Unemployment Insurance Claims Data Shed Light on the Local Economic Impacts of the COVID-19 Pandemic

By Lecia Parks Langston, Senior Economist; Michael Jeanfreau, Regional Economist

“You have power over your mind — not outside events. Realize this, and you will find strength.” Marcus Aurelius

In the wake of the COVID-19 pandemic, businesses lost revenues and workers lost jobs. But because of the time it takes to collect and collate data, economists have been left without much information to quantify the economic impacts at the local level.

But there is one ray of data illumination. Claims for unemployment benefits are promptly available and provide information about a large cross section of the economy. This post will outline what light unemployment claims data sheds on the state of Utah’s Bear River economy.

While not all workers are protected by unemployment insurance laws, roughly 95% of jobs are covered. This makes claims data an exceptional source of information about the economy. Not included under unemployment insurance laws are most self-employed workers, about half of agricultural employment, unpaid family workers, railroad personnel (covered separately) and many nonprofit organizations (such as churches). Also, some out-of-work employees may not have worked a sufficient work history to qualify for unemployment insurance benefits, but may file anyway. Fortunately, in this time of economic distress, the social safety nets of the unemployment insurance program, special national COVID-19 funding and social programs are working together to keep workers’ income and well-being stable.

Unemployment claimants and the unemployed; they aren’t the same

Also, keep in mind that, in addition to individuals drawing unemployment benefits, the unemployment rate includes those entering and re-entering the workforce and noncovered groups without current employment. This means the number of “unemployed” will be greater than the number of claimants. In “normal” times, only about 40% of the “unemployed” are claiming benefits. The generally reported unemployment rate also has a work-search requirement. If you haven’t made any minimal attempts to find work, you aren’t counted as “unemployed.”

Watch this Space

While this analysis won’t be updated on a regular basis, new data will be added to the data visualization on a weekly basis allowing readers to check back for the latest information.

An Unprecedented Event

Not surprisingly, first-time claims for unemployment benefits have soared in Utah and across the nation as the pandemic swept across the country. This increase is unprecedented since the creation of unemployment insurance coverage during the Great Depression. Week 12 (beginning March 16) marks the start of this unparalleled surge in claims. On a positive note, while new claims for unemployment benefits have skyrocketed in Utah, the state currently shows one of the lowest claims rates in the nation.

In Bear River, total claims peaked at week 15 (starting April 6), slightly after the state average of at or before week 14 (starting March 23). During the peak week 15, initial claims filed totaled 1,183 in Bear River. By week 19, claims measured considerably lower but continued to run substantially greater than in previous years — even during the “Great Recession.”

Here’s another example of the tremendous flood of new claims. Prior to the COVID-19 pandemic, counties in Bear River averaged a total of 49 first-time claims per week. This time period included seasonally high claims weeks in January. In the weeks after, an average of 738 claims were filed for an almost unbelievable increase of 1,506%.

Who took the hardest hit?

Across the state, there was an initial spike in food service, retail and healthcare/social assistance filing initial claims in the weeks immediately following the start of the pandemic in Utah. The Bear River region was affected similarly until week 15, which saw a sharp increase in the number of claims from the manufacturing industry. For weeks 12 through 14, about 8% of initial claims were from manufacturing. On week 15, that percentage increased to roughly 45% of initial claims as companies reacted to the national and international effects of COVID-19 on consumer demand and supply chain management and has remained high in comparison to other industries in the weeks following.

Manufacturing and COVID-19

Rich County and Cache County were both hit less heavily in comparison to the state — receiving first-time claims from 5% of covered employment in their areas compared to the state total of 10%. Box Elder County is recorded as having first-time claims from 12% of their covered employment.

The unemployment insurance system was first put in place in 1935 with the Social Security Act during the Great Depression and was designed largely around production and manufacturing jobs. In the 85 years since, the labor force has changed significantly and the prevalence of the service industry (food/retail) has increased. The early effects of COVID-19 largely impacted these service sectors while in the most recent weeks, the region has seen the large numbers of claims coming from the manufacturing industry. The numbers were enough to make manufacturing the regions overall largest contributor to unemployment benefit claims. This is indicative of both Bear River’s heavy concentration in the manufacturing industry as well as the expanding effects of pandemic slowdown across more industries over time.

The Industry Flow

While most of the high-claim industries felt the pain of the pandemic early on, other industries surged in later weeks. As the economic effects of other closures worked their way through the economy, both manufacturing and transportation/warehousing proved relative latecomers to the layoffs in the Bear River region.

