Section 3 Labor Force Analysis - Cooperative Extension · important labor force issues related to...

Transform Milwaukee 3-1 Labor Force Analysis

Section 3 – Labor Force Analysis

The knowledge and skills of the labor force, also known as human capital, is a primary driver and determinant

of economic growth. A large body of research has found that regions possessing higher levels of human capital

tend to be more innovative, experience greater economic activity, and enjoy faster rates of development than

areas with lesser endowments (Abel and Gabe 2011). In considering this research and its relevance to the

Transform Milwaukee Initiative, several important differences should be noted. First, much of the research

exploring the ties between human capital and economic growth has been conducted in the context of an

economy shifting from manufacturing and goods production, to one based on the creation of knowledge and

ideas. Second, research suggests that regional economic growth stemming from human capital found in a

broader, regional labor market does not necessarily translate to development in low income neighborhoods

(Blair and Carroll 2007). That is, a rising economy based on fostering greater skills and knowledge at the

regional level may not extend directly to new economic prospects for residents of the Transform Milwaukee

Study Area. Consequently, strategies for developing human capital in the areas surrounding Milwaukee’s

Industrial Corridor will differ somewhat from approaches pursued in other areas of the M7 Region.

Residents of the Transform Milwaukee Study Area are the primary focus of this labor force overview. As

depicted in Section 1, the Transform Milwaukee Study Area encompasses an area containing some of the

region’s lowest labor participation levels and highest unemployment rates. Given these conditions, it is not

surprising that portions of the Study Area are the focus of significant research by local organizations and

institutions. Accordingly, the information in this section does not seek to replicate existing information on

important labor force issues related to basic work-readiness, incarceration rates, availability of driver’s

licenses, and other identified concerns. Instead, this analysis focuses on the alignment between the Study

Area’s potential labor force and economic development strategies related to the Transform Milwaukee

Initiative. Individuals interested in labor force issues not covered in Section 3 should contact organizations

such as the UWM Employment Training Institute (ETI) and the Milwaukee Area Workforce Investment Board.

Other studies also have compared, benchmarked, and ranked aspects of Milwaukee’s labor market conditions

relative to those of other urban areas. While previous comparative research provides valuable context and

insights to Milwaukee’s economy, this analysis does not focus on rankings for several reasons:

• Rankings suggest that a formula for success can be found by looking at higher rated areas. While case

studies and success stories from other communities provide direction and ideas, focusing on rankings

ignores the role of a region’s economic history in shaping its current infrastructure, institutions,

technology, labor markets and entrepreneurial environment (Barkley and Dudensing, 2011);

• Rankings can increase awareness of local economic conditions, but they also have the potential to increase

inequality as lagging areas may be stigmatized by a low status and deficiencies beyond their local control

(Greene, Tracey and Cowling 2007). This observation is particularly relevant to the areas surrounding the

30th Street Industrial Corridor, which have been sufficiently benchmarked by past efforts. Consequently,

another study showing low rankings of Milwaukee’s central city labor market contributes little to

addressing these conditions.


While the following labor force overview is not a benchmarking study, comparisons are made among the

Transform Milwaukee Study Area, Milwaukee County, the Balance of the M7 Region and the United States.

Understanding similarities and differences between the Study Area and the surrounding region will help to

differentiate (or align) workforce development needs in the Transform Milwaukee Study Area from broader

regional initiatives. While some measures in Section 3 are based on the 2010 Census, many figures are derived

from the 2006-2010 American Community Survey (ACS) 5-year estimates. As noted in Section 1, using the

2006-2010 timeframe is beneficial as it covers the period before, during and after the latest official recession.

As also cautioned in Section 1, the ACS is a survey, not a true census, and each reported value should be

thought of as a period estimate rather than a five-year average. Given the potential wide margins of error

associated with ACS estimates, values for the Study Area, Milwaukee County and the Balance of the M7 Region

are tested for statistically significant differences relative to United States figures.

Population, Age and Race

The Transform Milwaukee Study Area includes over 378,000 residents and 138,000 households (Table 3.1).

While the Study Area has lost population over the last few decades, this portion of the community continues

to account for 40% of Milwaukee County’s population and almost 7% of the state’s population. Consequently,

improving economic opportunities for Study Area residents will not only increase the local quality of life, but

also could have a considerable impact on the State of Wisconsin. As an example, consider the long pursued

goal of increasing the state’s per capita income (PCI) to that of the national average.1 Currently, Wisconsin

residents would need to generate an additional $6.26 billion to grow the state’s per capita income from

$26,624 to the national PCI of $27,334.2 However, increasing the Study Area’s per capita income from its

current value of $16,135 to a modest $20,000 alone would account for $1.46 billion or 24% of Wisconsin’s per

capita income gap.

Table 3.1 – Population and Household Change 1990 to 2010

Time Period Transform Milwaukee

Study Area Milwaukee

County Balance of M7 Region

United States

Population

1990 415,619 959,275 851,089 248,709,873

2000 386,417 940,164 992,744 281,421,906

2010 378,222 947,735 1,072,235 308,745,538

Percent Change 1990 to 2000 -7.0% -2.0% 16.6% 13.2%

Percent Change 2000 to 2010 -2.1% 0.8% 8.0% 9.7%

Households

1990 150,016 373,048 303,059 91,947,410

2000 139,757 377,729 371,326 105,480,101

2010 138,246 383,591 416,496 116,716,292

Percent Change 1990 to 2000 -6.8% 1.3% 22.5% 14.7%

Percent Change 2000 to 2010 -1.1% 1.6% 12.2% 10.7% Source: U.S. Census Bureau 2010, 2000, 1990 Census and UW-Extension Center for Community and Economic Development

1 Increasing Wisconsin’s per capita income to that of the national average has been a stated goal of many economic development initiatives over the past decade, largely arising from discussions at the Wisconsin Economic Summits held between 2000 and 2003. 2 PCI figures are from the 2006-2010 American Community Survey. Per capita income figures from other sources (such as the BEA) will vary.


A particular asset of the Transform Milwaukee Study Area is its population density. The Study Area accounts

for some of the highest population densities in the State of Wisconsin, providing access to a large potential

labor pool within a small area. In particular, the 30th Street Industrial Corridor and the Menomonee Valley are

located among areas having population densities upwards of 12,000 residents per square mile, or more than

three times greater than Milwaukee County’s average density (Map 3.1).

Map 3.1 – Population Density (2010)


Age Structure

Age structure affects workforce considerations within specific businesses, broader industry sectors, and the

overall regional labor market. The age distribution of the current and future labor force determines the

potential numbers of workers entering the labor force versus those approaching retirement. An

understanding age structure can also direct the development of training or education curricula to the needs of

different age groups. Individuals between the ages of 16 and 64 are commonly considered to be of working

age, while ages 25 to 54 are often deemed as prime working years. Within these two age groups, the

distribution of residents in the Transform Milwaukee Study Area differs little from those of Milwaukee County,

the Balance of the M7 Region and the United States (Table 3.2). However, the broader age groups of 16 to 64

and 25 to 54 mask important characteristics in each area’s age structure. The Transform Milwaukee Study

Area is weighted towards younger age cohorts, with 31.9% of its residents between the ages 16 and 34,

compared to just 22.7% and 26.1% in the Balance of the M7 Region and United States respectively. While not

officially part of the labor force, individuals under the age of 16 also comprise a larger share of residents in the

Study Area.

The younger age distributions within the Transform Milwaukee Study Area are significant given the well-

documented aging of the labor force both nationally and in the State of Wisconsin. National age projections

from the Census Bureau indicate that the population age 65 and older will increase from about 1 in 8 people in

2004 to 1 in 5 people by 2030.3 While not directly comparable to the U.S. figures, projections from the

Wisconsin Department of Administration suggest that Wisconsin’s share of residents in the prime working ages

of 25 to 54 will decrease from 43.0% in 2005 to 36.5% in 2035.4

Table 3.2 – Population Distribution by Selected Age Groups (2010)

Age Transform Milwaukee



United States

Total Population 378,222 947,735 1,072,235 308,745,538

Under Age 16 26.1% 22.1% 21.4% 21.2%

Age 16 to 19 7.3% 6.0% 5.5% 5.8%

Age 20 to 24 9.1% 8.2% 5.6% 7.0%

Age 25 to 34 15.5% 15.4% 11.1% 13.3%

Age 35 to 44 12.7% 12.6% 13.3% 13.3%

Age 45 to 54 12.4% 13.4% 16.8% 14.6%

Age 55 to 64 9.1% 10.7% 12.8% 11.8%

Age 65+ 7.8% 11.5% 13.5% 13.0%

Age 25 to 54 40.6% 41.4% 41.2% 41.2%

Age 16 to 64 66.1% 66.4% 65.1% 65.8%

Source: U.S. Census Bureau 2010 Census and UW-Extension Center for Community and Economic Development

3 Taeuber, Cynthia and Matthew R. Graham, 2008. The Geographic Distribution and Characteristics of Older Workers in Wisconsin: 2004.

LED Older Workers Profile, LEDOW04-WI. U.S. Census Bureau, Washington, DC. 4 State and County Age-Sex Population Projections, 2005 – 2035. Demographic Services Center, Wisconsin Department of

Administration.


A projected decline in the share of prime working age residents is particularly relevant when comparing

Milwaukee County to its surrounding region. In 2005, Milwaukee County and the Balance of the M7 Region

had nearly identical shares of residents between the ages 25 and 54. Beginning in 2015, the percentage of

Milwaukee County residents within this age group is projected to exceed the share found within the Balance of

the M7 Region (Chart 3.1). Importantly, some of the difference in Milwaukee County is attributed to younger

residents of the Transform Milwaukee Study Area. If regional labor shortages arise from an aging population,

Milwaukee County and the Study Area should be positioned to benefit.5 Consequently, Study Area residents

ages 16 to 34 and under age 16 should be noted as a vital component of the Transform Milwaukee Study

Area’s comparative advantage.

Chart 3.1 – Projected Share of Population Age 25 to 54 (2005 to 2035)

Source: Wisconsin DOA Demographic Services Center and UW-Extension Center for Community and Economic Development

An aging labor force will impact some industries more than others. Shifts in the national age structure will

require firms to make locational or investment decisions partly based on labor availability. Consequently, local

industries with a high share of workers approaching retirement may need to start soon in devising worker

succession strategies. Within the region, these industries include large employment sectors such as education

and health services, financial activities, and construction (Chart 3.2). However, manufacturing is the private

sector industry with the largest share of workers between the ages 45 and 64 in both Milwaukee County and

the Balance of the M7 Region. Half of all workers in the region’s manufacturing sector are now age 45 to 64,

compared to just 40% of workers across all industries. Increased productivity might reduce the impact of

retiring workers, as could workers postponing retirement beyond age 65. Nonetheless, delayed retirement

within a given occupation is determined by an employee’s physical capacity to perform and remaining in

physically demanding jobs may not be an option for some workers.

5 The extent of any future labor shortage is an unknown due to changing consumption patterns, projected increases in productivity, and

immigration levels.

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

2005 2010 2015 2020 2025 2030 2035

Shar

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5 t

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4

Balance of M7 Region

State of Wisconsin

Milwaukee County


Chart 3.2 – Share of Workers Age 45 to 64 by Industry (2010 Annual Average)

Source: U.S. Census Bureau Local Employment Dynamics and UW-Extension Center for Community and Economic Development

Race

Efforts to enhance employment rates and earnings of low-income and less-educated workers have been

hallmarks of workforce development policies over the last few decades. Many of these programs have focused

on minorities as they have often been disproportionately represented among lower skilled workers (Stoll

2006). Accordingly, race and ethnicity is reported to provide insights to the diversity of the local labor force

(Table 3.3). The Transform Milwaukee Study Area has considerably more racial diversity when compared to

the Balance of the M7 Region and overall United States. Of particular difference is the share of Black or

African American residents in the Study Area compared to the Balance of the M7 Region. Other large

differences are noted among residents self-identifying as Hispanic or Latino.6 The highly concentrated

residential patterns among these two population groups are depicted on Map 3.2 and Map 3.3.

