BLS WORKING PAPERS 

 U.S. Department of Labor 

U.S. Bureau of Labor Statistics 

Office of Productivity and Technology 

The Strength of Occupation Indicators as a Proxy for Skill 

Alec Levenson, University of Southern California 

Cindy Zoghi, U.S. Bureau of Labor Statistics 

Working Paper 404

March 2007

All views expressed in this paper are those of the authors and do not necessarily reflect the views or policies of the 

U.S. Bureau of Labor Statistics. 
“The Strength of Occupation Indicators as a Proxy for Skill”*

Alec Levenson 

Center for Effective Organizations 

Marshall School of Business 

University of Southern California 

Los Angeles, CA 90089 

e-mail: alevenson@marshall.usc.edu

Cindy Zoghi 

US Bureau of Labor Statistics 

2 Massachusetts Ave NE #2180 

Washington DC 20212 

e-mail: Zoghi.Cindy@bls.gov

* The data used in this paper are restricted-use; we thank Brooks Pierce for his guidance in 

analyzing them. All views expressed in this paper are those of the authors and do not necessarily 

reflect the views or policies of the U.S. Bureau of Labor Statistics.

1
Abstract 

Labor economists have long used occupation indicators as a proxy for unobserved 

skills that a worker possesses. In this paper, we consider whether inter-occupational 

wage differentials that are unexplained by measured human capital are indeed due to 

differences in often-unmeasured skill. Using the National Compensation Survey, a large, 

nationally- representative dataset on jobs and ten different components of requisite skill, 

we compare the effects on residual wage variation of including occupation indicators and 

including additional skills measures. We find that although skills do vary across 3-digit 

occupations, occupation indicators decrease wage residuals by far more than can be 

explained by skill differentials. This indicates that “controlling for occupation” does not 

equate to controlling for skill alone, but also for some other factors to a great extent. 

Additionally, we find that there is considerable within occupation variation in 

skills, and that the amount of variation is not constant across skill levels. As a result, 

including occupation indicators in a wage model introduces heteroskedasticity that must 

be accounted for. We suggest that greater caution be applied when using and interpreting 

occupation indicators as controls in wage regressions. 

2
I. Introduction 

Occupations play a central role in labor markets. People get both formal education and 

on-the-job training to develop sets of skills that enable success in different categories of jobs. 

Those categories are occupations. Occupation classifications are used by both firms and workers 

to facilitate communication about the content of a job, which promotes more efficient screening 

of potential job applicants than otherwise might occur. The job requirements associated with any 

given occupation (e.g. doctor, lawyer, accountant, laborer, secretary, teacher, computer 

programmer) also provide a road map for those seeking to enter the occupation, whether by 

formal schooling, on the job training, or both. Without occupation classifications, therefore, there 

would be much less efficient resource allocation in the labor market. 

Despite this central role for occupations, there is no consensus within labor economics on 

the issue of how to use occupation classifications empirically. On the one hand, the conceptual 

argument for occupations as bundles of skills leads some economists to use occupation controls 

to hold constant unobserved skill differences, particularly when estimating human capital 

earnings or other labor market models. On the other hand, because people can and do switch 

occupations, and wages are presumed to be set at least partially by supply and demand, there are 

risks in viewing occupational wages as direct measures of the returns to skill.1

 For example, do 

doctors earn more than laborers because they have more skill, because of barriers to entry, or 

both? Clearly doctors learn real skills in medical school, so the occupational classifications 

represent more than just barriers to entry. Yet there also are barriers to entry created both 

naturally by the need for lengthy graduate education and on-the-job training, and artificially by 

 

1

 Also, because there is substantial occupational segregation along gender and racial lines, and that segregation is correlated with 

wages, there is intense debate whether occupations in these cases are proxies for true skills or for nonmarket factors (including 

discrimination) that are unrelated to skill. 

3
licensing requirements. Thus the question is to what extent are occupations proxies for skills 

versus a convenient way of classifying jobs that is unrelated to objective measures of skills? 

While this question is critical for understanding the role of occupations (and skills) in the 

labor market, traditional data sets in economics are not suitable for the task because they only 

record occupation title and demographic characteristics of the person holding the job. This paper 

uses the National Compensation Survey (NCS), a new nationally-representative data set with 

measures of skill that are based on the job to examine this question in depth. The skill measures 

are derived from the behavioral science literature on job design, which uses concepts such as 

complexity of the job and degree of autonomy to compare and rate dissimilar jobs throughout the 

economy using uniform measurements. We are thus able to compare the effects on wage 

estimations of including occupation indicators as a proxy for unobserved skill versus using direct 

measures of those skills. 

We find that although skills do vary across 3-digit occupations, occupation indicators 

decrease wage residuals by far more than can be explained by skill differentials. This indicates 

that “controlling for occupation” does not equate to controlling for skill alone, but also for some 

other factors to a great extent. Part of the inter-occupation wage variation is due to nonrandom 

sorting along demographic lines: gender and race controls decrease the residual wage variation 

that is not explained by the NCS skill controls but which is accounted for by occupation controls. 

Additionally, we find that there is considerable within occupation variation in skills, and that the 

amount of variation is not constant across skill levels. As a result, including occupation 

indicators in a wage model introduces heteroskedasticity that must be accounted for. We suggest 

that greater caution be applied when using and interpreting occupation indicators as controls in 

wage regressions. 

4
The paper is structured as follows. In the next section we review the development of the 

occupation classification system and the extent to which skills have not been a main organizing 

logic for dividing jobs into similar groups. The next sections describe the NCS data and compare 

human capital earnings models estimated using the NCS and Current Population Survey (CPS). 

Following that we focus on the amount of residual inter-occupational wage variation that is 

accounted for by the NCS skill measures. The final section examines how including traditional 

occupation controls beyond the NCS skill measures introduces heteroskedasticity in human 

capital earnings models.

