Title: Occupations and compensating wages for unemployment risk
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OCCUPATIONS AND COMPENSATING
WAGES FOR UNEMPLOYMENT RISK








By

Cynthia D. Stephens


A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE
UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE
REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY



UNIVERSITY OF FLORIDA


1992




























Copyright 1992

by

Cynthia D. Stephens















ACKNOWLEDGMENTS

The research reported in this paper was sponsored by

the firm of Deiter, Stephens and Durham. The author thanks

Lawrence Kenny, David Denslow, Douglas Waldo, Stephen

Donald, Robert Emerson, John Deiter, Stephen Durham and the

participants in the Micro-Macro Empirical Economics

Workshop for their valuable comments and suggestions. In

addition, most sincere thanks go to Elizabeth Fortier for

editorial assistance and to my parents and family for their

support.


iii
















TABLE OF CONTENTS
Page

ACKNOWLEDGMENTS.......................................iii

ABSTRACT...............................................vi

CHAPTERS

1 UNEMPLOYMENT RISK.............................. 1

2 OCCUPATIONAL WAGE..............................9

Long-Run Equilibrium........................... 9
Short-Run Equilibrium.........................14
Industry Wage Differentials...................17
Evidence of Occupational Wage Differentials...21
Conclusions...................................... 22

3 OCCUPATIONAL RISK MEASURES ....................23

4 EMPIRICAL STUDY..................................51

5 TEST RESULTS...................................59

Risk Measures..................................61
Growth Rates................................... 64
Unemployment Rates.............................67
Geographic Location...........................67
Fraction Female ...............................68
Fraction Employed in Industry..................68
Education and SVP.............................70
Experience..................................... 70
GED. .................................. .. 71
Physical Demands..............................71
Environmental Conditions......................72
Hazards.......................................... 72
Unionization..................................... 73
Fraction Nonwhite..............................73
Heteroskedasticity...........................73
Conclusions.................................... 76

APPENDICES

A INDUSTRY CLASSIFICATIONS......................79











B OCCUPATIONS AND INDUSTRY OF
LARGEST CONCENTRATION .......................81

C RISK MEASURES FOR DETAILED OCCUPATIONS........92

D VARIANCE RISK MEASURE BY
DETAILED OCCUPATION......................... 101

E ORDINARY LEAST SQUARES REGRESSIONS...........111


F GED SCORE REASONING..

G GED SCORE MATH........

H GED SCORE LANGUAGE...

I SVP SCORE..............

J DEXTERITY..............

K STRESS..................

L STRENGTH...............

M EXTREME COLD...........

N EXTREME HEAT...........

O EXTREME WET............

P EXTREME NOISE..........

Q VIBRATION..............

R ATMOSPHERIC CONDITIONS.

S MECHANICAL EQUIPMENT...

T SHOCK ..................

U HEIGHTS................

V RADIATION..............

W EXPLOSIVES.............

X TOXINS .................

Y OTHER HAZARDS..........

BIBLIOGRAPHY....................

BIOGRAPHICAL SKETCH.............


...................... 133

...................... 146

...................... 159

......................172

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...................... 187

...................... 190

.................... 203

...................... 205

..................... 209

...................... 214

...................... 222

...................... 230

...................... 236

...................... 238

..................... 239

...................... 240

...................... 241

..................... 242

.............. ..... 243

..................... 245

..................... 248















Abstract of Dissertation Presented
to the Graduate School of the University of Florida in
Partial Fulfillment of the Requirements for the Degree of
Doctor of Philosophy





OCCUPATIONS AND COMPENSATING
WAGES FOR UNEMPLOYMENT RISK





By

Cynthia D. Stephens

August 1992





Chairman: Dr. Lawrence Kenny
Major Department: Economics


The study tests the theory that occupations with

employment opportunities concentrated in an industry (or

similar industries) require a compensating wage relative to

occupations with diversified opportunities across many

industries. An occupational risk measure was constructed

that accounts for the variance and covariance of industry

employment. This risk measure was tested and earnings were

found to be positively related to occupational employment

risk.















CHAPTER 1
UNEMPLOYMENT RISK

Most studies of the effect of unemployment risk on

wages do not control for differences between occupations.

Since individuals choose education and training for an

occupation rather than an industry, this study will explore

compensating wage effects from an occupational standpoint.

The observed wage paid to an occupation reflects its

contribution to the employer's revenues, in addition to the

preferences of the individual providing the labor services.

In equilibrium, the wage is simultaneously determined by

these demand and supply factors. If all occupations were

identical in skill requirements, enjoyment and risk, wages

would not vary between occupations, but clearly these

features do differ. Individual preference is a factor.

Consider, for example, differences between occupations in

exposure to hazards. Occupations which are more hazardous

are compensated at a higher rate than those lacking such

risks. The increased wage level reflects individual

preference for safer occupations and the resulting demand

for a compensating wage in a riskier environment.

Wages also reflect unemployment risk, a factor which

differs between occupations. To see this, suppose that

utility is a function of consumption and leisure, as

































ME --



0I
.OJ


oI
0 I


UU


SI I



I I



0
T T
L Leisure Time


Figure (1-1)










depicted in Figure 1-1. Competition among firms will make

a worker indifferent between occupations offering the

combinations of consumption and leisure depicted by the

indifference curve "U." This individual would be

indifferent in selecting between an occupation offering (T-

TL) hours of work each week which provided consumption at

the level of Me and one with some unemployment if it

yielded consumption at the level of Mu when unemployed (and

enjoying T hours of leisure) and Me when employed. If

wages are paid only during employment weeks and the number

of weeks unemployed is equal to the number of weeks

employed, the occupation with unemployment must have Mu

higher wages than the occupation with no unemployment risk

for the individual to be indifferent between the

occupations. The formula which represents this

relationship is described by Equation 1-1. The occupation

with no unemployment risk is denoted by superscript "A" and

the occupation with unemployment risk is denoted by

superscript "B."

MBe = (Muu)/(52-u)+MAe Equation 1-1

As the unemployment period (u) lengthens, compensation for

occupation B (MBe) must be higher in order for an

individual to be indifferent between occupations A and B.

Others have estimated the effect of risk on wages.

King (1974) examines the relationship between occupational

choice and risk aversion. He defines two types of earnings

risk: How an individual will fare relative to others in










the same occupation and how the occupation fares in

response to structural and business cycle risk.

King tests for wage differentials due to the first

type of risk. He finds that riskier occupations offer

higher mean incomes and that individuals from wealthier

families choose the riskier occupations. King's measure of

risk is the variance of earnings within an occupational

classification.

In exploring wage differentials for unemployment risk,

Adams (1985) tests for compensating wage differentials

based on geographic and industry unemployment differences.

The regression model tested is outlined in Table 1-1.


Table 1-1
Adam's Risk Model

Log Hourly Wage = f(U,R,C,S,E,H,M,O,R,X,T,L,D,N,Y,G)

Variable Definitions:
U State Unemployment Rate O Union Membership
R State Unemployment Insurance R Race
Replacement Ratio (UI X Sex
Benefits/Employed Earnings) T City Size
C Current Industry L Climate
Unemployment Rate D Durable Goods
S Years of Schooling N Nondurable Goods
E Years of Experience Y Industry Sensitivity
H Health Limitation G Industry Growth Rate
M Marital Status


Differentials are divided into permanent and

transitory components by controlling for state unemployment

insurance benefits. The data comes from Waves 4-10 of the

Panel Study of Income Dynamics, which contain industry

identifiers for seventeen industries. The calculation of










the growth rate begins with a regression of log GNP on

Time, defining the predicted value as Trend and the

residual value as Deviation.

Adams calculates regressions of log Industry Value

Added on the Trend and Deviation values. The coefficient

on Trend is the industry growth variable, and the

coefficient on Deviation is a variable measuring the

industry's sensitivity to the business cycle. The results

show that wage differentials are related to long-run

unemployment differences between industries. Wages are

also found to be higher in industries which are cyclically

sensitive. Adams does not measure the effects of any risk

unrelated to the business cycle.

Li (1986) also measures the effect of unemployment

risk on the wage differential between industries. Li tests

whether wages are a function of both the systematic

(cyclical or market) unemployment risk and the

nonsystematic (industry-specific) risk. Li uses the Panel

Study of Income Dynamics covering white male heads of

household over the period 1969-1973. To measure the

systematic risk, individuals are grouped into fourteen two-

digit level industries. Those in the same industry are

assumed to face the same systematic and nonsystematic risk.

The hours worked value is regressed on the prior period's

hours and on the rate of change in real GNP. A pooled

industry regression and a full sample regression are

calculated. The predicted hours value from the industry










regression is the industry employment norm, and the

predicted value from the full sample is the economy-wide

norm. The residual variances (MSE) from the industry

regressions are the estimate of the industry-specific,

noncyclical risk of unemployment. The cyclical risk (COV)

is estimated by the residual variance derived from a

regression of the difference between industry predicted

hours and economy-wide predicted hours on the rate of

change in real GNP. Both MSE and COV are divided by the

mean number of hours in the industry for the empirical

measures of risk. Li's test of compensating wages is

outlined in Table 1-2.


Table 1-2
Li's Risk Model

Log Wage= f(E,D,PCOV,PMSE,EX,R,0)

Variable Definitions:
E-Education PMSE-Noncyclical
D-Difference in Hours from Unemployment Risk
Average Economy Hours EX-Experience
PCOV-Industry Cyclical Systematic R-Regional Dummies
Unemployment Risk O-Occupational Dummies


Positive compensating wage differentials are found for

both measures of risk. Differences in industry

unemployment risks can explain 14-41% of the observed

differences in wages. The noncyclical risk compensating

wage differentials are much higher than the cyclical risk

compensating wage differentials.

Li's study has two main deficiencies: It is concerned

with the industry of employment, yet only fourteen










industries are isolated, and the control for occupational

differences is broadly classified at six occupational

groupings. If the sample of occupations in the Current

Population Survey (CPS) data was as large as the sample of

industries of employment, Li's model could be reestimated

with occupations. However, observations for three-digit or

four-digit classification of occupations are too infrequent

for consistent estimates with the annual CPS database.

Both studies document wage differentials for

unemployment risk with measures based on the cyclical

behavior of the industry in which the individual is

employed. The industry data approach is used because

annual data are reported by industry rather than by

occupation. However, specific occupational risk appears

more relevant to an individual's human capital decision and

is therefore worthy of study. An occupation's unemployment

risk should reflect the combined unemployment risk of all

industries in which the occupation is employed. If

individuals require a compensating wage for unemployment

risk and occupations differ in their exposure to this risk,

then the wage differential between occupations should be

measurable. If an occupation is closely linked to an

industry, the cyclical and long-run unemployment risks of

that occupation reflect the risks of the industry. If an

occupation has employment opportunities in many industries,

the unemployment risk should be diversified.










Since occupations differ in employment opportunities

between industries, occupations may be considered as having

varying degrees of diversification. The data indicate that

industry-diversified occupations, such as that of secretary

or accountant, are relatively low-paying occupations. For

example, in 1979, 35-year-old males with college degrees

working full-time in securities and financial services

sales occupations earned an average of $20.25 per hour.

Similar males employed as accountants and auditors earned

$13.16 per hour. This differential could be considered a

compensating wage for the concentration of security sales

occupations in the finance industry; economic theory would

suggest that stockbrokers require a wage premium to cover

future downturns in the finance industry which would expose

them to a period of unemployment.

This study provides documentation of the importance of

an occupational perspective in the analysis of wage

differentials, especially the effect of compensation for

unemployment risk. The method of calculation of industry

risk used by Li and Adams defines risk based on variances

in industry employment. Building on their methodology,

this study adds an occupational employment variance measure

and includes factors such as skill requirements and hazard

features in order to comprehensively analyze wage

differentials between occupations.
















CHAPTER 2
OCCUPATIONAL WAGE

The primary determinants of an occupation's wage must

be identified before any compensating wage effect due to

risk can be studied. Wages paid to an occupation are

fundamentally determined by supply and demand factors; wage

levels fluctuate in response to changes in the number of

qualified people who are seeking employment and changes in

industry demand for such skills. The wage paid to an

occupation is thus determined simultaneously by the forces

of supply and demand.

Long-Run Equilibrium

Long-run occupational wage differences are defined as

differentials which have no tendency to change unless there

is a change in the long-run demand or supply. In

equilibrium, if the wage paid to one occupation exceeds the

wage paid to another, the difference exists due to

differing supply and demand characteristics for the

occupation.

In general, the labor supply for all occupations is

determined by individual preferences for work versus

leisure and the availability of nonwage income. The labor

supply to a particular occupation, however, is influenced

by the cost of acquiring the necessary skills to enter that










occupation. These costs include required education and

specific vocational preparation. If there is an initial

investment cost to train for a profession, that

occupation's wage would be higher than an occupation with

little or no initial training cost in order to generate a

return on the skill investment.

The nonwage aspects of the occupation, such as

prestige, health or safety risks and income variability,

also influence the supply of individuals to an occupation.

In long-run equilibrium, wages adjust to levels that enable

individuals to be indifferent in selecting occupations.

The resulting wage differentials are defined as

compensating wages for nonwage features.

In addition, market imperfections which restrict the

supply of workers to an occupation will result in wage

differentials. Barriers to entry into an occupation can be

established by licensing or certification requirements and

union control of job placements.

The long-run demand for labor in an occupation is

determined by the forces which affect the profit-

maximizing combination of a firm's capital and labor.

Therefore, changes in the demand for a firm's product, in

the cost of other production resources, or in the firm's

production technology will influence the demand for a

particular occupation.

The long-run supply and demand relationship is

illustrated in Figure 2-1. If individuals have identical










preferences, then the long-run equilibrium wage for an

occupation, denoted by w*, reflects characteristics of this

occupation relative to other occupations. For each of the

L* employed in the occupation, the wage compensates for

human capital brought into the occupation in addition to

any characteristics of the occupation which require a

compensating wage. Thus, the equilibrium wage includes any

compensation for the probability of cyclical or seasonal

unemployment.

Numerous studies of equilibrium wage differentials

primarily assume that the current wage is the long-run

equilibrium wage. These studies measure wage differentials

between individuals based on differences in skill levels

and educational accomplishments, commonly referred to as

human capital stocks, and in nonwage features of their

current industry or employer.

As Gary Becker (1975) argues, human capital is an

important determinant of wages. Human capital theory

proposes measuring an individual's stocks of human capital,

categorized as general and firm-specific skills. These

stocks can be considered capital on which individuals earn

a return. When an individual changes employer, firm-

specific skills do not transfer and the new wage rate is

determined by general skills transferred by the individual

which are applicable to the new employer.

Individuals can be envisioned as possessing other

classes of human capital skill stocks. Skills may be


































SLR














DLR


Quantity of Labor


Figure (2-1)


Wage










sorted by employer, occupation, or industry. An individual

facing a human capital investment decision, such as the

choice of a college major or a change of employer, is

evaluating an additional investment in the occupation or an

employer skill investment. Individuals do not typically

evaluate an investment of human capital in an industry

independent of an investment in an occupation or employer.

More commonly, individuals invest in education or training

specific to an occupation. Therefore, a comprehensive

analysis of wage differentials between occupations requires

information about differences between occupations. In

considering skills from the perspective of individual

investment, the relevant analysis concerns human capital

investments in occupations and the expected return.

A similar analysis was done by Shaw (1984). Using the

National Longitudinal Survey of men aged 14-24 over the

period 1966-1975, Shaw studies wages as a return on

occupational investment at a three-digit level of

occupational classification.

Shaw calculates the total occupational investment as

the sum of stocks of specific occupational investment

weighted by the transferability of skills between

occupations. As a first approximation of specific

occupational skills, Shaw employs information from the

Dictionary of Occupational Titles. The Standard Vocational

Preparation (SVP) score is derived from a nine-level scale










which indicates the amount of time necessary to acquire the

skills necessary to perform the job at an average level.

The second measure of specific occupational skill is the TQ

measure from the Michigan Panel Study of Income Dynamics.

This is the scored response to the question "How long would

it take an average person to become qualified in a job like

yours?" Transferability is measured by similarity of

occupational mobility patterns. Shaw finds that an

occupational investment measure that takes into account the

level of investment brought from previous occupations is a

stronger determinant of income than work force experience.

Short-Run Equilibrium

The relationship between short-run supply and demand

is depicted in Figure 2-2. A new level of long-run demand

for an occupation, which is represented by the shift of the

demand curve, denoted by D1LR, may result in an increase in

wages if no trained, unemployed individuals are available

to meet the excess demand. In the short-run, the labor

supply curve is steeper than in the long-run. Labor

markets are not as efficient as goods markets. If one has

both the skills to compete in an occupation that is in

great demand and the ability to move to the job site, one

will earn rents as an early adapter until others relocate

and drive the wage down. Additionally, a higher wage is

required to motivate established workers in other

occupations to forgo the return on human capital specific

to the occupation and enter an alternative occupation.










Similarly, a higher wage is required to motivate

established workers in other firms to forgo the return on

human capital specific to a firm and seek employment with a

new firm.

If time is required for individuals to acquire the

skills necessary to perform an occupation and there is an

insufficient number of qualified unemployed individuals,

the short-run supply curve would be vertical for any wage

greater than w The dynamic effects of changes in demand

for an occupation are evident in unemployment rates for the

occupation. Current unemployment rates should be lower

than average in occupations which have experienced a recent

increase in demand, since the existing unemployed move to

fill the vacancies. Over time, individuals receive the

required training and enter the profession. The short-run

supply then shifts to the right over time, driving the wage

down toward w .

In order to induce individuals to leave an occupation,

the wage must fall below w In Figure 2-2, the decrease

in long-run demand for an occupation is depicted by a

downward shift in the demand curve to D2LR. The

equilibrium wage falls, the number of individuals employed

decreases, and the unemployment rate in the profession is

higher than average. Individuals in this occupation will

experience unemployment while they change occupations.

