economic conditions in st. louis county
TRANSCRIPT
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Mikhail Kublanov
Methods in Planning Analysis II
4/11/2014
Analyzing Economic Conditions in
St. Louis County, Missouri
Earth City, Distribution and Warehousing Hub
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Introduction
St. Louis County, indicated by Figure 1 below, is by far the most populous county in the state of
Missouri. It comprises the bulk of the economic activity in the St. Louis metropolitan region.
Because it plays such a vital economic role for the region, planners must understand the many
aspects of the local economy. Planners must investigate which sectors of the economy are
doing well and which ones are struggling. They must understand whether the employees within
the county also live in the county, or if they come from other areas. They must look at
inequalities within the county and address them accordingly. Finally, they must analyze
numerous other economic indicators that greatly affect the planning process. This report offers a
comprehensive look at the economic state of St. Louis County. Using this information, planners
will have a better understanding of the county and the economic implications rooted in their
planning decisions.
Economic Composition and Trends (1995-2006)
During the 1980’s, St. Louis County emerged as the economic powerhouse of the St. Louis
area. By this time, the county contained the largest resident labor force and the largest number
of jobs in the region. The unemployment rate has been consistently lower than the national
average as well as the St. Louis region altogether until recent times. It has become more closely
aligned with the national unemployment rate. The county contains roughly one fourth of all jobs
in the state, as well as half the jobs of the metro area. During the late 1990s, there was a
significant economic boom and many jobs
were created, consistent with the rest of the
nation, but after 9/11, a recession took a
toll on the region. After modest growth
through the mid-2000s, the great recession
of 2009 threw another blow at the
economy. To this day, St. Louis County is
recovering from the great recession, and a
major diversification of the economy is one
of the key reasons for this recovery. As in
many rust-belt cities, manufacturing has
been slowly declining since the 1980s,
being replaced by more specialized service
industries. For example, the share of
manufacturing employment fell from 15% in
1995 to 11% in 2005, to 6% in 2011. At the
same time, the education and health
service fields have grown substantially.
Other fields, including information
technologies, financial activities,
construction, leisure and hospitality, and
professional and business services have
also seen significant growth between 1995
and 2005.1
1 http://www.stlouisco.com/OnlineServices/MappingandData/CountyFactBook
Figure 1
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Location Quotient
To gauge the economic health of St. Louis County, a broader reference geography is used for
comparison sake. From an economic standpoint, this reference is usually the United States as a
whole. Thus we can conclude how well St. Louis County is doing by comparing various
economic statistics of the county with the national averages. The most common economic
indicator is the Location Quotient (LQ). The LQ is a ratio that compares an area’s distribution of
employment, on an industry-by-industry basis, to a reference distribution.2 When the LQ is
greater than 1, that industry is said to be “basic”. This means that this particular sector
comprises a larger percentage of the workforce in this particular county than is witnessed at the
national average level. These basic industries tend to serve not only the analysis area, but also
people outside of this area (exports), whereas industries with LQs under 1 are termed “non-
basic” and usually only serve the analysis area. In 2011, St. Louis County’s largest LQs were
from the management and
professional, scientific and
technical services sectors, as
indicated in Table 1 below. These
sectors bring in capital from not
only the county itself, but likely the
city of St. Louis, as well as other
counties, and possibly other states.
On the flipside, the forestry and
mining industries are almost non-
existent in St. Louis County, as
evidenced by their miniscule LQs.
Nationally, the mining industry
employs roughly 8 times more
employees, as a percentage of all
employees, than does St. Louis
County. As a result, the little mining
that does go on in St. Louis County
tends to only affect the county itself, with virtually no impact on surrounding areas. Table 1
indicates all of the basic sectors in St. Louis County, including those mentioned above as well
as real estate and rental leasing, wholesale trade, finance and insurance, information,
construction, and other services excluding public administration.
