Unemployment rate continues to decline in July 2015.

The unemployment rate continues to decline in July 2015 for most of the metropolitan areas within the United States. 92% of the metropolitan areas in the Nation experienced lower unemployment rates than a year earlier while only 20 metro areas showed higher rates. In 8 out of 389 metropolitan areas, rates were unchanged. Metro areas within both Dakotas had unemployment rates below 3 percent, as well as Lincoln in Nebraska and, Ames and Iowa City. According to the US Bureau of Labor Statistics (BLS), “a total of 187 areas had July Unemployment rates below the US figure of 5.6 percent, 185 areas had rates above it and 15 areas had rates equal to that of the nation”.

The highest unemployment rate, locally speaking, was in Yuma Arizona, which registered 26.6 percent for the month of July 2015. El Centro in California had the second highest unemployment rate, 24.2 percent. Otherwise, the lowest rate (2.3 percent) was in Bismarck, North Dakota. Following data from BLS, “of the 51 metropolitan areas with a 2010 Census population of 1 million or more, Austin-Round Rock, Texas, had the lowest unemployment rate in July, 3.5 percent”.

In regards to employment levels, the largest over-the-year increase happened in New York-Newark-Jersey City where the difference in July 2015 is 164,400 more jobs than in July 2014. The Los Angeles metropolitan area added 157,500 over-the-year. In Texas, the Dallas-Fort Worth-Arlington also augmented the payroll by 121,700 jobs over-the-year. On the other hand, the largest decrease in employment level occurred in New Orleans-Metairie where the metric contracted by 3,800 jobs. This contraction also happened in Davenport-Moline-Rock Island (Iowa and Illinois) in which the decrease was around 3,600 jobs. Barnstable Town in Massachusetts also declined its employment level by 3,000.

Although these data have not been adjusted by season yet, estimates are derived from a comprehensive model-based approach that covers several data sources. In fact, since the average over-the-year change in state rates is 0.7 percentage points, current estimates are somehow reliable. The Local and Urban Statistics (LAUS) program at BLS utilizes a method that aggregates weighted data from the Current Population Survey (Household data), the Current Employment Statistics (Establishment data), and State Unemployment Insurance programs. Estimates for the State-level data are produced by using time-series models, though. The BLS uses 90 percent confidence level when reporting statistically significant data.

Below are the graphs for the unemployment rate in the fifty states and its correspondent metro areas.

 

Alabama Alaska Arizona Arkansas California 1 California 2 Cannecticut Colorado Delaware District of Columbia Florida Georgia Hawaii Idaho Illinois Michigan Minnesota Mississippi Missouri Montana Nebraska

New Mexico New York North Carolina

Ohio

Oklahoma Oregon Pennsylvania Puerto Rico Rhode Island South Carolina South Dakota Tennessee Texas Utah Vermont Virginia Washington West Virginia Wisconsin Wyoming

 

Alabama
Anniston-Oxford-Jacksonville.
Auburn-Opelika
Birmingham-Hoover.
Daphne-Fairhope-Foley.
Decatur.
Dothan.
Florence-Muscle Shoals.
Gadsden.
Huntsville.
Mobile.
Montgomery.
Tuscaloosa.

Alaska.
Anchorage.
Fairbanks.

Arizona.
Flagstaff.
Lake Havasu City-Kingman.
Phoenix-Mesa-Scottsdale.
Prescott.
Sierra Vista-Douglas.
Tucson.
Yuma.

Arkansas.
Fayetteville-Springdale-Rogers.
Fort Smith.
Hot Springs.
Jonesboro.
Little Rock-North Little Rock-Conway.
Pine Bluff.

California.
Bakersfield.
Chico.
El Centro.
Fresno.
Hanford-Corcoran.
Los Angeles-Long Beach-Anaheim.
Madera.
Merced.
Modesto.
Napa.
Oxnard-Thousand Oaks-Ventura.
Redding.
Riverside-San Bernardino-Ontario.
Sacramento–Roseville–Arden-Arcade.
Salinas.
San Diego-Carlsbad.
San Francisco-Oakland-Hayward.
San Jose-Sunnyvale-Santa Clara.

San Luis Obispo-Paso Robles- Arroyo Grande.
Santa Cruz-Watsonville.
Santa Maria-Santa Barbara.
Santa Rosa.
Stockton-Lodi
Vallejo-Fairfield.
Visalia-Porterville.
Yuba City.

Colorado.
Boulder
Colorado Springs.
Denver-Aurora-Lakewood.
Fort Collins.
Grand Junction.
Greeley.
Pueblo.

