The document summarizes analysis of labor data from the Bureau of Labor Statistics for Iowa from 2006-2015. Key points: - Data was downloaded, cleaned, filtered to only include Iowa data, and concatenated into a single dataset for analysis. - Occupations were clustered and categorized into 3 groups: Professional, Manual Labor, and Personal Services. Geographical areas were also grouped. - Analysis found higher salaries in college towns and metro areas for professionals, with salaries exceeding $60k recently. Median incomes were highest for professionals, followed by manual labor, and lowest for personal services statewide. - Salary distributions were explored, finding the gap between lower and higher salaries growing for most areas and occupations, particularly

1.  The initial step was to download the XLS data for both Metropolitan and
Non-metropolitan areas for the years 2006-2015. 2006 was the start date
for this analysis because it was the first year which contained both metro
and non-metro information. We then saved these files as comma
separated value files. CSV is a simple file format used to store tabular
data, such as a spreadsheet or a database.
 First we uploaded each year’s data individually into JMP to clean and
filter it since we only wanted Iowa’s data and the Bureau of Labor
Statistics had every state in the U.S.’s labor data.
 After we filtered, we subset, and matched column titles for the data from
2006-2015, we then concatenated all of the data. Concatenation is making
a series of interconnected things or events. By doing this, it will create a
new spreadsheet with all of the data combined into one massive data set
ready for analysis.
 For the professional category, there appear to be two groups: the college towns (Ames and Iowa City) and
the large metro areas are on one group, with the rest of the state (including Dubuque) having lower
professional salaries. Notable is the fact that in both College towns and metro areas in the most recent years,
annual median salaries for professional occupations have approached or exceeded 60K, where as in the other
areas the annual median salary for the professional category resides around 50K.
 Median incomes are highest for professional occupations, followed by the manual labor category, with
personal services the lowest throughout Iowa.
• http://www.bls.gov/oes/tables.htm The Bureau of Labor Statistics is a
government run website that has a subcategory called “Occupational
Employment Statistics” and within the subcategory is the data we
downloaded.
Throughout the last two semesters, working with multiple data
sources and software in order to perform both analysis and
visualizations, we have gained an abundance of knowledge
and unveiled some interesting things about the economy in
Iowa. The monitor contains more visualizations that drill
down in a more interactive way of visualizing the data that we
gathered.
 What is reported here is aggregate categories.
 Throughout our search for insight from the data given, we noticed that
there were some similar trends in the data in regards to the varying career
fields that might provide us with some generalized insight about trends in
employment. In order to dive into this idea in an accurate fashion we
performed a principle components analysis with the salary data provided.
When we did this we found that the median salary was the principal
component for analysis. We then proceeded to use JMP’s clustering
feature to cluster the occupation titles based on median salary…
 From this procedure’s findings, we proceeded to collapse the 22 major
Occupation Titles provided by the Bureau of Labor Statistics into just 3
categories.
 Professional
 Manual Labor
 Personal Services
 This Process was performed in JMP (JMP is a SAS product that is
marketed as a ‘Statistical Discovery’ tool) as follows:
 And, for geographical areas, collapsed 12 (the Bureau of Labor Statistics
reports data for 12 separate regions in Iowa) into 5 as follows:
 These groupings permit a high level view of the Dubuque economy, how
it has changed recently, and how it compares with other areas of the state.
Any of these analyses can be drilled down to a more disaggregated level –
but the reliability of the data will be reduced the more targeted the
analysis (due to smaller sample sizes).
INTRODUCTION
BACKGROUND
SALARY ANALYSIS
SALARY DISTRIBUTION
CONCLUSIONS
REFERENCES
Project completed in correspondence with Dr. Dale Lehman
Iowa Economic Report with Dubuque Focus
Michael Perhats & Brendan Doyle
Loras College Center for Business Analytics
 It is also useful to explore how the distributions of salaries compare. The next
figure shows the 10th percentile, median income, and 90th percentile of income,
by occupational category across the regions of Iowa:
 For most areas – and most occupational categories – the 10th and 90th percentiles
have been spreading apart. This is part of the more general phenomenon of the
growing gap between rich and poor. Not surprisingly, the gap is the greatest for
professional occupations and that is where the gap is growing most quickly.
Again, Dubuque is an exception, showing declining professional salaries,
particularly at the highest end of the distribution. Also, not the relatively high top
salaries for manual labor in the college towns. In virtually all areas and
categories, the bottom of the income distribution appears flat.
 NOTE: This visualization was created in the Graph Builder provided by JMP,
which is where all of our analysis was computed.
LORAS.EDU
SHARE OF EMPLOYMENT ANALYSIS
 When trying to gather actionable information from our data, we thought that it would be advantageous to calculate
what share of employment was held for each occupation type. We did this with a simple formula, dividing the Total
Employment number provided by the sum of the Total employment across all categories for the year; leaving us with
a percentage.
 The Formula in JMP: (TOT_EMP was a provided field in the original data set)
 Using the same three occupational categories (Professional, Personal Services, Manual Labor) that we calculated for
the examples for the Dashboard representing the change in median salaries over time, we have depicted what share
of local employment falls into these three categories and how this ‘share’ has been changing overtime.
 This pattern is shown in the following visualization:
NOTE: The Visualizations for this Analysis were created in Microsoft Power BI, which is an open source, cloud
delivered visualization stack which allows for and promotes engagement from Microsoft users in the data.