Monthly Labor Review December 1998 3

The Food-at-Home Budget

I

n the 1960s, it was wheat germ and yoga. In

the 1970s, it was granola and jogging. In the

1980s, it was oat bran and aerobics. Every

decade appears to have had its own prescription

for good health, and the 1990s are no exception.

The news continually reports findings from medical studies that link foods with health conditions,

either good or bad. For instance, studies have

linked the consumption of cruciferous vegetables

to a reduced risk of certain types of cancer, polyunsaturated fats to lower levels of total blood cholesterol, and monounsaturated fats to lower “bad”

cholesterol and maintenance levels of “good”

cholesterol.1 Americans are advised to lower

their consumption of red meats and to increase

the amount of fiber and complex carbohydrates

they consume by eating more breads, rice, pasta,

and fresh fruits and vegetables.

But are consumers following this advice? Evidence from the U.S. Department of Agriculture

suggests that some changes in dietary patterns

have occurred recently.2 For instance, per capita

consumption of red meat fell 10 percent between

1980 and 1992, while per capita consumption of

poultry rose 48 percent over the same period.3

Per capita consumption also increased for fish (19

percent), flour and cereal products (29 percent),

dairy products (4 percent),4 fresh fruits (14 percent), and fresh vegetables (18 percent).5 The 15-

percent increase in per capita consumption of fats

and oils from 1980 (57.2 pounds) to 1992 (65.6

pounds) was due to a 23-percent increase in consumption of vegetable fats (44.8 pounds to 55.2

pounds), combined with a 15-percent decrease in

consumption of animal fats (12.3 to 10.4 pounds

per capita).

But there is a limitation to the data: because

the figures cited rely on estimates of food disappearance, they may not accurately reflect changes

in actual food intake. For example, the report

overstates turkey consumption, because an increasing amount of the supply of turkey is used

for pet foods.6 Similarly, consumption of fats and

oils may not be accurately measured, because the

figures include waste grease from restaurants.

After use in deep frying, waste grease is utilized

in animal and pet foods, as well as in industrial

operations, and is also sold for export, amounting to about 9 percent of the 1992 disappearance

of fats and oils.7 Furthermore, it is not clear from

the figures how Americans are consuming these

foods. The increase in fresh vegetable consumption may be in part due to the proliferation of

salad bars in grocery stores and of fast-food and

other restaurants.8 And changes in fat consumption may also be due to changes at fast-food establishments, and to the use of salad oils at salad

bars.9

Although any improvement in diet is good, it

is more important to look at patterns in food eaten

at home, for several reasons. First, most families

eat more meals at home (where the family has

more control over the ingredients used) than away

from home.10 Furthermore, the figures cited

The changing food-at-home

budget: 1980 and 1992 compared

Consumers appear to have made changes

to their diet between 1980 and 1992;

many have replaced red meat with poultry

and have reduced their consumption of eggs

Geoffrey D. Paulin

Geoffrey D. Paulin is an

economist in the

Division of Consumer

Expenditure Surveys,

Bureau of Labor

Statistics. All opinions

expressed in this article

are those of the

author and do not

constitute policy of the

Bureau of Labor

Statistics.
4 Monthly Labor Review December 1998

The Food-at-Home Budget

above do not make clear who is purchasing the foods: are

changes observed in the population in general or only in certain segments of the population? As the report says, “Data

from the periodic NFCS [Nationwide Food Consumption Survey] and Consumer Expenditures [sic] Survey conducted by

the Bureau of Labor Statistics are more useful for measuring

the effect of socioeconomic and demographic characteristics

on food consumption behavior.”11 Because the NFCS is conducted only about once every 10 years (most recently, in

1987–88), data from the continuing Consumer Expenditure

Surveys are an attractive alternative.12

Many authors have modeled demand for major food groups

as it relates to consumer characteristics.13 Some have looked

at demand for selected food items, such as dairy products.14

Others have examined income or other elasticities for specific food items.15 One article attempts, at least in part, to

compare the consumption of fruits and vegetables with levels

of intake suggested by various sources from the U.S. Department of Agriculture to see how far away consumers of different demographic backgrounds are from consuming the recommended intake.16 But a search of the literature revealed no

publications that address two critical issues, which are examined in this article: How have nationwide consumption patterns changed, if at all, over time? and How aware are consumers of nutritional issues, and, to the extent of their

awareness, do consumption patterns appear to be consistent

with consumers’ knowledge of nutrition?

The discussion that follows examines data from the Diary

portion of the 1980 and 1992 Consumer Expenditure Surveys

and newly published results from the Diet and Health Knowledge Survey (DHKS), a national survey of nutritional attitudes

conducted by the U.S. Department of Agriculture’s Agricultural Research Service. First, using the Consumer Expenditure data, shares of total food spending for several demographic groups are analyzed for differences over time. An

index is described that accounts for the influence of price

changes on shares and that estimates the change in quantities

of specific foods consumed relative to all food consumed.

Second, logistic regressions are performed to see if the probability of purchasing certain types of food has changed for

different groups. The demographic characteristics examined

include the age of the reference person,17 family income

level,18 race, and the marital status of the reference person.

Third, regressions using the Heckman two-stage procedure

are performed to estimate income elasticities of selected food

groups for different demographic groups. At the same time,

results from the DHKS are also cited to ascertain the level of

nutritional awareness attained by different demographic

groups. These data help explain changing patterns. For example, if older consumers were found to be the group most

concerned about saturated-fat and cholesterol intake, it would

not be surprising to see their expenditures for eggs decreasing over time. This article likely represents the first time that

results from the DHKS have been linked to another nationwide

survey (the Consumer Expenditure Survey) to investigate

changing food expenditure patterns.

The data

Consumer Expenditure Survey data. The Diary component

of the Consumer Expenditure Survey is composed of reports

from more than 5,000 consumer units19 annually. Participating families receive a diary for two consecutive 1-week periods in which they record expenditures for many different

items. Purchases of food for home consumption are documented in great detail. In 1980, 10,433 diaries were available

for study; in 1992, 11,713 diaries were. Each observation represents one family’s response for 1 week. Dividing the number of observations by 2 yields an approximate count of

unique families.20 All observations are treated independently.

The results are weighted to represent the total population of

about 85 million families (including single persons) in 1980

and 100 million families in 1992. Unless otherwise specified,

the sample described includes all families participating in the

1980 and 1992 surveys.

Report on attitudes toward nutrition. Between April 1989

and May 1992, the Department of Agriculture conducted the

DHKS for the first time. This survey was “designed so that individuals’ attitudes and knowledge about healthy eating...could

be linked with their food choices and nutrient intakes.”21 The

survey interviews main meal planners or preparers in U.S.

households, who are asked specific questions designed to find

out their knowledge and attitudes about dietary issues.22 For

example, respondents are asked, “In your opinion, should your

diet be lower or higher in saturated fat or is it just about right

compared with what is most healthful?”23 The data are broken out by several demographic groupings, including age, income, race, and gender.

One important fact about the DHKS is that it is the first survey designed to link dietary attitudes and food consumption

on a nationwide basis. Because it is a recent source of data, it

is not yet possible to see whether knowledge about nutrition

has changed over any length of time on a national basis.24

However, combined with results from the Consumer Expenditure Survey, the DHKS data may be used both to confirm that

families in the later period are at least aware of what they

should be eating and to investigate whether expenditure patterns are moving in that direction.

Shares analysis

In this section, five major food groups are considered: cereal

and bakery products; meat, poultry, fish, and eggs; dairy prod-
Monthly Labor Review December 1998 5

ucts; fruits and vegetables; and other food at home. Subcomponents of each major food group are shown in tables 1

through 5. A detailed breakdown of the subcomponents appears in the appendix.

The share index. The Diary survey results do not include

information on quantities of food purchased—only the level

of expenditure for each item is available. Because it is not

possible to directly compare quantities purchased over time

using these results, alternative methods must be utilized to

ascertain whether food-purchasing

habits have changed. One method is

to examine how the total food budget

is allocated. Although a Student’s ttest on the shares25 might seem appropriate at first, such a test does not conclusively indicate whether food purchases are changing. For example, if

the price of butter doubles, and, as a

result, the average family purchases

half the quantity it usually purchases,

then the share of the food budget spent

on butter does not change, assuming

that there are no changes in other food

prices or quantities purchased. So

price changes must be incorporated

into the analysis. In order to do this, a

share index is proposed to measure

relative changes in the amount of food

purchased. This index compares

shares over time after they are adjusted

using the Consumer Price Index

(CPI),26 which measures price changes

in detail for many goods and services,

including food at home. Therefore,

price changes for specific food items

can be compared with the change in

overall food-at-home prices. (See

chart 1.) If the share of total food expenditures for a particular food item

in 1992 is different from the share

found in 1980, and if the difference

cannot be accounted for by price

changes alone, then, at least relative to

total food purchased, the amount of the

specific food item purchased must

have changed over time.

The computation of the share index

is straightforward. Table 1 shows that

meat, poultry, fish, and eggs accounted

for 34.4 percent of total expenditures

on food at home in 1980. The figure

dropped to 26.4 percent in 1992. During that time, the price of

meat, poultry, fish, and eggs rose 42.3 percent, compared with

54.8 percent for all food at home. This means that meat, poultry, fish, and eggs cost 1.423 times more in 1992 than they did

in 1980, while all food at home cost 1.548 times more in 1992

than in 1980. If the quantities purchased of meat, poultry, fish,

and eggs and of total food at home remained constant, then

the share in 1992 should have been about 92 percent of the

1980 level (1.423/1.548 = 0.919). That is, if the quantities

remained unchanged, meat, poultry, fish, and eggs should have

Table 1. Changes in food purchases, all consumer units, 1980 and 1992

Consumer units (thousands) .......... 85,188 100,082 ... ...

Income before taxes1 ...................... $17,985 $33,407 ... ...

Average number of persons ........... 2.7 2.5 ... ...

Age of reference person ................. 46.1 47.4 ... ...

Number in consumer unit:

Persons under age18 ................. .8 .7 ... ...

Earners ....................................... 1.4 1.4 ... ...

Average weekly expenditures for:

Food at home ............................. $33.22 $49.99 ... ...

Cereal and bakery products ..... 4.27 7.90 ... ...

Meat, poultry,

fish, and eggs ........................ 11.43 13.22 ... ...

Dairy products ......................... 4.47 5.80 ... ...

Fruits and vegetables ............... 4.92 8.24 ... ...

Other food at home .................. 8.11 14.84 ... ...

Share of food at home (percent):

Food at home ............................. 100.0 100.0 54.8 ...

Cereal and bakery products ..... 12.9 215.8 80.6 1.05

Cereal and cereal products ... 4.2 25.4 82.1 1.09

Bakery products .................... 8.7 2

10.4 79.5 1.03

Meat, poultry, fish, and eggs .... 34.4 226.4 42.3 .83

Beef ...................................... 13.2 2

8.1 34.5 .71

Pork ....................................... 7.3 26.0 56.0 .82

Other meats .......................... 4.6 23.6 41.3 .86

Poultry ................................... 4.5 4.7 40.2 1.15

Fish and seafood ................... 2.8 2.9 73.4 .92

Eggs ...................................... 1.9 21.1 22.2 .73

Dairy products ......................... 13.5 2

11.6 41.4 .94

Fresh milk and cream ............ 7.1 25.1 36.4 .82

Other dairy products ............. 6.4 6.5 48.3 1.06

Fruits and vegetables ............... 14.8 16.5 89.3 .91

Fresh fruits ............................ 4.3 24.9 117.2 .81

Fresh vegetables ................... 4.2 2

4.9 99.9 .90

Processed fruits .................... 3.5 23.9 67.7 1.03

Processed vegetables ........... 2.8 2.9 55.0 1.03

Other food at home .................. 24.4 229.7 43.9 1.31

Sugar and other sweets ........ 3.6 23.9 47.1 1.14

Fats and oils .......................... 2.9 2.8 45.4 1.03

Miscellaneous foods .............. 8.8 214.8 67.6 1.55

Nonalcoholic beverages ........ 9.2 28.2 25.1 1.10

1 Complete income reporters only.

2

 Change in share is statistically significant at the 95-percent confidence level.

Percent

change in CPI,

198092

Item 1980 1992

Share

index
6 Monthly Labor Review December 1998

The Food-at-Home Budget

accounted for about 31.6 percent of the food budget in 1992.

Because they accounted only for 26.4 percent, however, the

quantities purchased of meat, poultry, fish, and eggs declined

relative to total food consumption. In fact, the share (26.4 percent) was only about 83 percent of its expected value in 1992;

hence, the share index is shown as 26.4/31.6, or 0.83.

The share index is easy to interpret. If it is greater than

unity, the quantity purchased of the specific item has risen

relative to the total quantity of food purchased. If the share

index is less than unity, the quantity purchased of the specific

item has fallen relative to the total quantity of food purchased.

Subtracting 1 from the level of the index also shows by what

percent the specific quantity has changed relative to that of all

food. Thus, as shown in table 1, the food group consisting of

meat, poultry, fish, and eggs accounted for about 17 percent

less of total food in 1992 than it did in 1980. Cereal and bakery products, with a share index of 1.05, increased 5 percent

relative to total food quantities purchased over the same period. It is worth emphasizing that the index does not measure

absolute changes in quantities. For example, if the average

family purchased twice as much meat, poultry, fish, and eggs

in 1992 than in 1980, but 3 times as much of all other foods,

the index is less than unity. Still, the measure is important in

that it shows how the family purchased certain items relative

to all food. And because it controls for price changes, it eliminates false interpretations that might arise from looking at

changes in the share of total food alone, even if the changes

are statistically significant. For example, the shares the average family devoted to fresh fruits and fresh vegetables each

rose almost 1 percent from 1980 to 1992, but relative to all

food purchased, the quantities actually declined substantially—10 percent for fresh vegetables and 19 percent for fresh

fruits. (These and other annual changes in the share index are

shown in chart 2.)

For ease of analysis, terms relating to food purchases and

food consumption are used interchangeably to denote relative

changes, as described by the share index. Changes of 5 percent or more, as indicated by the share index, are considered

analytically significant.

Overview. Tables 1 through 5 show that, for most demographic groups, the share indexes for cereal and bakery products and for other food at home indicate an increase in relative

purchases of these products between 1980 and 1992, while

for all other food items (meat, poultry, fish, and eggs; dairy

products; and fruits and vegetables), the indexes indicate a

relative drop in purchases. In most cases, the index for cereal

and cereal products is larger than the index for bakery products; similarly, the index for other food at home appears to

derive its large magnitude from strong increases in purchases

Chart 1. Changes in CPI food price indexes, 198092

1980 1982 1984 1986 1988 1990 1992

100

110

120

130

140

150

160

170

180

190

200

100

110

120

130

140

150

160

170

180

190

200

Food at home

Cereal and bakery products

Meat, poultry, fish, and eggs

Dairy products

Fruits and vegetables

Other food

Index

(1980 = 100)

Index

(1980 = 100)
Monthly Labor Review December 1998 7

of miscellaneous foods, probably because miscellaneous

foods include frozen meals, which proliferated in the 1980s.

More varieties became available during that period, including meals that were marketed as being gourmet-style foods or

low in calories. Also, an increase in ownership of microwave

ovens27 and an increase in dual-income families,28 making

leisure time more valuable and prepared foods more affordable,29 coincided during the period, rendering frozen meals a

timesaving, convenient, and, therefore, attractive option for

many families. Finally, the share of the food budget allocated

for eggs was cut by about half for most groups between 1980

and 1992, a reduction that is consistent with results from the

Department of Agriculture stating that “U.S. per capita egg

consumption has declined steadily since the end of World War

II from an all-time recorded high of 403 eggs in 1945”30 to (a

preliminary estimate of) 234 eggs in 1992.31 (See chart 2.)

Although the majority of these changes are in more healthful directions, consumption patterns in two food groups—fish

and seafood, and fruits and vegetables—declined unexpectedly for most demographic groups. The percentage of all families reporting expenditures on fish and seafood was fairly

stable around a mean of 28 percent from 1980 through 1990.

Table 2. Food purchases, by age of reference person, 1980 and 1992

Consumer units (thousands) ......... 29,092 28,716 39,568 49,713 16,528 21,654 ... ... ...

Income before taxes1 ..................... $16,975 $28,500 $22,450 $42,807 $9,108 $19,624 ... ... ...

Average number of persons .......... 2.5 2.5 2.5 2.9 1.7 1.7 ... ... ...

Age of reference person ................ 26.8 27.6 46.5 47.2 73.0 74.1 ... ... ...

Number in consumer unit:

Persons under age 18 ................. .9 .9 1.0 .9 .1 .1 ... ... ...

 Earners ...................................... 1.4 1.4 1.7 1.7 .5 .5 ... ... ...

Average weekly expenditures for:

Food at home .............................. $27.91 $41.70 $39.86 $58.30 $26.65 $41.92 ... ... ...

Cereal and bakery products ...... 3.51 6.54 5.19 9.16 3.40 6.81 ... ... ...

Meat, poultry,

fish, and eggs ......................... 9.47 10.68 13.88 15.99 9.03 11.14 ... ... ...

Dairy products .......................... 3.91 4.90 5.34 6.78 3.38 4.75 ... ... ...

Fruits and vegetables ................ 3.85 6.36 5.85 9.43 4.60 8.00 ... ... ...

Other food at home ................... 7.17 13.22 9.60 17.35 6.23 11.23 ... ... ...

