Wednesday, October 25, 2017

Economic Hurdles in Rural Utah

by Mark Knold

Utah is a geographically large state. Based on total area, it is the 13th largest state, implying there is room to spread out. Despite all this space, Utah’s population distribution is quite concentrated. According to the U.S. Census Bureau, Utah is the nation’s 9th most urbanized state. This dichotomy has shaped a state with two economic profiles — one urban, one rural. It can be challenging for a state dominated and prospering within the urban to extend its economic bounty to the betterment of the rural.

What is rural? It depends upon one’s objective behind the question. Most define rural by a visual scan of the landscape. A lot of open land and not many people — rural. Yet economically, the view can be different. An area may look rural, but if the economic vitality of its populace is strongly integrated with a nearby urban area, then this creates a different perspective. The latter is a preference of the federal government — an entity that often makes allocation or distribution decisions based upon economic factors.

No matter how one technically defines rural, the Governor’s Office recognizes a recent dichotomy in Utah’s economic prosperity. Since the Great Recession, Utah has had compelling economic success. Yet, most of this is concentrated in Utah’s urban centers. Portions of Utah’s rural communities are not seeing matching levels of success. Utah’s Lt. Governor recently observed, “Not all of Utah’s communities are full participants in this economic success. Many counties off the Wasatch Front are experiencing challenges.”

In response to this economic disparity, the Governor’s Office has launched the 25k Jobs initiative — an effort for businesses to create 25,000 new jobs in 25 Utah counties by 2020. With this spotlight on rural Utah’s economics, let’s take a look at some of these rural challenges.

To most, jobs deliver their income and means for living sustenance. Therefore, employment, and peripheral variables associated with employment, becomes the strongest proxy for measuring the Utah economy’s health. We will look at Utah’s counties through the lens of employment, unemployment, the labor force and how the industry structure speaks to the underlying performance of these variables.

A profile of job growth becomes a starting point. Economic performance needs to be viewed with a somewhat long lens. The Governor’s 25k Jobs initiative was not born from a short-term disorder, but instead is recognition of weak longer-term fundamentals. To illustrate this perspective, one needs to backdrop the short-term mechanics against the longer-term dynamics.

The County Job Profile chart is an intersection of the short-term trend with the moderate-term. Each county is a bubble, and the bubble size reflects job counts. The chart is divided into four quadrants. The quadrants tell the story of the intersection of the short and moderate-term trends (growth or contraction) and the general health of the county’s economy.

There are two axes of measure. First, the vertical axis represents the short-term. It is the percentage of county job change between 2015 and 2016. Above the horizontal axis is growth — below is contraction.

Second, the horizontal axis measures the moderate-term. It is the percentage of job change over the past five years (2011-2016). To the right of the vertical axis is growth — to the left is contraction. Where a bubble lies is the intersection of the short and the moderate term.

To illustrate, find Beaver County on the chart. Beaver aligns with around -4.0 percent on the vertical axis, and 8.0 percent on the horizontal axis. This says that over the past five years, Beaver County’s job count has grown by 8.0 percent, but over the past year it has contracted by around 4.0 percent. This implies that Beaver County’s economy may be slipping a bit. A one-year view would imply a problem. A longer-term view places this short-term setback against a broader perspective of overall prosperity.

The quadrant of concern is the Contracting quadrant. These economies have contracted over both the most recent year and the past five years. No matter how one wants to define rural as outlined above, all of these contracting counties identify as rural.

In-county jobs alone are not the complete picture. For example, a large percentage of Morgan County’s residents commute to Weber or Davis counties for work. If jobs are not being germinated in Morgan County, the county and its population can still prosper from its ties with the urban area.

An additional way to look at the economy is through the lens of the labor force. The labor force consists of those 16-years and older who are either working or looking for work. It is based upon where people live, not where they work. A worker living in Morgan County will be represented in Morgan County on the following chart (County Labor Force Change); yet, if they work in Weber County, their job is represented in Weber County on the prior chart. Adding this perspective helps to round out a county’s profile.

