Chart 1. Correlation between income and education for wage

f
i and salary earners, Consumer Expenditure Survey, 1986
| and 1987 Interview Surveys
' Income
| before taxes
| $50,000
t
} $45,000 -
Managers,
professionals @
$40,000 |
$35,000 |

Technicians, sales,
administrative eupport
$30,000 | Precision craft @ e

and repair workers

t

'

'

| $25,000 | Operators and @

| laborers

\ $20,000 +

! © Service workers

j

4 $15,000 }

S| jg00 Le
! ° 10 20 30 40 450 60 70 480 90

Percent obtaining college education

' than is the case for richer households.'? Also, women’s clothing is higher as well, with a

the average size of households headed by craft greater percentage reporting expenses for
and repair workers or operators and laborers is dresses, coats, and furs.

‘ larger than those headed by managers and pro- Transportation expenditures were higher for

, fessionals and technicians, salespeople, and ad- the blue-collar groups at 23 percent of total

' ministrative support personnel, and therefore, expenditures, significantly more than the 20-

those households require greater expenses for percent share spent by managers and profes-

food. sionals. Blue-collar families also allocate a

: Food away from home, in contrast, is highest much higher share of total expenditures for

: for managers and professionals and lowest for vehicle purchases (both new and used cars and

the blue-collar groups. This can be explained, in _ trucks) and gasoline and motor oil. The average

part, by the fact that managers and professionals blue-collar worker evidently relies mostly on

are likely to eat at more expensive restaurants the automobile for transportation needs and may

' than those that blue-collar workers patronize. It be commuting a greater distance to work as

: is also possible that, in general, managers and well, thus consuming more gasoline. The

professionals eat out more often than do blue- average number of vehicles owned by families

collar workers. headed by craft and repair workers and by

The share for rented dwellings is highest for families headed by operators and laborers is

service workers, reflecting the high percentage 3.0, much higher than the figure for the other

of renters in this group. On the other hand, the groups. Some transportation expenses of man-

share for owned dwellings is highest for man- agers and professionals and salesworkers are

agers and professionals, as is that for other lodg-_ likely to be reimbursed by their employer as

ing, indicating a greater probability on the part business expenses—particularly costs for gas-

of that group’s families to incur expenses for line. Also, the use of a company car may contri-

vacation homes and lodging while out of town. bute to the lower share of total transportation

Managers and professionals also spend a expenses for managers and professionals.

higher share of income on men’s clothing than Another reason for the lower transportation

i all other groups, due to the preponderance of expenditures of managers and professionals

suits purchased as working attire. The share for _ may be that a large proportion of these individ-

Monthly Labor Review December 1989 37


TT

Spending Differences by Occupation

Education also is influential in predicting the
level of expense for housing, apparel, trans-
portation, personal care products, reading mate-
rials, occupation-related items, and personal
insurance and pensions.

Conclusions

The shares analysis illustrated that differences
exist among the five occupational groups with
Tespect to their distributions of expenditures.
The demographics and family characteristics as-
sociated with these occupational groups help ex-
plain some of the differences. The multivariate
tobit analysis demonstrated that income was
a driving force in determining the level of
expense for all expenditure categories investi-
gated. Occupation and education proved to have
significant effects for most items; however, the
only variables that occupation alone influenced
that education did not were food at home and
food away from home. This suggests that some
occupational fields are associated with the same
level of educational attainment. The data, fur-
thermore, indicate that managers and profes-
sionals have higher degrees of educational
attainment and higher incomes, on average,
than blue-collar and service’ workers. These so-
cioeconomic characteristics result in a greater
allocation of expenditures towards housing,
reading materials, pensions, and entertainment
by families headed by managers and profession-
als, while service workers and blue-collar
families are more likely to spend a larger share
for food and transportation. The tobit analysis

Footnotes

Table 4. Tobit regression analysis
results: significance of
occupation and education
on selected expenditures

Dependent variable | Occupation | Education
Food at home .. oa 12.0 40
Food away from home ....... "12.0 0.0

i . "30.0 “16.0
40 "80
"92.0 “38.0
60 20
50.0 "220
Reading materials . . 40.0 "62.0
Cash contributions ........... 40 00
Wes... "94.0 *8.0

Personal insurance and
pensions 0.0.2.2... "52.0 "8.0

* Significant at the 95-percent confidence interval.
“Significant at the 99-percent confidence interval.
Nore: y2= ~2 (Log likelinoodpasritea — Log likelihood).

