State Employment Recovery since the Pandemic

Total employment in the U.S. has recovered nicely over the last four years, since the pandemic shutdown back in 2020. But, as is always the case, not all the states have enjoyed a similar experience. Before reviewing the states' employment performance let's take a look at the overall U.S. employment situation.

U.S. total employment stood at 158.1 million jobs in March of this year, almost 6.5 million jobs above the pre-pandemic level of 152.0 million. Employment shrunk by 21.6 million jobs between February and May 2020, the result of the government's draconian reaction to the pandemic. But two years later, by June 2022, employment reached 152.3 million jobs, slightly higher than the pre-pandemic high reached in February 2020. This is illustrated in the chart below.

Employment growth over the last four years can be split into two periods. The first one is from April 2020 to June 2022, when employment rose from 130.4 million to 152.3 million. The 21.6 million increase in the number of jobs brought the total employment level to what it was right before the pandemic. That is, the 21.6 million jobs were not "new" jobs, but rather "recovered" jobs from the pandemic collapse.

The second period is from June 2022 to March 2024, when the number of jobs rose from 152.3 million to their current level. That is a  6.49 million (3.8%) gain, truly new jobs.

Furthermore, a majority of the recent new jobs are not full-time. Nearly six of ten new jobs (58%) created in the last year were part-time. Also, unlike prior history when the number of part-time jobs fell during economic expansions, we have seen them increasing in the current recovery.

All the states are not equal

Despite the recovery and strength of national employment, we find different responses at the state level. That is, there are several states which are still lagging in their employment recovery. At the same time there are many which already exceed their  pre-pandemic levels.

The chart below displays fifteen states with employment below their pre-pandemic peak. Their current employment is still lower than that as of February 2020, or earlier.  

Leading with the biggest gap is North Dakota where latest employment of nearly 440 thousand is 6.2% below its maximum level, reached back in 2014 at the height of the state's fracking boom. Also this state's latest employment is lower than its level in February 2020.

The next state is Washington DC (considered a state for most geographical analysis) with employment still 4.4% below its pre-pandemic level. DC is followed by Hawaii lagging 3.9% behind its employment in February 2020. Both of these two states are characterized by high employment concentration in one sector. 

Nearly twenty percent of the jobs in Hawaii originate from the Leisure & Hospitality sectors which, as we know, have been hit the hardest since the pandemic.

Employment in DC is heavily concentrated in the Government sector and the city has had a persistently high unemployment rate. At least for the last two decades, the unemployment rate in DC has exceeded the national rate by more than one percent.

West Virginia and Wyoming lag their previous employment peak by similar amounts. Current employment in West Virginia is 2.8% lower than its peak employment back in 2013, and Wyoming is 2.7% below the maximum employment it had reached back around 2009, in the middle of the Great Recession. 

Employment in Louisiana is still 2.5% lower than in February 2020. Further, employment recovery in the state has stalled over the last twelve months. 

Connecticut is an unusual place. The latest employment actually exceeds its level right before the pandemic, by a minimal amount (just 8,400 jobs out of 1.7 million). But the state's employment has never recovered to its level before the 2008-2009 recession, more than fifteen years ago. 

Changes in Consumer Spending

 It is well known that the Covid pandemic, and the government's response which resulted in the also well known disastrous aftermath, altered consumer spending patterns. There was a sharp drop in consumer spending between January and April 2020, with overall spending falling from an annual rate of $14.1 trillion in January to $11.8 trillion three months later. That is, consumption dropped 16.9% in that short period. 

This is a sharp contrast to historical spending which averages an annual increase of 3.1%. Of course, there are several recessionary periods when spending also fell, but never as much. The worst case was in the middle of the 1930s Great Depression, when consumer spending fell 9% in 1932, still much less than the mentioned 16.9% drop during the pandemic.

Consumer spending fell for most goods and services categories during the January to April 2020 period. For several of these categories, as can be seen in the chart to the right, spending dropped more than 40% in those short four months. 

