10

C. Occupational Structure

and Urban Growth

Audrey Watson

Audrey Watson is an economist in the Division of Occupational

Employment Statistics, Bureau of Labor Statistics.

Urban areas have exhibited widely divergent growth

in recent years. Between 1990 and 2003, the estimated U.S. population growth rate was 16.9 percent.1

Over the same period, Las Vegas, Nevada, the most rapidly

growing Metropolitan Area (MA), grew by 109.1 percent,

while the most rapidly declining area, the Steubenville–

Weirton, OH–WV MA, shrank by nearly 10 percent. Previous literature has identified a number of factors correlated

with population growth at the city and MA level. While

some of these, such as climate, have no obvious implications for occupational structure, others, such as a negative

correlation between growth and employment in manufacturing, suggest ways in which occupational structure may differ between fast- and slow-growing urban areas. This article

uses Occupational Employment Statistics (OES) data to look

for relationships between occupational composition and MA

population growth rates. Following a brief literature review,

the article outlines some predictions for specific occupational groups and then describes the methodology used to

compare occupational structure across groups of fast-, slowand moderately growing MAs. After summarizing the results for selected occupational groups and comparing them

with the earlier predictions, the article concludes with some

general remarks and issues lending themselves to future research.

Some facts about recent urban growth

This section summarizes findings from previous literature on

urban growth between the 1990 and 2000 censuses. Although

much of the discussion focuses on cities rather than MAs,

differences between growth relationships at the city and MA

levels are mentioned where relevant. Except as noted, the

primary sources are Edward Glaeser and Jesse Shapiro, “Is

There a New Urbanism? The Growth of U.S. Cities in the

1990s,” NBER Working Paper 8357, 2001; and, by the same

authors, “City Growth and the 2000 Census: Which Places

Grew, and Why?” Brookings Institution, May 2001. Among

the interesting findings to come out of these sources are the

following:

• Growth rates of cities showed a high degree of persistence: the correlation between growth rates in the 1990s

and the 1980s was greater than 75 percent. Thus, one

of the primary predictors of an area’s growth between

1990 and 2000 was its growth in the recent past.

• Western and Southern cities grew the most rapidly,

with average growth rates of 19.5 percent and 12 percent, respectively. Cities in the Midwest grew by an

average of 3.4 percent, an improvement over their negative growth rate in the 1980s. Cities in the Northeast

shrank by an average of 1 percent.

• Hot, dry cities gained population at the expense of

cool, wet cities. With regions controlled for, higher

temperatures and lower rainfall were significant predictors of city growth.

• There was no significant correlation between initial

population and later growth. However, the average

growth rate of large cities (those with more than 1 million inhabitants) relative to that of smaller cities did

appear to increase somewhat in the 1990s.

• Cities with younger residents and higher per capita

incomes grew more rapidly. However, these relationships do not always remain statistically significant after controlling for other variables; for example, age

becomes insignificant at both the city and MA level

after college education is taken into account, presumably because college education is more prevalent among

younger people.

1

 Population growth rates are calculated from the U. S. Census

Bureau’s 1990 Census population data and 2003 population estimates, the latest data available for all components of OES metropolitan areas.

 C.
11

• High-density cities built around public transportation

and pedestrians declined in population, while low-density cities in which driving represents the primary mode

of transportation grew. This correlation is interpreted,

not as a causal effect of public transportation, but as

evidence of a movement away from older cities built

around earlier forms of transportation. Although the

relationship between the percentage driving to work

and population growth is not significant at the MA

level, where driving is more universal than at the city

level, other variables representing MA age and density are still significant predictors of population growth.

• Cities with larger immigrant populations grew more rapidly in general, but only up to a certain level: cities with

the highest concentrations of immigrants (more than

15 percent foreign born) grew less rapidly than those

with immigrant populations of 8 percent to 15 percent.

• Cities with high human capital grew more rapidly. The

percentage of the population 25 years and older with a

college degree or higher is a significant predictor of

growth at both the city and MA level. The percentage

of the population 25 years and older with a high school

degree or higher is positively correlated with growth

at the city level, but not at the MA level. Other research shows that areas with high numbers of colleges per capita before World War II tended to have

both high levels of college-educated residents several

decades later and higher population growth rates over

the 1970–2000 period (Glaeser and Saiz, 2003).2 However, this research also suggests that the human capital and skill effects matter more in “potentially declining cities,” but are less important in warm cities with

large immigrant communities, two factors that tend to

be highly correlated with rapid growth.

• Cities with high levels of poverty and unemployment

grew less rapidly. The civilian unemployment rate is a

significant negative predictor of growth at both the

city and MA level. The percentage of the population

living below the poverty level is a significant negative

predictor of growth at the city level, but insignificant

at the MA level, where average per capita income appears to be more important. The percentage of the

population that is black is significantly negatively correlated with growth at both the city and MA level,

even after controlling for average income and percentage living below the poverty level.

• Manufacturing cities had low population growth rates.

The percentage of civilian employment in manufacturing is significantly negatively correlated with both city

and MA growth. Employment in public administration

and employment in health services also are negatively

correlated with growth. According to the authors, the

latter does not reflect differences between the demographic structures of growing and declining cities: although older populations are positively correlated with

employment in healthcare, the correlation between age

and growth of the population is relatively weak. The

authors speculate that this phenomenon may occur

instead because healthcare and public administration

are among the last industries to leave a declining area.

• Cities with larger governments grow more slowly. Per

capita city government expenditures are a significant

negative factor in growth. Controlling for the overall

size of the government budget, expenditures on education, and expenditures on police are significantly

negatively correlated with growth. This relationship

was tested only at the city level, because cities, not

MAs, represent a more natural political unit.