confirms these findings for the most part. Fur-
ther investigation into the effects of occupa-
tional status, and even its relationship to income
and education, would be worthwhile, given that
the composition of the labor force is changing.
An analysis of the interaction among income,
education, and occupation could be employed in
future research. As employment of service and
white-collar workers continues to grow, and
that of high-wage blue-collar positions contin-
ues to decline, changes in consumption at the
aggregate level may occur. oO

' See Eva Jacobs, Stephanie Shipp, and Gregory Brown,
“Families of working wives spend more on services and
nondurables,” Monthly Labor Review, February 1989,
pp. 15-23.

2Nancy Gibbs, “How America has run out of time,”
Time, Apr. 24, 1989, pp. 58-67.

> See H. S. Houthakker and Lester D. Taylor, Consumer
Demand in the U.S. (Cambridge, Ma, Cambridge University
Press, 1970), pp. 254-57.

“Houthakker and Taylor, Consumer Demand, p. 59.

° See Angus Deaton and John Muellbauer, Economics
and Consumer Behavior (New York, Cambridge University
Press, 1980),

© See Gary S. Becker, Human Capital (Chicago, Univer-
sity of Chicago Press, 1975).

7 See Jacob Mincer, “On-the-Job Training: Costs, Re-
turns, and Some Implications,” Journal of Political Econ-
omy, October 1962, pp. 50-59.

* Publication of the data occurred in BLS news releases
88-175 (Apr. 14, 1988) and 89-330 (July 6, 1989).

° A consumer unit is (1) a single person living alone or
sharing a household with others but who is financially inde-

42 Monthly Labor Review December 1989

pendent, (2) members of a sample household related by
blood, marriage, adoption, or some other legal arrange-
ment, or (3) two or more persons living together who share
responsibility for at least two out of three major types of
expenses—that is, food, housing, and other expenses. The
terms “household,” “family,” and “consumer” are used for
convenience.

10 A 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 this home.”
Other consumer unit members are then referenced to this
person by their relationship to him or her.

1"! Managerial and professional specialty occupations in-
clude officials, administrators, financial managers, person-
nel and labor relations managers, purchasing managers,
managers, marketing advertising and public relations ad.
ministrators, administrators in education and related fields,
medicine and health managers, properties and real estate
‘managers, accountants and auditors, architects, engineers,
mathematical and computer scientists, natural scientists,
physicists, dentists, nurses, therapists, teachers, counselors
(educational and vocational), librarians, social scientists,
social recreation counselors, religious workers, writers,
artists, entertainers, athletes, lawyers, and judges.



J).

Chart 2. Shares of total expenditures for wage and salary earners, Consumer
Expenditure Survey, 1986 and 1987 Interview Surveys
Expenditure Expenditure
res shares
39 39
@ Transportation
36 oF 4 36
3 Housing
33 Food 4 33
30 + 4 30
27 - 4 27
24 - 4 24
21+ 4 21
18 + 4 18
16 4 15
12 12
Service Operators Craft and Technicians, Managers,
workers and repair sales, professionals
laborers administrative
support

tional attainment, living in an owned home in an
urban area.

The sample used for the analysis was re-
stricted to one-earner salaried and wage
families, and two-earner salaried and wage
families if both earners belonged to the same
occupational group. This way, the family ex-
penditures of a specific occupational group
would be highlighted without introducing error
caused by families whose earners belong to dif-
ferent occupational groups. The sample con-
sisted of 4,101 consumer units.

The descriptive data indicated that expendi-
ture differences exist among occupational
groups. The regression analysis tests whether
these differences still exist after controlling for
other demographic variables. Results are listed
in table 3. To test the overall significance of the
set of variables included in each expenditure
model, the likelihood ratio test statistic was
used.!” The resulting chi-square values were
statistically significant at the 0.01 level. This
allowed for the rejection of the null hypoth-
esis that all of the coefficients (except the inter-
cept) were equal to zero for all the models
considered.

An asterisk (*) indicates that the parameter
estimate was significantly different from zero at
the 95-percent confidence interval, while two

asterisks (**) indicate significance at the 99-
percent confidence interval. The parameter esti-
mates for total expenditures were significantly
different for all of the dependent variables at the
99-percent confidence interval. This indicates
that total expenditures (used as a proxy for in-
come) has a major influence in determining the
probability and level of incurring an expense for
the expenditures upon which the regression is
performed. In fact, total expenditures proved to
be the only significant variable, along with age
squared, influencing entertainment and cash
contributions.