II. A brief history of the occupation classification system 

“It should be the purpose of statistics of occupation... [to] show, so far as 

possible, not only the skill and intelligence of the worker, and his position in the 

industry,...but, as a means for the study of the risk, healthfulness, and numerous 

other problems connected with his occupation, they should show, also, the 

specific services rendered, work done, or processes performed by him” (Alba M. 

Edwards, 1911) 

In the nineteenth century, occupational classifications generally focused on the industry 

in which a worker was employed. For example, one category in the 1880 Census occupation 

system is car makers, which clearly involves many different skills and types of tasks. In fact, 

there are nearly 100 distinct manufacturing and professional occupations that contribute to 

making cars today. On the other hand, the 1880 Census also contained separate categories for 

“clerks in stores”, “bookkeepers in stores”, “clerks and bookkeepers in banks”, “clerks and 

bookkeepers in companies”, “clerks and bookkeepers in offices” and “clerks and bookkeepers in 

railroad offices”, all of whom presumably possess similar skills and perform similar tasks. 

5
In arguing for a major revision of the Census method of classifying occupations, Alba 

Edwards (1911, 1938, 1941) proposed that a worker should be classified not by the product she 

was making, but by the kind of work she was doing or service she was rendering. He argued that 

since the aim of collecting statistics is to “better the social and economic condition of man” that 

such statistics should, in fact, measure this condition. The Census Bureau first fully adopted this 

methodology in the 1940 Decennial Census. At the same time, Edwards also realized that no 

perfect categorization of occupations is possible, both because the division of labor and vast 

number of industries in the economy prohibits a categorization that is both “sufficiently broad 

and sufficiently detailed”, and because the occupations are not always well-defined and change 

over time as “new processes in manufacturing are being devised.” (Edwards, 1911: 619-20) 

Labor economists often control for the effect of otherwise unobserved human capital on 

wages by including a series of occupation indicator variables in a wage regression, on the 

assumption that occupation categories measure the skills a worker must have to perform a 

particular job in that occupation. It is not certain, however, that the occupational classification 

system successfully distinguishes the skills of workers. First, it may not have correctly 

distinguished initial skill differences between occupations in 1940. As Margo Conk (1978) 

notes, “Without providing an adequate definition of ‘skill’ or a criterion for determining it, the 

United States Census began to classify occupations according to skill. To do this, the Census fell 

back on the ‘social’ component of its definition of an occupation, in short, on the divisions of 

ethnicity, race, sex and age within the American population.” Thus, an occupation might be 

categorized as unskilled merely because there were many minority or female workers in the 

occupation. 

6
The second problem with inferring skill from occupational classifications is that there is 

significant within-occupation skill variation that is entirely obscured by the Census classification. 

Certainly, there are differences among workers in their ability to perform tasks of high 

complexity, and there are differences among jobs in the level of task complexity and 

responsibility bestowed on the worker. It is harder, for example, to build an entire house than to 

install a bookshelf, yet the people who do part of that work in both cases are classified as 

carpenters by the Census. A copy editor for children’s books may require less knowledge than 

one responsible for scientific texts. And a police officer in a quiet rural area faces quite different 

job demands than one in a high crime urban area. In each case the job demands differ 

significantly within occupations (even “narrowly” defined 3 digit occupations), and so, too, 

might the skills of the people who work in those jobs. 

The third potential difficulty with inferring skill from occupational classifications is that 

both the mean skill level and the variation in skill within an occupation can change over time 

without being captured by changes in the occupational classification system adjustments. As 

technology changes the nature of the production methods, jobs become up-skilled or downskilled, depending on the nature of the technology (see, for example, Autor, Levy and Murnane, 

2002) This can lead to an occupation category becoming more skilled, less skilled, or having a 

higher variation in skill over time. 

III. The National Compensation Survey 

The National Compensation Survey (NCS) is a restricted-use dataset of information on 

up to 20 jobs each at nearly 20,000 nationally representative establishments in the non-farm, 

non-Federal U.S. economy in 1999. The data are collected by field economists who visit 

sampled establishments and randomly select 5-20 workers from the site’s personnel records, 

7
depending on establishment size. Through interviews with human resources representatives, 

detailed information about the jobs those workers hold is obtained. 

The data include information on the establishment, including its location, industry, 

whether privately or publicly owned, and whether operating for profit. In addition, for the 

selected jobs, data are collected on unionization status, work hours, incentive pay, occupation 

and earnings. The NCS data measure the skills and wages of jobs—thus they do not measure 

anything about a particular worker who might hold that job. There are therefore no demographic 

details about workers. Rather than measuring the human capital stock possessed by a worker, as 

proxied by his education level and experience, the NCS measures the human capital 

requirements of a given job. These requirements are encompassed by ten “generic leveling 

factors,” which are intended to measure various job design attributes consistently across 

occupations. These factors are based on the Federal Government’s Factor Evaluation System, 

which is used to set Federal pay scales,2

 and are measured on Likert scales with ranges varying 

from 1-3 to 1-9. 

• Knowledge (1-9): The nature and extent of applied information that the workers must 

possess to do acceptable work. The lowest numbers correspond to blue collar jobs, 

requiring minimal skills or education; the highest numbers correspond to very highskilled jobs. 

• Supervision Received (1-5): The nature and extent of supervision and instruction 

exercised by the supervisor, the extent of modification and participation permitted by 

the employee, and the degree of review of completed work. Larger values correspond 

to less supervision. 

 

2

 For a detailed description of the NCS, see Pierce (1999). 

8
• Guidelines (1-5): How specific and applicable the guidelines are for completing the 

work, and the extent of judgment needed to apply them. Larger numbers correspond 

to less use of guidelines. 

• Complexity (1-6): The nature, number, variety, detail and originality of the tasks, and 

the difficulty in determining what needs to be done. Larger numbers correspond to 

more complex jobs. 

• Scope and Effect (1-6): The extent to which the nature of the work impacts the work, 

output or service of others within or outside the organization. Larger numbers 

correspond to greater impact on other activities and persons. 