The structure of the County Labor Force Change graphic is the same as the prior chart. The area of vibrancy is the upper-right quadrant where the labor force is increasing. The quadrant of labor force contraction is the lower left. A decline in the labor force occurs when people become discouraged and leave the labor force — yet stay in the county, or when people leave the county altogether. Either way, a decline in the labor force signals a fundamental negative in the economic trend.

Depending upon the variables measured, a gain in one and a decline in another can both be positive. Job growth and an unemployment decline are both positive. To associate the positive with low unemployment, the quadrant message on the Unemployment Rate chart has been transposed.

Every month an unemployment rate is calculated for Utah and each of its counties. A county’s unemployment rate can be measured against the Utah statewide average unemployment rate. In the following graphic, county rates are mathematically compared against the statewide rate (seasonally adjusted), recorded and then summed across time.

For example, if a county’s unemployment rate is 5.5 percent and the statewide rate is 4.0 percent, then that county’s difference for that month is 1.5. If a county’s rate were to be 3.5 percent against the statewide rate of 4.0 percent, then the difference is -0.5. These monthly differences are tallied and summed. A high score speaks to a consistent and persistent unemployment rate above the statewide average. In other words, these are counties with a continuous environment of high unemployment.

The horizontal axis is a measure since 2000 and the vertical axis a measure since the beginning of the Great Recession (2008). The axis intersection is not at zero to isolate the “concern area” within the upper right quadrant. The statewide average is consistently close to the Salt Lake County average, so a sizeable number of counties will have sums slightly above the statewide average; yet, this doesn’t imply an unemployment problem. But the non-zero intersection is utilized to emphasize the counties that do have an outstanding unemployment disparity.

Across these various charts, a common group of rural counties emerge in the weak quadrant. These include Carbon, Emery, Garfield, Piute and San Juan counties; with Duchesne and Uintah hanging on the edge. There is a common theme that surrounds this grouping and it centers upon low economic diversity.

An economy’s ability to be consistently positive has a strong foundation in a diverse mix of industrial employment. Think of it in terms of “not putting all your eggs in one basket.” Economic diversity is spreading jobs across many baskets. Diversity is desirable because the overall economy is not dominantly influenced by one or a handful of industries whose poor performance weighs upon the whole.

A Hachman Index is an evaluation tool measuring to what degree an economy may or may not have all its eggs in one basket. In the Hachman Index, a measure of 1.0 means your eggs are well distributed across many industries. Conversely, numbers approaching zero point to a high concentration in one or a handful of industries.

Many of the counties that score low on the previous charts are the same ones on the lowest tier of the following Hachman Index chart. This chart represents the placement of economic diversity upon employment change of the past five years. A county will be placed high or low (vertical axis) on the chart depending upon its Hachman Index score. It will align right or left (horizontal axis) depending upon its five-year employment change. Metropolitan counties have higher economic diversity than rural counties — placing them higher on the chart. They are also further to the right on the chart, showing stronger employment growth. There can be individual exceptions, but the general theme is that lack of economic diversity is a foundational impediment to economic viability. Industrial diversity, though difficult to artificially induce, is a desired remedy to counter sluggish economic performance.

Lack of diversity does not mandate a poor economy. A reproduction of this chart five years ago would have placed Uintah and Duchesne counties still low on the chart, but their five-year growth rates would have been off the chart, needing arrows to point out beyond the chosen 40 percent horizontal axis limit.

Those economies are dominated by energy production. When energy prices are high, their economies can soar. When energy falters, they often do likewise. They are striking examples of economic outcome being determined by a dominant industry.

In summary, there is a dichotomy within the Utah economy between urban and rural. The urban economies are diverse and, therefore, more economically balanced; while many rural economies are not. With some rural counties the economic distinction is not a wide divide; but in the rural counties where the divide is pronounced, the underlying theme is often a low level of economic performance.

Monday, October 23, 2017

Utah's Seasonally Adjusted Unemployment Rates

Seasonally adjusted unemployment rates for all Utah counties have been posted online here.

Each month, these rates are posted the Monday following the Unemployment Rate Update for Utah.