In summary, there is a positive relationship between

wages and the number of employed individuals. A growing














SSR


Wage


SLR


DLR


DLR
LR


Quantity of Labor


Figure (2-2)


\y










occupation will command a higher wage. This is necessary

to bring about an unusually high rate of entry into an

occupation. The relationship between unemployment rates

and wages is inverse, however. The wage rate decreases as

unemployment increases. Unemployed individuals must

relocate in order to find alternative employment or be

retrained.

Wage levels and short-run demand shifts due to a

business cycle or seasonal demand are not necessarily

correlated. If the long-run wage w already reflects

compensation for the probability of short-run unemployment,

there is no need to further adjust the wage level to induce

individuals to enter or leave the profession.

If an occupation is expanding in the long-run and has

high seasonal unemployment, fluctuations in employment are

absorbed through changes in the rate of new hiring and

layoffs. If the occupation is contracting in the long-run,

the occupation will have a slower rate of new entry.

Industry Wage Differentials

Research on wage differentials between industries

includes studies compiled by Krueger and Summers (1988) and

Katz and Summers (1989). These studies use the CPS data to

test for wage differentials between industries. The

findings include the observation that some industries

(e.g., mining and petroleum) consistently pay higher wages

than other industries (e.g., food and beverage

establishments and household services). This ranking of










industry wage differentials holds over time and between

large classes of occupations, even with controls for

unions, education, and experience. The results hold even

between countries. The inclusion of fringe benefits

increases rather than decreases the industry wage

differential.

Krueger and Summers attribute these industry

differentials to efficiency wage practices in some

industries. Efficiency wage practices should be examined,

because they account for involuntary unemployment. The

literature defines "efficiency wages" as the payment of a

wage higher than the value of marginal product (VMP). The

firm benefits because the higher wage discourages shirking,

excessive turnover or malfeasance, all of which increase

the firm's costs. Firms "share rents" with workers;

employees are motivated to perform in the firm's best

interest due to the risk of unemployment if their behavior

results in dismissal. Current unemployment must exist in

order to make dismissal an effective deterrent. Without

current unemployment, the employee would have the option of

finding a position at another firm.

Current unemployment is not required in other

compensation schemes in which the wage paid differs from

the VMP. Lazear (1976) proposes an age-dependent wage

profile in which an implicit wage contract exists between

the firm and the employee. The employee is paid a wage

lower than the VMP early in his or her career and is paid a










wage in excess of the VMP at the end of his or her career.

Essentially, employees post a performance bond with the

firm while working for a wage lower than the VMP. The

employee is repaid in the form of higher wages at the end

of his or her career only if job performance is

satisfactory to the firm. Otherwise, the employee is

dismissed and the bond is forfeited. The loss of the bond

is the deterring force in this case, rather than the risk

of unemployment.

Becker and Stigler (1974) also describe a model in

which wages are paid in excess of VMP. If the opportunity

for undetected malfeasance is high, as in the case of a

police officer accepting a bribe, the employee should be

compensated at a wage higher than the potential earnings in

an alternative position. The loss of the higher earnings

stream reduces the expected benefits from malfeasance after

weighting for the probability of detection resulting in

dismissal. In this model, unemployment is not preventing

the malfeasance; it is prevented by the risk of losing the

higher earnings stream.

These wage theories are not easily tested, since data

on both the firm and the employee are required to compare

wages and costs of shirking, malfeasance and turnover, and

comprehensive data sets of both firms and employees have

not been constructed. However, the main implication of the

efficiency wage theory is that higher wages are paid in

industries with higher levels of unemployment. Indeed, the










theory is specifically designed to explain higher

unemployment. The effects of efficiency wages are not

easily isolated from the effects of compensating wages for

expected unemployment discussed in this study, since both

theories relate wages and unemployment. However, the

distinction is found in the difference between levels and

variability. For example, if a normal unemployment level

generated by efficiency wages is assumed to exist in an

industry, compensating wages for unemployment risk could

still be required if cyclical volatility is higher in the

industry.

Differences in wage levels between industries

documented by Krueger and Summers could also be attributed

to any factors that differ between industries, such as risk

characteristics. For example, many occupations in the

mining industry are not found in any other industry,

therefore, different levels of unemployment risk exist for

mining occupations than for industry-diversified

occupations. Thus, the efficiency wages attributed to the

mining industry are expected to be correlated with

occupational risk premiums.

A test of efficiency wage theory would exploit the

relationship between wages and unemployment rates. The

expected negative short-run relationship between wages and

unemployment rates would be tempered by the positive

relationship between wages and unemployment when efficiency

wages are paid. The positive relationship between wages










and unemployment proposed by efficiency wage theory can be

indirectly tested by estimating the coefficients on

unemployment rates in a wage model which concurrently

isolates compensating wages for unemployment risk. If

efficiency wages are paid, the coefficient on unemployment

would be biased positive when industry controls are not

included. A more negative coefficient on the unemployment

rate would be expected in the model with the industry

controls when efficiency wages are paid. Again,

significant coefficients on industry control regressors

could account for anything that differs between industries,

and the behavior of the coefficient on the unemployment

rate is a more precise test of the theory.

The association between industry and wages provides

indirect evidence in favor of efficiency wages only if

controls are in place for all differences in human capital

and job conditions. Murphy and Topel (1987) find that

individual characteristics may explain this industry wage

differential, i.e., the efficiency wage studies were unable

to adequately control for differences in human capital.

Evidence of Occupational Wage Differentials

Thaler and Rosen (1976) measure compensating wage

differentials between occupations associated with

differences in safety. Wages are found to be positively

related to the mortality rate within the occupation.

Many other wage studies control for occupation by

using large occupation classification dummies, usually










finding significant occupational wage differentials. For

the most part, no theory is proffered for, or tested by,

this approach.

Conclusions

In order to measure long-run wage differentials

between occupations which are caused by exposure to

employment risk, controls for other factors which would

also cause wage differentials between occupations are

required. These factors include controls for differences

in occupational skill levels, exposure to health and safety

risks, potential industry efficiency wages, and short-run

supply and demand fluctuations for the occupation.















CHAPTER 3
OCCUPATIONAL RISK MEASURES

In order to test the effect of employment

diversification on wages, a measure of employment risk must

be designed that takes into account the mobility of the

occupation between industries. Employment offers to recent

four-year college graduates by industry classification are

examined for evidence of occupational mobility between

industries over time. Of the nontechnical degree

graduates, those with General Business majors are recruited

by virtually all industries. Offers to graduates in

Accounting are primarily from the Public Accounting

Industry. Humanities and Marketing majors receive 40-50%

of their offers from the Merchandising Industry. Among the

Technical majors, Civil Engineers and Agricultural Sciences

majors are the most widely recruited. Chemistry and

Chemical Engineering majors are primarily recruited by the

Chemical and Petroleum Industries. Offers to Computer

Science, Industrial and Mechanical Engineering and Math

majors are 40-50% concentrated in the industry

classification which includes Aerospace, Electrical

Machinery and Computer Manufacturing. The majority of

offers to Civil Engineers are from the Construction and

Government Industries.










As an example of the effects of diversification on

employment opportunities, Civil Engineering majors were

exposed to a significant decline in offers from the

Government in 1981; however, increased offers from the

Construction Industry offset the reduction, producing no

net effect on the total number of job offers. In contrast,

Chemical Engineering majors are repeatedly exposed to

fluctuations in the Petroleum and Chemical Industries.

Between 1981 and 1983, total offers to Chemical Engineers

declined by 85%. This was driven by an 87% decline in

offers from the two primary industries. A summary of the

percentage of offers by industry classification is

presented in Table 3-1. Table 3-2 presents ten-year

averages and variances of the number of offers per graduate

and a Herfindahl-Hirschmann (H) concentration statistic for

each curriculum category. The H statistic is calculated as

the sum of square values of the industry shares of an

occupation's total employment, enabling the measurement of

the degree of concentration of an occupation on a 0 to 1

scale. Among the engineering fields, there is some

evidence that when a curriculum is less industry-

diversified, as represented by a high H statistic, the

employment opportunities are more volatile over time, as

represented by a higher variance in offers per graduate.

The correlation between H and the variance is .04 overall.

The correlation is much stronger (.97) for the engineering

fields.









Table 3-1

Percentage of Total Job Offers by Industry to Bachelor
Degree Candidates by Curriculum 1978-1988

CURRICULUM
Computer Marketing & General
Accounting Science Distribution Business
INDUSTRY
Public
Accounting 76% 3% 1% 4%

Banking, Finance,
& Insurance 3% 5% 8% 23%

Merchandising 2% 5% 47% 28%

Aerospace, Electronic
& Computers 3% 53% 12% 13%

Automotive & Mechanical
Equipment 1% 2% 3% 2%

Construction & Building
Materials 1% 1% 2% 2%

Chemical, Drugs &
Allied 1% 4% 7% 4%

Food & Beverage
Processing 1% 1% 7% 5%

Glass, Paper &
Packaging 1% 1% 3% 2%

Metals & Metal
Products 0% 1% 2% 2%

Petroleum & Allied
Products 6% 8% 3% 5%

Research &
Consulting 0% 5% 1% 2%

Tire & Rubber 0% 0% 0% 0%

Public Utilities &
Transportation 2% 6% 4% 4%

Government 3% 4% 1% 3%

Nonprofit &
Education 0% 1% 1% 1%









Table 3-1--continued
CURRICULUM
Chemical Mechanical Industrial Civil
Engineers Engineers
INDUSTRY
Public
Accounting 0% 0% 4% 1%

Banking, Finance &
Insurance 0% 0% 1% 1%

Merchandising 0% 0% 2% 0%

Aerospace, Electronic
& Computers 8% 39% 39% 9%

Automotive & Mechanical
Equipment 2% 11% 8% 2%

Construction & Building
Materials 2% 3% 4% 25%

Chemical, Drugs &
Allied 43% 8% 10% 2%

Food & Beverage
Processing 4% 2% 4% 0%

Glass, Paper &
Packaging 6% 2% 4% 1%

Metals & Metal
Products 2% 6% 7% 4%

Petroleum & Allied
Products 27% 10% 2% 9%

Research &
Consulting 2% 3% 4% 10%

Tire & Rubber 1% 1% 0% 0%

Public Utilities &
Transportation 2% 8% 6% 10%

Government 2% 6% 5% 25%

Nonprofit &
Education 0% 0% 0% 0%









Table 3-1--continued

INDUSTRY

Chemistry I
Public
Accounting 1%

Banking, Finance &
Insurance 2%

Merchandising 3%

Aerospace, Electronic &
& Computers 8%

Automotive & Mechanical
Equipment 1%

Construction & Building
Materials 1%

Chemical, Drugs &
Allied 49%

Food & Beverage
Processing 3%

Glass, Paper &
Packaging 3%

Metals & Metal
Products 1%

Petroleum & Allied
Products 7%

Research &
Consulting 7%

Tire & Rubber 4%

Public Utilities &
Transportation 2%

Government 3%

Nonprofit &
Education 4%


CURRICULUM


Humanities

2%


18%

42%


6%


1%


1%


3%


3%


2%


1%


1%


3%

0%


2%

7%


Agricultural
Mathematics Sciences

4% 1%


28%

3%


35%


13%

15%


12%


26%


1%

15%


1% 5%









Table 3-2

Job Offers per Bachelor Degree Candidate

1978-1988 Offers/Grads

Curriculum Average Variance H
Statistic

Accounting 0.16 0.0013 0.58
Agricultural Sciences 0.02 0.0001 0.15
Business General 0.09 0.0017 0.16
Chemistry 0.02 0.0001 0.27
Computer Science 0.14 0.0061 0.08
Humanities 0.24 0.0053 0.23
Marketing 0.08 0.0005 0.25
Mathematics 0.05 0.0003 0.22
Civil Engineering 0.24 0.0207 0.17
Chemical Engineering 0.60 0.1726 0.27
Industrial Engineering 0.39 0.0326 0.19
Mechanical Engineering 0.51 0.1000 0.20


The analysis of wages in Chapter 4 utilizes the three-

digit level of detail for occupations in 1980. In order to

perform a quantitative identification and ranking of

occupations by degree of exposure to unemployment risk and

to later test for compensating wage effects, three measures

of unemployment risk are calculated. To compute these

measures, data referring to occupational employment by

industry were obtained from the Commerce Department's 1980

Census Subject Reports. Responses to questions regarding

occupation and industry of current employment are compiled

in these reports. Occupations are identified by the 1980

detailed classification system consisting of 434 three-

digit level specific occupational categories describing the

nature of the occupation.

The industry classification of the employer, or the

nature of the employer's business, is also identified from










this data source. This industry classification consists of

231 categories based on the Standard Industrial

Classification Manual. The industry variance and

covariance measures, however, are derived from the Labor

Department's Bureau of Labor Statistics (BLS) annual

employment estimates for 100 industries from 1968-1990, as

reported in Employment and Earnings. Although the Census

reports the industry of employment at a three-digit level,

in some cases the three-digit level industries were not

available for the full 32-year period. These industries

were regrouped to the two-digit level. Similarly, because

the BLS uses one-digit level reporting in the industries of

Agriculture, Construction and Finance, Insurance and Real

Estate (FIRE), employment in these industry classes was

grouped to maintain compatibility between the two sources.

The resulting industry classifications are presented in

Appendix A.

The first two measures test whether employment risk is

lower in occupations that are diversified between

industries. The first measure calculated is a simple dummy

variable. If greater than 50% employment is found in any

one industry, an occupation is defined as high-risk. The

industry with the largest concentration of employment for

each occupation is presented in Appendix B. Table 3-3

presents the dummy and H risk measures at a three-digit

occupational level ranked from highest to lowest

concentration.










The third, more sophisticated, risk measure is

calculated based on employment variability over time.

Since time series data on employment at a three-digit

occupation level have not been compiled for more than a few

large occupational classifications, the study must rely on

time series data on industry employment, similar to the

studies by Adams and Li, together with the industry-by-

occupation matrix of employment to make inferences about

the time series of occupational employment.

A regression was computed using the annual industry

employment growth rates for each industry for the years

1960-1990 as the dependent variable and the average of the

three prior years' U.S. employment growth rate as the

independent variable. This method controls for labor force

changes due to varying labor force participation rates

(e.g., for women) and fluctuations in demographic patterns,

which should not be included in a measure of unemployment

risk.

The residual will embody changes in employment due to

business cycle fluctuations and shifting demand for

industry products, which should be included in an

employment risk measure.

The variance of industry deviations from the predicted

values is considered the unemployment risk for the

industry, or the industry variance. The covariances of

industry deviations from predicted values are also

calculated. The unemployment risk of each occupation is










Table 3-3
Risk Measures for Detailed Occupations

SOC TITLE DUMMY H

227 Air Traffic Controllers 1 1.00
383 Bank Tellers 1 1.00
424 Correctional Institution 1 1.00
488 Graders and Sorters 1 1.00
253 Insurance 1 1.00
375 Insurance Adjusters 1 1.00
179 Judges 1 1.00
003 Legislators & Public Administration 1 1.00
355 Mail Carriers, Postal Service 1 1.00
418 Police and Detective, Private Service 1 1.00
354 Postal Clerks, except Mail Carriers 1 1.00
017 Postmasters 1 1.00
255 Securities and Financial Services Sales 1 1.00
423 Sheriff, Bailiffs and Other Law Enforce 1 1.00
414 Supervisors, Police 1 1.00
317 Hotel Clerks 1 0.99
457 Barbers 1 0.94
254 Real Estate Sales 1 0.94
595 Roofers 1 0.94
614 Driller, Oil Well 1 0.93
573 Drywall Installers 1 0.93
005 Administrators, Officials, Pub. Admin. 1 0.92
176 Clergy 1 0.92
458 Hairdressers 1 0.92
006 Administrators, Protective Services 1 0.91
024 Underwriters 1 0.91
588 Concrete and Terrazzo Finishers 1 0.90
445 Dental Assistants 1 0.90
204 Dental Hygienists 1 0.90
417 Firefighting 1 0.90
018 Funeral Directors 1 0.90
089 Health Diagnosing nec 1 0.90
085 Dentists 1 0.87
823 Railroad Conductors and Yardmasters 1 0.87
745 Shoe Machine Operator 1 0.87
425 Crossing Guards 1 0.86
413 Supervisors, Firefighting 1 0.86
869 Construction Laborers 1 0.84
845 Longshore Equipment Operators 1 0.84
177 Religious 1 0.84
876 Stevedores 1 0.84
465 Public Transportation 1 0.83
825 Railroad Brake, Signal & Switch Operator 1 0.83
584 Plasterers 1 0.82
088 Podiatrists 1 0.82
679 Bookbinders 1 0.81
498 Fishers 1 0.81
594 Paving, Surfacing and Tamping Equipment 1 0.81
553 Supervisors, Brickmasons, Stonemasons 1 0.81









Table 3-3--Continued

SOC TITLE DUMMY H
558 Supervisors, nec 1 0.81
437 Short-Order Cooks 1 0.80
826 Rail Vehicle Operators, nec 1 0.78
387 Teachers' Aides 1 0.77
563 Brickmasons and Stonemasons 1 0.76
306 Chief Communications 1 0.75
678 Dental Laboratory and Medical Appliance 1 0.75
455 Pest Control Occupations 1 0.75
556 Supervisors, Painters, Paperhangers 1 0.75
198 Announcers 1 0.74
087 Optometrists 1 0.74
529 Telephone Installers and Repairers 1 0.74
527 Telephone Line Installers and Repairers 1 0.74
438 Food Counter 1 0.72
565 Tile Setters, Hard and Soft 1 0.72
086 Veterinarians 1 0.72
435 Waiters and Waitresses 1 0.72
495 Forestry Workers 1 0.71
773 Motion Picture Projectionists 1 0.71
598 Driller, Earth 1 0.70
016 Managers Properties 1 0.70
554 Supervisors, Carpenters and Related 1 0.70
205 Health Record 1 0.68
694 Water & Sewage Treatment Plant Operators 1 0.68
434 Bartenders 1 0.67
824 Locomotive Operating Occupations 1 0.67
875 Garbage Collectors 1 0.66
186 Musicians and Composers 1 0.66
583 Paperhangers 1 0.66
206 Radiologic Technicians 1 0.66
464 Ushers 1 0.66
567 Carpenters 1 0.65
047 Petroleum Engineers 1 0.64
865 Helpers, Construction Trades 1 0.63
278 News Vendors 1 0.63
844 Operating Engineers 1 0.63
557 Supervisors, Plumbers, Pipefitters 1 0.63
173 Urban Planners 1 0.63
353 Communications Equipment Operators, nec 1 0.62
577 Electrical Power Installers and Repair 1 0.62
737 Miscellaneous Printing Machine Operators 1 0.62
744 Textile Sewing Machine Operators 1 0.62
496 Timber Cutting 1 0.62
203 Clinical Laboratory 1 0.61
178 Lawyers 1 0.61
226 Airplane Pilots 1 0.60
183 Authors 1 0.60
829 Sailors and Deckhands 1 0.60
155 Teachers, Prekindergarten 1 0.60
885 Garage and Service Station Related 1 0.59
738 Winding and Twisting Machine Operators 1 0.59