Share of food at home (percent):

Food at home .............................. 100.0 100.0 100.0 100.0 100.0 100.0 ... ... ...

Cereal and bakery products ...... 12.6 215.7 13.0 215.7 12.8 216.2 1.07 1.04 1.08

Cereal and cereal products ..... 4.4 2

5.9 4.1 25.3 4.1 25.2 1.14 1.10 1.08

Bakery products ...................... 8.2 2

9.8 8.9 210.4 8.7 211.1 1.03 1.01 1.10

Meat, poultry, fish, and eggs ..... 33.9 2

25.6 34.8 226.7 33.9 226.6 .82 .83 .85

Beef ........................................ 13.9 2

8.2 13.2 28.2 12.2 27.5 .68 .72 .71

Pork ........................................ 6.9 5.6 7.6 26.0 7.2 6.5 .81 .78 .90

Other meats ............................ 4.4 2

3.4 4.8 23.7 4.2 3.5 .85 .84 .91

Poultry .................................... 4.4 4.7 4.4 4.7 5.0 4.7 1.18 1.18 1.04

Fish and seafood .................... 2.4 2.6 3.0 3.1 3.1 3.0 .97 .92 .86

Eggs ....................................... 1.8 2

1.0 1.9 21.0 2.1 21.2 .70 .67 .72

Dairy products .......................... 14.0 211.7 13.4 211.6 12.7 211.3 .91 .95 .97

Fresh milk and cream ............. 7.4 2

5.5 7.1 25.0 6.5 25.3 .84 .80 .93

Other dairy products. .............. 6.6 6.3 6.3 6.7 6.2 6.1 1.00 1.11 1.03

Fruits and vegetables ................ 13.8 15.3 14.7 2

16.2 17.3 219.1 .91 .90 .90

Fresh fruits .............................. 3.5 4.2 4.4 4.8 5.5 6.0 .86 .78 .78

Fresh vegetables .................... 3.9 4.7 4.3 24.8 4.8 5.4 .93 .86 .87

Processed fruits. ..................... 3.5 3.6 3.2 2

3.7 4.3 4.7 .95 1.07 1.01

Processed vegetables ............ 2.9 2.8 2.7 2.9 2.7 2.9 .96 1.07 1.07

Other food at home ................... 25.7 2

31.7 24.1 229.8 23.4 226.8 1.33 1.33 1.23

Sugar and other sweets .......... 3.5 3.7 3.6 2

3.9 3.6 24.2 1.11 1.14 1.23

Fats and oils ........................... 2.8 2.4 2.9 2.8 3.2 3.2 .91 1.03 1.06

Miscellaneous foods ............... 10.5 216.9 8.4 214.8 7.0 212.0 1.49 1.63 1.58

Nonalcoholic beverages .......... 8.8 8.7 9.3 2

8.3 9.6 27.3 1.22 1.10 .94

1

 Complete income reporters only.

2

 Change in share is statistically significant at the 95-percent confidence level.

Item

 Under 35 years 35 to 64 years 65 and older Share index

1980 1992 1980 1992 1980 1992 Under 35

years

65 and

older

35 to 64

years
8 Monthly Labor Review December 1998

The Food-at-Home Budget

However, the percentage fell from 28.2 percent in 1990 to

24.9 percent in 1992. The drop may have been due to an increase in prices over this period: fish and seafood prices rose

3.4 percent, compared with a price hike of 2.7 percent for

beef and decreases of 1.5 percent for pork and 0.8 percent for

poultry. To the extent that poultry and fish are substitutes,32 it

makes sense that families would purchase less fish and seafood and more poultry, given an increase in fish prices and a

decrease in poultry prices. Evidence from other surveys also

shows declining purchases of fish and seafood from 1990

through 1992. For example, the Bureau of Economic Analysis Personal Consumption Expenditure Survey, which measures expenditures of individuals and nonprofit institutions;

the Supermarket Business annual survey of food manufacturers, packers, wholesalers, and retailers; and the Progressive

Grocer study of stores with annual food sales of at least $2

million each show decreasing real weekly expenditures on

fish and seafood from 1990 to 1992 (9.8 percent, 8.3 percent,

and 15.2 percent, respectively).33 Data from the Department

of Agriculture show that fish and seafood consumption was

12.4 pounds per capita in 1980, peaked in 1987 at 16.1 pounds

per capita, and dropped to 14.7 pounds in 1992.

Data on fruits and vegetables are more intriguing. The share

index for total expenditures on fruits and vegetables (including processed fruits and vegetables) indicates a decrease in

purchases for all demographic characteristics examined, although the percentage of all families reporting such expenditures increased slightly from 1980 (75 percent) to 1992 (78

percent). The share index for fresh fruits and vegetables was

found to decline in every case examined, although the increase

in the percentage of all families reporting expenditures on

fruits and vegetables was about 7 percent for fresh fruits and

fresh vegetables alike. However, the share indexes agree with

data from the NFCS, which show a decrease in average annual

household food use, measured in pounds per 21-meal equivalent person. Specifically, consumption of fresh fruits decreased from about 150 pounds in 1977–78 to about 147

pounds in 1987–88. Consumption of fresh vegetables (including potatoes, for consistency with the Consumer Expenditure

Survey) fell from about 214 pounds to 185 pounds over the

same period.34

But how can the percentage of families reporting increase,

yet the amounts consumed decrease? To explain this phenomenon, families can be categorized into new purchasers and

continuing purchasers. New purchasers are those who were

not likely to purchase fresh fruits and vegetables in 1980, but

who were more likely to purchase them in 1992. Continuing

purchasers are those who were likely to purchase fresh fruits

Chart 2. Chart 2. Consumer expenditure share indexes, 198092

1980 1982 1984 1986 1988 1990 1992

0.6

0.7

0.8

0.9

1

1.1

1.2

1.3

1.4

1.5

0.6

0.7

0.8

0.9

1

1.1

1.2

1.3

1.4

1.5

Cereal and bakery products

Meat, poultry, and fish

Eggs

Dairy products

Fruits and vegetables

Other food at home

Share index

(1980 = 1)

Share index

(1980 = 1)
Monthly Labor Review December 1998 9

and vegetables regardless of the year. Two factors are undoubtedly at work. First, new purchasers are induced to purchase, perhaps because of a greater awareness of the relationship of the consumption of fresh fruits and vegetables to

health. Second, by contrast, continuing purchasers, perhaps

reacting to the fact that prices of fruits and vegetables have

increased faster than prices of any other foods (see chart 1),

cut back on purchases of fruits and vegetables. Now, if the

new purchasers are a small fraction of all purchasers, then

their average purchase must be large to counteract even a small

decrease in average purchases by continuing purchasers, or

else average purchases will decline for the population. Evidence from the Consumer Expenditure Survey data shows that

this is indeed what happened. The real (that is, inflation-adjusted) mean weekly expenditure for fresh fruits in 1980 was

$1.70, compared with $1.33 in 1992. Only bananas showed

an increase in real mean expenditures, from $0.26 to $0.36.

This is not so surprising, because the price increase for bananas (53 percent) was by far the smallest of all fresh fruits.

(Oranges had the largest increase, 143 percent.) Fresh vegetables showed a similar decline in real mean weekly expenditures, from $1.78 in 1980 to $1.54 in 1992. Although expenditures for potatoes decreased only $0.01 (2.9 percent),

expenditures for lettuce decreased $0.06 (23.1 percent), toTable 3. Food purchases and family income, 1980 and 1992

Consumer units (thousands) ....... 22,642 26,148 22,894 26,184 21,731 26,209 ... ... ...

Income before taxes1 ................... $5,270 $9,232 $15,597 $26,143 $33,749 $64,784 ... ... ...

Average number of persons ........ 2.0 1.9 2.8 2.5 3.4 3.1 ... ... ...

Age of reference person .............. 50.7 51.5 41.8 45.8 42.6 44.4 ... ... ...

Number in consumer unit:

Persons under age 18 ............... .5 .5 .9 .7 1.1 .9 ... ... ...

Earners ..................................... .7 .7 1.4 1.4 2.0 2.0 ... ... ...

Average weekly expenditures for:

Food at home ............................ $23.42 $35.61 $34.78 $49.29 $47.19 $67.38 ... ... ...

Cereal and bakery products .... 3.17 5.55 4.51 7.72 5.92 10.85 ... ... ...

Meat, poultry, fish, and eggs ... 7.55 9.93 12.02 12.92 16.73 16.78 ... ... ...

Dairy products ........................ 3.19 4.22 4.63 5.86 6.36 7.63 ... ... ...

Fruits and vegetables .............. 3.66 6.10 4.93 7.88 6.69 11.12 ... ... ...

Other food at home ................. 5.85 9.81 8.70 14.90 47.19 67.38 ... ... ...

Share of food at home (percent):

Food at home ............................ 100.0 100.0 100.0 100.0 100.0 100.0 ... ... ...

Cereal and bakery products .... 13.5 2

15.6 13.0 215.7 12.5 216.1 .99 1.04 1.10

Cereal and cereal products ... 4.9 5.6 4.5 25.5 3.7 25.3 .97 1.04 1.22

Bakery products .................... 8.6 29.9 8.5 2

10.2 8.8 210.8 .99 1.04 1.06

Meat, poultry,

fish, and eggs ....................... 32.2 2

27.9 34.6 226.2 35.5 224.9 .94 .82 .76

Beef ...................................... 10.1 2

8.2 13.8 28.3 14.9 27.6 .93 .69 .59

Pork ...................................... 7.4 7.0 7.2 6.1 7.5 25.1 .94 .84 .67

Other meats .......................... 4.5 23.5 4.6 2

3.8 4.4 23.4 .85 .90 .85

Poultry .................................. 5.3 5.1 4.4 4.4 4.0 4.6 1.06 1.10 1.27

Fish and seafood .................. 2.6 2.6 2.8 2.4 3.0 3.3 .89 .77 .98

Eggs ..................................... 2.3 2

1.3 1.9 21.1 1.7 2.9 .72 .73 .67

Dairy products ........................ 13.6 211.9 13.3 11.9 13.5 2

11.3 .96 .98 .92

Fresh milk and cream ........... 7.7 2

5.8 7.3 25.5 6.7 24.6 .86 .86 .78

Other dairy products ............. 5.9 6.1 6.0 6.4 6.8 6.7 1.08 1.11 1.03

Fruits and vegetables .............. 15.6 17.1 14.2 16.0 14.2 2

16.5 .90 .92 .95

Fresh fruits ............................ 4.6 4.9 3.9 24.8 4.2 2

5.0 .76 .88 .85

Fresh vegetables .................. 4.4 4.9 4.3 4.6 3.9 25.0 .86 .83 .99

Processed fruits .................... 3.7 4.1 3.2 23.8 3.5 3.8 1.02 1.10 1.00

Processed vegetables .......... 3.0 3.2 2.8 2.8 2.6 2.7 1.07 1.00 1.04

Other food at home ................. 25.0 27.5 25.0 2

30.2 24.3 231.2 1.18 1.30 1.38

Sugar and other sweets ........ 3.8 3.9 3.6 3.9 3.6 2

4.2 1.08 1.14 1.23

Fats and oils ......................... 3.3 22.8 3.0 3.0 2.7 2.6 .90 1.06 1.03

Miscellaneous foods ............. 8.2 2

12.7 8.9 214.9 9.2 216.0 1.43 1.55 1.61

Nonalcoholic beverages ........ 9.6 2

8.1 9.5 8.5 8.9 8.3 1.04 1.11 1.15

1 Complete income reporters only.

2

 Change in share is statistically significant at the 95-percent confidence level.

Item

 Low income Middle income High income Share index

1980 1992 1980 1992 1980 1992 Low

income

 High

income

Middle

income
10 Monthly Labor Review December 1998

The Food-at-Home Budget

matoes $0.05 (18.5 percent), and other fresh vegetables $0.14

(15.1 percent). When real mean weekly expenditures are divided by the percentage of consumer units reporting expenditures, to find the average real expenditure for those who actually purchased fresh fruits or vegetables, the results are even

more convincing. Average weekly expenditures for families

that reported purchases of fresh fruit declined from $3.35 in

1980 to $2.29 in 1992, while expenditures for fresh vegetables

decreased from $3.28 to $2.52. Even bananas showed a small

decrease in weekly expenditure ($0.03) when values for those

reporting expenditures are compared. The fact that the percentage of families reporting increased, while average expenditures for fruits and vegetables decreased, underscores the

importance of using both a share index and logistic regression to get a fuller picture of how purchases are changing.

Age. Age is expected to have a strong relationship to food

expenditures, although which group is expected to eat more

foods that are currently described as healthful is not clear a

priori. Older persons have different health concerns than

younger persons and so may be more inclined to eat carefully.

But older persons also may find that lifelong eating habits are

difficult to change. Surprisingly, when the share indexes are

examined by age, few differences in the direction of change

are found. (See table 2.) In almost every case, if the index

indicates a change in one direction for one age group, it indicates a change in the same direction for all three groups. For

example, purchases of beef, pork, fish, and eggs declined for

all age groups, while purchases of poultry increased. One notable exception is the share index for fats and oils, which indicates a 9-percent decrease in relative consumption for the

youngest group and an increase of 6 percent for the oldest

group. This difference is particularly interesting when it is

compared with analogous results from the DHKS: only 40.6

percent of meal planners older than 60 years thought that their

diet should be lower in fat, compared with 60.3 percent of

those under 39 years.35

Income. Level of income also is related to food expenditures.36 Families with lower incomes have less flexibility than

higher income families to adjust their food expenditure patterns should prices of foods change. In addition, reference

persons of families with lower incomes have lower average

levels of education, so they may not be as informed about

health issues.37

In almost every case, the share indexes for the highest income group indicate a change in a more healthful direction.

(See table 3.) For example, the largest increase in purchases

of cereals and cereal products (22 percent) was associated

with this group. Although each group showed a decrease in

its share index for meat, poultry, fish, and eggs, the share index was smallest for the high-income group (0.76), followed

by the middle- (0.82) and low-income groups (0.94). The indexes for beef and pork indicate a substantial drop in consumption for the high- and middle-income groups and a much

lesser decline for the low-income group. Similarly, poultry

consumption rises for each group, but the most for the high

group. The fish and seafood index indicates the smallest drop

for the high-income group (2 percent), but the largest for the

middle-income group (23 percent). And while egg purchases

decreased more than one-fourth for the low- and middleincome groups, they decreased by one-third for the highincome group. The index for fresh milk and cream shows

the largest decrease in consumption for the high-income

group, while the index for other dairy products shows that

purchases increased least for the high-income group. The index for fresh fruits indicates decreased consumption for all

groups, with the highest income group showing the smallest

decline; by contrast, the index for fresh vegetables decreased

more than one-eighth for the low- and middle-income

groups, but showed virtually no change for the high-income

group.

These findings are generally consistent with attitudes revealed by the DHKS. For example, middle-income families (defined in that survey as having income 1.31 to 3.50 times the

poverty line) and high-income families (those with incomes

greater than 3.5 times the poverty line) are the least likely to

think that they have sufficient fiber in their diets: only 34 percent of meal planners in low-income families thought that their

diets lacked fiber, compared with more than 40 percent of

middle- and high-income families’ meal planners.38 Furthermore, the higher the family’s income, the more likely the meal

preparer was to have heard of health problems related to lack

of fiber: less than 40 percent of low-income families’ meal

preparers had heard of such problems, compared with 52 percent of middle-income and 61 percent of high-income families1

 meal planners.39 As these results show, low-income families cut back more on fresh fruits and vegetables than do the

other groups and fail to increase their expenditures on cereal

and bakery products, unlike the other groups.

The findings regarding meat expenditures are less easily

reconciled with nutritional awareness. For example, when the

meal planner is asked, “Should your diet be lower or higher in

fat or is it just about right compared to what is most healthful,”40 high- (56 percent) and middle-income (57 percent)

meal planners are more likely than low-income families (47

percent) to think that their diet should be lower in fat; but

when the meal planner is asked specifically about saturated

fat,41 less than half (42 percent to 46 percent) think that their

diets should include less, regardless of income. On the other

hand, there appears to be a positive relationship between level

of income and knowledge of links between saturated fats and

health problems:42 about half of the low-income group is

aware of these links, compared with less than two-thirds of
Monthly Labor Review December 1998 11

middle-income families and slightly more than three-fourths

of high-income families. When the meal planner is asked

about the link between fat (without specifying whether it is

saturated) and health problems,43 the numbers increase for

each group, but the relationship is still apparent: now, 2 out of

3 low-income families say that they have heard of a link, compared with 7 out of 10 middle-income families and 8 out of 10

high-income families.

Gender and race. Because gender and race characteristics

can be broken into two groups (male and female; blacks, on

the one hand, and white and other races, on the other), these

groups can be compared easily within a year, as well as across

years. It is assumed that each group faces the same prices

within the same year; therefore, a t-test is useful in comparing

the groups within a year. For comparison within a group across

years, the share index is useful.

1. Gender. In the Consumer Expenditure Survey, it is not

clear that the reference person is making the decisions about

grocery purchases for the family, or even if the reference person exerts a great deal of influence in those decisions. FurTable 4. Food purchases by single persons, 1980 and 1992

Consumer units (thousands) ........ 9,289 13,556 ... 11,799 17,089 ... ... ...

Income before taxes1 .................... $11,932 $7,462 ... $21,290 $16,318 ... ... ...

Average number of persons ......... 1.0 1.0 ... 1.0 1.0 ... ... ...