Occupation proved to be a significant vari-
able for all of the dependent variables except
apparel, entertainment, and cash contributions.
This implies that income is a better predictor of
the level of expense for most of these expendi-
ture categories.

Although, as a proportion of total expendi-
tures, service workers spend the most on food at
home, the negative parameter estimate in the
regression implies that they spend significantly
less than managers and professionals in terms of
average dollar amount. All occupational groups
were less likely to incur an expense for food
away from home compared to managers and
professionals, except service workers, which is
similar to the result obtained in the shares anal-

Monthly Labor Review December 1989 39



Income was a
driving force in
determining all
expenditure levels
investigated;
occupation and
education were
significant for
most items.

Spending Differences by Occupation

uals are employed in urban areas and, therefore,
have public transportation at their disposal for
commuting needs. Ninety-three percent of all
managers and professionals in the Consumer
Expenditure Survey sample live in urban areas,
compared to only 82 percent of the blue-collar
groups. Together with the fact that managers
and professionals are more likely to incur ex-
penses related to travel, such as the cost of air-
line fares, the higher concentration of this group
in urban areas might explain the greater share
they spend for public transportation.

Managers and professionals spend a higher
share than do any of the other groups for fees
and admissions to sporting events and clubs.
These include club membership dues, which are
generally very expensive. By contrast, the share
of expenditures spent for tobacco and smoking
supplies is significantly higher for the blue-
collar groups and lower for managers and
professionals.

Finally, costs of education, cash contribu-
tions, and monies allocated to retirement funds,
pensions, and Social Security are significantly
higher for managers and professionals com-
pared to the other groups. Interestingly, of all
types of expenditures made by technicians,
salespeople, and administrative support per-
sonnel, only the share allocated for rented
dwellings proved to be significantly different
from the average such expenditure for all the
groups. This implies that, on average, this
group does not distribute expenditures signifi-
cantly differently from all other salaried and
wage earners. Therefore, it is most representa-
tive of the “average” worker.

It is obvious from these results that differ-
ences exist among the five occupational groups,
although, with respect to demographics and
consumption patterns, the white-collar groups
tend to be similar to each other and the blue-
collar groups to each other. Chart 2 highlights
the major differences in expenditure distribution
among the five groups.

Regression analysis

In this section, regression analysis determines
the effect occupation alone has on the probabil-
ity and level of incurring an expense after
controlling for variation due to income and edu-
cation. Tobit analysis utilizes maximum like-
lihood estimation in a single equation when a set
of continuous observations on a dependent vari-
able is truncated.'* For houschold expenditure
data, it is more advantageous than ordinary least
squares, because many households might not
incur an expense for some goods and services.
The data have a lower bound of zero.

38 Monthly Labor Review December 1989

es

Eleven different expenditure categories,
listed in table 3, were chosen as dependent vari-
ables. These include most major categories for
which, according to the shares analysis, the five
occupational groups have significantly different
mean expenditures. Among such categories are
food at home and away from home, housing,
transportation, apparel, reading, cash contribu-
tions, and personal insurance and pensions. Ex-
penses for personal care and occupational
expenses were also chosen, as it was reasoned
that occupational status would have an effect on
these items.

The independent, or causal, variables were
chosen from the socioeconomic characteristics
of the family. Dummies were created for the
five occupational groups, with managers and
professionals being left out of the model as the
control group. Parameter estimates produced for
the occupational dummies indicate the relation-
ship of these groups to managers and profes-
sionals with respect to the probability and level
of incurring an expense for the dependent vari-
able. Education was accounted for by including
dummies for the educational level of the refer-
ence person.

As mentioned previously, income is an im-
portant determinant of consumption. The cate-
gory of total expenditures was used as an
approximation of income in these models as a
continuous variable. Total expenditures are cho-
sen as a proxy for income primarily because (1)
in the short run, families have more control over
expenditures than income, and (2) total ex-
penditures give a better fit than income in mod-
els designed to predict expenditures in a number
of different categories. !>

Because Houthakker and Taylor argue that
family composition is so important that no
cross-sectional analysis should ignore it,!®
dummies were included in the models with hus-
band-and-wife-only families being the control
group.

