the effects of income, gender, and age on global diets · • 11 food categories: fruit, fruit...

The Effects of Income, Gender, and Age on Global Diets

Andrew Muhammad, PhD Chief of the International Demand and Trade Branch

U.S. Department of Agriculture, Economic Research Service, Washington, DC

Research Funded by the Gates Foundation (PI) Dariush Mozaffarian, MD, DrPH, Dean, Gerald J. and Dorothy R. Friedman School of Nutrition Science & Policy, Tufts University.

The views expressed here are those of the author(s), and may not be attributed to the Economic Research Service or the U.S. Department of Agriculture.

2

Background The global financial crisis and rise in world food prices brought

attention to the importance of income to global diets/health (Brinkman et al., 2010).

Knowing how income affects food choice is critical to determining the impact of rising incomes, economic development, and government/assistance programs.

There have been significant changes in global eating habits resulting in increased rates of non-communicable diseases in developing and wealthy countries (Popkin, Adair, and Ng, 2012).

It is important to take a more global perspective when examining options for improving diets.

3

Overview… Assess the relationship between income and global

dietary habits, from least developed to wealthy countries, and the heterogeneity in intake and income responsiveness due to gender, age, and region.

The analysis is the first to use nationally representative data on individual food intake to derived income elasticities of food consumption globally.

4

Distribution of an additional $1 of food expenditures

1 Countries are arranged in ascending order of Affluence. Source: Author’s estimates using the 2005 ICP data from the World Bank.

0.00

0.10

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0.50

0.60

0.70

0.80

0.90

1.00

Congo, Dem. Rep. United States

Mar

gina

l Sha

re V

alue

Per capita income

Beverages &TobaccoFood Other

Fruits &VegetablesOils & Fats

Dairy

Fish

Meats

Cereals

5

Global Dietary Database

6

Metric code Metric (grams/day) Fruit Total fruit intake, including fresh, frozen, cooked, canned, or dried fruit. Exclude fruit juices and

salted or pickled fruits.

Fruit juices Total fruit juices intake. 100% juice.

Vegetables Total vegetable intake, including fresh, frozen, cooked, canned, or dried vegetable. Exclude salted or pickled vegetables, vegetable juices, starchy vegetables (e.g., potatoes, corn), legumes, nuts and seeds.

Beans, legumes Total intake of beans and legumes (excluding soy milk, and including tofu).

Nuts, seeds Total intake of nuts and seeds.

Whole grains Fiber content: Total intake of whole grain foods, including breakfast cereals, bread, rice, pasta, biscuits, muffins, tortilla, pancake etc. A whole grain is defined as a food with ≥1.0 g of fiber per 10 g of carbohydrate (reference to the fiber content of whole wheat).

Fish Total seafood intake (fish & shellfish), OR total fish intake.

Unprocessed red meat

Total red meat intake from all livestock, both domesticated and non domesticated (i.e., game). Exclude poultry, fish, eggs, and all processed meats.

Processed meat Total processed meat intake (e.g., processed deli or luncheon meats (ham, turkey, pastrami etc.), bacon, salami, sausages, bratwursts, frankfurters, hot dogs).

Milk Total milk intake (combined non-fat, low-fat and full-fat milk). Exclude soya milk or other plant-derived alternatives.

Sugar-sweetened beverages

Caloric content: Total sugar-sweetened beverages intake defined as any sugar-sweetened beverage with ≥50 kcal per 8 oz (226.8 g) serving, including carbonated beverages, soft drinks, sodas, energy drinks, fruit drinks etc. Exclude 100% fruit and vegetable juices.

7

Plant-Base Intake: 2010

Source: Global Dietary Database

0

25

50

75

100

125

150

175

200

225

Sub-Saharan Africa Latin America and theCaribbean

North Africa/MiddleEast/South Asia

Former Soviet Union Asia Rest of the World

Mea

n In

take

(gra

ms

per d

ay)

Fruit Vegetables Beans, legumes Nuts, seeds Whole grains

8

Meat and Fish Intake: 2010


0

10

20

30

40

50

60

70

Sub-Saharan Africa Latin America and theCaribbean

North Africa/MiddleEast/South Asia

Former Soviet Union Asia Rest of the World

Mea

n In

take

(gra

ms

per d

ay)

Unprocessed red meat Processed meat Fish

9

Beverage Intake: 2010


0

50

100

150

200

250

300

350

400

450

500

Sub-Saharan Africa Latin America andthe Caribbean

NorthAfrica/Middle

East/South Asia

Former SovietUnion

Asia Rest of the World

Mea

n In

take

(gra

ms

per d

ay)