Age of reference person ............... 41.3 53.2 ... 42.9 55.6 ... ... ...

Number in consumer unit:

 Persons under age 18 ............... .0 .0 ... .0 .0 ... ... ...

 Earners ..................................... .8 .5 ... .8 .5 ... ... ...

Average weekly expenditures for:

Food at home ............................ $12.55 $13.90 ... $22.75 $24.93 ... ... ...

 Cereal and bakery products ... 1.57 1.72 ... 3.47 3.97 ... ... ...

 Meat, poultry,

 fish, and eggs ...................... 3.75 4.24 ... 5.88 5.87 ... ... ...

 Dairy products ....................... 1.63 1.86 ... 2.81 2.93 ... ... ...

 Fruits and vegetables ............. 2.13 2.71 ... 3.89 4.88 ... ... ...

 Other food at home ................ 3.47 3.36 ... 6.70 7.28 ... ... ...

Share of food at home (percent):

Food at home ............................ 100.0 100.0 ... 100.0 100.0 ... ... ...

Cereal and bakery products ... 12.5 12.4 – .09 15.3 15.9 .43 1.05 1.10

Cereal and cereal products .. 3.5 3.7 .35 4.9 4.9 .03 1.19 1.13

Bakery products ................... 10.7 8.6 –1.71 10.4 11.0 .52 .84 1.10

Meat, poultry,

 fish, and eggs ........................ 29.9 30.5 .18 25.8 23.5 – .95 .94 .84

 Beef ..................................... 10.2 9.6 – .35 8.1 6.4 –1.81 .91 .77

 Pork ..................................... 6.5 5.8 – .43 5.0 5.6 .78 .76 .96

 Other meats ........................ 3.3 4.2 1.51 3.7 3.1 –1.22 1.23 .81

 Poultry ................................. 4.1 2

5.8 2.08 5.0 4.6 –.52 1.35 .88

 Fish and seafood ................. 3.6 3.2 –.47 3.0 2.6 –.83 .74 .73

 Eggs .................................... 2.1 2.0 –.20 1.1 1.4 1.31 .66 .89

Dairy products ........................ 13.0 13.4 .30 12.4 11.8 –.46 1.04 .96

Fresh milk and cream ......... 6.9 5.9 – 1.30 5.9 4.7 –1.47 .97 .90

Other dairy products ........... 6.1 2

7.5 2.01 6.5 7.0 .73 1.11 .97

Fruits and vegetables ............. 17.0 19.5 .98 17.1 19.6 1.40 .82 .82

Fresh fruits .......................... 4.1 26.5 3.57 5.1 6.1 1.50 .89 .67

Fresh vegetables ................ 6.0 5.6 –.18 4.7 5.7 1.64 .61 .79

Processed fruits .................. 4.2 4.5 .39 4.4 4.6 .23 .97 .94

Processed vegetables ........ 2.7 2.9 .54 2.9 3.2 .65 1.07 1.10

Other food at home ................ 27.6 24.2 – 1.31 29.5 29.2 –.10 1.15 1.30

Sugar and other sweets ...... 3.9 3.7 –.24 3.2 4.1 1.88 .86 1.17

Fats and oils......... ............... 2.0 23.1 2.68 2.4 2.9 1.33 1.28 1.00

Miscellaneous foods... ........ 9.3 7.8 –1.33 14.6 14.2 –.29 1.45 1.68

Nonalcoholic beverages ...... 12.4 2

9.5 – 2.08 9.3 8.1 – 1.23 .93 1.06

1

 Complete income reporters only.

2

 Difference in shares by gender is statistically significant at the 95-percent confidence level.

Item

Single persons, 1980 Share index

Men Women t-statistic Men Women t-statistic Men Women

Single persons, 1992
12 Monthly Labor Review December 1998

The Food-at-Home Budget

thermore, changes in family composition over time may influence purchasing decisions. By contrast, because a single

person is the sole decisionmaker for his or her “family,”

differences in purchases by gender are likely the result of

differences in taste or other influences that vary by gender.

However, regression analysis is still necessary to achieve the

greatest degree of certainty that gender influences food consumption, because incomes and other demographics may differ for single men and women.

Table 4 shows differences in expenditure shares for single

males compared with single females. The t-statistics indicate

that there were few statistically significant differences in expenditure shares by gender in 1980 and none in 1992. Yet,

when t-statistics are compared across time (not shown in the

table), single women are seen to have had significant changes

in their share of expenditures for cereal and bakery products

(the share increased), meat, poultry, fish, and eggs (decreased), and other food at home (increased), while single

men have no significant changes in share over time for any

major food category. This “paradox” can be resolved by considering the variances in expenditures for single men and

women. If single women have a lower variance in expenditures than single men do, then smaller changes in shares over

time for women will be statistically significant, while those

for men will not. And in comparisons by gender over the same

period, the larger variance in shares for men means that the

difference in shares by gender must also be large to be statistically significant. These facts suggest that in both 1980 and

1992, the average single person, regardless of gender, followed certain dietary habits, but that single men were more

likely than single women to deviate from those habits.

Nevertheless, there are some interesting differences in

share indexes when single men and women are compared.

Both sexes exhibit a decrease in purchases of meat, poultry,

fish, and eggs, but the share index for women (0.84) is 10

points lower than for men (0.94). This difference is due in

part to the fact that women cut back on all meat purchases,

while men increased their consumption of poultry and other

meats. The index for fruits and vegetables is the same for both

sexes (0.82), but men cut back less on fresh fruits and more

on fresh vegetables, while women did just the opposite. Similarly, women increased their purchases of sugar and sweets,

while leaving their consumption of fats and oils unchanged,

whereas men decreased their purchases of sugar and sweets

and increased their consumption of fats and oils.

The DHKS does not break out data by family size, but it

does have data comparing the knowledge and attitudes of male

and female meal planners. Some differences in these two

groups’ perceptions of diet are worth noting.

The percentage of women (55 percent) who believe that

their diets should be lower in fat is slightly larger than the

percentage of men (51 percent) who believe similarly.44

Women (51 percent) are also much more likely to believe that

their diets are too high in sugar and sweets than are men (38

percent).45 This difference is especially noteworthy, considering that the share index for women rose 17 points from 1980

at the same time the index for men fell 14 points. Men and

women are probably closest in attitudes on fiber, with nearly

40 percent of each group believing that their diets should be

higher in fiber and about 54 percent to 55 percent believing

that their fiber intake is “about right.”46

2. Race. Although only single individuals are examined by

gender of the reference person, families can be examined by

race of the reference person. Table 5 shows that, except for

beef and other meats, shares spent for meat, poultry, fish, and

eggs differ significantly by race, regardless of the year (1980

or 1992) the survey was conducted. Blacks spent larger shares

than whites and others on pork, poultry, fish and seafood, and

eggs in either year. However, both groups consumed less of

most meat products in 1992 than in 1980, with two exceptions: whites and others increased their consumption of poultry, and blacks increased their consumption of fish and seafood. Changes in the consumption of other foods are not

clearly related to race. The indexes indicate that whatever

changes there were were in the same direction and similar in

magnitude for most items, even at the subcomponent level.

The fact that black families spend larger shares than white

and other families for meat products is interesting when taken

in conjunction with the DHKS results.47 Meal preparers in black

families are more likely than meal preparers in white families

to respond that their saturated fat intake should be lower (48

percent, compared with 43 percent), and the same holds true

for cholesterol intake (48 percent, compared with 40 percent).

This may indicate that at least the meal preparers are aware of

the problem, even if habits are hard to break.

Logistic regression results

Even when accompanied by the share index, share analysis

does not give a complete picture of changes in food consumption patterns. Changes in one segment of a group can affect

the average share, even though not all members of that group

are changing their patterns. For example, suppose that in 1980,

families whose reference person is under age 35 ate mostly

meat and a few vegetables at every meal, while single persons

under age 35 ate only meat and no vegetables. Suppose further that in 1992, families under age 35 ate less meat and

more vegetables, while singles under age 35 continued to eat

only meat. Then, in the absence of price changes, the share

for consumers under age 35 should rise for vegetables and

fall for meat. Yet in this example, only families, and not single

persons, reap the benefits of the inclusion of more vegetables

in their diet. Logistic regression, or logit,

48 is used to estimate
Monthly Labor Review December 1998 13

the probability that a particular family will purchase a certain

type of food, given the family’s characteristics. If such an

analysis were performed on a group of data described in the

current example, the results would likely predict a higher

probability of purchasing vegetables for families whose reference person is under age 35 and no change in probability

for single persons. Whether the predicted probability of families purchasing vegetables would actually increase is an open

question. The reason is that it does not matter how much a

family purchases: all positive expenditures on vegetables are

recorded as a “yes,” the family purchased vegetables, on the

survey. So if, in 1980, the family purchased a small amount of

vegetables every week, and in 1992, it purchased a large

amount of vegetables every week, the probability of purchasing vegetables does not change, even though the quantity purchased does. Still, logit analysis offers some insight into foodpurchasing patterns. The way the data are collected, an increase in the probability of purchasing suggests that more

families are reporting purchases of the food and is thus a good

indicator of whether consumption is increasing due to an increase in the number of families that purchase the food, rather

than an increase in the number of purchases by families that

already consume the food regularly.

Because the logit procedure is a form of regression analyTable 5. Food purchases and race of reference person, 1980 and 1992

Consumer units (thousands) ........ 76,163 9,026 ... 88,754 11,328 ... ... ...

Income before taxes1 .................... $18,601 $12,860 ... $34,486 $24,612 ... ... ...

Average number of persons ......... 2.6 2.9 ... 2.5 2.7 ... .... ...

Age of reference person ............... 46.3 44.4 ... 47.6 45.6 ... ... ...

Number in consumer unit:

Persons under age 18 ............... .7 1.1 ... .7 .9 ... ... ...

Earners ..................................... 1.4 1.3 ... 1.4 1.3 ... ... ...

Average weekly expenditures for:

Food at home ............................ $33.81 $28.17 ... $22.75 $24.93 ... ... ...

Cereal and bakery products .. 4.35 3.57 ... 8.09 6.45 ... ... ...

Meat, poultry, fish, and eggs .. 11.41 11.58 ... 13.10 14.14 ... ... ...

Dairy products ....................... 4.65 2.94 ... 6.07 3.69 ... .. ...

Fruits and vegetables ............ 5.03 4.07 ... 8.39 7.05 ... ... ...

Other food at home ............... 8.36 6.01 ... 15.37 10.66 ... ... ...

Share of food at home (percent):

Food at home ............................ 100.0 100.0 ... 100.0 100.0 ... ... ...

Cereal and bakery products ... 12.9 12.7 .15 15.9 15.4 .56 1.06 1.04

Cereal and cereal products . 4.1 4.9 –1.29 5.4 6.1 –1.81 1.12 1.06

Bakery products ................. 8.8 7.8 1.25 10.5 2

9.2 1.99 1.03 1.02

Meat, poultry, fish, and eggs ... 33.7 2

41.1 –2.20 25.7 233.7 –5.26 .83 .89

Beef .................................... 13.5 11.1 1.74 8.0 8.9 –1.49 .68 .92

Pork .................................... 7.0 2

10.9 –3.38 5.7 28.6 –4.59 .81 .78

Other meats ........................ 4.6 5.0 –.65 3.5 4.1 –.75 .83 .90

Poultry ................................ 4.2 7.8 –4.41 4.6 2

6.1 –3.49 1.21 .86

Fish and seafood ................ 2.8 2

3.8 –2.07 2.8 24.6 –3.70 .89 1.08

Eggs ................................... 1.8 2

2.6 –2.98 1.0 21.3 –3.08 .70 .63

Dairy products ........................ 13.8 210.4 2.59 11.9 28.8 5.84 .94 .93

Fresh milk and cream ......... 7.2 6.0 1.36 5.3 2

3.9 4.09 .84 .74

Other dairy products ........... 6.5 2

4.4 3.74 6.6 24.9 4.90 1.06 1.16

Fruits and vegetables ............. 14.9 14.4 .28 16.4 16.8 –.39 .90 .95

Fresh fruits .......................... 4.4 3.8 1.17 4.9 4.9 –.07 .79 .92

Fresh vegetables ................ 4.3 4.2 .07 4.9 4.6 1.01 .88 .85

Processed fruits .................. 3.5 3.7 –.47 3.8 4.0 –.45 1.00 1.00

Processed vegetables ........ 2.8 2.8 .03 2.8 3.3 –1.44 1.00 1.18

Other food at home ................ 24.7 21.3 1.63 30.1 2

25.4 3.35 1.31 1.28

Sugar and other sweets ...... 3.5 3.7 –.26 4.0 3.5 1.95 1.20 1.00

Fats and oils ....................... 2.9 2.8 .35 2.8 2.8 –.01 1.03 1.06

Miscellaneous foods ........... 8.9 2

6.9 2.27 15.1 211.7 4.02 1.57 1.57

Nonalcoholic beverages ...... 9.3 8.0 1.50 8.3 7.5 1.51 1.10 1.16

1 Complete income reporters only.

2

 Difference in shares by race is statistically significant at the 95-percent confidence level.

Item

Race of reference person, 1980 Race of reference person, 1992 Share index

Black White and

other t-statistic White and

other Black t-statistic White andother Black
14 Monthly Labor Review December 1998

The Food-at-Home Budget

sis, several demographic characteristics can, in effect, be held

constant, and predictions can be made for specific families.

For example, the probability that a family in the middle-income group whose reference person is 35 to 64 years old and

black purchases fruits and vegetables can be predicted. The

characteristics that might be examined in assessing this probability are family income, composition, and size; participation in the food stamp program; whether the family lives in an

urban area; the month in which the family participated in the

survey (because some food items, such as fresh fruits, may be

available only seasonally or, like poultry, may be purchased

more frequently at holiday time regardless of other characteristics); and the age, race, and education of the reference person. The number of adults and children (that is, persons less

than 18 years old) are included separately as continuous variables, while all other variables are binary. A variable for the

square of the number of children is also included to account

for potential nonlinear relationships between the number of

children and the probability of purchasing a particular food.

For example, if milk is deemed a good food for children, but

not so important for adults, then a family consisting of a husband and wife with one child is expected to have a much

higher probability of purchasing dairy products than a family

of only a husband and wife. But a husband and wife with two

children is not expected to have a substantially higher probability of purchasing milk than a family with one child.

By using these characteristics, a “standard” or “control”

family can be described, against which other families can be

compared. For example, if the effect of age on the probability

of purchasing is the characteristic to be isolated, one can compare the control family with another family with identical characteristics except for age. In this way, the impact of age on

probability can be more carefully measured. For these purposes, the control group is defined as a family

consisting of a husband, wife, and one child;

in the middle-income group;

living in an urban area;

not participating in the food stamp program;

participating in the Diary survey in the spring (April, May,

or June); and

whose reference person is 35 to 64 years old, not black,

and never attended college.

The predicted probability for each characteristic is shown

for 1980 and 1992 in tables 6 through 8. For example, table 6

shows that the probability that a family in the control group

purchased cereal and bakery products in 1980 was 95.8 percent. A family whose reference person was under 35, but was

otherwise identical to the control family, was 2.8 percent less

likely to purchase cereal and bakery products that year. (That

is, the probability of that family’s purchasing those products

in 1980 was 93.0 percent.) In 1992, the control group’s probability was down slightly (94.5 percent), but the probability

of the family whose reference person was under 35 making

such purchases was virtually unchanged (92.7 percent). Presenting the results in this manner facilitates comparisons

across time and demographic groups.

The specification of the logit equation is

Y = a1

 + a2D1992 + biXi + (bjD1992Xi), (1)

where

Y equals 1 if a purchase occurs and 0 otherwise;

ai

, bi, and bj are parameter estimates;

D1992 is a dummy variable, coded 1 for 1992 data and 0

 for 1980 data; and

Xi is the ith characteristic for each family (for

 example, age, income level, and so forth).

Using the results of equation (1), tables 6 through 8 show the

various probabilities that the control family made a purchase

of a certain kind in 1980 and 1992. These probabilities are

calculated with the formula

P = 1/{1 + exp[–1(a1

 + a2D1992 + biXi

+ [bjD1992Xi])]}, (2)

where

P is the probability of the family’s purchasing a

 given food; and

all other symbols are as in equation (1).

Thus, from table 6, the probability that a single man who otherwise fits into the control group purchased cereal and bakery

products in 1980 is calculated by adding the value of the intercept (2.5555), the parameter estimate for single men

(–1.3905), and the parameter estimate for the number of adults

in the family (0.1628); summing these factors (which yields

1.3278); multiplying by negative 1 and exponentiating (that

is, taking exp[–1.3278]); adding 1 to the result (yielding

1.2651); and taking the reciprocal of this value. After all these

operations, P is estimated to be 0.7905, or about 79 percent.

If the family consists of a husband and wife only, then the

parameter estimate for married couples (–0.2679) is added

instead of the value for single males, and the parameter estimate for the number of adults is doubled, because there are

two adults present. Similarly, the addition of a child49 changes

the equation, through the change in both the number of children and the square of the number of children, and the family

type variable must change accordingly; that is, if the family is

in the control group, no family type parameter estimate is

added; however, if the family consists of a single parent with

children, the parameter estimate for single parents must be

included, and the number of adults must be adjusted accordingly. In each case, the results presented in tables 6 through 8

are calculated with the composition of the family taken into

account.












Monthly Labor Review December 1998 15

Calculating results for 1992 is slightly more complicated.