Table 3-3--Continued

SOC TITLE DUMMY H
193 Dancers 1 0.56
095 Registered Nurse 1 0.56
828 Ship Captains and Mates, except Fishing 1 0.56
597 Structural Metal Workers 1 0.55
096 Pharmacists 1 0.53
593 Insulation Workers 1 0.52
695 Power Plant Operators 1 0.52
497 Captains & Other Officers Fishing Vessel 1 0.51
207 Licensed Practical Nurses 1 0.51
015 Managers Medicine 1 0.51
366 Meter Readers 1 0.51
735 Photoengravers and Lithographers 1 0.51
277 Street and Door-to-door 1 0.51
514 Automobile Body and Related Repairers 1 0.50
683 Electrical and Electronic Equipment 1 0.50
036 Inspectors and Compliance 1 0.50
028 Purchasing Agents 1 0.49
318 Transportation Ticket & Reservations 1 0.49
344 Billing, Posting and Calculating Oper. 1 0.48
329 Library Clerks 1 0.48
579 Painters, Construction and Maintenance 1 0.48
877 Stock Handlers and Baggers 1 0.48
066 Actuaries 1 0.47
025 Other Financial Officers 1 0.47
084 Physicians 1 0.46
494 Supervisors 1 0.46
467 Welfare Service 1 0.46
014 Administrators 1 0.45
599 Construction Trades, nec. 1 0.45
726 Wood Lathe, Routing and Planing Machine 1 0.45
855 Grader, Dozer and Scraper Operators 1 0.44
164 Librarians 1 0.44
747 Pressing Machine Operator 1 0.44
459 Attendants, Amusement 1 0.43
466 Baggage Porters 1 0.43
536 Locksmiths and Safe Repairers 1 0.43
433 Supervisors, Food Preparation 1 0.43
808 Bus Drivers 1 0.42
739 Knitting, Looping, Taping and Weaving 1 0.42
616 Mining Machine Operators 1 0.42
447 Nursing Aides, Orderlies 1 0.42
693 Adjusters and Calibrators 1 0.41
487 Animal Caretakers 1 0.41
686 Butchers and Meat Cutters 1 0.41
436 Cooks except Short Order 1 0.41
596 Sheetmetal Duct Installers 1 0.41
063 Surveyors 1 0.41
187 Actors and Directors 1 0.40
566 Carpet Installers 1 0.40
097 Dieticians 1 0.40
669 Shoe Repairers 1 0.40









Table 3-3--Continued

SOC TITLE DUMMY H
443 Waiters and Waitresses Assistants 1 0.40
736 Typesetters and Compositors 1 0.39
228 Broadcast Equipment 1 0.38
517 Farm Equipment Mechanics 1 0.38
446 Health Aides except Nursing 1 0.38
585 Plumbers, Pipefitters and Steamfitters 1 0.38
044 Aerospace Engineers 1 0.37
508 Aircraft Engine Mechanics 1 0.37
687 Bakers 1 0.37
853 Excavating and Loading Machine Operators 1 0.37
515 Aircraft Mechanics except Engine 1 0.36
377 Eligibility Clerks 0 0.36
613 Supervisors, Extractive Occupations 1 0.36
867 Helpers, Extractive Occupations 1 0.35
058 Marine Engineers 1 0.35
684 Miscellaneous Precision Workers, nec 1 0.35
647 Precious Stones & Metal Workers-Jewelers 1 0.35
284 Auctioneers 1 0.34
034 Business and Promotion Agents 1 0.34
748 Laundering and Dry Cleaning Machine 1 0.34
646 Lay-out Workers 1 0.34
734 Printing Machine Operators 1 0.34
349 Telegraphers 1 0.34
054 Agricultural Engineers 1 0.33
035 Construction Inspectors 1 0.33
163 Counselors 1 0.33
208 Health Technologists 1 0.33
848 Hoist and Winch Operators 1 0.33
275 Sales Counter Clerks 1 0.33
667 Tailors 1 0.33
199 Athletes 1 0.32
079 Forestry 0 0.32
439 Kitchen Workers 1 0.32
763 Roasting and Baking Machine Operators 1 0.32
174 Social Workers 0 0.32
866 Helpers, Surveyor 1 0.31
617 Mining Occupations, nec. 0 0.31
727 Sawing Machine Operators 1 0.31
165 Archivists 1 0.30
468 Child Care Workers except Private 0 0.30
543 Elevator Installers and Repairers 1 0.30
416 Fire Inspection and Fire Prevention 0 0.30
729 Nailing and Tacking Machine Operators 1 0.30
677 Optical Goods Workers 0 0.30
636 Precision Assemblers, Metal 1 0.30
707 Rolling Machine 1 0.30
688 Food Batchmakers 0 0.29
037 Management Related 1 0.29
833 Marine Engineers 1 0.29
814 Motor Transportation Occupations nec 1 0.29
175 Recreation 0 0.29









Table 3-3--Continued

SOC TITLE DUMMY H
668 Upholsterers 0 0.29
589 Glaziers 0 0.28
749 Miscellaneous Textile Machine Operator 1 0.28
733 Misc. Woodworking Machine Operator 1 0.28
813 Parking Lot Attendants 0 0.28
194 Artists 1 0.27
834 Bridge, Lock and Lighthouse Tenders 0 0.27
657 Cabinet Makers and Bench Carpenters 0 0.27
658 Furniture and Wood Finishers 0 0.27
444 Miscellaneous Food Preparation 1 0.27
784 Solderers & Braziers 1 0.27
026 Management Analysts 1 0.26
539 Mechanical Controls and Valve Repairer 0 0.26
259 Sales Reps. 1 0.26
499 Hunters and Trappers 1 0.25
218 Surveying Technologists 0 0.25
053 Civil Engineers 0 0.24
007 Financial Managers 1 0.24
486 Groundskeepers 0 0.24
234 Legal Assistants 0 0.24
106 Physicians Assistants 0 0.24
485 Supervisors 0 0.24
555 Supervisors, Electricians & Power 1 0.24
809 Taxicab Drivers and Chauffeurs 1 0.24
505 Automobile Mechanics 0 0.23
325 Classified-ad Clerks 1 0.23
575 Electricians 1 0.23
075 Geologists 0 0.23
538 Office Machine Repairers 0 0.23
169 Social Scientists 0 0.23
348 Telephone Operators 1 0.23
256 Advertising and Related Sales 0 0.22
535 Camera, Watch and Musical Instruments 0 0.22
276 Cashiers 0 0.22
427 Protective Service Occupations 0 0.22
456 Supervisors 0 0.22
074 Atmospheric 0 0.21
525 Data Processing Equipment Repairers 0 0.21
195 Editors 0 0.21
083 Medical 0 0.21
076 Physical Scientists nec 0 0.21
384 Proof Readers 0 0.21
168 Sociologists 0 0.21
167 Psychologists 0 0.20
757 Separating, Filtering and Clarifying 0 0.20
316 Interviewers 0 0.19
449 Maids and Housemen 0 0.19
046 Mining Engineers 0 0.19
509 Small Engine Repairers 0 0.19
673 Apparel and Fabric Patternmakers 0 0.18
068 Mathematical Scientists 0 0.18









Table 3-3--Continued

SOC TITLE DUMMY H
674 Misc. Precision Apparel & Fabric 0 0.18
189 Photographers 0 0.18
336 Records Clerks 0 0.18
314 Stenographers 0 0.18
804 Truck Drivers, Heavy 0 0.18
077 Agricultural 0 0.17
526 Household Appliance & Power Tool Repair 0 0.17
489 Inspectors 0 0.17
188 Painters, Sculptors, Craft Artists 0 0.17
743 Textile Cutting Machine Operators 0 0.17
029 Buyers 0 0.16
048 Chemical Engineers 0 0.16
224 Chemical Technologists 0 0.16
463 Guides 0 0.16
516 Heavy Equipment Mechanics 0 0.16
328 Personnel Clerks except Payroll 0 0.16
257 Sales Occupations, Other Business 0 0.16
507 Bus, Truck & Stationary Engine Mechanic 0 0.15
534 Heating, Air Conditioning & Refrigeration 0 0.15
723 Metal Plating Machine Operators 0 0.15
656 Patternmakers and Model Makers, Wood 0 0.15
774 Photographic Process Machine Operators 0 0.15
666 Dressmakers 0 0.14
649 Engravers, Metal 0 0.14
347 Office Machine Operators, nec 0 0.14
653 Sheet Metal Workers 0 0.14
803 Supervisors, Motor Vehicle Operators 0 0.14
078 Biological 0 0.13
326 Correspondence Clerks 0 0.13
343 Cost and Rate Clerks 0 0.13
055 Electrical Engineers 0 0.13
787 Hand Molding, Casting and Forming 0 0.13
849 Crane and Tower Operators 0 0.12
806 Driver-Sales Workers 0 0.12
523 Electronic Repairers, Communications 0 0.12
454 Elevator Operators 0 0.12
615 Explosive Workers 0 0.12
426 Guards and Police, except Public Service 0 0.12
786 Hand Cutting and Trimming 0 0.12
793 Hand Engraving 0 0.12
689 Inspectors, Testers and Graders 0 0.12
376 Investigators except Insurance 0 0.12
703 Lathe and Turning Machine Set-up Operator 0 0.12
715 Miscellaneous Metal, Plastic, Stone 0 0.12
699 Miscellaneous Plant and System Operator 0 0.12
049 Nuclear Engineers 0 0.12
069 Physicists 0 0.12
067 Statisticians 0 0.12
303 Supervisors General Office 0 0.12
243 Supervisors & Proprietors, Sales 0 0.12
887 Vehicle Washers and Equipment Cleaners 0 0.12









Table 3-3--Continued

SOC TITLE DUMMY H
023 Accountants 0 0.11
378 Bill and Account Collectors 0 0.11
643 Boilermakers 0 0.11
213 Electrical Technologists 0 0.11
864 Helpers, Mechanics and Repairers 0 0.11
704 Lathe and Turning Machine Operators 0 0.11
374 Material Recording, Scheduling 0 0.11
714 Numerical Control Machine Operators 0 0.11
319 Receptionists 0 0.11
285 Sales Support Occupations nec 0 0.11
728 Shaping and Joining Machine Operators 0 0.11
415 Supervisors, Guards 0 0.11
634 Tool and Die Makers 0 0.11
389 Administrative Support nec 0 0.10
223 Biological Technologists 0 0.10
064 Computer Systems 0 0.10
708 Drilling and Boring 0 0.10
755 Extruding and Forming Machine Operator 0 0.10
765 Folding Machine Operators 0 0.10
675 Hand Molders & Shapers except Jewelers 0 0.10
789 Hand Painting, Coating and Decorating 0 0.10
724 Heat Treating Equipment Operators 0 0.10
705 Milling and Planing Machine Operators 0 0.10
533 Miscellaneous Electrical and Electronics 0 0.10
659 Miscellaneous Precision Woodworkers 0 0.10
754 Packaging & Filling Machine Operators 0 0.10
469 Personal Service Occupations, nec 0 0.10
706 Punching and Stamping Press 0 0.10
315 Typists 0 0.10
283 Demonstrators, Promoters and Models 0 0.09
185 Designers 0 0.09
359 Dispatchers 0 0.09
059 Engineer, nec. 0 0.09
713 Forging Machine Operators 0 0.09
794 Hand Grinding 0 0.09
637 Machinists 0 0.09
027 Personnel 0 0.09
258 Sales Engineers 0 0.09
448 Supervisors, Cleaning & Building Services 0 0.09
785 Assemblers 0 0.08
229 Computer Programmers 0 0.08
217 Drafting Technologists 0 0.08
335 File Clerks 0 0.08
799 Graders and Sorters 0 0.08
357 Messengers 0 0.08
045 Metallurgical Engineers 0 0.08
065 Operations and Systems Researchers 0 0.08
327 Order Clerks 0 0.08
676 Pattern Makers, Lay-out Workers & Cutters 0 0.08
645 Patternmakers and Model Makers, Metal 0 0.08
644 Precision Grinders, Filers, and Tool 0 0.08









Table 3-3--Continued

SOC TITLE DUMMY H
797 Production Testers 0 0.08
386 Statistical Clerks 0 0.08
304 Supervisors, Computer Equipment 0 0.08
503 Supervisors, Mechanics and Repairers 0 0.08
235 Technicians nec 0 0.08
805 Truck Drivers, Light 0 0.08
385 Data Entry Keyers 0 0.07
345 Duplicating Machine Operators 0 0.07
717 Fabricating Machine Operators, nec 0 0.07
883 Freight Stock & Material Handlers, nec 0 0.07
323 Information Clerks nec 0 0.07
453 Janitors and Cleaners 0 0.07
346 Mail & Paper Handling Machine Operators 0 0.07
544 Millwrights 0 0.07
725 Miscellaneous Metal & Plastic Processors 0 0.07
719 Molding and Casting Machine Operators 0 0.07
197 Public Relations 0 0.07
225 Science Technologists nec 0 0.07
313 Secretaries 0 0.07
305 Supervisors, Financial Records 0 0.07
843 Supervisors, Material Moving Equipment 0 0.07
043 Architects 0 0.06
073 Chemists 0 0.06
308 Computer Operators 0 0.06
166 Economists 0 0.06
216 Engineering Technologists 0 0.06
379 General Office 0 0.06
709 Grinding, Abrading, Buffing and Polish 0 0.06
878 Machine Feeders and Offbearers 0 0.06
356 Mail Clerks, except Postal Service 0 0.06
057 Mechanical Engineers 0 0.06
215 Mechanical Technologists 0 0.06
759 Painting and Paint Spraying Machine 0 0.06
309 Peripheral Equipment Operators 0 0.06
184 Technical Writers 0 0.06
783 Welders and Cutters 0 0.06
337 Bookkeepers, Accounting & Auditing 0 0.05
753 Cementing and Gluing Machine Operators 0 0.05
768 Crushing and Grinding Machine Operator 0 0.05
766 Furnace Kiln & Oven Operators except Food 0 0.05
056 Industrial Engineers 0 0.05
214 Industrial Technologists 0 0.05
013 Managers Marketing 0 0.05
655 Miscellaneous Precision Metal Workers 0 0.05
795 Misc. Hand Working 0 0.05
307 Supervisors, Distributions 0 0.05
863 Supervisors, Handlers, Equipment Cleaners 0 0.05
233 Tool Programmers 0 0.05
764 Washing, Cleaning and Pickling Machine 0 0.05
339 Billing Clerks 0 0.04
758 Compressing and Compacting Machine 0 0.04









Table 3-3--Continued

SOC TITLE DUMMY H
373 Expediters 0 0.04
888 Hand Packers and Packagers 0 0.04
519 Machinery Maintenance Occupations 0 0.04
019 Managers and Administrators nec 0 0.04
777 Miscellaneous Machine Operators, nec 0 0.04
756 Mixing and Blending Machine Operator 0 0.04
796 Production Inspectors, Checkers 0 0.04
009 Purchasing 0 0.04
369 Samplers 0 0.04
769 Slicing and Cutting Machine Operators 0 0.04
547 Specified Mechanics and Repairers, nec 0 0.04
696 Stationary Engineers 0 0.04
365 Stock and Inventory 0 0.04
856 Industrial Truck and Tractor Equipment 0 0.03
889 Laborers, except Construction 0 0.03
779 Machine Operators, not Specified 0 0.03
859 Miscellaneous Material Moving Equipment 0 0.03
549 Not Specified Mechanics and Repairers 0 0.03
338 Payroll and Timekeeping Clerks 0 0.03
008 Personnel and Labor Relations 0 0.03
363 Production Coordinators 0 0.03
873 Production Helpers 0 0.03
798 Production Samplers and Weighers 0 0.03
033 Purchasing Agents nec 0 0.03
364 Traffic, Shippings 0 0.03
368 Weighers, Measurers 0 0.03
518 Industrial Machinery Repairers 0 0.02
633 Supervisors, Production Occupations 0 0.02


estimated by the sum of the weighted industry variance and

covariances. The weights are the frequencies of the

occupation's employment in industries in 1980. If an

occupation is employed exclusively in one industry, the

occupation's risk measure is equivalent to that industry's

risk measure. If the occupation is employed in many

industries, the covariance of the industries is also

included. For example, an occupation with 80% employment

in Industry One and 20% employment in Industry Two in 1980,

would have its occupational variance calculated as in

Equation 3-1.










Var=80%2(Varl)+20%2(Var2)+2*80%*20%(CoVl,2) Eq. 3-1

This procedure downweights the risk of an occupation if its

opportunities are in industries which are negatively

correlated. The risk measure of an occupation is increased

if the industries are positively correlated. In contrast

to Adams' and Li's measures of risk, the occupational

measure will capture the combined industry unemployment

risk as measured by the covariance terms, and not just the

risk of the industry as measured by the variance terms.

Occupation variance measures ranked from highest to

lowest volatility are presented in Table 3-4, which reports

the sum of weighted industry variances as the occupation's

variance. The sum of weighted industry covariances is

reported as the occupation's covariance. The total is the

sum of the variance and covariance, representing the total

occupational variance.