Table 3.3 – Population by Race (2010)

Race Transform Milwaukee



United States

Total Population 378,222 947,735 1,072,235 308,745,538 White 36.0% 60.6% 89.7% 72.4% Black or African American 47.1% 26.8% 3.8% 12.6% American Indian and Alaska Native 0.9% 0.7% 0.3% 0.9% Asian 3.1% 3.4% 1.8% 4.8% Native Hawaiian and Other Pacific Islander 0.0% 0.0% 0.0% 0.2% Some Other Race 9.5% 5.4% 2.6% 6.2% Two or More Races 3.5% 3.0% 1.7% 2.9% Hispanic or Latino (of any race) 21.4% 13.3% 6.9% 16.3%

Source: U.S. Census Bureau 2010 Census and UW-Extension Center for Community and Economic Development 6 In the 2010 Census “Hispanic or Latino” refers to a person of Cuban, Mexican, Puerto Rican, South or Central American, or other Spanish culture or origin regardless of race. Accordingly, people who identify their origin as Spanish, Hispanic, or Latino may be of any race.

0% 10% 20% 30% 40% 50% 60%

All Sectors

Public Administration

Other Services

Leisure & Hospitality

Education and Health Services

Professional & Business Services

Financial Activities

Information

Trade, Transportation, Utilities

Manufacturing

Construction

Natural Resources and Mining

Balance of M7Region

MilwaukeeCounty


Map 3.2 – Black or African American Residents as a Share of Total Population


Map 3.3 – Hispanic or Latino Residents as a Share of Total Population


Labor Participation, Unemployment and Employment Rates Labor participation rates, unemployment rates and employment rates provide three perspectives on the local

labor market. Although labor participation rates and unemployment rates were examined briefly in Section 1

of this report, definitions of these measures are worth reviewing, along with a description of employment

rates:

Unemployment rate – Measured as the share of the civilian labor force that is without a job and actively

seeking one. Unemployment rates exclude individuals not in the labor force;

Labor participation rate – Share of the civilian population that is part of the labor force; either employed or

unemployed and actively seeking a job. Individuals not in the labor force can include discouraged workers

(i.e. those not actively seeking employment), students, retired workers, seasonal workers surveyed in the

off-season, institutionalized individuals, and people doing incidental unpaid family work;

Employment Rate – A commonly noted deficiency with using unemployment rates to examine the local

labor market is that unemployment rates do not include individuals without a job and not counted as part

of the labor force. That is, there may be individuals interested in a job that are not currently seeking

employment for a variety of reasons, and therefore not counted in official unemployment rate figures.7

Employment rates, also known as employment-population ratios, address this issue by measuring the share

of the total population that is employed, regardless of participation in the labor force.

Recent debate has surrounded the use of these measures to describe Milwaukee’s labor market conditions.

One perspective is that the employment rate is a more robust measure of the labor market. Proponents of

using the employment rate submit that unemployment rates are misleading when measuring employment

among historically disadvantaged groups. Specifically, unemployment rates do not include discouraged

workers and other working age individuals who may be disabled or incarcerated. Consequently, some

economists and sociologists suggest that unemployment rates underestimate employment conditions and do

not provide a comprehensive view of an area’s economic health.8

An alternate perspective suggests disadvantages to using employment rates. As the employment rate includes

individuals not in the labor force, an emphasis on this measure may redirect workforce development efforts

towards individuals who may not wish to work for valid reasons. Consequently, inefficiencies in program

delivery can be created and the needs of workers who are actively seeking new or full employment may not be

fully met. Furthermore, emphasizing employment rates over unemployment rates may promote stereotypes of

7 As a result, unemployment rates can actually decline as a result of individuals halting their employment search, rather than an

increasing number of unemployed workers finding new jobs. This phenomenon has been commonplace during the current economic recovery.

8 For a full discussion of using employment rates to measure Milwaukee’s labor conditions, see: Levine, Marc V. The Crisis of Black

Male Joblessness in Milwaukee: Trends, Explanations, and Policy Options.” University of Wisconsin-Milwaukee Center for Economic Development Working Paper, March 2007. Also see: Levine, Marc V. Race and Male Employment in the Wake of the Great Recession: Black Male Employment Rates in Milwaukee and the Nation’s Largest Metro Areas 2010.” University of Wisconsin-Milwaukee Center for Economic Development Working Paper, January 2012.


some individuals as not willing to seek active employment.9 In truth, there are likely advantages to using both

measures together, as they provide different perspectives on community development and workforce training

policies.

In context of the Transform Milwaukee Initiative, the discussion surrounding unemployment rates and

employment rates raises a more specific policy question. Specifically, can a sufficient level of economic

revitalization occur in the Study Area if workforce development efforts only focus on those who are officially

unemployed? Or should significant efforts also focus on re-engaging individuals who could work, but have

dropped out of the labor force for some reason? Furthermore, do unemployment and employment rates

among different age groups, genders or races suggest specific segments within the Transform Milwaukee

Study Area that might have greater needs?

To further explore these questions, the following analysis compares unemployment rates, labor participation

rates and employment rates in the Transform Milwaukee Study Area. Figures are provided by age group,

gender and race. When considering these measures by race and gender, note that Black or African American

men and women and white men and women are by far the largest groups in the Study Area. These four

population segments account for 85.2% of the Study Area’s total population between the ages 16 and 64

(Black or African American men = 19.5%; Black or African American women = 25.0%; white men = 20.7%; white

women = 20.0%). Figures for Hispanic and Latino men and women are also provided, but as noted previously,

Hispanic and Latino self-identification can include any race.

Labor Participation Rates

Labor participation rates vary greatly by age, with participation rates traditionally peaking in the prime working

years of 25 to 54. Within these prime years, participation rates among Transform Milwaukee Study Area

residents differ somewhat from the nation (Table 3.5). Participation rates of Study Area residents age 35 to 44

are only slightly lower than the national average, while participation rates for the Study Area’s 45 to 54 age

cohort are significantly below the national average (72.7% vs. 80.9%). In contrast, participation rates among

Study Area individuals under the age of 35 and over the age of 65 do not vary significantly from U.S. averages.

Additional variations in labor participation rates are apparent when comparing the Study Area to the

surrounding region. High participation rates are found in the Balance of the M7 Region, with rates in almost

every age cohort (except 65 to 74) exceeding the national average. The rates in the Balance of the M7 Region

reflect the overall high participation rates found throughout the State of Wisconsin and suggest stark contrasts

between labor market conditions in the Study Area and the outlying region. Milwaukee County also has

participation rates above the national average for most age groups. As depicted in Section 1, participation

rates are particularly high among many county residents outside of the Study Area (see Map 1.4).

9 This perspective is presented in: Drilldown on African American Male Employment and Workforce Needs, prepared for the Milwaukee

Area Workforce Investment Board by the University of Wisconsin-Milwaukee Employment and Training Institute, December 2009.


Table 3.4 – Labor Participation Rates for the Civilian Population by Age Group (2006-2010)

Age Group Transform Milwaukee



United States

Age 16 to 19 38.0% * 42.7% - 55.3% *** 41.7%

Age 20 to 24 73.6% - 78.7% *** 82.7% *** 73.2%

Age 25 to 34 81.4% - 85.4% *** 86.9% *** 81.4%

Age 35 to 44 79.1% *** 83.3% ** 87.2% *** 82.2%

Age 45 to 54 72.7% *** 81.0% - 87.3% *** 80.9%

Age 55 to 64 56.1% ** 65.2% ** 69.4% *** 63.7%

Age 65 to 74 20.8% - 23.9% - 25.9% *** 24.3%

Age 75 and Over 4.6% - 4.9% ** 6.0% - 5.7%

Age 25 to 54 78.0% *** 83.3% *** 87.1% *** 81.5%

Age 16 to 64 70.2% *** 76.3% *** 80.7% *** 74.0%

Source: U.S. Census Bureau 2006-2010 ACS and UW-Extension Center for Community and Economic Development Estimates Based on a 90% CI. Significance vs. United States * = 90% ** = 95% *** = 99%

In comparing participation rates among individuals ages 16 to 64 of different genders and races, additional

differences are apparent among the Transform Milwaukee Study Area, the Balance of the M7 Region and the

United States (Table 3.5). Overall participation rates are somewhat lower for white and African American men

residing in the Study Area than their respective national averages. Similar differences are seen within Black or

African American women and Hispanic or Latino women. However, the overall participation rates of Black or

African American men are below those of white and Hispanic or Latino men across all areas. The participation

rate among Black or African American men is also lower than that of African American women, a reversal of

gender participation rates seen among other groups in Table 3.5.

Table 3.5 – Labor Participation Rates by Gender and Race for the Civilian Population Ages 16 to 64 (2006-2010)

Gender and Race Transform Milwaukee



United States

Men

White 79.0% *** 83.4% *** 85.9% *** 80.3%

Black or African American 62.4% *** 64.1% *** 61.3% *** 66.6%

Hispanic or Latino (of any Race) 81.8% - 81.8% * 82.4% ** 80.4%

Women

White 73.2% *** 77.8% *** 77.5% *** 70.4%

Black or African American 66.7% *** 69.5% - 67.8% * 70.6%

Hispanic or Latino (of any Race) 60.2% * 62.8% - 68.3% *** 63.6%



Unemployment Rates

Unemployment rates provide a perspective on individuals who are unemployed and actively seeking

employment. Again, unemployment rates do not count individuals who are not included in the labor force, but

active pursuit of employment should not be confused with willingness to work. Unemployment rates are

valuable as they provide some perspective on the availability of jobs and the relative tightness of the labor

market. As noted in Section 1, unemployment rates derived from the American Community Survey will differ

from the monthly and annual figures reported by the Bureau of Labor Statistics and should not be directly

compared (see Section 1 for a discussion of these differences). Furthermore, these rates may not reflect

current conditions and should be used to provide relative comparisons, rather than absolute figures.

Within the Balance of the M7 Region, unemployment rates among all age groups under the age of 65 are

statistically below those of the national average. The unemployment rates in the broader region also coincide

with the high participation rates reported earlier (Table 3.6). Despite being part of the same regional labor

market, unemployment rates among all age groups 25 to 54 in the Transform Milwaukee Study Area are well

above the national average. (Unemployment rates in the 16 to 19 and 20 to 24 age cohorts also appear to be

higher, but are not statistically different due to their large margins of error.) In all, unemployment rates in the

Transform Milwaukee Study Area translate to over 25,000 officially unemployed individuals between the ages

of 16 and 64.10 As unemployment rates are based on individuals looking for work, the differences in rates

could suggest that jobs in the Balance of the M7 Region are perhaps more numerous, more accessible, or

better aligned to the skills of workers residing in the area. More specifically, unemployment rates in the Study

Area may be partially attributed to so-called structural conditions that will be further explored in this section.