The largest declines are in services, or in goods that require consumers going out of their house and typically having contact with other humans. The fear of Covid infections drove many consumers to avoid contact, coupled with strict "stay at home" mandates from the Federal and many state governments. These forced practices drove sales for many product and service categories down.

Food Services and Lodging is the service category with the biggest drop in consumer spending. It can be seen in the chart above that it fell by 52%. Food Services includes spending at restaurants, bars and similar establishments. The Lodging category includes hotels, motels and similar types of temporary residence.

Other "service" categories with substantial drops in spending are Recreation Services, down 45%, and Transportation Services falling by 44% in that short period. Both of them imply continuous contact with other people, that the government attempted to reduce by setting mandatory distances to be maintained by business establishments. Who does not remember the "six-foot rule" established by many organizations, with markings on the floor indicating where one needed to stand n line? At the end, the basis for this rule proved to be arbitrary and totally ineffective.

Scanning down the chart we can see that the likelihood of human contact is reduced the further down the category is. At the bottom there are two categories, the green bars, where spending actually increased. Housing & Utilities rose 2% over this period. Spending here is either necessary or fixed, such as utilities, or rental costs which we know rose during this period. 

The largest gain is labeled Food and Drink, which rose 5% over those four months. This category refers to food and beverages that are purchased to be consumed off-premises. That is, the likelihood of contact with others is minimized.

Are Consumer Spending Changes Permanent?

A natural question is whether some of these changes are temporary or do they reflect permanent changes in consumer spending. Of course the question can only be answered after some time has elapsed, for now we can only speculate.

The chart below shows annual changes in consumer spending over several periods, with green bars denoting "Goods" and light blue represent "Services." Note that the scales used are different in all four charts, thus a visual comparison is deceiving. We must look at the percentage in each of the bars. Also, all the percentages are stated as average annual rates, even in the chart displaying change between January and April 2020, the third chart from the left..

The two left-most charts show that growth in spending for Goods accelerated since the pandemic, while for most Services is either comparable to pre-pandemic changes, or slightly higher. Comparing the pre-pandemic to the after period, that is from January 2007 to January 2020 and  from January 2020 to December 2023, we can see that the growth rate for many goods is faster in the second period.

Spending for Recreational Goods & Vehicles had increased 12% annually between 2007 and 2020, but jumped to 17% since 2020. Spending for Household Goods, e.g. furniture or appliances, rose from 4% to 5%, and spending for the Other Durable Goods category jumped to 8% since the pandemic, from 4% annual growth previously. This category includes items such as personal jewelry, luggage, etc.

The fifth bar, which shows spending for Other Nondurable Goods, increased from 2% annually pre-pandemic, to 6% since January 2020. Here are included items such as paper plates and cups, disposable diapers, etc. 

Further down, we can see Clothing & Footwear changes in spending that also rose from 2% to 6%, and Motor Vehicles & Parts increasing from 2% to 4%. In contrast, the annual pace of spending all types of services has slowed down. Health Care has dropped from 3% to 2%, Recreation Services moved from 2% to 1%, and Transportation Services as well as Other Services have also dropped from 2% in the thirteen years before the pandemic, to 1% since then. 

Long Term Consumers are Spending More on Services

On a long-term basis the biggest change is the well-known shift from purchasing goods towards services. Over nearly a hundred years, that is since 1929, consumers' spending for Services rose from 43.4% of total spending to last year's 66.6%. A shift of 23.2% towards services.

Spending for services had actually reached an all-time high in 2019, when it accounted for 68.6% of all consumer spending, two percentage points more than last year. The Covid pandemic drove spending for many types of services down although there is a modest recovery underway.

The biggest contributor to the increase in services over the last hundred years is Health Care, which rose from just under three percent in 1929,  to last year's 16.1%. That is, one of every six dollars that consumers spend goes for health care services.

The two major categories within Health Care gained significant share since 1929, they are shown in the chart nearby. One of them, Hospital & Nursing Home services, rose from under one percent (0.7%) of consumer spending in 1929, to last year's 8.6%. The other category, Outpatient services, rose from 2.2% to 7.5% in 2023. 