Milk Sugar-Sweetened Beverages Fruit juices

10

Fruit 11.3%

Fruit juice 1.9%

Vegetables 15.8%

Beans, legumes 16.3%

Nuts, seeds 1.0%

Whole grains 12.4% Unprocessed

red meat 5.8%

Processed meat 1.1%

Milk 12.1%

Sugar-Sweetened Beverages

18.8%

Fish 3.5%

Sub-Saharan Africa

Fruit 19%

Fruit juices 7%

Vegetables 18%

Beans, legumes

2% Nuts, seeds

0%

Whole grains

7% Unprocessed

red meat 8%

Processed meat 3%

Milk 21%


11%

Fish 4%

Western Europe

11

Fruit 11.3%

Fruit juice 1.9%

Vegetables 15.8%

Beans, legumes 16.3%

Nuts, seeds 1.0%

Whole grains 12.4% Unprocessed

red meat 5.8%

Processed meat 1.1%

Milk 12.1%


18.8%

Fish 3.5%

Sub-Saharan Africa

Fruit 11%

Fruit juices 10%

Vegetables 11%

Beans, legumes

2%

Nuts, seeds 1%

Whole grains

6%

Unprocessed red meat

5%

Processed meat 4%

Milk 17%


31%

Fish 2%

United States

12

Data and Analysis • 2010 Global Dietary database data (188 countries) • Number of Countries (181) - limited by World Bank GDP data • 11 food categories: fruit, fruit juices, vegetables, beans and legumes, nuts and seeds,

whole grains, unprocessed red meat, processed meat, milk, fish, and sugar-sweetened beverages.

• Income - GDP per capita, PPP constant 2011 international dollars • Sex (binary variable); Age and Age2 (continuous variables); • 6 Regions (binary variables)

– Asia: East, So. East, & Asia Pacific (16 countries) – Former Soviet Union (FSU): Cent./East Europe and Cent. Asia (29 countries) – Latin America and Caribbean (LAC) (31 countries) – Sub-Saharan Africa (SSA) (46 countries) – North Africa, Middle East, and South Asia (NAME) (25 countries) – Rest of the World: U.S., Canada, Western Europe, and Oceania (34 countries)

13

Intake Demand Model

Let 𝑞𝑖𝑖 and 𝑌𝐶 denote the intake of the ith food or nutrient category and income in country c, respectively. We assume a quadratic relationship between intake and the log of income:

(1) 𝑞𝑖𝑖 = 𝛼𝑖∗ + 𝛽𝑖∗ log𝑌𝐶 + 𝛾𝑖∗(log𝑌𝐶)2+𝜇𝑖𝑖

𝛼𝑖∗,𝛽𝑖∗, and 𝛾𝑖∗ are parameters to be estimated and 𝜇𝑖𝑖 is the error term.

𝛼𝑖∗,𝛽𝑖∗, and 𝛾𝑖∗ can be expanded to account for differences in intake and income

responsiveness across gender and age subgroups and regions.

𝛼𝑖∗ = 𝛼𝑖0 + 𝛼𝑖1𝐹𝐹𝐹𝐹𝐹𝐹 + 𝛼𝑖2𝐴𝐴𝐹 + 𝛼𝑖3𝐴𝐴𝐹2 + 𝛼𝑖5𝑘 ∑𝑅𝐹𝐴𝑅𝑅𝑅𝑘

𝛽𝑖∗ = 𝛽𝑖0 + 𝛽𝑖1𝐹𝐹𝐹𝐹𝐹𝐹 + 𝛽𝑖2𝐴𝐴𝐹 + 𝛽𝑖3𝐴𝐴𝐹2 + 𝛽𝑖5𝑘 ∑𝑅𝐹𝐴𝑅𝑅𝑅𝑘

𝛾𝑖∗ = 𝛾𝑖0 + 𝛾𝑖1𝐹𝐹𝐹𝐹𝐹𝐹 + 𝛾𝑖2𝐴𝐴𝐹 + 𝛾𝑖3𝐴𝐴𝐹2 + 𝛾𝑖5𝑘 ∑𝑅𝐹𝐴𝑅𝑅𝑅𝑘

14

From (1) we can derive the marginal propensity to consume (MPC) and income elasticity for each country c: (2) 𝑀𝑀𝑀𝑖𝑖 = ∆𝑖𝑖𝑖𝑖𝑘𝑖