Table 3.6 – Unemployment Rates for the Civilian Population by Age Group (2006-2010)

Age Group Transform Milwaukee



United States

Age 16 to 19 34.1% - 25.6% - 17.2% *** 23.9% Age 20 to 24 20.6% - 13.7% - 10.8% *** 13.2%

Age 25 to 34 14.9% *** 8.8% ** 6.2% *** 8.0%

Age 35 to 44 12.5% *** 8.2% *** 4.7% *** 6.2%

Age 45 to 54 11.5% *** 7.3% *** 4.5% *** 5.9%

Age 55 to 64 7.5% - 5.7% - 4.8% *** 5.5%

Age 65 to 74 6.7% - 5.8% - 4.5% - 5.3%

Age 75 and Over 8.2% - 6.3% - 4.3% - 4.8%

Age 25 to 54 13.1% *** 8.1% *** 5.0% *** 6.6%

Age 16 to 64 14.8% *** 9.4% *** 6.2% *** 8.0%

Source: U.S. Census Bureau 2006-2010 ACS and UW-Extension Center for Community and Economic Development. Estimates Based on a 90% CI. Significance vs. United States * = 90% ** = 95% *** = 99%

Unemployment rates by race and gender provide additional insights into local labor market conditions. While

the labor participation rates among Black or African American men in the Transform Milwaukee Study Area are

statistically no different than those of the Balance of the M7 Region and only slightly lower than the U.S.

average, unemployment rates in this group are dramatically higher in the Study Area (Table 3.7).

10

This figure does not include underemployed or discouraged individuals. Furthermore, some level of frictional unemployment will always be present regardless of local economic conditions.


Unemployment rates among white and Hispanic or Latino men are also higher in the Study Area compared to

their counterparts in the Balance of the M7 Region and the United States. The higher unemployment rates

among Study Area men in every group could suggest an absence of appropriate, local employment

opportunities compared to other areas in the region.

While a lack of jobs may partially account for the higher unemployment rates among men in the Study Area, it

does not fully explain why males have higher unemployment rates than females. Furthermore, it fails to

explain why male unemployment rates vary greatly among men of different races and ethnicities. These

differences might be partially explained by industry of employment and geographic population distributions.

For instance, unemployment differences by gender may be a reflection of job opportunities (or lack thereof) in

industries that are traditionally dominated by men (manufacturing, construction, etc.) versus jobs in industries

that tend to have a higher share of employment by women.11 The influence of geographic location must also

be considered given the residential distributions depicted on Map 3.2 and Map 3.3. Specifically, African

American and Hispanic or Latino residents are tightly clustered in different parts of Milwaukee. If suitable

employment opportunities (e.g. those aligned with existing skill sets) are fewer in areas with a large number of

African American residents, unemployment rates among this group also may be higher.

Table 3.7 – Unemployment Rates by Gender and Race for the Civilian Population Ages 16 to 64 (2006-2010)

Gender and Race Transform Milwaukee



United States

Men

White 9.7% ** 7.3% - 6.5% *** 7.3%

Black or African American 27.9% *** 23.2% *** 18.4% - 15.7%

Hispanic or Latino (of any Race) 14.5% * 12.8% *** 8.9% - 9.0%

Women

White 7.6% - 5.1% *** 5.0% *** 6.5%

Black or African American 11.0% - 10.2% * 10.3% - 9.0%

Hispanic or Latino (of any Race) 13.6% - 11.6% 8.5% * 10.5% Source: U.S. Census Bureau 2006-2010 ACS and UW-Extension Center for Community and Economic Development. Estimates Based on a 90% CI. Significance vs. United States * = 90% ** = 95% *** = 99%

Employment Rates

As previously mentioned, employment rates measure the share of a given population group that currently has

a job, regardless of whether individuals are active participants in the labor force. Consequently, a low

employment rate may be influenced by 1) a high share of individuals not in the labor force; 2) high levels of

unemployment; 3) or some combination of both factors. Conversely, a high employment rate is a function of

both low unemployment and high labor participation. That is, a relatively high employment rate cannot occur

in areas experiencing either elevated unemployment or a low labor participation rate. The high employment

rates in the Balance of the M7 Region relative to the United States and the Study Area are certainly a

combination of these factors, with unemployment rates in the Balance of the M7 Region below the U.S.

average and labor participation rates exceeding national values in all age groups (Table 3.8).

11

A similar dynamic was seen during the recent recession, with male unemployment rates exceeding those of women.


Depending on the age group, lower employment rates found in the Transform Milwaukee Study Area are

driven by a variety of factors. For instance, Study Area labor participation rates in the 25 to 34 age cohort are

nearly identical to the national average. Consequently, the lower Study Area employment rate in this age

group is driven largely by unemployment. Similar differences are likely seen in the 20 to 24 age group, but

cannot be verified through statistical testing. Within the 35 to 44 and 45 to 54 age groups, the lower Study

Area employment rates are largely driven by high unemployment, rather than low labor participation. In

contrast, a low Study Area employment rate in the 55 to 64 age group is mostly attributed to lower labor

participation.

Table 3.8 – Employment Rates for the Civilian Population by Age Group (2006-2010)

Employment Rate by Age Group

Transform Milwaukee Study Area

Milwaukee County


United States

Age 16 to 19 25.0% *** 31.8% - 45.8% *** 31.7%

Age 20 to 24 58.4% ** 67.9% *** 73.8% *** 63.5%

Age 25 to 34 69.3% *** 77.9% *** 81.5% *** 74.9%

Age 35 to 44 69.2% *** 76.4% - 83.1% *** 77.1%

Age 45 to 54 64.4% *** 75.1% ** 83.4% *** 76.1%

Age 55 to 64 51.9% ** 61.5% ** 66.1% *** 60.2%

Age 65 to 74 19.4% - 22.5% - 24.7% *** 23.0%

Age 75 and Over 4.2% - 4.6% *** 5.7% - 5.4%

Age 25 to 54 67.7% *** 76.5% - 82.8% *** 76.1%

Age 16 to 64 59.8% *** 69.1% *** 75.7% *** 68.1% Source: U.S. Census Bureau 2006-2010 ACS and UW-Extension Center for Community and Economic Development Estimates Based on a 90% CI. Significance vs. United States * = 90% ** = 95% *** = 99%

When comparing employment rates by race and gender, the largest differences by far are seen when

comparing employment rates among Black or African American men to the rates for white males and Hispanic

or Latino men. These differences are to be expected given the low participation rates and high unemployment

rates previously noted among Black or African American men. Employment rates among the other groups in

Table 3.9 are also lower than those of the Balance of the M7 Region as well and also below the national

averages in many instances. Again, these rates are based on conditions during the 2006-2010 period, but

other research employing different timeframes largely echo these findings.12

Table 3.9 – Employment Rates by Race for the Civilian Population Ages 16 to 64 (2006-2010)

Employment Rate by Race


Milwaukee County


United States

Men

White 71.3% *** 77.3% *** 80.3% *** 74.4%

Black or African American 45.0% *** 49.2% *** 50.0% *** 56.1%

Hispanic or Latino (of any Race) 69.9% * 71.3% - 75.1% * 73.1%

Women

White 67.6% ** 73.8% *** 73.7% *** 65.9%

Black or African American 55.7% *** 59.3% *** 57.5% * 61.5%

Hispanic or Latino (of any Race) 52.0% ** 55.5% - 62.5% *** 56.9%


12

Again, see Levine 2007.


The various rates by age group, race and gender in the preceding tables return us to the earlier question of

workforce development priorities in the Study Area. That is, are workforce development initiatives that

prioritize unemployed individuals participating in the labor force sufficient, or should efforts provide equal

engagement for individuals who are not currently part of the labor force? One way to explore workforce

development priorities is to model the potential impacts of reduced unemployment rates on different groups

in the Transform Milwaukee Study Area. As an example, consider how employment conditions would change

by reducing Study Area unemployment rates to 7.5% among white and Black or African American residents

ages 16 to 64.13 While a 7.5% unemployment rate is still high, it is slightly below the overall national

unemployment rate for this age group (see Table 3.6). A 7.5% rate is also a significant reduction from the

27.9% unemployment rate found among Black or African American males in the Study Area. The results of this

scenario are shown in Table 3.10.

Table 3.10 – Changes to Employed Population and Employment Rates (Age 16 to 64) Using a 7.5% Unemployment Rate

Category Current Employed

Population Employed Population at

7.5% Unemployment Change in Employed

Population

Black or African American Men 21,582 27,690 6,108

Black or African American Women 34,201 37,874 3,673

White Men 36,356 37,258 902

White Women 33,332 33,385 353

Total 125,471 136,207 11,036

Source: U.S. Census Bureau 2006-2010 ACS and UW-Extension Center for Community and Economic Development Estimates based on a 90% CI and are subject to estimation errors

A 7.5% unemployment rate among these demographic groups would add approximately 11,000 individuals to

the working population, with more than half of this increase attributed to Black or African American men. This

impact is significant. However, an additional 11,000 employed workers among these groups would increase

the Study Area’s overall employment rate to just 64.3%, still well below the overall employment rates of the

M7 Region and the United States. Moreover, if these new workers were employed at county average earnings,

their additional wages would directly increase Study Area income by a modest seven percent (indirect and

induced increases will also occur).

Consequently, a large reduction in unemployment rates will be important, but only will provide a partial path

to revitalizing the Study Area. The scale of unemployment and potential underemployment in the Study Area

likely requires a range of strategies that put active job seekers to work, move underemployed workers to full-

employment and re-engage potential workers who are not in the labor force. While not all working age

individuals can nor should be fully employed, those that may be discouraged could be a particular target of

local efforts.14

13

Hispanic and Latino men and women are not part of this calculation as they may be part of other categories in Table 3.10 and could result in double-counting. 14

Potential reasons for labor force non-participation among African American males are explored in the aforementioned Drilldown on African American Male Employment and Workforce Needs from the UWM Employment and Training Institute.


Education, Occupations and Journey to Work

Addressing both high unemployment and encouraging some individuals to re-enter the job market are not

unrelated tasks. As labor market conditions improve, discouraged workers may start seeking employment

once again (Riddell 2000). However, improving the labor market for residents of the Transform Milwaukee

Study Area requires addressing structural unemployment conditions. Structural unemployment describes

conditions where unemployment rates are largely driven by mismatches of skills or geography. 15 Geographic

mismatches occur when there are unemployed workers with needed skills living in one area and unfilled

positions located in other regions. In contrast, skills mismatches are incompatibilities between the abilities of

job applicants and the abilities required by employers trying to fill vacant positions.

The presence of skills mismatches in Milwaukee, the State of Wisconsin, and the United States have been the

focus of much discussion during the current economic recovery. From a broad policy perspective, skills

mismatches are important as vacant positions tend to remain open longer, resulting in production challenges

for existing firms and higher unemployment rates in the labor market. However, there is some debate on the

existence and extent of skills mismatches in the economy. One specific suggestion sparked by this discussion is

that worker re-training is a primary path to putting individuals back to work and lowering the unemployment

rate. However, are worker training and re-training initiatives sufficient to improve unemployment and

employment rates in the Transform Milwaukee Study Area? Furthermore, what influence does the geographic

distribution of jobs have on employment conditions? The following overview of educational attainment,

occupations and journey to work provides perspectives on these questions.

Educational Attainment

Educational attainment is a basic means for quantifying human capital. While educational attainment has

shortcomings as a measure and does not independently qualify individuals for an occupation, it is a reflection

of traits such as perseverance, cooperation or discipline. Furthermore, since many specific qualities and skills

are not directly observable on a job application, employers often rely on educational attainment as one

method of screening applicants, along with other credentials such as past work experience and references

(Stoll 2006).