Other categories with substantial increases are Financial & Insurance Services, which captured 3.8% of consumer spending in 1929 and rose to 7.1% last year- a gain of 3.4% over the full period. Consumers are spending $1.32 trillion for these services today.

The second largest category is an estimated one, that is the so-called Imputed Rental Value of Owner-Occupied Housing. The government economists come up with a dollar value of what consumers who are homeowners would be spending, if they were renting their house rather than owning it. 

This spending area was estimated at $2.17 trillion last year, accounting for 11.7% of total consumer spending. It has increased by 4.1% share points since 1929. (In a future article I want to investigate consumer spending and growth if I eliminate this "imputed" spending from the total.)

   

...and Spending Less on Goods

The converse of consumers spending more on Services is that they spend less on Goods naturally. Thus their spending for all types of goods fell from 56.6% back in 1929, to 33.5% last year. That is a loss of 23.2% over this long period. 

The long-term drop in the goods share, which fell from 56.6% in 1929 to 33.5% last year,  is driven by nondurables share losses. They fell from 43.9% in 1929 to 21.5% last year. Durables barely moved less than a percent from 12.7% to 11.9%.

The vast majority of the drop in spending for goods is centered consumer purchases of two major categories of nondurable goods, shown in the right panel above. One is spending for Food & Beverages consumers purchase for consumptions "off premises." The share of spending for this category has fallen by 13.% since 1929. It represented 21% of consumer spending in that year but fell to 7.8% last year. 

The second category is spending for Clothing & Footwear which had a 11.7% share of spending in 1929, only to drop to just 2.8% last year. This is an 8.9% decline over those 97 years.

The spending data comprehends roughly 100 different categories. The share of spending of he vast majority of them, 78 goods or services groups, changed less than 2% over 1929 to 2023 period. But two important areas where consumer spending pattern has changed little over the last 97 years are Motor Vehicles and for Housing.

Share of Spending on Motor Vehicles

The automobile industry has seen dramatic changes over the last century. Cars have more features, are more efficient, households have more than one vehicle, etc. But despite all these changes consumers are allocating  virtually the same proportion of their spending on vehicles today as they did back in 1929. 

Back then, as can be seen in the chart below, consumers spent 5.3% of their 1929 budget on motor vehicles (including spending for the vehicle itself, plus service parts and maintenance). Fast forward 97 years and we find that spending increased just 0.7% to 6.0% last year. The more things change the more things stay the same.

Note, in the chart above, the relative stability of spending share in the second half of last century, from 1950 to 2000. That is the period marked by the horizontal red line in the chart. In this period share of spending averaged 7.8%, almost two percent higher than today. But it moved within a narrow range from a low of 6.7% to a high of 8.8%. 

Spending on this group,  that is Vehicles, Parts & Maintenance, falls in almost every recession. But the largest declines are during the Second World War, the are highlighted in light red, and between 2000 and the Great Recession of 2008-2009.

But the 0.7% increase in overall spending for vehicles since 1929 is the result of an 0.8% increase in Vehicle Services, including maintenance, repair and other services. Its share was 1.0% in 1929 but rose to 1.8% last year.

 Parts and Accessories share of spending has remained virtually unchanged over time, falling by only 0.1%. But more interesting is the fact that spending for both New & Used Motor Vehicles was the same in 2023 as it was back in 1929- 3.5% share. This is the result of consumers increasing their purchases of used vehicles by 1.3%, while dropping their share of new vehicles by an identical 1.3%.

Consumer Spending for Housing

Similar to the case of motor vehicles, consumers' spending to meet their housing needs has changed very little over nearly a hundred years. The share of consumer spending for Housing & Utilities increased by just 1.1% since 1929. Consumers were allocating 16.6% of their 1929 spending to Housing & Utilities, which increased to 17.7% last year.

The chart below shows that, except for the sharp swings in spending between the Great Depression and the end of the Second World War, spending has been relatively flat. Big changes in household and family structure have had little impact on the amount of spending to meet housing needs. 