• Other than the strong negative impact of crime, variables representing lifestyle amenities, such as eating

and drinking establishments and museums, are generally not strong predictors of growth rates (Glaeser and

Saiz, 2003).3

Possible implications for occupational structure

The preceding literature summary suggests a number of ways

in which occupational structures might differ between fastand slow-growing urban areas:

• The negative correlation between growth and manufacturing employment suggests that the employment

share in production occupations should be higher in

slow-growing areas. This group of occupations contains many that typically are associated with manufacturing activities, such as welders, cutters, solderers, and

brazers; sewing machine operators; and engine and

other machine assemblers.

• Because employment in the healthcare industry was

higher in slow-growing areas, population growth

should be negatively correlated with occupations concentrated in this industry.

• The lack of evidence that lifestyle amenities such as

restaurants, movie theaters, and museums are associated with urban growth suggests that there should be

no clear correlation between MA growth and employment in occupations associated with providing various consumption amenities. Occupations that might

2

 Glaeser, Edward L., and Saiz, Albert, The Rise of the Skilled City,

NBER Working Paper (Cambridge, MA, National Bureau of Economic Research, 2003). 3

 Glaeser and Saiz, The Rise of the Skilled City.
12

be associated with lifestyle amenities include food

preparation and serving related occupations; arts, design, entertainment, sports, and media occupations,

such as actors, athletes and sports competitors, musicians and singers, and fine artists; personal care and

service occupations, such as barbers, fitness trainers

and aerobics instructors, and manicurists; and salesrelated occupations that might indicate a consumption

amenity in the form of a vibrant retail sector.

• High levels of crime are associated with slower population growth. To the extent that high-crime areas combat this problem by employing more law enforcement

personnel, we might expect to see higher concentrations of protective service workers in slow-growing

areas. In addition, because law enforcement workers

are public employees, the negative correlation between

growth and share of employment in public administration tends to reinforce this prediction.

• If poverty and large local government employment

are correlated with higher levels of social services, we

might expect to see higher concentrations of community and social services occupations, such as social

workers and counselors, in slow-growing areas.

• Because previous literature has found a positive correlation between population growth and educational

attainment, slow-growing areas, particularly in the

Northeast and Midwest, might be expected to have

smaller concentrations of occupations requiring higher

levels of education or skill. Occupations that may be

associated with higher educational attainment include

management occupations; business and financial operations occupations, such as management analysts

and accountants and auditors; computer and mathematical science occupations; architecture and engineering occupations; life, physical, and social science

occupations; legal occupations; and education, training, and library occupations. Because the number of

colleges was found to be associated with higher population growth, one might in particular expect high concentrations of postsecondary teachers in fast-growing areas.

• In addition to these predictions based on the literature, because rapid population growth is likely to spur

demand for new housing, fast-growing areas might

also be expected to have higher employment shares in

construction and extraction occupations.

Data and methodology

In order to test the preceding implications, MAs were first

assigned to growth classes on the basis of their population

growth rates. For each growth class, employment data from

the May 2004 OES survey were used to calculate the percentage of total employment in each major occupational

group, as defined by the 2000 Standard Occupational Classification system (SOC).4

 These employment shares were then

compared across MA growth classes. To better isolate effects of geography or the size of the area, metropolitan areas

were further subdivided by initial population size and by

region.

On the basis of their 1990 to 2003 population growth rates,

the 331 MAs were divided into three growth classes: 72 fastgrowing MAs, with 1990–2003 growth rates greater than 25

percent; 145 moderately growing MAs, with growth rates of

10 percent to 25 percent; and 114 slow-growing MAs, with

growth rates less than 10 percent. These intervals were chosen to be of similar orders of magnitude in terms of number of

MAs, to offer intuitive endpoints, and to include the 1990–

2003 U.S. population growth rate (16.9 percent) and median

MA growth rate (15.5 percent, Lawrence, MA–NH MA)

within the center interval.

The MAs also were grouped by region—Northeast, Midwest, South, or West—and into one of three possible size

classes, based on their initial (1990) population. “Small”

MAs had an initial population of less than 250,000; “medium-sized” MAs had an initial population of 250,000 to 1

million; and “large” MAs had an initial population of more

than 1 million.

For each set of MAs, May 2004 OES employment figures

were used to calculate the percentage of total employment in

each SOC major group. Three sets of comparisons were then

performed, checking for significant differences in employment shares for each occupational group across MA growth

classes. In all comparisons, fast- or slow-growing MAs were

compared with their moderately growing counterparts, rather

than directly with one another. Because not all subgroups

contained MAs in each growth class, making all comparisons relative to moderately growing MAs allowed the analysis to be performed consistently across all three sets of comparisons.

The initial inquiry involved comparing employment distributions across growth classes for the full data set. Because geographical area is one of the major determinants of

urban growth, and these strong regional effects may obscure the effects of other growth factors on occupational

structure, the second stage of the inquiry involved comparing MAs within the same region across growth classes wherever possible. Because the fast-growing MA group contained only two Midwestern MAs and no Northeastern ones,

comparisons for those regions were possible only between

slow- and moderately growing MAs. Similarly, only five

MAs in the West met the definition of “slow growing.” Comparisons were performed between these MAs and moderately growing Western MAs, but the results should be interpreted with caution, given the small size of the slow-growing

group. Finally, although previous literature found no signifi4

 For more information on the 2000 Standard Occupational Classification, see Appendix A at the end of this publication.
13

cant correlation between initial size and subsequent growth,

it is possible that correlations between growth rate and occupational structure differ by size of MA. Therefore, the

third stage of the inquiry involved comparing MAs of similar size across growth classes. Findings for selected occupational groups are presented in the next section.