The High and the Low

Although manufacturing is the dominant industry in the Bear River region and has generated the largest number of initial claims during the COVID-19 pandemic, in percentage terms, other industries have actually suffered more. For example, in the small real estate and rental and leasing industry, roughly 20% of workers have filed for claims. The Other Industries sector, which comprises mainly of auto work and personal beauty services, had a first-time claims rate of 13%. Accommodation and food services also has a higher first-time claims (11%) rate than manufacturing, despite manufacturing having more than twice as many claims total.

Because of its job-to-job nature, the construction industry typically accounts for 20-30% of first-time claims. However, although construction’s new claims have also increased, they have increased at a much slower-than-average rate. After the start of the COVID-19 pandemic, construction contributed only about 4% of first-time claims. Ease of social-distancing and good weather have helped construction maintain employment levels. New claims measured just 4% of covered construction employment.

Only a portion of agricultural employment is covered by unemployment insurance laws. However, as companies work to keep America fed, agribusiness has laid off few employees. In the Bear River region, covered agriculture plays a notable role in the economy. However, less than 1% of Bear River’s covered agricultural workers have filed a claim during the pandemic.

Public administration, educational services (including public and higher education), finance/insurance, professional and scientific services, and utilities have also managed to keep a higher percentage of their workforces employed.

County by County

Box Elder County

• Prior to the pandemic slowdown, Box Elder County averaged 18 unemployment claims per week compared to 330 new claims afterward, an increase of 1,688%.
• Because of its large share of employment in manufacturing, the worst effects of COVID-19 were delayed from the initial effect on food accommodation, retail trade and nonessential healthcare. As a result, the peak of initial claims was on week 15, with 607 initial claims.
• New claims, as a percent of covered employment, measured at 12% — higher than the state average and reflective of the region’s industrial strengths.
• While manufacturing had the highest total initial claims at 1,213, it did not have the highest percent of covered employment submitting initial claims. Real estate and rental services, personal care services, accommodation and food services, and information all had higher initial claims as a percent of covered employment.
• Box Elder County accounted for 40% of the Bear River Region’s new claims prior to the pandemic. For weeks 12 through 14, it dropped to around 35%, and then rose as manufacturing was impacted on week 15 and has rested about 50% since. Overall, manufacturing accounts for 46% of all claims in Box Elder County.

Cache County

• Cache County shares a regional specialization in manufacturing but has not been as affected as sharply in the sector as Box Elder. Cache County saw 14% of total initial claims compared to Box Elder’s 46%. Across all industries, only 5% of total covered employment in Cache County has filed initial claims, half of the state of Utah’s average of 10%.
• Prior to the COVID-19 slowdown, Cache County averaged 30 first-time claims per week, compared to an average of 404 claims per week afterwards. This change represents an increase of 1,243%.
• Although all industries have been affected by COVID-19, no single industry was overwhelmingly represented in initial unemployment benefit claims. Manufacturing and food services were both 14% of total initial claims in the county, followed by retail trade (12%), health care and social assistance (11%) and administrative support (7%).
• 11% of total claims are from unknown industries, which will largely reflect the distribution of known industries.
• The high unemployment claims from manufacturing across the Bear River Region has actually lowered Cache’s share of first-time claims after the COVID-19 slowdown from 61% before quarantine procedures to 55% after.

Rich County

• In the weeks before business reacted to the pandemic, Rich County averaged one initial claim per week. After the pandemic hit, an average of four claims were filed per week, marking an increase of 368%.
• In Rich County, first-time claims in the restricted period measured 5% of covered employment. That places Rich County in the bottom half of a county-by-county ranking. Only 34 claims were filed in total.
• As in many counties, Rich County’s accommodations/food service industry accounted for the highest number of new claims after the COVID-19 slowdown, but was tied with real estate and rental and leasing, both accounting for 21% of claims total.
• Public administration, construction, and health care and social services followed, each have three or less claims.
• First-time claims from Rich County have gone from 2% of the Bear River Regional total before the COVID-19 slowdown down to 1% or less in the weeks following.

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 March 26th.

Utah's Employment Situation for January 2018

Utah's Employment Situation for January 2018 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 March 23rd, 2018.

Ten Years Later: Differing patters of recession and recovery in Bear River

December 2017 marked 10 years since the Great Recession first cast its long shadow across the American economy. The recession officially lasted 18 months, but its consequences can still be seen across the country without having to look very hard. We have not had another recession since.