Since 1929, when the typical household consisted of 4.3 persons, the average size has fallen to about 2.5 persons per family. Although there are no reliable statistics on the number of single person households in 1929, we can assume there were very few since by 1950 single households were less than 5% of the total. That in 1929 fewer than one in 25-30 households were single-person ones, last year one in five households were single.

Despite these radical changes in the composition of households and families, consumers are allocating to housing roughy the same amount they did nearly one hundred years ago.

"If things are to remain as they are, everything has to change.", Il Gattopardo, Giuseppe Tomasi di Lampedusa

©2024 ManuelDJGutierrez, LLC

     

New houses continue to get smaller

The trend of building smaller houses continued further last year. The median size of a new single house started in 2023 was 2,179 sq. ft., down 90 sq. ft. from the prior year. Recall that the median is the figure indicating that half of the homes are smaller, and conversely. The average, i.e. mean, house size also fell to 2,411 sq. ft., that is 74 sq. ft. , or 3.0%, smaller than the year before. 

The chart above shows that single family houses have become smaller since 2015, when the largest new houses were built. The median size of a new single family house in 2015 was 2,466 square feet, shown by the blue line in the chart above. That is, houses today are 287 sq. ft smaller, reflecting almost the loss of a full room.

On an average size basis, the biggest new houses were also built in 2015 when the mean size of a single family house reached 2,689 sq. ft. This is 278 sq. ft. more than those built last year.

The steady shrinkage of house sizes can be appreciated better in the chart to the right. It displays the quarterly changes in house size for the last five years. Note that we are using quarterly data here, rather than the annual data shown in the chart above.

The bars represent the change in house sizes from one quarter to the next.  For instance, the average house size fell 60 sq. ft. in the fourth quarter of last year to 2,374 sq. ft.

Also, although we are not showing it here, the number of houses built each quarter changes. There were 259 houses started in Q3 down to 238 thousand in Q4.

Size of Multifamily Housing Units 

A similar pattern is observed in the multifamily sector. That is, new housing units were biggest earlier this century and their size has been almost in a free fall. The only difference is that while both the median and average size of single family houses fell last year, for multi family units we find the average falling to 1,050 sq. ft. in 2023, but the median rose to 1,020 sq. ft. Last year was the only time since 1999, when these data first became available, when the average and median sizes moved in different direction. 

The chart to the right displays the historical annual trend in the size of multifamily housing units. Biggest units were built in 2006, with the median at 1,192 square feet. The average multi unit size was almost 100 sq. ft. larger than the median that year, measuring 1,291 sq. ft. 

One reason for the significantly larger multifamily units built in 2006, when the median size rose from around 1,100 sq. ft. to nearly 1,200 that year, is that a larger number of those units were built for condominium purposes, rather than for apartment rentals. Condominium units, which are homes owned by the persons living in them, tend to be larger and with more amenities than rental units.  

In 2006 nearly half of the multifamily units, 45% actually, were built for condominium purposes. Prior to that year fewer than a quarter of the units were condominium with the significant exception in the early 80s. In 1981 condominiums accounted for 42% of all multifamily units built that year.

Last year, as can be seen in the chart nearby, condos had fallen to just 3% of the multifamily units, the lowest historical level. Incidentally,  a few of the single family homes are also built/sold as condominiums. In 2022 they were less than 3% of all single family houses.

Regional Patterns

Similar to the national trend in the size of single family houses, all four regions also had reductions in house size. The declines ranged from a small drop of 2.2% in the South region, to a relatively large one in the Northeast were the average single family house was 8.4% smaller.  

The average size of new houses in the Northeast region, which always leads the nation with the largest homes, fell to 2,572 sq. ft. This is down from over 2,800 sq. ft. in 2022 and, as can be seen in the chart below, this region's single family homes have also exceeded 2,800 sq. ft. in two previous years- 2014 and 2015.

Builders in the Midwest region produce the smallest houses in the nation. In contrast to the Northeast, new single family houses in this region are always smaller than any other region's. Since 1999, when house data at the region level first became available, Midwest new houses have been 150 sq. ft. smaller than the national average. Last year they were 159 sq. ft. smaller. 