Base Multiplier
Using the above LQs, a base multiplier can now be calculated to understand the relationship
between basic and non-basic industries. A base multiplier is the ratio of the total employment in
a specific year to the total basic sector employment in that year.3 To calculate basic employment
at the sector level, this equation is used: (1-1/LQ) * (Total Employment for That Year in That
Sector). The equation is used only when the LQ is greater than 1, since any number below 1
signifies no basic employment. The equation subtracts the percentage of jobs required to bring
the LQ back to exactly 1, and then multiplies this number by the total number of employment in
2 http://www.bls.gov/help/def/lq.htm 3 http://mailer.fsu.edu/~tchapin/garnet-tchapin/urp5261/glossary.htm#sectB
Location Quotients in St. Louis County (2011)
Industry LQ B or NB
Management of companies & enterprises 2.77 basic
Professional, scientific & technical services 1.31 basic
Real estate & rental & leasing 1.18 basic
Wholesale trade 1.14 basic
Finance & insurance 1.09 basic
Information 1.04 basic
Construction 1.02 basic
Other services (except public administration) 1.02 basic
Arts, entertainment & recreation 0.98 non-basic
Educational services 0.96 non-basic
Health care and social assistance 0.95 non-basic
Retail trade 0.95 non-basic
Admin, support, waste mgt, remediation services 0.92 non-basic
Accommodation & food services 0.88 non-basic
Utilities 0.74 non-basic
Transportation & warehousing 0.73 non-basic
Manufacturing 0.66 non-basic
Mining 0.12 non-basic
Forestry, fishing, hunting, and agriculture support 0.09 non-basic
Table 1 Source: CBP
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that sector for that year. We know that every job above this mark is considered basic, because
the share of that sector grows higher than the national average. The base multiplier strives to
answer the question: How many total jobs can be created if 1 basic sector job is added to the
economy? As of 2011, St. Louis County has a base multiplier of 11.79. This means that on
average, adding a basic job creates a total of 11.79 new jobs. To put this in perspective, if 1,000
jobs are to be added to the management sector, there will be a total creation of 11,790 jobs in
St. Louis County. 10,790 of those jobs will be created in non-basic industries. Non-basic
industries rely on basic industries to function. For example, the influx of management
employees to the county will create the need for more grocery stores, more car dealership
employees, more landscaping services, and a multitude of other jobs in many sectors to support
the basic employment. The managers are exporting their services all over the country based on
their highly skilled nature of work, while the non-basic sectors are only serving the county. It is
thus paramount that St. Louis County has a strong, basic economy. This will support the larger,
non-basic economy, and a healthy mixture of employment will ensue.
Shift-Share Analysis
Another effective way to investigate local industries is to utilize a shift-share analysis. This
analysis provides a picture of how well a region’s mix of industries is performing. It can also be
used to understand how individual industries are doing. This metric breaks down regional
employment growth into three components, the national share, the industry mix, and the
regional shift. The national share tells us how many jobs would be created in our regional
industry if it grew at the national rate. For example, the national growth rate between 2001 and
2011 is 99.5%, meaning employment has actually gone down half a percent nationally. If we
multiply this rate by each industry’s 2001 employment count in St. Louis County, we can obtain
the national share. For example, there were 806 mining jobs in 2001 within St. Louis County. If
the 99.5% national growth rate is applied, then we expect there to be 802 mining jobs by 2011,
a decrease of half a percent. The industry mix focuses on how much growth can be attributed to
the region’s mix of industries. This component estimates how many jobs were created in each
industry due to differences in industry and total national growth rates. When the industry mix is
positive, that particular sector has a higher growth rate than the national rate. This component
does not take into account the actual growth that occurred in these local sectors, but only how
their rates would look given national job growth statistics. For example, the national growth rate
for mining between 2001 and 2011 is 134.1%. This is much higher than the overall national
employment growth for that decade, 99.5%; thus the industry mix for mining results in a positive
number (279). The final component, regional shift, is likely the most important of the three. This
component measures the ability of a local economy to capture a share of a particular sector’s
growth.4 A positive regional shift indicates that an industry gained additional jobs over those due
to national growth and its industrial structure. For example, the mining industry in St. Louis
County started with 806 employees in 2001. If we multiply this number by the difference
between the growth rate of this sector nationally and locally, we obtain the number -695.
Accounting for national growth and industrial structure, the mining industry in St. Louis County
did not add any additional jobs, in fact it lost them. Industries that exhibit positive regional shift
figures are said to have competitiveness, meaning they fare better in the county than nationally.
These industries are the driving force of local economies, so it is vital for planners to understand
the regional shifts in their regions. Table 2 on page 4 displays the shift-share analysis for St.
4 http://aese.psu.edu/nercrd/economic-development/tools/tred/basic-tools/shift-share-analysis
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Louis County between 2001 and 2011. We can see that 13 of the 19 sectors exhibit negative
regional shifts, meaning no new jobs were added to these sectors above the national growth
rate. The healthiest industries in the county are management, professional, scientific and
technical services, the arts, entertainment, and recreation sectors, and the information sector.