Educational attainment is reported for individuals age 25 and over. Consequently, the following measures of

educational attainment do not include the large share of individuals ages 16 to 24 in the Transform Milwaukee

Study Area. When compared to the national average and the Balance of the M7 Region, the Study Area has a

greater share of individuals age 25 and over with a high school degree as their highest level of education.

Conversely, the share of the population without a high school degree is also significantly larger. Almost a

quarter of the population in the Transform Milwaukee Study Area does not have a high school degree,

compared to 15% in the nation and about eight percent in the Balance of the M7 Region (Table 3.11). These

differences are also stark when considering that the United States and the Balance of M7 Region have higher

15

At a macro level, structural unemployment is defined as the amount of unemployment that cannot be affected by monetary policy, requiring other strategies for addressing unemployment.


shares of their populations in older age groups (e.g. age 75 and older) that are less likely to have completed a

high school degree than their younger counterparts.

Table 3.11 - Highest Level of Educational Attainment for the Population Age 25 and Over (2006-2010)

Highest Level of Educational Attainment (Age 25 and Over)


Milwaukee County


United States

Total Population Age 25 and Over 216,594 597,175 711,594 199,726,659

Less than 9th grade 8.9% *** 5.2% *** 2.6% *** 6.2%

9th to 12th grade, no diploma 15.3% *** 9.8% *** 5.8% *** 8.7%

High school graduate (includes GED) 33.8% *** 30.4% *** 30.4% *** 29.0%

Some college, no degree 20.6% - 20.9% - 21.5% *** 20.6%

Associate's degree 5.8% *** 6.9% *** 8.6% *** 7.5%

Bachelor's degree 10.6% *** 17.3% * 20.8% *** 17.6%

Graduate or professional degree 5.0% *** 9.5% *** 10.4% - 10.3%

High School or Greater 75.8% *** 84.9% - 91.6% *** 85.0%

Bachelor's or Greater 15.6% *** 26.7% *** 31.2% *** 27.9%


In areas with low levels of educational attainment, it is not surprising that workforce development efforts

frequently focus on traditional Adult Basic Skills education (ABE) programs to provide proficiencies in reading,

writing, and math. The typical objective of these initial preparation programs is helping individuals obtain a

high school diploma or GED, with the viewpoint that this achievement is a primary factor in future

employment opportunities or earning potential. However, basic education programs often fail to significantly

enhance employment and retention of disadvantaged workers due to a stigmatization of program participants’

short training times and a lack of skills appropriate to specific employers (Stoll 2006). While the need to

develop basic skills is important, these programs fail to recognize the difference between education and

training for job specific needs (Grubb 2009). Consequently, ABE efforts may be more successful when

conducted in concert with employers who are able to provide on-the-job training and connections to

employment opportunities upon completion of the program (Stoll 2006).

The spatial distribution of residents with a high school degree is shown on Map 3.4 and largely mirrors the

income distributions shown in Section 1 (Map 1.3). That is, areas with higher levels of educational attainment

tend to show higher incomes. These correlations are expected given the ties between education and earning

potential. The influence of population density on educational attainment distributions is also an important

labor force consideration and will be examined later in Section 3.


Map 3.4 – Population Age 25 or Over with a High School Degree or Higher


Occupations

Educational attainment provides an incomplete perspective on a worker’s knowledge and skills. At best,

formal education figures capture differences in vertical skills, or the amount of skill possessed by people, but

says nothing about the specific types of skills people possess (Marigee, Blum, and Strange, 2009). Other

approaches to exploring skills have been based on examining employment levels in different industries.

However, an industry-based approach only measures what is produced, rather than the skills needed in the

production process. That is, an industry-based analysis considers all workers in a manufacturing facility to be

similar, regardless of the specific position held by an employee (e.g. CNC operator, manager, IT services or

custodian).

Ideally, primary data on specific skills possessed by individuals living in the Transform Milwaukee Study Area

would be available for quantifying different skill sets. Unfortunately, a detailed skills inventory for all potential

workers in the Study Area does not currently exist and is considered to be a common need among labor force

economists and analysts. In lieu of a comprehensive skills inventory, employment by occupation provides

some perspective on the levels and types of skills held by employed residents of the Study Area (Table 3.12).

Note that these figures only report occupations for individuals who are employed and do not capture latent

skills that may be present among unemployed workers.

Table 3.12 - Occupation for the Civilian Employed Population Age 16 and Over (2006 – 2010)

Occupation Transform Milwaukee



United States

Total 150,697 442,542 546,841 141,833,331

Management 5.7% *** 7.7% *** 10.9% *** 9.6%

Business and financial operations 3.2% *** 4.7% - 5.3% *** 4.6%

Computer and mathematical 1.6% - 2.3% - 2.4% - 2.4%

Architecture and engineering 1.1% - 1.7% ** 2.6% *** 1.9%

Life, physical, and social science 0.5% - 0.7% *** 0.7% ** 0.8%

Community and social service 2.0% - 1.8% ** 1.3% *** 1.6%

Legal 0.8% - 1.1% - 0.9% *** 1.2%

Education, training, and library 5.6% - 6.1% - 5.6% *** 5.9%

Arts, design, entertainment, sports and media 1.4% - 1.8% - 1.6% *** 1.9%

Healthcare practitioners and technical 3.8% *** 5.6% *** 5.8% *** 5.2%

Healthcare support 5.0% *** 3.3% *** 2.1% *** 2.3%

Protective service 2.7% - 2.4% ** 1.5% *** 2.2%

Food preparation and serving related 6.8% *** 5.7% * 4.5% *** 5.4%

Building/grounds cleaning and maintenance 5.0% *** 3.4% *** 2.8% *** 3.9%

Personal care and service 4.2% * 3.3% - 2.8% *** 3.3%

Sales and related 8.4% *** 10.1% *** 11.6% ** 11.2%

Office and administrative support 15.3% *** 15.7% *** 14.6% ** 14.2%

Farming, fishing, and forestry 0.6% - 0.4% *** 0.4% *** 0.7%

Construction and extraction 3.7% *** 3.6% *** 4.8% *** 5.7%

Installation, maintenance, and repair 2.3% ** 2.6% *** 3.2% ** 3.4%

Production 12.1% *** 9.4% *** 9.1% *** 6.3%

Transportation 3.6% - 3.3% *** 3.0% *** 3.6%

Material moving 4.7% *** 3.1% *** 2.5% - 2.5% Source: U.S. Census Bureau 2006-2010 ACS and UW-Extension Center for Community and Economic Development. Estimates Based on a 90% CI. Significance vs. United States * = 90% ** = 95% *** = 99%


In terms of its occupational distribution, the Transform Milwaukee Study Area differs from both the Balance of

the M7 Region and the United States in several ways. Specific occupational categories where the Study Area

exceeds the national average include health care support; food preparation; building and grounds

maintenance; office and administrative support; personal care services; production; and material moving. In

comparison, the Study Area has occupational employment shares statistically below the national average in

management; business and financial operations; construction and extraction; health care practitioners; and

sales.

While skills within these broad occupational categories certainly vary, most of the occupational categories that

show high shares in the Transform Milwaukee Study Area tend to require lower overall preparation times and

training requirements. In contrast, the Balance of the M7 Region tends to more concentrated in categories

requiring longer preparation times such as management, business and financial operations and health care

practitioners. Several occupation categories concentrated in the Study Area have also seen some of largest

recent occupational declines in the Milwaukee Metropolitan Statistical Area.16

Given the Transform Milwaukee Study Area’s occupational distribution, along with its aforementioned

educational attainment levels, what are the prospects for aligning residents with the potential needs of

industries? One approach that has been cited as effective in urban settings is a sectoral-based workforce

development strategy (Fitzgerald 1999, Harper-Anderson 2008). Sectoral-based strategies position workforce

development strategies around the needs of specific industries that are well-suited to an area. In terms of the

Transform Milwaukee Initiative, these could be sectors noted in the industry structure analysis presented in

Section 2 or industries in the broader region. Importantly, sectoral-based strategies emphasize living wage

industries and occupations, as well as those that provide opportunities for advancement.

To explore potential alignments between industries and educational attainment and preparation

requirements, the following analysis uses occupational-employment matrices from the Bureau of Labor

Statistics to decompose industry employment by shares of high-skill and low-skill occupations. Specifically, the

shares of occupations in each industry are reported by their job zones as defined by the U.S. Department of

Labor/Employment and Training Administration’s Occupational Information Network (O*NET). O*NET

describes occupations by their required levels of knowledge, skills, and abilities and is based on data collected

from occupational experts and ongoing surveys of current workers in each occupation. Using this information,

O*NET assigns a job zone to each occupation based on the usual type of preparation needed, as well as the

typical length of time workers need to acquire information; learn techniques; and develop the capacity needed

for average performance. Overall, occupations in Job Zone 1 have lower preparation and skills requirements

while occupations in Job Zone 5 require the largest amount of preparation (Figure 3.1).

16

Information on recent shifts in the region’s occupation structure is available in Appendix 3A.


Figure 3.1 – Understanding Job Zones

Job Zone One: Little or No Preparation Needed

Education - Some of these occupations may require a high school diploma or GED certificate.

Related Experience - Little or no previous work-related skill, knowledge, or experience is needed for these

occupations. For example, a person can become a waiter or waitress even if he/she has never worked before.

Job Training - Employees in these occupations need anywhere from a few days to a few months of training. Usually,

an experienced worker could show you how to do the job.

Specific Vocational Preparation Time – Short demonstration, up to one month or one to 3 months.

Job Zone Two: Some Preparation Needed

Education - These occupations usually require a high school diploma.

Related Experience - Some previous work-related skill, knowledge, or experience is usually needed. For example, a

teller would benefit from experience working directly with the public.

Job Training - Employees in these occupations need anywhere from a few months to one year of working with

experienced employees. A recognized apprenticeship program may be associated with these occupations.

Specific Vocational Preparation Time – 3 to 6 months, 6 months to 1 year

Job Zone Three: Medium Preparation Needed

Education - Most occupations in this zone require training in vocational schools, related on-the-job experience, or

an associate's degree.

Related Experience - Previous work-related skill, knowledge, or experience is required for these occupations. For

example, an electrician must have completed three or four years of apprenticeship or several years of vocational

training, and often must have passed a licensing exam, in order to perform the job.

Job Training - Employees in these occupations usually need one or two years of training involving both on-the-job

experience and informal training with experienced workers. A recognized apprenticeship program may be

associated with these occupations.

Specific Vocational Preparation Time – 1 to 2 years

Job Zone Four: Considerable Preparation Needed

Education - Most of these occupations require a four-year bachelor's degree, but some do not.

Related Experience - A considerable amount of work-related skill, knowledge, or experience is needed for these

occupations. For example, an accountant must complete four years of college and work for several years in

accounting to be considered qualified.

Job Training - Employees in these occupations usually need several years of work-related experience, on-the-job

training, and/or vocational training.

Specific Vocational Preparation Time – 2 to 4 years

Job Zone Five: Extensive Preparation Needed

Education - Most of these occupations require graduate school. For example, they may require a master's degree,

and some require a Ph.D., M.D., or J.D. (law degree).

Related Experience - Extensive skill, knowledge, and experience are needed for these occupations. Many require

more than five years of experience. For example, surgeons must complete four years of college and an additional

five to seven years of specialized medical training to be able to do their job.

Job Training - Employees may need some on-the-job training, but most of these occupations assume that the

person will already have the required skills, knowledge, work-related experience, and/or training.