Other socioeconomic variables included in all
of the models were family size (squared), age
(squared), race, urbanization, housing tenure,
and number of earners. The region of residence,
season, and sex of the reference person were
included in some of the models if it was be-
lieved that they influenced the level of expendi-
tures for the dependent variable in question. The
models produce an intercept which represents
the expected quarterly expenditures for the de-
pendent variable, before accounting for the con-
tinuous variables (total expenditures, age
squared, family size squared, and number of
eamers), of the control group: a husband-and-
wife-only family headed by a white manager or
professional with some college or more educa-


im |

Table 3. Continued—Tobit regression analysis: coefficients of estimation of causal variables

ttem Personal | Reading cash —_| Occupational | pernonel,
care | meterais | contributions | expenses | ,vitanciong
F-statistic . 58.9 407 43.1 13.4 119.5
Chi-square statistic . . "899.5 “814.8 "821.6 “2174 “1736.3
Adjusted R-squared statistic 0.19 0.18 0.18 0.05 0.34
Independent variables
Constant . . . "412 "27.39 *-20.19
Total ‘expenditures - sees **,0027 "0025 "0024
Occupation (managers/protessionals):
Technicians, sales, administrative support . “-7.31 —10.21 142
"14.41 24.08 1.44
13.35 70.05 "43.03
+1285 34.33 11.65
Education (some college or more):
‘Some high school or less 11.83 | *-17.16 24.56 *-7.80
High school graduate -3.81 | *-11.10 4,70 2.36
Single ... 4.06 99.45 12.45 08.68
‘Husband 4.97 *-98.50 2.32 14.43
Other families 162 | 44.86 8.90 67.35
Urbanization (urban)
lural . *=15.34 *-6.88 16.24 1.29 "71.24
Housing tenure (owner)
hens *-4.94 44.71 3.54 **— 203.96
**,0049 **,0500 0013 0201
Consumer unit size squared . 6.6... 6c cece eee eer e eens 51 —.26 42 “6.58
Race of reference person (wit):
seen we "14.40 *-6.84 28.64 1.15, 14.53
eee wee *~10.33 2.58 10.19 30 “472.71
‘Season (fall):
Spring -.- = = = =
Summer... 22... eee eee - _ - -
Region (midwest):
Northeast 0... eee eee ee = 2.48 **— 104.94 - -
South . - "4.21 -29.47 _ -
West ... bee deeneeceeaeneeneesetaeee = 3.28 -37.92 _ -
‘Sex of reference person (male)
Female ... - 2.37 24.96 - —45.06
‘Number of eamers weeeteeeaetenrersareeee 2.78 3.22 42.66 "11,81 "232.67
Number of vehicles .... 5.0... eee e cnc een eee ener nent teen ee - - _ - -

* Significantly different from zero at the 95-percent confidence interval.
** Significantly different from zero at the 99-percent confidence interval.

Nore: Dash indicates that the variable was not used in the model.

The blue-collar job fields are, on the face of
it, more likely to have workers who belong to
labor unions and, consequently, have greater
occupational expenses because of union dues.
The tobit analysis reflects this intuitive assump-
tion: both operators and laborers and craft and
repair workers spend more on occupational ex-
penses, which include the cost of union dues,
tools, uniforms, and licenses and permits, than
do managers and professionals.

To determine the overall significance of oc-
cupation on the dependent variables, a chi-

square test was performed using the logarithm
of the likelihood of the variables in the restricted
model (without the occupational dummies) and
the logarithm of the likelihood of the variables
in the full model. (See table 4.) The test was
also performed for education. The results
demonstrate that, after control for education and
income, occupation has a significant effect on
the probability and level of incurring expenses
for food, housing, transportation, personal care
products, reading materials, occupation-related
items, and personal insurance and pensions.

Monthly Labor Review December 1989 41



Robert Cage

Robert Cage is an
economist formerly in the
Division of Expenditure
Surveys, Bureau of Labor
Statistics. He was assisted
by Carolyn White, an
economic assistant in the
same division, in preparing
this article and in
constructing tables and
charts for it.