For 21 of 434 total occupations, the weighted sum of

the industry covariances has a negative sign, thereby

reducing the total variance for the occupation. Among the

occupations with negative industry covariances are timber

cutters, hoist and winch operators, veterinarians,

groundskeepers and actors and directors. These occupations

appear to have little in common. The inclusion of the

covariance in the total variance significantly changes the

risk ranking of occupations. For example, purchasing

agents are found in industries which have a relatively low










variance, but the high positive industry covariance makes

it a riskier occupation.

Based on the total risk measure, mining, lumber and

petroleum occupations rank highest, due to high industry

employment volatility. These occupations are found

primarily in the same industries which purportedly pay

efficiency wages. Low-risk occupations include government,

administrative, retail and service positions. Performers,

artists and athletes also have a low ranking.

The correlation between the risk measures Dummy and H

is very high at .81. Both measure the concentration of an

occupation in industries. The correlation between the

total occupational variance and H is .06; with the Dummy,

it is .09. The correlations with total occupational

variance are expected to be low, since the variance measure

is measuring risk by the employment volatility of the

relevant industries instead of the dependence of an

occupation's employment on the industry. Appendix C

presents the Dummy and H measures, and Appendix D presents

the occupational variance measures. Occupations are ranked

from lowest to highest risk within each two-digit

classification.

The risk measures are very small for most classes at

the one and two-digit levels, with the exception of

construction, extractive and farming occupations which have

high risk measures. The variation in risk measures within

the two-digit classes from the weighted average of the










Table 3-4
Occupation Variance Measures


SOC Title


Variance Covariance Total


496
494
614
047
613
693
616
745
617
044
867
726
715
703
683
707
634
727
706
724
729
784
713
848
708
704
636
709
656
595
573
675
728
733
785
588
644
684
705
594
553
645
646
714
869
563
584
565
657


Timber Cutting
Supervisors, Forestry
Driller, Oil Well
Petroleum Engineer
Supervisors, Extractive
Adjusters & Calibrators
Mining Machine Operators
Shoe Machine Operator
Mining Occupations, nec
Aerospace Engineers
Helpers, Extractive
Wood Lathe, Routing
Miscellaneous Metal
Lathe & Turning Machine
Electrical
Rolling Machine
Tool and Die Makers
Sawing Machine Operator
Punching & Stamping
Heat Treating Equipment
Nailing & Tacking
Solderers and Braziers
Forging Machine
Hoist & Winch
Drilling and Boring
Lathe and Turning
Precision Assemblers
Grinding, Abrading
Patternmakers and Model
Roofers
Drywall Installers
Hand Molders & Shapers
Shaping and Joining
Misc. Woodworking
Assemblers
Concrete and Terrazzo
Precision Grinders
Miscellaneous Precision
Milling and Planing
Paving, Surfacing
Supervisors, Brickmason
Patternmakers and Model
Lay-out Workers
Numerical Control
Construction Laborers
Brickmasons
Plasterers
Tile Setters
Cabinet Makers & Bench


0.04841
0.03013
0.00721
0.00492
0.00269
0.00254
0.00292
0.00307
0.00225
0.00203
0.00238
0.00174
0.00075
0.00044
0.00174
0.00115
0.00045
0.00132
0.00038
0.00036
0.00113
0.00090
0.00029
0.00253
0.00034
0.00035
0.00127
0.00023
0.00045
0.00201
0.00201
0.00034
0.00038
0.00106
0.00034
0.00194
0.00031
0.00101
0.00034
0.00175
0.00174
0.00027
0.00097
0.00042
0.00180
0.00163
0.00176
0.00154
0.00068


-0.00025
0.00037
0.00005
0.00024
0.00090
0.00092
0.00049
0.00013
0.00093
0.00114
0.00078
0.00108
0.00206
0.00232
0.00098
0.00150
0.00207
0.00108
0.00201
0.00202
0.00119
0.00139
0.00197
-0.00028
0.00190
0.00187
0.00089
0.00192
0.00165
0.00005
0.00006
0.00174
0.00167
0.00098
0.00171
0.00009
0.00171
0.00100
0.00165
0.00018
0.00019
0.00163
0.00091
0.00146
0.00007
0.00022
0.00008
0.00028
0.00114


0.04816
0.03051
0.00726
0.00516
0.00358
0.00346
0.00341
0.00320
0.00318
0.00317
0.00316
0.00282
0.00281
0.00277
0.00272
0.00265
0.00252
0.00240
0.00240
0.00239
0.00232
0.00229
0.00226
0.00224
0.00224
0.00222
0.00216
0.00215
0.00210
0.00207
0.00207
0.00207
0.00205
0.00204
0.00204
0.00202
0.00202
0.00200
0.00199
0.00193
0.00192
0.00190
0.00188
0.00188
0.00187
0.00185
0.00184
0.00182
0.00182










Table 3-4--continued


Variance Covariance Total


SOC Title


558
725
797
597
723
717
556
719
515
655
554
544
567
046
045
637
598
865
583
075
057
557
849
596
755
844
593
796
659
783
689
738
215
759
658
579
794
056
465
566
653
676
739
599
753
749
779
585
744
777


Supervisors, nec
Misc. Metal & Plastic
Production Testers
Structural Metal Worker
Metal Plating Machine
Fabricating Machine
Supervisors, Painters
Molding & Casting
Aircraft Mechanics
Misc. Precision Metal
Supervisors, Carpenters
Millwrights
Carpenters
Mining Engineers
Metallurgical Engineers
Machinists
Driller, Earth
Helpers, Construction
Paperhangers
Geologists
Mechanical Engineers
Supervisors, Plumbers
Crane and Tower
Sheetmetal Duct
Extruding & Forming
Operating Engineers
Insulation Workers
Production Inspectors
Misc. Precision Wood
Welders and Cutters
Inspectors, Testers
Winding & Twisting
Mechanical Engin. Tech
Painting & Paint
Furniture & Wood
Painters, Construction
Hand Grinding
Industrial Engineer
Public Transportation
Carpet Installers
Sheet Metal Workers
Pattern Makers, Lay-out
Knitting, Looping
Construction Trades nec
Cementing & Gluing
Miscellaneous Textile
Machine Operators
Plumbers, Pipefitters
Textile Sewing Machine
Misc. Machine Operator


0.00174
0.00019
0.00027
0.00119
0.00035
0.00021
0.00161
0.00020
0.00124
0.00016
0.00150
0.00025
0.00140
0.00128
0.00030
0.00026
0.00152
0.00135
0.00140
0.00145
0.00025
0.00135
0.00039
0.00086
0.00026
0.00136
0.00111
0.00015
0.00022
0.00017
0.00040
0.00088
0.00030
0.00014
0.00059
0.00103
0.00016
0.00016
0.00126
0.00060
0.00033
0.00016
0.00062
0.00096
0.00012
0.00043
0.00009
0.00081
0.00072
0.00009


0.00007
0.00161
0.00149
0.00057
0.00141
0.00155
0.00014
0.00155
0.00050
0.00156
0.00019
0.00143
0.00027
0.00040
0.00135
0.00137
0.00009
0.00027
0.00020
0.00012
0.00130
0.00016
0.00109
0.00059
0.00119
0.00006
0.00031
0.00127
0.00119
0.00124
0.00099
0.00050
0.00109
0.00122
0.00077
0.00030
0.00115
0.00113
0.00002
0.00068
0.00094
0.00111
0.00064
0.00029
0.00112
0.00079
0.00112
0.00039
0.00040
0.00102


0.00181
0.00180
0.00177
0.00176
0.00176
0.00176
0.00175
0.00175
0.00174
0.00172
0.00168
0.00168
0.00167
0.00167
0.00165
0.00163
0.00162
0.00162
0.00160
0.00158
0.00155
0.00151
0.00148
0.00145
0.00145
0.00142
0.00142
0.00142
0.00141
0.00141
0.00139
0.00138
0.00138
0.00136
0.00135
0.00132
0.00131
0.00129
0.00128
0.00128
0.00127
0.00127
0.00126
0.00125
0.00124
0.00122
0.00121
0.00120
0.00112
0.00110










Table 3-4--continued


SOC Title


Variance Covariance Total


054
543
766
615
575
787
769
878
668
743
855
226
258
853
825
823
055
213
647
217
589
856
555
824
508
669
514
673
758
633
318
043
214
354
355
017
826
516
518
525
643
764
233
053
793
529
455
789
768
306


Agricultural Engineer
Elevator Installers
Furnace Kiln & Oven
Explosive Workers
Electricians
Hand Molding, Casting
Slicing & Cutting
Machine Feeders
Upholsterers
Textile Cutting Machine
Grader, Dozer & Scraper
Airplane Pilots
Sales Engineers
Excavating & Loading
Railroad Brake, Signal
Railroad Conductors
Electrical Engineer
Electrical Technician
Precious Stones & Metal
Drafting
Glaziers
Industrial Truck
Supervisors, Electrician
Locomotive Operating
Aircraft Engine Mechanic
Shoe Repairers
Automobile Body
Apparel & Fabric Pattern
Compressing & Compacting
Supervisors, Production
Transport Ticket Agent
Engineers, Architects
Industrial Engin. Tech
Postal Clerks
Mail Carriers, Postal
Postmasters
Rail Vehicle Operators
Heavy Equipment
Industrial Machinery
Data Processing Equip
Boilermakers
Washing, Cleaning
Tool Programmers
Civil Engineer
Hand Engraving
Telephone Installers
Pest Control Occupation
Hand Painting, Coating
Crushing, Grinding
Chief Communications


0.00083
0.00063
0.00015
0.00063
0.00049
0.00022
0.00007
0.00013
0.00038
0.00027
0.00105
0.00092
0.00011
0.00083
0.00079
0.00083
0.00037
0.00030
0.00046
0.00011
0.00034
0.00010
0.00049
0.00065
0.00057
0.00066
0.00049
0.00021
0.00006
0.00005
0.00074
0.00011
0.00006
0.00078
0.00078
0.00078
0.00074
0.00028
0.00005
0.00030
0.00021
0.00006
0.00009
0.00040
0.00013
0.00068
0.00068
0.00011
0.00007
0.00068


0.00026
0.00044
0.00093
0.00041
0.00056
0.00083
0.00098
0.00091
0.00065
0.00077
-0.00003
0.00006
0.00086
0.00013
0.00016
0.00009
0.00055
0.00060
0.00044
0.00077
0.00053
0.00077
0.00037
0.00018
0.00026
0.00015
0.00033
0.00061
0.00076
0.00077
0.00007
0.00069
0.00073
0.00000
0.00000
0.00000
0.00004
0.00049
0.00072
0.00046
0.00055
0.00070
0.00065
0.00033
0.00060
0.00004
0.00004
0.00059
0.00064
0.00002


0.00109
0.00108
0.00108
0.00105
0.00105
0.00105
0.00105
0.00104
0.00103
0.00103
0.00102
0.00098
0.00097
0.00096
0.00095
0.00092
0.00092
0.00091
0.00090
0.00089
0.00086
0.00086
0.00085
0.00083
0.00083
0.00082
0.00082
0.00082
0.00082
0.00082
0.00081
0.00080
0.00079
0.00078
0.00078
0.00078
0.00078
0.00077
0.00077
0.00076
0.00076
0.00076
0.00074
0.00073
0.00073
0.00072
0.00072
0.00071
0.00071
0.00070










Table 3-4--continued


Variance Covariance Total


SOC Title


885
519
859
488
527
765
866
387
533
033
795
804
505
509
216
873
534
353
363
798
373
364
059
063
536
887
457
507
674
458
498
018
535
218
756
667
547
495
809
526
843
549
864
883
048
009
699
064
747
284


Garage & Service Station
Machinery Maintenance
Misc. Material Moving
Graders and Sorters
Telephone Line Installer
Folding Machine Operator
Helpers, Surveyor
Teachers' Aides
Misc. Electrical Repair
Purchasing Agents nec
Misc. Hand Working
Truck Drivers, Heavy
Automobile Mechanics
Small Engine Repairers
Engineering Tech, nec
Production Helpers
Heating, Air Condition
Communications Equipment
Production Coordinators
Production Samplers
Expediters
Traffic, Shippings
Engineer, nec.
Surveyors
Locksmiths & Safe Repair
Vehicle Washers
Barbers
Bus, Truck & Stationary
Misc. Precision Apparel
Hairdressers
Fishers
Funeral Directors
Camera, Watch Repair
Surveying
Mixing & Blending
Tailors
Specified Mechanics
Forestry Workers
Taxicab Drivers
Household Appliance
Supervisors Material
Not Specified Mechanics
Helpers, Mechanics
Freight Stock & Material
Chemical Engineer
Purchasing Manager
Misc. Plant Operator
Computer Systems Analyst
Pressing Machine
Auctioneers


0.00057
0.00013
0.00011
0.00069
0.00067
0.00011
0.00040
0.00066
0.00014
0.00007
0.00006
0.00024
0.00024
0.00023
0.00011
0.00006
0.00023
0.00056
0.00006
0.00007
0.00006
0.00004
0.00009
0.00041
0.00039
0.00013
0.00058
0.00017
0.00013
0.00056
0.00055
0.00055
0.00020
0.00028
0.00004
0.00033
0.00004
0.00051
0.00036
0.00019
0.00007
0.00004
0.00012
0.00009
0.00014
0.00005
0.00021
0.00011
0.00034
0.00027


0.00014
0.00057
0.00059
0.00000
0.00002
0.00058
0.00028
0.00001
0.00053
0.00060
0.00062
0.00042
0.00041
0.00042
0.00056
0.00060
0.00042
0.00008
0.00058
0.00056
0.00057
0.00059
0.00053
0.00019
0.00021
0.00046
0.00000
0.00041
0.00045
0.00001
0.00001
0.00001
0.00037
0.00027
0.00051
0.00021
0.00050
0.00002
0.00016
0.00033
0.00045
0.00048
0.00038
0.00042
0.00035
0.00044
0.00027
0.00036
0.00013
0.00020


0.00070
0.00070
0.00070
0.00069
0.00069
0.00069
0.00068
0.00067
0.00067
0.00067
0.00067
0.00066
0.00066
0.00066
0.00066
0.00066
0.00065
0.00064
0.00064
0.00063
0.00063
0.00063
0.00062
0.00060
0.00060
0.00059
0.00058
0.00058
0.00058
0.00057
0.00057
0.00057
0.00057
0.00055
0.00055
0.00054
0.00054
0.00053
0.00052
0.00052
0.00052
0.00052
0.00050
0.00050
0.00049
0.00049
0.00048
0.00048
0.00047
0.00046










Table 3-4--continued


SOC Title


Variance Covariance Total


649 Engravers, Metal
185 Designers
086 Veterinarians
889 Laborers, except Const
677 Optical Goods Workers
224 Chemical
184 Technical Writers
349 Telegraphers
049 Nuclear Engineers
329 Library Clerks
026 Management Analysts
523 Electronic Repairers
799 Graders and Sorters
888 Hand Packers & Packager
845 Longshore Equipment
876 Stevedores
229 Computer Programmers
259 Sales Reps., Mining
365 Stock & Inventory Clerk
164 Librarians
829 Sailors and Deckhands
503 Supervisors, Mechanics
338 Payroll and Timekeeping
445 Dental Assistants
204 Dental Hygienists
014 Administrators, Educ
497 Captains & Other Officer
035 Construction Inspectors
813 Parking Lot Attendants
757 Separating, Filtering
538 Office Machine Repairers
089 Health Diagnosing nec
085 Dentists
225 Science Tech, nec
307 Supervisors Distribution
013 Managers Marketing
828 Ship Captains and Mates
058 Marine Engineer, Naval
275 Sales Counter Clerks
088 Podiatrists
678 Dental Lab Tech
206 Radiologic Technicians
803 Supervisors, Motor
068 Mathematical Scientists
359 Dispatchers
666 Dressmakers
437 Short-Order Cooks
205 Health Record Tech
774 Photographic Process
426 Guards and Police


0.00009
0.00006
0.00049
0.00003
0.00020
0.00013
0.00008
0.00031
0.00011
0.00038
0.00022
0.00013
0.00009
0.00003
0.00040
0.00040
0.00008
0.00008
0.00003
0.00036
0.00031
0.00010
0.00003
0.00038
0.00038
0.00036
0.00034
0.00030
0.00024
0.00015
0.00013
0.00038
0.00037
0.00011
0.00005
0.00003
0.00028
0.00025
0.00020
0.00035
0.00032
0.00031
0.00016
0.00014
0.00010
0.00009
0.00033
0.00032
0.00012
0.00010


0.00037
0.00041
-0.00005
0.00042
0.00023
0.00030
0.00036
0.00012
0.00033
0.00003
0.00020
0.00029
0.00033
0.00040
0.00001
0.00001
0.00033
0.00033
0.00038
0.00004
0.00009
0.00030
0.00037
0.00001
0.00001
0.00003
0.00005
0.00009
0.00015
0.00024
0.00026
0.00000
0.00001
0.00027
0.00034
0.00036
0.00009
0.00011
0.00016
0.00001
0.00003
0.00005
0.00020
0.00022
0.00025
0.00026
0.00002
0.00003
0.00022
0.00025


0.00046
0.00046
0.00045
0.00045
0.00044
0.00044
0.00044
0.00043
0.00043
0.00042
0.00042
0.00042
0.00042
0.00042
0.00041
0.00041
0.00041
0.00041
0.00041
0.00040
0.00040
0.00040
0.00040
0.00039
0.00039
0.00039
0.00039
0.00039
0.00039
0.00039
0.00039
0.00038
0.00038
0.00038
0.00038
0.00038
0.00037
0.00037
0.00037
0.00036
0.00036
0.00036
0.00036
0.00036
0.00036
0.00036
0.00035
0.00035
0.00035
0.00035