Specific Vocational Preparation Time – 4 to 10 years, over 10 years

Source: O*NET


Reflecting the Transform Milwaukee Initiative’s goals and findings of the industry structure analysis in Section

2, the distributions of employment by job zone for manufacturing, construction and transportation related

industries are reported at the three-digit NAICS level in Chart 3.3. Distributions for all industries are reported

in Appendix 3B. Note that these occupational percentages are based on national averages in each industry and

local distributions will vary somewhat. Again, Job Zone 1 tends to include occupations requiring the lowest

skill levels and preparation times, while Job Zone 5 requires the largest amounts. Occupations in Job Zone 3

are often considered middle-skilled jobs, but even these jobs can require notable amounts of formal training

and preparation times of one-to-two years. A number of important observations can be made when

examining these distributions and their relevancy to the Transform Milwaukee Initiative:

Current skill and educational attainments in the Transform Milwaukee Study Area suggest a large share of

residents would mostly match jobs present in Job Zone 1 and Job Zone 2. Very few manufacturing

industries have a significant share of employment in Job Zone 1, reflecting the growing skill sets required

by many manufacturers. Textile product mills and apparel manufacturing have more than 25% of their

employment in this job zone, but this industry employs a relatively small share of employees both locally

and nationally. The low skill requirements within this sub-sector have contributed to a large share of

production migrating overseas;

Food manufacturing has a relatively high share of employment in Job Zone 1, but also a high share in Job

Zone 2. Combined, 80% of employment in this industry is found in Job Zone 1 and Job Zone 2. As noted in

Section 2, food manufacturing is a relative strength within the region (and broader State of Wisconsin).

The skills required by the industry, combined with its prominence, suggests that food product

manufacturing could be aligned to the local labor force and well-suited to recruitment and expansion

efforts in the industrial core;

While plastics and rubber products manufacturing has a small share of employment in Job Zone 1, it has a

significant share within Job Zone 2. As with food manufacturing, this industry is also concentrated in the

region and could be a good match for aligning workforce development strategies with recruitment and

expansion efforts;

Several manufacturing industries showing concentrations within the region have a high share of

employment in Job Zone 3, including printing, machinery, primary metals and fabricated metal products.

Any efforts to recruit or expand these industries in Milwaukee’s Industrial Corridor should recognize this

higher skill distribution;

Employment in construction industries are dominated by middle-skill occupations in Job Zone 2 and Job

Zone 3, with some employment in Job Zone 1 also present. The diversity of occupations within these

industries could provide a mix of occupations and career paths for workers in the Transform Milwaukee

Study Area. However, employment in middle skill jobs across all industries is strongly cyclical, increasing

during economic expansion and contracting rapidly during downturns (Grubb 2009).

Very few jobs in transportation, logistics and warehousing are found in Job Zone 1. Nonetheless, these

industries have very large shares of occupations in Job Zone 2, with water transportation and support

activities for transportation also having large shares of employment in Job Zone 3;


Chart 3.3 – Construction, Manufacturing and Transportation, Logistics and Warehousing Employment by Job Zone

Source: Bureau of Labor Statistics, O*NET and Author’s Calculations

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

493 Warehousing & Storage

492 Couriers & Messengers

491 Postal Service

488 Support Activities for Transportation

487 Scenic & Sightseeing Transportation

486 Pipeline Transportation

485 Transit & Ground Passenger Trans

484 Truck Transportation

483 Water Transportation

482 Rail Transportation

481 Air Transportation

339 Miscellaneous Mfg

337 Furniture & Related Product Mfg

336 Transportation Equipment Mfg

335 Electrical Equipment & Appliance Mfg

334 Computer & Electronic Product Mfg

333 Machinery Mfg

332 Fabricated Metal Product Mfg

331 Primary Metal Mfg

327 Non-metallic Mineral Product Mfg

326 Plastics & Rubber Products Mfg

325 Chemical Mfg

324 Petroleum & Coal Products Mfg

323 Printing & Related Support Activities

322 Paper Mfg

321 Wood Product Mfg

316 Leather & Allied Product Mfg

315 Apparel Mfg

314 Textile Product Mills

313 Textile Mills

312 Beverage & Tobacco Product Mfg

311 Food Mfg

238 Specialty Trade Contractors

237 Heavy & Civil Engineering Construction

236 Construction Of Buildings

Zone 1 Zone 2 Zone 3 Zone 4 Zone 5 N/A


Education and Occupations from a Density Perspective

When considering the potential labor force residing in the Transform Milwaukee Study Area, it may be

tempting to focus on deficits in the labor market. Industries and economic development professionals may

discount the area when seeing its share of residents having low levels of educational attainment or working in

lower skill occupations. Educational and occupational distributions may also suggest a need to emphasize

efforts to upgrade skills or retrain the local labor force. However, focusing on low percentages masks the

overall density of the labor force residing in the Study Area and hides potential workforce assets. For instance,

compare the highest level of educational attainment for residents living within a three-mile radius of the

Century City development to the residents living in a ten-mile radius around the industrial area located at the I-

94/Highway 67 interchange in Oconomowoc (Table 3.13). Despite a notably smaller radius, the area

immediately surrounding Century City has:

A greater number of high school graduates;

A greater number of college graduates;

A larger number of residents with some college, but no degree; and

A comparable number of residents with associate degrees.

Similar comparisons can be also be made by occupation and for other sites in the region. While this example is

not intended to directly compare all attributes of a given industrial site, it does suggest that skilled labor in the

Transform Milwaukee Study Area may available in comparable (or greater) numbers relative to other suburban

locations.

Table 3.13 – Number of Residents by Educational Attainment within Radii of Selected Industrial Sites

Highest Level of Educational Attainment (Age 25 and Over)

Three-Mile Radius of Century City (Number of Residents)

Ten-Mile Radius of I-94 and Highway 67 Interchange (Number of Residents)

High School Degree or Equivalent 59,256 47,776

Some College, No Degree 42,236 36,395

Associate Degree 12,192 14,757

Bachelor’s Degree or Higher 60,155 55,861

Source: 2006-2010 American Community Survey and UW-Extension Center for Community and Economic Development. Figures are

subject to margins of error.


Journey to Work and Job Accessibility

A large body of literature examines the geographic mismatch between jobs and residences as a primary driver

of structural unemployment conditions and central city poverty. The spatial mismatch theory speculates that

available jobs are located in areas inaccessible to residents of central city areas due to insufficient

transportation options, high travel costs, or a lack of information about jobs in more distant locations. The

spatial mismatch theory has been examined frequently over the last 40 years and found valid (Blair and Carroll

2007). Subsequently, basic measures of job accessibility deserve attention as part of this analysis, but a full

spatial mismatch study is beyond the scope of this overview.

Mode of transit is one accessibility factor influencing an individual’s job search. Workers (and potential

workers) without access to a car depend on other means of transportation to work. Public transportation may

be viable in some instances, but workers relying on this mode confront issues related to bus route proximity,

increased travel time and frequency of service. Availability of public transportation also is a particular concern

facing workers employed during non-traditional work hours (e.g. night shifts). Walking to work may be an

option as well, but weather and distance may affect this travel choice.

In comparing means of transportation to work and car availability across the region, households in the

Transform Milwaukee Study Area are much less likely to have access to a car. Just 4.8% of households in the

Balance of the M7 Region do not have a car, while 20.5% percent of households in the in the Study Area are

without an automobile (Table 3.14).17 Reduced accessibility to cars among Study Area households is reflected

in the larger shares of workers who commute by bus, carpooling and walking. The share of Study Area

employees commuting by public transportation is particularly high, especially among workers living in census

tracts surrounding the 30th Street Industrial Corridor and Menomonee Valley (Map 3.4).18

Table 3.14 – Vehicles Available per Household and Means of Transportation to Work (2006-2010)

Household Vehicle Availability and Means of Transportation to Work


Milwaukee County


United States

Number of Vehicles Available per Household

Total Households 138,581 - 379,372 - 413,329 - 114,235,996

No vehicle available 20.5% *** 13.4% *** 4.8% *** 8.9%

1 vehicle available 44.4% *** 42.1% *** 28.5% *** 33.3%

2 or more vehicles available 35.1% *** 44.5% *** 66.7% *** 57.9%

Means of Transportation to Work

Drove alone 67.7% *** 76.2% * 84.7% *** 76.0%

Carpooled 13.3% *** 10.6% * 7.8% *** 10.4%

Public transportation (excluding taxicab): 10.3% *** 5.8% *** 0.9% *** 4.9%

Bicycle 0.6% * 0.6% * 0.3% *** 0.5%

Walked 4.9% *** 3.5% *** 1.9% *** 2.8%

Taxicab, motorcycle, or other means 0.7% * 0.8% *** 0.8% *** 1.2%

Worked at home 2.5% *** 2.5% *** 3.6% *** 4.1% Source: U.S. Census Bureau 2006-2010 ACS and UW-Extension Center for Community and Economic Development. Estimates Based on a 90% CI. Significance vs. United States * = 90% ** = 95% *** = 99%

17

Vehicle availability is based on the number of working vehicles kept at home and available for the use of household members. 18

Limited availability of public transportation within the Balance of the M7 Region is also a factor in this difference.


Map 3.4 – Percent of Workers Commuting by Public Transportation


Vehicle availability and means of transportation to work suggest that workers in the Transform Milwaukee

Study Area face some barriers in traveling to job opportunities. Another perspective on journey to work is

provided by examining the origins of employees working in the 30th Street Industrial Corridor, Menomonee

Valley, Port of Milwaukee or the 440th Air Base Redevelopment Area. That is, are jobs filled by local workers or

by employees commuting from elsewhere? Using origin-destination figures from the LODES data introduced in

Section 1, Table 3.15 and Table 3.16 examine two perspectives on worker origins within these four industrial

areas. Again, the four employment centers used in these two tables are not based on the specific boundaries

for each area, but rather selected census tracts that approximate these areas (see Section 1 for the rationale

for using these tracts). Consequently, these figures include the areas in and around each employment center.

Table 3.15 details each employment center by its number of jobs filled by residents of the Transform

Milwaukee Study Area. These jobs are further examined by their range of monthly earnings. Despite the

Study Area containing 63% of the City of Milwaukee’s working age population, no more than 39% percent of

jobs in these four industrial centers are filled by residents of the Study Area. While the share in the 440th Air

Base Redevelopment area is somewhat expected given its more distant location, the low shares in the 30th

Street Industrial Corridor and Menomonee Valley are especially surprising given the proximity of these two job

centers to the high density residential areas within the Study Area (Map 3.1). Equally surprising is the share of

low-to-medium earnings jobs filled by Study Area workers. No more than 48% of these jobs are filled by Study

Area residents.

Table 3.15 – Jobs Filled by Residents of the Transform Milwaukee Study Area (2010, beginning of Q2)

Jobs by Earnings

30th St. Corridor Menomonee Valley Port of Milwaukee 440th Air

Base Area

Total Jobs

% Filled by Study Area

Residents

Total Jobs


Residents

Total Jobs


Residents

Total Jobs


Residents

Total Jobs 15,888 33.8% 22,416 23.1% 3,065 39.4% 11,338 19.1%

$1,250 per month or less 2,994 48.3% 4,494 24.2% 1,229 45.8% 2,917 26.6%

$1,251 to $3,333 per month 4,328 45.7% 8,067 35.4% 1,037 42.4% 4,426 23.3%

More than $3,333 per month 8,566 22.6% 9,855 14.2% 799 27.5% 3,995 10.0%

Source: LEHD Origin-Destination Employment Statistics (2010) and UW-Extension Center for Community and Economic Development

As suggested in Section 1, both Milwaukee County and the City of Milwaukee are large importers of labor.