Utah was hit hard at the time, losing a larger share of jobs than the national average; but, we were fortunate to be one of the most resilient states in terms of economic rebound. There are plenty of states where the Great Recession continues to weigh upon them. Employment levels in 14 states are still not back to their pre-recession peak, and another 29 states have only grown 5.0 percent or less. As the working-age population has grown by more than 5.0 percent, the job gains nationally have not been enough to fully employ working-age labor.

Utah lost 7.0 percent employment during the recession. Since that low, employment has recovered by 18 percent. That is the second best rebound in the nation. From Utah’s pre-recession employment peak to now, Utah’s employment has increased by 9.5 percent, third best in the nation. Yet, Utah’s job growth has not been enough to absorb all of the labor force growth during that time. Utah’s unemployment rate is low, but the percent of the working-age population in the labor force is several percentage points below the pre-recession norm — telling us that potential labor is still not as fully engaged with the job market as before the recession.

As a whole, Utah has had a notable recession rebound, but those gains have not been shared equally across all regions. Just like the national profile, some areas have bounced back strong while others are still lagging behind. The state’s metropolitan areas have grown well, but many of Utah’s rural areas cannot say the same. Nine counties have employment levels below their pre-recession peaks.

In this issue of Local Insights, we profile Utah’s regional and county economies in light of the 10-year span since the Great Recession.

Bear River Region

Although the three northernmost counties in Utah (Cache, Box Elder and Rich) are often viewed as a single region, the reality is they each have distinct economies and experienced differing recessionary impacts and recoveries. For instance, Box Elder County is reliant on the manufacturing sector, which was hit hard during the recession. Rich County is highly subject to swings in tourism spending, which is among the first things consumers pull back on when money is tight. Cache County, on the other hand, has a more diverse economy and recession-resilient industries (like education) that help to mitigate the severity of economic downturns.

Box Elder County

Just prior to the recession in 2008, the manufacturing sector accounted for nearly 40 percent of Box Elder County’s total employment — or about 8,100 workers. By 2012, that number had been slashed to around 4,500. Layoffs at ATK Launch Systems and the La-Z-Boy closure slashed deep into the region’s economic tissue. Although the initial ATK cuts were the end of NASA’s shuttle program and not a direct result of recessionary pressures, the recession did put budgetary constraints on the federal government leading to less money for NASA and other types of contracts that might have kept ATK at higher employment levels. In addition, the recession meant virtually no other jobs were available and many people left the area. Net migration turned outward with about 500 leaving each year in 2011 and 2012.

Box Elder is only now getting back to its pre-recession employment level of about 21,000. The good news is that since it turned the corner in 2013, growth has been robust — averaging more than 5.0 percent annually. Manufacturing is still the primary driver of new growth; but within manufacturing, the products being produced are more diverse. Motor vehicle parts, food and fabricated metals now make up larger shares of manufacturing employment.

In addition to manufacturing’s resurgence and diversification, other industries have begun to rise in economic importance adding to the economy’s overall diversity and future recessionary resilience. For example, health care services did not even dip during the recession and has been on the rise since. Prior to the recession, heath care was about 6.0 percent of employment. Now it is up to 9.0 percent.

Rich County

Rich County lost about 150 jobs during the last recession — nearly 27 percent of its average employment. Only Piute County lost a larger employment share. Tourism is the key to Rich County’s economy; and during recessions, travel and recreation are among the first luxuries cut to pinch pennies. Traveler accommodations alone shed more than half of its employment between 2007 and 2012 (from 90 to nearly 40).

Construction took a significant hit as well, shedding about 90 jobs over the same period. Most of the losses were in residential construction with fewer vacation and retirement homes being built. In 2007, there were 43 single-family homes permitted in Rich County. By 2012, that number had dropped to just four.

Rich County’s recovery since late 2012 does not appear to be driven by tourism spending. Some moderate growth in restaurants indicates improvement in visitor spending, but the accommodations industry has basically plateaued since the recession’s end. New single-family home construction is again on the rise but has yet to return to pre-recession levels. Most of the recovery growth is local business services, such as repair and maintenance, and building services. Retail sales employment in lawn and garden, and building materials is also a significant contributor to the recovery — indicating that local demand is currently keeping Rich County on its growth trajectory. This is encouraging as it suggests a shift away from tourism dependence and leaves room for even more growth once travel spending resumes.