Table 3-4--continued

SOC Title Variance Covariance Total

786 Hand Cutting & Trimming 0.00006 0.00029 0.00035
805 Truck Drivers, Light 0.00006 0.00029 0.00035
073 Chemists 0.00005 0.00030 0.00035
065 Operations, System 0.00005 0.00030 0.00035
368 Weighers, Measurers 0.00003 0.00032 0.00035
694 Water and Sewage 0.00033 0.00002 0.00034
679 Bookbinders 0.00033 0.00001 0.00034
875 Garbage Collectors 0.00032 0.00002 0.00034
808 Bus Drivers 0.00027 0.00007 0.00034
203 Clinical Laboratory 0.00029 0.00005 0.00033
863 Supervisors, Handlers 0.00005 0.00027 0.00033
087 Optometrists 0.00031 0.00001 0.00032
438 Food Counter 0.00030 0.00002 0.00032
435 Waiters and Waitresses 0.00030 0.00003 0.00032
366 Meter Readers 0.00021 0.00011 0.00032
277 Street & Door-to-door 0.00020 0.00012 0.00032
166 Economists 0.00004 0.00027 0.00032
095 Registered Nurse 0.00026 0.00004 0.00031
737 Misc. Printing Machine 0.00025 0.00006 0.00031
015 Managers Medicine 0.00024 0.00008 0.00031
257 Sales Occupations, Other 0.00013 0.00018 0.00031
415 Supervisors, Guards 0.00009 0.00023 0.00031
008 Personnel & Labor 0.00002 0.00028 0.00031
198 Announcers 0.00028 0.00001 0.00030
434 Bartenders 0.00027 0.00003 0.00030
833 Marine Engineers 0.00015 0.00014 0.00030
304 Supervisors, Computer 0.00005 0.00025 0.00030
283 Demonstrators, Promoters 0.00005 0.00025 0.00030
369 Samplers 0.00004 0.00027 0.00030
019 Managers, Administrator 0.00002 0.00028 0.00030
207 LPN 0.00024 0.00005 0.00029
577 Electrical Power 0.00023 0.00005 0.00029
517 Farm Equipment Mechanics 0.00014 0.00016 0.00029
374 Material Recording 0.00008 0.00021 0.00029
069 Physicists 0.00008 0.00021 0.00029
696 Stationary Engineers 0.00005 0.00024 0.00029
163 Counselors 0.00026 0.00003 0.00028
348 Telephone Operators 0.00020 0.00008 0.00028
084 Physicians 0.00020 0.00007 0.00027
189 Photographers 0.00012 0.00015 0.00027
309 Peripheral Equipment 0.00003 0.00024 0.00027
487 Animal Caretakers 0.00027 0.00000 0.00026
414 Supervisors, Police 0.00026 0.00000 0.00026
227 Air Traffic Controllers 0.00026 0.00000 0.00026
423 Sheriff, Bailiffs 0.00026 0.00000 0.00026
424 Correctional Institution 0.00026 0.00000 0.00026
418 Police & Detective 0.00026 0.00000 0.00026
179 Judges 0.00026 0.00000 0.00026
003 Legislators & Public 0.00026 0.00000 0.00026
317 Hotel Clerks 0.00026 0.00000 0.00026










Table 3-4--continued


SOC Title


Variance Covariance Total


735
499
539
029
327
345
027
308
385
447
754
006
005
466
834
734
339
326
417
425
413
097
695
446
243
285
337
305
468
736
235
453
814
763
343
007
335
433
378
376
436
443
173
256
188
325
067
346
748
208


Photoengravers
Hunters and Trappers
Mechanical Control
Buyers
Order Clerks
Duplicating Machine
Personnel Specialist
Computer Operators
Data Entry Keyers
Nursing Aides, Orderlies
Packaging & Filling
Admin., Protective
Admin., Public Admin.
Baggage Porters
Bridge, Lock, Lighthouse
Printing Machine
Billing Clerks
Correspondence Clerks
Firefighting
Crossing Guards
Supervisors, Fire
Dieticians
Power Plant Operators
Health Aides
Supervisor Proprietor
Sales Support Occupation
Bookkeepers, Accounting
Supervisors, Financial
Childcare Worker
Typesetters & Compositor
Technicians
Janitors and Cleaners
Motor Transportation
Roasting & Baking
Cost and Rate Clerks
Financial Managers
File Clerks
Supervisors, Food Prep
Bill & Account Collect
Investigator
Cooks except Short Order
Waiters Assistant
Urban Planners
Advertising and Related
Painters, Sculptors
Classified-ad Clerks
Statisticians
Mail Preparing
Laundering
Health Technologists


0.00021
0.00018
0.00013
0.00008
0.00006
0.00005
0.00005
0.00003
0.00003
0.00019
0.00003
0.00024
0.00024
0.00018
0.00018
0.00013
0.00003
0.00003
0.00023
0.00022
0.00022
0.00019
0.00019
0.00017
0.00005
0.00005
0.00002
0.00002
0.00017
0.00013
0.00005
0.00005
0.00015
0.00007
0.00003
0.00005
0.00003
0.00018
0.00005
0.00004
0.00018
0.00017
0.00016
0.00010
0.00005
0.00004
0.00004
0.00003
0.00020
0.00015


0.00006
0.00008
0.00014
0.00018
0.00020
0.00021
0.00021
0.00023
0.00023
0.00006
0.00021
0.00001
0.00000
0.00006
0.00006
0.00010
0.00022
0.00021
0.00000
0.00001
0.00000
0.00004
0.00004
0.00005
0.00018
0.00018
0.00020
0.00021
0.00005
0.00008
0.00017
0.00017
0.00006
0.00014
0.00018
0.00016
0.00017
0.00001
0.00014
0.00015
0.00000
0.00000
0.00002
0.00008
0.00013
0.00013
0.00014
0.00015
-0.00003
0.00002


0.00026
0.00026
0.00026
0.00026
0.00026
0.00026
0.00026
0.00026
0.00026
0.00025
0.00025
0.00024
0.00024
0.00024
0.00024
0.00024
0.00024
0.00024
0.00023
0.00023
0.00023
0.00023
0.00023
0.00023
0.00023
0.00023
0.00023
0.00023
0.00022
0.00022
0.00022
0.00022
0.00021
0.00021
0.00021
0.00020
0.00020
0.00019
0.00019
0.00019
0.00018
0.00018
0.00018
0.00018
0.00018
0.00018
0.00018
0.00018
0.00017
0.00017










Table 3-4--continued


SOC Title


Variance Covariance Total


028
036
357
347
023
356
253
375
383
255
278
083
037
328
454
379
313
254
024
439
025
384
076
456
314
323
386
489
344
316
074
448
034
197
444
016
228
806
303
389
079
486
155
106
167
066
195
315
176
773


Purchasing Agents
Inspectors
Messengers
Office Machine Operators
Accountants
Mail Clerks
Insurance
Insurance Adjusters
Bank Tellers
Securities & Financial
News Vendors
Medical Scientists
Management Related
Personnel Clerks
Elevator Operators
General Office
Secretaries
Real Estate Sales
Underwriters
Kitchen Workers
Other Financial Officers
Proof Readers
Physical Scientists nec
Supervisors
Stenographers
Information Clerks nec
Statistical Clerks
Inspectors, Agricultural
Billing, Posting
Interviewers
Atmospheric & Space
Supervisors, Cleaning
Business Agent
Public Relations
Miscellaneous Food Prep
Managers Properties
Broadcast Equipment
Driver-Sales Workers
Supervisors Office
Administrative Support
Forestry Scientists
Groundskeepers
Teachers Prekindergarten
Physicians Assistants
Psychologists
Actuaries
Editors
Typists
Clergy
Motion Picture Project


0.00014
0.00013
0.00004
0.00004
0.00003
0.00002
0.00016
0.00016
0.00016
0.00016
0.00010
0.00010
0.00008
0.00004
0.00003
0.00002
0.00002
0.00015
0.00015
0.00013
0.00009
0.00008
0.00007
0.00007
0.00006
0.00004
0.00003
0.00011
0.00008
0.00006
0.00006
0.00006
0.00006
0.00003
0.00013
0.00012
0.00010
0.00003
0.00003
0.00003
0.00017
0.00014
0.00012
0.00010
0.00010
0.00008
0.00004
0.00003
0.00011
0.00009


0.00003
0.00003
0.00013
0.00013
0.00015
0.00014
0.00000
0.00000
0.00000
0.00000
0.00006
0.00006
0.00008
0.00012
0.00013
0.00014
0.00014
0.00000
0.00000
0.00002
0.00006
0.00007
0.00008
0.00008
0.00009
0.00011
0.00012
0.00003
0.00006
0.00008
0.00008
0.00008
0.00008
0.00011
0.00000
0.00001
0.00002
0.00010
0.00010
0.00010
-0.00005
-0.00001
0.00001
0.00002
0.00002
0.00004
0.00007
0.00009
0.00000
0.00002


0.00017
0.00017
0.00017
0.00017
0.00017
0.00017
0.00016
0.00016
0.00016
0.00016
0.00016
0.00016
0.00016
0.00016
0.00016
0.00016
0.00016
0.00015
0.00015
0.00015
0.00015
0.00015
0.00015
0.00015
0.00015
0.00015
0.00015
0.00014
0.00014
0.00014
0.00014
0.00014
0.00014
0.00014
0.00013
0.00013
0.00013
0.00013
0.00013
0.00013
0.00012
0.00012
0.00012
0.00012
0.00012
0.00012
0.00012
0.00012
0.00011
0.00011










Table 3-4--continued


SOC Title


Variance Covariance Total


193
186
877
688
416
177
096
464
168
336
078
469
276
319
194
183
199
169
459
178
686
223
234
187
449
377
165
427
485
077
175
174
463
687
467


Dancers
Musicians & Composers
Stock Handlers
Food Batchmakers
Fire Inspection
Religious
Pharmacists
Ushers
Sociologists
Records Clerks
Biological Scientist
Personal Service Occup
Cashiers
Receptionists
Artists
Authors
Athletes
Social Scientists
Attendants, Amusement
Lawyers
Butchers & Meat Cutters
Biological Tech
Legal Assistants
Actors and Directors
Maids and Housemen
Eligibility Clerks
Archivists
Protective Service
Supervisors, Agriculture
Agricultural Scientist
Recreation Workers
Social Workers
Guides
Bakers
Welfare Service


0.00008
0.00008
0.00006
0.00006
0.00013
0.00010
0.00010
0.00008
0.00007
0.00006
0.00005
0.00004
0.00004
0.00004
0.00004
0.00008
0.00008
0.00006
0.00006
0.00008
0.00006
0.00004
0.00004
0.00008
0.00007
0.00007
0.00005
0.00005
0.00010
0.00008
0.00007
0.00006
0.00005
0.00005
0.00007


combined class warrants the analysis of risk at the three-

digit level. There is much variation in industrial

concentration and risk at the three-digit level which is

not captured at the two-digit level, much less the one-

digit level. This is evidence of how control for

differences between occupations using one and two-digit

level classifications will lead to distorted results.


0.00003
0.00002
0.00005
0.00005
-0.00004
0.00000
0.00000
0.00002
0.00003
0.00004
0.00005
0.00006
0.00006
0.00006
0.00005
0.00001
0.00002
0.00003
0.00003
0.00000
0.00002
0.00005
0.00003
-0.00001
0.00000
0.00000
0.00002
0.00002
-0.00004
-0.00002
-0.00001
0.00000
0.00001
0.00001
-0.00001


0.00011
0.00011
0.00011
0.00011
0.00010
0.00010
0.00010
0.00010
0.00010
0.00010
0.00010
0.00010
0.00010
0.00010
0.00010
0.00009
0.00009
0.00009
0.00009
0.00008
0.00008
0.00008
0.00008
0.00007
0.00007
0.00007
0.00007
0.00007
0.00006
0.00006
0.00006
0.00006
0.00006
0.00006
0.00005















CHAPTER 4
EMPIRICAL STUDY

The empirical study employs data from the Department

of Commerce's 1980 Census Subject Reports. The Subject

Report Earnings by Occupation and Education is the source

of the wage data.

The Decennial Census was selected as the data source

because alternative panel data sources, such as the Panel

Study of Income Dynamics and the Current Population Survey,

contain too few observations at the three-digit

occupational level for reliable estimates of occupational

differences. To regroup the three-digit occupational

categories at the two-digit or one-digit classification

level for increased observations would misrepresent the

level of exposure to industry concentration experienced by

most occupations as shown in Appendices C and D.

Although earnings are reported by occupation for men

and women in earlier Censuses, comparability between years

is reduced by the emergence or disappearance of certain

occupational classifications. If 1970 data were added and

the study restricted to occupations consistently defined in

each Census, the number of occupations deleted from the

study would be greater than the number of 1970 observations

added. Therefore, the study uses only 1980 data.










The Census data report the mean annual earnings for

males and females employed full-time, year-round in 1979.

This sample includes persons who usually worked 35 hours or

more per week for 50-52 weeks in 1979, compiled by the

three-digit level Standard Occupational Classification

code. If the occupation changed during the course of the

year, the occupation of longest duration is specified.

Earnings observations are delineated for age groups

25-34, 35-44, 45-54, 55-64, and 65+, and by education

levels 0-8, 9-11, 12, 13-15, 16, and 17+ years. Earnings

are defined as the algebraic sum of wage or salary income,

nonfarm self-employment income and farm net self-employment

income. This earnings figure represents income before

deductions for personal income taxes, Social Security, bond

purchases, union dues, Medicare and the like. Mean

earnings is defined as the aggregate earnings of a

particular occupation's wage and education class divided by

the number of observations included in that cell.

Given the age and education groupings, 30 potential

earnings observations exist for each occupation for each

sex. For males, a total of 12,229 mean earnings

observations are available and for females, 10,391

observations are available. The means and standard

deviations for the male and female samples are presented in

Table 4-1.

Years of education and potential work experience are

used to measure human capital. Potential work experience











Full Sample M

Variable:
LOG WAGE
EDUCATION
EXPERIENCE
GED REASONING
GED MATH
GED LANGUAGE
SVP
DEXTERITY
STRESS
STRENGTH
EXTREME COLD
EXTREME HEAT
EXTREME WET
EXTREME NOISE
VIBRATION
ATMOSPHERIC CONDITIONS
MECHANICAL EQUIPMENT
SHOCK
HEIGHTS
RADIATION
EXPLOSIVES
TOXINS
OTHER HAZARDS
FRACTION NORTH
FRACTION NORTH CENTRAL
FRACTION SOUTH
FRACTION WEST
GROWTH DUMMY
FRACTION UNEMPLOYED
FRACTION FEMALE
FRACTION AGRICULTURE
FRACTION MINING
FRACTION CONSTRUCTION
FRACTION DURABLE
FRACTION NONDURABLE
FRACTION TRANSPORTATION
FRACTION WHOLESALE
FRACTION RETAIL
FRACTION FIRE
FRACTION BUSINESS SERV.
FRACTION PERSONAL SERV.
FRACTION ENTERTAINMENT
FRACTION PROFESSIONAL
FRACTION PUBLIC ADMIN.
FRACTION UNION COVERAGE
FRACTION NONWHITE
DUMMY RISK
H
VARIANCE
COVARIANCE


Table 4-1
means & Standard Deviations


Males


MEAN
9.7725
L2.7760
25.6430
3.1892
2.2863
2.6133
1.3703
3.6758
0.0425
2.2335
0.0046
0.0300
0.0526
0.1890
0.1745
0.0822
0.0041
0.0023
0.0027
0.0029
0.0008
0.0017
0.0085
0.2233
0.2498
0.3216
0.2053
0.3867
0.0614
0.3135
0.0212
0.0274
0.0701
0.1777
0.1101
0.1070
0.0313
0.0794
0.0461
0.0543
0.0279
0.0188
0.1434
0.0781
0.2643
0.0866
0.4785
0.3416
0.0006
0.0004


STD.DEV
0.4024
3.1600
14.4060
1.1022
1.2132
1.2929
1.2416
0.3747
0.1491
0.7167
0.0246
0.0981
0.1331
0.2525
0.2548
0.1599
0.0234
0.0167
0.0191
0.0297
0.0111
0.0113
0.0508
0.0645
0.0796
0.0915
0.0686
0.9223
0.0457
0.2850
0.1060
0.1264
0.1956
0.2599
0.1995
0.2221
0.0737
0.1641
0.1453
0.1080
0.1194
0.0873
0.2514
0.1770
0.2334
0.0591
0.4996
0.2860
0.0028
0.0005


Females
MEAN STD.DEV
9.3207 0.5325
12.5840 3.0957
24.6430 14.2430
3.1985 1.1136
2.2841 1.2107
2.6498 1.3057
1.3312 1.2274
3.7028 0.3775
0.0392 0.1431
2.1644 0.7000
0.0046 0.0251
0.0275 0.0905
0.0454 0.1182
0.1709 0.2416
0.1520 0.2363
0.0727 0.1446
0.0032 0.0187
0.0022 0.0162
0.0022 0.0161
0.0029 0.0297
0.0008 0.0109
0.0018 0.0118
0.0087 0.0525
0.2283 0.0626
0.2509 0.0743
0.3174 0.0882
0.2033 0.0659
0.4214 0.9069
0.0591 0.0431
0.3540 0.2892
0.0182 0.0981
0.0210 0.1074
0.0530 0.1658
0.1717 0.2531
0.1175 0.2062
0.0973 0.2071
0.0313 0.0713
0.0861 0.1722
0.0530 0.1564
0.0546 0.1050
0.0305 0.1244
0.0187 0.0842
0.1604 0.2663
0.0796 0.1758
0.2469 0.2134
0.0826 0.0552
0.4588 0.4983
0.3314 0.2860
0.0005 0.0025
0.0003 0.0005










is measured as the age less years of education less six.

Since labor force participation rates differ between sexes,

the total sample is separated into male and female samples

for estimation.

The U.S. Government's Dictionary of Occupational

Titles (DOT) was originally developed in 1939 by the

Department of Labor to assist in providing occupational

guidance in local employment service offices. The DOT

comprehensively identifies and defines virtually all

civilian sector occupations. It is now widely used in

career guidance counseling to assist in making occupational

choices.

The DOT data are based on more detailed definitions of

occupations than those used by the Standard Occupational

Coding (SOC) of the Census. The DOT codes have fortunately

been associated with their respective SOC code by the

National Crosswalk Service. To aggregate the DOT data to

the SOC level, the average value of a job characteristic

for the corresponding DOT observation is used to represent

the score for the SOC.