According to LODES figures, 58% of employees working in the City of Milwaukee live outside its municipal

boundaries. The workforce commuting to the 30th Street Industrial Corridor, the Menomonee Valley, Port of

Milwaukee and the 440th Air Base Redevelopment Area contribute to these employee origin figures for the City

of Milwaukee (Table 3.16). Almost half of the employees working in the 30th Street Industrial Corridor and the

Port of Milwaukee commute from outside the city. In contrast, only 36.6% of workers in the Menomonee

Valley live in the City of Milwaukee, while just 27.9% of workers in 440th Air Base Redevelopment Area reside

in the city. The smaller share in the Menomonee Valley is not surprising given the area’s exceptional

accessibility arising from the Marquette Interchange and the intersection of Interstate 94 and U.S. Highway 41

anchoring each end of the Valley. Accessibility to the 440th Air Base Redevelopment Area also benefits from its

location directly adjacent to Interstate 94.


Table 3.16 – Origins of Workers Employed in Subarea by Place of Residence (2010, beginning of Q2)

Place of Residence For Workers

30th St. Corridor Menomonee Valley Port of Milwaukee 440th Air

Base Area

City of Milwaukee 8,347 52.5% 8,207 36.6% 1,660 54.2% 3,166 27.9%

City of West Allis 404 2.5% 897 4.0% 113 3.7% 497 4.4%

City of Oak Creek 277 1.7% 675 3.0% 78 2.5% 743 6.6%

City of Wauwatosa 432 2.7% 812 3.6% 32 1.0% 167 1.5%

City of Greenfield 294 1.9% 666 3.0% 62 2.0% 361 3.2%

City of Franklin 244 1.5% 648 2.9% 48 1.6% 429 3.8%

City of New Berlin 274 1.7% 619 2.8% 48 1.6% 245 2.2%

City of South Milwaukee 142 0.9% 349 1.6% 34 1.1% 599 5.3%

City of Cudahy 104 0.7% 394 1.8% 59 1.9% 347 3.1%

City of Waukesha 264 1.7% 389 1.7% 29 0.9% 194 1.7%

City of Brookfield 290 1.8% 405 1.8% 24 0.8% 134 1.2%

City of Muskego 163 1.0% 378 1.7% 29 0.9% 199 1.8%

Village of Menomonee Falls 240 1.5% 314 1.4% 0 0.0% 102 0.9%

City of Racine 125 0.8% 212 0.9% 0 0.0% 287 2.5%

Village of Caledonia 86 0.5% 190 0.8% 21 0.7% 274 2.4%

All Other Locations 4,202 26.6% 7,261 32.4% 828 27.1% 3,594 31.5%

Total 15,888 100.0% 22,416 100.0% 3,065 100.0% 11,338 100.0%

Milwaukee County 11,043 69.5% 14,357 64.0% 2,379 77.6% 6,904 60.9%

Source: LEHD Origin-Destination Employment Statistics (2010) and UW-Extension Center for Community and Economic Development

The commuting patterns depicted in Table 3.15 are not specific evidence of a spatial mismatch, but they do

suggest that jobs in Milwaukee’s Industrial Corridor are disproportionately filled by residents outside of the

Study Area. Furthermore, the distributions of worker origins for each employment center shown in Table 3.16

are significant as they show the importance of these industrial areas to the entire region. Overall, these

commuting patterns suggest that there is no current guarantee that future jobs created in these industrial

areas will be filled by local residents, such as those of the Study Area. If employment of Study Area residents is

a priority of the Transform Milwaukee Initiative, then other mechanisms may be needed to ensure some level of

local hiring.

Commuting patterns, job creation and local hiring practices should also be considered in the context of a

decentralizing regional job market. Between 1970 and 2010 a net of 280,000 jobs were created in the M7

Region. However, Milwaukee County only accounted for 8,800 of these jobs, with the remaining 271,200

located in the six surrounding counties (Chart 3.4).19 The trends in manufacturing employment were even

more dramatic. Over the same four decades, manufacturing employment grew in the Balance of the M7

Region by 7,900 jobs, despite large losses during the last two recessionary periods. However, manufacturing

employment in Milwaukee County declined by almost -114,000 jobs during the period between 1970 and

2010.

19

Similar trends were noted by Levine (2006).


Chart 3.4 – Distribution of Regional Employment – All Industries and Manufacturing (1970 to 2010)

Source: Bureau of Economic Analysis and Author’s Calculations

Given a decentralized job market and potential transportation barriers facing Study Area residents, other

strategies for providing job access include developing residential mobility policies (e.g. workforce housing) or

improving transportation options to outlying areas. However, these programs can be costly, politically

challenging and difficult to take to a sufficient scale given the number of potential workers in the Transform

Milwaukee Study Area (Stoll 2006). These programs also do not fundamentally address the underlying

structural issues facing this area and potentially abandon a large amount of tax base and physical

infrastructure in the City of Milwaukee.

Other economic development professionals and policy makers may point to the worker origin figures as

evidence of a skills mismatch. That is, workers are not being employed locally due to a lack of appropriate

skills. While skills mismatches undoubtedly influence unemployment rates among residents of the Transform

Milwaukee Study Area, the existence of this mismatch is not a recent phenomenon. Furthermore, it is unlikely

that simply re-training potential workers residing in the Study Area will greatly reduce unemployment rates

when considering the potential number of jobless workers in the area relative to the number of job openings

both locally and regionally. Research by UWM’s Employment and Training Institute reported 13 job seekers

for every full-time job opening in the M7 Region in 2009. Within the inner city of Milwaukee (which

encompasses a large portion of the Study Area), this disparity increased to 25 job seekers for every full-time

opening.20 While these numbers likely have changed since 2009, it is unlikely that the ratio of job openings to

job seekers has improved dramatically given broader state and national employment trends. Other research

also suggests that these conditions have been chronic for many decades. Consequently, addressing skills

mismatches and transportation needs will be important strategies. However, these strategies should be

pursued in concert with a larger focus on job creation in the industrial core.

20

See: www4.uwm.edu/eti/2009/RegionalJobOpenings.pdf

0

100,000

200,000

300,000

400,000

500,000

600,000

1970 1975 1980 1985 1990 1995 2000 2005 2010

Tota

l Jo

bs

Milwaukee County -All Industries

Balance of M7 Region -All Industries

Milwaukee County -Manufacturing

Balance of M7 Region -Manufacturing


Figure 3.2 – Potential Workforce Development Initiatives

Potential Workforce

Development Initatives

2. Quantify Real-Time Skills Demand

3. Leverage Density and Young

Residents

4. Encourage Employment of Local Residents

5. Consider Alternative

Training and Education Offerings

1. Sector-Based Strategy

Coordinating Economic & Workforce

Development

Conclusions, Policy Implications and Potential Strategies

The preceding overview of the potential labor force residing in the Transform Milwaukee Study Area shows a

youthful and diverse population that is highly concentrated around Milwaukee’s Industrial Corridor. The

density of potential workers also provides an overlooked asset of the Study Area, one that could be leveraged

even further by fostering greater shares of basic education and developing industry-specific skills. As a variety

of workforce development programs in Milwaukee are already pursing these goals, the following strategies

and potential policy options are not intended to be duplicative of these efforts. Instead, they are intended to

provide broad guidance to workforce development considerations within the Transform Milwaukee Initiative

and perhaps supplement existing programs. Importantly, these strategies are not recommendations per se,

but rather provide possible direction based on the research in this section. Specific policies and

recommendations will need to be developed between WHEDA and its partners.

Workforce development initiatives within

the Transform Milwaukee Initative could

include new strategies, and renewed

emphasis on past proposals that have not

been fully acted upon (Figure 3.2). These

potential initiatives also extend beyond

traditional educational and training services

currently offered by local workforce

development intermediaries. While each

intermediary has its own unique abilities

and capacities, these organizations will be

instrumental partners in furthering the

Transform Milwaukee Initiative’s goals and

filling service gaps. The possible roles of

intermediaries ranging from higher

educational institutions to community

organizations to labor groups are

summarized in Appendix 3C.

Strategy 1 – Develop a Sector-Based Strategy Specific to Milwaukee’s Industrial Corridor that Coordinates

Workforce Development and Economic Development Efforts

Issues related to scale suggest that economic revitalization and improved job opportunities for residents of the

Study Area will not occur solely through workforce training efforts. In addition to an unspecified number of

discouraged and underemployed workers, the Transform Milwaukee Study Area may contain upwards of

25,000 unemployed workers ages 16 to 64. The previously noted job opening-to-applicant ratios suggest that

there is a severe shortage of job opportunities. Even if vacant positions could be filled through re-training

programs, these efforts will only provide moderate progress in lowering unemployment rates and increasing

labor participation among Transform Milwaukee Study Area residents. While job growth has occurred in


outlying areas, transportation programs and other strategies to connect Study Area workers to jobs in the

Balance of the M7 Region likely will face financial challenges. Consequently, economic development efforts

are needed to produce large-scale job creation.

When considering an emphasis on job creation, it is important that workforce development is coordinated

with these efforts. Too often, workforce development and economic development have been approached as

stand-alone disciplines. As suggested earlier, these coordinated initiatives should focus on industry sectors

that are well-suited to Milwaukee’s Industrial Corridor. When conducted effectively, sector-based approaches

creating market efficiencies between the labor supply produced by workforce development efforts and the

labor demand created through economic development efforts. Cooperation and conversations between

workforce and economic development organizations improve chances that jobs can be matched to the local

workforce by developing skills for jobs being created, rather than the reverse (Harper-Anderson 2008).

Notably, alignment between workforce development and economic development organizations are occurring

in the M7 Region and throughout the nation. Many of these discussions occur at the regional level and are a

result of the U.S. Department of Labor’s Workforce Innovation in Regional Economic Development (WIRED)

model and grants. However, it is unlikely that regional sector-based approaches can fully meet the needs of an

area such as Milwaukee’s Industrial Corridor. A growing body of literature suggests that regional economic

growth does not translate to equitable central city development (Jonas 2012, Pastor and Benner 2010, Blair

and Carroll 2007). As specifically noted by Lynch and Kamins (2012):

“… Generic policies aimed at stimulating all economic areas within a region are unlikely to

have a significant impact on most inner city economies. Therefore a distinct strategy for

improving economic outcomes in distressed urban areas should be part of any regional

policy framework.” Pg. 20.

In other words, broad-based or regional economic and workforce development strategies may not be able to

delivery economic equity for the Study Area. Developing jobs and skills concurrently in Milwaukee’s Industrial

Corridor will require a dedicated or standalone sector-based effort that recognizes the unique skills and needs

of the Study Area and not necessarily those of the greater region. Sectors mentioned earlier in Section 3 and in

Section 2 provide a starting point for these efforts.

Importantly, this is not a criticism of local economic development organizations as each has their own mission

and constituents to serve. In fact several companies recruited by groups such as M7 have actually located

within Milwaukee’s Industrial Corridor. M7 also notes the importance of a strong inner city to the regional

economy as part of their strategic framework. Furthermore, a number of sectors that are aligned with the

Industrial Corridor are also targeted sectors for the M7 Region and the broader state of Wisconsin.(e.g. food

manufacturing). Consequently, other economic development organizations in the region should be viewed as

important partners in the Transform Milwaukee Initiative.