The parameter estimates for that year actually represent the

difference between the expected estimates for 1980 and 1992.

Therefore, parameter estimates for 1992 must be added to

their 1980 counterparts before a probability for 1992 families

can be calculated. Further, calculations for single women require an additional step, regardless of the year. A dummy variable, coded 0 if the consumer unit consists of more than one

person and 1 if the consumer unit consists of a single person,

is included in each model. A separate dummy variable, coded

0 if the consumer unit consists of more than one person or

a single man and 1 if the consumer unit consists of a single

woman, is also included in each model. Under this specification, the parameter estimate for the first dummy variable represents the estimated value for single men in 1980. The parameter estimate for single women represents the difference

Table 6. Probabilities of purchasing cereal and bakery products; beef, pork, and other meats; and poultry, all consumer

units, 1980 and 1992

Control group ....................... 3.1358 –0.2949 95.8 94.5 2.0803 –0.3940 88.9 84.4 –0.1277 –0.1660 46.8 42.7

Intercept1 ............................. 5

2.5555 –.5869 95.9 94.5 51.4871 5–.8120 89.0 84.5 5–.4477 –.2877 47.1 43.0

Age (35 to 64):

 Under 35 .......................... 5–.5463 5.2525 93.0 92.7 5–.5176 5.1694 82.7 79.2 5–.3302 5.1916 38.7 39.4

 65 and older ..................... 5.3762 –.0603 97.1 95.9 5.2578 –.1550 91.2 85.7 5.1924 –.1582 51.6 43.5

Income (middle tercile):

 Lowest tercile ................... 5–.2675 –.0369 94.6 92.7 5–.2730 5.2109 85.9 83.5 –.0901 .1706 44.6 44.7

 Highest tercile ................... 5–.2426 .2076 94.8 94.3 .0598 –.0887 89.5 84.0 –.0631 .1464 45.2 44.8

 Incomplete reporter .......... 5

–.4870 5.4236 93.4 94.1 5–.3645 5.2788 84.8 83.2 .0027 5.1883 46.9 47.4

Family composition

(husband and wife

with children):2

Single man ....................... 5–1.3905 5.6076 79.0 81.7 5–.9977 5.8323 67.1 70.0 5–.6555 .0483 27.0 23.6

Single woman .................. .1446 .0938 81.3 85.0 .0546 5

–.3041 68.3 64.6 .1811 –.1004 30.7 25.1

Husband and wife only ..... –.2679 .0871 93.2 91.8 –.2090 .2322 84.8 79.8 –.1152 .0278 41.5 38.0

Single parent ................... 5–.4383 .2058 92.7 90.9 5–.3476 .1052 81.9 75.1 –.1725 .0561 39.9 36.0

Other family ..................... 5

–.4942 –.0825 93.3 90.6 5–.3335 .1535 85.2 81.9 .0057 –.1751 47.0 38.6

Family size

(two adults, one child):

Number of adults3 ............ .1628 .1478 96.4 95.9 5

.2221 .1171 90.9 88.3 5.1096 .0567 49.5 46.8

Number of children4 ......... 5

.2807 –.0138 96.6 95.5 5.1875 5.1930 89.6 87.3 5.1194 –.0081 48.4 45.3

Square of number

of children ...................... –.0260 .0102 ... ... 5–.0385 –.0092 ... ... –.0186 .0164 ... ...

Race (white and other):

Black ................................ –.1586 .1382 95.2 94.4 .0323 .0572 89.2 85.5 5

.6868 5–.2538 63.6 53.5

Education (no college):

Some college ................... –.0700 –.1941 95.5 92.9 5

–.2097 –.1445 86.7 79.1 5–.1390 .0238 43.4 39.9

College graduate ............. –.0241 –.1378 95.7 93.6 –.1382 5–.3193 87.5 77.4 .0353 –.0890 47.7 41.4

Degree of urbanization:

Rural ................................ .2018 .0488 96.6 95.7 –.0686 .0316 88.2 83.9 –.0877 –.0697 44.6 38.9

Food stamp program:

Participates ...................... .2271 –.3786 96.7 93.6 .0071 .0068 89.0 84.6 .0699 –.0272 48.6 43.8

Month surveyed:

January, February,

 or March ........................ .0523 –.0068 96.0 94.7 5

–.1660 .1728 87.2 84.5 –.0667 .1590 45.2 45.0

July, August, or

September ..................... .1572 –.1503 96.4 94.5 .0081 .0217 89.0 84.8 –.0306 .1750 46.1 46.3

October, November,

 or December ................. –.1070 .1321 95.4 94.6 5

–.1877 .0886 86.9 83.0 –.0952 .1530 44.5 44.1

Characteristic

Cereal and bakery products Beef, pork, and other meats Poultry

Parameter

estimate

Predicted

probability

(percent)

Parameter

estimate

Predicted

probability

(percent)

Parameter

estimate

Predicted

probability

(percent)

1980 1992 1980 1992 1980 1992 1980 1992 1980 1992 1980 1992

1

Predicted probability is calculated for an increase of 0.01 in the intercept.

2

 Predicted probability is calculated with family size taken into account. For

example, a single-person consumer unit consists of one adult and no children.

A single-parent consumer unit consists of one adult and one child.

3

 Predicted probability is calculated for a husband and wife with two children, one of whom is at least 18 years old.

4

 Predicted probability is calculated for a husband and wife with two children,

both of whom are under 18 years old. Because the increase in the number of

children also affects the square of the number of children, the entire change is

included in the value listed for the number of children.

5

 Parameter estimate is statistically significant at the 95-percent confidence

level.
16 Monthly Labor Review December 1998

The Food-at-Home Budget

between the expected estimates for single men and single

women in 1980. Therefore, if single women in 1980 are considered, the parameter estimates for single men in 1980

(–1.3905) and single women in 1980 (0.1446) must be added

before calculating the probability of purchasing any food item.

If single women in 1992 are considered, the parameter estimates for single men in 1980 and 1992 must be added to the

parameter estimates for single women in 1980 and 1992 before calculating a probability.

The five major food groups examined in the previous section are too restrictive for the present purpose in some cases.

For example, the category of meat, poultry, fish, and eggs contains a mix of items that nutritionists would not consider to be

equal in health benefit. Poultry and many varieties of fish and

seafood are lower in fat than comparable servings of beef or

pork. Eggs are high in saturated fat and cholesterol and are

consumed in different ways than other meat products. They

may be eaten for their own sake (for example, hard boiled or

scrambled), as a major, visible ingredient in other foods (for

instance, a spinach soufflé or quiche), or as a not-so-visible

ingredient in yet other foods (say, cakes or egg noodles). If

kept at the aggregate level, substitutions within the meat, poultry, fish, and eggs group would be missed. So if families were

eating less red meat and more poultry, it would appear that

the probability of purchasing meat, poultry, fish, and eggs had

not changed much, even though the probability of purchasing

red meat had declined and the probability of purchasing poultry had increased. For these reasons, the major food groups

analyzed are cereal and bakery products; beef, pork, and other

meats; poultry; fish and seafood; eggs; dairy products; fruits

and vegetables; fats and oils; and other foods (defined as other

food at home minus fats and oils).

Before logits are run, families that do not report purchases

of groceries (about 11 percent) are omitted from the sample.

This is done to avoid bias: if no one in the family buys groceries in the first place, then the probability that the family buys

any specific food item is zero. Also omitted are families for

whom no Diary placement date could be found (less than 3

percent of the total sample). Placement dates are used to determine the season in which the expenditures are made.

Logistic regressions are not weighted to reflect the population. Preliminary experiments show that weighting does not

radically alter the probabilities derived from the parameter

estimates, although it does substantially reduce the standard

errors associated with the estimates. All but two parameter

estimates are statistically significant at the 99-percent confidence level when the weighted regression results are analyzed.

Therefore, to err on the side of caution in finding statistically

significant results, the regressions are not weighted. Table 9

shows the characteristics of the average family included in

the sample for each year. The statistics in the table are

unweighted.

General results. For some food groups, such as cereals and

bakery products, the predicted probabilities of purchasing

them change little over time. For others, such as eggs, the

change is dramatic. But the common result seems to be that if

one demographic group experiences a change in a certain direction, most other groups do also, at least where parameter

estimates are statistically significant.

The control group. Before examining changes by specific

segments of the population, changes in the control group's

probability of purchasing any item are described, to give a

sense of how probabilities are changing for the “average” or

“typical” family.

The control group exhibits statistically significant decreases in the probability of purchasing items from four food

groups: beef, pork, and other meats (5 percent); fish and seafood (6 percent); eggs (14 percent); and dairy products (4

percent). Changes in probability are not found to be statistically significant for any other food groups.

The foregoing groups contain foods that are high in saturated fat (dairy products), cholesterol (seafood),50 or both

(eggs; and beef, pork, and other meats). The DHKS shows that

54 percent of all main meal planners believe that their diets

should be lower in fat, and this may be one reason for the

decreased probabilities of purchasing the aforementioned

foods. When asked about saturated fat specifically, only 44

percent of respondents to the survey think that their diet should

be lower in this food component, compared with 48 percent

who think that it is about right. Similarly, when asked about

cholesterol, only 41 percent think that their diet should have

less of it, compared with 53 percent who think that it is about

right.51

Age. For families whose reference person is under age 35,

many statistically significant changes occurred from 1980

to 1992. Beef, pork, and other meats (4 percent), fish and

seafood (5 percent), and dairy products (3 percent) all exhibited moderate decreases in the said family’s probability of

purchasing them.52 The family’s probability of purchasing

poultry rose slightly (1 percent). These findings are consistent with results of the DHKS which indicate that, of all meal

preparers, those under age 39 are most likely to believe that

their diet should be lower in fat (60 percent) and, specifically,

saturated fat (52 percent). The Department of Agriculture’s

tables show a decreasing relationship between age and percent of meal planners who believe that their diets should be

lower in fats.53 For example, only 41 percent of those 60 years

and older believed that their diet should be lower in total fat,

and just 34 percent believed that it should be lower in saturated fat.

One possible explanation for this trend is that, as consumers get older, they become more concerned about fat intake
Monthly Labor Review December 1998 17

and so lower their consumption accordingly; thus, fewer older

consumers think that their diet should be lower in fats. This

finding is consistent with DHKS results which show an increasing relationship between the age of the meal planner and the

percentage of the group that places a high importance on limiting fat (especially saturated fat) and cholesterol intake.54 But

this explanation is not consistent with the logit result that families 65 and older were more likely to purchase beef, pork, and

other meats; poultry; eggs; dairy products; and fats and oils

than were younger families in both years. However, the estiTable 7. Probabilities of purchasing fish and seafood; eggs; and dairy products, all consumer units, 1980 and 1992

Control group ...................... –0.5714 –0.2795 36.1 29.9 .4102 –0.5722 60.1 46.0 3.1674 –0.6809 96.0 92.3

Intercept1 ............................ 5

–.8459 5–.4739 36.3 30.1 .0904 5–.9378 60.4 46.2 52.6929 5–1.0167 96.0 92.4

 Age (35 to 64):

 Under 35 ......................... 5–.2079 .0237 31.4 26.2 5–.3154 .1282 52.4 41.4 5–.5215 5.3387 93.4 90.9

 65 and older .................... 5

.2040 –.1020 40.9 32.1 5.2277 –.0944 65.4 49.3 5.3175 .0286 97.0 94.4

Income (middle tercile)

 Lowest tercile .................. 5–.1444 .1643 32.8 30.3 –.0482 .0102 59.0 45.0 5–.2691 .0126 94.8 90.3

 Highest tercile .................. .0763 –.0131 37.9 31.3 .0359 –.0622 61.0 45.3 .1795 –.1209 96.6 92.7

 Incomplete reporter ......... –.0709 .1407 34.5 31.4 5–.1587 5.2635 56.3 48.6 5–.2399 5.2737 94.9 92.6

Family composition

 (husband and wife

 with children):2

 Single man ...................... 5–.5233 .0701 22.2 17.0 5–.7515 .2944 35.5 25.4 5–1.3349 5.7175 80.8 78.6

 Single woman .................. .0113 .0892 22.4 18.4 –.0518 .1732 34.3 27.8 5.1994 –.0021 83.7 81.7

Husband and wife only .... –.1500 .1180 31.7 27.2 –.1094 .0951 52.6 39.9 –.2760 .2800 92.9 89.7

Single parent ................... –.1369 –.1187 30.5 21.6 5–.2837 .2531 51.6 39.7 –.3580 .4545 93.9 91.2

 Other family ..................... 5–.1652 –.0152 32.4 26.3 –.0753 –.0649 58.3 42.5 5

–.3944 .0569 94.1 89.6

Family size

 (two adults, one child):

 Number of adults3 ............ 5.1130 .0715 38.7 33.9 .0621 5.1645 61.6 51.6 .0805 .1601 96.3 93.9

 Number of children4 ......... .0534 .0479 37.0 32.0 5.2263 .0335 63.3 50.4 5.3777 –.0120 96.6 93.9

 Square of number

 of children ...................... –.0049 .0035 ... ... 5–.0307 .0031 ... ... 5

–.0642 .0276 ... ...

Race (white and other):

 Black ............................... 5.2511 –.0254 42.1 34.9 –.0032 .0821 60.0 47.9 5

–.6210 –.0551 92.7 85.9

Education (no college):

 Some college .................. .0487 –.0307 37.2 30.3 –.0302 –.0869 59.4 43.1 –.0644 –.1446 95.7 90.7

 College graduate ............. 5.2059 .0518 41.0 35.6 .0732 5–.3388 61.9 39.5 .0360 –.1591 96.1 91.4

Degree of urbanization:

 Rural ................................ 5–.3414 .1737 28.6 26.5 5–.2426 .1732 54.2 44.2 –.0211 .1447 95.9 93.2

Food stamp program:

 Participates ..................... .1222 –.1586 39.0 29.2 5

.1889 .0712 64.5 52.5 .1602 5–.4159 96.5 90.3

Month surveyed:

 January, February,

 or March ........................ 5.1879 –.0827 40.5 32.2 .0838 –.0213 62.1 47.5 –.0881 .2138 95.6 93.2

 July, August,

 or September ................. –.0061 .0033 36.0 29.9 .0512 .0199 61.3 47.7 .0052 .1469 96.0 93.3

 October, November,

 or December .................. 5–.1325 .1047 33.1 29.3 .0836 .0505 62.1 49.3 –.1157 .0646 95.5 91.9

Characteristic

Fish and seafood Eggs Dairy products

Parameter

estimate

Predicted

probability

(percent)

Parameter

estimate

Predicted

probability

(percent)

Parameter

estimate

Predicted

probability

(percent)

1980 1992 1980 1992 1980 1992 1980 1992 1980 1992 1980 1992

1Predicted probability is calculated for an increase of 0.01 in the intercept.

2

 Predicted probability is calculated with family size taken into account. For

example, a single-person consumer unit consists of one adult and no children.

A single-parent consumer unit consists of one adult and one child.

3

 Predicted probability is calculated for a husband and wife with two children,

one of whom is at least 18 years old.

4

 Predicted probability is calculated for a husband and wife with two children,

both of whom are under 18 years old. Because the increase in the number of

children also affects the square of the number of children, the entire change is

included in the value listed for the number of children.

5

 Parameter estimate is statistically significant at the 95-percent confidence

level.
18 Monthly Labor Review December 1998

The Food-at-Home Budget

mated probabilities of purchasing these foods for families in

the 65-and-older age category at least were lower in 1992 than

they were in 1980.55

Eggs are particularly interesting to study. Older families

showed a large decrease (16 percent) in the probability of purchasing eggs, while younger families exhibited a smaller, but

still notable, decrease (11 percent) over the 2 survey years.

However, older families were more likely to purchase eggs

regardless of the year (65 percent in 1980 and 49 percent in

1992) than either the control group (60 percent and 46

percent) or younger families (52 percent and 41 percent). The

difference may reflect the difficulty of breaking lifelong habits. Overwhelming majorities of main meal planners are aware

of the relationships between fat and cholesterol intakes and

health problems.56 For example, regardless of age, more than

three-fourths of respondents had heard that fat intake is

related to health problems. More than 6 out of 10 had heard

that saturated fat intake, in particular, is related to health problems. Similarly, 7 out of 8 of those under 60 (and nearly as

many at least age 60) responded that they were aware of a

relationship between health problems and cholesterol. When

asked specifically which health problem they had heard about

being related to fat or cholesterol intakes, once again, large

majorities mentioned heart disease. But the older family members grew up when eggs were considered a quintessential part

of any healthful breakfast, while the younger family members

grew up hearing about the relationship of cholesterol to heart

disease. So, if the younger families are raising their children

to be concerned about egg consumption, the relationship of

egg consumption to age will probably continue; that is, all

families will decrease consumption, but older families will

continue to purchase eggs more frequently than younger families, with the gap between older and younger families continuing to shrink. (The difference in the probability of purchasing

eggs between older and younger families was 13 percent in

1980, but only 8 percent in 1992.)

One bright spot for older consumers is that they have a

higher probability of purchasing fruits and vegetables than

younger consumers have. In 1980, the predicted probability

of older consumers purchasing food from this group (94 percent) was 4 percentage points higher than that of the control

group and 11 percentage points higher than that of the under35 group. There is no indication that the predicted probabilities changed from 1980 to 1992 for any age group.

Income. Relations are less strong by income group than by

age group. Middle- and high-income families appear to have

similar probabilities. For no food group is the high-income

parameter estimate statistically significant, regardless of year.

