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An Investigation of Industry

and Size Effects on Wage

Dispersion

John Ichiro Jones

The existence of wage dispersion in an industry is a

perplexing problem. For example, why would accountants working in the same industry earn vastly different incomes? Why are wages divergent or convergent for

the same occupation dependent on the industry? There are

several possible reasons.

One, there are size differences between firms. In general,

the larger the firm, the more likely it will pay higher wages.

This is due, in theory, to the phenomenon of “rent sharing.”

Firms share rents (profits) with workers to prevent shirking,

labor turnover, and disruptions to the firms’ own rent seeking. Smaller firms have more variation in the amount of rent

available to share with workers. This will lead to higher wage

dispersion in industries dominated by small firms.

Two, firms in the same industry may have dissimilar employment wage practices. The less structured the industry’s

wage practices, the greater the wage dispersion. By contrast, industries that are highly unionized may have tighter

or more structured wage practices. In theory, this would lead

to lower wage dispersion.

Three, there are skill level and seniority differences between individuals in the labor market. Finally, there may be

differences within an industry as to skill requirements, job

duties, or working environments that result in intraindustry

wage differences. The labor market may work to sort individuals into industries such that workers within an occupation in some industries have a narrow range of skills and

responsibilities, while workers in the same occupation in other

industries may have a wider range of responsibilities and

skills.

We use the OES occupational wage data to see which

industries have high and low wage dispersion. We then see

whether they have common characteristics—whether they

are dominated by small or large firms, for example, or whether

there are differences in industry wage practices, an example

being union affiliation employment rates.

Measuring industry occupation wage dispersion

For each three-digit NAICS industry, dispersion ratios were

calculated for six occupations that are found in every industry. The six occupations are: Accountants and auditors;

first-line supervisors/managers of office and administrative

support workers; bookkeeping, accounting, and auditing

clerks; executive secretaries and administrative assistants;

secretaries, except legal, medical, and executive; and general

office clerks. Using these six occupations in this study limits, in part, the effects of industry wage differences due to

varying job content within the occupation because workers

in these six occupations have similar duties and working

environments.

To generate a dispersion ratio, the 10th-percentile wage

rate was subtracted from the 90th-percentile wage rate, and

the difference was divided by the median wage rate. If the

ratio is below 1, the difference between the 90th- and 10thpercentile wages is less than the median wage. If the ratio

equals 1, the difference between the 90th and 10th percentiles equals the median. Finally, if the ratio is greater than 1,

the difference between the 90th and 10th percentiles is greater

than the median.

Following the generation of the dispersion ratio, the six

occupations were sorted and ranked by their respective dispersion ratios. This provides a relative measure of industry

wage dispersion; each industry was given a dispersion score

equal to the sum of the dispersion ranks for each of the six

occupations in the industry. Text table 1 shows the relative

ranks of wage dispersion for all three-digit NAICS. It is

sorted in an ascending manner from lowest wage dispersion

to highest wage dispersion. The fourth column, sum of ranks,

is the sum of the relative wage dispersion ranks for the six

occupations. The table illustrates that wages for the six

occupations in this study were, on average, closely clustered in hospitals, air transportation, monetary authorities,

social assistance, and rail transportation. Wages were more

highly dispersed in securities, commodities, contracts and

investments; apparel manufacturing; membership associations; gasoline stations; and lessors of nonfinancial intangible assets.

Union participation

In order to test the theory that wage dispersion would be

lower in industries with high union affiliation rates, the reJohn Ichiro Jones is an economist in the Division of Occupational

Employment Statistics, Bureau of Labor Statistics.
23

sults from text table 1 were related to the Current Population

Survey (CPS) union affiliation rates for two- or three-digit

NAICS industries. Because CPS estimates are published by

three-digit NAICS category for a limited number of industries, only 6 of the 88 industries in text table 1 could be directly related to union affiliation rates. As a result, 82 of the

three-digit NAICS industries are related to two-digit NAICS

union affiliation rates. For the purposes of this inquiry, the

industries in text table 1 were divided into four groups, based

on their respective dispersion ranks. The average union

affiliation rate for each of the four groups was calculated

using the CPS data. The results are shown in text table 2.

The summary data in text table 2 illustrates that industries

with lower wage dispersions have higher union affiliation

rates, supporting the hypothesis that, as unionization rates

decrease, wage dispersion increases. There may be exceptions to this general observation that are not apparent in the

table because average union affiliation rates were used in

the absence of data at the more detailed three-digit NAICS

level. Despite this caveat, the data suggest that there is a

unionization effect on wage dispersion. For instance, the

average union affiliation rate of the 22 industries with the

lowest dispersion ranks is 16.4 percent. The union affiliation

rates for three-digit NAICS groupings decline until they reach

a low of 6.8 percent for the 22 industries with the highest

wage dispersion.

Effects of employer size

To test the firm-size theory, which is that three-digit NAICS

industries that are populated by a preponderance of small

firms will have high wage dispersion, employment in each

three-digit NAICS category was divided into three size

classes. The small size class refers to the sum of employment by three-digit NAICS for firms with fewer than 50 employees. Medium size class is defined as the sum of employment by three-digit NAICS for firms with 50 to 249 employees.