Strategy 2 – Quantify Real-Time Demand for Skills

While an emphasis on job creation will be important, local workforce development programs will still need to

focus on the needs of existing industries. Developing efficient job training programs benefit employers who

depend upon their services and also help workers understand what jobs are in demand. However, quantifying

current and longer-term demand for skills and occupations is difficult for workforce development

intermediaries. The current debate over real or perceived skills gaps in some occupations certainly is

indicative of these challenges. Throughout the United States, understanding supply and demand for

occupations and skills is problematic for several common reasons:

Occupational forecasts are often inaccurate or outdated (Harper-Anderson 2008);

Many workforce development efforts have a supply-side focus guided by what Grubb (2009) has called the

“education gospel,” or the belief that increased education and training can explicitly solve a region’s local

economic development needs. Proponents of this strategy suggest that simply promising a well-prepared

labor force will spur economic growth for the region, without recognizing that training efforts do not

promise that positions will be available for these workers. Consequently, there is not an appropriate

emphasis on demand considerations in an area;

Information gaps related to occupational supply and demand have also been attributed to a lack of deep

and transformative relationships between intermediaries and local employers. For instance, strategies

that connect employers to workforce intermediaries through employer representation on advisory boards

do not guarantee the communications needed to influence workforce development program

implementations and outcomes (Harper-Anderson 2008). Moreover, these advisory boards frequently are

weighted towards larger firms even though the middle-skill labor market is often dominated by smaller

firms, particularly in entry level positions (Grubb 2009). Unless intermediaries are communicating with

small-to-medium enterprises on a regular basis, workforce development efforts may not fully understand

the need for many middle-skill occupations;

Additional efforts to deepen the involvement of specific industries at the training program level include

working directly with employers and industry partners in DACUM (development of a curriculum) design to

ensure that participants are being trained in skills that are in demand. However, curriculum development

efforts are likely not revisited frequently enough to ensure that workforce needs are continually

exchanged and developed (Harper-Anderson 2008).

One method for improving information between workforce development organizations and employers may

exist in new sophisticated “spidering” software packages that aggregate and analyze online job postings from a

wide variety of sources. Specific examples of these services include those offered through WANTED

AnalyticsTM and Burning Glass, which provide real-time information about the hiring and skill needs of local

employers. While this is not an endorsement of these specific providers, analyses of online job postings could

complement other methods for determining program demands and course offerings. Doing so may be

particular advantageous for many of the middle skill jobs that are important to the Transform Milwaukee


Initiative. If pursued as a strategy, some caution is needed as the effectiveness of real-time job posting

aggregation services is still being evaluated. Three specific limitations noted by Altstadt (2011) include:

1. As not all job openings are posted online, the employment picture can be distorted;

2. Current technology cannot eliminate all duplicate postings and discover all listings. Consequently, an

accurate count of job openings over time is not yet possible;

3. On-line job ads often fail to include complete information about desired qualifications.

Strategy 3 – Leverage Density and Young Residents in the Transform Milwaukee Study Area

The Transform Milwaukee Study Area’s density and share of young residents are two of its largest assets.

Instead of focusing on the shares of residents without basic levels of educational attainment, efforts should be

made to highlight the number of potential workers that do have necessary levels of education or skills. In

many instances, the Study Area’s density will provide a greater number of workers than found in suburban

locations.

Furthermore, the large number of Study Area residents under the age of 16 comprises an important portion of

the area’s future employees and entrepreneurs. While these residents are not yet officially part of the labor

force, they provide opportunities to support an aging population. Of particular interest is the need for young

workers in the region’s manufacturing sector over the next two decades. With 50 percent of the industry’s

workers between the ages of 45 and 64, manufacturers are facing a significant number of workers either at or

approaching retirement age. Avoiding or reducing costs related to recruitment, training, reduced

competitiveness, or lost productivity could be achieved through creating succession plans at both the firm level

and in cooperation with the greater industry sector in the region. With the broader M7 Region having an older

population distribution, young residents in the Transform Milwaukee Study Area will need to be part of these

plans. Consequently, recent conversations occurring in Milwaukee about promoting careers in manufacturing,

bringing training into the K-12 system, and leveraging organizations such as YouthBuild cannot just be rhetoric.

Action is needed.

Strategy 4 – Develop Mechanisms to Encourage Employment of Local Residents

The origin-destination analyses in this overview showed the high share of jobs in Milwaukee’s Industrial

Corridor filled by workers residing outside the Transform Milwaukee Study Area. If a goal of the Transform

Milwaukee Initiative is to improve employment opportunities for local residents, then these commuting

patterns suggest that a mechanism may be needed to guarantee that Study Area residents receive a share of

any new job opportunities. In addition to the aforementioned alignment between workforce development

and economic development efforts, another possible strategy is a community benefit agreement (CBA). A

CBA is a legally enforceable contract negotiated directly between a business or developer and a community

organization or lending institution. The business agrees to provide benefits to the community (e.g. jobs) in

return for financing or community support. While the type of benefits returned to communities can vary


greatly, ensuring jobs and training opportunities for local residents are most relevant to the Transform

Milwaukee Initiative. A wide variety of CBA examples (Staples Center in Los Angeles, CA; Oak to 9th in Oakland,

CA; Columbia University and Harlem in New York; Hill District in Pittsburgh; etc.) exist and could serve as

starting points for developing an appropriate local document.

If structured properly, a community benefit agreement could also assist in employee screening and hiring

procedures among small and medium-sized enterprises (SMEs). As previously mentioned, many middle-skilled

jobs are found in SMEs. There is often a perception among SMEs that the supply of potential employees is

fragmented and uneven (Grubb 2009). Many small and medium-sized enterprises also are limited in

information and expertise on matters related to hiring and compensation. These firms also often lack the

time, staff or financial resources to invest in more formal recruitment efforts. Consequently, recruiting and

screening choices are often made informally, and can reflect employer prejudices, perceptions and

experiences (Stoll 2006). A CBA could help overcome some of these challenges by providing employee

screening and evaluation as part of the agreement to ensure local jobs.

Strategy 5– Consider Alternative Training and Education Offerings

Providing workforce development through a career pathways approach is currently being emphasized as a

preferred strategy among many federal agencies and local organizations. Particularly directed toward low-

skilled or disadvantaged workers, career pathways have “the goal of increasing individuals’ educational and

skills attainment and improving their employment outcomes while meeting the needs of local employers and

growing sectors and industries. Career pathway programs offer a clear sequence, or pathway, of education

coursework and/or training credentials aligned with employer-validated work readiness standards and com-

petencies. This systems approach makes it easier for people to earn industry-recognized credentials (through

more flexible avenues and opportunities for relevant education and training) and to attain marketable skills so

that they can more easily find work in growing careers.”21

In addition to being industry sector driven, other career pathway characteristics are summarized in Figure 3.3.

In particular, the pathway model is much more intensive than other programs, with an emphasis on flexibility

and widespread support for individuals pursing a given career. Consequently, workforce intermediaries in a

region must coordinate to provide these services and accommodate individuals entering and exiting the

training program. Certainly, a career pathways approach is a means of providing workforce training for

targeted sectors in Milwaukee’s industrial core. While career pathways exist for some targeted industries,

others may need to be developed if they do not exist. In fact, M7’s Food and Beverage (FaB) advisory council is

partnering to develop career pathways for the food and beverage industry. 22

21

CAREER PATHWAYS TOOLKIT: Six Key Elements for Success. Developed on behalf of the U.S. Department of Labor by Social Policy Research Associates.

22 See: http://www.fabmilwaukee.com/?page=career_pathways


However, when considering the career pathway model, local intermediaries must recognize the scale of

residents who may need assistance. Again, the Transform Milwaukee Study Area may contain upwards of

25,000 unemployed workers ages 16 to 64 and 52,000 adults without a high school degree. Consequently, the

magnitude of individuals that may require some sort of basic education or skills development is a potential

challenge to workforce intermediaries. In particular, the magnitude of need may affect community colleges

that serve the public at large. These institutions must balance the needs of students across all socio-economic

backgrounds while also ensuring that disadvantaged job seekers succeed in training programs. As noted by

Lowe, Goldstein and Donegan, (2011):

“most community college initiatives are accessible to disadvantaged job seekers, but program

participation is not necessarily limited to those individuals facing identifiable barriers to employment.

Rather, college-based initiatives are usually designed to be open enrollment and therefore attract a wide

variety of participants representing diverse socioeconomic and educational backgrounds. In contrast,

most successful nonprofit and labor union–backed workforce intermediaries make it their stated priority

to target individuals who are low income and less educated or facing additional barriers to employment.

Given this targeting, participants in these programs often receive individualized counseling and case

management assistance in addition to structured vocational training. Community college programs,

although clearly accessible to and used by low-income and less educated individuals, may not have the

resources needed to offer this kind of targeted assistance. This raises important questions about their

ability to implement workforce intermediation in a manner that facilitates upward mobility for all

participants, especially those with greater needs.”

Figure 3.3 - Career Pathway Characteristics

1. Stackable Educational/Training Options—Programs include the full range of secondary, adult education, and postsecondary education options, including registered apprenticeships; they use a non-duplicative progression of courses clearly articulated from one level of instruction to the next; they provide opportunities to earn postsecondary credits; and they lead to industry-recognized and/or postsecondary credentials;

2. Contextualized Learning—Career pathway education and training programs focus on curriculum and instructional strategies that make work a central context for learning and help students attain work readiness skills

3. Integrated Education & Training—As appropriate, career pathway programs combine occupational skills training with adult education services, give credit for prior learning, and adopt other strategies that accelerate the educational and career advancement of the participant;

4. Industry-recognized Credentials—Effective career pathway programs lead to the attainment of industry-recognized degrees or credentials that have value in the labor market;

5. Multiple Entry & Exit Points—Career pathway programs allow workers of varying skill levels to enter or advance within a specific sector or occupational field;

6. Intensive Wrap-Around Services—Career pathway systems incorporate academic and career counseling and wrap-around support services (particularly at points of transition), and they support the development of individual career plans;

7. Designed for Working Learners—Career pathway programs are designed to meet the needs of adults and non-traditional students who often need to combine work and study. They provide childcare services and accommodate work schedules with flexible and non-semester-based scheduling, alternative class times and locations, and innovative uses of technology.

Source: U.S. Department of Labor Career Pathways Toolkit


The more intensive requirements of a career pathways model, combined with potential demand for services

and training, may necessitate education offerings to be extended beyond traditional classroom instruction. In

particular, there may be opportunities for offering education and training through other learning spaces such

as on-line courses, learning networks, peer learning, volunteering and after-school programs. On-line courses,

offered through so-called massive online open classrooms (MOOC’s), have particular potential as they could

provide access to a large number of learners at flexible times. The scale and flexibility of these programs could

accommodate work schedules; be offered locally in a manner that does not require extensive travel; and

pursued in the stackable manner suggested by the career pathways approach. The option also would allow

workforce development intermediaries to serve a large number of learners at once, maximizing personnel

resources for deployment in other areas.

There are upfront costs to this delivery model and certainly not all skills could be taught in this manner (e.g.

those that require training on specialized equipment). However, many skills related to adult basic education

programs, computer skills and math may be well-suited to these programs. Internet access is also a concern,

but access to computers could be provided through community development corporations, libraries, job

centers, and schools. Temporary staffing agencies may also provide an opportunity as this avenue would allow

them to develop workers having in demand skills.

An additional challenge is that these other means of education, if offered outside a formal educational

institution, do not provide a means for accreditation. One method for addressing this issue would be to

develop a pilot Mozilla Open Badges project or a similar program. Under the Open Badge system,

organizations or employers can issue badges backed by their own seal of approval. That is, if a worker can

prove his or her skills to an approved community organization, temporary staffing agency or employer, then

they could offer a badge that verifies that specific skill. Learners can then collect badges from different

sources and display them on their applications or resumes. For instance, badges could be issued for skills

ranging from basic math and reading comprehension to CNC machine programming and operation.