∆𝑖𝑖𝑖𝑖𝑖𝑖= ∆𝑞𝑖𝑖

∆𝑌𝐶= 𝛽𝑖

∗+2𝛾𝑖∗(log 𝑌𝐶)𝑌𝑖

(3) 𝜂𝑖𝑖 = %∆𝑖𝑖𝑖𝑖𝑘𝑖

%∆𝑖𝑖𝑖𝑖𝑖𝑖= ∆𝑞𝑖𝑖/𝑞𝑖𝑖

∆𝑌𝐶/𝑌𝐶= 𝛽𝑖

∗+2𝛾𝑖∗(log 𝑌𝐶)𝑞𝑖𝑖

𝜂𝑖𝑖> 1: luxury good 1 > 𝜂𝑖𝑖> 0: normal good 𝜂𝑖𝑖 < 0: inferior good.

Differences in 𝑀𝑀𝑀𝑖𝑖 across countries are due to differences in the Region estimates and income (𝑌𝐶). Differences in 𝜂𝑖𝑖 across countries are due to differences in the Region estimates, income (𝑌𝐶), and intake level (𝑞𝑖𝑖).

Intake Demand Model

15

Summary of plant-intake estimates…

Overall, income has a positive impact on intake (except unprocessed red meat). Vegetables and whole grains are the most responsive (plant intake).

Fruit is the only plant-base measure where income-responsiveness is influenced by age and sex. Female and older adults are more likely to spend additional income on fruit.

Beans in Latin America are “inferior” but “normal” in all other regions… …the only inferior relationship in terms of estimates.

Regions matter (true for all foods).

16

Meat- and fish-intake estimates… Income not an issue with unprocessed meat intake:

FSU and NAME/So. Asia are the exception. No diminishing affect of income, no affluence effect.

Female meat intake (both types) is less responsive to income.

Sex and age do not explain differences in fish intake and income.

17

Beverage-intake estimates…

Milk is the most responsive to income (all foods). Sugar-sweetened beverage is also relatively high.

Sex and/or age influence intake-responsiveness for all beverages. Sugar-sweetened beverages: age only. Small and positive female effect for milk and fruit juice intake. Small and negative age effect for all beverages.

Compared to ROW, regions are less responsive for milk but more responsive for sugar-sweetened beverages (estimates only).

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No. Africa/Mid. East/So. Asia

Marginal Propensity to Consume, Fruit Additional daily intake from a unit increase in income.

Marginal Propensity to Consume, Processed meat Additional daily intake from a unit increase in income.

-0.001

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Income Elasticity, Processed Meat Percentage change in daily intake given a percentage change in income.

-1.25

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Income Elasticity, Sugar Sweetened Beverages Percentage change in daily intake given a percentage change in income.

22

Marginal Propensity to Consume Estimates, Age 40 (additional daily intake, grams per day)

Democratic Rep. Congo versus Western Europe Democratic Rep. Congo

(Women) Democratic Rep. Congo

(Men) Western Europe

(Women)

Fruit 0.0369 (0.004)*** 0.0257 (0.004)*** 0.0003 (0.000)***

Vegetables 0.0315 (0.005)*** 0.0290 (0.005)*** -0.0005 (0.000)***

Beans, leg. 0.0187 (0.006)*** 0.0190 (0.006)*** 0.0000 (0.000)

Nuts, seeds 0.0094 (0.002)*** 0.0096 (0.001)*** -0.0002 (0.000)***

Whole grains 0.0523 (0.006)*** 0.0530 (0.006)*** -0.0001 (0.000)*

Unprocessed meat 0.0155 (0.002)*** 0.0168 (0.002)*** 0.0000 (0.000)

Processed meat 0.0059 (0.001)*** 0.0072 (0.001)*** 0.0000 (0.000)***

Fish 0.0088 (0.001)*** 0.0089 (0.001)*** -0.0003 (0.000)***

Milk 0.1201 (0.005)*** 0.1152 (0.005)*** 0.0007 (0.000)***

Sugar beverages 0.0713 (0.006)*** 0.0731 (0.006)*** -0.0005 (0.000)***

Fruit juice 0.0228 (0.002)*** 0.0180 (0.002)*** 0.0003 (0.000)***

Note: ***, **, and * denote the 0.01, 0.05, and 0.10 significance level, respectively. Standard errors are in parentheses. Estimates measure the additional intake (grams per day) given an extra dollar of income.