Cache County

Cache County did not contract nearly to the extent of Utah’s other counties during the recession. It lost less than 800 jobs and contracted only 1.6 percent. The downturn was short-lived too. The economy started expanding again in early 2010, and regained its pre-recession employment by mid-2012 when Box Elder and Rich counties were just bottoming out.

In Cache County there is no single sector that dominates the local economy the way manufacturing does in Box Elder County. Manufacturing is Cache County’s largest sector and comprises 19 percent of total employment. And, most of Cache County’s manufacturing employment is in food manufacturing (especially dairy and beef), which is highly recession-resilient. During recessions demand falls for many nonessential goods and services; but core food products, such as milk and meat, tend to hold steady. In fact, during the recession, employment in both dairy and beef manufacturing actually grew and helped prop up Cache’s economy.

Utah State University provided a similar stabilizing effect. The university is the region’s core economic engine, accounting for about 12 percent of employment. Like food manufacturing, education services are not prone to major recessionary setbacks. Historically, enrollment actually increases when unemployment is high as more people who find themselves idle choose to invest in education.

Cache County’s recovery has been steady and shared across most industrial sectors. Growth at the university and the human and intellectual capital it produces boost the vibrancy of other industries, as well. Professional, scientific and technical services have been expanding employment; as have medical services and even manufacturing industries, like chemical and computer and electronic products. Such industry diversification will further strengthen Cache County and its ability to weather the next recessionary downturn.

Economic Hurdles in Rural Utah

by Mark Knold

Utah is a geographically large state. Based on total area, it is the 13^th 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 9^th 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.

The New Retail Workforce:How Online Sales are Changing Retail Jobs in Northern Utah.

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 a large share is.

Broad-category examples of retail trade sectors are motor vehicle sales, furniture stores, electronic 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 on the Internet. Examples include Amazon, Zappos, Overstock.com, or eBay. These types of retailers have grown rapidly in the past 15 years and their presence is reshaping the retail trade landscape.

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 Bear River region:

Retail Matters in the Bear River Region

The retail trade industry is an important economic driver in Bear River. It employs more than 8,000 people in the region —almost 11 percent of total nonfarm employment. In Box Elder County, retail trade is the third largest industry, behind only manufacturing and health care. In Cache County, it is the fourth largest. Retail sales account for about 51 percent of total taxable sales in the region — very similar to the 52 percent statewide average.

The Rise of Online Retail

Due to consumers making more and more purchases online, the demand for brick-and-mortar retail workers in the region has been softening. Overall, employment in Bear River has been growing at a rate of about 2.4 percent on average since the recovery noticeably began in 2012; but in traditional retail, employment growth has been averaging just 1.4 percent. In the five years prior to the recession (and before online retail really took off), traditional retail employment was clipping along at a much quicker average growth rate of 2.6 percent growth.

Non-store retail, on the other hand, has been booming in the region. With the growth of online retailers, like Malouf Linens and Jensen Online Books in Cache County, employment in non-store retail has grown an average of 16 percent annually since 2012. The share of total employment represented by non-store retail has increased 105 percent over that time. The next highest employment share expansion in retail was in the motor vehicles category, which increased its share by a paltry 19 percent in comparison. Most other retail categories saw a decline in their share of total employment.

Non-store Taxable Sales Are Also Gaining, But Not as Fast as Employment. Why?

Taxable sales in non-store retail have not gained as a share of total taxable sales as quickly as the employment share. This is primarily because 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 Malouf sells a pillow 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.

An Aging Retail Workforce

Interestingly, the jobs in retail are not primarily younger workers as one might expect. In fact, about 70 percent of the region’s retail jobs are people 25 and older, and approximately 45 percent are at least 35. There used to be more young workers in the industry. Prior to the recession in 2007, the share of 35 and older retail workers in the Bear River Region was only 38 percent.

During the Great Recession, the share of teenagers working in retail plummeted from 9 percent to 4.5 percent and has remained low ever since. The reduced youth base means there are fewer workers who stay on and age into the older categories.

A Less Educated Retail Workforce

At the same time, the share of retail workers with less than a high school education has increased significantly. This has been primarily at the expense of individuals for whom educational attainment data are not available (i.e., workers under the age of 24 — mostly students). Since 2007, the share of workers with less than a high school education in Utah retail has increased by more than 25 percent.

This does not appear to be an actual increase in less educated workers. Rather, the drop in workers under 24-years-old is causing a share increase for the existing less educated workers. As a result, the retail workforce in Bear River (and in Utah in general) is trending toward an older and less educated demographic.