For more information about seasonally adjusted rates, read a DWS analysis here.

Next update scheduled for November 20th.

Friday, October 20, 2017

Utah's Employment Situation for September 2017

Utah's Employment Situation for September 2017 has been released on the web.

Find the Current Economic Situation in its entirety here.

For charts and tables, including County Employment, go to the Employment and Unemployment page.

Next update scheduled for November 17th, 2017.

Thursday, July 27, 2017

Retail Employment in the Age of Amazon:
A Profile of the Mountainland Region

By Mark Knold, Supervising Economist and Cory Stahle, Regional Economist

Consumer spending makes up around 68 percent of the nation’s gross domestic product. Consumer spending is individuals and families purchasing groceries, clothing, recreation, stocks, insurance, education and much more. The transactions cover a broad swath of economic activity.

Much of the nation’s consumer spending is captured via retail trade. A useful retail trade definition is “the re-sale (sale without transformation) of new and used goods to the general public, for personal or household consumption or utilization.” Not all consumer spending is captured through retail trade transactions, but majority large share is.

Whereas in the past nearly all retail transactions were done through traditional brick-and-mortar stores, now a significant and growing segment is diverted to internet sales. The consumer shops online and FedEx (or like) delivers the product. One can see that the number of brick-and-mortar stores and the level of local sales across the country are being endangered by this economic evolution.

The brick-and-mortar reduction is beginning to show its economic presence in the United States employment numbers. While the U.S. economy is finally expanding at a healthy pace this side of the Great Recession, one of the few industries not rising with this tide is retail trade. While overall retail sales are increasing, employment is not.

Traditionally, as a population increases, retail trade employment grows simultaneously, since population growth and consumer spending volume is an integrated dynamic. If studied deeply, a certain ratio of retail trade employment growth spawned from population growth would emerge. 

Before the internet, the vast majority of all consumer sales occurred in the immediate community or region. But now, the internet is diverting these sales away from the local community — and with internet sales growing, its market share will increase.

We do not yet know how much brick-and-mortar erosion will eventually occur. And will such a phenomenon hit some areas more than others (e.g., urban vs. rural, or local vs. tourist spending)? These are touch points that economists will be watching as this internet sales phenomenon continues to grow within the national and Utah economies.

In light of this change, in this quarter’s Local Insights we are profiling retail trade employment throughout Utah’s local regions. This can offer a profile of where retail trade is now in a local economy, and possibly how much of the sector could become vulnerable to the internet-sales phenomenon.

All regions can be viewed through the Local Insights web portal. The following is a retail trade profile for the Mountainland region:

Measuring Retail – NAICS Codes

In order to explore the relationship between internet and brick-and-mortar retail we need to look at data grouped through the North American Industry Classification System (NAICS), which “is the standard used by Federal statistical agencies in classifying business establishments.” Stated simply, the NAICS groups businesses together based upon what they do.

Hierarchical in nature, the NAICS begins with a broad categorization and narrows its focus at subsector levels. As an example, the educational services sector includes all institutions focused on providing instruction and training. At the subsector levels, that focus is narrowed so that data from elementary schools, colleges and trade institutions are separated.

In the case of retail, a broad sector known as retail trade includes several underlying categories such as: motor vehicle sales, furniture stores, electronics stores, building material stores, grocery stores, pharmacies, gas stations, clothing stores and department stores, among others.

Then there is the relatively new and emerging part of the retail trade sphere — non-store retailers. These are establishments that sell products primarily on the internet or through direct selling. Examples include Amazon,, Young Living and dōTERRA. These types of retailers have grown rapidly in the past 15 years and their presence is reshaping the retail trade landscape. We will look at this subsector in a later section.

Retail in the Mountainland Region

In 2016, retail trade accounted for 12.8 percent of employment in the four Mountainland counties making it one of the largest industries with nearly 35,000 jobs. The three largest retail subsectors in the area are general merchandise stores, food and beverage stores, and motor vehicle and part dealers. These three account for 41 percent of retail employment in the area are responsible for nearly a third of all taxable sales in the four counties.