Summary of results

The data appear to support the prediction of a negative

correlation between growth and the share of employment in

production occupations. This relationship generally holds

across regions and size groups. In the full data set, the

employment share in the production occupational group was

1.16 percentage points lower in fast-growing MAs than in

moderately growing ones, while the employment share in

slow-growing MAs was 0.62 percentage point above that in

moderately growing ones. (See table C1.) When the data are

broken down by region, fast-growing MAs in the South and

the West have significantly lower shares in production occupations than do moderately growing MAs in the same

regions. In 3 out of the 4 regions, slow-growing MAs have

significantly higher shares of production occupations than

do moderately growing MAs. The exception is the West, for

which slow-growing MAs contain a significantly lower share

of production employment; however, this result may reflect

the small number of slow-growing MAs in the West and the

unusually low percentage of production workers in that region (3.84 percent, compared with an average of 7.2 percent

across all MAs).

The size-class data show lower shares of production occupations in fast-growing small and medium-sized MAs, relative to moderately growing MAs in the same size class, while

the share difference was not significant for fast-growing

large MAs. In 2 out of 3 size classes, slow-growing MAs

had higher shares of production occupations than did their

moderately growing counterparts. Although this result did

not hold for slow-growing medium MAs, the difference in

share was relatively small in absolute value.

In a number of these comparisons, the differences in share

between growth classes are relatively large in magnitude,

frequently exceeding 1 percentage point in absolute value.

For example, in small and medium-sized MAs, the shares of

production occupations in fast-growing MAs are respectively 2.16 percentage points and 3.30 percentage points below those in moderately growing MAs. Similarly, slow-growing MAs in the Northeast have a 1.45-percentage-point higher

share in production occupations than do moderately growing MAs in the same region, while the corresponding difference in share for the Midwest is 1.24 percentage points. Both

the direction and magnitude of the share differentials support the finding that manufacturing, and hence employment

in production occupations, is negatively correlated with urban growth.

The occupational data also are largely consistent with

previous findings of a negative correlation between growth

and employment in the healthcare industry. Relative to moderately growing MAs, both healthcare practitioner and technical occupations and healthcare support occupations have

lower employment shares in fast-growing MAs, and higher

shares in slow-growing MAs, in the full data set. (See tables

C2 and C3.) In the regional data, all statistically significant

differences in share have the anticipated sign. Fast-growing

Southern MAs have lower shares of both healthcare groups

than do moderately growing Southern MAs. Slow-growing

MAs have higher shares of both healthcare groups in the

South and Midwest and of the first group—healthcare practitioner and technical occupations—in the Northeast. Share

differences are not statistically significant for both groups in

the West and for healthcare support in slow-growing Northeast MAs. Similarly, the size-class comparisons all show the

expected sign when the differences are statistically significant. Fast-growing MAs in all three size classes have lower

shares of both healthcare groups, with the exception of

healthcare support in medium-sized areas, for which the difference has the anticipated sign, but is not statistically sigEmployment share

(percent)

Share differential

(percentage point)

Fast

growing

Moderately

growing

Slow

growing

Between

fast and

moderate

Between

slow and

moderate

Region

and

size class

Table C1. Production occupations

Full data set 6.12 7.29 7.90 1

–1.16 1 0.62

By region:

Midwest — 8.90 10.14 — 11.24

Northeast — 5.33 6.78 — 11.45

South 6.24 7.02 7.72 1–.78 1.70

West 5.91 7.18 3.84 1–1.27 1–3.34

By size class:

Small 6.48 8.64 9.90 1–2.16 11.26

Medium 5.55 8.85 8.57 1–3.30 1–.28

Large 6.37 6.33 7.20 .04 1.87

1 Share differential is statistically significant.

NOTE: Dash indicates data not available.

Table C2. Healthcare practitioner and technical

occupations

Employment share

(percent)

Share differential

(percentage point)

Fast

growing

Moderately

growing

Slow

growing

Between

fast and

moderate

Between

slow and

moderate

Region

and

size class

Full data set 4.50 4.97 5.54 1–0.47 10.57

By region:

Midwest — 5.11 5.52 — 1.41

Northeast — 5.23 5.62 — 1.39

South 4.60 5.31 6.10 1–.70 1.80

West 4.29 4.14 4.00 .15 –.14

By size class:

Small 4.86 5.61 5.97 1–.75 1.36

Medium 4.85 5.40 5.57 1–.54 1.17

Large 4.26 4.66 5.44 1–.41 1.78

1

 Share differential is statistically significant.

NOTE: Dash indicates data not available.
14

nificant. Slow-growing areas have higher shares of both

healthcare groups across all size classes.

The data also appear to support the prediction of a positive correlation between growth and employment in construction and extraction occupations, at least for fast-growing MAs. In the full data set, both comparisons have the

expected sign and are statistically significant: the share of

employment in construction and extraction occupations is

1.20 percentage points higher in fast-growing MAs than in

moderately growing MAs, while the difference in share between slow- and moderately growing MAs is –0.32 percentage point. (See table C4.) Fast-growing MAs in both the

South and the West have higher shares of construction and

extraction occupations than do moderately growing MAs;

in addition, the share differential in the West is quite large in

absolute value: nearly two percentage points. However, the

regional data for slow-growing MAs show a more mixed pattern. While the share differentials have the expected sign for

both the Midwest and the West, the latter difference is not

statistically significant. Furthermore, in both the South and

the Northeast, slow-growing MAs have a significantly higher

share of construction and extraction occupations in comparison to moderately growing MAs.

The size-class comparisons exhibit a pattern similar to

that found in the regional data. Relative to moderately growing MAs, fast-growing MAs in all three size classes have

significantly higher shares of construction and extraction

occupations; in addition, the share differentials all exceed 1

percentage point in absolute value. However, the pattern is

less pronounced in the comparisons between slow- and moderately growing MAs. In 2 out of 3 size classes, the differences in share have the expected sign, but are relatively

small in absolute value, while the difference is not statistically significant for small MAs.