Connecticut.
Bridgeport-Stamford-Norwalk.
Danbury.
Hartford-West Hartford-East Hartford.
New Haven.
Norwich-New London-Westerly.
Waterbury.

Delaware.
Dover.
Salisbury (1).

District of Columbia.
Washington-Arlington-Alexandria.

Florida.
Cape Coral-Fort Myers
Crestview-Fort Walton Beach-Destin.
Deltona-Daytona Beach-Ormond Beach.
Gainesville.
Homosassa Springs.
Jacksonville.
Lakeland-Winter Haven.
Miami-Fort Lauderdale-West Palm Beach.
Naples-Immokalee-Marco Island.
North Port-Sarasota-Bradenton
Ocala.
Orlando-Kissimmee-Sanford.
Palm Bay-Melbourne-Titusville.
Panama City.
Pensacola-Ferry Pass-Brent.
Port St. Lucie.
Punta Gorda.
Sebastian-Vero Beach.
Sebring.
Tallahassee.
Tampa-St. Petersburg-Clearwater.
The Villages.

Georgia.
Albany.
Athens-Clarke County.
Atlanta-Sandy Springs-Roswell.
Augusta-Richmond County.
Brunswick.
Columbus.
Dalton.
Gainesville.
Hinesville.
Macon.
Rome
Savannah.
Valdosta.
Warner Robins

Hawaii.
Kahului-Wailuku-Lahaina.
Urban Honolulu.

Idaho.
Boise City.
Coeur d’Alene.
Idaho Falls.
Lewiston
Pocatello.

Illinois.
Bloomington.
Carbondale-Marion.
Champaign-Urbana.
Chicago-Naperville-Elgin.
Danville
Davenport-Moline-Rock Island (1).
Decatur.
Kankakee
Peoria.
Rockford.
Springfield.

Indiana.
Bloomington.
Columbus.
Elkhart-Goshen.
Evansville.
Fort Wayne.
Indianapolis-Carmel-Anderson.
Kokomo.
Lafayette-West Lafayette.
Michigan City-La Porte.
Muncie.
South Bend-Mishawaka.
Terre Haute.

Iowa.
Ames.
Cedar Rapids.
Des Moines-West Des Moines.
Dubuque.
Iowa City.
Sioux City.
Waterloo-Cedar Falls.

Kansas.
Lawrence
Manhattan.
Topeka
Wichita.

Kentucky
Bowling Green.
Elizabethtown-Fort Knox.
Lexington-Fayette.
Louisville/Jefferson County.
Owensboro.

Louisiana.
Alexandria.
Baton Rouge.
Hammond.
Houma-Thibodaux.
Lafayette
Lake Charles.
Monroe.
New Orleans-Metairie.
Shreveport-Bossier City.

Maine.
Bangor.
Lewiston-Auburn.
Portland-South Portland.

Maryland.
Baltimore-Columbia-Towson.
California-Lexington Park.
Cumberland.
Hagerstown-Martinsburg.

Massachusetts.
Barnstable Town.
Boston-Cambridge-Nashua.
Leominster-Gardner.
New Bedford.
Pittsfield.
Springfield.
Worcester.

Michigan.
Ann Arbor.
Battle Creek.
Bay City.
Detroit-Warren-Dearborn
Flint.
Grand Rapids-Wyoming.
Jackson.
Kalamazoo-Portage.
Lansing-East Lansing.
Midland.
Monroe.
Muskegon.
Niles-Benton Harbor.
Saginaw.

Minnesota.
Duluth.
Mankato-North Mankato.
Minneapolis-St. Paul-Bloomington.
Rochester.
St. Cloud.

Mississippi.
Gulfport-Biloxi-Pascagoula.
Hattiesburg.
Jackson.

Missouri.
Cape Girardeau.
Columbia.
Jefferson City
Joplin
Kansas City.
St. Joseph.
St. Louis (2).
Springfield.

Montana.
Billings.
Great Falls.
Missoula.

Nebraska.
Grand Island.
Lincoln.
Omaha-Council Bluffs

Nevada.
Carson City.
Las Vegas-Henderson-Paradise.
Reno.

New Hampshire.
Dover-Durham.
Manchester.
Portsmouth

New Jersey.
Atlantic City-Hammonton.
Ocean City.
Trenton.
Vineland-Bridgeton.

New Mexico.
Albuquerque.
Farmington.
Las Cruces.
Santa Fe.