This and other data are represented in the visual below, which illustrates trends in retail employment and taxable sales between 2000 and 2016. From this visual we see that retails’ share of employment has declined in recent years. On the surface, this may indicate that the internet is replacing “vulnerable” brick-and-mortar jobs with employment in electronic shopping related industries. 

While many of these online retail jobs are counted in non-store retailers, some worksites may be placed in other industries like transportation and warehousing. As an example, despite their online storefront, the distribution center for in West Valley City is classified as a warehousing establishment. This structural difference between online and traditional retail may account for some of the employment share reduction, but to what extent is it driven by the internet supplanting vulnerable retail locations?

Infrastructure, Perishability and Behavior – Identifying Vulnerable Retail Sectors

One way to identify potential vulnerabilities in brick-and-mortar retail employment is to explore trends in e-commerce sales relative to total retail sales. While this data is unavailable at the region and state level it is provided for the U.S. Despite the lack of geographical specificity in the data, it captures trends and relationships in an industry where borders are disappearing due to the growing accessibility of the internet.

In order to identify vulnerable employment, we will identify subsectors which have been impacted most by online shopping. We can do this by looking at internet sales penetration within retail, which is the share of online sales for a particular subsector, and comparing it to employment in Utah, Summit, Juab and Wasatch counties. For this portion of the analysis, we will exclude non-store retailers and look at “traditional” brick-and-mortar retail groupings and their online sales.

Mountainland Employment vs. Internet Sales Penetration

          Source: Census Bureau E-Stats

This graph illustrates four main points about the connection/competition between brick-and-mortar and online stores.

First, not all industries are currently well-suited for internet sales and may lack the infrastructure required. This is seen primarily in the low online sales levels in general merchandise (one-stop shopping) and food/beverage (grocery) stores. While the idea of having all your groceries and toiletries shipped is appealing, the infrastructure to transfer these perishable goods to a large amount of households in a timely manner does not yet exist. Conversely, the electronics subsector appears to be much better suited for online sales. Perhaps it is because individuals purchasing technology are comfortable with using technology to purchase it.

The second insight we can glean from the graph is the impact of product on labor needs. A food and beverage store will require more employees to care for perishable goods relative to a furniture store where products rotate only a few times annually. Books, clothing and electronics are also examples of products requiring less labor to maintain. Therefore, the less maintenance an inventory requires, the higher the likelihood that it will transition to online sales.

Third, the motor vehicle and part dealer subsector is unique. While perishability of goods can explain the need for higher employment levels in the grocery groupings, a car doesn’t expire the way a gallon of milk does. While some of the higher employment in motor vehicles may be explained by a lack of infrastructure in shipping cars purchased online, it may be more of a behavioral driver. According to a 2015 Autotrader survey, 88 percent of consumers said they wouldn’t buy a car before a test drive.
These first three points illustrate that infrastructure, perishability and behavior all influence the degree to which certain retail groups sell online.

The final point is the negative relationship between internet sales penetration and employment. In general, higher levels of internet sales penetration are correlated to low regional employment in that industry. This confirms visually that jobs gained with online retailers reduce employment among traditional retail.

What About Non-Store Retailers?

As mentioned previously, the graph above excluded non-store retailers. In the Mountainland area, this subsector employs more than 6,200 individuals, making it the largest retail category. Of these jobs, over 4,000 are found in the direct selling category which encompasses in-house sales, truck or wagon sales and portable stalls. With the rise of essential oil companies like Young Living and dōTERRA, among other multi-leveling marking (MLM) companies, Utah County has become a bastion of this type of retail activity. Since 2000, employment in this subsector has grown by 595 percent (mostly since 2007) and taxable sales have grown by 63 percent. So why are non-store taxable sales gaining at a slower pace than employment?

In the case of direct selling, many MLM workers are contracted labor, in other words they operate as self-employed workers. This means their taxable sales will not be captured in a retail NAICS code, and cannot be tracked. In online sales, taxes are collected by the state of the purchaser, and then, only if the seller has a physical presence in that state. This means that when sells a rug to someone outside of Utah, there is money coming into Utah (in terms of the jobs that the sale supports), but there is no sales tax coming in to Utah. The only non-store sales taxes captured in Utah are Utah consumers purchasing goods from retailers with a presence in Utah. Since a large share of sales by local online retailers are to customers in other states, it means that sales tax revenue lags compared to employment growth in the industry.