What is Driving This Trend?

This is likely the result of young people choosing to take jobs in other industries with better pay, as wages in retail have lagged. Or they may be opting to finance their education rather than work while attending school. But some portion of this shift is also being driven by the structural changes taking place in retail due to increasing online sales.

The Occupational Shift

The transition to non-store retail translates to shifting demand for a different set of occupations required by non-store retail operations. Traditional brick-and-mortar retail stores primarily need people to work on the sales floor, such as retail sales workers and cashiers. Those two occupations alone represent about 45 percent of all employees in traditional retail. In non-store retail, on the other hand, the top two occupations are customer service reps and shipping/receiving clerks. Freight and inventory movers, order clerks/fillers, and truck drivers all play a much more prominent role in non-store retail as well.

Generally speaking, these kinds of jobs tend to require more time commitment than the most demanded traditional retail jobs. According to the Conference Board’s Help Wanted Online® product (analyzes online job postings), about 40 percent of job openings for cashiers and retail sales workers (the top jobs for traditional retail) posted in Utah in the second quarter of 2017 were part-time jobs. Only 20 percent of job postings for customer service reps and shipping/receiving clerks (the top jobs for non-store retail) were part-time. Positions that require more time commitment and more fixed schedules are likely to be less attractive to young people — especially students — who may be looking for opportunities that are less time consuming.

The Geographic Shift

In addition, there is a geographic component to this transition. Traditional retailers tend to have many more locations spread out geographically, making them more likely to have that cover a broader footprint within the labor force. Online retailers, however, are generally centralized in large warehouses, distribution centers, and office buildings that runs counter to the disperse spread of traditional brick-and-mortar. As a result, it may be harder for workers — especially younger workers — to get to and from these jobs.

What It All Means

These structural changes are having a profound effect on the retail workforce, and we can reasonably expect the resulting trends to continue for some time. As new technologies and retail processes emerge, there will doubtless be more shifts in this rapidly evolving sector. But for now, in the Bear River region, we can expect fewer traditional brick-and-mortar retail jobs, more non-store retail jobs, and an increasing share of retail employment opportunities that may be challenging for our young population to access.

Check Out the Viz

If you are interested in the details, the data visualization below breaks out the various retail categories and allows you to compare sales (as a share of total taxable sales) and employment (as a share of total nonfarm employment) in each category (by county) over time. The relative changes in taxable sales compared to employment are telling in relation to some of these structural changes, although direct links are difficult to establish as there are many other confounding factors. The tables at the bottom give the actual sales and employment levels, summed-up for whatever you have selected in the county and retail category filters.

Census Bureau Tool Provides Labor-Force Insight for Bear River

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.

Various data “tabs” are available, presenting Utah’s economy from different angles, ranging from industry shares within the economy to the age-group distributions of the labor force, to gender and race distributions. These labor variables can be viewed for the state as a whole, or by each individual county.

Some statewide highlights:

Industry — industrial distribution is quite diverse, which provides strength within the economy. Distributions do fluctuate with time, with manufacturing seeing its share lessen while health care and professional and business services shares have increased.

Age — the bulk of Utah’s labor force is composed of 25- to 44-year-olds. Older worker shares have increased over the past 15 years, yet still remain a non-dominant portion of Utah’s labor force. The youngest segments of the labor force declined noticeably during the Great Recession due to less participation, and that trend remains.

Educational Attainment — turnover rates are understandably highest with workers under the age of 25 as they strive to build their educational foundation and also find their niche in the labor market. A trend does stand out where the more education that a worker attains, the lower the turnover rate businesses experience from said educational classes.

Race/Ethnicity — Whites account for around 80 percent of Utah’s labor force. The Asian community is small but slowly increasing in share, and is also characterized with the lowest turnover rate and the highest new-hire wages.

Gender — males comprise about 55 percent of Utah’s labor force. The female share of 45 percent is higher than the national average. Roughly 35 percent of working females work part-time compared to 15 percent for males. Therefore, female new-hire wages are considerably lower than male new-hire wages. (Note: employer reporting into the unemployment insurance system is not hourly wage rate reporting but instead total calendar quarter wages paid. Therefore, calculations can only be made upon total quarterly wages, and part-time employment weakens this measure).