The size of new houses in the South and West is closer to the national averages. The South region's are on average 34 sq. ft. larger than the nation's, although last year were 43 sq. ft. bigger.

The West region's houses have been smaller than the national average since 2009. But overall this region's houses have been 24 sq. ft. smaller. Last year the gap was 72 sq. ft.

Regional Multifamily New Housing

On the multifamily housing side we find that one of the regions, the Midwest, bucked the national decrease in the average size of multi housing units. That is, the average size of multifamily units in the region rose last year to 1,050 sq. ft., just barely higher than in 2022.

The declines in housing unit size in the other three regions were relatively mild. New multi units in the South region saw the largest drop, falling 2.7% or 30 sq. ft. to 1,068 sq. ft.

New multifamily housing units in the West region were 1.2% smaller last year, with a reduction of just 12 sq. ft. in one year. 

Implications of the trend towards smaller houses

Smaller houses naturally imply a reduced demand for a variety of products. For instance, in terms of volume last year's reduction in the size of new single family houses represents a drop of nearly 43 million square feet of floor space. This implies a reduced demand for flooring

materials such as carpeting, wood, or vinyl. 

Although the reasons for smaller houses are unclear, they could be the result of builders trying to reduce costs or simply that consumers are finally demanding smaller houses. The average family size or persons per household has been declining for many years. Additionally, the number of one-person households has increased. All these factors point towards smaller houses. 

New Housing in Metro Areas

 Housing starts fell nearly nine percent last year to 1.41 million units, for only the second annual decline since 2009. Both the single and multifamily segments fell. Single family was down 6.0% to a preliminary 944 thousand houses, but new multifamily construction fell more sharply, down 14.4% to 469 thousand units.

Similarly, the number of housing permits also fell, although by a larger 12%. Total number of permits issued last year was 1.47 million, single family was 6.9% fewer at 908 thousand units, and multifamily was down 18.6% to 562 thousand units.

The right panel in the chart, shows monthly starts and permits data for the last two years. Latest data are for January of this year, with housing starts running at a 1.46 million unit annual rate, and permits slightly higher at a 1.49 million rate. While both starts and permits fell throughout 2022, activity stabilized most of last year.

Based on the Fed's latest pronouncements, we do not expect significant changes in mortgage rates over the next couple of months. This suggests we should not see material improvement or deterioration in the housing markets.  

Last year's decline in both housing starts and permits naturally results of declines in a majority of metropolitan areas. Although new residential construction improved in a number of metro areas.

On a geographic basis, we find that new housing construction is concentrated in a few of the metropolitan areas. The top 15 metros, depicted in the chart to the right, account for 43% of  the total housing permits issued last year. But these 15 are only 4% of all the 387 official metropolitan areas in the country. That is, just 4% of the metros represent 43% of all housing construction.

Along with the nation overall, most of these 15 metro areas saw fewer housing permits last year than in 2022, but three of them actually saw an increase. We can see in the right panel that Nashville permits were 27% higher in 2023 than the prior year, Charlotte saw 8% more permits issued and the Miami-Ft. Lauderdale metro area had 7.7% more permits. 

Note that the two top metros, Houston and Dallas, combined represent 12% of the nation's new housing construction. In other words, one in eight house permits took place in both of these areas. The top six metros in the chart account for one quarter (25%) of the nation's housing permits. And the fifteen metros shows in that chart contribute 43% of the total housing permits.

 Twenty one metro areas account for half of the total permits, and just 58 of the 387 metropolitan areas account for three-quarters (75%) of the total housing permits.

The geographic concentration of housing varies whether it's single or multifamily housing. Although several metropolitan areas are dominant in both market segments, such as Houston or Dallas-Ft. Worth, other metros predominate in either single family or multifamily housing, but not both. Such is the case of Jacksonville, Florida which shows up with high number of single family houses but vary few multifamily, or Minneapolis metro area with high number of multifamily housing but relatively few single family units.