Direct sales (MLM) and companies like illustrate the difficulties in quantifying the impact of non-store retailers on brick-and-mortar locations. When Utah residents purchase tax-free goods from a non-store retailer like Amazon or from a direct sales company, establishing a relationship between non-store and traditional retail is challenging.

Perhaps the state’s recent agreement with Amazon will be helpful in unraveling part of this puzzle. Amazon recently established a nexus with the state of Utah and therefore became obligated to collect sales taxes. Amazon reportedly captured 33 percent of all US online purchases in 2015, according to the magazine Internet Retailer, up from 25 percent in 2012. Conversely, direct selling firms will continue to create challenges as long as the current data methodology and MLM business model is preserved.

Monday, May 1, 2017

Census Bureau Tool Provides Labor-Force Insight for Utah

By Mark Knold and Cory Stahle

Across the United States, jobs are quantified through each state’s unemployment insurance program. Those programs provide the potential for laid-off workers to receive unemployment benefits — the goal being to bridge the gap between workers’ lost jobs and their next jobs. An eligible recipient’s weekly benefit amount is based upon their earnings from recent work. This begs the question, how does Utah’s unemployment insurance program know how much an individual recently earned while working?

That answer is supplied by all businesses that hire workers, as they must report their employees and pay as mandated by the unemployment insurance laws. Companies identify their individual workers and those workers’ monetary earnings for a calendar quarter. As businesses are identified by their industrial activity and geographic location, it is through the unemployment insurance program that aggregate employment counts by industry and location are calculated.
Yet each state’s profiling of individuals is quite minimal in the unemployment insurance program. The U.S. Census Bureau can bring more light to the overall labor force by supplementing said information with gender, age, race/ethnicity and educational attainment (imputed from American Community Survey responses) for Utah’s labor force.

The Census Bureau packages this information through their Local Employment Dynamics program and makes available said data on its website. Here at the Department of Workforce Services, we recently downloaded and packaged Utah-specific data from said website and summarized it in the attached visualization.

Tuesday, February 14, 2017

Better, Faster, Smarter . . .Check out our new website design

Information is the treasure of the current age. The instant access to information since the advent of the Internet has transformed societies in ways that thousands of years prior had not. Information can lead to knowledge, and — with increased knowledge — better efficiencies and way of life. If information is vital, then the presentation of information has also risen to a prominent level. With this, the Utah Department of Workforce Services has made some organizational improvements to its economic webpages. Various economic data categories are not mutually exclusive, but we made an effort to compartmentalize economic data for a better organizational display and navigation. We also added a new feature area that taps into various national data elements and measurements from the Federal Reserve Economic Data (FRED), the database of the Federal Reserve Bank of St. Louis. FRED’s added value is national — and Utah — economic indicators. More on FRED’s contribution below.

Depending on the subject, economic data can be categorized as either broad or specific. For example, the demographic makeup of an area and how that impacts an economic structure is a broad-subject approach. Conversely, a current monthly snapshot of the Utah economy, its job growth and unemployment rate is a more specific observation. Our economic webpage has four “portals” through which to “categorize” and search for information. One portal is broad, while the other three are more specific in nature.

Topic Portals

The monthly employment profile just mentioned is a specific topic and gets its own “portal,” entitled Employment Update. Here, the most current Utah economic performance can be explored and summarized. The information found here is what often gets cited in the local news media in reference to the current Utah job performance and unemployment rate.

The second specific “portal” is labeled Local Insights. This is a quarterly profile of the Utah economy down to a county level. Each county is summarized with its own economic performance, including job growth, unemployment rate, housing starts, taxable sales and other profile variables. The common theme here is a county-specific approach.

