficients:
## (Intercept) Labor_Fource_Rate Fertility_Rate
## 13.842905 -0.008813 -2.784413model1 <-
model1 %>%
tbl_regression()
model1| Characteristic | Beta | 95% CI1 | p-value |
|---|---|---|---|
| Labor_Fource_Rate | -0.01 | -0.03, 0.01 | 0.4 |
| Fertility_Rate | -2.8 | -3.2, -2.4 | <0.001 |
|
1
CI = Confidence Interval
|
|||
regression model for Model 1(Hypothesis 1) \[\widehat{Y_i} = 13.842905 -2.8 X1_i \]
model2 <- lm( CO2 ~ GDP+ Animal_Products_Consumption, #First is Y to answer Hypothesis 2
data=our.data)
model2##
## Call:
## lm(formula = CO2 ~ GDP + Animal_Products_Consumption, data = our.data)
##
## Coefficients:
## (Intercept) GDP
## 0.4495314 0.0003639
## Animal_Products_Consumption
## -0.0151971model2 <-
model2 %>%
tbl_regression()
model2| Characteristic | Beta | 95% CI1 | p-value |
|---|---|---|---|
| GDP | 0.00 | 0.00, 0.00 | <0.001 |
| Animal_Products_Consumption | -0.02 | -0.03, 0.00 | 0.10 |
|
1
CI = Confidence Interval
|
|||
Regression model for model 2 GDP has no effect \[\widehat{Y_i} = 0.4495314 -0.02 X1_i \]
#to draw model
#modelsummary (model1)
#modelplot (model1)#to draw model
#modelsummary(model2)
#modelplot(model2)4.2.1 Alternative to modelsummary()
Based on my rdf dataset, I will regress gnipc (y) on year (x). Again, you won’t be able to run the following codes without my rdf dataset. I will not provide you the dataset. Please adapt the following codes to suit your final project.
# Summary statistics of year to find year0 = 1999
#summary(joined.data.1$year)
# Fit a linear model
#lm1 <- lm(CO2 ~ Animal_Products_Consumption+ GDP, data = joined.data.1)
# Tidy and print fitted model summary
#tidy(lm1) %>%
# mutate(sig = joined.data.1(p.value)) %>%
# as_hux_table()
# Assess model fit using glance()
# Convert glance() to a long table
#lm1_gof <-
# glance(lm1) %>%
#pivot_longer(
# everything(),
#names_to = "goodness of fit",
#values_to = "value")
# Add detailed summary of the fitted linear model to the data
#joined.data.1_lm1 <- augment(lm1, joined.data.1)
# Fitted model plot
#joined.data.1_lm1 %>%
# ggplot() +
#geom_point(aes(x = Animal_Products_Consumption+ GDP,
y = CO2)) +
#geom_line(aes(x = year,
# y = .fitted), colour = "blue") +
#labs(title = "Fitted plot",
subtitle = "Actual against fitted value: model 1")
# Residual plot
joined.data.1_lm1 %>%
ggplot(aes(x = .fitted,
y = .resid)) +
geom_point(colour = "brown") +
labs(title = "Residual plot",
subtitle = "Residual against fitted value: model 1")Start your regression model by including all the possible predictor variables. Then remove the non-significant variable one by one until you arrive at your final best model.
Please document each of the modelling steps - how you build the initial model (e.g. model 1) and how you fix it (e.g. transform/ remove a variable) resulting in the in a better model 2).
Until you reach your final best model. Yes, you are allowed to have 1 best model for each research question.
4.3 Results presentation
Provides a comprehensive summary of all the models developed in table format. Explain understandably the model development to a general audience with no technical background, such that they can understand why the best model is clearly justified and how is it being derived.
4.3.1 Our Best model
The best model must be clearly described and justified.
Best model: Crave out a subsection to describe and interpret the best model.
An example of how your final model can be expressed as follows (Use \\ to break the equation into two lines):
\[\widehat{Y_i} = -153.305 + 0.081 X1_i \\ -2.637 lowmid{\_}X2_i - 1.566 uppmid{\_}X3_i \]
Interpret your results. Do they support/ not support your hypotheses? Positive/ negative/ no impact on the response variable? Do they make sense in answering your research questions? (Pitch #H What’s new?)
5 Conclusion
5.1 Implications
Discuss “why” (why not) the results are expected (not expected). Given the positive/ negative/ no impact on the response variable in your results, “how different” would it change the current practice in the real world? Practical suggestions or insights must have supporting results in your project or hard facts from the real world (properly cited from journal article or newspaper or credible website sources). (Pitch #I So what?)
5.2 Limitations
List the rationale of data choice and methodologies used in this project. Briefly identify any other concern/ limitation/ obstacle you face when conducting the research. (Pitch #K Other consideration)
5.3 Summary and Contributions
Summarise the problems of your interest and the answers (results) to your research questions. Summarises also the rationale of data choice and methodologies used in the report. (Pitch #I So what?)
5.4 Appendix
You can add the full documentation of your codes for model development here. Or any other document that will be useful to understand your project.
Other criteria:
- Accuracy and completeness: Apply accurate and consistent coding, code formatting, and R language syntax, with no errors. Comments are used effectively. Knit your final report to a HTML or PDF output document.
- Expression and grammar: scholarly, succinct with correct spelling, grammar, and punctuation.
- References: application of accurate and consistent APA 6th referencing style. See citing and referencing tutorial, or a Quick reference guide - APA 6th Citing & Referencing style.
Word limit: no word limit (You must address all eight criteria above)
Grade: 35% of your total unit assessment. Feedback as per marking rubrics.