The large size class is defined as the sum of employment by

three-digit NAICS for firms with 250 or more employees. For

this study, a three-digit NAICS industry is defined as being

dominated by small, medium, or large firms whenever 50 percent or more of total employment is found in one of the size

classes. The results are shown in text table 3.

Text table 3 illustrates the relationship between firm size,

the dominance of firm size in a three-digit NAICS industry,

and wage dispersion. The hypothesis is that, as small firms

dominate a three-digit NAICS category, the wage dispersion will increase due to greater variability in rent sharing in

small firms than in large firms. The data demonstrate that

this is the case. As wage dispersion increases, dominance

by small firms increases. Industries with the smallest wage

dispersion (groups 1-22) are the likeliest to be dominated by

large firms, with 11 three-digit NAICS industries being so

dominated while only 3 three-digit NAICS industries in the

same group are dominated by small firms. At the opposite

end of the wage dispersion spectrum, there are 12 three-digit

NAICS categories dominated by small firms in the industries

with the highest wage dispersion (groups 67-88), whereas

there are no three-digit NAICS industries dominated by large

firms in that group.

To further test whether there is a relationship between the

industry’s dispersion ratio and firm size, dispersion ranks

were correlated to the industry’s average firm size. Data from

the Bureau of Labor Statistics 2003 Quarterly Census of Employment and Wages were used to calculate the average firm

size by three-digit NAICS code. The result of this test was a

correlation coefficient of -.40, which indicates that, as firm

size increases, the wage dispersion rank decreases. The

industries’ average firm size and dispersion ranks are shown

in text table 4.

Discussion and summary

The purpose of this article was to investigate the relationships between wage dispersion, industry wage practices,

and firm size. The case for wage dispersion being related to

industry wage practices was supported using CPS union

affiliation data for industries. While a detailed industry analysis could not be conducted, available data shows that there

seems to be an effect. The evidence for a dominant firm size

effect was stronger. When wage dispersion was higher, small

firms dominated more of the three-digit NAICS category.

Conversely, if large firms dominated more of the three-digit

category, wage dispersion was lower.

Of course, there are limitations to this study. The occupations used to generate the wage dispersion ratio are located

in only two occupational groups: Business and financial

operations and office and administrative support. While

using these occupations for analysis may have its advantages, using a larger number of occupations may provide

additional information on wage dispersion, but this will be

left for a future study. As mentioned previously, almost all of

the CPS unionization data related to the two-digit NAICS

level. If the CPS ever yields industry data at the three-digit

NAICS level for all 88 industries used in this study, a more

precise result would be obtained. Finally, the existence of

skill and seniority differences within occupations was not

accounted for in this study.

In summary, OES data support the theory that wage dispersion will be greatest in industries that have an unstructured wage policy and in those that are dominated by small

firms, due to the variability of rent sharing.
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Text table 1. Wage dispersion ranks by industry