But for some food items, low-income families had significantly different probabilities from other income groups. Lowincome families were slightly less likely (by 1 percent) to purchase cereal and bakery products than were control group

families, at least in 1980. They were also less likely to purchase fish and seafood (by 3 percent), dairy products (1 percent), fruits and vegetables (2 percent), fats and oils (8 percent), and other foods (1 percent) than the control group was

in 1980. However, only for beef, pork, and other meats was

their probability statistically significantly lower than the control group’s in both 1980 (3 percent) and 1992 (1 percent).

The DHKS results show a smaller spread across income groups

in concerns about fat and cholesterol intake than they do for

age groups,57 so it is not surprising that there is very little difference in probabilities of purchasing among income groups.

Similarly, the spread across income groups in the percentage

of each group that thinks its fiber intake is about right (2 percent) is much less than that for age groups (24 percent).58

Gender. For the most part, probabilities of purchasing food

do not differ significantly by gender. Further, single women

do not have many coefficients that are statistically significant.

In fact, the only statistically significant difference they exhibit

is for purchasing beef, pork, and other meats. Single men

showed a statistically significant increase in the probability of

purchasing these foods, from 67 percent in 1980 to 70 percent

in 1992. By contrast, single women exhibited a decrease from

68 percent to 65 percent. Single women were more likely to

purchase fats and oils in 1980 than single men were (the predicted probabilities were 36 percent and 29 percent, respectively), and neither group showed a statistically significant decrease from these percentages in 1992. The DHKS shows that

women are slightly more concerned about fat intake than are

men.59 For example, 55 percent of female meal preparers believe that their diet should be lower in total fat, compared with

52 percent of male meal preparers. Similarly, 42 percent of

female preparers believe that their intake of cholesterol should

be reduced, compared with 39 percent of male preparers.

Slightly more than half of each group think that their intake is

about right, but still, a larger percentage of female preparers

(53 percent) than male preparers (51 percent) agrees with that

statement.

Other differences within and across time are worth noting.

For example, single women had a higher probability of purchasing dairy products in 1980, although both single women

and single men had lower probabilities in 1992. (Only the

men’s decline, however, was statistically significantly.) Far

more women (41 percent) than men (25 percent) believe that

their diets need more calcium,60 and this belief may be a factor in explaining the difference.

Also, single men appear to have a substantially lower probability of purchasing fruits and vegetables than single women

have, regardless of the year. (The parameter estimates for

single men and women were statistically significant in 1980,

but not in 1992. This finding indicates that single men and
Monthly Labor Review December 1998 19

Table 8. Probabilities of purchasing fruits and vegetables; fats and oils; and other foods, all consumer units, 1980 and 1992

Control group ....................... 2.1767 0.0546 89.8 90.3 0.2866 –0.0652 57.1 55.5 2.5529 0.4246 92.8 95.2

Intercept1 ............................. 51.4008 .1546 89.9 90.4 –.2612 .1620 57.4 55.8 51.7735 .3610 92.8 95.2

Age (35 to 64):

 Under 35 .......................... 5–.5749 .1145 83.2 85.5 5–.3041 –.0947 49.6 45.6 5–.1627 .1397 91.6 95.0

 65 and older ..................... 5.5780 .1751 94.0 95.2 5.2651 –.1133 63.5 59.2 –.1022 .2504 92.1 95.8

Income (middle tercile):

 Lowest tercile ................... 5–.2408 .0413 87.4 88.4 5–.2019 .1087 52.1 53.2 –.0340 5–.2761 92.5 93.5

 Highest tercile ................... .1258 .1232 90.9 92.3 .0144 –.0075 57.5 55.7 .1174 –.1838 93.5 94.8

 Incomplete reporter .......... 5

–.2616 .1655 87.2 89.4 5–.2136 .1559 51.8 54.1 5–.5344 .0974 88.3 92.7

Family composition

 (husband and wife

 with children):2

 Single man ....................... 5–.7800 .0827 71.6 75.1 5–.8205 .0995 29.3 32.2 5–.7289 .1805 79.0 86.0

 Single woman ................... 5

.5454 .0349 81.3 84.3 5.3145 –.0782 36.2 37.5 .1090 .1779 80.8 89.1

Husband and wife only ..... .0223 –.0233 88.4 88.5 –.1349 5.2906 50.2 55.1 5

–.3119 .2528 88.3 92.6

Single parent ................... –.0221 –.1153 86.4 86.4 5–.3719 .0480 42.9 45.6 –.3273 .1244 87.5 92.7

Other family ..................... –.1069 –.0920 88.8 88.4 5–.2279 .0715 51.5 51.6 5

–.5403 5.4517 88.2 94.7

Family size

 (two adults, one child):

 Number of adults3 ............. 5.3027 –.0580 92.3 92.2 5

.2019 5–.1280 62.0 57.3 5.2812 –.0529 94.4 96.1

Number of children4 .......... 5

.1906 .0081 90.9 91.6 5.1566 .0403 60.0 58.6 5.2465 .1998 93.8 96.2

 Square of number

 of children ....................... –.0201 .0079 ... ... –.0126 –.0115 ... ... –.0295 –.0304 ... ...

Race (white and other):

 Black ................................ 5–.1822 .1956 88.0 90.4 5–.2168 .1464 51.7 53.8 5–.4001 –.0614 89.6 92.5

Education (no college):

 Some college ................... .0933 5

–.2226 90.6 89.1 .0093 5–.2030 57.3 50.7 .1561 –.1286 93.8 95.3

 College graduate .............. 5.3670 –.1054 92.7 92.4 .0271 5–.1678 57.8 52.0 .1000 .0247 93.4 95.7

Degree of urbanization:

 Rural ................................. 5

–.2467 5.2872 87.3 90.7 5.1941 –.0620 61.8 58.7 –.0436 5.3348 92.5 96.3

Food stamp program:

 Participates ...................... 5

–.2500 .0129 87.3 88.0 –.1209 .0281 54.1 53.2 –.0722 –.0040 92.3 94.8

Month surveyed:

 January, February,

 or March ......................... –.0445 –.0015 89.4 89.9 5.2225 5–.2519 62.5 54.8 .1290 –.1662 93.6 95.0

 July, August,

 or September .................. .1069 –.0444 90.8 90.8 5.1400 –.0899 60.5 56.7 5

.1906 5–.3147 94.0 94.5

 October, November,

 or December ................... –.0803 –.0775 89.1 88.8 .0382 .0322 58.0 57.2 .1640 –.2192 93.8 94.9

Characteristic

Fruits and vegetables Fats and oils Other foods

Parameter

estimate

Predicted

probability

(percent)

Parameter

estimate

Predicted

probability

(percent)

Parameter

estimate

Predicted

probability

(percent)

1980 1992 1980 1992 1980 1992 1980 1992 1980 1992 1980 1992

1Predicted probability is calculated for an increase of 0.01 in the intercept.

2 Predicted probability is calculated with family size taken into account. For

example, a single-person consumer unit consists of one adult and no children.

A single-parent consumer unit consists of one adult and one child.

3

 Predicted probability is calculated for a husband and wife with two children,

one of whom is at least 18 years old.

4 Predicted probability is calculated for a husband and wife with two children, both of whom are under 18 years old. Because the increase in the number of children also affects the square of the number of children, the entire

change is included in the value listed for the number of children.

5

 Parameter estimate is statistically significant at the 95-percent confidence

level.

women had different probabilities of purchasing fruits and

vegetables in 1980, but that single men cannot be said with an

acceptable degree of statistical confidence to have had a different probability of purchasing the same than single women

had in 1992. Further, the change over time in the probability

of singles purchasing fruits and vegetables is statistically significant, regardless of gender.) In 1980, the probability of

single men purchasing fruits and vegetables was 72 percent,

compared with 81 percent for single women. In 1992, the figures were 74 percent and 85 percent, respectively. So, although

in 1992, single men appear to have purchased fruits and vegetables more frequently than they did in 1980, they still ap-
20 Monthly Labor Review December 1998

The Food-at-Home Budget

pear not to have caught up to the frequency with which single

women purchased items from this food group in 1980. Even

so, about 54 percent of male meal preparers and 55 percent of

female meal preparers believe that they are getting adequate

fiber. About the same amount of men (38 percent) and women

(39 percent) believe that they should consume more fiber.61

By contrast, women are more likely to believe that they are

consuming adequate amounts of vitamin C (62 percent) than

men are (57 percent). However, about one-third of each group

believe that their diet should be higher in vitamin C. (Approximately 9 percent of men either did not know whether their diet

was adequate in vitamin C, did not answer the question, or

thought that their intake of vitamin C should be lower, compared with 4 percent of women.)

Race. There do not appear to be any changes across time in

the probability of purchasing food that can be linked to race.

Only poultry has a statistically significant coefficient for race,

in 1992. Therefore, except for poultry, black families’ probabilities of purchasing food in 1992 were not statistically distinguishable from those of white and other families in that year.

However, black families were more likely to purchase fish and

seafood, and less likely to purchase dairy products, fruits and

vegetables, fats and oils, and other foods, than the control

group was, at least in 1980.

Although this relationship leads to no discernible pattern

by race, there are clear differences in nutritional attitudes by

race. For example, about 8 percent more black meal preparers

believe that their diet should be lower in fat and cholesterol

than do white meal preparers.62 Also, more black meal

preparers (44 percent) than white meal preparers (39 percent)

believe that their diet should be higher in fiber. However,

blacks are less likely to be aware of the relationship between

health problems and certain foods. For example, 68 percent of

black meal preparers associated fat intake with health problems, compared with 80 percent of white meal preparers; and

54 percent of black meal preparers named heart disease as an

illness related to fat intake, compared with 67 percent of white

meal preparers.63 Similarly, 6 out of 8 black meal preparers

had heard of links between cholesterol and health problems,

compared with 7 out of 8 white meal preparers.64 Clearly, the

link between knowledge of nutrition and expenditures by race

warrants further investigation.

Family size and composition. Although the DHKS does not

provide information on the size of the meal preparer’s family

or the meal preparer’s marital status, it is nonetheless interesting to examine expenditures for families of differing size and

composition.

Single persons have smaller probabilities of purchasing

specific goods than the control group has. This is to be expected, because the control group consists of married couples

with one child. With families, there are more members to be

satisfied, and the more members present, the more likely is the

probability of purchasing different items, for two reasons: first,

families may consume certain foods more quickly than singles

do, so that they need to replace these foods more frequently;

and second, more family members means more tastes for the

shopper to satisfy.

But how much does the addition of a child to a family

change the probabilities, compared with an adult? For some

goods, the answer is “not much.” For example, the probability

of purchasing cereal and bakery products was about 93 percent for the single-parent family in 1980, compared with 96

percent for the control group. Adding one child to a control

group family raises the probability to 97 percent. But if an

adult instead of a child is added to the control group family,

there is no statistically significant difference produced by the

addition. Further, there is no evidence to support a change in

these patterns for 1992.

For other expenditures, the differences are more noticeable.

For instance, married couples with no children show a smaller

decrease (4 percent) than the control group does in the probability of purchasing fish and seafood, while other families

show a decrease similar to that of the control group. Single

parents show a larger decline (9 percent). Married couples with

no children and single parents have a smaller probability of

purchasing fish and seafood than the control group has in both

1980 and 1992. For other families, the probability is about the

same as that of the control group in 1980, but is smaller than

the probability of the control group in 1992.

Other characteristics. Most of the other characteristics

tested (degree of urbanization, participation in the food stamp

program, and month surveyed) show few statistically significant parameter estimates and no real discernible patterns.

However, one characteristic warrants a brief mention: education. It seems reasonable to assume that, the more educated a

person is, the more exposure the person has had to issues involving health and nutrition. Therefore, one could expect to

observe some differences in expenditure patterns for persons

with different levels of education. Indeed, some such differences are observed in the logit results. For example, the probability of purchasing beef, pork, and other meats decreases

substantially—about 10 percentage points—for college graduates from 1980 to 1992.65 Families whose reference person is

a college graduate also show declines in the probability of purchasing eggs and fats and oils. Once again, the DHKS results

show that meal preparers with at least some college are more

likely to believe that their diets should be lower in total fat

(including saturated fat) than meal preparers with less education believe.66 Those with at least some college are also much

more aware of relationships between the intake of certain nutrients and health problems.67 For example, 86 percent of those
Monthly Labor Review December 1998 21

with some college are aware of links between total fat intake

and health problems, compared with 73 percent of those whose

last grade attended was any of grades 9 through 12 and 67

percent of those whose last grade attended was grade 8 or

lower. Similarly, more than 9 out of 10 meal preparers with

some college were aware of the relationship between cholesterol intake and health problems, compared with 8 out of 10

whose highest grade attended was high school and 7 out of 10

whose highest grade attended was grade 8 or lower.

Income elasticities

One important economic measure is income elasticity. In this

article, income elasticity is used to show by what percent expenditures for a selected food group are expected to increase,

given a 1-percent increase in income. An increase in income

elasticity over time indicates that it takes less of an increase in

income to induce a purchase of a particular item than it did

before. Also important, but not estimated here, are price elasticities, which show the effect of a 1-percent increase in price

on expenditures for a commodity. In addition, price elasticities show whether goods are complements of, or substitutes

for, each other. (For example, if expenditures for coffee decrease when tea prices fall, the goods are substitutes.) The

reason price elasticities are not estimated in what follows is

that the Consumer Expenditure Survey lacks data on prices.68

However, the analysis that is presented does control for price

changes over time.

Income elasticity can be estimated by using regression results. However, before regressions can be performed, several

factors must be taken into account.

First, if prices for selected food items change at a different

rate than incomes, changes in income elasticities will not be

accurately measured. For instance, if prices rise faster than

incomes, it may appear that a given expenditure has increased

for the same level of income, yet the family may actually be

consuming less of the product. For this reason, both expenditures and incomes are adjusted for inflation before one performs a regression. To achieve these adjustments, each individual expenditure is divided by the level of the CPI for that

item in the appropriate year, with the resulting “real” expenditures aggregated to the food group level. Thus, 1980 expenditures for apples are divided by 0.921 before being added to

(real) expenditures for other fruits and vegetables. (The appendix to this article has a complete listing of individual foods

and the deflators for each year.) Income is divided by the CPI

for all goods and services. By regressing real expenditures on

real income (with base year 1982–84), a “real” Engel curve is

estimated. The estimated relationship between income and

expenditures is then used to calculate income elasticities.

Second, a method of regression must be selected. Although

all families buy food at home of some type at some time or

another, not all families buy each kind of food all the time.

Table 9. Characteristics of families included in logistic

regressions

Variable 1980 1992

 Number

Sample ................................. 9,055 10,186

Adults ................................... 1.9 1.9

Persons less than age 18 ..... .8 .8

Percent

Age:

Under 35 .............................. 33.6 28.0

65 and older ......................... 19.3 20.7

Income group:

Low income .......................... 27.6 23.9

High income ......................... 27.2 29.4

Incomplete reporters ............ 18.1 19.9

Type of family:

Single man ........................... 9.9 10.0

Single woman ...................... 16.1 14.5

Husband and wife only ......... 21.7 21.9

Single parent ........................ 5.0 6.1

Other family .......................... 12.8 16.3

Race:

Black .................................... 11.7 9.7

Education:

Some college ....................... 21.2 23.3

College graduate .................. 18.6 25.4

Degree of urbanization:

Rural .................................... 12.5 10.6

Food stamp program:

 Participant .............................. 7.4 7.5

Month surveyed:

January, February,

or March ............................. 21.3 24.6

July, August,

or September ..................... 23.0 21.3

October, November,

or December ...................... 34.3 32.1

Reporting purchases of:

Cereal and bakery

products ............................. 88.1 89.2

Beef, pork,

and other meats ................. 74.9 73.0

Poultry .................................. 38.9 40.1

Fish and seafood ................. 31.2 28.5

Eggs .................................... 50.8 40.2

Dairy products ...................... 88.0 87.0

Fruits and vegetables ........... 83.9 86.8

Fats and oils ......................... 48.5 46.0

Other food at home .............. 87.5 91.2

Some families would choose not to purchase some kinds of

foods, even if they were available for very low prices or even

if they were given away free of charge. Unfortunately, there is

no way to identify whether families are true nonconsumers of

a certain food or whether they just did not happen to purchase

the food during the survey’s 2-week reference period for some

other reason. If conventional ordinary least squares regressions are run only on families with positive expenditures, biases will result, because some “would-be” purchasers are

thrown out with the “never” purchasers. Several methods are

available to adjust for this problem. The method used in this

article is the Heckman two-stage procedure.69 In this method,
22 Monthly Labor Review December 1998

The Food-at-Home Budget

a probit regression is run to estimate the probability that a

family would purchase the food in question. Results of this

regression are manipulated to create a variable, called the inverse of Mill’s ratio. The second stage is an ordinary least

squares regression of expenditures for those who made a purchase on selected variables, including the inverse of Mill’s

ratio. One major advantage of the Heckman procedure over

its competitors (such as Tobit models) is that the variables can

differ from the first to the second stage, making the Heckman

procedure much more flexible. Because the second stage is

performed using ordinary least squares, the coefficients are

directly interpretable; no adjustment is necessary before using them in further analysis, as would be necessary with Tobit

models.70

Third, once a model is selected, it is important to make

sure that relevant coefficients are unbiased and efficient. Because income data are often found to be nonnormally distributed, some correction for heteroskedasticity is in order. Accordingly, the income data in the second stage are subjected

to a Box-Cox transformation,71 the standard formula for which

is

X( ) = (X – 1)/ ,

where

X is the variable to be transformed (income before taxes in

this case) and

 is a parameter obtained through estimation.