New York.
Albany-Schenectady-Troy.
Buffalo-Cheektowaga-Niagara Falls.
Elmira.
Glens Falls.
Ithaca.
Kingston.
New York-Newark-Jersey City.
Rochester.
Syracuse.
Utica-Rome.
Watertown-Fort Drum.

North Carolina.
Asheville.
Burlington.
Charlotte-Concord-Gastonia
Durham-Chapel Hill.
Fayetteville.
Goldsboro
Greensboro-High Point.
Greenville.
Hickory-Lenoir-Morganton.
Jacksonville.
New Bern.
Raleigh.
Rocky Mount.
Wilmington.
Winston-Salem.

North Dakota.
Bismarck
Fargo.
Grand Forks.

Ohio.
Akron.
Canton-Massillon.
Cincinnati.
Cleveland-Elyria.
Columbus.
Dayton.
Lima.
Mansfield
Springfield.
Toledo.
Weirton-Steubenville (1)
Youngstown-Warren-Boardman.

Oklahoma
Lawton.
Oklahoma City
Tulsa.

Oregon.
Albany
Bend-Redmond.
Corvallis.
Eugene.
Grants Pass
Medford.
Portland-Vancouver-Hillsboro.
Salem.

Pennsylvania.
Allentown-Bethlehem-Easton.
Altoona.
Bloomsburg-Berwick.
Chambersburg-Waynesboro.
East Stroudsburg.
Erie.
Gettysburg.
Harrisburg-Carlisle.
Johnstown.
Lancaster.
Lebanon.
Philadelphia-Camden-Wilmington.
Pittsburgh.
Reading.
Scranton–Wilkes-Barre–Hazleton.
State College.
Williamsport.
York-Hanover.

Rhode Island.
Providence-Warwick.

South Carolina.
Charleston-North Charleston.
Columbia
Florence.
Greenville-Anderson-Mauldin.
Hilton Head Island-Bluffton-Beaufort.
Myrtle Beach-Conway-North Myrtle Beach
Spartanburg.
Sumter.

South Dakota.
Rapid City.
Sioux Falls.

Tennessee.
Chattanooga.
Clarksville.
Cleveland.
Jackson
Johnson City.
Kingsport-Bristol-Bristol.
Knoxville.
Memphis.
Morristown.
Nashville-Davidson–Murfreesboro– Franklin.

Texas.
Abilene.
Amarillo.
Austin-Round Rock.
Beaumont-Port Arthur.
Brownsville-Harlingen.
College Station-Bryan.
Corpus Christi.
Dallas-Fort Worth-Arlington.
El Paso.
Houston-The Woodlands-Sugar Land.
Killeen-Temple.
Laredo.
Longview.
Lubbock.
McAllen-Edinburg-Mission.
Midland.
Odessa.
San Angelo.
San Antonio-New Braunfels.
Sherman-Denison.
Texarkana.
Tyler.
Victoria
Waco
Wichita Falls.

Utah.
Logan.
Ogden-Clearfield.
Provo-Orem
St. George
Salt Lake City.

Vermont
Burlington-South Burlington.

Virginia.
Blacksburg-Christiansburg-Radford.
Charlottesville
Harrisonburg.
Lynchburg.
Richmond.
Roanoke.
Staunton-Waynesboro.
Virginia Beach-Norfolk-Newport News
Winchester.

Washington.
Bellingham
Bremerton-Silverdale.
Kennewick-Richland
Longview
Mount Vernon-Anacortes.
Olympia-Tumwater.
Seattle-Tacoma-Bellevue.
Spokane-Spokane Valley.
Walla Walla.
Wenatchee.
Yakima.

West Virginia.
Beckley.
Charleston.
Huntington-Ashland.
Morgantown.
Parkersburg-Vienna.
Wheeling

Wisconsin.
Appleton.
Eau Claire.
Fond du Lac.
Green Bay
Janesville-Beloit.
La Crosse-Onalaska.
Madison.
Milwaukee-Waukesha-West Allis.
Oshkosh-Neenah.
Racine.
Sheboygan.
Wausau.

Wyoming.
Casper.
Cheyenne.

Puerto Rico.
Aguadilla-Isabela
Arecibo.
Guayama.
Mayaguez.
Ponce.
San German.
San Juan-Carolina-Caguas.

Do Workers on Unemployment Insurance make Other Workers’ Income Worst?