1 622000 Hospitals 34

2 481000 Air transportation 49

3 521000 Monetary authorities—central bank 58

4 624000 Social assistance 70

5 482000 Rail transportation 79

6 322000 Paper manufacturing 82

7 331000 Primary metal manufacturing 102

8 326000 Plastics and rubber products

 manufacturing 105

9 623000 Nursing and residential care facilities 112

10 486000 Pipeline transportation 117

11 517000 Telecommunications 130

12 325000 Chemical manufacturing 147

13 492000 Couriers and messengers 149

14 621000 Ambulatory healthcare services 159

15 311000 Food manufacturing 160

16 327000 Nonmetallic mineral product manufacturing 160

17 721000 Accommodation 163

18 483000 Water transportation 170

19 333000 Machinery manufacturing 175

20 493000 Warehousing and storage 179

21 335000 Electrical equipment, appliance, and

 component manufacturing 181

22 312000 Beverage and tobacco product

 manufacturing 184

23 334000 Computer and electronic product

 manufacturing 195

24 611000 Educational services 200

25 999000 Federal, State, and local government

 (OES designation) 202

26 336000 Transportation equipment manufacturing 206

27 522000 Credit intermediation and related activities 207

28 221000 Utilities 214

29 337000 Furniture and related product

 manufacturing 219

30 445000 Food and beverage stores 221

31 516000 Internet publishing and broadcasting 223

32 314000 Textile product mills 234

33 321000 Wood product manufacturing 234

34 332000 Fabricated metal product manufacturing 236

35 339000 Miscellaneous manufacturing 239

36 561000 Administrative and support services 241

37 551000 Management of companies and

 enterprises 250

38 452000 General merchandise stores 251

39 712000 Museums, historical sites, and similar

 institutions 252

40 323000 Printing and related support activities 253

41 484000 Truck transportation 260

42 524000 Insurance carriers and related activities 263

43 313000 Textile mills 266

44 4485000 Transit and ground passenger

 transportation 266

45 324000 Petroleum and coal products

 manufacturing 275

46 423000 Merchant wholesalers, durable goods 282

47 212000 Mining (except oil and gas) 296

48 713000 Amusement, gambling, and recreation

 industries 297

49 316000 Leather and allied product

 manufacturing 297

50 443000 Electronics and appliance stores 298

51 424000 Merchant wholesalers, nondurable

 goods 298

52 236000 Construction of buildings 298

53 211000 Oil and gas extraction 303

54 454000 Nonstore retailers 305

55 562000 Waste management and remediation

 services 313

56 532000 Rental and leasing services 318

57 531000 Real estate 320

58 519000 Other information services 320

59 425000 Wholesale electronic markets and

 agents and brokers 320

60 451000 Sporting goods, hobby, book, and

 music stores 321

61 444000 Building material and garden equipment

 and supplies dealers 322

62 237000 Heavy and civil engineering

 construction 328

63 512000 Motion picture and sound recording

 industries 338

64 488000 Support activities for transportation 344

65 442000 Furniture and home furnishings stores 350

66 487000 Scenic and sightseeing transportation 355

67 453000 Miscellaneous store retailers 355

68 115000 Support activities for agriculture and

 forestry 356

69 525000 Funds, trusts, and other financial

 vehicles 361

70 811000 Repair and maintenance 369

71 213000 Support activities for mining 373

72 446000 Health and personal care stores 377

73 511000 Publishing industries (except Internet) 379

74 518000 Internet service providers, Web search

 portals, and data processing service 383

75 515000 Broadcasting (except Internet) 383

76 722000 Food services and drinking places 385

77 441000 Motor vehicle and parts dealers 386

78 711000 Performing arts, spectator sports, and

 related industries 387

79 812000 Personal and laundry services 395

80 113000 Forestry and logging 399

81 238000 Specialty trade contractors 400

82 541000 Professional, scientific, and technical

 services 411

83 448000 Clothing and clothing accessories

 stores 415

84 533000 Lessors of nonfinancial intangible

 assets (except copyrighted works) 419

85 447000 Gasoline stations 424

86 813000 Religious, grantmaking, civic,

 professional, and similar

 organizations 432

87 315000 Apparel manufacturing 450

88 523000 Securities, commodity contracts, and

 other financial investments and

 related activities 462

Industry

ranks NAICS NAICS title Sum of

ranks

Industry

ranks NAICS NAICS title Sum of

ranks
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Text table 3. Effects of firm size on wage dispersion

1-22 ............................... 3 1 11 7

23-44 ............................. 0 1 6 15

45-66 ............................. 9 0 1 12

67-88 ............................. 12 0 0 10

Industry rank

Count of three-digit NAICS industries

dominated by 1 of 3 size classes

Small Medium Large Not

1-22 ............................. 16.4 dominated

23-44 ........................... 14.8

45-66 ........................... 10.8

67-88 ........................... 06.8

Industry rank by

dispersion ratio

CPS average percent of employed represented

by unions, 2003, by dispersion groups1

Text table 2. Effects of union representation on wage dispersion

1

 Data refer to members of a labor union or an employee association similar to a

union, as well as to workers who report no union affiliation but whose jobs are

covered by a union or an employee association contract.

Text table 4. Effect of firm size on wage dispersion

1 622000 707 31 516000 9 61 444000 20

2 481000 105 32 314000 23 62 237000 19

3 521000 129 33 321000 31 63 512000 15

4 624000 22 34 332000 24 64 488000 17

5 482000 12 35 339000 21 65 442000 11

6 322000 88 36 561000 21 66 487000 11

7 331000 78 37 551000 48 67 453000 8

8 326000 56 38 452000 199 68 115000 20

9 623000 76 39 712000 38 69 525000 15

10 486000 49 40 323000 17 70 811000 6

11 517000 58 41 484000 13 71 213000 21

12 325000 66 42 524000 14 72 446000 20

13 492000 51 43 313000 56 73 511000 28

14 621000 11 44 485000 36 74 518000 19

15 311000 58 45 324000 67 75 515000 39

16 327000 35 46 423000 12 76 722000 22

17 721000 34 47 212000 33 77 441000 19

18 483000 40 48 713000 25 78 711000 10

19 333000 35 49 316000 31 79 812000 8

20 493000 48 50 443000 12 80 113000 7

21 335000 64 51 424000 16 81 238000 9

22 312000 54 52 236000 7 82 541000 8

23 334000 69 53 211000 19 83 448000 17

24 611000 125 54 454000 15 84 533000 9

25 999000 95 55 562000 18 85 447000 14

26 336000 125 56 532000 16 86 813000 11

27 522000 35 57 531000 6 87 315000 24

28 221000 52 58 519000 25 88 523000 13

29 337000 24 59 425000 4

30 445000 26 60 451000 13

 Correlation coefficient = -.40

Industry

ranks NAICS

Average

firm

size

Industry

ranks NAICS

Average

firm

size

Industry

ranks NAICS

Average

firm

size