• Personal Contact (1-4): Extent of contacts with persons not in the supervisory chain, 

including the difficulty of initiating and performing communication. Larger numbers 

correspond to more contacts or higher-ranking contacts. 

• Purpose of Contacts (1-4): Nature of contacts, ranging from exchange or clarification 

of information to justifying, defending or negotiating matters involving significant or 

controversial issues. Larger numbers correspond to more significant contacts. 

• Physical Demands (1-3): Physical abilities and exertion involved in the work. Larger 

numbers correspond to more physical demands. 

• Work Environment (1-3): Risks and discomforts in the physical surroundings or the 

nature of the duties. Larger numbers correspond to more discomfort or risk. 

• Supervisory Duties (1-5): Level of supervising responsibility. Larger numbers 

correspond to more levels of subordinates supervised. 

Table 1 shows the distribution of values of the skill measures. To better compare across 

skills, Table 1 also includes the mean of each variable, normalized to range from zero to one. 

9
These statistics show that most jobs have very minimal supervisory duties. There are few jobs 

with severe physical demands or extremely risky or uncomfortable work environments. For the 

remaining skills, there are few jobs in either the lowest or the highest skill categories. 

IV. Estimating human capital earnings models using CPS vs. NCS 

The canonical human capital earnings model uses schooling and imputed experience to 

infer the person’s skill level. Over the years labor economists have used a variety of more direct 

measures of skill, particularly scores from “objective” measurement tools such as the IQ test and 

the Armed Forces Qualifying Test, to show that traditional human capital measures are positively 

correlated with these measures of skill. The problem with these measures, however, is that they 

are focused on the individual's innate skills, not the job requirements. Thus they are suitable for 

isolating the portions of earnings that are due to fixed factors related to the individual. They are 

not, however, good measures of the skill requirements of the jobs themselves. 

The NCS job skill most closely related to traditional worker’s human capital measures is 

knowledge, which is typically coded as that which “would be acquired through a pertinent 

[degree] or its equivalent in experience, training, or independent study”. To see how a worker’s 

human capital measures compare to the knowledge requirements of the jobs those workers hold, 

we obtain the average knowledge required for NCS jobs in each three-digit occupation and 

compare this to the average education and years of potential experience for workers in those 

occupations in the March 2001 Current Population Survey. The correlation between knowledge 

and education is .824, while the correlation between knowledge and potential experience is only 

.0803

. Table 2 summarizes this comparison, aggregating the numbers to the 2-digit occupation 

 

3

 Interestingly, the correlation between the average education and average potential experience is -.0783. 

10
level. In general, many of the occupations are ranked similarly along knowledge and education. 

It is less apparent for knowledge and potential experience that the two measure a similar concept. 

In many particular occupations, such as secretary, mail distribution, building and personal 

services, vehicle and machine operating, the workers have many years of potential experience, 

while the jobs do not require a high amount of knowledge. In these jobs, the additional years of 

potential experience do not result in accumulating additional human capital relevant to 

performing this job. 

We consider whether knowledge plays a similar role to traditional human capital 

variables in predicting wages by estimating a wage model in the CPS using only education, and 

potential experience and its square as explanatory variables, and estimating a wage model in the 

NCS using only knowledge. Table 3 shows the results of these estimations. The explanatory 

power of the NCS model is much higher than that of the CPS model. In both samples we predict 

log hourly wages and calculate the average within each 3-digit occupation code. The correlation 

between these two sets of predicted wages is quite high – .87, suggesting that the measures, 

although somewhat different, do explain wages comparably for the average worker. 

We are interested not only in the explanatory power for the average worker, however, but 

also in the relative explanatory power across occupation groups. Figure 1 shows both the CPS 

and the NCS residuals of these estimations, averaged across all workers/jobs in each 3-digit 

occupation code. The data points are sorted in an approximate white collar-blue collar order, 

with executives and managers on the far left and unskilled laborers on the far right. As indicated 

earlier by the R-squareds of Table 3, neither model fully explains wage variation, but the NCS 

model that controls for knowledge requirements results in smaller overall residuals than the CPS 

model that controls for accumulated human capital. In both samples there are important 

11
differences across 3-digit occupations in the explanatory power of the human capital controls. In 

the CPS, accumulated human capital explains less variation in both the high skill occupations 

and also in the production occupations. In the NCS, knowledge explains less variation for the 

production workers. 

V. Occupational indicators as a proxy for unobserved skill 

Such findings of inter-occupational wage differentials that are not explained by 

differences of worker’s observed human capital are common. In fact, such wage differentials 

motivate the inclusion of occupation indicators in many empirical wage estimations. This makes 

sense if worker ability is poorly measured and the omitted ability variable varies systematically 

across occupations. When occupation controls are included, oftentimes along with other “job” 

controls like industry4

 and union status, they typically account for a significant portion of wage 

differentials in both gender (Blau and Kahn, 2000) and part-time (Blank, 1990) wage 

regressions. On the other hand, occupation indicators may control for other non-market factors 

that affect wages, including, but not limited to, discrimination and sorting on the basis of 

nonmarket characteristics. 

If we believe that ability is the only unobserved variable, then we believe that wages are 

given by the following wage model: 

(1) Wj = β0 + β1Xj + β2Zj + uj 

where Wj is the wage rate earned in job j, Xj is the observed component of human capital in job j, 

Zj is the unobserved component of human capital and uj is a random disturbance that is 

distributed N(0,σu

2

). It is well established that estimating model (1) without controlling for the 

 

4

 Occupation controls sometimes are entered by themselves, other times in conjunction with controls for industry. However, 

because there are only a limited number of occupations represented within many industries (Helwege, 1992), the identification of 

either when both are included is driven from a minority of industry-occupation groups. 