Economists like to think that wages are set depending upon two basic factors plus a “catchall” variable. The two basic factors are expected price level and unemployment rate. The “catchall” variable stands for all other overlooked factors affecting wage. The way in which the relationship is established by labor theory is that expected price level affects wage determination positively (since the economy has not experienced deflation effect systematically); and, unemployment does it negatively (supposedly, given that workers compete for jobs, employers take advantage of it through price-taking behavior). All other factors affecting wages are assumed to be positive.

Among those all other factors –which are believed to affect positively wage levels- is the Unemployment Insurance benefit. However, depending upon ideology, Unemployment Insurance benefits may be interpreted as affecting wage determination either positively, or affecting wages negatively. On one side, Unemployment Insurance may affect upward wages given that it sums up into the so-called reserve salary, which is the minimum amount of money that makes a person indifferent to the choice between working and not working. In other words, if a person has Unemployment Insurance for any given dollar amount, why would that person work for less that such a figure? The flip side of the coin is that, if Unemployment Insurance contributes to keep people from work, then the unemployment rate goes up due to the UI, thereby pushing down the wages. At first glance, analysts might be tempted to think that those two forces cancel off each other. There is where data becomes important in determining the real breadth of those factors without binding to any ideology.

By Catherine De Las Salas

By Catherine De Las Salas.

By the way, in case you have not noticed it yet, right wing politicians tend to believe that UI pressures upwards wages thereby increasing production costs. Therefore, right wing politicians believe that such a pressure constraints hiring within the United States affecting negatively production and forcing employers to find cheap labor elsewhere overseas.

Managers play a roll either in cutting or increasing wages:

It is important to note that wage laws create downward wage rigidity, which prevents managers to lower nominal salaries. However, and despite of such a rigidity, administrators may manage to cut ‘earnings’ by lowering workloads. Therefore, looking at measures such as hourly wage, or minimum legal wage does not capture the reality of compensation. Instead, looking at ‘earnings’ might give a hint about the variance created by unemployment insurance, unemployment rate and inflation.

The model:

So, the logic goes as follows: wage levels are an outcome of unemployment rate (negatively); plus, unemployment benefits (positively); plus, expected price level (positively). In other words, wage setting gets affected by those three factors since a manager ‘virtually’ would adjust her payroll based on how easy is for her to either hire or fire an employee, and how enthusiastic she is to increase or decrease the employee workload.

Thus, the statistical model would look like the following:

Model

Where y is the dependent variable Average weekly earnings for November 1980 to November 2014; x1 represents Unemployment Rate at its annual average; x2 represents Unemployment Insurance Rate for November’s weeks seasonally adjusted average; x3 stands for inflation rate at its annual average.

Data and method:

Thus, I took data on three variables: Average weekly earnings for the month of November starting from 1980 through 2014. These data, taken from the U.S. Bureau of Labor Statistics (BLS), were adjusted by the average inflation rate of the correspondent year. The second variable is year average inflation rate from 1980 to 2014, taken also from BLS too. I use Inflation Rate as a proxy for the “expected price level”. The third variable is the November’s Unemployment Insurance rate from 1980 to 2014, which was taken from the Unemployment Insurance Division at the U.S. Department of Labor. I chose data on November series given that this month’s Average weekly earnings has the greatest standard deviation among all other months.

Ordinary Least Square Method was used to run the multiple regression.

Results:

Data for the month of November, starting 1980 through 2014, show that Unemployment Insurance Rate could have a negative effect on average weekly earnings for Americans. Apparently, the statistical relation of the data is negative. The actual estimated coefficient for these data points out toward a figure of (+/-) $123 less for U.S. Worker’s average weekly earnings per each percent point increase in Unemployment Insurance Rate. In other words, the greater the share of people collecting Unemployment Insurance, the lower the average weekly earnings of U.S. workers. One limitation of the regression model is that it only captures the employees effect of the variable, the model is not intended to explain costs of employers. In such a case the dependent variable should be some variable capable of capturing employer’s labor costs. The statistical significance for the effect of Unemployment Insurance on November average weekly earnings data is at 95%.

Furthermore, data also show that inflation rate (proxy for “expected price level”) actually works against average weekly earnings. The estimated coefficient for the months of November is (+/-) 28 dollars less for the average paycheck. The statistical significance for the effect of Inflation Rate on November average weekly earnings data is at 95%.

Finally, the Unemployment Rate shows a positive effect on average weekly earnings indicating that, per each percent point increase in Unemployment Rate, average weekly earnings increases by an estimated figure of (+/-) 49 dollars. The statistical significance for the effect of Unemployment Rate on November average weekly earnings data is at 90%.