Other issues include the need for employers to trust and accept badges. Badges also may be viewed as

competition by traditional educational institutions that require fees for service. However, this program is not

meant to replace formal certificates, training or degrees, but rather provide another educational path. For

instance, badging is being tested by large educational institutions such as MIT. Consequently, a pilot program

may be one method for exploring some of these issues. For more information on the Open Badges concept,

see openbadges.org


Appendix 3A - Shift Share Analysis of Occupations in the Milwaukee Region (2005 – 2010)

The following description of shift share analysis is derived from Using Employment Data to Better Understand

Your Local Economy. Shift-Share Analysis Helps Identify Local Growth Engines by Martin Shields, formerly of

Penn State University. For more information see: http://cecd.aers.psu.edu/pubs/Tool%204.pdf

Shift-share analysis is an exploratory method for examining a region’s economic base and analyzing the

competitiveness of local industries. The analysis facilitates comparisons between the local economy of interest

and the larger economy. Specifically, shift-share analysis can be used to explore whether the local economy

has experienced a faster or slower growth rate in employment than the larger (national or state) economy has

observed. The analysis is based on apportioning local employment changes over a specific period of time into

three contributing factors:

1. National growth share – This factor refers to local occupational changes that can be attributed to national

economic growth. If the nation is experiencing employment growth, it is reasonable to expect that this

growth will have a positive impact on a local area as well. The national growth share factor describes the

local employment change that would be anticipated from an area’s connection a dynamic national

economy. Ultimately, the national growth share is the number of jobs lost or gained in a region if total

employment in the region had changed at the same rate as overall national employment;

2. Occupational mix share - Some occupations are added more rapidly than others, while other occupations

decline. The occupational mix share factor reflects differences in occupational distributions between the

local and national levels. The mix factor considers how national growth or decline of a particular

occupation translates into local growth or decline of that occupation. Consequently, this component

represents the effects that occupational trends at the national level have had on the change in the number

of jobs in the region;

3. Local share – Even during periods of prosperity, economic growth is uneven. Some regions and some

occupations grow faster or slower than others. These differences are commonly attributed to sources of

local comparative advantage such as natural resources, agglomeration economies, or local labor

conditions. The local share describes the extent to which unique local factors might contribute to regional

occupational growth or decline. The local share is useful in identifying a local area’s economic strengths

and represents how a region’s competitive position can contribute to job growth.

Noting that shift-share is a simple analytical technique that does not account for many factors is important.

When interpreting the results, consider the following:

The technique minimizes the impact of issues such as business cycles;

The method does not identify actual sources of comparative advantages for a given region;

A shift-share analysis provides a “snapshot” of two particular points in time. Accordingly, the results are

sensitive to the period of time chosen;

The results are sensitive to differences caused by levels of occupational detail.


A shift-share analysis for occupational changes in the Milwaukee region shows that almost half of the region’s

loss of -45,000 employees can be attributed to national trends.23 The change due to the local share also

contributed to a notable loss of employment. The loss attributed to the local share suggests that the

Milwaukee region is less competitive overall than the national average, but does not identify specific sources

of comparative disadvantage. Changes within individual occupations vary significantly, but the categories of

production; transportation and material moving; and construction and extraction accounted for three of the

four largest total employment declines. These categories are of particular interest to the Transform

Milwaukee Initiative due given their importance to the manufacturing, construction and freight transportation

industries. In all three of these occupational categories, the occupational share is the largest driver of decline

and reflects the region’s reliance on industries that employ these occupations.

Occupational Shift-Share in the Milwaukee-Waukesha-West Allis MSA and Racine MSA – May 2005 to May 2010

SOC Code

Occupation Title Total

Employment in 2010

Total Change

from 2005

National Share

Occupational Share

Local Share

00-0000 All Occupations 855,720 -45,060 -22,190 -3,850 -19,040

11-0000 Management 38,470 3,920 -850 1,210 3,560

13-0000 Business and Financial Operations 44,060 3,080 -1,010 6,160 -2,070

15-0000 Computer and Mathematical 23,710 4,470 -470 2,630 2,310

17-0000 Architecture and Engineering 15,930 -2,720 -460 -140 -2,120

19-0000 Life, Physical, and Social Science 5,060 -1,250 -160 -490 -600

21-0000 Community and Social Service 10,200 -1,100 -280 1,670 -2,490

23-0000 Legal 6,130 -10 -150 190 -50

25-0000 Education, Training, and Library 46,560 -1,780 -1,190 3,460 -4,050

27-0000 Arts, Design, Entertainment, Sports, and Media 12,720 250 -310 550 0

29-0000 Healthcare Practitioners and Technical 54,810 7,860 -1,160 6,890 2,130

31-0000 Healthcare Support 31,000 6,640 -600 4,940 2,300

33-0000 Protective Service 16,430 -310 -410 1,130 -1,030

35-0000 Food Preparation and Serving Related 69,350 580 -1,690 3,160 -880

37-0000 Building and Grounds Cleaning and Maintenance 26,680 -3,200 -740 -410 -2,050

39-0000 Personal Care and Service 28,680 -570 -720 2,890 -2,740

41-0000 Sales and Related 84,730 -4,170 -2,190 -950 -1,030

43-0000 Office and Administrative Support 138,940 -17,960 -3,870 -4,950 -9,140

45-0000 Farming, Fishing, and Forestry 520 120 -10 -20 150

47-0000 Construction and Extraction 24,300 -7,780 -790 -5,750 -1,240

49-0000 Installation, Maintenance, and Repair 30,070 -650 -760 -1,420 1,530

51-0000 Production 88,160 -21,480 -2,700 -18,830 50

53-0000 Transportation and Material Moving 59,210 -9,020 -1,680 -5,760 -1,580

Source: Bureau of Labor Statistics Occupational Employment Statistics and Author’s Calculations

23

The Milwaukee region includes the Milwaukee-Waukesha-West Allis MSA and the Racine MSA. Occupational data is not available for Walworth County and Kenosha County is included in the Chicago MSA.


0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

339 Miscellaneous Mfg

337 Furniture & Related Product Mfg

336 Transportation Equipment Mfg

335 Electrical Equipment & Appliance Mfg

334 Computer & Electronic Product Mfg

333 Machinery Mfg

332 Fabricated Metal Product Mfg

331 Primary Metal Mfg

327 Non-metallic Mineral Product Mfg

326 Plastics & Rubber Products Mfg

325 Chemical Mfg

324 Petroleum & Coal Products Mfg

323 Printing & Related Support Activities

322 Paper Mfg

321 Wood Product Mfg

316 Leather & Allied Product Mfg

315 Apparel Mfg

314 Textile Product Mills

313 Textile Mills

312 Beverage & Tobacco Product Mfg

311 Food Mfg

238 Specialty Trade Contractors

237 Heavy & Civil Engineering Construction

236 Construction Of Bldgs

221 Utilities

213 Support Activities For Mining

212 Mining Exc Oil & Gas

211 Oil & Gas Extraction

115 Ag & Forestry Support Activities

113 Forestry & Logging

Share of Industry Employment by Job Zone Natural Resources, Utilities, Construction and Manufacturing


Appendix 3B – Industry Employment Distribution by Job Zone (Three Digit NAICS)

Source: Bureau of Labor Statistics, O-NET and Author’s Calculations



0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

493 Warehousing & Storage

492 Couriers & Messengers

491 Postal Service

488 Support Activities for Transportation

487 Scenic & Sightseeing Transportation

486 Pipeline Transportation

485 Transit & Ground Passenger Trans

484 Truck Transportation

483 Water Transportation

482 Rail Transportation

481 Air Transportation

454 Non-store Retailers

453 Misc Store Retailers

452 General Merchandise Stores

451 Sporting Goods Hobby Book & Music Stores

448 Clothing & Clothing Accessories Stores

447 Gasoline Stations

446 Health & Personal Care Stores

445 Food & Beverage Stores

444 Building Material & Garden Supply Stores

443 Electronics & Appliance Stores

442 Furniture & Home Furnishings Stores

441 Motor Vehicle & Parts Dealers

425 Electronic Markets & Agents & Brokers

424 Merchant Wholesale - Nondurable Goods

423 Merchant Wholesale - Durable Goods

Share of Industry Employment by Job Zone Wholesale, Retail and Transportation and Warehousing




0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

813 Membership Associations & Organizations

812 Personal & Laundry Services

811 Repair & Maintenance

722 Food Services & Drinking Places

721 Accommodation

713 Amusements Gambling & Recreation

712 Museums Historical Sites Zoos & Parks

711 Performing Arts & Spectator Sports

624 Social Assistance

623 Nursing & Residential Care Facilities

622 Hospitals

621 Ambulatory Health Care Services

611 Educational Services

562 Waste Management & Remediation Services

561 Administrative & Support Services

551 Management Of Companies & Enterprises

541 Professional & Technical Services

533 Lessors Of Nonfinancial Intangible Assets

532 Rental & Leasing Services

531 Real Estate

525 Funds Trusts & Other Financial Vehicles

524 Insurance Carriers & Related Activities

523 Securities Commodity Contracts Investments

522 Credit Intermediation & Related Activities

521 Monetary Authorities Central Bank

519 Other Information Services

518 Data Processing Hosting & Related Services

517 Telecommunications

515 Broadcasting Exc Internet

512 Motion Picture & Sound Recording Ind

511 Publishing Ind Exc Internet

Share of Industry Employment by Job Zone Information, Financial, Business Professional, Education, Health,

Hospitality and Other Services



Appendix 3C – Potential Partner Workforce Development Intermediaries and Roles

Type of Intermediary Potential Roles

Community Colleges

Offer certificate programs to develop entry-level or specific skills and associate degree programs for more comprehensive training;

Provide student career counseling and job placement assistance;

Provide short-term customized training to support learning and career development among incumbent workers;

Provide technical assistance to employers;

Collaborate with other partners in region to share resources and create centers of excellence in particular technical specialties;

Offer ABE programs.

High Schools

Administer school-to-work or career-specific programs;

Provide instruction to develop technical foundations;

Encourage students to pursue careers in technical fields by providing exposure through career awareness, internships, etc.

Provide college and job placement assistance;

Community and Faith-Based Organizations

Recruit community residents for employment programs;

Provide basic literacy for youth and adults tied to technical education and employment;

Provide education on soft-skills;

Offer career counseling;

Provide support services for community residents in community college or other training programs (day care, transportation assistance, etc.);

Provide job and college placement assistance;

Work with clients to develop job-keeping skills and promote job retention.

Social Service Agencies

Provide transportation;

Recruit community residents;

Refer for health care;

Provide day care.

Economic Development and Workforce Development Organizations

Align economic development programs with workforce development needs;

Identify emerging employment and training needs among local employers;

Identify key industries and occupations to guide comprehensive economic and workforce development programs;

Recruit employers, community colleges, and organizations to participate in DACUM creation;

Assist colleges and high schools in identifying internship and employment opportunities for students;

Employers

Participate in DACUM creation;

Encourage career interest through job shadowing and mentoring programs;

Provide internships for students and teachers;

Establish hiring agreements;

Labor Participate in DACUM creation;

Establish new points of entry for apprenticeship programs;

Universities

Offer baccalaureate programs in applied science and technology for graduates of associate degree programs;

Serve as intermediaries in developing integrated pathway or systems for workforce development;

Provide applied research for workforce development initiatives;

Develop program assessment tools;

Offer career counseling and job placement assistance.

Adapted from Fitzgerald, J. (1999). Principles and Practices for Creating Systems Reform in Urban Workforce Development. Great Cities Institute Working Paper.

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