Spending differences
across occupational fields

Multivariate analysis reveals

that income is the most significant factor
in determining levels of various expenditures;
occupation and education also play a role

ince the late 1940’s, the U.S. labor force
has undergone several substantial changes
affecting its composition and structure.
Female participation has grown rapidly since
World War II, and consequently, there are more
dual-earner families.' Growth of the suburban
population has contributed to increased com-
muting time to and from work. And although
average weekly work hours have decreased for
the civilian labor force, the average American
household has less time available for leisure
activities:
. . . the amount of leisure time enjoyed by the
average American has shrunk 37 percent since
1973. Over the same period, the average work
week, including commuting, has jumped from 41
hours to nearly 47 hours. In some professions,
predictably law, finance, and medicine, the de-
mands often stretch to 80-plus hours a week. Vaca-
tions have shortened to the point where they are
frequently no more than long weekends. And the
Sabbath is for, what else, shopping.?

According to the same source, the course of
the rat race has led to less time available for
family activities, increased consumption of
service-oriented items, and more labor-saving
gadgetry. This increased demand for services is
reflected in the change in employment by occu-
pational group. Projections by BLs indicate that
employment by the year 2000 wiil increase most
for service workers and least for operators and
laborers. (See table 1.)

Does the shift in employment towards service
workers have any implications regarding con-

sumption? In other words, does occupation as a
demographic variable affect the level and distri-
bution of consumption for any given expendi-
ture category? Do white-collar workers, for
example, spend differently than blue-collar
workers, or are these distinctions becoming an-
tiquated? Does one’s working environment have
a measurable effect on one’s perception of so-
cial class, formation of tastes and preferences,
and, consequently, spending patterns? The pur-
pose of this article is to compare and contrast
various occupational groups to investigate what
effect, if any, occupational status has on family
expenditures for certain goods and services after
controlling for income, education, number of
earners, and other demographic variables.

Background

It is well documented that, throughout a per-
son’s life cycle, permanent income has a sig-
nificant effect on consumption.? Age also
influences consumption with respect to the na-
ture of the bundle of goods and services an in-
dividual consumes. As one ages, tastes and
preferences are likely to change, as are such
needs as medical care and transportation. Not
much attention has been given to the study of
the effects of occupational status, a means of
obtaining income, on consumption behavior.
Perhaps this is because such a study is more a
sociological question of tastes and preferences
than an economic question of constraints on
opportunities.

Monthly Labor Review December 1989 33

ns 1 iad


Spending Differences by Occupation

ysis. Household composition had a significant
influence on food expenditures as well, with
single consumer units more likely to have
higher expenditures for food away from home
while husband-and-wife families with children
are more likely to eat at home. Education had
little effect on the probability or level of food
expenses.

All occupational groups had significant neg-
ative coefficients for housing, indicating the
greater likelihood of managers and_profes-
sionals incurring higher expenses for housing.
Those families headed by someone with some
college or more educational attainment also

are more likely to have higher housing
expenditures.

The much larger proportion of expenditures
allocated to transportation by the blue-collar
groups and service workers is reflected in the
significantly positive parameter estimates for
transportation expenses by these groups. Recall
that these workers own more vehicles and are
more likely to use them in their business.

All groups were less likely to incur expenses
for reading materials and for personal insurance
and pensions compared to managers and pro-
fessionals. This is consistent with the shares
analysis.

‘See footnotes at end of table.

40 Monthly Labor Review December 1989

Table 3. Tobit regression analysis: coefficients of estimation of causal variables
Food at Food away ‘Trans- Enter-
ftom home | trom home | Housing | Apparel | sorstion | tainment
F-statistic - 211.0 52.9 203.5 76.5 215.2 60.2
Chi-square statistic. “2585.6 816.9 *2939.1 "1525.5, *3160.2 “916.6
Adjusted R-squared statistic 0.47 0.18 0.51 031 0.53 0.20
"133.88 "941.28 30.75 “1791.40 ~106.91
"0201 **.2061 "0499 "4250 **.0666
Occupation (managersprtessionals) |
Technicians, sales, administrative suppor . ~16.03 "31.64 *~125.11 ~ 12.08 259.12 13.82
‘Service workers . “*—47.90 ~23.62 28.54 “44147 23.27
Production, craft, and repair 20.95 *~36.55 32.33 "341.28 25.20
‘Operators and laborers 4.94 *-29.01 7.85 "424.93 57.44
Education (some college or more):
‘Some high school or less . *-29.70 *-32.69 "186.94 “38.19 "489.53 11.68
High school graduate . ~13.02 1.66 "~182.24 *-34.71 "338.27 5.14
Household composition (husband and wife oni
Single... eee eee ee "~203.85 "30.91 *=148.29 31.55 "392.48 66
Husband and wife with ‘children 99.90 "50.35 "206.45 4.45 —183.76 —15.61
‘Other families . sees . -3.93 *-41.23 106.88 *-52.68 60.80 20.35
Urbanization mn (urban)
Rural Peete eee ee ete tere eee e ee nne “"-83.37 "42.56 “406.26 "58.50 “473.43 27.52
Waxing ‘tenure rome:
sees 20.81 ~18.53 “*-258.57 "29.24 -7.00
Age of reference person squared... eee "0338 ~.0054 *~.0382 *~,0122 *-.0243
‘Consumer unit size squared . "11.28 -1.03 2.65 1.69 98
Race of reference Person (whitey:
Black ...... *-38.53 *~42.04 37.96 "50.96 139.33 44.31
35.05 3.68 76.00 48.29 154.30 24,22
- - - "66.06 -
= - - -28.31 - =
- - _- -11.73, -
_ - 73.17 7.85 52.92 -
_ - 17.57 5.16 50.47 -
- _ "186.46 -11.43 *-187.20 _
_ > 52.63 "101.88 111.00 -
*39.08 24.12 4.33 12.42 **—400.54 39.07
- - - 200.56 -