Regression output table:

Insurance

Despite GDP estimates, U.S. industries experienced growth on level of employment in April to April comparison.

Despite news showing negative growth in Gross Domestic Product for the first quarter 2015, most of the U.S. industries experienced growth on level of employment in April 2014 to April 2015 comparison. Besides Construction, which tends to grow faster as weather allows for outdoor activities, Leisure and hospitality industry experienced the highest average growth rate in level of employment, 2.8%. Education and Health Services seconded Hospitality with an average of 2.3%. Professional Business had 2.2% increase, while Trade and Transportation and Utilities recorded 1.8% increase.

Industry
The lowest rate of change showed up unsurprisingly in Manufacturing. Aggregate data for the industry exhibited an anemic .9% change in job creation when comparing April 2014 to April 2015. Indeed, several surveys are showing May might not have made any better difference for the sector. For instance, the Texas Manufacturing Outlook Survey revealed its main Index fell to -13.5. Moreover, the employment Index declined to -8.2, which translates into shorter workweeks for employees in Texas Manufacturing Industry. On the other hand, the Federal Reserve Bank of Richmond reported the employment gauge in their survey decreased from 7 to 3, though the average workweek actually increased.


In General, manufacturing conditions in Texas reflected continuing contraction during May 2015. The Federal Reserve Bank of Dallas claims that these readings are the lowest in the recent six years. On the other hand, the composite manufacturing index in Richmond’s survey moved a bit up to 1, from a reading of -3 in the previous month. Manufacturing Activity “flattened in May” Richmond reported.

Utah ranks on top in job creation for April 2015.

Perhaps the state that showed the best performance in level of employment was Utah. Industries seem to be booming there, where 52K jobs positions were added. Construction registered an increase of 7.7%, while Leisure and Hospitality did on 7.4%. Trade and Transportation, and Financial Services also increased their levels by 5.0% and 4.9%.

Utah Level of Employment
Utah ranked first on industry growth for Leisure and hospitality along with Vermon. Arkansas, Georgia and Florida also experienced increases in such industry. In education Utah was surpassed by Oregon and Colorado both with 4.9% increase in job creation for the industry in April 2015. In Financial Activities Utah also made an appearance in April 2015, though the state was surpassed by Oregon, South Carolina and Washington State. Even in the Manufacturing sector Utah made it to the fourth place in April 2015. The only two sector in which the State did not make it to the four top rank were Construction and Professional Business.

California Level of Employment

Data source: US Bureau of Labor Statistics.

Change in employment

Unemployment rate is still at 5.4%: BLS.

The US Bureau of Labor Statistics released on April 27th 2015 its preliminary data on unemployment. On the national level the unemployment rate is still at 5.4%. By regions, the Midwest had the lowest unemployment rate, 5.0% The Western region had the highest rate at 5.8%. The highest rates of unemployment were in Nevada and the District of Columbia, 7.1% and 7.5% correspondingly. Unemployment rate rose .4 percentage points to 3.1% in North Dakota, which registered a rate of 2.7% one year ago. On the other hand, largest percentage changes over the year were in Michigan which decreased its unemployment rate by -2.1 %, and both Kentucky and Rhode Island where the decrease in the unemployment rate was of -2%.

Unemployment april 2015
The largest over-the-month decrease occurred in New York, -14,700, followed by Missouri with -5,700. The largest increase from March to April 2015 happened in California which experimented +29,500 jobs gains, Pennsylvania and Florida with +27,000 and +24,500 jobs gains respectively. For the case of New York City and Los Angeles, some scholars at the Brookings Institute are suggesting that population growth in both states has slowed down in the recent years, which may be affecting level of employment and unemployment statistics for those states.
Employment level increased significantly in California where 457K new positions were created. Gains in employment over the year were in Construction, 6.4%, Leisure and hospitality 3.4%, and Education with 2.9%. Texas, where roughly 287K workers found a new job, showed the largest increase in Leisure and hospitality with 4.9% change from the previous year. Construction increased 3.9% from April 2014 in Texas. The third place in job creation went to Florida where approximately 277K jobs were added. There, the industries that pulled up job creation were Construction with 8.2%, Leisure and hospitality and Professional Business with 5.2% and 4.6% respectively.

Falling Oil Price and Unemployment: Inferences from TMOS.