The top single and multifamily housing markets are shown in the chart below.

Finally, for illustration purposes, the two charts below display the trend in total housing permits for each of the top 16 metro areas. The red line in each of the individual graphs represents the growth of total housing permits since 2010. That is, each point indicates the percent change in housing permits from 2010. For comparison, the U.S comparative growth is shown by the black lines. 

The numbers at the end of each line is the average growth in permits between 2010 and 2023. That is, the total growth in the number of permits divided by 14- the number of years. Also, we have colored three of the charts in pale yellow to highlight the fact that permits in these metros grew further last year, unlike the other areas where growth has stalled or fallen.

U.S. Homeownership

Data recently released by the Census Bureau reveals that the U.S. homeownership rate fell to 65.7% in the fourth quarter, down from the year before. The decline was very modest, the rate fell by just 0.3% keeping homeownership within the narrow band it has maintained over the last five years.

The homeownership rate has ranged in the last four years less than one percent, from a low 65.1%, in the fourth quarter of 2019, to the 2022 high of 65.9%. However, the gains made in homeownership between 2016 and 2020, when the rate rose by two percentage points, have stopped.

Further, as can be seen in the chart, the current rate is barely higher than what it was in 1980, when 65.5% of households owned their home. More than four decades of government policies have not made the American dream possible to a greater percentage of households.

But the latest drop in homeownership was not widespread across all age groups. Households headed by a 55 to 64 year-old saw their ownership rise by just under half a percent, 0.3% in fact, to  76%. This is the second highest rate among all ages, as can be seen in the left panel in the chart below.

The biggest decline in ownership was among the youngest households, that is those headed by persons 34 years or younger. Their homeownership rate fell by six-tenths of a percent (0.6%) to 62% in the fourth quarter. Yet this decline does not make a major dent on the gains effected in the five year period 2016-2020, when the homeownership among the youngest households had risen by 3.8%. The under 35 year-old households enjoyed the biggest gains among all age groups in that period.

 

The other groups with a drop in the homeownership rate were the 35 to 44 years, down by two-tenths of a percent (0.2%), and the 45 to 54 year olds, whose rate fell by three-tenths to 70.3%.

Back to the future

The earliest homeownership data by age group from the Census Bureau is available beginning in 1994, even though overall rate for all U.S. households is from 1964.  Based on the age of household data, the national homeownership rate has fluctuated from 64.2% in 1994 to 65.7% today, but rising as high as 69.2% in 2004 at the height of the housing boom/crash earlier this century. 

The 1.5% increase in homeownership over the last 19 years results from gains among the youngest and oldest households, compensated with losses among the middle age groups. Households headed by persons under 35 years rose by just 0.1%, but elder households 65 and over rose by 1.3%.

The homeownership rate among the other three age groups is lower today than in 1994. The 35 to 44 year olds is 2.7% lower, the 45 to 54 is 4.6% down, and the 55 to 64 group is down 3.2%.

Homeownership Among Race & Ethnic Groups

The Census Bureau also provides homeownership data for major race groups, including Hispanics. The left panel in the chart below displays the latest ownership rate for the various groups. White households, as is well known, have the highest rate with 73.8% of households owning their home. Although the rate fell last year by 0.7% it is 3.6% higher than its level back in 1994.

Asian households post the second highest rate, 63% of them owned their house at the end of last year, 1.1% higher than in 2022. Although data for Asian households had not been collected prior to 2016, since that year their homeownership has risen by 6.4%.

Hispanic- and Black-headed households have the lowest homeownership rates, with Hispanic latest rate of 49.8%, and that of Blacks almost four points lower at 45.9%. The homeownership for Hispanics and Blacks had been nearly equal until 2004 when both declined as a consequence of the housing collapse that started that year. But when the housing market began to improve around 2010, the homeownership among Hispanics recovered faster. 

Hispanic households' rate has risen 7.6% since 1994, while Black's have gained less than half, 3.3%.

Politics anyone?