The data available from the DOT include measures of

skill requirements, such as the level of general

educational development (GED) and the years of SVP for each

occupation. The GED scores reasoning, math and language

skill requirements separately on a scale of 1-6. The GED

scores for reasoning, math and language skills for each SOC

are listed in Appendices F, G, and H. The SVP ordinal










scale from 1-9 was transformed to a time-based scale

ranging from 0-10 years. The SVP score for each occupation

is listed in Appendix I.

Occupational complexity is also reported in the DOT

data. Worker function ratings measure the dexterity and

eye-foot coordination requirements on a scale of 1-5 for

each variable. Higher wages should be observable for

occupations requiring these skills if they are scarce in

the labor force. Scores for each occupation requiring

dexterity are listed in Appendix J.

Information regarding working conditions by occupation

is also extracted from the DOT information, since adverse

working conditions should also result in higher wages.

Extreme physical demands and adverse working conditions

such as exposure to hazards, weather, vibration, noise and

stress, are coded with a zero or one dummy variable which

denotes the existence of adverse conditions in an

occupation. Appendix K contains a listing of stressful

occupations by SOC code. Appendix L lists occupations by

order of strength requirements. Occupations that are

exposed to extreme heat, cold, wet noise and vibration are

listed in Appendices M, N, O, P and Q. Occupations exposed

to hazards such as atmospheric conditions, mechanical

devices, shock, heights, radiation, explosives, toxins and

other hazards are listed by order of exposure in Appendices

R, S, T, U, V, W, X and Y.










Further data have been accumulated from the Census

reports. Each occupation's location is reported for the

South, West, North Central and Northeast United States.

Wages vary between regions to compensate for differences in

amenities and in the cost of living.

Current unemployment rates and the fraction employed

in major industry classes are also available from the

Census reports. These data are included to test for

efficiency wages and industry wage differentials.

Additionally, percentages female and nonwhite in an

occupation are also reported by the Census. These are

included to measure any wage discrimination practices.

Higher wages may be required to lure workers into

growing occupations. Occupations which experienced growth

in employment from 1970 to 1980 are coded one, denoting a

growth occupation. If an occupation appears for the first

time in the 1980 Census, it is considered a growth

occupation. Any occupation experiencing negative growth

from 1970 to 1980 is coded -1. In 1980, 31% of the

occupations experienced no increase in employment from 1970

employment levels.

A growing occupation with high employment volatility

should not command as high a level of compensating wage for

risk of unemployment as a no-growth occupation with

volatility. Employment fluctuation in a high-growth

occupation can be absorbed by reducing new hires rather

than by laying off employees. This hypothesis can be










tested with an interaction variable which equals the

product of the long-run growth and the occupational

variance measure. A +1/-1 dummy is employed instead of a

0/1 dummy so that interaction of the risk variables with

the growth variable retains information on the sign of the

risk variable.

Unionization rates are available from Curme, Hirsch

and Macpherson (1990), who have calculated union coverage

and membership rates by occupation. Their rates are based

on the Current Population Survey for the years 1983-1985.

This study utilizes the union coverage rate, which is

defined as the fraction of workers covered by a collective

bargaining agreement, since wages should be more closely

associated with it than with union membership. This

variable will be used to test whether unions raise wages.

To test the hypothesis that increased employment risk

commands a compensating wage, an empirical study using an

ordinary least squares regression is performed as specified

by the model presented in Table 4-2. Transformation of the

wage to the log of the wage is the specification that

others have found provides the best fit when wage is the

dependent variable and traditional human capital measures

are the independent variables.











Table 4-2
Occupation Based Risk Model
Ois=f(F,E,X,X2,X3,S,Q,Dp,Hz,EC,U,GL,T,Z,II,Rj,L)

Variable Definitions:

O = Log Occupation i's Mean Hourly Earnings

F = Fraction Female of Total Occupational Employment

E = Median Years Education for Cell

X = Experience = Age less Education less 6

S = Years Specific Vocational Preparation (SVP)

Q = SVP Education

Dp = Physical Demands Indicator Vector
P = Dexterity, Stress, Strength Indicator

HZ = Hazardous Working Conditions Indicator Vector
Z = Atmospheric Conditions, Mechanical Parts, Shock,
Heights, Radiation, Toxins, Other Hazards

EC = Environmental Conditions Indicator Vector
C = Cold, Heat, Noise, Other

U = Unionization Rate of Occupation

GL = Fraction of Occupation in Geographic Location
L = North Central, North, South, West

T = Long Run Occupational Employment Growth Indicator

Z = Current Unemployment Rate for Occupation

II = Fraction of Occupation Employed in Industry I
I = Agriculture, Mining, Construction, Durable Goods,
Transportation, Wholesale, Retail, FIRE, Business
Services, Personal Services, Entertainment,
Professional Services & Public Administration

Rj = Unemployment Risk Measure
J = Crude, Herfindahl, or Occupational Variance Measures
of Risk

L = Interaction of Long-run Growth and Variance Measure















CHAPTER 5
TEST RESULTS

In order to test for the effect of unemployment risk

on wages, an ordinary least squares regression model was

estimated. Tests of the residuals found

heteroskedasticity, so White's (1980) correction was

employed for significance tests of the ordinary least

squares regression coefficients. Since additional

information could be obtained if the heteroskedasticity was

corrected, many modifications of the model were tested, but

no reduction in heteroskedasticity resulted. These results

are discussed at the end of this chapter.

Regressions were run in six passes. The first

regression pass was the base wage model with no risk

measure. Regressions two through five included one of the

four risk measures described in Chapter 3. The sixth

regression estimated the model with no industry control

variables. Detailed regression results for all models and

both samples are presented in Appendix E.

Due to differences between the male and female labor

force attachment rates, the calculated experience measure

may misrepresent work experience for males relative to

females. Separate regressions were calculated for each sex

to allow for this and to determine the sensitivity of the

regression results to sex.










The models which include industry controls are

preferable, since the hypothesis that the industry control

regressors are jointly equal to zero can be rejected.

However, this does not indicate conclusively that

efficiency wages are the cause. Table 5-1 reports the

estimates of the coefficient for unemployment in models

five and six, which differ only by the inclusion of

industry control variables. If efficiency wages are the

cause of the industry wage differentials, the unemployment

coefficient should be more negative when industry controls

are included. This is indeed the case in both the male and

female samples. However, the unemployment rate

coefficients in these two specifications are not more than

two standard deviations from each other and therefore do

not appear to be significantly different. Thus, these

regressions provide no support for this implication of

efficiency wages.


Table 5-1
OLS Unemployment Rate Coefficients
and Corrected t-statistics (in Parenthesis)

Males Females
Model 5 -0.35 -1.16
With industry controls (-2.71) (-4.95)

Model 6 -0.11 .98
No industry controls (-1.07) (-5.06)


A Hausman specification test can be performed to test

the efficiency wage hypothesis based on the difference in

the coefficients from each model. The estimate of the










coefficient of the unemployment rate is asymptotically

efficient under the null hypothesis, which is a condition

for this test. The null hypothesis that no efficiency wage

exists is rejected with the male sample, but it is not

rejected with the female sample.

Risk Measures

It was expected that compensating wages would be

required for increased unemployment risk. The occupation

variance measure was expected to fit the data more closely

than when an industry variance alone is included, as in the

Adams and Li studies, since the occupational variance would

better account for the mobility of certain occupations

between industries. Table 5-2 presents the coefficient

estimates and correlated t-statistics for both samples and

all risk variables.

Model four includes a variance measure alone which is

positive and significant at the 90% level in the male

sample only. When the covariance and its interaction with

growth is added to the model, the coefficient on the

variance variable increases in significance with the female

sample, but decreases in significance with the male sample.

The coefficients with the covariance measure and its

interaction with growth are jointly significant in both

samples. In the female sample it is significant at

a 95% level. In the male sample the joint test is

significantly different from zero at a 99% level.














MODEL 2
Dummy

Dummy*Gr

MODEL 3
H

H*Growth

MODEL 4
Variance

MODEL 5
Variance

Covarian

Occ. Var


Table 5-2
OLS Risk Variables Coefficients
and Corrected t-statistics (in Parenthesis)
by Model and Sex of Sample

Male Female
-.013 -.015
(-1.75) (-1.17)
owth .022 -.010
(3.34) (-0.86)

-.057 -.053
(-3.73) (-2.02)
.040 -.039
(3.50) (-1.76)

1.588 2.564
(1.72) (1.09)

1.120 4.016
(1.04) (1.56)
ce 18.207 67.01
(1.02) (1.93)
.* Growth 29.733 29.253
(2.93) (2.02)


Thus, taking account of the covariance of employment

across industries provides a better fit, as predicted. The

magnitude of the effect of covariance risk on wages is the

combined effect of the covariance and the interaction of

total variance and growth. Based on the estimates computed

with the female sample, if the covariance increases by one

standard deviation and the occupation is scored one for

growth, a compensating wage increase of 5% is measured. If

the occupation is scored as a no-growth occupation, the

compensating wage differential is increased by 2%. Based

on the estimates computed with the male sample, if the

covariance increases by one standard deviation and the

occupation is scored one for growth, a compensating wage

increase of 2.4% is measured. If the occupation is scored










as a no-growth occupation, wages are estimated to be

decreased by .58%. These results generally support the

hypothesis that earnings are higher in occupations with

greater employment risk, but do not support the theory that

growing occupations would require smaller compensating

wages for employment risk than declining occupations.

Regression model two tests the risk dummy variable

(Dummy), which denotes whether an occupation's

concentration in an industry is 50% or greater. Using the

male sample this model specification finds that wages are

.9% higher in concentrated growth occupations, and 3.5%

lower in concentrated no-growth occupations. Using the

female sample, the effect of concentration is a 2.5%

decrease for positive growth occupations and a .5% decrease

for negative growth occupations. Thus, industry

concentration is associated with higher wages in only one

of these four cases. The joint significance test of the

dummy variable and the growth interaction is significant at

the 99% and 85% level with the male and female samples

respectively.

The H measure is a risk variable which measures the

degree of concentration of an occupation in all industries

on a scale from 0-1. This measure is employed in model

three. The hypothesis that concentrated occupations

require positive compensating wages is not supported with

this definition of risk. Negative differentials are










estimated as the H measure increases whether or not the

occupation is growing.

Regression model five, which contains the covariance

risk measure and industry variables, is presented in Table

5-3. Since the dependent variable is the log wage, a

coefficient less than or equal to 10% can be interpreted as

the percentage change in the wage due to a change in the

independent variable. For coefficients greater than 10%,

the wage is calculated for a one-unit change in the

independent variable. The effect of the independent

variable is then calculated based on the percentage change

of this wage from the mean sample wage. The following

detailed discussion of the coefficients is based on

regression model which includes the covariance risk measure

and industry dummies.

Growth Rates

The growth rate is a -1 or +1 dummy variable which

measures whether an occupation experienced increased or

decreased employment from 1970 to 1980. The model

estimated also includes the interaction of growth and the

occupation variance risk measure. The combined effect of

these growth variables on wages is significant at 99.5% in

both samples. If the mean occupational variance measure of

.001 is employed, earnings in growing occupations are

estimated to be 2% and 6.4% than in declining occupations

in the female and male samples respectively. This supports

the hypothesis that earnings in rapidly growing occupations









Table 5-3
Model Five Regression Results
Log Wage is Dependent Variable
Males Females
Coefficient Coefficient
(t-statistic) (t-statistic)

FRACTION NORTH 0.2085 -0.2525
(2.66) (-1.92)
FRACTION SOUTH -0.1847 -0.6191
(-3.27) (-4.19)
FRACTION WEST -0.0067 -0.1726
(-0.09) (-1.51)
GROWTH DUMMY -0.0024 0.0113
(0.63) 1.53
UNEMPLOYMENT RATE -0.3523 -1.1555
(-2.71) (-4.95)
FRACTION FEMALE -0.2597 -0.1012
(-11.23) (-3.00)
FRACTION AGRICULTURE -0.0517 -0.1865
(-1.01) (-1.71)
FRACTION MINING 0.2898 0.4798
(8.12) (7.11)
FRACTION CONSTRUCTION 0.1456 0.3102
(4.09) (4.72)
FRACTION NONDURABLE 0.0255 0.0858
(0.77) (1.32)
FRACTION TRANSPORTATION 0.1443 0.3027
(4.14) (4.67)
FRACTION WHOLESALE 0.1410 0.1173
(2.60) (1.21)
FRACTION RETAIL -0.1811 -0.1381
(-5.06) (-2.12)
FRACTION FIRE 0.1424 0.1482
(3.83) (2.50)
FRACTION BUSINESS SERV. -0.0657 -0.0119
(-1.68) (-0.18)
FRACTION PERSONAL SERV. -0.2626 -0.1671
(-6.54) (-2.61)
FRACTION ENTERTAINMENT 0.0741 0.1919
(1.48) (2.40)
FRACTION PROFESS. SERV. -0.1323 -0.0293
(-3.48) (-0.49)
FRACTION PUBLIC ADMIN. -0.1382 0.1093
(-3.85) (1.72)
EDUCATION 0.0337 0.0151
(21.64) (5.32)
EXPERIENCE 0.0312 0.0032
(17.99) (0.88)
EXPERIENCE SQUARED -0.0007 0.0001
(-8.65) (0.45)
EXPERIENCE CUBED 3.00 x 106 -4.00 x 106
(2.51) (-1.83)










Table 5-3--continued


GED-REASONING

GED-MATH

GED-LANGUAGE

SVP

SVP EDU

DEXTERITY

STRESS

STRENGTH

EXTREME COLD

EXTREME HEAT

EXTREME WET

EXTREME NOISE

VIBRATION

ATMOSPHERIC

MECHANICAL

SHOCK

HEIGHTS

RADIATION

EXPLOSIVES

TOXINS

OTHER HAZARDS

NONWHITE

UNION REPRESENTATION

VARIANCE


Males
Coefficient
(t-statistic)

-0.0139
(-1.82)
0.0204
(3.22)
0.0516
(6.98)
-0.0279
(-2.24)
0.0047
(5.40)
0.0519
(4.23)
0.1292
(5.14)
-0.0435
(-5.63)
-0.1678
(-1.48)
0.1016
(3.26)
0.1276
(4.66)
0.0067
(0.39)
0.0403
(-2.23)
-0.0357
(-1.59)
0.0362
(0.27)
0.0928
(0.55)
-0.0169
(-0.13)
0.3692
(4.35)
-0.2282
(-1.18)
-0.2635
(-1.30)
-0.0699
(-1.14)
-0.7503
(-8.74)
0.0743
(3.86)
1.1198
(1.04)


Females
Coefficient
(t-statistic)

0.0025
(0.17)
-0.0479
(-5.74)
0.0325
(2.86)
-0.1581
(-6.64)
0.0129
(7.41)
0.0526
(3.59)
0.0649
(1.94)
-0.0455
(-3.90)
0.0712
(0.51)
0.0130
(0.25)
-0.0011
(-0.02)
0.0272
(0.87)
0.0330
(0.84)
0.0409
(0.97)
0.1379
(0.50)
0.4180
(1.23)
-0.2935
(-0.90)
0.3039
(2.37)
0.2185
(0.56)
-0.2883
(-0.91)
0.1271
(1.95)
0.0419
(0.24)
0.0156
(0.41)
4.0155
(1.56)









Table 5-3--continued
Males Females
Coefficient Coefficient
(t-statistic) (t-statistic)
COVARIANCE 18.2070 67.0110
(1.02) (1.93)
GROWTH TOTAL VARIANCE 29.7330 29.2530
(2.93) (2.02)
CONSTANT 8.9453 9.0766
(110.24) (65.42)
Statistics:

R-SQUARE .3897 .2115
ADJ. R-SQUARE .3872 .2078
VARIANCE .0992 .2246
STANDARD ERROR .3150 .4739
SUM OF SQUARED ERRORS 1209 2323
LOG LIKELIHOOD -3201 -6961


are driven up in order to draw workers from other

occupations.

Unemployment Rates

The current unemployment rate is expected to be

negatively related to wages. Lower wages help to induce

workers to leave an occupation that has recently

experienced a fall in long-run demand. In both samples,

the current unemployment rate has a significant negative

coefficient. Based on the male sample, if the unemployment

rate increases by 1% wages will drop by .3%. Based on the

results from the female sample, as the unemployment rate

increases by 1%, wages decrease by .7%.

Geographic Location

The coefficient on geographic regions is expected to

be higher in urban areas to reflect higher costs of living

and lower in geographic areas with positive amenities.










The test results differ greatly in the two samples. Using

the male sample, the regression estimates a 23% higher wage

for employment in the Northeastern states relative to the

omitted variable, the North Central states. Wages are

estimated to be 17% lower for employment in the South. For

the female sample, the differential for wages in the South

is -46%. At a reduced level of statistical significance,

wages are estimated to be 22% lower in the North and 16%

lower in the West, relative to wages in the North Central

States.

Fraction Female

The coefficient on fraction female is expected to be

negative due to the combination of differing labor force

participation rates and discrimination. In the male

sample, as the fraction female increases from 0 to 100% in

an occupation, wages drop 23%. With the female sample,

wages are estimated to drop 10% as the fraction female

increases by 100%.

Fraction Employed in Industry

This variable controls for efficiency wages as well as

any omitted variable which systematically differ by

industry, such as percent self-employed, benefit packages,

hours of work and safety. Positive wage differentials are

found with the male sample for the Mining, Construction,

Transportation, Wholesale and FIRE Industries. Negative

wage differentials are found for the male sample for the

Retail, Personal Services, Professional Services and Public










Administration Industries. These results differ for the

female sample, however. Relative to the Durable Goods

Manufacturing Industry, positive wage differentials are

found with the female sample for the Mining, Construction,

Transportation, FIRE and Entertainment Industries.

Negative wage differentials are found for the Retail and

Personal Services Industries.

A comparison of regression models one and five shows

the effect of including risk measures in a model measuring

industry differentials. A simple correlation between the

set of industry coefficients from models one and five is

.999 for males and .989 for females. The standard

deviation of the sets of coefficients is reduced from .159

to .156 with the male sample and from .198 to .194 with the

female sample. For the full set of coefficients, the

effect of including a risk measure does not measure a

notable difference.