The third specific “portal” is Reports and Analysis. Workforce Services’ economic forte is the labor market. Things over and above the everyday reporting on the labor market are presented here. Sometimes we do special economic studies, other times we will report on specific economic groups within the labor force, like women or veterans. Anything we do that is not an often repeated or ongoing report are grouped here.

The final “portal,” and possibly the one that will be most used, is labeled Economic Data. The core of our data collection and analysis is concentrated here. Employment data, occupational data, wage information and demographic profiles are just some of the major economic themes found in this area.

FRED's on site

As mentioned earlier, we have added an economic indicator area tapping into FRED, which is a massive compilation of economic data from various sources — primarily government statistical agencies, but also some nongovernmental organizations. Workforce Services economists have gone through the list and selected a handful of the most useful data series for gauging the performance of Utah’s macro economy and gaining insights into expected trends. Utah functions within the national economy, so the national economic indicators profiled here are intended to also be guiding influences on the Utah economy. These indicators include composite indexes; a recession probability indicator; leading indicators, such as construction permits and the yield curve; coincident indicators, such as real GDP and employment; and price indicators, such as the consumer price index, regional housing prices, and oil and gas prices. Each chart has a detailed description of what the data represent and how they may be useful.

Keeping relevant with the fast-changing pace of the Internet and data presentation is our goal at Workforce Services. We hope these changes help to better present our broad package of economic data offerings.

Monday, October 24, 2016

Show Me the Economy - New Occupational Projections for the Mountainland Region

Mark Knold, Supervising Economist

“The government knows everything about everyone.”

Fortunately, that statement is not true. Yet society still looks to the government to provide answers to comprehensive and complex questions that have their foundation within individual decisions and activities. One subject frequently directed toward the government is individual-level information about the economy — particularly, what occupations are in demand, what occupations pay well and have lucrative outlooks, and ultimately, what occupation(s) should I build my career upon?

It takes the accumulation of a wide array of individual information to answer these questions. Employers provide the foundation information about the occupations they employ. Jobs are held by individuals, but employers provide the profile information about the job itself, not any particular individual.

Since society desires to profile such a broad spectrum of the economy — occupational profiles and the occupational distribution within the economy — only government is in the unique position to collect, analyze and provide answers for said desire. Yet, no government program or regulatory agency mandates any comprehensive occupational reporting from individuals or businesses. Therefore, government attempts to fill the void with an ongoing, robust and voluntary survey of employers — a survey where employers are asked to provide details about their various occupations, including descriptions, quantities, wages/salaries and location. Through this survey emerges an occupational portrait of an economy.

The U.S. Bureau of Labor Statistics (BLS) structures and funds the survey, yet the individual states conduct the survey. Under BLS administration, all states use the same methodology; therefore, occupational profiles are comparable across states.

Through this survey, analysts discover how industries are populated with various occupations. Accountant is an occupation, yet accountants can be found across many different industries. Other occupations may be more exclusive to certain industries; for example, doctors are largely found only in the healthcare industry. One of the survey’s products is that industries can be profiled with their general mix of occupations. This is called an industry’s occupational staffing pattern.

This brings us back to the original questions: what occupations are in demand, what occupations pay well and have lucrative outlooks, and ultimately, what occupation(s) should I build my career upon?

The foundation is to make informed forecasts about how industries will expand/contract over the next 10 years. By applying existing occupational staffing patterns to each industry’s projected change, a trained economic analyst can then make an extrapolation about how occupations will correspondingly increase/decrease. Knowledgeable analyst judgment further refines the occupational expectations, such as knowing an occupation will grow faster than in the past, with the result being a set of occupational projections that accumulate to profile a state or regional economy.

A new set of occupational projections are done every two years to keep the information fresh even though economies do not change dramatically in short order. Because of slow change, updated occupational projects generally continue the overall message of preceding occupational projections. But economies do modify with time, and therefore, subtle changes will arise with each new set of occupational projections.

Utah’s most recent occupational projections are found here: These projections look forward to the year 2024.

The occupational profile is structured from the general to the detailed, mimicking the structure of a family tree. First, broad occupational categories are defined, such as management or healthcare occupations; then, subcategories are defined; and finally, individual occupations are defined. Individual occupations are the heart of the occupational projections. But overall patterns and characteristics do emerge when observing the broader categories.