However, as Box and Cox point out, the variable need only be

raised to the power of if it is to be used in a regression.

72 For

convenience in interpreting results, this simplification is used.

In these models, the value of , estimated by using a maximum-likelihood technique described by Scott and Rope,73 is

found to be one-fourth. Therefore, in the second stage, instead of regressing expenditures directly on income, expenditures are regressed on the fourth root of income.

Fourth, not all families provide information on even one

major source of income. For this reason, the sample is limited

to complete income reporters before is estimated and before

first- or second-stage regressions are run. Because the focus

of the analysis set forth in this article is contingent upon having appropriate income values, omitting the incomplete reporters from the regression is presumably the best solution to

the problem. This omission eliminates the excess variation

that would result from their inclusion; the only other solution

would be to greatly complicate the regression equation by

adding appropriate main effects and interaction terms to control for this variation.

Finally, results from the probit stage are not shown. In general, logit and probit regressions yield similar results. Because

the same variables are used in the probit stage and in the logit

regressions already described, providing both sets of estimates

would be duplicative.74 Also, only the most important second-stage results (that is, those dealing with the expenditureincome relationship) are shown in table 10.75

The models. The probit model is identical to the logit model

described earlier; the same dependent and independent variables are used in all cases. The second-stage model differs

only slightly. First, instead of utilizing a dichotomous dependent variable, the second stage uses a continuous dependent

variable (that is, the actual level of expenditures for the food

group under study). Second, instead of using binary variables

to represent income classes, a continuous income variable is

employed (after being transformed as described above). With

this variable, one can still estimate income elasticities for each

income group (low, middle, or high) by evaluating each elasticity using the mean income for each group. Additionally, the

second stage includes several interaction terms used to evaluate whether income elasticities are different both across demographic groups and across time. The interactions are limited to the most basic demographic differences, including age,

gender (single male and single female), and race.

Estimating income elasticity. The definition of income elasticity is the ratio of the percent change in quantity purchased

to the percent change in income. (In this article, “real” expenditure is used as a proxy for quantity purchased, as described earlier.) Although income elasticities can be positive

or negative, in general, they are positive; therefore, goods with

positive elasticities are often called “normal” goods. (Those

with negative elasticities are often called “inferior” goods.)

The larger the absolute value of the elasticity, the more responsive the good or service is to a change in income.

The formula for income elasticity is

 = Y/ I × I/Y,

where

Y/ I is the rate of change of the expenditure with respect

to income;

I is the level of income at which elasticity is evaluated; and

Y is the level of the real expenditure at which elasticity is

evaluated.

Because a Box-Cox transformation is used on income before the regression is carried out, the value of Y/ I is found

as follows:76

Y = a + bI0.25;

Y/ I = 0.25bI

–0.75

.

The elasticity then becomes

= 0.25bI

–0.75 I/Y.
Monthly Labor Review December 1998 23

Simplifying yields

 = (0.25bI

0.25

)/Y.

Results. Table 10 shows the income elasticities derived from

the second stage of the regression, plus other information. The

first two columns present real weekly expenditures in 1980

and 1992 for purchasers of the food item in each demographic

group. The next two columns show the percentage of each

demographic group reporting an expenditure for the food item

in question. For example, of the 2,655 consumer units in the

under-35 age group in 1980 who reported purchasing food at

home, 2,214, or about 83 percent, included cereal and bakery

products in their grocery baskets. The third set of columns

shows real annual income (that is, income before taxes, divided by the Consumer Price Index for all goods and services)

in 1980 and 1992, by demographic group. The next set of

columns shows the estimated income parameters for each

group in each year. (For convenience, all interaction terms

have been added as appropriate before inserting them into the

tables; see the section titled “Logistic regression results,”

above, for more details.)

The income elasticities shown in the table require a bit more

explanation. According to the formula derived earlier, income

elasticity is dependent on both the level of income and the

level of expenditure in each year. If these have changed for

some reason, how can one be certain that differences in observed elasticities are due to changes in tastes or other factors

influencing expenditures, and not just differences in incomes?

To correct for this problem as much as possible, real expenditures and real incomes are averaged for each group across

time before using them in the income elasticity equations. In

that way, differences in elasticity are due solely to differences

in the income parameter estimates.

As with the logit models, many parameters are based on

interactions across characteristics as well as time and must be

added appropriately before calculating elasticities. The parameter estimates are accompanied by footnotes designating

their statistical significance. Parameter estimates are considered to be statistically significant on the basis of standard

t-test results. To say that an estimate is statistically significant means that it is significantly different from zero before

addition.77

Elasticities calculated from these parameter estimates are

analyzed for statistical significance in a different way. The

first part of the elasticity equation ( Y/ I) is calculated by adding parameters appropriately, regardless of their statistical significance. Elasticities in 1980 are considered to be statistically significantly different from zero if the sum of the 1980

parameter estimates does not differ significantly from zero

according to an F-test. Elasticities in 1992 are analyzed to see

whether they are statistically significantly different from the

1980 elasticities, rather than from zero. (That is, if the difference between the estimated effects for 1980 and 1992 is statistically significant according to an F-test, the difference in

elasticities is considered to be statistically significant.)

General observations. As expected, the individual food categories are income inelastic in each year; that is, a 1-percent

increase in income yields an increase of less than 1 percent in

expenditures for each individual food category. Even so, the

food categories can be placed into three distinct groups: those

for which elasticities increase over time for most groups (cereal and bakery products, fish and seafood, and other food at

home); those for which elasticities were positive in 1980 for

most groups and do not change over time (beef, pork, and

other meats; dairy products; and fruits and vegetables); and

those for which elasticities were statistically indistinguishable

from zero, regardless of the year (poultry, eggs, and fats and

oils). Expenditures in the last group are called “perfectly inelastic,” indicating that quantities purchased do not change

with income. Expenditures in the other groups are called “necessities,” because their elasticities are greater than zero, but

less than unity. Those with elasticities less than zero are called

“inferior,” because a negative elasticity means that expenditures decrease as income increases. Although some predicted

elasticities fit this category, they are not statistically distinguishable from zero, as noted above. No “luxury” foods (those

with elasticities greater than unity) are found. In only one case

is a decrease in elasticity found to be statistically significant:

fruits and vegetables purchased by families whose reference

person is older than 65. Even here, though, the change is just

barely significant at the 90-percent confidence level.

Those expenditures with increasing elasticities are especially interesting. The increasing elasticity indicates that the

Engel curve has become steeper over time, which, in turn,

indicates that it took a smaller increase in income to induce a

purchase of the good in 1992 than it did in 1980, at least for

the demographic group under study. The best example of this

phenomenon is other food at home, which shows an increase

in elasticity for almost every demographic group. As mentioned earlier, this is not surprising, due to the ever-changing

nature of society. The category contains a substantial amount

of “convenience” foods, such as frozen foods and prepackaged meals. Again, the rise of two-earner families, the proliferation of a variety of frozen meals since 1980, and increased

ownership of microwave ovens undoubtedly contribute to the

increased elasticity of frozen meals.

Age. For most food expenditures, elasticity varies little with

age. For example, for cereal and bakery products, estimated

elasticities range only from 0.10 (for those under 35) to 0.14

(for those 65 and older) in 1980. In 1992, only the middle

group (aged 35 to 64) shows a statistically significant increase

in elasticity, although the range of predicted elasticities is

–0.18 to 0.19.

But some differences are worth noting. A substantial gap
24 Monthly Labor Review December 1998

The Food-at-Home Budget

Table 10. Income elasticity estimates and related information, Heckman second-stage results, complete reporters only,

1980 and 1992

Cereal and bakery products

Age of reference person:

Under 35 ......................... $5.81 $5.83 83.4 85.5 $21,466 $22,883 40.186 50.341 40.10 0.18

35 to 64 ........................... 7.77 7.46 91.8 90.6 28,251 32,508 5

.277 5.423 5.12 4.18

65 and older .................... 5.02 5.67 90.3 90.0 11,583 16,167 5

.284 5.369 5.14 .19

Income group:

Low ................................. 4.80 5.06 83.7 84.9 6,709 7,161 5

.277 5.423 5.13 4.20

Middle ............................. 6.57 5.98 90.1 89.8 18,961 18,684 5.277 5.423 5.13 4.20

High ................................ 8.26 8.37 91.7 91.6 41,481 47,586 5

.277 5.423 5.12 4.18

Single person:

Man ................................ 3.33 3.59 73.3 77.6 13,309 17,964 .068 5.277 .05 .22

Woman ........................... 3.24 3.77 78.0 82.7 8,820 12,633 .065 5

.308 .05 .22

Race of reference person:

Black ............................... 5.69 5.67 87.6 87.3 15,380 19,097 .086 –.014 .04 –.01

White or other ................. 6.71 6.72 88.6 89.2 23,835 27,113 5.277 5.423 5.13 4.20

 Beef, pork, and other meats

Age of reference person:

Under 35 ......................... 11.98 9.39 66.7 67.4 22,756 22,729 4.582 .087 4.17 .02

35 to 64 ........................... 15.82 11.37 81.5 76.1 28,751 32,438 5

.571 .236 5.14 .06

65 and older .................... 10.10 9.03 77.3 73.3 12,013 15,979 5.969 .409 5.28 .12

Income group:

Low ................................. 9.27 8.86 66.4 69.1 6,884 7,294 5

.571 .236 5.14 .06

Middle ............................. 13.94 10.14 77.0 73.3 19,098 18,750 5.571 .236 5

.14 .06

High ................................ 16.58 11.63 83.0 75.9 41,527 47,096 5.571 .236 5.15 .06

Single person:

Man ................................ 6.93 6.25 53.4 60.7 14,811 17,343 .289 .481 .12 .21

Woman ........................... 6.01 5.96 58.3 57.7 9,011 12,075 –.036 205 –.02 .09

Raceof reference person:

Black ............................... 13.48 12.08 75.1 74.9 15,993 18,966 .483 .200 .11 .04

White or other ................. 13.54 10.16 75.5 72.8 24,740 27,182 5

.571 .236 5.15 .06

Poultry

Age of reference person:

Under 35 ......................... 4.58 4.59 32.4 36.2 22,113 24,321 .103 .043 .07 .03

35 to 64 .......................... 5.22 5.57 42.4 41.6 28,622 33,127 .033 6

.090 .02 .06

65 and older ................... 3.76 4.54 40.5 37.7 11,424 17,620 .182 –.006 .12 .00

Income group:

Low ................................. 4.05 4.45 35.3 36.3 6,827 7,206 .033 6

.090 .02 .05

Middle ............................. 4.72 4.75 39.1 37.4 19,053 18,806 .033 6.090 .02 .06

High ................................ 5.37 5.82 41.1 43.3 41,707 48,429 .033 6

.090 .02 .06

Single person:

Man ................................ 3.31 4.40 23.4 24.5 13,575 23,082 –.017 5.283 –.01 .21

Woman ........................... 3.26 3.54 29.1 27.7 8,743 11,843 .012 4

.277 .01 .21

Race of reference person:

Black ............................... 5.07 5.02 51.3 50.3 15,872 19,385 –.148 .053 –.08 .03

White or other ................. 4.68 5.11 36.3 38.1 24,844 28,795 .033 6

.090 .02 .06

Fish and seafood

Age of reference person:

Under 35 ......................... 3.56 3.38 27.2 25.0 22,382 24,148 .065 4

.238 .06 .21

35 to 64 ........................... 4.65 4.35 34.6 30.3 30,483 36,498 .114 5

.439 .09 4.33

65 and older .................... 3.56 3.67 30.9 27.2 12,851 16,932 6.474 6.394 6

.36 .30

Income group:

Low ................................. 3.09 3.18 26.0 24.0 6,937 7,226 .114 5

.439 .08 4.32

Middle ............................. 4.12 3.40 31.1 26.7 19,554 18,793 .114 5.439 .09 4.34

High ................................ 4.83 4.84 36.8 32.7 41,842 50,138 .114 5

.439 .09 4.33

Single person:

Man ................................ 3.93 3.10 20.3 18.1 15,126 21,060 .268 5.476 .22 .39

Woman ........................... 2.86 2.72 21.8 19.9 8,839 12,823 –.031 6

.340 –.03 .31

Characteristic

Real weekly

expenditures1

Percent

reporting2 Real annual

income

Parameter estimate

I = (I

0.25)

Income

elasticity3

1980 1992 1980 1992 1980 1992 1980 1992 1980 1992
Monthly Labor Review December 1998 25

Race of reference person:

Black ............................... 4.28 4.41 35.9 31.4 16,610 21,117 .041 4.336 .03 .23

 White or other ................ 4.09 3.91 30.7 27.8 26,041 30,330 .114 5

.439 .09 4.36

Eggs

Age of reference person:

Under 35 ......................... 1.51 1.26 45.7 36.0 23,014 22,627 –.040 .004 –.09 .01

35 to 64 ........................... 1.78 1.47 56.0 41.9 29,060 31,280 .001 .040 .00 .08

65 and older .................... 1.47 1.41 50.3 38.6 12,474 16,816 –.014 –.013 –.03 –.02

 Income group:

Low ................................. 1.55 1.40 44.7 36.6 6,930 7,272 .001 .040 .00 .06

Middle ............................. 1.61 1.36 51.3 39.6 19,159 18,744 .001 .040 .00 .08

High ................................ 1.73 1.44 57.8 41.6 41,966 45,668 .001 .040 .00 .09

Single person:

Man ................................ 1.18 1.10 32.6 23.9 14,244 17,780 –.001 .030 .00 .07

Woman ........................... 1.14 1.46 34.3 27.2 8,986 11,855 .064 .116 .12 .23

Race of reference person:

Black ............................... 1.89 1.41 52.6 43.2 16,116 19,059 –.010 –.006 –.02 –.01

White or other ................. 1.60 1.40 51.1 39.1 25,254 26,845 .001 .040 .00 .08

Dairy products

Age of reference person:

Under 35 ......................... 5.95 5.34 83.8 84.1 21,792 23,429 5

.250 5.167 5.14 .09

35 to 64 ........................... 7.35 6.57 91.5 88.1 28,587 32,820 5

.264 5.166 5.13 .08

65 and older .................... 4.65 4.71 88.6 87.2 11,674 16,109 5.255 6.149 5.15 .09

Income group:

Low ................................. 4.58 4.58 81.8 81.6 6,719 7,253 5

.264 5.166 5.13 .08

Middle ............................. 6.23 5.58 89.2 87.4 18,983 18,728 5.264 5.166 5.13 .08

High ................................ 8.10 6.97 93.8 90.3 41,562 47,794 5

.264 5.166 5.13 .08

Single person:

Man ................................ 3.50 3.61 71.4 74.1 13,968 18,859 .142 .116 .11 .09

Woman ........................... 3.27 3.43 77.1 78.8 8,963 12,963 .155 .150 .12 .11

Race of reference person:

Black ............................... 4.54 4.25 81.0 78.9 15,677 19,085 .040 –.052 .03 –.03

White or other ................. 6.60 5.99 89.2 87.6 24,074 27,379 5

.264 5.166 5.13 .08

Fruits and vegetables

Age of reference person:

Under 35 ......................... 7.11 6.28 76.7 79.9 22,076 23,653 5

.317 4.367 5.15 .17

35 to 64 ........................... 9.17 7.89 87.8 88.6 28,885 33,052 5

.379 5.353 5.15 .14

65 and older .................... 6.91 6.54 88.8 92.2 11,819 16,113 5.467 4.219 5.19 6.09

Income group:

Low ................................. 6.13 5.76 78.0 82.3 6,731 7,214 5

.379 5.353 5.15 .14

Middle ............................. 7.89 6.56 84.4 86.2 19,036 18,761 5.379 5.353 5.15 .14

High ................................ 9.91 8.71 89.7 91.0 41,940 47,961 5

.379 5.353 5.15 .14

Single person:

Man ................................ 5.18 4.47 63.9 72.2 14,924 19,205 4.301 4.288 4.18 .17

Woman ........................... 5.19 4.53 77.8 84.1 8,964 13,024 .215 6

.226 .11 .12

Race of reference person:

Black ............................... 7.38 6.60 80.3 85.6 15,891 19,343 5.326 .148 5.13 .06

White or other ................. 8.15 7.22 84.5 86.9 24,304 27,548 5

.379 5.353 5.16 .15

Fats and oils

Age of reference person:

 Under 35 ........................ 2.38 2.53 41.5 36.7 23,310 23,842 .007 .117 .01 .15

 35 to 64 ......................... 2.78 2.81 54.2 49.8 29,817 32,812 .026 5

.076 .03 .09

 65 and older .................. 2.14 2.40 50.3 49.0 12,218 16,266 –.056 .058 –.07 6.07

Characteristic

Real weekly

expenditures1

Percent

reporting2 Real annual

income

Parameter estimate

I = (I

0.25)

Income

elasticity3

1980 1992 1980 1992 1980 1992 1980 1992 1980 1992

Table 10. ContinuedIncome elasticity estimates and related information, Heckman second-stage results, complete

reporters only, 1980 and 1992
26 Monthly Labor Review December 1998

The Food-at-Home Budget

exists between the estimated elasticities of families younger

than 65 and families 65 and older for beef, pork, and other

meats. The 1980 elasticity for families 65 and older was 0.28,

compared with 0.17 for those younger than 35 and 0.14 for

the 35- to 64-year-old group. Although no statistically significant change occurred over time, the oldest group still had the

largest predicted elasticity, 0.12, compared with 0.06 for the

middle group and 0.02 for the youngest group. And although

there was some variation in elasticities for other food at home

in 1980 (0.13 for the youngest group, 0.09 for the middle

group, and an estimate not significantly different from zero

for the oldest group), by 1992 all age groups had elasticities

in the low 0.20 range, with figures statistically significantly

different from their 1980 estimates.