12
unknown variable Zj results in biased estimates of β1. To improve upon our estimate of β1, we 

use a set of occupation indicators as a proxy for the unknown data, where the occupations are 

related to Zj by: 

(2) Zj = γ0 + γ1(occ=1) + γ2(occ=2) + ... + γN(occ=N) + vj 

where vj represents the measurement error in the proxy, or the extent to which occupation 

indicators are a “good” proxy for unobserved ability. Generally we cannot observe this 

relationship, and must assume that the error is distributed N(0, σv

2

). This leads us to estimate: 

(3) Wj = β0 + β1Xj + β2[γ0 + γ1(occ=1) + γ2(occ=2) + ... + γN(occ=N) + vj] + uj 

or , 

(4) Wj = (β0 + β2γ0) + β1Xj + β2(γ1(occ=1) + γ2(occ=2) + ... + γN(occ=N)) + (uj + β2vj) 

Absent any measurement error, estimation of the model (4) would yield unbiased 

estimates of β1. Even with measurement error, the bias will be smaller in estimating equation (4) 

than if we simply left unmeasured the omitted variable. Generally, we would interpret 

significance of the coefficients on the proxy to mean that the unobserved skills are correlated 

with the wages, although we could not separate β2 and γn. Caution should be exercised in such 

interpretation of this model, however. In particular, suppose that another unobserved variable, 

Dj, is important to determining wages, so that: 

(5) Wj = β0 + β1Xj + β2Zj + β3Dj + uj

where the unobserved variable Dj is likewise related to the occupation indicators by: 

(6) Dj = α0 + α1(occ=1) + α2(occ=2) + ... + αN(occ=N) + wj

with wj again representing the measurement error in the proxy. Then the wage model we 

estimate is actually: 

(7) Wj = (β0 + β2γ0 + β3α0) + (β2γ1 + β3α1)(occ=1) + ... + (β2γN + β3αN)(occ=N) + error 

13
which is completely undistinguishable from equation (4). Thus, we may attempt to interpret the 

significance of the occupation indicators and the increased overall explanatory power of the 

model, as an indication that we have “controlled” for unobserved skill differences, but we may 

just as likely have, in fact, controlled for some other non-market characteristics of jobs that vary 

across occupations as well. 

Before we can address this issue, we first show the bias in estimating the wage model (1) 

without any proxy for the unobserved skills, and then show how the coefficient on knowledge 

and the explanatory power of the model change when we add a full set of 3-digit occupation 

indicators. In addition to knowledge, we include an indicator for whether the job is a part-time 

job, an indicator for whether it is unionized, and an indicator for whether it earns incentive pay. 

It is important to remember that these variables are characteristics of the job, not of any 

particular worker who performs the job. In addition to these job features, we include the log of 

the establishment size, indicators for whether the establishment is a non-profit institution, and 

indicators for whether the establishment is located in the Northeast, Midwest or South regions. 

After controlling for these job and workplace features, we then add a full set of indicators for 

each 3-digit occupation. 

Table 4 compares these two estimations. Adding the explanatory variables reduces the 

size of the knowledge coefficients and increases the overall explanatory power by 5%. The 

inclusion of occupation indicators further reduces the measured effect of knowledge and 

increases the adjusted R-squared from .747 to .8075

. Figure 2 shows the dramatic change in the 

pattern and size of the wage residuals resulting from including occupation indicators in the wage 

estimation. The mean of the average wage residuals is now quite close to zero, and the 

 

5

 Due to the extremely large sample size, standard errors of the coefficient estimates are quite small for all variables and we do 

not report them here. 

14
differences in explanatory power across occupations is largely eliminated. Including occupations 

clearly improves the ability of the model to predict the inter-occupation wage differentials that 

are evident in the data and are not explained by human capital. 

Again, it is not at all clear how to interpret this method of “explaining” wage variation 

across occupations. On the one hand, unobserved skills may vary across occupation, and the 

inclusion of indicators for the worker’s/job’s occupation may be capturing the effect of such 

omitted variables. If this is the case, it seems quite reasonable to use occupation indicators as a 

proxy for the unobserved variables. On the other hand, if the wage differentials are due to other 

non-market features of a worker/job, it is important for the researcher to know that the 

occupation indicators are not proxies for unobserved skills, but rather for differences across 

occupation in some other variable. 

With most data on worker demographics, it is not possible to distinguish between these 

two alternatives, because the skill variables are unobserved. Using the NCS data, however, we 

can investigate the relationship between occupation indicators and some of these oftenunobserved requisite job skills. In addition to knowledge, the NCS contains nine additional 

measures of job skill that are not necessarily captured by traditional human capital variables. In 

order for occupation indicators to capture these omitted skills, the skills must, in fact, vary 

systematically across occupations. Figures 3A-I show the average value of each skill within a 3-

digit occupation group. In a sense, these graphs indicate the estimated γs from equation (2). 

There is considerable variation across occupations. For supervision received, guidelines, 

complexity and scope and effect, the means are highest for managers, professionals and technical 

jobs. Sales, clerical and most service jobs have fairly low values of these skills. Construction 

and precision craft workers have intermediate values, while the machine operators, assemblers 

15
and other laborers have the lowest values. Personal contacts and the purpose of those contacts 

have high values only for the managers, professionals and technical workers, and are consistently 

low across most administrative, service and production jobs. Physical demands and work 

environment are similarly low for most managers, professionals, technical, sales and clerical 

jobs, and higher for those in services and production. Lastly, supervisory duties are quite high 

for the executive managers, much lower for professional and technical jobs, and at their lowest 

for all other groups, with the exception of occupation codes that are specifically designated as 

“supervisory”, such as administrative supervisors and construction supervisors. The patterns in 

these figures do suggest that often-unmeasured skills such as these do, in fact, vary 

systematically across occupation and may provide justification for including occupation 

indicators to control for these omitted skill variables. 

To further investigate whether occupation indicators serve as a proxy for such omitted 

skills, we consider how adding the skills to a wage regression affects the overall explanatory 

power of the model, as well as the relative explanatory power across occupations. We expect 

that including additional skills in a wage regression will not only push the average residual 

toward zero, but will also flatten out the distribution of those residuals across occupation groups. 