As for the various counties in the Bear River region, here are some labor highlights:

Cache County – 

People are often surprised to learn that manufacturing is the largest sector in Cache County. With Utah State University in Logan, it is often assumed that education would dominate, but in fact manufacturing composes about 21 percent of employment while education is about 13 percent. Food product manufacturing is the largest sub-industry with nearly 4,000 employees represented by companies like E.A. Miller, Gossner, and Schreiber.

Health care/social services is the only sector that has substantially increased its share of employment over the last 15 years (the bump in manufacturing in 2006 was due to a classification change from professional/business services). Since 2000, the share of employment in health care/social services has almost doubled from around 6 percent to nearly 11 percent – that’s an increase of 2,300 jobs. Growth in this sector has been shared across ambulatory health care as well as hospitals, but the fastest growth has been in residential intellectual and developmental disability facilities.

Box Elder County – 

Manufacturing is an even bigger driver of the economy in Box Elder County than it is in Cache County. Despite an employment share that has declined from 46 percent to 29 percent over the last 15 years, it is still by far the most dominant sector. Over the years, job losses at ATK, and La-Z-Boy among others, have resulted in the declining share of manufacturing jobs; but overall, manufacturing employment has been rebounding since the end of the recession and is back up to nearly 6,000. Autoliv, Thiokol, West Liberty Foods, and Nucor Steel are just few examples of major manufacturing employers in Box Elder County.

Like the rest of Northern Utah, the health care/social services sector in Box Elder has seen notable gains in its employment share over the last 15 years. Individual and family social assistance services in particular have grown quickly — more than 100 new jobs just in the last four years.

Rich County – 

Not surprisingly, leisure/hospitality services is the dominant sector in Rich County with about 26 percent of employment, which has remained relatively consistent across the last 15 years. In that same time, the education sector has declined from around 20 percent to around 14 percent. In fact, the actual level of employment in education (which is primarily public schools in Rich County) has fallen from about 120 in 2000 to about 100 in 2015. Meanwhile, public administration has gained about 20 jobs over the same period, so its share has increased to nearly 10 percent.

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

Go to: JOBS.UTAH.GOV/WI to check it out

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.

Show Me the Economy - Occupational Projections for Cache County, Utah

The biennial update to Utah's occupational projections have been released and can be found here: http://www.jobs.utah.gov/wi/pubs/outlooks/state/index.html.  But first. check out these highlights:

Cache County Highlights

Matt Schroeder, Regional Economist

The projected occupational growth rate in Cache County is slightly below the rest of the state on average at 2.3 percent annually through 2024. Utah statewide projected growth is 2.7 percent. The 2,530 projected annual openings in Cache County from 2014 to 2024 represent about 4 percent of all projected openings in the state.

The occupations with the highest growth expectations are, on average, those that require the most education. Jobs that typically require a doctoral or professional degree are projected to grow 2.7 percent annually through 2024. Growth in openings for physicians, physical therapists and psychologists are driving this trend.

Expectations for healthcare practitioners and healthcare support occupations in Cache County are worth highlighting. The healthcare industry is supplying large numbers of annual openings and is expected to grow at more than 3 percent every year over the next eight years. Registered nurses have the strongest demand outlook among healthcare jobs, with expected growth of 3.7 percent or about 40 openings a year, and they earn median wages of nearly $58K per year. Nursing, medical and dental assistants are also expected to be in demand with about 60 openings per year combined. These jobs offer median wages between $22K and $28K per year, but require less education.

After registered nurses, perhaps the most noteworthy occupations in terms of expected demand and wages are applications software developers, mechanical engineers and accountants/auditors. Jobs such as these in the areas of business/finance, engineering and information technology, tend to offer high wages for the level of education required and consistently exhibit a strong growth outlook. Application software engineers are projected to grow by about 3.2 percent or 20 openings per year in Cache County. They typically require a bachelor’s degree and earn median wages of $69K per year. Similarly, accountants and auditors are projected to grow by about 3.2 percent or 20 openings annually and earn median wages of $55K per year.

There are many other occupations in the region that are projected to offer excellent opportunities as well — electrical engineers, mechanical engineers, engineering techs, cement masons and concrete finishers, environmental science techs, and management analysts just to name a few. You can learn more about these occupations and others through the Utah Occupational Explorer where you can explore and compare occupations of interest in detail by region, wage level, typical education required, projected growth, and demand. Before digging into the details though, take a look at the interactive data visualization above to see the big picture of the occupational outlook for Cache County.

About Utah's Occupational Projections

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: http://www.jobs.utah.gov/wi/pubs/outlooks/state/index.html. 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.