 

Government policies naturally have an impact on housing and homeownership, often unfavorable despite the good intentions. Most dramatic, and damaging it turns out, were the policies enacted in the late 1990s and early 2000s aimed at bolstering homeownership, such as the Community Reinvestment Act. These policies combined with easier lending practices adopted by banks and mortgage companies, and encouraged by the government, led to what came to be known as the subprime mortgage crisis with dire consequences for millions of households who subsequently lost their home. 

For illustration purposes, the chart below displays the path of homeownership for the last thirty years, highlighting the president and his political party throughout this period. Without any deep analysis we can easily see that neither party's policies proved to be more beneficial or detrimental to homeownership than the other party.

U.S. Unemployment Rate

 The U.S. unemployment rate has deteriorated slightly over the last year. While the rate was 3.5% at the end of 2022, by the end of last year it had worsened to 3.7%. Not a big change but an adverse change nonetheless. In fact, the rate has been at or above 3.7% in each of the latest five months. 

On a generational basis, the unemployment rate has worsened for people in two age groups. For the youngest workers, those between 16 and 19 years of age, the rate rose by half a point from 11.4% in December of 2022 to 11.9% at the end of last year. The other group posting higher rate at the end of 2023, is the 25 to 34 year olds, which went from 3.9% to 4.2%. This can be seen in the chart below.

Another perspective is a comparison of the unemployment rate geographically. As one would expect, there are differences in unemployment across the various regions in the U.S. Naturally, we find that some states have a rate higher than the national average of 3.7% and, conversely, the rate for other states is better (i.e. lower) than the total U.S. 

States in the map below in red shades are those whose current unemployment rate is higher than the national average and, conversely, those states with rates below the national average are shown by green shades.

We can readily see that states in a swath from Washington to Texas all have a rate higher than the national average; except for Oregon which posted a December rate of 3.7%. There are also some states towards the East-Central part of the nation, from Illinois to New York with (relatively) high unemployment rates. 

 

Nevada currently posts the highest rate in the nation with a December 2023 rate of 5.4% unemployment. It also has exceeded the U.S. rate every month since 2007.  The average unemployment rate in Nevada, from 2005 to December last year, has been 1.8% higher than the overall U.S. rate.

California, Illinois, and New Jersey are other states with currently highest rates. California is also one of a handful of states whose unemployment rate has been above the national average for all of the available data history. The other states with unemployment rates mostly above the national average are Illinois, Michigan and Kentucky.  

At the same time, there are several states which have consistently maintained unemployment rate below the national average. Among this group we find Maryland with 1.9% unemployment rate in December. This state's average rate is 0.81% below the nation's rate.  Montana's rate is 1.4% below the U.S. rate, Utah is 1.9% below,  and Iowa is 1.8% lower than the U.S. Both Dakotas are also better than the nation, with South Dakota's average 2.6% below the U.S., and North Dakota even better with 2.9% gap from the national rate. 

Other states performing better than the overall U.S. are Idaho with an average unemployment rate 1.1% below the U.S. , Virginia is 1.4%, Kansas is 1.3% lower and Vermont is 1.9% below. 

While the total U.S. unemployment rate has deteriorated over the last year, as we mentioned above, many states bucked this trend. 

The map below displays the change in unemployment rate between December 2022 and December of last year. States whose unemployment rate has deteriorated more than the nation, that is the rate has increased more than the U.S. change of 0.2%, are shown in shades of red. 

A few states stand out with an oversize increase. New Jersey's unemployment is 1.5% higher than a year ago. Similarly, California is one percent higher and Alaska is 0.8% higher. Montana, Colorado and Missouri post rate 0.6% higher than in December 2022. 

States with improving unemployment rate are Oregon, 1.1% lower today, Maryland also 1.1% although impossible to see in the map. Other states are Wyoming (0.9% lower), Hawaii (0.8%), and Pennsylvania and Vermont both also 0.8% lower. 

An exploration of the relationship between industry concentration and states' unemployment rate was unsuccessful. Deeper analysis is required for an explanation of the variation of unemployment among states.