Across the board, the data exhibit a pattern of higher

shares in construction and extraction occupations in fastgrowing MAs, consistent with the presence of booming construction markets in those areas. However, this pattern appears to hold mainly in comparisons between fast- and

moderately growing MAs; in slow-growing areas, the relationship between these occupations and growth is less pronounced.

Previous research has found no evidence of a correlation

between growth and various measures of consumption amenities, such as number of eating and drinking establishments

or motion picture establishments per capita. This finding

suggests that no relationship should exist between growth

and employment shares in occupational groups associated

with consumption amenities, such as food service, personal

care, and arts and entertainment. However, that prediction is

not fully supported by the data. Instead, employment in

food preparation and serving related occupations appears

to follow a bimodal peak, with the share of this occupational

group higher in both fast-growing areas and slow-growing

areas relative to moderately growing ones. In the full data

set, the employment share of these occupations is 0.8 percentage point higher in fast-growing MAs than in moderately growing MAs, while the employment share in slowgrowing MAs is 0.31 percentage point higher than in

moderately growing MAs. (See table C5.) The same pattern

can be observed in the regional data: both fast-growing and

slow-growing MAs have higher shares of this occupational

group than do moderately growing MAs, with all differences

statistically significant. The size-class data also show evidence of the same bimodal peak, with the exception of small

fast-growing MAs, for which the difference in share is positive, but not statistically significant.

One possible explanation for this result is that different

factors are at work in fast- and slow-growing MAs with respect to this occupational group. Restaurants and other

eating and drinking places produce a service that is not easily traded across geographical areas and thus must locate

near their potential customers. Therefore, in slow-growing

cities, these occupations may represent a “residual” source

Table C3. Healthcare support occupations

Full data set 2.19 2.39 2.89 1–0.20 10.49

By region:

Midwest — 2.28 2.82 — 1.54

Northeast — 3.04 2.99 — –.05

South 2.19 2.34 3.06 1–.15 1.72

West 2.18 2.18 2.09 .00 –.09

By size class:

Small 2.41 2.77 3.17 1–.36 1.40

Medium 2.50 2.57 2.93 –.07 1.36

Large 2.00 2.24 2.81 1–.25 1.56

1

 Share differential is statistically significant.

NOTE: Dash indicates data not available.

Employment share

(percent)

Share differential

(percentage point)

Fast

growing

Moderately

growing

Slow

growing

Between

fast and

moderate

Between

slow and

moderate

Region

and

size class

Table C4. Construction and extraction occupations

Full data set 5.69 4.49 4.17 1

1.20 1–0.32

By region:

Midwest — 4.40 4.25 — 1–.15

Northeast — 3.64 3.83 — 1.19

South 5.12 4.89 5.46 1.23 1.57

West 6.52 4.55 4.43 11.97 –.12

By size class:

Small 5.85 4.75 4.80 11.10 .05

Medium 5.92 4.83 4.31 11.09 1–.52

Large 5.54 4.29 3.99 11.25 1–.31

1

 Share differential is statistically significant.

NOTE: Dash indicates data not available.

Employment share

(percent)

Share differential

(percentage point)

Fast

growing

Moderately

growing

Slow

growing

Between

fast and

moderate

Between

slow and

moderate

Region

and

size class
15

of employment when the production of more highly tradable

goods and services has relocated elsewhere. In fast-growing MAs, however, a relatively high share of employment in

this occupational group may reflect a thriving food service

industry that represents a lifestyle amenity to the area’s growing population. To the extent that the latter explanation is

correct, these data fail to support earlier findings that consumption amenities, such as eating and drinking establishments, are not strongly correlated with growth.

Personal care and service occupations represent another

occupational group that may be associated with consumption amenities. Like food preparation and serving related

occupations, personal care and service occupations show

evidence of a bimodal distribution, although the pattern is

less pronounced. Both slow- and fast-growing MAs in the

full data set have higher shares of this group than do moderately growing MAs; the differences in share, though slight,

are statistically significant. (See table C6.) In the regional

data, both fast- and slow-growing MAs have higher shares

of this occupational group in all comparisons except for

slow-growing Northeastern MAs, for which the share of

personal care and service occupations is 0.88 percentage

point lower than in moderately growing Northeastern MAs.

The size-class data show higher shares for both fast- and

slow-growing MAs in all comparisons where the differences

are statistically significant, although the differences are not

statistically different from zero for fast-growing large MAs

and slow-growing small ones. Like food service employees,

personal care and service workers generally must locate near

their customers. Thus, the explanation offered for the bimodal peak in food preparation and serving occupations may

be advanced for this group as well.

Employment in arts, design, entertainment, sports, and

media occupations appears to display the opposite pattern,

with shares of this group generally higher in moderately growing MAs than in either slow- or fast-growing ones. In the

full data set, the employment share is 0.44 percentage point

lower in fast-growing MAs, and 0.36 percentage point lower

in slow-growing MAs, relatively to moderately growing ones.

(See table C7.) The regional data show a similar pattern, with

moderately growing MAs exhibiting higher employment

shares in this group than either fast- or slow-growing MAs,

with the exception of the small group of slow-growing Western MAs, for which the difference in share is negative, but

not significant. The size-class data exhibit this pattern less

strongly, however. Although moderately growing MAs have

higher shares of arts, design, entertainment, sports, and media workers in 3 out of 6 comparisons, the difference in share

between moderate and fast-growing small MAs is not statistically significant. Furthermore, medium-sized MAs deviate

from the pattern, with the share of these occupations significantly higher in both slow- and fast-growing medium-sized

MAs compared with moderately growing ones. Overall, the

data for this group provide some evidence that slow-growing MAs may have particularly low shares of employment in

arts, design, entertainment, sports, and media occupations,

but little evidence that the presence of these workers constitutes a consumption amenity in fast-growing areas.

