the effects of income, gender, and age on global diets · • 11 food categories: fruit, fruit...
TRANSCRIPT
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
0.20
0.30
0.40
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
Source: Global Dietary Database
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
Source: Global Dietary Database
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%
Sugar-Sweetened Beverages
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%
Sugar-Sweetened Beverages
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%
Sugar-Sweetened Beverages
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).
-0.01
0.00
0.01
0.02
0.03
0.04
0.05C
ongo
, Dem
. Rep
.M
ozam
biqu
eG
uine
a-B
issa
uM
adag
asca
rB
enin
Sene
gal
Sao
Tom
e an
d Pr
inci
peN
iger
iaN
amib
iaEq
uato
rial G
uine
aB
oliv
iaB
eliz
eD
omin
ica
Cos
ta R
ica
Mex
ico
Ant
igua
and
Bar
buda
Nep
alIn
dia
Jord
anTu
rkey
Om
anTa
jikis
tan
Mon
golia
Turk
men
ista
nB
elar
usC
roat
iaH
unga
ryTi
mor
-Les
tePh
ilipp
ines
Thai
land
Sing
apor
eV
anua
tu Fiji
New
Zea
land
Fran
ceC
anad
aIr
elan
dLu
xem
bour
g
Male, 20 Female, 20 Male, 80 Female, 80
Sub-Saharan Africa
Latin America & Caribbean
Former Soviet Union
Asia Rest of World
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
0.000
0.001
0.002
0.003
0.004
0.005
0.006
0.007
0.008C
ongo
, Dem
. Rep
.M
ozam
biqu
eG
uine
a-B
issa
uM
adag
asca
rB
enin
Sene
gal
Sao
Tom
e an
d Pr
inci
peN
iger
iaN
amib
iaEq
uato
rial G
uine
aB
oliv
iaB
eliz
eD
omin
ica
Cos
ta R
ica
Mex
ico
Ant
igua
and
Bar
buda
Nep
alIn
dia
Jord
anTu
rkey
Om
anTa
jikis
tan
Mon
golia
Turk
men
ista
nB
elar
usC
roat
iaH
unga
ryTi
mor
-Les
tePh
ilipp
ines
Thai
land
Sing
apor
eV
anua
tu Fiji
New
Zea
land
Fran
ceC
anad
aIr
elan
dLu
xem
bour
g
Male, 20 Female, 20 Male, 80 Female, 80
Sub-Saharan Africa
Latin America & Caribbean
Former Soviet Union
Asia
Rest of World
No. Africa/Mid. East/So. Asia
Income Elasticity, Processed Meat Percentage change in daily intake given a percentage change in income.
-1.25
-1.00
-0.75
-0.50
-0.25
0.00
0.25
0.50
0.75
1.00
1.25
1.50
1.75C
ongo
, Dem
. Rep
.N
iger
Eritr
eaR
wan
daB
urki
na F
aso
Ben
inK
enya
Djib
outi
Zam
bia
Con
go, R
ep.
Nam
ibia
Gab
onN
icar
agua
Gua
tem
ala
Jam
aica
Dom
inic
aG
rena
daPa
nam
aV
enez
uela
, RB
Bah
amas
, The
Nep
alY
emen
, Rep
.Tu
nisi
aIr
aqIs
rael
Om
an
Uzb
ekis
tan
Ukr
aine
Mac
edon
ia, F
YR
Bel
arus
Kaz
akhs
tan
Esto
nia
Cze
ch R
epub
licC
ambo
dia
Phili
ppin
esM
aldi
ves
Japa
nK
iriba
tiM
icro
nesi
a, F
ed. S
ts.
Fiji
Gre
ece
Italy
Finl
and
Swed
enIr
elan
dN
orw
ay
Male, 20 Female, 20 Male, 80 Female, 80
Sub-Saharan Africa
Latin America & Caribbean
Former Soviet Union
Asia
Rest of World
No. Africa/Mid. East/So. Asia
-2.00
-1.50
-1.00
-0.50
0.00
0.50
1.00
1.50
2.00
2.50
3.00C
ongo
, Dem
. Rep
.N
iger
Eritr
eaR
wan
daB
urki
na F
aso
Ben
inK
enya
Djib
outi
Zam
bia
Con
go, R
ep.
Nam
ibia
Gab
onN
icar
agua
Gua
tem
ala
Jam
aica
Dom
inic
aG
rena
daPa
nam
aV
enez
uela
, RB
Bah
amas
, The
Nep
alY
emen
, Rep
.Tu
nisi
aIr
aqIs
rael
Om
an
Uzb
ekis
tan
Ukr
aine
Mac
edon
ia, F
YR
Bel
arus
Kaz
akhs
tan
Esto
nia
Cze
ch R
epub
licC
ambo
dia
Phili
ppin
esM
aldi
ves
Japa
nK
iriba
tiM
icro
nesi
a, F
ed. S
ts.
Fiji
Gre
ece
Italy
Finl
and
Swed
enIr
elan
dN
orw
ay
Male, 20 Female, 20 Male, 80 Female, 80
Sub-Saharan Africa
Latin America & Caribbean Former Soviet
Union Asia
Rest of World
No. Africa/Mid. East/So. Asia
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
Source: Global Dietary Database
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.
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.