These industries underwent the highest job growths in the county, going above and beyond the
national growth rates. These numbers coincide with the largest LQs mentioned above, further
solidifying these industries as front runners in St. Louis County. Using the information in Table 2
below, an economic profile can be drawn about the county. As discussed above, the service
industries in St. Louis County have prevailed in the decade since 2001. Manufacturing,
construction, transportation, retail trade, and accommodation and food service have not been
keeping pace with national growth, in all cases decreasing over the decade. These decreases
can be explained by the dwindling population growth in the county. Many of the jobs in these
sectors are non-basic, meaning they only serve the county itself. Because the country is still
growing significantly, non-basic jobs are still in demand, while in St. Louis County, the capacity
has been reached for the current population. There are of course industry specific explanations
for the declines. For example, Lambert International Airport has seen massive declines in
passenger volume partially due to less layovers, in addition to less cargo handling. The overall
trend wherein manual labor sectors have declined is a reflection of a declining demand for new
infrastructure in the built up county, as well as a sign of more labor efficient operations. Luckily
St. Louis County has a diversified economy that can absorb the punches of shifting industries.
Four Digit LQ
Earlier we examined the LQs of various industries in St. Louis County. While the LQs of those
industries is important, a planner might want more specific data about a specific industry. For
example, the professional, scientific and technological service sector contains several sub-
sectors. Even though the industry as a whole was found to be basic, some sub-sectors might
actually be non-basic, counterbalanced by basic ones. Each sub-sector is further divided into
more sub-sectors, creating a tree of different occupations relating to the parent industry. In this
Shift-Share Analyis, St. Louis County (2001-2011)
Industry National Share Industry Mix Regional Shift
Forestry, fishing, hunting, and agriculture support 70 -10 11
Mining 802 279 -695
Utilities 2246 -40 71
Construction 39692 -7807 -6393
Manufacturing 58775 -18111 -5851
Wholesale trade 36586 -2917 -2699
Retail trade 73827 -612 -5818
Transportation & warehousing 24078 2406 -12101
Information 17530 -2889 927
Finance & insurance 37218 -1992 -4268
Real estate & rental & leasing 12420 -537 -1024
Professional, scientific & technical services 38988 4413 6499
Management of companies & enterprises 30775 599 7551
Admin, support, waste mgt, remediation services 47484 1947 -8002
Educational services 14079 4254 -2719
Health care and social assistance 70857 17590 -5523
Arts, entertainment & recreation 7339 957 1163
Accommodation & food services 47610 7818 -6602
Other services (except public administration) 29039 -890 -2805
Table 2 Source: CBP
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instance, we are looking into four digit sub-sectors. (Three digit sub-sectors in St. Louis County
are redundant and offer little more information than the 2 digit analysis.) Table 3 below shows
the four digit sub-sectors of the professional, scientific, and technical service sector in St. Louis
County. We can see that only four of the nine sub-sectors are basic. The computer systems
design sub-sector is the largest exporter, and thus the most basic of all sub-sectors. The
scientific research and development sub-sector has the lowest LQ, meaning it lags behind the
national proportion of scientific researchers. It is no coincidence that in recent years, many new
tech companies have relocated to, or formed in St. Louis County. The cost of living is cheaper
than in traditional tech metros such as San Francisco and Seattle, and this translates into more
profits. St. Louis County has transformed itself into a technologically driven service sector job
market, and the four digit sub-sector LQ analysis helps to illustrate this.
Local Inequality
Planners must also understand where local inequality takes place within their communities.
This section covers several ways in which inequality can be measured and understood. Table 4
below shows the wealthiest and poorest county subdivisions (CSDs) within St. Louis County.
These are not all the CSDs in the county, just the extremes in terms of wealth.5 A large income
inequality can be seen between the wealthiest and the poorest CSDs. St. Louis County is a
highly segregated place, and this should come as no surprise to an urban planner familiar with
the area. Western parts of the county tend to be wealthier than the rest of the county, and the
north side tends to be poorer than the rest of the county. This is perfectly illustrated in Figure 2
on the next page; all five of the wealthiest CSDs are in West County and all five of the poorest
are in North County. The wealthiest CSDs tend to be newer suburban single family residential,
while the poorer CSDs have a larger presence of multifamily living, a larger ethnic diversity, and
are older.