The data suggest some evidence of a positive correlation

between growth and employment in sales and related occupations. Shares of this group in the full data set are 0.59

percentage point higher in fast-growing MAs, and 0.10 percentage point lower in slow-growing MAs relative to moderately growing ones. (See table C8.) In both regions for

which the comparison is possible, fast-growing MAs have a

higher share of sales and related occupations than moderately growing MAs have. However, the regional comparisons between slow- and moderately growing MAs are generally not statistically significant; only the Northeast shows

a significantly lower share of this group in slow-growing

MAs. Fast-growing MAs have a higher share of sales and

related workers in 2 out of 3 size classes, but no significant

Table C5. Food preparation and serving related

occupations

Full data set 8.59 7.78 8.09 10.80 10.31

By region:

Midwest — 7.78 8.53 — 1.75

Northeast — 6.89 7.36 — 1.47

South 8.46 8.17 9.24 1.29 11.07

West 8.74 7.80 9.67 1.94 11.87

By size class:

Small 9.77 8.89 9.01 1.88 .12

Medium 9.00 8.35 8.48 1.65 1.13

Large 8.20 7.33 7.74 1.87 1.41

1

 Share differential is statistically significant.

NOTE: Dash indicates data not available.

Employment share

(percent)

Share differential

(percentage point)

Fast

growing

Moderately

growing

Slow

growing

Between

fast and

moderate

Between

slow and

moderate

Region

and

size class

Table C6. Personal care and service occupations

Full data set 2.56 2.37 2.51 1

0.19 10.14

By region:

Midwest — 2.26 2.46 — 1.20

Northeast — 3.40 2.52 — 1–.88

South 2.54 2.10 2.64 1.44 1.53

West 2.59 2.22 2.59 1.37 1.36

By size class:

Small 2.53 2.34 2.37 1

.19 .03

Medium 2.83 2.21 2.49 1.62 1.28

Large 2.42 2.45 2.55 –.03 1.11

1

 Share differential is statistically significant.

NOTE: Dash indicates data not available.

Employment share

(percent)

Share differential

(percentage point)

Fast

growing

Moderately

growing

Slow

growing

Between

fast and

moderate

Between

slow and

moderate

Region

and

size class
16

difference for small MAs. Similarly, 2 out of 3 size classes

show a lower share of sales and related workers in slowgrowing MAs, with the difference in share insignificant for

large MAs. In general, where the difference is significant,

the data support a positive correlation between employment

shares of sales and related workers and MA growth. However, several of the comparisons are not statistically different from zero, and overall the evidence of a positive correlation is not strong. In addition, while at least two-thirds of the

employment in this group is retail sales related, it also contains nonretail salesworkers such as telemarketers, real estate sales agents and brokers, and wholesale and manufacturing sales representatives.

Although previous research has failed to find a correlation between growth and consumption amenities such as

eating and drinking establishments, high levels of crime, as

proxied by the murder rate, appear to be “a very salient

disamenity” from the point of view of population growth

(Glaeser and Saiz, 2003).5 Assuming that high-crime areas

respond to their situation by devoting a larger share of local

resources to law enforcement, we might expect to see a higher

employment share for protective service occupations in slowgrowing areas. In addition, because law enforcement personnel are public employees, the correlation between large

government and slow growth provides a further reason to

expect a negative correlation between the share of these

workers and population growth. However, the data do not

appear to support this prediction. In the full data set, both

fast- and slow-growing MAs employ a smaller proportion of

protective service workers than do moderately growing MAs.

(See table C9.) Fast-growing MAs in the South employ a

lower share of protective service workers than do their moderately growing counterparts, but for fast-growing areas in

the West, the reverse is true. Relative to moderately growing

MAs in the same region, slow-growing Western MAs employ a higher share of protective service workers, slow-growing Northeastern MAs employ a lower share, and the differences in share for the other two regions are not statistically

significant. The size-class comparisons show a similarly mixed

pattern. These data suggest that, although high crime may

be correlated with low population growth, it is not necessarily correct to assume that high-crime areas respond by devoting a higher share of resources to the employment of

police and other protective service workers.

Similarly, previous findings have indicated negative correlations between poverty and growth, as well as between

the size of local government and growth. These findings

suggest that slow-growing areas might have relatively higher

concentrations of community and social service occupations,

both because government tends to be a large employer in

Table C8. Sales and related occupations

Full data set 11.18 10.59 10.49 10.59 1–0.10

By region:

Midwest — 10.55 10.38 — –.17

Northeast — 10.74 10.52 — 1–.22

South 11.44 10.59 10.63 1.85 .04

West 10.77 10.54 10.71 1.23 .17

By size class:

 Small 10.73 10.73 10.55 .00 1–.18

Medium 10.78 10.61 10.32 1.17 1–.29

Large 11.45 10.56 10.55 1.89 –.01

1

 Share differential is statistically significant.

NOTE: Dash indicates data not available.

Table C7. Arts, design, entertainment, sports, and media

occupations

Full data set 1.10 1.54 1.19 1–0.44 1–0.36

By region:

 Midwest — 1.26 1.17 — 1–.08

 Northeast — 2.03 1.17 — 1–.85

 South 1.09 1.15 .86 1–.06 1–.29

 West 1.11 2.11 1.94 1–.99 –.17

By size class:

 Small 1.08 1.09 .92 –.01 1–.17

 Medium 1.14 1.03 1.12 1.11 1.09

 Large 1.09 1.85 1.27 1–.77 1–.58

1 Share differential is statistically significant.

NOTE: Dash indicates data not available.