Spending Differences by Occupation

tures for food, which is significantly higher than
the average of any other salaried and wage
earner groups.

Thus, the cost for food as a share of total
expenditures is lower for managers and profes-

sionals and higher for service workers and oper-
ators and laborers. Because service workers re-
port the lowest average income, the data support
Friedrich Engel’s contention that a higher share
of the income of poorer households goes to food

Table 2. Selected characteristics and percent distribution of total expenditures
of salaried workers and wage earners, Consumer Expenditure
Survey, interview survey, 1986-87

Managers | Technicians, | sevice pero Operators
tem and salesworkers, workers repair and
Professionals | administrators workers laborers.
Number of consumer units (thousands) 20,128 16,686 6,503 6,449 12,610
Percent of total salaried workers and
Wage CAMENS eee eee 323 268 10.4 103 202
Consumer unit characteristics:
Income before taxes .. $42,021 $28,590 $17,848 $29,289 $24,172
Income after taxes ©... 2... cece eee ee 37,620 25,661 16,449 26,491 22,047
‘Average number of persons in consumer unit 26 26 26 a4 3.0
‘Age of reference person ..... coves 409 38.9 402 38.6 39.0
Average number of vehicles 23 20 1.6 27 24
Percent reporting:
Homeownership. 67 55 43 66 58
Black... 7 " a1 7 12
Education level:
Elementary (1-8)... 2 2 8 10
High school (9-12). 4 54 61 66
College... eee 4 34 30 23
Estimated market value of owned home $45,731 $29,149 | $45,111 | $33,827
‘Average annual expenditures $24,772 $17,311 | $25,185 | $20,828
FOOd 2. eeeeee eee 14.33 6.51 15.04 "16.20
Food at home . . 9.65 "11.96 "10.94 "12.10
Food away from home 4.68 454 "4,09 “4a
Alcoholic beverages 1.29 1.28 1.18 1.29
Housing ......... 30.22 31.05 "27.96 "28.55
Owned dwellings 9.80 "8.01 *9.60 "B71
Rented dwellings .. "6.61 *9.54 "5.05 "6.31
Other lodging 1.81 “1.01 1.49 "1.07
‘Apparel and services . .. 5.55 5.18 “4.65 “an
Men, 16 and over 1,07 85 89 92
Women, 16 and over .... 1.95 1.64 "1.33 11.38
Transportation : 21.23 20.91 "28.73 "23.05
Vehicle purchases... 10.05 9.67 “11.61 10.62
Gasoline and motor oil... 2.2... 3.89 4.44 “4.99 "521
Public transportation 111 1.04 “59 "62
Health care . 3.66 *4.00 351 3.62
Entertainment wee 5.32 491 "439 5.49 476
Fees and admissions 2... "1.67 1.43 “4.02 *.98
Television, radios, and sound equipment... . 1.65 1.61 1.83 1.85
Personal cate... eee eee eee 84 92 99 85
Reading 66 58 50 50
Education ....... weve cceeeeees "1.86 1.46 1.29 +90
Tobacco products and smoking supplies “87 24 at 1.58
Miscellaneous 2... ee ee eevee 1.88 2.10 172 2.07
Cash contributions 2.0.0... "3.56 2.63 "2.05 "1.94
Personal insurance and pensions "11.43 10.17 "8.72 9.95
Life and other personal insurance 1.22 117 1.18 1.25
Retirement, pensions, Social Securit "10.20 9.00 "7.54 "8.69

* | Chi-square test of proportions was significant at the 99-percent confidence interval for this line item. See N. M. Downie and R. W.
Heath, Basic Statistical Methods (New York, Harper and Row, 1974), pp. 200-01.