5 Noteworthy is the fact that in Missouri, CSDs are only used for census data gathering. While states like New Jersey
have actual townships, counties in Missouri consist of cities, towns, census designated places, or unincorporated land. As a result, a native St. Louisan will have trouble recognizing the names of the townships that are designated by the census as CSDs. For example, Meramec Township might be a mystery to residents in the County, yet this township consists of several cities that residents can easily identify.
Location Quotients in St. Louis County (2011)
Professional, Scientific, and Technical Services LQ B or NB
Computer systems design and related services 1.78 basic
Management, scientific, and technical consulting services 1.56 basic
Advertising, public relations, and related services 1.03 basic
Specialized design services 1.01 basic
Other professional, scientific, and technical services 0.79 non-basic
Architectural, engineering, and related services 0.68 non-basic
Accounting, tax preparation, bookkeeping, and payroll services 0.65 non-basic
Legal services 0.65 non-basic
Scientific research and development services 0.62 non-basic
Table 3 Source: CBP
Table 4 Source: ACS 2012 5Y
Median Household Income by County Subdivision in St. Louis County, (2012, Inflation Adjusted)
Wealthiest CSDs Median Household Income Poorest CSDs Median Household Income
Missouri River Township $122,684 Norwood Township $31,119
Chesterfield Township $119,393 Normandy Township $31,823
Meramec Township $96,042 St. Ferdinand Township $35,480
Wildhorse Township $88,527 Airport Township $35,727
Lafayette Township $88,286 Midland Township $41,496
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When Compared to the national figures, St. Louis County is actually quite similar in terms of
economic inequality. Median household income in St. Louis County is $58,485 while the
national figure is $53,046. Both the county and the nation’s mode household income is in the
$75,000-$99,999 cohort. Figures 3 and 4 on the next page show the income distribution in St.
Louis County and nationally. The county has higher percentages of households that earn over
$75,000 than the nation. Additionally, the nation has slightly higher rates of households earning
less than $40,000. Suburban counties tend to have higher incomes than rural and highly
urbanized counties, which is the likely reason that St. louis County is wealthier than the national
average.
Figure 2 Source: ACS 2012 5Y
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5.5%
4.2%4.6%
5.3%5.0% 5.0%
4.6%4.9%
4.0%
8.0%
9.8%
12.7%
8.3%
5.5%6.0%
6.6%
0.0%
2.0%
4.0%
6.0%
8.0%
10.0%
12.0%
14.0%
Perc
enta
ge o
f H
ousehold
s
Median Household Income
Median Household Incomes in St. Louis County (2012)
Figure 3 Source: ACS 2012 5Y
Figure 4 Source: ACS 2012 5Y
7.2%
5.4% 5.3% 5.4% 5.2% 5.2%4.7% 4.8%
4.2%
8.1%
10.1%
12.2%
8.0%
4.8% 4.8% 4.6%
0.0%
2.0%
4.0%
6.0%
8.0%
10.0%
12.0%
14.0%
Perc
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f H
ousehold
s
Median Household Income
Median Household Incomes in the US (2012)
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Pen’s Parade, Lorenz Curve, & Gini Coefficient
There are three ways to further examine local inequality. Figures 5 and 6 below illustrate the
Pen’s Parade for St. Louis County, as well as for the nation. Pen’s Parade paints a picture of the
influence of the wealthy. Half of the wealth in St. Louis County is held by about 18% of the
wealthiest residents, as the minimal majority indicates. Put another way, the poorest 330,861
residents in the county hold half the wealth, while the wealthiest 73,290 hold the other half. The
income distributions are quite similar for both areas of analysis. It is clear that a select few
people have the bulk of the economic influence in the county, and in the country as a whole.