The importance of the risk measures is demonstrated

for specific industries when they are isolated from the

full set. For example, the Business Services Industry

differential is 10% lower and significant when risk

measures are excluded. The differential is not

significantly different from zero when the risk measures

are included. In six of the thirteen industries, the

coefficients estimated with the male sample move closer to

zero when the risk measure is included. In seven of the

industries, the coefficients move further away from zero










when the risk measure is included. In the female sample,

seven industry coefficients moved closer to and six

coefficients moved further away from zero when risk

measures were included.

Education and SVP

It is expected that education and wages are

significantly and positively related. The effect of an

additional year of education is estimated to increase

earnings by 4%, using the male sample and a score of 1.37

for the mean years of SVP. The corresponding effect of an

additional year of education for the female sample is 3.2%,

based on a mean SVP of 1.33 years. The positive

coefficient on the interaction variable indicates that

education is more valuable in an occupation that requires

more training.

Experience

The coefficient for experience is expected to be

positively related to earnings. As expected, the estimated

return on the experience variable differs for the male and

female samples. This could be explained as the result of

differing labor force attachment rates between the sexes,

rather than the result of the effect of sex on the return

on experience. Using the male sample, the regression

estimates that wages increase until 27 years of experience

and decline thereafter. Employing the female sample, wages

are estimated to increase until 24 years of experience, and

decline thereafter. These estimates were derived by taking










the derivative with respect to experience and solving the

quadratic derivative for the positive root.

GED

The coefficient on the educational requirements of the

occupation is expected to be large and positive for math

skills, which are relatively scarce, but lower for language

and reasoning skills, which are relatively common.

However, the estimated coefficients for the skill variables

differ by sex. Math skills are associated with 2% higher

wages in the male sample and 5% lower wages in the female

sample.

Language skills also earn a premium in both samples.

Based on the male sample, a 5% premium is assessed for

language skills. The comparable coefficient measured with

the female sample is 3%.

The results for reasoning skills are puzzling.

Reasoning skills are associated with significantly lower

wages in the male sample, but this is not significant at a

95% level. No discernible wage differential is estimated

with the female sample.

Physical Demands

It is expected that higher physical demands in an

occupation will be associated with higher wages. This

differential reflects the relative scarcity of these skills

in addition to compensating wages required for physically

demanding jobs. Employing the male sample, the regression

estimates a differential of 12% higher compensation for










stressful occupations. The wage differential for the

female sample is estimated at 6.5%, but at a lower level of

significance.

Strength requirements in the male sample are estimated

to reduce earnings by 4.3%. Similarly, for the female

sample, the model estimates a -4.5% wage differential for

strength requirements. Dexterity requirements increase

wages by 5% in both samples.

Environmental Conditions

It is expected that the environmental conditions

variables will have positive coefficients which reflect

compensating wages for discomfort. The coefficient for

exposure to extreme heat estimated with the male sample

indicates 10% greater compensation for this discomfort. No

discernible differential is found with the female sample.

Exposure to extreme wetness for the male sample is

estimated to command 14% higher wages. Again, the female

sample does not estimate any wage differential due to

exposure to wetness.

Exposure to cold, noise or vibration was not

associated with a wage differential in either sample.

Hazards

Significant positive wage differentials are expected

for exposure to hazards as compensation for increased

health and safety risks. Exposure to radiation is

estimated by the regression to require a 36% wage










differential in the female sample and a 45% differential in

the male sample.

Exposure to atmospheric conditions, risk of shock,

heights, mechanical equipment, explosives and toxins were

not estimated to require any significant wage differential

by either sample. Exposure to other hazards was reported

to increase wages by 14% in the female sample, but this is

estimated at a slightly reduced level of significance.

Unionization

Representation by unions is expected to be positively

related to wages. However, the regression results differ

by sex of sample. The regression for males estimated 7%

higher wages at a high level of significance. The presence

of unions does not indicate any improvement in earnings for

females, however.

Fraction Nonwhite

It is expected that the fraction nonwhite will be

negatively related to wages due to the combined effect of

low-quality schools and the practice of discrimination

against nonwhites. The regression estimates a negative

earnings effect of 5.3% for a 10% increase in percent

nonwhite. No effect is indicated with the female sample.

Heteroskedasticity

Heteroskedasticity was discerned when residuals from

the OLS regression of this model failed three tests of

homoskedasticity. The first of the three tests which

failed was the Breusch-Pagan-Godfrey Test; this is a










regression of the squared residuals on the independent

variables. Similarly, the two other tests, the Harvey Test

and the Glejser Test, are regressions of the log of the

squared residuals and the absolute residual on the

independent variables. With heteroskedasticity, the

coefficients are not efficiently estimated.

To correct for heteroskedasticity, interpretations of

coefficients are based on t-statistics constructed with

White's (1980) consistent variance covariance matrix. This

correction is asymptotically efficient in large samples,

which is the case in this study. Elimination of the

heteroskedasticity can sometimes provide more information;

therefore, further investigation of the source is

warranted.

Tests of the OLS residuals indicated that the squared

OLS errors were positively related to the level of SVP and

years of education. Based on this information, the sample

was divided into two groups: Those with more than one year

of SVP and those with less than one year of SVP. This

division of the sample did not reduce the

heteroskedasticity.

The model was reestimated with additional variables

for each level of education and SVP. Each level became a

dummy variable. Again, this did not reduce the

heteroskedasticity problem.

In order to reduce the heteroskedasticity, weights

were constructed and weighted least squares regressions










were employed. Weights based on the number of individuals

representing the wage cell, both relative to the total

individuals in the sample and to the total in the

occupation, did not reduce the heteroskedasticity. In

fact, these weights and their inverses increased the tested

heteroskedasticity of the residuals.

Two weights were found to individually reduce the

heteroskedasticity of the errors in the male sample only.

First, a weight was constructed in which all occupations

were represented with the 30 possible wage observations.

Since some occupations had less than 30 actual

observations, each occupations' observations were weighted

by the fraction 30 divided by the number of wage

observations for the occupation.

Secondly, the weight which was found to most reduce

the heteroskedasticity of the errors was the inverse of the

ratio of the number of individuals in the occupation to the

number of individuals in the sample. This procedure

essentially downweights the largest occupations.

In another procedure, the inverses of the squared OLS

residuals were used as weights. This procedure downweights

the observations with the largest errors. The weighting

procedure failed due to the presence of residuals with no

error which implied division by zero. A constant was then

added to each squared residual and the model was

reestimated. The heteroskedasticity was not reduced by as










much as by the model where larger occupations were given

less weight.

Further research in this area should capitalize on the

work of King regarding earnings differences between

occupations. He found that the variance of earnings within

occupations differs across occupations. This may be the

cause of the heteroskedasticity problem in this study.

Correcting for this form of heteroskedasticity and

reestimating the model would result in more efficient

estimates.

Conclusions

Data from the United States Government Departments on

industries and occupations have become more standardized

through the use of standard industrial classification and

occupation coding systems. This facilitates creation of

large data sets from multiple sources, such as the Bureaus

of Census and Labor Statistics. In this study, the

standard occupation code was used to link the data from

multiple sources.

The premise that occupations exposed to higher

employment volatility require compensating wage

differentials was tested, and some evidence supporting the

theory was found. The results are sensitive to the

specification of the risk measure. Based on the

unemployment risk measure which accounts for the covariance

of employment opportunities across industries, both males










and females were found to require compensating wages for

unemployment risk.

Other factors which were found to increase wages

include higher occupational growth rates, education,

experience, the occupation's skill requirements, dexterity

requirements and exposure to radiation. Unemployment, high

percentage female and strength requirements were found to

be negatively related to wages.

Nonwhite males were found to have lower wages than

white males, and no differential was found for the female

sample. Occupations exposed to stress, heat, or wetness

require differentials in the male sample only.

Additionally, the coefficients of the regional dummies

differed by sex. Collinearity between percent nonwhite and

geographic region dummies was tested and rejected as the

source of the differences in estimates between males and

females.

The inconsistencies in the results between the male

and female samples may reflect omitted variable problems.

Further research could include pooling the samples and

adding the 1990 Census data as it becomes available.

Potential omitted variables include a measure of the degree

of transferability between occupations, better measures of

occupational earnings which would include fringe benefits

and geographic concentration of employment.

Despite these issues, it is worthwhile to study wages

from an occupational perspective. Measures of unemployment






78



risk differ across occupations as compiled in this study.

Others have found that compensating wages are required for

exposure to unemployment risk as measured by industry

employment variance alone. This study improves the risk

measure by incorporating the covariance between industries'

employment.















APPENDIX A
INDUSTRY CLASSIFICATIONS

Agriculture
Metal Mining
Coal Mining
Crude Petroleum and Natural Gas Mining
Nonmetallic Minerals Except Fuels Mining
Construction
Meat Products Manufacturing
Dairy Products Manufacturing
Grain Mill Products Manufacturing
Beverages Manufacturing
Other Food Manufacturing
Tobacco Manufacturing
Knitting Mills Manufacturing
Textile Finishing, Except Wool Manufacturing
Floor Coverings Manufacturing
Miscellaneous Textile Goods Manufacturing
Other Textile Manufacturing
Apparel and Other Textile Products Manufacturing
Paperboard Containers and Boxes Manufacturing
Other Paper Manufacturing
Newspapers Manufacturing
Other Printing Manufacturing
Plastics Manufacturing
Drugs Manufacturing
Soap, Cleaners and Toilet Goods Manufacturing
Paints and Allied Products Manufacturing
Other Chemicals Manufacturing
Petroleum Refining Manufacturing
Other Petroleum Manufacturing
Rubber Manufacturing
Leather Tanning and Finishing Manufacturing
Other Leather Manufacturing
Other Nondurable Goods Manufacturing
Logging Manufacturing
Other Lumber Manufacturing
Furniture and Fixtures Manufacturing
Cement, Concrete, Gypsum & Plaster Manufacturing
Structural Clay Products Manufacturing
Pottery and Related Products Manufacturing
Other Stone Manufacturing
Blast Furnace & Basic Steel Production
Manufacturing
Iron and Steel Foundries
Other Metal Manufacturing
Cutlery, Handtools, and Hardware Manufacturing









Screw Machine Products, Bolts, etc. Manufacturing
Other Metal Fabrication Manufacturing
Engines and Turbines Manufacturing
Construction and Related Machinery Manufacturing
Metalworking Machinery Manufacturing
Other Office Machinery Manufacturing
Household Appliances Manufacturing
Other Electronic Equipment Manufacturing
Motor Vehicles and Equipment Manufacturing
Aircraft Space Vehicles and Parts Manufacturing
Ship and Boat Building and Repairing
Railroad Equipment Manufacturing
Other Transportation Equipment Manufacturing
Watches, Clocks, Watchcases and Parts Manufacturing
Other Durable Goods Manufacturing
Railroad Transportation
Bus
Taxicabs
Trucking Service
Public Warehousing and Storage
U.S. Postal Service
Airline
Pipelines, Except Natural Gas
Radio and Television Broadcasting
Telephone Communications
Other Transportation and Public Utilities
Wholesale Electrical Goods
Apparel, Piece Goods and Notions Wholesale
Wholesale Groceries and Related Products
Other Wholesale
Retail Department Stores
Retail Food Stores
Retail Gasoline Service Stations
Other Automotive Retail
Shoe Stores
Other Apparel
Furniture and Homefurnishings Stores
Other Retail Furniture
Eating and Drinking Places
Drug Stores and Proprietary Stores
Other Retail
Hotels
Advertising
Other Business Services
Hospitals
Other Medical
College
Other Education
Engineering
Other Services
















APPENDIX B
OCCUPATIONS AND INDUSTRY OF LARGEST CONCENTRATION


Industry


SOC Occupation


Concentration


Advertising

Agriculture










Airline


Aircraft &
Space Vehicle
Parts


Apparel &
Other Textile






Blast Furnace
Basic Steel


256 Advertising & Related Sales

086 Veterinarians
485 Supervisors
486 Groundskeepers
487 Animal Caretakers
488 Graders and Sorters
489 Inspectors
495 Forestry Workers
497 Captains & Other Officers Fishing
498 Fishers
499 Hunters and Trappers

226 Airplane Pilots
318 Transportation Ticket &
Reservation Agents
465 Public Transportation
508 Aircraft Engine Mechanics
863 Supervisors, Handlers, Equipment
Cleaners & Laborers

044 Aerospace Engineers
515 Aircraft Mechanics Except Engine
636 Precision Assemblers, Metal
714 Numerical Control Machine Operators


659
667
673
744
765
769
798


Misc. Precision Woodworkers
Tailors
Apparel & Fabric Patternmakers
Textile Sewing Machine Operators
Folding Machine Operators
Slicing & Cutting Machine Operators
Production Samplers and Weighers


045 Metallurgical
544 Millwrights
707 Rolling Machine
724 Heat Treating Equipment Operator
766 Furnace, Kiln & Oven
Operators, Except Food
849 Crane & Tower Operators
873 Production Helpers


35%

84%
31%
43%
61%
100%
37%
84%
70%
90%
47%

78%

69%
91%
55%

12%

51%
50%
53%
22%

20%
50%
41%
79%
24%
8%
7%

20%
16%
52%
22%

13%
27%
8%










Industry


Bus


Business
Services


































Chemicals,
other




Coal
Mining




College


Concentration


SOC Occupation

808 Bus Drivers


026 Management Analysts
064 Computer Systems
069 Physicists
184 Technical Writers
229 Computer Programmers
257 Sales Occupations, Other Business
284 Auctioneers
304 Supervisors-Computer Equipment
345 Duplicating Machine Operator
353 Communications Equipment
Operator nec
415 Supervisors-Guards
426 Guards & Police Except Public Serv.
455 Pest Control Occupations
505 Automobile Mechanics
514 Automobile Body & Related Repairers
523 Electronic Repairers Communications
525 Data Processing Equipment Repairers
526 Household Appliance and
Power Tool Repairer
533 Misc. Electronic Equipment Repairer
535 Camera, Watch & Musical
Instrument Repairers
536 Locksmiths & Safe Repair
547 Specified Mechanics & Repairers nec
549 Not Specified Mechanics & Repairers
668 Upholsterers
759 Painting & Paint Spraying Machine
774 Photographic Process Machine
789 Hand Painting, Coating & Decorating
793 Hand Engraving
813 Parking Lot Attendants
864 Helpers, Mechanics & Repairers
887 Vehicle Washers & Equipment Cleaner


048
073
224
756
757


Chemical Engineers
Chemists
Chemical Engineering Technician
Mixing & Blending Machine Operator
Separating, Filtering & Clarifying


046 Mining Engineers
615 Explosive Workers
616 Mining Machine Operators
859 Misc. Material Moving Equipment
867 Helpers, Extractive Occup.

225 Science Technicians
235 Technicians except Health & Science


47%

48%
23%
20%
11%
20%
27%
52%
17%
13%

78%
28%
31%
87%
40%
67%
29%
36%

31%
26%

42%
65%
12%
8%
45%
18%
34%
27%
26%
51%
29%
28%

37%
17%
37%
12%
42%

34%
23%
62%
7%
52%

21%
20%










SOC Occupation


Construction 053 Civil Engineers
216 Engineering
516 Heavy Equipment Mechanics
519 Machinery Maintenance Occupations
534 Heating, Air Conditioning &
Refrigeration Mechanics
543 Elevator Installers & Repairers
553 Supervisors, Brick & Stonemasons
554 Supervisors, Carpenters & Related
555 Supervisors, Electricians &
Power Transmission
556 Supervisors, Painters,
Paperhangers & Plasterers
557 Supervisors, Plumbers, Pipefitters
558 Construction Supervisors nec
563 Brickmasons & Stonemasons
565 Tilesetters, Hard & Soft
567 Carpenters
573 Drywall Installers
575 Electricians
579 Painters, Construction & Maintenance
583 Paperhangers
584 Plasterers
585 Plumbers, Pipefitters & Steamfitters
588 Concrete & Terrazzo Finishers
593 Insulation Workers
594 Paving, Surfacing & Tamping
595 Roofers
596 Sheetmetal Duct Installers
597 Structural Metal Workers
598 Driller, Earth
599 Construction Trades nec
643 Boilermakers
653 Sheet Metal Workers
783 Welders and Cutters
844 Operating Engineers
853 Excavating & Loading Machine
855 Grader, Dozer & Scraper Operators
865 Helpers, Construction Trades
869 Construction Laborers

Crude 047 Petroleum Engineers
Petroleum 075 Geologists
& Natural 613 Supervisors, Extractive Occupations
Gas 614 Drillers, Oil Well
617 Mining Occupations nec
848 Hoist & Winch Operators


Drugs


223 Biological Technicians


Durable Goods 645 Patternmakers & Model Makers, Metal
other 647 Precious Stones & Metals (Jewelers)


40%
18%
31%
11%

29%
51%
90%
83%

47%

86%
79%
90%
87%
85%
81%
97%
47%
69%
80%
90%
61%
95%
71%
90%
97%
62%
73%
84%
66%
26%
30%
12%
79%
60%
65%
79%
91%

80%
43%
53%
96%
39%
55%

22%

15%
53%


Concentration


Industry










SOC Occupation


Education
other







Electrical




Electrical
other














Engineering






Metal
Fabricating





Financial
Services,
Insurance,
Real Estate


656
676
794

014
163
164
329
387
448
453
468

049
366
577
695

055
056
213
363
633
683
689
758
777
779
784
785
796
797

043
059
063
217
218
866

655
706
713
717
723
725

007
016
024
025
066


Patternmakers & Model Makers, Wood
Patternmakers, Layout Workers
Hand Grinding

Administrators
Counselors
Librarians
Library Clerks
Teachers' Aides
Supervisor Cleaning & Building
Janitors & Cleaners
Child Care Workers Except Private

Nuclear Engineers
Meter Readers
Elec. Power Installers & Repairers
Power Plant Operators