Employment share

(percent)

Share differential

(percentage point)

Fast

growing

Moderately

growing

Slow

growing

Between

fast and

moderate

Between

slow and

moderate

Region

and

size class

Employment share

(percent)

Share differential

(percentage point)

Fast

growing

Moderately

growing

Slow

growing

Between

fast and

moderate

Between

slow and

moderate

Region

and

size class

Table C9. Protective service occupations

Full data set 2.32 2.38 2.27 1

–0.06 1–0.10

By region:

Midwest — 2.02 2.02 — .00

Northeast — 2.99 2.37 — 1–.62

South 2.31 2.48 2.47 1–.17 –.01

West 2.34 2.22 2.70 1.12 1.48

By size class:

Small 2.13 1.96 2.22 1.16 1.26

Medium 2.41 2.15 2.25 1.26 1.11

Large 2.29 2.56 2.29 1–.27 1–.27

1

 Share differential is statistically significant.

NOTE: Dash indicates data not available.

Employment share

(percent)

Share differential

(percentage point)

Fast

growing

Moderately

growing

Slow

growing

Between

fast and

moderate

Between

slow and

moderate

Region

and

size class

5

 Glaeser and Saiz, The Rise of the Skilled City.
17

faster growing areas should have heavier concentrations of

employment in occupational groups characterized by higher

levels of skill, such as management occupations; business

and financial operations occupations; computer and mathematical science occupations; architecture and engineering

occupations; life, physical, and social science occupations;

legal occupations; and education, training, and library occupations. Interestingly, the data largely fail to bear out this

prediction, with many comparisons showing a lower concentration of these “high-skill” occupations in fast-growing

areas.

In the full data set, fast-growing MAs have lower employment shares than moderately growing MAs in all of the

foregoing groups except architecture and engineering occupations. (See tables C11–C17.) The comparisons between

slow- and moderately growing MAs are more consistent with

the predicted pattern. Of the aforementioned seven occupational groups, only one—education, training, and library occupations—has a higher share in slow-growing areas, although the difference in share for architecture and engineering

occupations is statistically insignificant.

The regional data reveal that the West appears to be characterized by a negative correlation between growth and employment shares in skilled occupations. Relative to moderately growing Western MAs, fast-growing Western MAs

exhibit lower concentrations of all of the “high-skill” groups,

with the exception of education, training, and library occupations, for which the difference is statistically insignificant.

Similarly, the small group of slow-growing Western MAs

has a higher share of all of these groups except architecture

and engineering occupations and education, training, and

library occupations, with the latter share differential again

statistically insignificant. In the South, only architecture

and engineering occupations have a higher share in fastgrowing areas than in moderately growing ones; of the remaining six groups, four have lower shares in fast-growing

areas and two show no significant difference in shares. SlowTable C10. Community and social services occupations

Full data set 1.08 1.29 1.45 1–0.21 10.16

By region:

Midwest — 1.15 1.13 — –.02

Northeast — 1.71 1.70 — –.01

South .90 1.26 1.31 1–.36 .05

West 1.33 1.21 1.46 1.12 1.25

By size class:

Small 1.19 1.38 1.60 1–.19 1.22

Medium 1.25 1.24 1.47 .01 1.23

Large .97 1.29 1.41 1–.32 1.11

1

 Share differential is statistically significant.

NOTE: Dash indicates data not available.

Employment share

(percent)

Share differential

(percentage point)

Fast

growing

Moderately

growing

Slow

growing

Between

fast and

moderate

Between

slow and

moderate

Region

and

size class

Table C11. Management occupations

Full data set 4.84 5.13 4.84 1

–0.29 1–0.29

By region:

Midwest — 5.01 4.28 — 1–.74

Northeast — 5.14 5.16 — .02

South 5.06 5.14 4.51 1–.08 1–.63

West 4.57 5.23 6.20 1–.66 1.97

By size class:

Small 4.22 4.43 4.23 1–.20 1–.20

Medium 4.40 4.79 4.49 1–.40 1–.30

Large 5.16 5.41 5.11 1–.25 1–.30

1 Share differential is statistically significant.

NOTE: Dash indicates data not available.

Employment share

(percent)

Share differential

(percentage point)

Fast

growing

Moderately

growing

Slow

growing

Between

fast and

moderate

Between

slow and

moderate

Region

and

size class

this occupational group and because high levels of poverty

might be reflected in high levels of some social services.

Where differences are statistically significant, this predicted

pattern appears to hold. For example, in the full data set, the

employment share for the community and social services

occupational group is 0.21 percentage point lower in fastgrowing MAs than in moderately growing ones, while the

share in slow-growing MAs is 0.16 percentage point higher

than in moderately growing areas. (See table C10.) Three out

of 6 regional comparisons are statistically insignificant; of

the remainder, only 2 have the expected sign, with fast-growing MAs in the West having a higher, rather than lower, share

of these occupations. As regards the size-class data, for all

three size classes, slow-growing MAs had a higher share of

community and social service occupations than did moderately growing MAs, while fast-growing MAs had a lower

share of these occupations in two size classes, with the difference in share for medium-sized MAs not statistically significant.

The statistical insignificance of several comparisons, as

well as the generally small magnitude of the share differences

in the remaining comparisons, suggests that employment in

community and social service occupations is only weakly

correlated with slow growth. Previous research had found a

negative correlation between poverty and growth at the city

level only, while the correlation was not significant at the MA

level. In addition, only city-level data were examined for

correlations between size of government and growth, because the city, and not the MA, was perceived as the basic

political unit. Thus, the use of MA rather than city data in

this article may weaken any potential connections among

slow growth, poverty, and large government. In addition, to

the extent that slow growth is associated with poverty even

at the MA level, it is not necessarily the case that poorer

areas devote more resources to the provision of social services.