23

Mean Daily Intake (Age 40): Democratic Rep. Congo versus Western Europe


0

50

100

150

200

250

Fruit Vegetables Beans,legumes

Nuts, seeds Whole grain Unprocessedmeat

Processedmeat

Fish Milk Sugarbeverages

Fruit juice

Mea

n In

take

(gra

ms

per d

ay)

Democratic Rep. Congo (Women) Democratic Rep. Congo (Men) Western Europe

24

Fruit 18%

Vegetables 32%

Beans, legumes

22% Nuts, seeds 0%

Whole grain 3%

Unprocessed meat 6%

Processed meat 1%

Fish 4%

Milk 4%

Sugar beverages

9%

Fruit juice 1%

Democratic Rep. Congo (before)

Fruit 15%

Vegetables 25%

Beans, legumes

17% Nuts, seeds 1%

Whole grain 6%

Unprocessed meat 6%

Processed meat 1%

Fish 4%

Milk 11%

Sugar beverages

12%

Fruit juice 2%

Democratic Rep. Congo (after)

The effects of income on diet composition, Men, Age 40: $25 per month increase in PPP-adjusted income.

25

Closing and implications… • Changes in Diet Composition, Rising Income, and Assistance/Aid:

Fruit and vegetable intake as a share of total intake could decrease. – “Unhealthy” intake share increases. – Sugar-sweetened beverage intake, a problem among older adults in Sub-

Saharan Africa? • Fruit, the only plant-base metric where gender and age affect income

responsiveness. – Older adults are more responsive to income in a positive way. – Young men in Sub-Saharan Africa show a negative response.

• Unprocessed red meat consumption.

– Does income matter? – Education and information.

26

Thank you. [email protected]

mailto:[email protected]

27

Variables Fruit Vegetables Beans,

legumes Nuts, seeds

Whole grains

Constant -211.610 (68.99)*** -400.849 (81.37)*** -244.546 (83.57)*** -158.773 (21.11)*** -661.404 (89.07)***

Female (F) -41.479 (9.66)*** 0.775 (11.39) 2.670 (11.70) 1.622 (2.96) 6.534 (12.47)

Age -3.766 (1.58)** -0.168 (1.86) 3.310 (1.91)* 0.708 (0.48) 1.010 (2.04)

Age2 0.023 (0.02) 0.001 (0.02) -0.028 (0.02) -0.007 (0.00) 0.003 (0.02)

Asia 46.788 (22.72)** 40.017 (26.80) 44.162 (27.53) 92.684 (6.95)*** 407.626 (29.34)***

FSU -158.123 (25.84)*** -332.385 (30.48)*** 4.559 (31.31) -16.812 (7.91)** 146.527 (33.37)***

LAC 111.466 (28.06)*** -74.241 (33.10)** 514.160 (34.00)*** 6.578 (8.59) 22.816 (36.23)

SSA 134.282 (21.04)*** 102.886 (24.81)*** 126.330 (25.49)*** 25.259 (6.44)*** 199.765 (27.16)***

NAME -149.814 (19.51)*** -23.610 (23.02) 100.446 (23.64)*** -36.509 (5.97)*** 43.024 (25.19)*

log(income) 66.521 (13.23)*** 120.229 (15.61)*** 40.529 (16.03)** 35.886 (4.05)*** 136.288 (17.09)***

F × log(income) 7.052 (1.06)*** 1.576 (1.25) -0.216 (1.28) -0.097 (0.32) -0.470 (1.37)

Age × log(income) 0.537 (0.17)*** 0.227 (0.20) -0.285 (0.21) -0.037 (0.05) -0.078 (0.22)

Age2 × log(income) -0.003 (0.00)* -0.002 (0.00) 0.002 (0.00) 0.000 (0.00) 0.000 (0.00)

Asia × log(income) -3.947 (2.39)* -1.151 (2.82) -3.300 (2.90) -7.060 (0.73)*** -40.799 (3.09)***

FSU × log(income) 11.737 (2.72)*** 31.112 (3.21)*** -0.936 (3.29) 1.386 (0.83)* -20.582 (3.51)***

LAC × log(income) -9.459 (3.00)*** 1.802 (3.54) -48.417 (3.63)*** -1.045 (0.92) -5.183 (3.87)

SSA × log(income) -19.497 (2.40)*** -16.996 (2.84)*** -0.549 (2.91) -3.202 (0.74)*** -16.274 (3.10)***

NAME × log(income) 13.965 (2.01)*** 7.163 (2.37)*** -8.462 (2.43)*** 4.104 (0.61)*** -6.417 (2.59)**

log(income)2 -3.655 (0.68)*** -7.063 (0.80)*** -1.581 (0.82)* -1.994 (0.21)*** -6.460 (0.87)***

Adjusted R2 0.461 0.323 0.443 0.313 0.269

Table Model estimates for plant-based intake

Note: ***, **, and * denote the 0.01, 0.05, and 0.10 significance level, respectively. Standard errors are in parentheses.