To the initial wage model showed in column one of Table 4, we add each of the nine skills, first 

one at a time and then all simultaneously. In each case we use a set of indicator variables for 

each Likert value the skill takes on. 

The resulting adjusted R-squareds are shown in Table 5. For supervision received, 

guidelines, complexity and scope and effect, the overall adjusted R-squared of the wage model 

increases with their addition. The remaining four skill requirements do not add to the 

explanatory power of the model. Not only do the skill requirement variables improve the fit of 

16
the model, but even after controlling for occupations, these variables still matter, although the 

change in adjusted R-squared is small. This indicates that there are aspects of skill not 

represented by the traditional occupation classifications. On the other hand, the large amount of 

additional residual variation explained by the occupation indicators after controlling for the full 

set of NCS skills indicates that something other than skills as measured by traditional human 

capital and the other NCS factors is driving differences in mean wages across occupations. 

One possible nonmarket factor that occupations may proxy for, but that may be unrelated 

to skill is the gender or race of the worker. The NCS data do not contain any demographic 

information on the workers who hold the jobs surveyed. Thus, occupation indicators may 

control for unmeasured gender or racial differences that affect wages. To provide some evidence 

on this question, we calculate from the March CPS the percent of workers that are female and the 

percent of workers that are non-white in each 3-digit occupation, and add the result to the wage 

model with controls for all skills. The explanatory power increases somewhat, owing to the 

significant negative coefficients on both variables. Wage differentials across gender and racial 

lines persist even with strong controls for skills, and this may be one nonmarket factor for which 

occupation indicators are controlling. 

Table 5 indicates that the inclusion of additional skills increases the ability of the wage 

model to explain the variation in wages for the average job, although not quite as much as the 

inclusion of occupation indicators. As we saw in Figure 2, however, the biggest impact of the 

occupation indicators was in reducing the unexplained inter-occupational wage differentials. In 

order to compare the effect of the skills to the effect of occupation indicators, we calculate the 

average residuals within each 3-digit occupation and examine the distribution across occupations 

of these average wage residuals under each model estimated. 

17
Table 6 shows the first four moments of this distribution for each model. Consistent with 

the visual representation of the residuals in Figure 2, the data indicate that the model with 

occupation controls has mean zero residuals, with very little variation, a slight negative skew 

(with several negative average residuals among the highest and lowest skill groups), and some 

leptokurtosis. While several of the skills also reduce the mean of these within-occupation 

average residuals, especially guidelines, complexity and scope and effect, even with the 

inclusion of all nine skills, the average remains around 0.015, while including the occupation 

indicators reduces the average to 2.6 E-11. Additionally, guidelines, complexity and scope 

reduce only slightly the amount of variation across occupations in the size of the unexplained 

component of wages (from .153 in the model with only knowledge to around .14). Again, this is 

very little reduction compared to the effect of including occupation indicators, which removes 

nearly all the variation across occupations, by design. Including additional skills increases the 

extent of negative skew and leptokurtosis in the average residuals. Unlike in the model with 

occupation indicators, the negative skew in these cases does not indicate more negative than 

positive residuals, but rather indicates that more of the average residuals lie below the (positive) 

mean than above, which makes sense. 

To summarize, the addition of often-unmeasured job skill variables does reduce the size 

of the residuals somewhat, and does diminish the differences in explanatory power across 

occupations to a small extent. The magnitude of these effects is tiny compared to the effect of 

introducing occupation indicators. This suggests that for the most part, occupation indicators are 

controlling for factors much more important to determining inter-occupational wage differentials 

than the skill differentials measured here. This leaves two possibilities: there are other important 

18
skills not measured by the NCS, or inter-occupational wage differentials are due to other factors 

than differential skill requirements across occupations. 

VI. Heteroskedasticity 

One further difficulty with occupation proxies that we have not addressed is that the 

assumptions about the distribution of the measurement error might not hold. In particular, 

looking at Figures 3A-I, it appears that there is significant within-occupation variation in the 

level of the skills. If the amount of variation is consistent across occupations, so that Var(vj) = 

σv

2

, this is not problematic. However, the standard errors suggest otherwise for several of the 

skill requirements. In particular, the figures show that the within-occupation variation is highest 

for scientists, teachers and professors, other professionals, technicians, clerical and 

administrative jobs, construction jobs and precision craft jobs. This indicates that for these 3-

digit occupation categories, jobs are not as similar as they are within other 3-digit occupations—

especially managers, engineers, sales, supervisors, service jobs and very low-skilled jobs. If the 

variance of the proxy’s measurement error is related to the proxy itself, then the composite error 

term of the wage model will also suffer from heteroskedasticity. 

To assess the extent to which this is a problem, we test for heteroskedasticity in the wage 

estimation model that controls for knowledge, job and establishment characteristics and the 

occupation indicators as proxies for omitted skills. Since the patterns of within-occupation skill 

variability are evident in all nine measures of skill, we test whether the heteroskedasticity is a 

function of each measure separately as well as all measures combined. Although we perform 

these tests on the NCS sample, we are somewhat more concerned about the likely 

heteroskedasticity in a sample where occupation indicators are used in place of additional 

19
information on skill requirements beyond the standard human capital measures. To check 

whether this is a problem in such data, we calculate the 90-10 skill differential by 3-digit 

occupation in the NCS, and test whether this variable is related to the variance of the CPS wage 

regression. The CPS wage regression includes controls for the worker’s race, gender, marital 

status, whether working part-time, whether a veteran, and region of residence. Table 7 shows the 

results of both sets of tests. There is strong evidence of heteroskedasticity in the NCS wage 

model with occupation controls, related to each of the skills. Even in the much weaker CPS test 

where the skill level can only vary across occupations, there is strong evidence of 

heteroskedasticity in the model that is related to several of the skills. 