Finally, previous research found that local skill levels, as

measured by educational attainment, were a strong predictor

of population growth rates. This observation suggests that
18

growing areas in the South, however, largely display the

expected pattern. Shares of all the “high-skill” occupational

groups, with the exception of education, training, and library

occupations, are lower in slow-growing Southern MAs relative to moderately growing MAs. In some cases, the share

differentials also are quite substantial. For example, the

share of computer and mathematical occupations is 1.69 percentage points lower in slow-growing Southern MAs, while

the share of business and financial occupations is 1.47 percentage points lower. Slow-growing Midwestern MAs exhibit shares that also are largely consistent with the predicted pattern: all of the groups except architecture and

engineering occupations have lower shares in slow-growing

areas, although the difference in share for education, training, and library occupations is not significant. Finally, the

results for slow-growing MAs in the Northeast are somewhat mixed, with business and financial operations occupations; legal occupations; and education, training, and library

occupations having lower shares in slow-growing areas, architecture and engineering occupations having a higher

share, and the remaining three groups showing no significant differences.

In the size-class data for fast-growing MAs, only life,

physical, and social science occupations had a higher share

in small MAs, with the remaining groups not following the

predicted pattern. In medium-sized MAs, 3 out of the 7 occupational groups had higher shares in fast-growing areas, 1

group had a lower share, and the remaining differences were

statistically insignificant. Fast-growing large MAs deviated

strongly from the predicted pattern, with lower shares of all

the “high-skill” occupational groups except architecture and

engineering occupations. The size-class comparisons between slow- and moderately growing MAs, however, appear

to be more consistent with the predicted pattern. Slow-growing small MAs have lower shares of all seven groups except

Table C12. Business and financial operations

occupations

Full data set 4.13 4.52 4.19 1–0.39 1–0.33

By region:

Midwest — 4.85 4.28 — 1–.57

Northeast — 4.57 4.30 — 1–.26

South 4.17 4.26 2.80 1–.10 1–1.47

West 4.09 4.51 5.11 1–.42 1.60

By size class:

Small 3.35 3.59 2.83 1–.24 1–.76

Medium 3.72 3.78 3.81 –.06 .03

Large 4.46 5.03 4.64 1–.57 1–.39

1

 Share differential is statistically significant.

NOTE: Dash indicates data not available.

education, training, and library occupations, for which the

difference in share has the expected sign, but is not statistically significant. Medium-sized slow-growing MAs have

lower shares for 4 of the 7 groups, with education, training,

and library occupations showing a higher share and the remaining two differences insignificant. Finally, slow-growing

large MAs have lower shares of five of the seven groups,

with architecture and engineering and education, training,

and library occupations the exceptions.

Overall, the data fail to show a positive correlation between growth and employment concentrations in “high-skill”

occupational groups. This negative result is particularly

noticeable in the comparisons between fast- and moderately

growing MAs, in which the correlation between skill and

growth frequently appears to run the other way. Note that

earlier research found the correlation between skill and

growth to be important only for areas that might otherwise

tend to be declining—that is, cities that are not located in

warm, dry areas with significant immigrant communities.

Thus, we would expect positive correlations between skill

and growth to be less likely in the West and South and more

likely in the Midwest and Northeast. Data for the West bear

out this assumption; in fact, the region shows clear evidence

of a negative correlation between growth and employment in

skilled occupations. Thus, the importance of the West among

the fast-growing cities, and the lack of Northeastern and

Midwestern cities in this category, may help explain some of

the unusual results. The South, however, displays a more

complex pattern, with fast-growing Southern MAs appearing similar to fast-growing Western ones, while slow-growing Southern MAs display a positive correlation between

skilled occupations and growth. One possible explanation is

that, in the South, different growth factors may be relevant

to slow- and fast-growing MAs. Specifically, fast-growing

Southern MAs may have higher rates of immigration, thus

falling into the category of MAs for which skill is not strongly

Employment share

(percent)

Share differential

(percentage point)

Fast

growing

Moderately

growing

Slow

growing

Between

fast and

moderate

Between

slow and

moderate

Region

and

size class

Table C13. Computer and mathematical occupations

Full data set 2.51 2.74 2.28 1–0.23 1–0.46

By region:

Midwest — 2.60 2.02 — 1–.58

Northeast — 2.51 2.57 — .07

South 2.76 2.72 1.03 .04 1–1.69

West 2.18 3.07 3.55 1–.90 1.48

By size class:

Small 1.59 1.87 1.12 1

–.28 1–.75

Medium 2.29 1.95 1.86 1.35 1–.08

Large 2.75 3.26 2.70 1–.51 1–.56

1

 Share differential is statistically significant.

NOTE: Dash indicates data not available.

Employment share

(percent)

Share differential

(percentage point)

Fast

growing

Moderately

growing

Slow

growing

Between

fast and

moderate

Between

slow and

moderate

Region

and

size class
19

Table C14. Architecture and engineering occupations

Full data set 2.06 1.95 1.94 10.10 –0.02

By region:

Midwest — 1.88 2.30 — 1.42

Northeast — 1.40 1.80 — 1.41

South 2.02 1.85 1.35 1.17 1–.50

West 2.13 2.56 1.86 1–.43 1–.70

By size class:

Small 1.80 1.75 1.39 .05 1–.37

Medium 1.90 1.93 1.74 –.03 1–.19

Large 2.18 2.00 2.14 1.17 1.13

1

 Share differential is statistically significant.

NOTE: Dash indicates data not available.

predictive of growth. Slower growing Southern MAs, by

contrast, may be less likely to contain significant immigrant

communities and may therefore rely more on skill to generate

growth. Indeed, a check of the data reveals that most of the

fast-growing Southern MAs (24 out of 35) are in Florida or

Texas; the corresponding figures for moderately and slowgrowing Southern MAs are 15 out of 60 and 7 out of 33,

respectively. Census data show that these two states have

higher estimated annual rates of net international migration

than the United States as a whole and than most of the other

Southern states, the majority of which are below the national

average in this regard.6 In sum, it is possible that the general

population shift to warm, dry areas and the importance of

immigration as a source of population growth dominate

among fast-growing MAs, leading to negative correlations

or none at all between skill and population growth in these

areas. In the comparisons between slow and moderately

growing MAs, where these two factors are less important,

share differentials for high-skill occupational groups follow

the predicted pattern more closely.