36 Monthly Labor Review December 1989



im |

considered blue collar. Service workers have
developed into a third distinct group comprising
such occupations as firefighters, police officers,
food preparation and service workers, dental as-
sistants and health aides, and cleaning and per-
sonal care service workers.

Analysis was restricted to salaried workers
and wage earners and excluded self-employed
workers for two reasons. First, salaried workers
and wage earners comprise 91 percent of all
workers in the sample, and second, many differ-
ent occupations are lumped together in the self-
employed category. Thus, it would have been
difficult to interpret any results with respect to
differences caused by specific occupational
fields. Two years of data were used to ensure a
large enough sample size for each of the groups
under investigation.

Table 2 shows some selected characteristics,
including income and percent distribution of
total expenditures, for the five salaried and
wage earner groups. Managers and profession-
als make up 32 percent of all salaried workers
and wage earners, with service and craft and
repair workers having the smallest representa-
tion, at about 10 percent each. The blue-collar
groups of craft and repair workers and operators
and laborers have the largest consumer units
with an average size of 3.1 and 3.0 persons,
respectively, while the households of white-
collar and service workers are smaller, with an
average of 2.6 persons each.

Chart 1 illustrates the relationship between
income and education for the five salaried and
wage earner groups. Although there is a trend
which suggests that income is greater as educa-
tional level increases, the two variables are not
perfectly correlated for the groups. Managers
and professionals represent the greatest devia-
tion from the mean, with an average income of
$42,000 and 80 percent having obtained a col-
lege education. Although both white-collar
groups are characterized by higher educational
attainment, average income for technicians,
salespeople, and-administrative support person-
nel is less than that of craft and repair workers.

A study by the Conference Board and the
Census Bureau on discretionary income, or
“spare cash,” reports similar findings. Accord-
ing to that study, the second strongest determi-
nant of discretionary income, after household
income, is education. Certain occupations are
associated with high levels of education and in-
come. More than half (53 percent) of all house-
holds headed by a person in a professional or
managerial job have discretionary income. But
still, 27 percent of all households with discre-
tionary income are headed by an individual in a
blue-collar, farm, or service job.'?

The spending power of the five groups is also
represented by the market value of an owned
home and the ratio of total expenditures to in-
come. Managers and professionals have the
highest percentage of homeownership, while
the majority of service workers are renters. Al-
though managers and professionals and craft
and repair workers have roughly the same per-
centage of homeownership (about 66 percent
each), the estimated market value of an owned
home for managers and professionals is almost
twice as high.

The ratio of total expenditures to income be-
fore taxes is around 87 percent for technicians,
salespeople, and administrative support person-
nel, as well as for both blue-collar groups, while
the ratio for service workers is considerably
higher at 97 percent and for managers and pro-
fessionals, considerably lower at 80 percent.
Although this does not necessarily indicate the
level of saving for any of the groups, it empha-
sizes the higher income of managers and profes-
sionals. Almost all of the average income after
taxes of the other groups, especially service
workers, is absorbed by the expenditure cate-
gories, while only 80 percent of managers’ and
professionals’ average income is used for that
group’s expenditure needs. This indicates that a
greater share of the average income of managers
and professionals is available for investments,
such as second or vacation homes, financial se-
curities, or savings. Property income, of which
a large part is interest and dividends, is greatest
for managers and professionals, supporting this
conjecture.

Shares analysis

In terms of average dollar amounts, managers
and professionals spend more on all major ex-
penditure categories. The percent distribution of
total expenditures is more useful, however, in
determinining any difference in consumption
patterns across the five occupational groups. A
chi-square test of proportions was used to meas-
ure the significance of mean expenditures as a
proportion of total expenditures for one group
compared to the average of the others. This
was done for all major expenditure categories
and for more specific items for which occu-
pational status was hypothesized to influence
expenditures.