VII. Conclusion 

In this paper, we have examined the use of occupation indicators as proxies for oftenunobserved ability or job skill requirements. We use the National Compensation Survey to 

compare the effects on wage estimations of including occupation indicators versus including 

actual measures of job skills. In general, researchers cannot do this using traditional data sets 

because skill is often measured only by education and experience, rather than the amount of 

particular skills required by a job (and thus rewarded by wages). 

We find that while including measures of actual job skills requirements improves the fit 

of a human capital earnings model, the extent of the improvement is much less than with 

occupation indicators. Thus, “controlling for occupation” does not appear to be the same thing 

as controlling for unobserved ability, but rather controlling partially for unobserved ability and to 

a larger extent, other factors that are also related to occupation classifications. 

20
We also find that part of the inter-occupation wage variation is due to nonrandom sorting 

along demographic lines: gender and race controls decrease the residual wage variation that is 

not explained by the NCS skill controls but which is accounted for by occupation controls. This 

perhaps is not surprising given the long-running debate regarding the merits of controlling for 

occupation when estimating gender and race wage differentials. Our contribution to the debate is 

the inclusion of direct measures of skill requirements which enable a comparison with variation 

in gender and racial composition as factors explaining individual wage differentials. The 

evidence indicates that part of the residual wage variation accounted for by occupation controls 

is due to skill requirement differences, but part is also due to apparent non-market sorting, 

including sorting along gender and racial lines. Thus controlling for occupation in gender and 

racial wage differential models appears to over-control for skill requirements and potentially bias 

such models away from non-market explanations of wage differentials (e.g. due to 

discrimination), even when the “true” model includes non-market drivers of wage differentials. 

Additionally, we find that there is considerable within-occupation variation in skills, and 

that the amount of variation is not constant across skill levels. As a result, including occupation 

indicators in a wage model introduces heteroskedasticity that must be accounted for. This is at 

least in part because skills are more homogenous in certain occupation categories than in other 

ones. This causes the measurement error in the proxy to be heteroskedastic, which affects the 

wage error term as well. In the absence of direct skill measures such as these in datasets like the 

CPS, human capital earnings models that include controls for occupation should also use 

heteroskedasticity-consistent standard errors. 

Based on these results, we urge caution in both the use and interpretation of occupation 

indicators as proxies for unobserved skills. Greater efforts can and should be made to collect 

21
data on job skills, which would obviate the need for using occupation indicators. Additionally, 

new occupation categories might be designed that incorporate job skills data such as the NCS. 

22
References 

Autor, David H., Frank Levy and Richard J. Murnane. 2002. “Upstairs, Downstairs: ComputerSkill Complementarity and Computer-Labor Substitution on Two Floors of a Large Bank,” 

Industrial and Labor Relations Review 55(3): 432-447. 

Blank, Rebecca M. 1990. “Understanding Part-Time Work,” in Laurie J. Bassi and David L. 

Crawford, eds., Research in Labor Economics, Volume 11, 137-158. 

Blau, Francine D. and Lawrence M. Kahn. 2000. “Gender Differences in Pay,” Journal of 

Economics Perspectives, 14:4, 75-99. 

Conk, Margo Anderson. 1978. “Occupational Classification in the United States Census: 1870-

1940,” Journal of Interdisciplinary History 9(1): 111-130. 

Edwards, Alba M. 1911. “Classification of Occupations: The Classification of Occupations, 

with Special Reference to the United States and the Proposed New Classification for the 

Thirteenth Census Report on Occupations,” Publications of the American Statistical Association

12(94): 618-646. 

Edwards, Alba M. 1941. “Occupation and Industry Statistics,” Journal of the American 

Statistical Association 36(215): 387-392. 

Helwege, Jean. 1992. “Sectoral Shifts and Interindustry Wage Differentials,” Journal of Labor 

Economics, Vol. 10, No. 1, pp. 55-84. 

Pierce, Brooks. 1999. “Using the National Compensation Survey to Predict Wage Rates,” 

Compensation and Working Conditions. Winter: 8-16.