$0
$50,000
$100,000
$150,000
$200,000
$250,000
$300,000
$350,000
$400,000
0% 20% 40% 60% 80% 100%
Household
Incom
e
Percent of Households (Cumulative)
Pen's Parade (St. Louis County, 2012)
Pen's Parade Minimal Majority
$0
$50,000
$100,000
$150,000
$200,000
$250,000
$300,000
$350,000
0% 20% 40% 60% 80% 100%
Household
Incom
e
Percent of Households (Cumulative)
Pen's Parade (US, 2012)
Pen's Parade Minimal Majority
Figure 5 Source: ACS 2012 5Y
Figure 6 Source: ACS 2012 5Y
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A Lorenz Curve also demonstrates income distribution, but it has additional components that
help clarify just how unequal household incomes can be. Figures 7 and 8 show the Lorenz
Curves for St. Louis County and for the nation. The Y axis now represents the cumulative
percentage of income, as opposed to just household income in the case of Pen’s Parade. If
income was completely equal for all, then the equality lines below would represent income
distribution. But because we know income distribution is not equal, we can use a Gini
Coefficient to obtain a statistic for just how much inequality exists. The Gini Coefficient is a
measure of statistical dispersion intended to represent the income distribution in a given region.
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0% 20% 40% 60% 80% 100%
Perc
ent
of
Incom
e (
Cum
ula
tive)
Percent of Households (Cumulative)
Lorenz Curve and Line of Equity (St. Louis County, 2012)
Lorenz Curve Equality Line Minimal Majority
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0% 20% 40% 60% 80% 100%
Perc
ent
of
Incom
e (
Cum
ula
tive)
Percent of Households (Cumulative)
Lorenz Curve and Line of Equity (US, 2012)
Lorenz Curve Equality Line Minimal Majority
Figure 7 Source: ACS 2012 5Y
Figure 8 Source: ACS 2012 5Y
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The Gini Coefficient specifically measures economic inequality. To calculate this, we simply
divide the area between the line of equality and the Lorenz Curve by all the area under the line
of equality. The resulting ratio will be between 0 and 1; 1 meaning complete inequality and 0
meaning complete equality. St. Louis County has a Gini Coefficient of .47 while the national
figure is .46. Thus the county has slightly more income inequality than does the country.
Planners must understand these income inequalities and keep them in mind when speaking to
stakeholders. Not everyone has the influence to be heard, and a select few hold the bulk of the
economic influence.
Racial Segregation
Another area of concern in terms of inequality is racial segregation. De Jure segregation ended
in the mid-20th century, but De Facto segregation remains prevalent throughout the U.S. Inner-
city St. Louis has much stronger segregation than the county, but St. Louis County is not devoid
of this inevitable occurrence. We can use a dissimilarity index to gauge the extent of racial
segregation in the county. The index basically measures the percentage of a group’s population
that would have to change residence within the county in order to create an even distribution of
the races in all subdivisions. The index is calculated by first dividing the number of people in a
particular race in each county subdivision by the total number of people in that race in the entire
county. This quotient is then subtracted from another quotient (a second race), since we are
comparing two races. The finalized dissimilarity index is simply the sum of all dissimilarities
obtained in the previous step, divided by 2. An index of 0 signifies complete integration while a
value of 1 signifies complete segregation. For this report, the dissimilarity index is being
calculated for St. Louis County using county subdivisions and census tracts for the 2000 and
2010 census years. The findings are displayed in Table 5 below. The findings appear to be
conflicted, since each variable changed inversely
over the course of 10 years. The results must be
taken with a grain of salt for three reasons. First, this
index only accounts for whites and blacks. In many
cases, there are significant concentrations of other
races that would not be accounted for by this metric.
Secondly, because census tracts are smaller in size
than county subdivisions, they offer a more in depth
look at segregation. CSDs may overgeneralize patterns within themselves and incorrectly
assume that segregation is low, whereas numerous census tracts within that CSD can expose
these micro-patterns. In addition, during data acquisition, it was found that in 2010, St. Louis
County has 25 more census tracts than it did in 2000, yet the county did not physically grow any
larger. This further division of the county creates different geographic subdivisions for
measuring dissimilarity. Thus the census tract results are not consistent and cannot be used for
comparison sake. For this analysis, the CSDs offer the most reliable data for computing the
dissimilarity index for the decade. They indicate an insignificant increase in racial segregation.
An index of .667, which is considered high, is lower than the figure for the entire St. Louis
region, which actually went down from .734 to .706 between 2000 and 2010.6 The city of St.