While a Utah statewide profile leads the way, Utah’s local economies are not homogenous; therefore, nine Utah subregions are also profiled. Due to confidentiality restraints and statistical reliability, the amount of occupations available will diminish the smaller a subregion; but, occupations comprising the backbone of a regional economy will be available.

Mountainland Region
Cory Stahle, Regional Economist

Of the nine subregions referenced above, two encompass the Mountainland service area region: the Provo-Orem Metropolitan Statistical Area (MSA) — Utah and Juab counties combined, and the Wasatch Back area — Summit, Wasatch and Rich counties. The following are some general highlights from each area’s occupational projections:

Provo-Orem MSA
As mentioned above, individuals often look to government to understand the outlook for specific jobs. Through the occupational projections, outlook is measured in three ways: 1) job openings, 2) job growth and 3) wages.

Job openings are estimated based upon two major factors: replacements and growth. Replacements occur as individuals change occupations, retire or leave the labor force; while growth refers to labor market expansion and the addition of new jobs. The sum of these two factors result in total openings for each occupation or the economy as a whole. Current projections indicate that, on average each year between 2014 and 2024, occupations in the Provo-Orem metro area will generate 12,890 total annual openings.

In addition to openings, job growth rates are calculated for the total of all occupations and their subgroups. For the Provo-Orem area, total occupations are projected to grow at 3.8 percent annually through 2024, with computer related occupations expecting the highest growth at 6.3 percent.

These two statistics — job openings and job growth rate — measure different things. Annual openings are influenced by occupations with higher levels of employment and turnover, because these jobs require more replacements. This is illustrated in the Provo-Orem area as the occupations with the highest number of average annual openings are fast food workers, customer service representatives and retail salespersons. Conversely, annual growth does not account for employment size and turnover and can often overstate the outlook for smaller occupations.

However, when openings and growth are combined, the outlook becomes clearer. On the embedded visualization above, we limited annual growth rates to occupations with at least 75 annual openings and found interpreters, computer systems analysts and software developers to have the strongest employment outlook in the Provo-Orem MSA.

The final piece used to form the occupational outlook is wages. In order to understand the relationship between openings, growth and wages, the Department of Workforce Services publishes star ratings. Through a process of statistical weighting, occupations are assigned ratings from zero to five stars based on opportunity and compensation. As an example, a job with strong growth/openings and relatively high pay would be assigned the highest or five-star ranking. In contrast, a job with a limited employment outlook and low wages would be assigned zero stars.

In the current projections, 51 occupations were identified as five-star jobs in the Provo-Orem area. These jobs range from computer systems managers to registered nurses. The five-star occupation with the largest number of openings over the next 10 years is general and operations managers.

Wasatch Back Non-Metropolitan Area
Current projections indicate that, on average each year between 2014 and 2024, occupations in the Wasatch Back area will generate 2,070 openings and grow at 3.3 percent annually through 2024. When limiting annual growth rates to occupations with at least 75 annual openings, software developers, childcare workers and electricians had the strongest employment outlook.

As was evident in the Provo-Orem area, the occupations with the highest number of average annual openings in the Wasatch Back area were those with higher replacement rates. At the top of this list were waiters and waitresses, fast food workers and cashiers.

Additionally, 11 occupations were identified as five-star jobs in the Wasatch Back area, compared to 51 in the Provo-Orem MSA. This gap is primarily the result of a small base of occupations being assigned star ratings in the Wasatch Back area. In order to receive a star rating, occupations must meet a minimum level of employment. In the Provo-Orem MSA more than 350 occupations met this requirement, while the Wasatch Back area only disclosed about 90. Therefore, while other jobs may meet the criteria for a five-star occupation, they are suppressed to protect individual business confidentiality.

Whether you are a policy maker, business owner or individual looking to make decisions about a career or education, you can begin to see what a valuable informational tool occupational projections are. In addition to the embedded visualizations, please visit the Occupational Comparison Dashboard for more projections data.