28

Variables Unprocessed

red meat Processed

meat Fish Milk Sugar-Sweet

Beverages

Fruit juice

Constant 66.123 (29.57)** -48.416 (12.78)*** -41.313 (17.64)** -1019.880 (88.28)*** -117.744 (134.82) -238.593 (43.84)***

Female (F) 1.697 (4.14) 4.388 (1.79)** 0.102 (2.47) -16.312 (12.36) -2.712 (18.88) -19.824 (6.14)***

Age -0.260 (0.68) -0.026 (0.29) 0.428 (0.40) 3.478 (2.02)* -1.942 (3.08) 3.927 (1.00)***

Age2 0.003 (0.01) 0.001 (0.00) -0.003 (0.00) -0.041 (0.02)** 0.011 (0.03) -0.031 (0.01)***

Asia -2.549 (9.74) 34.404 (4.21)*** -173.220 (5.81)*** 268.910 (29.08)*** -163.700 (44.41)*** -67.478 (14.44)***

FSU -161.871 (11.08)*** -48.319 (4.79)*** -112.644 (6.61)*** 357.643 (33.07)*** -131.786 (50.50)*** 1.603 (16.42)

LAC -68.655 (12.03)*** 44.219 (5.20)*** -135.607 (7.18)*** 436.694 (35.91)*** -91.102 (54.84)* 112.129 (17.84)**

SSA -90.812 (9.02)*** 4.466 (3.90) -46.969 (5.38)*** 174.863 (26.92)*** -230.021 (41.11)*** 15.092 (13.37)

NAME -138.830 (8.36)*** 11.435 (3.62)*** -103.254 (4.99)*** 208.987 (24.97)*** -213.089 (38.13)*** -28.871 (12.40)**

log(income) -1.750 (5.67) 12.326 (2.45)*** 21.707 (3.38)*** 195.146 (16.94)*** 102.530 (25.86)*** 45.515 (8.41)***

F × log(income) -0.807 (0.45)* -0.798 (0.20)*** -0.089 (0.27) 3.114 (1.36)** -1.150 (2.07) 3.028 (0.67)***

Age × log(income) 0.067 (0.07) -0.003 (0.03) 0.044 (0.04) -0.625 (0.22)*** -0.969 (0.34)*** -0.591 (0.11)***

Age2 × log(income) -0.001 (0.00) 0.000 (0.00) 0.000 (0.00) 0.008 (0.00)*** 0.007 (0.00)** 0.005 (0.00)***

Asia × log(income) -2.896 (1.02)*** -4.920 (0.44)*** 18.628 (0.61)*** -32.945 (3.06)*** 13.915 (4.67) 3.816 (1.52)**

FSU × log(income) 16.285 (1.17)*** 5.426 (0.50)*** 9.023 (0.70)*** -35.666 (3.48)*** 8.721 (5.31)*** -3.483 (1.73)**

LAC × log(income) 5.885 (1.29)*** -4.503 (0.56)*** 12.677 (0.77)*** -42.334 (3.84)*** 36.198 (5.86)*** -11.175 (1.91)***

SSA × log(income) 9.349 (1.03)*** -1.672 (0.45)*** 3.085 (0.61)*** -14.141 (3.08)*** 27.758 (4.70)*** -4.981 (1.53)***

NAME × log(income) 13.436 (0.86)*** -3.047 (0.37)*** 8.390 (0.51)*** -23.486 (2.57)*** 20.646 (3.92)*** -0.036 (1.28)

log(income)2 0.114 (0.29) -0.444 (0.13)*** -1.578 (0.17)*** -7.397 (0.86)*** -4.388 (1.32)*** -1.004 (0.43)**

Adjusted R2 0.326 0.564 0.489 0.448 0.666 0.449

Table Model estimates for meat, fish, and beverage intake

Note: ***, **, and * denote the 0.01, 0.05, and 0.10 significance level, respectively. Standard errors are in parentheses.

the effects of income, gender, and age on global diets · • 11 food categories: fruit, fruit...

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