Elasticities for fish and seafood also appear to differ by

age. In 1980, only those in the 65-and-older group had a statistically significant elasticity (0.36). In 1992, the middle

group had a statistically significant increase in elasticity, which

rose to 0.33. Because the older group does not change significantly over time, one can say the middle age group “caught

up” to the older one. The elasticity of the under 35 group was

not statistically significant in either year.

Income. There is almost no variation in the elasticity of income by age group. Although the same parameter estimate is

used to calculate elasticity regardless of income class (as described earlier), clear differences in real income and real expenditures can be seen for several food groups. So it appears

that, because inverse income shares are used to calculate elasticities, conventional shares of income must be similar for the

various income groups, a finding confirmed by table 3. Although some differences in shares by income in each year are

observed, they are not generally large.

Even so, some trends are interesting. For example, regardless of income, families exhibited statistically significant increases in their purchases of cereal and bakery products, fish

Income group:

Low ................................. 2.23 2.36 39.6 40.3 6,897 7,334 .026 5

.076 .03 .08

Middle ............................. 2.51 2.62 50.5 46.2 19,229 18,807 026 5

.076 .03 .09

High ................................ 2.75 2.87 56.7 50.0 41,631 46,715 .026 5.076 .03 .10

Single person:

Man ................................ 1.59 1.95 27.2 27.7 13,784 19,244 .040 .067 .06 .11

Woman ........................... 1.74 1.89 34.7 34.3 9,001 12,310 .056 .027 .08 .04

Race of reference person:

Black ............................... 2.49 8.05 41.9 41.9 15,777 19,190 –.016 .048 –.01 .03

White or other ................. 2.55 2.66 49.8 46.2 21,017 27,767 .026 5.076 .03 .09

Other food at home

Age of reference person:

Under 35 ......................... 9.86 12.00 87.4 91.4 21,140 22,881 5

.456 5.767 5.13 6.21

35 to 64 ........................... 12.22 14.49 91.2 92.3 28,480 32,588 5.356 5.894 5.09 5.22

65 and older .................... 7.96 9.59 84.9 90.4 11,717 16,160 .280 5

.772 .09 4.24

 Income group:

Low ................................. 7.38 8.90 84.1 88.1 6,656 7,117 5.356 5.894 5.10 5.25

Middle ............................. 10.91 11.86 89.1 92.6 18,974 18,779 5

.356 5.894 5.09 5.23

High ................................ 13.36 16.49 92.9 93.7 41,595 47,832 5

.356 5.894 5.09 5.22

Single person:

Man ................................ 6.00 7.16 79.3 83.9 13,953 18,264 5

.141 5.630 .06 6.27

Woman ........................... 5.45 7.06 81.2 87.2 8,911 12,748 5

.227 5.733 .09 5.30

Race of reference person:

Black ............................... 8.31 9.86 83.9 87.0 15,536 19,485 .119 .064 .04 .02

White or other ................. 10.93 13.05 89.3 92.1 23,877 27,074 5.356 5

.894 5.09 5.24

Characteristic

Real weekly

expenditures1

Percent

reporting2 Real annual

income

Parameter estimate

I = (I

0.25)

Income

elasticity3

1980 1992 1980 1992 1980 1992 1980 1992 1980 1992

1

 Mean for those who reported an expenditure for the specific food item.

2 Percent of consumer units in each group reporting at least one food

expenditure. 3

 Elasticity calculated using average of 1980 and 1992 real weekly expenditures and real incomes. 4

 Statistically significant at the 95-percent confidence level.

5 Statistically significant at the 99-percent confidence level.

6

 Statistically significant at the 90-percent confidence level.

NOTE: For 1980, statistically significant elasticities are different from zero;

for 1992, statistically significant elasticities are different from the corresponding

1980 elasticities.

Table 10. ContinuedIncome elasticity estimates and related information, Heckman second-stage results, complete

reporters only, 1980 and 1992
Monthly Labor Review December 1998 27

and seafood, and other food at home. Although the 1980 elasticities for other food at home were statistically significantly

different from zero, in 1992 they more than doubled for each

food group. For fish and seafood, elasticities rose from a value

not significantly different from zero to the low 0.30s.

Gender. Elasticities are slightly more complicated to analyze by gender. Except for other food at home, no elasticity

appears to have changed over time for either single men or

single women. But to interpret the results this way is slightly

misleading. Actually, in several cases (cereal and bakery products, poultry, fish and seafood, and fruits and vegetables), the

1992 parameter estimate was statistically significantly different from zero for at least one gender, whereas the 1980 parameter estimate was not statistically significant for that

gender. So why does it appear that there was no statistically

significant change? It has to do with the question the statistical tests are asked to answer. For example, table 10 shows

that, for both single men and women, the estimated elasticity

for cereal and bakery products was 0.05 in 1980 and 0.22 in

1992. The 1980 parameter estimate was not statistically significantly different from zero. Although the 1992 parameter

estimate was statistically significantly different from zero, it

was not statistically significantly different from 0.05. Thus,

there is no indicator of statistical significance associated with

the 1992 elasticity. Given that many elasticities were at least

significantly different from zero in 1992, but were not in 1980,

the analysis proceeds assuming that a difference from zero

implies a change in elasticity.

Single men and women had very similar elasticities for cereal and bakery products, poultry,78 and other food at home

regardless of the survey year. However, single men appear to

have had a higher elasticity for fruits and vegetables than

single women did regardless of the year, and also for fish and

seafood in 1992. Neither group had a statistically significant

elasticity for beef, pork, and other meats; eggs; dairy products; or fats and oils, regardless of the year.

Race. Expenditures by race are subject to the same caveat

as those for singles: occasionally, an elasticity was statistically significantly different from zero in 1992, whereas it was

not in 1980, but the difference in the estimated elasticities for

each year is not itself statistically significant. Even bearing

this in mind, however, there are only two expenditures for

which black families showed an income elasticity that was

both positive and statistically significantly different from zero:

fish and seafood (0.23 in 1992) and fruits and vegetables (0.13

in 1980). However, neither of these figures represents a statistically significant change in elasticity over time.

Still, for white and other families, many changes are evident. Elasticities increased for cereal and bakery products, fish

and seafood, and other food at home and were significantly

different from zero for beef, pork, and other meats (1980),

poultry (1992), dairy products (1980 and 1992), fruits and

vegetables (1980 and 1992), and fats and oils (1992). No statistically significant elasticities were calculated for eggs.

HAVE CONSUMER EXPENDITURES ON FOOD AT HOME changed to reflect current nutritional attitudes? To answer that question, this

article has analyzed data from the Consumer Expenditure Survey in three different ways. The first compares how shares of

the food budget were allocated in 1980 and 1992 for families

with different demographic characteristics, including how

price changes account for the observed differences. The second uses logistic regressions to see whether the frequency of

purchasing specific food items has changed over time and how

that frequency relates to demographic characteristics. The

third utilizes results from the second stage of a Heckman twostage regression analysis to estimate income elasticities for

various food groups. Data from the new Diet and Health

Knowledge Survey are used to analyze current nutritional attitudes by demographic group. These data are generally consistent with changing food expenditure patterns.

On the one hand, consumers are substituting poultry for

meats with a higher fat content and are reducing their consumption of eggs. On the other hand, the consumption of fruits

and vegetables has declined, although most demographic

groups are purchasing fruits and vegetables more frequently.

Both the consumption of, and the frequency of purchasing,

fish and seafood have declined for most demographic groups.

However, this finding is mitigated by the fact that income elasticities for fish and seafood have increased substantially, at

least for families in the control group. Apparently, expenditures on fish and seafood increased more rapidly with income

in 1992 than in 1980.

Among other interesting changes, single men consumed

relatively more poultry in 1992 than in 1980, while single

women consumed less; by contrast, single women were more

likely to purchase fruits and vegetables than single men were,

regardless of the year, although both groups purchased fruits

and vegetables more frequently in 1992. Some generational

differences were found. For example, families of all ages decreased their egg consumption from 1980 to 1992, but families 65 and older were most likely (and families under 35 least

likely) to purchase eggs regardless of the year. Conversely,

older families were most likely (and younger families least

likely) to purchase fruits and vegetables. Substantial differences by income group were found through an examination of

share indexes, with the high-income group exhibiting relatively

more healthful changes. However, as regards the frequency of

purchasing the various foods, gaps in probability are not generally large across income groups. The largest was for fish and

seafood in 1980, with the high-income group about 5 percent

more likely to purchase this item than the low-income group.
28 Monthly Labor Review December 1998

The Food-at-Home Budget

Race appears to have little relationship to food consumption

as measured by share indexes, except for the meat group. Also,

blacks are more likely than whites and others to purchase poultry or fish and seafood, and less likely to purchase dairy products or fats and oils, regardless of the year.

Some income elasticities exhibit notable changes over time.

Income elasticities for several foods—cereal and bakery products, fish and seafood, and other food at home—have increased

for many demographic groups. This rise indicates that a 1-percent increase in income yielded a larger percent increase in

expenditures for these items in 1992 than it did in 1980. However, income elasticities for all other foods were generally

stable across the 2 years that the survey was taken. For the

most part, income elasticities for beef, pork, and other meats;

dairy products; and fruits and vegetables were positive in 1980.

However, elasticities for poultry, eggs, and fats and oils were

not statistically significantly different from zero in 1980.

In general, the findings indicate that consumers are reacting to the ever-changing news about relationships of food to

health, but that some demographic groups respond differently

than others. Future work analyzing trends by demographic

groups should provide further insight into changing food expenditure patterns, especially as more data on nutritional attitudes and awareness become available.

Footnotes

1

 “Try These New Secrets: Live Longer and Love Every Day,” Prevention, August 1993, p. 73.

2

 Judith Jones Putnam and Jane E. Allshouse, Food Consumption, Prices,

and Expenditures, 1970–92, Statistical Bulletin No. 867 (U.S. Department

of Agriculture, September 1993).

3

 These and the figures that follow are taken from Putnam and Allshouse,

Food Consumption, table 1, “Major foods: Per capita consumption, 1970–

92,” p. 27.

4

 Unlike the per capita consumption of other foods described here, which

was more or less steadily up or steadily down from 1980 through 1992, per

capita dairy product consumption reached a low in 1981 (543.2 pounds),

rose from 1982 (554.6 pounds) through 1987 (601.2 pounds), and then

dropped sharply in 1988 (565.2 pounds), at which level it approximately

remained until 1992 (564.6 pounds). Even so, per capita consumption in

1992 was 4 percent higher than in 1981.

5

 The consumption of both fresh fruits and fresh vegetables dropped

from 1989 to 1991, probably due to sharp increases in prices after the 1988

drought. Per capita fresh fruit consumption increased strongly from 1991

(86.6 pounds) to 1992 (95.3 pounds), but that of fresh vegetables declined

slightly over the same period, falling from 110.4 pounds to 109.3 pounds.

6

 Putnam and Allshouse, Food Consumption, p. 3.

7 Ibid.

8

 “Fruit and Vegetable Consumption,” Family Economics Review, vol.

6, no. 2, 1993, p. 24.

9

 Putnam and Allshouse, Food Consumption, p. 18.

10 The Consumer Expenditure Diary Survey shows that in 1980, 88 percent of all families reported expenditures for food at home, compared with

73 percent for food away from home. In 1992, 90 percent reported expenditures for food at home, compared with 74 percent for food away from home.

11 Putnam and Allshouse, Food Consumption, p. 3.

12 In fact, the Continuing Survey of Food Intake for Individuals (CFSII)

has superseded the NFCS to some degree, although it is not a perfect replacement for that survey. As the name implies, the CFSII collects information on

what foods individuals actually consumed, but it does not collect household

data (including food usage and demographic characteristics) that were formerly available in the NFCS. Furthermore, the CFSII is itself conducted only

periodically. The first nationwide sample was collected in 1989–91, and a

later sample was collected in 1994–96. The next scheduled surveys will take

place starting in 1999.

13 See, for example, Ching-Fan Chung, “A Cross-Section Demand Analysis

of Spanish Provincial Food Consumption,” American Journal of Agricultural Economics, August 1994, pp. 513–21; and Hwang-Jaw Lee, “An Analysis of a Food Demand System for the United States,” ACCI [American Council on Consumer Interests] Proceedings, Toronto, 1992, pp. 271–78.

14 Dale M. Heien and Cathy Roheim Wessells, “The Demand for Dairy

Products: Structure, Prediction, and Decomposition,” American Journal of

Agricultural Economics, May 1988, pp. 219–28.

15 Chung, “Cross-Section Demand Analysis”; Lee, “Analysis of a Food

Demand System”; and Marilyn E. Manser, “Elasticities of Demand for Food:

An Analysis Using Non-Additive Utility Functions Allowing for Habit Formation,” Southern Economic Journal, July 1976, pp. 879–91.

16 Susan M. Krebs-Smith, Annetta Cook, Amy F. Subar, Linda Cleveland, and James Friday, “U.S. Adults’ Fruit and Vegetable Intakes, 1989 to

1991: A Revised Baseline for the Healthy People 2000 Objective,” American Journal of Public Health, December, 1995, pp. 1623–29.

17 The reference person is the first member mentioned by the respondent

when asked to “Start with the name of the person or one of the persons who

owns or rents the home.” It is with respect to this person that the relationship

of the other family members is determined.

18 The income groups include complete income reporters only. To be

classified as a complete income reporter, the family must have reported at

least one major source of income (such as wages or salaries, self-employment income, or Social Security income) for at least one of its members,

although even complete reporters may not provide a full accounting of income for all levels and sources.

19 A consumer unit, which is the basic unit of comparison in the Consumer Expenditure Survey, is defined as a single person living alone or sharing a household with others, but who is financially independent; members

of a household related by blood, marriage, adoption, or some other legal

arrangement; or two or more persons living together who share responsibility for at least two of three major types of expenses—food, housing, and

other expenses. For convenience, the terms “consumer unit” and “family”

are used interchangeably throughout the article.

20 Although an attempt is made to keep all participating families in the

sample for both weeks, some families are available only for 1 week. Thus,

dividing by 2 underestimates the number of unique families participating.

21 Nutritional Attitudes and Dietary Status of Main Meal Planners/

Preparers, 1989–91, Agricultural Research Service NFS Report No. 91-1 (U.S.

Department of Agriculture, January 1996). See especially page 1, and abstract for dates survey was conducted.

22 The main meal planner or preparer is the person “most responsible for

planning or preparing the household’s meals.” (Ibid., p. 201.)

23 Ibid., p. 1.

24 The second DHKS covers the period 1994–96.

25 See technical note for formula.

26 The CPI-U, which measures price changes in urban areas, is used. Although the Consumer Expenditure Survey data include both urban and rural

consumers, most families (86 percent) in the 1980 and 1992 Diary surveys

live in urban areas.

27 Maureen Boyle Gray, “Consumer spending on durables and services in
Monthly Labor Review December 1998 29

the 1980’s,” Monthly Labor Review, May 1992, pp. 18–26; see especially p.

20.

28 Although results from the 1980 and 1992 Diary surveys show that the

average number of earners per consumer unit is 1.4 in both years, there has

been a shift in the distribution of earners. Looking at single persons only,

one finds that about 63 percent are earners in each year. But examining

consumer units with at least two members, one readily sees that single-earner

consumer units drop from 30 percent to 25 percent of these families, and

two-earner consumer units increase from 44 percent to 49 percent of such

families.

29 Eva Jacobs, Stephanie Shipp, and Gregory Brown, “Families of working wives spending more on services and nondurables,” Monthly Labor Review, February 1989, pp. 15–23; see p. 16.

30 Putnam and Allshouse, Food Consumption, p. 16.

31 Ibid., p. 37. Table 10 shows that per capita egg consumption declined

at a fairly steady rate from 1980 (271 eggs) to 1987 (254 eggs), at which

point it fell sharply until 1990 to a level (233 eggs) at which it stabilized.

32 Lee, “Analysis of a Food Demand System.” Lee finds that poultry and

seafood are substitutes (p. 275), with a cross-price elasticity of 0.36 (p. 278).

In other words, for every 1-percent increase in the price of seafood, expenditures on poultry rise 0.36 percent.

33 The BLS Division of Consumer Expenditure Surveys routinely compares results of the Diary survey with those of other surveys. Ratios of 1991

Consumer Expenditure Survey data to data from other sources are published

in Consumer Expenditure Survey, 1990–91, Bulletin 2425 (U.S. Department of Labor, 1993), p. 11. Ratios also are found in internal reports, including Maureen Gray, Ratios of Consumer Expenditure Diary survey to alternative sources, 1989–92, 1994, table completed January 4. The Consumer

Expenditure Survey data for fish and seafood are divided by the appropriate

ratio in the table for conversion into values found by the other sources described. These values are then divided by the CPI for fish and seafood in each

year, converting the values into real dollars for 1990, 1991, and 1992. The

change from 1990 to 1992 can then be calculated for each source. For more

information on the other sources, see Raymond Gieseman, “The Consumer

Expenditure Survey: quality control by comparative analysis,” Monthly Labor Review, March 1987, pp. 8–14.