23
TABLE 1. Mean and distribution of responses to leveling factors 

Normalized Raw distribution of responses

Mean 1 2 3 4 5 6 7 8 9 

Knowledge .401 10.5 25.7 19.9 13.5 7.0 14.4 7.2 1.6 0.1

Supervision rec’d .449 21.6 41.5 28.8 7.1 1.0

Guidelines .408 33.3 36.1 24.7 5.2 0.7

Complexity .399 19.3 35.1 35.7 6.8 3.0 0.2 

Scope and effect .352 30.9 34.5 28.5 4.6 1.3 0.2 

Personal contacts .420 44.9 42.7 12.2 0.3 

Purpose of contacts .369 62.2 28.6 8.5 0.7 

Physical demands .528 43.7 54.0 2.3 

Work environment .501 51.5 46.8 1.7 

Supervisory duties .272 78.7 8.9 10.4 1.8 0.2

24
TABLE 2. Human capital measures in the 1997 NCS and 2001 CPS 

NCS 

knowledge

CPS years 

education

CPS years potential 

experience

Mean Rank Mean Rank Mean Rank 

Health diagnostic 0.8119 1 19.281 1 21.521 18 

Lawyer/judge 0.788 2 17.813 2 19.02 28 

University prof. 0.7515 3 17.494 3 19.023 27 

Executive 0.7342 4 14.303 16 22.99 9 

Engineers 0.7325 5 15.043 12 16.805 36 

Natural sciences 0.7241 6 17.092 4 17.872 34 

Math/CS 0.7226 7 15.858 6 10.729 43 

Public admin 0.7198 8 14.596 13 20.898 21 

Health treatment 0.66 9 15.76 8 12.764 41 

Mgmt-related 0.6555 10 15.19 11 19.79 25 

Teachers 0.6373 11 15.564 9 22.139 12 

Other professional 0.5833 12 16.321 5 20.226 23 

Admin. supervisor 0.5644 13 12.763 29 23.386 8 

Service sales 0.5498 14 14.361 15 21.347 20 

Other technical 0.5478 15 15.815 7 30.405 1 

Sales manager 0.4964 16 13.667 22 22.062 14 

Finance/bus. sales 0.4902 17 14.45 14 20.366 22 

Engineering tech. 0.4854 18 13.459 24 18.86 29 

Health technician 0.4417 19 14.025 18 18.6 30 

Construction 0.4077 20 11.451 38 22.721 10 

Mechanic 0.405 21 12.215 34 18.553 31 

Other precision craft 0.3848 22 12.381 32 22.286 11 

Protective service 0.3831 23 13.52 23 14.64 38 

Computer operator 0.3593 24 14.191 17 21.809 15 

Secretary 0.3406 25 13.695 20 26.373 2 

Other sales 0.3385 26 15.358 10 21.395 19 

Forestry/fishing 0.3232 27 11.402 39 22.065 13 

Records 0.3231 28 13.733 19 19.88 24 

Other transportation 0.2913 29 13.158 25 21.655 16 

Other administrative 0.2901 30 13.148 26 21.579 17 

Machine operator 0.2635 31 11.363 40 23.492 7 

Assembler 0.2564 32 11.908 35 19.288 26 

Vehicle operator 0.2552 33 12.33 33 25.4 4 

Health service 0.2543 34 12.939 28 13.521 39 

Retail sales 0.2484 35 12.677 31 13.034 40 

Personal service 0.2431 36 13.677 21 24.273 6 

Farm laborer 0.2329 37 10.618 43 18.099 32 

Construction labor 0.208 38 10.76 42 17.602 35 

Mail distribution 0.2031 39 12.704 30 25.654 3 

Food service 0.203 40 11.623 36 11.545 42 

Other laborer 0.1944 41 11.089 41 17.926 33 

Building service 0.1928 42 11.463 37 24.315 5 

Handlers 0.1803 43 13.135 27 15.509 37 

25
TABLE 3. Comparison of 1997 NCS and 2001 CPS wage 

estimations with only human capital controls 

CPS NCS

HS grad .297***

Some college .468***

College grad .835***

Advanced degree 1.10***

Potential experience .035***

Potential experience2 -.001***

Knowledge = 2 .280***

(.003) 

Knowledge = 3 .554***

(.003) 

Knowledge = 4 .843***

(.003) 

Knowledge = 5 .954***

(.004) 

Knowledge = 6 1.16***

(.003) 

Knowledge = 7 1.50***

(.004) 

Knowledge = 8 1.93***

(.007) 

Knowledge = 9 2.10***

(.031) 

R-squared .222 .693 

No. obs. 64,924 135,408 

26
TABLE 4. Effect of controlling for occupation in NCS wage estimation 

1 2 

Knowledge = 2 .227 .179 

Knowledge = 3 .467 .369 

Knowledge = 4 .731 .589 

Knowledge = 5 .883 .783 

Knowledge = 6 1.08 .995 

Knowledge = 7 1.43 1.30 

Knowledge = 8 1.86 1.69 

Knowledge = 9 2.03 1.89 

Part-time job -.164 -.087 

Incentive pay .212 .174 

Unionized -.066 -.033 

Non-profit estab. .169 .166 

Ln(estab. size) .022 .023 

Located in northeast .011 .005 

Located in midwest -.010 -.026 

Located in south -.059 -.079 

Controls for occupation? NO Yes 

R-squared .747 .807 

No. obs. 135,408 135,408 

NOTES: Due to the large sample size, standard errors are very small and are not reported here. 

27
TABLE 5. Effect of adding skills to wage estimation on 

explanatory power of model 

Adjusted 

R-squared

Knowledge .747 

Including supervision received .764 

Including guidelines .768 

Including complexity .763 

Including scope and effect .762 

Including personal contacts .749 

Including purpose of contacts .748 

Including physical demands .747 

Including work environment .748 

Including supervisory duties .749 

Including all skills simultaneously .779 

Including all skills plus % female and % minority .784 

Including all skills and occupation indicators .824 

Including occupation indicators .807 

NOTES: Each line represents the adjusted R-squared from a wage estimation that 

includes all variables listed in TABLE 4 plus the human capital control indicated. 

28
TABLE 6. Moments of the distribution of within-occupation average wage residuals 

for various sets of human capital controls 

Mean Standard Dev. Skewness Kurtosis

Knowledge alone .0280 .1532 -.9349 14.16 

Including occupation indicators 2.74e-11 9.97e-10 -.0516 8.61 

Including supervision received .0246 .1434 -1.00 18.17 

Including guidelines .0195 .1366 -.7194 17.33 

Including complexity .0190 .1445 -1.438 21.40 

Including scope and effect .0188 .1406 -1.402 18.86 

Including personal contacts .0253 .1513 -.8431 14.66 

Including purpose of contacts .0302 .1545 -.8568 14.02 

Including physical demands .0277 .1531 -.9514 14.26 

Including work environment .0241 .1504 -1.14 15.61 

Including supervisory duties .0311 .1532 -.9129 13.94 

Including all skill requirements .0151 .1361 -1.321 23.20 

Including all skills plus % female and 

% minority 

.0012 .1345 -1.54 25.26 

NOTES: Each line results from a separate wage estimation including all variables listed in TABLE 4 plus the human 

capital control indicated. Residuals were collected and then averaged within each of 468 separate 3-digit 

occupations. 

29
30

TABLE 7. Breusch-Pagan tests of whether job skills are a source 

of heteroskedasticity in NCS (CPS) wage estimations that control 

for knowledge (human capital) and occupation 

NCS CPS

Knowledge -- 11.41***

Supervision rec’d 1,500*** 2.72*

Guidelines 1,216*** 0.18 

Complexity 1,289*** 3.77**

Scope and effect 1,562*** 0.00 

Personal contacts 2,207*** 147.5***

Purpose of contacts 2,226*** 162.2***

Physical demands 163*** 33.2***

Work environment 429*** 214.1***

Supervisory duties 1,456*** 126.4***

All factors 3,964*** 863.4***

NOTES: Breusch-Pagan test of heteroskedasticity related to skill indicated. For 

NCS, this tests individual wage residuals with individual reported skill level. 