The results of the test are listed in table 2. An
asterisk (*) on an expenditure category indicates
that the share of total expenditures distributed to
that category was significantly different, at the
99-percent confidence interval, from the aver-
age of all other groups. For example, service
workers spend 16.5 percent of total expendi-

A greater share
of the average

income of

managers and
professionals is
available for
investments,

financial
securities,
savings.

or

Monthly Labor Review December 1989 35

nr i ied


Even after
controlling for
income and
education,
expenditures for
certain goods and
services are
affected by
occupational
factors.

Spending Differences by Occupation

In developing variables for their analysis of
consumer demand, H. S. Houthakker and Les-
ter D. Taylor observed that the effect of oc-
cupation, or more generally, social class, on
consumption is usually less obvious than that of
other factors.* Of course, developing an appro-
priate measure of social class is inherently dif-
ficult. Sociologists define the “classes” as social
strata sharing essential economic, political, or
cultural characteristics. In this regard, income,
education, and occupation are arguably factors
which influence one’s position in society:
income determines economic power or bud-
getary constraints, while education and occupa-
tion contribute to the formation of preferences
and cultural characteristics.

Income, education, and occupation, how-
ever, are interrelated. Human-capital theorists
have long argued that individuals can, by fore-
going earnings and obtaining higher levels of
education, augment the quality of their labor
services in such a way as to raise their future
market value.’ It is typically believed, there-
fore, that more educated individuals attract
higher wages.° Also, at least one authority
maintains that on-the-job training may be just as
significant as formal schooling in determining
an individual’s labor productivity and market
value.”

An in-depth technical discussion of the re-
lationships among education, occupation, and
income is beyond the scope of this article.
However, distinguishing education levels and
income levels among different occupational
groups is important in understanding the spend-
ing patterns associated with various occu-
pations. It may be that members of the same
occupational group are characterized by similar
educational attainment and income level. For
example, intuitively, one might expect
managers and professionals, or white-collar
workers in general, to have higher incomes and
higher levels of education than blue-collar
workers such as operators and laborers. If so,
the question then would be whether these differ-
ences account for any differences in spending
between the two groups. It is hypothesized in
this article that, even after controlling for in-
come and education, expenditures for selected
goods and services such as food, transportation,
housing, reading materials, entertainment,
occupation-related items, and apparel are di-
rectly affected by the circumstances surround-
ing one’s occupational environment and job
field. An analysis of the distribution of total
expenditures will be used to determine any dif-
ferences in spending behavior among different
occupational groups, while a multivariate tobit
regression (see later) will be employed to pin-

34 Monthly Labor Review December 1989

point the effects of occupational status alone on
the expenditures.

Data and demographics

The BLS Consumer Expenditure Survey pro-
vides an excellent source of household data for
cross-sectional studies. The survey collects ex-
penditure data which provide a continuous flow
of information on the buying habits of American
consumers. The data are used in a wide variety
of research endeavors by Government, busi-
ness, labor, and academic analysts. The survey
consists of two components: a diary survey and
an interview survey. The data used for this arti-
cle are from the 1986 and 1987 interview
surveys.®

It is important to note that the reference group
in the Consumer Expenditure Survey is a con-
sumer unit? and that the income and expenditure
data are those of the entire consumer unit, or
household. Data for individual or personal ex-
penditures are not available. The sample of con-
sumer units was divided into occupational
groups based on the occupation of the reference
person. !°

Five occupational groups were compared:
managers and professional specialists; techni-
cians, sales, and administrative support; preci-
sion production, craft, and repair workers;
operators and laborers; and service workers.!!
Specific occupations are classed into these
groups by the Census Bureau and are commonly
used in producing labor market data at BLS.
Managers, professionals, technicians, and sales
and administrative support personnel are gener-
ally considered employed in white-collar fields,
while precision production workers, craft and
repair employees, operators, and laborers are

Table 1. Employment by occupation,
1986, projection to 2000
(moderate alternative), and
percent change

[Number in thousands]

Percent
Occupation 1986 | 2000 | change
17,536 | 22,917 | 30.7
24,121 | 30,808 | 27.7
36,183 | 43,594] 20.5
Precision production, craft,
and repair...............] 13,924 | 15590] 12.0
Operators, laborers, farmers,
fOr@StrY eee eeee 19,556 | 20,117] 29

SouRCE: George T. Silvestri and John M. Lukasiewicz, “A
look at occupational employment trends to the year 2000,"
Monthly Labor Review, September 1987, pp. 46-63.