34 Steven M. Lutz, David M. Smallwood, James Blaylock, and Mary Y.

Hama, Changes in Food Consumption and Expenditures in American Households During the 1980’s, Statistical Bulletin No. 849 (U.S. Department of

Agriculture, December 1992), table 2, “Household size: Average annual food

use (per 21-meal equivalent person),” p. 20.

35 See page 21 of the survey.

36 To classify families by income group, complete income reporters are

sorted by their total reported income and numbered consecutively, with the

lowest income reported receiving a value of 1. The values are then all divided by the value associated with the family with the highest income, so

that the family with the lowest income has a rank close to 0 and the rank of

the family with the highest income is exactly 1. Families whose rank is less

than one-third are counted in the lowest group. Those whose value is between one-third and two-thirds are placed in the middle group. The remaining families are placed in the highest group.

37 In 1980, about 27 percent of low-income families had a reference

person with at least some college education, compared with 54 percent of

high-income families. In 1992, approximately 30 percent of low-income

families had a reference person with at least some college education, compared with 66 percent of high-income families. Differences in level of education are controlled for in the logistic regressions.

38 See p. 24 of the survey.

39 See p. 127 of the survey.

40 See p. 21 of the survey.

41 See p. 22 of the survey.

42 See p. 125 of the survey.

43 See p. 124 of the survey.

44 See pp. 38–39 of the survey.

45 See pp. 48–49 of the survey.

46 See pp. 44–45 of the survey.

47 See pp. 22–23 of the survey.

48 Peter Kennedy, A Guide to Econometrics, 3rd ed. (Cambridge, MA, MIT

Press, 1992), Chapter 15; see especially pp. 228–30, 241–42.

49 In this article, children are defined as persons less than 18 years old.

Hence, in considering the addition of an adult to the husband-wife family

with one child, the interpretation is that the spouses have two offspring living at home, one who is at least age 18 and the other under age 18. Otherwise, it is necessary to include the parameter estimate for “other family” in

the probability equation, which complicates the comparison. Furthermore,

the percent change from adding one child older than age 18 can be compared with the percent change from adding one child under age 18 to see

whether the age of the older child affects the probability of purchasing a

particular item of food, thus adding more flexibility to the analysis without

further complicating the model.

50 According to Jean A. T. Pennington and Helen Nichols Church, Food

Values of Portions Commonly Used, 14th ed. (New York, Harper & Row

Publishers, Inc., 1985), a large boiled egg contains 274 milligrams of cholesterol (p. 52). This compares with 31⁄2-ounce servings of canned oysters

(230 milligrams; p. 68), lobster (200 milligrams; p. 67), and shrimp (150

milligrams; p. 70).

51 Nutritional Attitudes, pp. 21–23.

52 Although the decrease in the family’s probability of purchasing cereal

and bakery products over the period (0.3 percent) is statistically significant

for this food group, it is not economically significant.

53 Nutritional Attitudes, pp. 21–22.

54 Ibid., pp. 78–80.

55 Although the parameter estimates for the older-than-65 and 1992 interaction terms are not statistically significant for any food group, the dummy

variable for 1992 is statistically significant for all but two (poultry, and fats

and oils) of the foods just mentioned. Therefore, one can conclude that the

control group experienced a decrease in the probability of purchasing these

foods in 1992 and those older than 65 had no significant difference in the

probability of purchasing the foods in the 2 years the survey was

conducted.

56 Nutritional Attitudes, pp. 124–26.

57 Ibid., pp. 21–23.

58 Ibid., p. 25.

59 Ibid., pp. 38–43.

60 Ibid., pp. 32–33.

61 Ibid., pp. 44–45.

62 Ibid., pp. 21, 23.

63 Ibid., p. 124.

64 Ibid., p. 126.

65 For those with some college, the decline also appears to be sharp (about

8 percentage points), although the parameter estimate just misses statistical

significance at the 90-percent level. 66 Nutritional Attitudes, pp. 21–22. 67 Ibid., p. 124. 68 Although the CPI is sometimes used as a proxy for price levels (see, for

example, Lee, “Analysis of a Food Demand System”), there is insufficient

variation in price levels in the 2 years investigated in this study to warrant

the attempt. 69 James J. Heckman, “Sample Selection Bias as a Specification Error,”

Econometrica, January 1979, pp. 153–61.

70 John F. McDonald and Robert A. Moffitt, “The Uses of Tobit Analysis,” Review of Economics and Statistics, vol. 62, no. 2, 1980, pp. 318–21.

71 G. E. P. Box and D. R. Cox, “An Analysis of Transformations,” Journal of the Royal Statistical Society, Series B, 1964, pp. 211–43.

72 Ibid., p. 214.

73 Stuart Scott and Daniel J. Rope, “Distributions and Transformations
30 Monthly Labor Review December 1998

The Food-at-Home Budget

for Family Expenditures,” 1993 Proceedings of the Section on Social Statistics (Alexandria, VA, American Statistical Association), pp. 741–46.

74 Had incomplete reporters been included in the probit sample, the parameters from the logit and probit regressions would presumably predict

identical probabilities (or extremely similar ones) for each demographic

group.

75 It may be of interest to note that the coefficient for the inverse of Mill’s

ratio is statistically significant only three times. The first is for cereal and

bakery products in 1992; the coefficient (8.293) is significant at the 90-

percent confidence level. The second is for fruits and vegetables; the 1980

coefficient (6.552) is statistically significant at the 90-percent confidence

level, and the 1992 coefficient (–3.098) is not statistically significant, indicating that the 1980 estimate holds for both periods. The third time is for

dairy products. As with fruits and vegetables, only the 1980 coefficient

(10.292) is statistically significant, but this time at the 99-percent confidence level; the 1992 coefficient (–3.858) is not statistically significant.

76 The regression model presented in the equation that follows is greatly

simplified. Let a stand for all parameter estimates and independent variables not associated with income. Let I stand for the income of the group

under study; b is the parameter estimate related to income for the group.

Note that in the Heckman procedure, it is necessary to include the inverse of

Mill’s ratio and its coefficient when taking the derivative ∂Y/∂I. However,

recall that the first stage of the model which uses the Heckman procedure

includes no continuous income variables—only categorical variables defining the income class (low, high, or incomplete reporter). Therefore, the inverse of Mill’s ratio can be treated the same way as all the other variables

included in a; that is, the partial derivative of the inverse of Mill’s ratio

equals zero. (For more on calculating this derivative, see Atanu Saha, Oral

Capps, and Patrick J. Byrne, “Calculating marginal effects in dichotomouscontinuous models,” Applied Economics Letters, vol. 4, 1997, pp. 181–85.)

77 In other words, if the parameter estimate for 1980 is statistically significant, it is interpreted in the usual way. If the parameter estimate for 1992

is statistically significant for a particular characteristic, then the effect of

this characteristic in 1992 is different from the effect it had in 1980, and this

difference is statistically significant. As explained later in the text, the two

parameters can be added, and the resulting sum can be tested with an F-test.

The purpose of the F-test is to determine whether there is a statistically

significant relationship between the characteristic in question and a particular food expenditure in 1992. (Consider a case where a 1980 parameter is,

say, 5.0, and the 1992 counterpart is –5.0. Each value may be statistically

significant according to a t-test: the effect in 1980 is 5 points, whatever that

means in this case, and in 1992 it is 5 points less than in 1980. Combining

the two yields a parameter estimate of zero, suggesting that there is no statistically significant relationship between the characteristic and expenditures

in 1992. The absence of such a relationship is confirmed with the F-test.)

78 The change in elasticity for poultry for single men just barely fails the

test for statistical significance at the 90-percent confidence level: the calculated F-test value is 2.69; the critical value is 2.71.

The following is a detailed list of food items contained in each food

group.

Cereal and bakery products:

Cereal and cereal products

Flour; prepared flour mixes; ready-to-eat and cooked cereals;

rice; pasta, cornmeal, and other cereal products.

Bakery products

White bread; bread other than white; cookies; crackers; frozen

and refrigerated bakery products; biscuits and rolls; cakes and

 cupcakes; bread and cracker products; sweet rolls, coffee cakes,

 and doughnuts; pies, tarts, and turnovers.

Meat, poultry, fish, and eggs:

Beef

Ground beef; chuck roast; round roast; other roast; round steak;

sirloin steak; other steak; other beef.

Pork

Bacon; pork chops; ham, not canned; canned ham; sausage;

other pork.

Other meats

Frankfurters; bologna, liverwurst, and salami; other lunch

meats; lamb and organ meats; mutton, goat, and game.

Poultry

Fresh whole chicken; fresh and frozen chicken parts; other poultry, including whole frozen chicken.

Fish and seafood

Canned fish and seafood; fresh and frozen shellfish; fresh and

frozen finfish.

Eggs

Dairy products:

Fresh milk and cream

Whole milk; other milk and cream.

Other dairy products

Butter; cheese; ice cream and related products; miscellaneous

dairy products.

Fruits and vegetables:

Fresh fruits

Apples; bananas; oranges; other fresh fruits.

Fresh vegetables

Potatoes; lettuce; tomatoes; other fresh vegetables.

Processed fruits

Frozen orange juice; other frozen fruits and juices; canned and

dried fruits; fresh, canned, or bottled fruit juices.

Processed vegetables

Frozen vegetables; canned beans; canned corn; other canned

and dried vegetables and juices.

Other food at home:

Sugar and other sweets

Candy and chewing gum; sugar; artificial sweeteners; jams, preserves, and other sweets.

Fats and oils

Margarine; other fats, oils, and salad dressing; nondairy cream

and imitation milk; peanut butter.

Miscellaneous foods

Frozen meals; other frozen prepared foods; canned and packaged soups; potato chips and other snacks; nuts; salt, spices,

and other seasonings; olives, pickles, and relishes; sauces and

gravies; baking needs and miscellaneous products; salads and

desserts; baby food; miscellaneous prepared foods.

APPENDIX 1: Food groups and Consumer Price Index categories
Monthly Labor Review December 1998 31

Nonalcoholic beverages

Cola; other carbonated drinks; roasted coffee; instant and

 freeze-dried coffee; noncarbonated fruit-flavored drinks;

tea; other nonalcoholic beverages.

The following data show the levels of the CPI for detailed expenditure items. These are used to obtain real expenditures for the income elasticity estimates presented in the text.

CPI category CPI deflator

1980 1992

Cereal and bakery products:

Flour and prepared flour

mixes ................................ 0.917 1.326

Cereal ................................. .763 1.754

Rice, pasta, and cornmeal... .909 1.283

White bread ........................ .859 1.462

Other breads ....................... .859 1.483

Fresh biscuits, rolls,

and muffins ....................... .841 1.445

Fresh cakes and cupcakes... .822 1.484

Cookies ............................... .808 1.563

Crackers; bread and cracker

products ............................ .829 1.749

Fresh sweet rolls, coffee cakes,

and doughnuts .................. .835 1.449

Frozen and refrigerated bakery

products, pies, tarts, and

turnovers ........................... .820 1.491

Meats, poultry, fish, and eggs:

Ground beef other than

canned .............................. 1.046 1.189

Chuck roast ........................ .998 1.371

Round roast ........................ 1.013 1.259

Round steak ........................ .989 1.299

Sirloin steak ........................ .962 1.324

Other beef and veal1 ........... .932 1.461

Bacon ................................. .735 1.046

Pork chops .......................... .829 1.389

Ham2 .................................. .855 1.356

Other pork .......................... .838 1.295

Pork sausage ....................... .822 1.236

Frankfurters ........................ .925 1.313

Bologna, liverwurst,

and salami ......................... .905 1.353

Other lunch meats .............. .903 1.269

Lamb and organ meats3 ...... 1.025 1.314

Fresh whole chicken4 ......... .944 1.319

Fresh and frozen chicken

parts .................................. .917 1.344

Other poultry ...................... .950 1.269

Canned fish and seafood .... .937 1.187

Fresh and frozen fish

and seafood5 ..................... .841 1.687

Eggs .................................... .886 1.083

CPI category CPI deflator

1980 1992

Dairy products:

Fresh whole milk ................ 0.935 1.264

Other fresh milk and cream .923 1.278

Cheese ................................ .887 1.355

Ice cream and related

products ............................ .864 1.309

Butter ................................. .894 .924

Other dairy products ........... .875 1.384

Fruits and vegetables:

Apples ................................ .921 1.795

Bananas .............................. .915 1.399

Oranges, including tangerines .726 1.762

Other fresh fruits ................ .862 2.007

Potatoes .............................. .810 1.415

Lettuce ................................ .778 1.557

Tomatoes ............................ .819 1.718

Other fresh vegetables ........ .778 1.616

Frozen fruit and fruit juices6 .793 1.327

Other fruit juices7 ............... .822 1.411

Canned and dried fruits ...... .844 1.310

Frozen vegetables ............... .776 1.309

Cut corn, canned beans excluding

lima beans8 ....................... .825 1.317

Other processed vegetables9 .865 1.269

Other food at home:

Sugar and artificial

sweeteners ........................ 1.073 1.204

Candy and chewing gum .... .846 1.375

Other sweets10 .................... .822 1.404

Margarine ........................... .928 1.320

Other fats, oils, and salad

dressing ............................ .913 1.233

Nondairy substitutes and peanut

butter11 .............................. .804 1.375

Carbonated drinks12 ........... .866 1.149

Roasted coffee .................... 1.169 1.087

Instant and freeze-dried

coffee ................................ 1.065 1.146

Other noncarbonated

drinks13 ............................. .859 1.313

Canned and packaged soup .866 1.537

Frozen prepared food14 ...... .857 1.375

Snacks15 ............................. .808 1.331

Seasonings, condiments, sauces,

and spices16 ...................... .823 1.430

Seasonings, olives, pickles,

and relish .......................... .804 1.439

Other condiments17 ............ .835 1.381

Miscellaneous prepared food,

including baby food ......... .840 1.503

Other canned and packaged

prepared foods18 ................. .848 1.278

Income before taxes19 ........ .824 1.403
32 Monthly Labor Review December 1998

The Food-at-Home Budget

Footnotes to appendix 1

1

 Used to deflate other roast; other steak; other beef.

2

 Used to deflate ham, not canned; canned ham.

3

 Also used to deflate mutton, goat, and game.

4

 Used to deflate fresh and frozen whole chicken.

5

 Used to deflate fresh and frozen shellfish; fresh and frozen finfish.

6

 Used to deflate frozen orange juice; other frozen fruits and juices.

7

 Used to deflate canned or bottled fruit juices.

8

 Used to deflate canned beans; canned corn.

9

 Used to deflate other canned and dried vegetables and juices.

10 Used to deflate jams, preserves, and other sweets.

11 Used to deflate nondairy cream and imitation milk; peanut butter.

12 Used to deflate cola; other carbonated beverages.

13 Used to deflate noncarbonated fruit-flavored drinks; tea; other nonalcoholic beverages.

14 Used to deflate frozen meals; other frozen prepared foods.

15 Used to deflate potato chips and other snacks; nuts.

16 Used to deflate salt, spices, and other seasonings; sauces and gravies.

17 Used to deflate baking needs and miscellaneous products.

18 Used to deflate salads and desserts.

19 Deflator is CPI for all items.

APPENDIX 2: The t-test

In comparing the means of two samples, a t-test is frequently used to

see whether observed differences are statistically significant. For

large samples, the formula for the standard t-test is

t = (M1

 – M2)/SEp,

where

M1 is the mean of the first sample,

M2

 is the mean of the second sample, and

SEp

 is the pooled standard error of the samples.

The pooled standard error is calculated by squaring the standard errors of the first and second samples, adding the squares together, and

taking the square root of the summed squares. If the value of t is

greater than 1.96, the difference is said to be statistically significant

at the 95-percent confidence level.

However, the above equation is not appropriate for testing differences in shares, because, as defined in the text, shares are calculated

by dividing an average by an average. For example, if the average

family in, say, group 1 spends $2 on beef and $50 on total food at

home, its share Sb1 is 0.04. If the average family in group 2 spends $3

on beef and $25 on total food at home, its share Sb2 is 0.12. Both the

mean expenditure for beef and the mean expenditure for total food at

home have their own associated standard errors, which most likely

differ for groups 1 and 2. These facts must be taken into account

before a t-test can be computed.

Fortunately, a formula is available for the comparison of shares.

(See Geoffrey Paulin, “Consumer expenditures on travel, 1980–87,”

Monthly Labor Review, June 1990, p. 60.) This formula uses the

relative standard error of the mean (RSE) for each element of the

share (beef and total food), where the RSE is defined as the standard

error of the expenditure, divided by the mean expenditure. (That is,

SEb1

/Mb1 equals RSEb1, in which b1 indicates group 1’s expenditures

for beef.) To calculate the pooled standard error for use in the shares

test, the following formula is employed:

(SEpb)

2

 = Sb1[RSEb1 + RSEf1 – 2Sb1RSEb1] + Sb2[RSEb2+ RSEf2

– 2Sb2RSEb2].

Here, the subscripts 1, 2, and f indicate 1980, 1992, and total food at

home, respectively. To test whether the change in a group of consumers’ share of beef from 1980 to 1992 is statistically significant,

the following formula is used:

t

b

 = (Sb1 – Sb2)/SEpb.

2 2 2 222 2

2 2