Louis, along with other segregated counties in the metro area exhibit higher rates of segregation
than St. Louis County on its own. While planners in St. Louis County are fortunate enough to
inherit lower rates of racial segregation than adjacent counties (and cities), St. Louis County’s
6 http://www.s4.brown.edu/us2010/segregation2010/msa.aspx?metroid=41180
Table 5 Source: Census 2000-2010
Dissimilarity Indexes in St. Louis County (2000)
Level of Measurement DI
Census Tracts 0.695
CSDs 0.665
Dissimilarity Indexes in St. Louis County (2010)
Level of Measurement DI
Census Tracts 0.705
CSDs 0.667
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dissimilarity index still illustrates a common problem found throughout the country. Efforts must
be made to encourage the mixing of races. Planners must not abuse zoning in order to exclude
certain races from particular communities. A culture of acceptance will inevitably follow.
Gravity Model Analysis
The final tool for analysis in this report is the gravity model. Gravity models communicate
relationships between different places and how their size and distance from one another affects
their interdependence. In this report, we shall use a gravity model to decide on the best location
for a materials recovery facility in St. Louis County. An assumption is made that each
municipality in the county has a cost effective site at the ready, and has adequate roads to
support this facility. In addition, we are assuming that the amount of recyclables is directly
proportional to population, and that each household produces 10 pounds of recyclables per
person per week. Our goal is to place the facility in the municipality that will result in the least
ton-miles being produced. In order for a municipality to be chosen, it needs to be in the most
centralized location. To figure out which municipality is the most centralized, two components
are critical. First, an origin-destination matrix must be made for all eligible county subdivisions.
Table 6 below is this matrix. The second component calculates the estimated ton-miles that will
be associated with locating the facility in every eligible municipality. First, the ton-mileage
contribution at every site must be calculated. To obtain these numbers we simply multiply the
distance between two municipalities by the population of the first municipality, and then multiply
this figure by .005, which represents the per person weekly contribution of recyclable material.
Total ton-mileage for each potential site is then calculated by summing the aforementioned
components for each site against all other sites. The results are shown below in Table 7.
Building the facility in Creve Coeur Township will produce the least ton-miles of any other
municipality. This means that trucks can access all destinations and drive the least to do so,
while helping preserve the environment. In addition, less time will be needed to collect
recyclable materials, resulting in less labor and generally
more efficient operations. However, while Creve Coeur is the
desirable CSD for the proposed facility, there are many other
factors that would typically influence this decision. For
example, zoning might be a huge issue, as well as neighbor
opposition, availability of utilities, and proximity to similar
facilities already in existence. Planners and developers would
need to thoroughly investigate their options and base their
decisions on more criteria than just least ton-mile production.
Origin Destination Matrix, St. Louis County
Chesterfield Clayton Creve Coeur Florissant Lemay Maryland Heights Normandy St. Ferdinand Tesson Ferry Wildhorse
Chesterfield 0 15.2 10.4 24.7 23.5 7.8 21.7 31.5 19.1 8.3
Clayton 15.2 0 8.7 15.8 11.1 14.8 12.3 22.7 15.8 21.9
Creve Coeur 10.4 8.7 0 17.8 19.4 4.1 11.5 24.6 15 16.2
Florissant 24.7 15.8 17.8 0 29.8 12.4 5.6 10.1 28.4 29.6
Lemay 23.5 11.1 19.4 29.8 0 22.6 22.9 25.1 7.1 28.1
Maryland Heights 7.8 14.8 4.1 12.4 22.6 0 9.9 20 18.6 19.8
Normandy 21.7 12.3 11.5 5.6 22.9 9.9 0 7.2 27 29.8
St. Ferdinand 31.5 22.7 24.6 10.1 25.1 20 7.2 0 29.3 37.2
Tesson Ferry 19.1 15.8 15 28.4 7.1 18.6 27 29.3 0 23.6
Wildhorse 8.3 21.9 16.2 29.6 28.1 19.8 29.8 37.2 23.6 0
Table 6 Source: Google Maps
CSD Total Ton-Miles
Creve Coeur 22,714.9
Maryland Heights 23,269.6
Clayton 24,909.0
Normandy 27,353.7
Chesterfield 28,580.5
Florissant 32,058.9
Tesson Ferry 32,592.5
Lemay 34,022.6
Wildhorse 38,000.9
St. Ferdinand 38,381.8
Table 7
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Conclusion
Planners must understand the economic conditions that drive their local economies. They must
know which industries are thriving and which industries are lagging. They must understand the
inequality that takes place both economically and in terms of racial segregation. They must
contain the knowledge needed to analyze various factors in order to pick where a specific
industrial facility should be placed. All of these vital things can be analyzed using the methods
described in this report.