 On a note of caution, although all of these groups contain occupations generally associated with higher levels of

education and skill, there is some variance in the skill levels

associated with specific occupations in each group, as well

as in the skill levels of individual practitioners of these occupations. Thus, compared with measuring the population’s

educational attainment, employment in these groups represents a less direct measure of local skill levels. In addition,

although education, training, and library occupations are

associated with high levels of skill, the demand for primary

and secondary school teachers, a major component of this

group, is also likely to be highly dependent on a community’s

demographics, a feature that may tend to weaken the results

for this occupational group as a whole.

Conclusion

Occupational Employment Statistics data reveal a number of

ways in which occupational structures at the metropolitanarea level appear to vary with population growth rates. These

comparisons are not intended to imply any causal relationship between occupational structure and growth. In particular, because May 2004 OES data are used in conjunction with

population growth rates for 1990–2003, it cannot be concluded that differences in occupational structure contributed to subsequent differences in population growth rates.

Rather, the comparisons presented here suggest ways in

which MAs with a recent history of rapid population growth

may differ in terms of their current occupational structures

from those with a less remarkable growth history.

Some of the observed differences appear to be consistent

with predictions based on earlier findings. For example, em6

 Table 6: Estimates of Average Annual Rates of the Components

of Population Change for the United States and States: April 1, 2000

to July 1, 2004 (NST-EST2004-06), Population Division, U.S. Census Bureau, January 28, 2004.

Employment share

(percent)

Share differential

(percentage point)

Fast

growing

Moderately

growing

Slow

growing

Between

fast and

moderate

Between

slow and

moderate

Region

and

size class

Employment share

(percent)

Share differential

(percentage point)

Fast

growing

Moderately

growing

Slow

growing

Between

fast and

moderate

Between

slow and

moderate

Region

and

size class

Table C15. Life, physical, and social science occupations

Full data set 0.86 0.97 0.87 1–0.12 1–0.11

By region:

Midwest — .91 .68 — 1–.23

Northeast — .95 1.00 — .04

South .83 .92 .60 1–.09 1–.32

West .90 1.13 1.34 1–.23 1.21

By size class:

Small 1.44 .94 .61 1.50 1–.33

Medium .89 .84 .76 1.05 1–.07

Large .76 1.04 .96 1–.28 1–.08

1

 Share differential is statistically significant.

NOTE: Dash indicates data not available.

Employment share

(percent)

Share differential

(percentage point)

Fast

growing

Moderately

growing

Slow

growing

Between

fast and

moderate

Between

slow and

moderate

Region

and

size class

Table C16. Legal occupations

Full data set 0.75 0.89 0.78 1

–0.14 1–0.11

By region:

Midwest — .72 .64 — 1–.08

Northeast — 1.11 .86 — 1–.25

South .81 .99 .66 1–.18 1–.33

West .67 .77 1.17 1–.10 1.40

By size class:

Small .56 .55 .46 .01 1

–.09

Medium .74 .66 .65 1.08 –.01

Large .77 1.05 .90 1–.27 1–.15

1

 Share differential is statistically significant.

NOTE: Dash indicates data not available.
20

ployment in production occupations appears to be negatively associated with growth; for this occupational group,

differences in share frequently exceeded 1 percentage point

in absolute value, suggesting that these differences were

both statistically and economically significant. As predicted,

employment in both healthcare occupational groups also

appeared to be higher in slow-growing areas. In other cases,

relationships between occupational distributions and growth

appeared to diverge from those predicted. For instance, rather

than finding no relationship between growth and occupational groups that might be associated with lifestyle amenities, the data suggest that food preparation and serving occupations and, to a lesser extent, personal care and service

occupations may have a bimodal peak, displaying higher

concentrations in both fast- and slow-growing MAs relative to moderately growing ones. One possible explanation

of this observation is that fast-growing areas may indeed

provide higher levels of amenities to their residents, while in

slow-growing areas higher concentrations of employment

in these occupations may merely reflect the lack of employment opportunity in other occupational groups. Finally, in

comparisons between fast- and moderately growing MAs,

the data showed no evidence of correlation between growth

and employment in more highly skilled educational groups;

in fact, fast-growing areas frequently appeared to have lower

concentrations of highly skilled occupations. However, comparisons between slow- and moderately growing MAs conform more closely to the predicted pattern, displaying at

least some evidence of a positive correlation between skill

and growth. Because the fast-growing group was composed

almost entirely of MAs in the South and West, this finding

was not inconsistent with previous evidence that skill was

not a significant growth factor in warm, dry areas with large

immigrant communities, but mattered more elsewhere. Examining in more detail the relationships among immigration,

population growth, skill, and occupational composition remains a subject for future research.

Table C17. Education, training, and library occupations

Full data set 5.80 5.95 6.21 1–0.15 10.26

By region:

Midwest — 5.72 5.53 — –.19

Northeast — 6.99 6.82 — 1–.17

South 5.79 5.65 5.89 .14 1.24

West 5.85 5.96 5.75 –.11 –.21

By size class:

Small 6.83 6.61 6.50 .21 –.11

Medium 5.83 5.80 6.38 .03 1.59

Large 5.63 5.89 6.08 1–.26 1.18

1 Share differential is statistically significant.

NOTE: Dash indicates data not available.

Employment share

(percent)

Share differential

(percentage point)

Fast

growing

Moderately

growing

Slow

growing

Between

fast and

moderate

Between

slow and

moderate

Region

and

size class