For CPS, this tests individual wage residuals with occupation-specific median 

skill level. * indicates p<.10, ** indicates p<.05, *** indicates p<.01.
FIGURE 1. CPS (NCS) Wage Residuals by Occupation with Human Capital (Knowledge) 

Controls

-1

-0.2-0.4-0.6-0.8

0

0.80.60.40.2

1

Managers

Professional

Health Diagnostics

Teacher/Prof.

Soc. Sci./Law/Arts

Technicians

Sales

Admin. Super.

Clerical/Admin.

Protective Svc

Pers./Hlth/Food

Ag/For/Fish &

Mechanics/Repair

Constr. Super.

Construction 

Mining

Precision Crafts

Plant/Sys. Op.

Machine

Fabricators/Asse

Transportation

Helpers/Laborers

Residuals

CPSNCS

31
32

FIGURE 2. Effect on R

-0.000000005

-2.5E-09

0

2.5E-09

0.000000005

Managers

Professional

Health Diagnostics

Teachers/Profess

Wage Residuals

esiduals of Adding Occupation Indicators to Wage Regression

Soc. Sci./Law/Arts

Technicians

Sales

Admin. Superv.

Clerical/Administr

Protective

Prof./Hlth/Food

Ag./For./Fish &

Mechanics/Repair

Constr. Superv.

Construction 

Mining

Precision Crafts

Plant/System Op.

Machine

Fabricators/Asse

Transportation

Helpers/Laborers

Quartic trendline
FIGURE 3A. Supervision Received by Occupation (with quartic trendline)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Managers

Engineers

Scientists

Health 

Teachers/Profes

Other

Technicians

Supervisors

Sales

Clerical/Administ

Protective

Pers./Hlth/Food

Farm/For/Fish/H

Mechanics/Repai

Construction 

Precision Crafts

Machine

Fabricators/Asse

Transportation

Helpers/Laborers

Supervision Received

Mean

± 1 Standard Deviation

Figure 3B. Guidelines by Occupation (withquartic trendline)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Managers

Engineers

Scientists

Health 

Teachers/Profes

Other

Technicians

Supervisors

Sales

Clerical/Administ

Protective

Pers./Hlth/Food

Farm/For/Fish/H

Mechanics/Repai

Construction 

Precision Crafts

Machine

Fabricators/Asse

Transportation

Helpers/Laborers

Guidelines

Mean

± 1 Standard Deviation

33
FIGURE 3C. Complexity by Occupation (with quartic trendline)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Managers

Engineers

Scientists

Health 

Teachers/Profes

Other

Technicians

Supervisors

Sales

Clerical/Administ

Protective

Pers./Hlth/Food

Farm/For/Fish/H

Mechanics/Repai

Construction 

Precision Crafts

Machine

Fabricators/Asse

Transportation

Helpers/Laborers

Complexity

Mean

± 1 Standard Deviation

FIGURE 3D. Scope and Effect by Occupation (with quartic trendline)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Managers

Engineers

Scientists

Health 

Teachers/Profes

Other

Technicians

Supervisors

Sales

Clerical/Administ

Protective

Pers./Hlth/Food

Farm/For/Fish/H

Mechanics/Repai

Construction 

Precision Crafts

Machine

Fabricators/Asse

Transportation

Helpers/Laborers

Scope and Effect

Mean

± 1 Standard Deviation

34
FIGURE 3E. Personal Contacts by Occupation (with quartic trendline)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Managers

Engineers

Scientists

Health 

Teachers/Profes

Other

Technicians

Supervisors

Sales

Clerical/Administ

Protective

Pers./Hlth/Food

Farm/For/Fish/H

Mechanics/Repai

Construction 

Precision Crafts

Machine

Fabricators/Asse

Transportation

Helpers/Laborers

Personal Contacts

Mean

± 1 Standard Deviation

FIGURE 3F. Purpose of Contacts by Occupation (with quartic trendline)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Managers

Engineers

Scientists

Health 

Teachers/Profes

Other

Technicians

Supervisors

Sales

Clerical/Administ

Protective

Pers./Hlth/Food

Farm/For/Fish/H

Mechanics/Repai

Construction 

Precision Crafts

Machine

Fabricators/Asse

Transportation

Helpers/Laborers

Purpose of Contacts

Mean

± 1 Standard Deviation

35
FIGURE 3G. Physical Demands by Occupation (with quartic trendline)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Managers

Engineers

Scientists

Health 

Teachers/Profes

Other

Technicians

Supervisors

Sales

Clerical/Administ

Protective

Pers./Hlth/Food

Farm/For/Fish/H

Mechanics/Repai

Construction 

Precision Crafts

Machine

Fabricators/Asse

Transportation

Helpers/Laborers

Physical Demands

Mean

± 1 Standard Deviation

36
FIGURE 3H. Work Environment by Occupation (with quartic trendline)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Managers

Engineers

Scientists

Health 

Teachers/Profes

Other

Technicians

Supervisors

Sales

Clerical/Administ

Protective

Pers./Hlth/Food

Farm/For/Fish/H

Mechanics/Repai

Construction 

Precision Crafts

Machine

Fabricators/Asse

Transportation

Helpers/Laborers

Work Environment

Mean

± 1 Standard Deviation

37
FIGURE 3I. Supervisory Duties by Occupation (with quartic trendline)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Managers

Engineers

Scientists

Health 

Teachers/Profes

Other

Technicians

Supervisors

Sales

Clerical/Administ

Protective

Pers./Hlth/Food

Farm/For/Fish/H

Mechanics/Repai

Construction 

Precision Crafts

Machine

Fabricators/Asse

Transportation

Helpers/Laborers

Supervisory Duties

Mean

± 1 Standard Deviation

38