Electrical Engineers
Industrial
Electrical
Production Coordinators
Supervisors, Production Occupations
Electronic Equipment Assemblers
Inspectors, Testers & Graders
Compressing & Compacting Machine
Misc. Machine Operators nec
Machine Operators, Not Specified
Solderers and Braziers
Assemblers
Production Inspectors, Checkers
Production Testers

Engineers, Architects
Engineer nec
Surveyors
Drafting Technicians
Surveying Technicians
Helpers, Surveyor

Misc. Precision Metal Workers
Punching and Stamping Press
Forging Machine Operators
Fabricating Machine Operators nec
Metal Plating Machine Operators
Misc. Metal & Plastic Processing

Financial Managers
Managers Properties
Underwriters
Other Financial Officers
Actuaries


30%
21%
26%

60%
50%
62%
62%
88%
15%
18%
39%

21%
58%
78%
72%

30%
14%
27%
8%
6%
70%
31%
8%
9%
7%
49%
19%
12%
19%

15%
24%
62%
22%
44%
51%

11%
22%
23%
15%
32%
15%

48%
83%
95%
67%
64%


Concentration


Industry










SOC Occupation


166
253
254
255
285
305
308
309
326
328
335
336
337

343
344
347
356
357
375
378
383
385
454


Food, other





Furniture
& Fixtures

Government


688 Food Batchmakers
754 Packaging & Filling Machine
763 Roasting & Baking Machine Operators
764 Washing, Cleaning & Pickling Machine
888 Hand Packers & Packagers

657 Cabinet Makers & Bench Carpenters
658 Furniture & Wood Finishers


003
005
006
008
027
033
035
036
037
065
067
068
074
076
077
078
079
168


Legislators & Public Administration
Administrators & Officials, Public
Administrators, Protective Services
Personnel & Labor Relations Manager
Personnel Specialists
Purchasing Agents nec
Construction Inspectors
Inspectors and Compliance
Management Related
Operations & Systems Researchers
Statisticians
Mathematical Scientists
Atmospheric Scientists
Physical Scientists nec
Agricultural Scientists
Biological Scientists
Forestry Scientists
Sociologists


43%
25%
55%
10%
11%

43%
44%

100%
96%
95%
9%
22%
9%
46%
71%
53%
20%
31%
36%
42%
42%
27%
24%
45%
34%


Economists
Insurance
Real Estate Sales
Securities & Financial Sales
Sales Support Occupations nec
Supervisors, Financial Records
Computer Operators
Peripheral Equipment Operators
Correspondence Clerks
Personnel Clerks, Except Payroll
File Clerks
Records Clerks
Bookkeepers, Accounting & Auditing
Clerks
Cost & Rate Clerks
Billing, Posting & Calculating
Office Machine Operators nec
Mail Clerks, Except Postal Service
Messengers
Insurance Adjusters
Bill & Account Collectors
Bank Tellers
Data Entry Keyers
Elevator Operators


14%
100%
97%
100%
22%
22%
15%
20%
33%
35%
22%
28%

14%
34%
69%
31%
17%
20%
100%
25%
100%
17%
31%


Concentration


Industry










SOC Occupation


169
173
179
227
228
303
314
315
316
338
376
379
386
389
413
414
416
417
418
423
424
425


Grain Mill
Products

Hospitals















Hotels



Iron & Steel
Foundries


Leather

Logging


768 Crushing & Grinding Machine Operator 16%


015
083
095
097
106
203
205
206
207
208
339
446
447
696

317
449
466


Managers, Medicine
Medical Scientists
Registered Nurse
Dieticians
Physicians Assistants
Clinical Laboratory Technologists
Health Record Technologists
Radiologic Technicians
Licensed Practical Nurses
Health Technologists
Billing Clerks
Health Aides, Except Nursing
Nursing Aides, Orderlies
Stationary Engineers

Hotel Clerks
Maids & Housemen
Baggage Porters


675 Hand Molders & Shapers Except Jewel 21%


745 Shoe Machine Operators

494 Supervisors
496 Timber Cutting


Social Scientists
Urban Planners
Judges
Air Traffic Controllers
Broadcast Equipment
Supervisors, General Office
Stenographers
Typists
Interviewers
Payroll & Timekeeping Clerk
Investigators, Except Insurance
General Office
Statistical Clerks
Administrative Support nec
Supervisors, Firefighting
Supervisors, Police
Fire Inspection & Prevention Occup.
Firefighting
Police & Detective, Private Service
Sheriff, Bailiff, Other Law
Correctional Institution
Crossing Guards


38%
79%
100%
100%
58%
26%
38%
23%
34%
11%
23%
15%
20%
21%
93%
100%
43%
95%
100%
100%
100%
92%


62%
39%
73%
60%
32%
76%
82%
79%
66%
53%
13%
47%
50%
9%


100%
32%
61%


93%

61%
78%


Concentration


Industry










SOC Occupation


Lumber, other





Machinery,
other


726
727
728
729
733

054
233
637
644
684
703
704
705
708
709


Wood Lathe, Routing & Planing
Sawing Machine Operators
Shaping & Joining Machine Operator
Nailing & Tacking Machine Operator
Misc. Woodworking Machine Operator

Agricultural Engineers
Tool Programmers
Machinists
Precision Grinder, Filer & Tool
Misc. Precision Workers nec
Lathe & Turning Machine Setup
Lathe & Turning Machine Operator
Milling & Planing Machine Operator
Drilling and Boring
Grinding, Abrading, Buffing


Meat Products 786 Hand Cutting and Trimming


Medical
other


Metalworking
Machinery

Motor
Vehicles &
Equipment

Newspapers




Paperboard


084
085
087
088
089
167
204
319
445
678


Physicians
Dentists
Optometrists
Podiatrists
Health Diagnosing nec
Psychologists
Dental Hygienists
Receptionists
Dental Assistants
Dental Lab & Medical Appliance Tech


634 Tool & Die Makers


057
215
715


Containers & Boxes


Paper, other


Personal
Services


Mechanical Engineers
Mechanical Technicians
Misc. Metal, Plastic, Stone & Glass


195 Editors
278 News Vendors
325 Classified Ad Clerks
346 Mail Preparing & Paper Handling

753 Cementing & Gluing Machine Operator


214 Industrial
369 Samplers


Funeral Directors
Photographers
Sales Counter Clerks
Barbers


018
189
275
457


64%
53%
25%
51%
49%

53%
10%
26%
15%
58%
20%
28%
25%
25%
14%

29%

56%
93%
86%
90%
95%
29%
95%
25%
95%
86%

21%


14%
17%
30%

39%
78%
46%
17%

9%


7%
10%

95%
34%
52%
97%


Industry


Concentration










SOC Occupation


458
666
669
747
748


Hairdressers
Dressmakers
Shoe Repairers
Pressing Machine Operators
Laundering & Drycleaning Machine


Pottery 787 Hand Molding, Casting and Forming
& Related Products


Printing,
other


384
649
679
734
735
736
737


Proofreaders
Engravers, Metal
Bookbinders
Printing Machine Operators
Photoengravers and Lithographers
Typesetters and Compositors
Misc. Printing Machine Operators


Radio & 198 Announcers
Television Broadcasting


Railroad
Transport





Retail
Apparel

Retail
Automotive

Retail
Department
Stores


Retail Drug
& Proprietary

Retail
Eating &
Drinking
Places


349
823
824
825
826
843


Telegraphers
Railroad Conductors & Yardmasters
Locomotive Operating Occupations
Railroad Brake, Signal & Switch
Rail Vehicle Operators nec
Supervisors Material Moving Equip.


674 Misc. Precision Apparel & Fabric


503 Supervisors, Mechanics & Repairers
509 Small Engine Repairers


009
029
373
374


Purchasing Managers
Buyers
Expediters
Material Recording & Scheduling


096 Pharmacists


019
433
434
435
436
437
438
439
443
444


Managers and Administrators nec
Supervisors, Food Preparation
Bartenders
Waiters & Waitresses
Cooks, Except Short Order
Short Order Cooks
Food Counter
Kitchen Workers
Waiters' & Waitresses' Assistants
Misc. Food Preparation


96%
27%
51%
55%
54%

31%


40%
27%
90%
57%
71%
54%
79%

85%


55%
93%
82%
91%
88%
15%

33%


21%
33%

13%
24%
10%
29%

69%


10%
64%
81%
85%
62%
89%
84%
54%
61%
47%


Concentration


Industry










SOC Occupation


Retail
Food Stores




Retail
Furniture


276
686
687
795
877


Cashiers
Butchers & Meat Cutters
Bakers
Misc. Hand Working
Stock Handlers & Baggers


566 Carpet Installers


Retail 885 Garage & Service Station Related
Gasoline Service Stations


Retail, other 185
243
277
283
589
677


Rubber


Designers
Supervisors & Proprietors, Sales
Street & Door-to-door Sales
Demonstrators, Promoters & Models
Glaziers
Optical Goods Workers


719 Molding & Casting Machine Operators
755 Extruding & Forming Machine Operator


Services,
other


023
034
155
165
174
175
176
177
178
183
186
187
188
193
194
197
199
234
313
377
427
456
459
463
464
467
469
773


Accountants
Business and Promotion Agents
Teachers, Pre-kindergarten
Archivists
Social Workers
Recreation
Clergy
Religious workers
Lawyers
Authors
Musicians and Composers
Actors and Directors
Painters, Sculptors, Craft Artists
Dancers
Artists
Public Relations
Athletes
Legal Assistants
Secretaries
Eligibility Clerks
Protective Service Occupations
Supervisors Personal Service
Attendants, Amusement
Guides
Ushers
Welfare Service
Personal Service Occupations nec
Motion Picture Projectionists


42%
58%
54%
11%
69%

46%


75%


22%
23%
71%
19%
39%
45%

22%
27%

26%
56%
74%
52%
44%
40%
96%
91%
77%
77%
81%
53%
34%
71%
50%
14%
49%
37%
14%
42%
34%
36%
64%
29%
81%
64%
16%
84%


Industry


Concentration










SOC Occupation


Ship & Boat
Building

Taxicabs

Telephone
Communication





Textile,
other





Transport.
Communication
& Public
Utilities









Trucking
Service





U.S. Postal
Service


646 Layout Workers


809 Taxicab Drivers & Chauffeurs


306
323
327
348
527
529

518
738
739
743
749
878

058
539
694
699
814
828
829
833
834
845
875
876

359
507
803
804
805
883

017
307
354
355


Chief Communications
Information Clerks nec
Order Clerks
Telephone Operators
Telephone Line Installers & Repairer
Telephone Installers & Repairers

Industrial Machinery Repairers
Winding & Twisting Machine Oper.
Knitting, Looping & Weaving Mach.
Textile Cutting Machine Oper.
Misc. Textile Machine Operators
Machine Feeders & Offbearers

Marine Engineers
Mechanical Controls & Valve Repairer
Water & Sewage Treatment Plant
Misc. Plant & System Operators
Motor Transportation Occup. nec
Ship Captains & Mates Except Fishing
Sailors & Deckhands
Marine Engineers
Bridge, Lock & Lighthouse Tenders
Longshore Equipment Operators
Garbage Collectors
Stevedores

Dispatchers
Bus, Truck & Stationary Engine Mech.
Supervisors, Motor Vehicle Operators
Truck Drivers, Heavy
Truck Drivers, Light
Freight Stock & Material Handler nec

Postmasters
Supervisors, Distributions
Postal Clerks, Except Mail Carriers
Mail Carriers, Postal Service


Watches, 693 Adjusters & Calibrators
Clocks, Watchcases & Parts


Wholesale
Grocery

Wholesale
other


799 Graders and Sorters
806 Driver-Sales Workers

013 Managers Marketing
028 Purchasing Agents


57%


48%

87%
13%
21%
46%
86%
86%

7%
76%
59%
33%
50%
15%

55%
45%
82%
23%
51%
74%
77%
53%
43%
91%
80%
91%

24%
29%
34%
41%
17%
22%

100%
19%
100%
100%

63%


20%
25%

14%
68%


Industry


Concentration






91


Industry SOC Occupation Concentration

258 Sales Engineers 24%
259 Sales Rep., Mining, Mfg. & Wholesale 49%
364 Traffic, Shipping 10%
365 Stock & Inventory 8%
368 Weighers & Measurers 9%
517 Farm Equipment Mechanics 58%
538 Office Machine Repairers 38%
856 Industrial Truck & Tractor Equipment 6%
889 Laborers, Except Construction 10%
















APPENDIX C
RISK MEASURES FOR DETAILED OCCUPATIONS

SOC Three-digit Level Classification Dummy H

Managerial and Professional Specialty 0 0.07
Executive, Administrative & Managerial 0 0.05
003 Legislators & Public Administration 1 1.00
005 Administrators, Officials, Pub. Admin. 1 0.92
006 Administrators, Protective Services 1 0.91
007 Financial Managers 1 0.24
008 Personnel and Labor Relations 0 0.03
009 Purchasing 0 0.04
013 Managers Marketing 0 0.05
014 Administrators 1 0.45
015 Managers Medicine 1 0.51
016 Managers Properties 1 0.70
017 Postmasters 1 1.00
018 Funeral Directors 1 0.90
019 Managers and Administrators nec 0 0.04
023 Accountants 0 0.11
024 Underwriters 1 0.91
025 Other Financial Officers 1 0.47
026 Management Analysts 1 0.26
027 Personnel 0 0.09
028 Purchasing Agents 1 0.49
029 Buyers 0 0.16
033 Purchasing Agents nec 0 0.03
034 Business and Promotion Agents 1 0.34
035 Construction Inspectors 1 0.33
036 Inspectors and Compliance 1 0.50
037 Management Related 1 0.29
Professional Specialty 0 0.14
043 Architects 0 0.06
044 Aerospace Engineers 1 0.37
045 Metallurgical Engineers 0 0.08
046 Mining Engineers 0 0.19
047 Petroleum Engineers 1 0.64
048 Chemical Engineers 0 0.16
049 Nuclear Engineers 0 0.12
053 Civil Engineers 0 0.24
054 Agricultural Engineers 1 0.33
055 Electrical Engineers 0 0.13
056 Industrial Engineers 0 0.05
057 Mechanical Engineers 0 0.06
058 Marine Engineers 1 0.35
059 Engineer nec 0 0.09
063 Surveyors 1 0.41










Soc
064 Computer Systems
065 Operations and Systems Researchers
066 Actuaries
067 Statisticians
068 Mathematical Scientists
069 Physicists
073 Chemists
074 Atmospheric
075 Geologists
076 Physical Scientists nec
077 Agricultural
078 Biological
079 Forestry
083 Medical
084 Physicians
085 Dentists
086 Veterinarians
087 Optometrists
088 Podiatrists
089 Health Diagnosing nec
095 Registered Nurse
096 Pharmacists
097 Dieticians
106 Physicians Assistants
155 Teachers, Prekindergarten
163 Counselors
164 Librarians
165 Archivists
166 Economists
167 Psychologists
168 Sociologists
169 Social Scientists
173 Urban Planners
174 Social Workers
175 Recreation
176 Clergy
177 Religious
178 Lawyers
179 Judges
183 Authors
184 Technical Writers
185 Designers
186 Musicians and Composers
187 Actors and Directors
188 Painters, Sculptors, Craft Artists
189 Photographers
193 Dancers
194 Artists
195 Editors
197 Public Relations
198 Announcers
199 Athletes
Technical Sales and Administrative


Dummy
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
1
0
1
1
1
1
0
0
0
0
1
0
0
1
1
1
1
1
0
0
1
1
0
0
1
1
0
0
1
1
0


H
0.10
0.08
0.47
0.12
0.18
0.12
0.06
0.21
0.23
0.21
0.17
0.13
0.32
0.21
0.46
0.87
0.72
0.74
0.82
0.90
0.56
0.53
0.40
0.24
0.60
0.33
0.44
0.30
0.06
0.20
0.21
0.23
0.63
0.32
0.29
0.92
0.84
0.61
1.00
0.60
0.06
0.09
0.66
0.40
0.17
0.18
0.56
0.27
0.21
0.07
0.74
0.32
0.05










Soc
Technicians
203 Clinical Laboratory
204 Dental Hygienists
205 Health Record
206 Radiologic Technicians
207 Licensed Practical Nurses
208 Health Technologists
213 Electrical Technologists
214 Industrial Technologists
215 Mechanical Technologists
216 Engineering Technologists
217 Drafting Technologists
218 Surveying Technologists
223 Biological Technologists
224 Chemical Technologists
225 Science Technologists nec
226 Airplane Pilots
227 Air Traffic Controllers
228 Broadcast Equipment
229 Computer Programmers
233 Tool Programmers
234 Legal Assistants
235 Technicians nec
Sales Occupations
243 Supervisors & Proprietors, Sales
253 Insurance
254 Real Estate Sales
255 Securities & Financial Services Sal
256 Advertising and Related Sales
257 Sales Occupations, Other Business
258 Sales Engineers
259 Sales Reps.
275 Sales Counter Clerks
276 Cashiers
277 Street and Door-to-door
278 News Vendors
283 Demonstrators, Promoters and Models
284 Auctioneers
285 Sales Support Occupations nec
Administrative Support
303 Supervisors General Office
304 Supervisors, Computer Equipment
305 Supervisors, Financial Records
306 Chief Communications
307 Supervisors, Distributions
308 Computer Operators
309 Peripheral Equipment Operators
313 Secretaries
314 Stenographers
315 Typists
316 Interviewers
317 Hotel Clerks
318 Transportation Ticket & Reservation:


e


Dummy
0
1
1
1
1
1
1
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
1
1
1
0
0
0
1
1
0
1
1
0
1
0
0
0
0
0
1
0
0
0
0
0
0
0
1
1


H
0.08
0.61
0.90
0.68
0.66
0.51
0.33
0.11
0.05
0.06
0.06
0.08
0.25
0.10
0.16
0.07
0.60
1.00
0.38
0.08
0.05
0.24
0.08
0.09
0.12
1.00
0.94
1.00
0.22
0.16
0.09
0.26
0.33
0.22
0.51
0.63
0.09
0.34
0.11
0.05
0.12
0.08
0.07
0.75
0.05
0.06
0.06
0.07
0.18
0.10
0.19
0.99
0.49


s




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