With oil prices at year’s lowest, U$55.25 per barrel, everyone wonders about possible spillovers in the United States national economy. Although many people started to enjoy low prices of gas and others turned recklessly their furnace up during the winter, many other people started worrying about their own jobs, inflation/deflation, future demand for new orders and future working hours, among other concerns. Manufactured goods producers in Texas are among the ones who worry the most when oil prices go down. Usually they are the first economic sector to perceive drastic changes in the economy. They almost can smell future crisis.

Graph # 1.

Oil prices

Every month roughly 107 Texans manufacturing-firm-executives take time out of their busy schedules to answer a couple of question that the Federal Reserve Bank of Dallas send them in the Texas Manufacturing Outlook Survey (TMOS). Those questions can be grouped in two subsets: first a group of question inquiring about economic activity changes from previous month (Summarized findings in Graph # 3); and a second group of question inquiring about expected economic activity six month from the date they fill out the survey (Summarized findings in Graph # 2).

December 2014 TMOS’ findings (Briefly):

Although mostly all indicators marked positive from November 2014 to December 2014 (Graph # 3), it is possible to perceive a moderate optimism among manufacturers who responded the Texas Manufacturing Outlook Survey (TMOS). Economic activity expectations were actually adjusted as the price of oil went down (Graph #2). Responders of the TMOS in December 2014 filled out the forms between December 15th and December 23rd. It is precisely the same week in which oil prices went down below U$60 a barrel. This very fact may have had an impact for expected economic activity on them. TMOS data showed that “Production” expectations six months from December 2014 were adjusted 6.7 points less than the previous month of November. This change could mean two thing, either Texans manufacturers were over-optimistic in November 2014, or they started to seriously feel the economic spillover of dropping oil prices during December of the same year.

So far not so bad for manufacturers. But how about workers? Texans manufacturers that responded the survey do not expect future increase in hours worked for their employees, at least for the six months following December 2014. The index “Hours worked” dropped almost toward negative terrain (0.4). Notice that -just one month before- the same index was at 14.2 (Graph #2). This is bad news for workers if you consider that expected sales drive current business hiring. In other words, a manager expecting an increase of sales tomorrow will decide to hire people today in order to fulfill future demand. Otherwise, a manager who does not expect sales to be higher tomorrow will not hire today.

Graph # 2.

TMOS Expectations Dec 2014

Another two indicators severely adjusted from November to December were “Shipments” and “Prices Received for Finished Goods”. The former indicator dropped 8.5 points whereas the latter did so by 11.5 points in the index. Both indicators remained positive and solid, but it is also true that both went down.

Graph # 3.

TMOS dec 2014

Obviously, the US economy works as a system in which someone losses’ is somebody else’s gains. In other words, although low oil prices may hit the economy the way described above, it also may create and foster job opportunities for many other people in other economic sectors. Nonetheless, it is important for policy makers and business leaders to track these trends in order to better adjust where and whenever it is necessary.

About TMOS and Texas:

Economists at the Federal Reserve Bank of Dallas construct a series of indexes by taking the answers responders give in the Texas Manufacturing Outlook Survey (TMOS). “Survey responses are used to calculate an index for each indicator. Each index is calculated by subtracting the percentage of respondents reporting” either a decrease or an increase. TMOS is a monthly survey of area manufacturers. It is important to note that besides oil industry in Texas, this state produces roughly 9.5% of the country’s manufacturing output, as well as roughly 10% of nation’s computer and electronics products. Texas ranks first in manufactured goods exports.

Insured Unemployment increased from 2,378,000 to 2,403,000 in December 2014.

There were no major changes in data from Unemployment Insurance during December 2014. Seasonally adjusted data showed that Insured Unemployment rate kept the same level for the week ending December 20th. Initial claims though went down roughly 9,000 for the same period. Average figure of Initial Claims for the month was 290,250, which represents a decrease of 8,500 from the previous 298,750 unrevised data. Insured Unemployment data refers to people continuously claiming Unemployment Benefits, whereas Initial Claims statistics refer to new people filing for benefits.
Total Insured Unemployment increased from 2,378,000 to 2,403,000 for the week ending December 13th. These numbers represent the total people claiming continuously unemployment benefits in the United States. Within this category of Insured civilians are former Federal employees which accounted for 16,828 people. These former Federal civilian employees were joined by a group of new claimants of roughly 1,331 former Federal employees. 1,752 recently discharged Veterans also filed claims, which represented a decrease of 266 from the second week of December 2014.

As of December 6th 2014, the highest Insured Unemployment Rates were in Alaska, 4.2%. It is worth noting that Alaska’s Unemployment rate for the same period was 6.8%. It is still above the Nation’s average. Puerto Rico’s Insured Unemployment Rate was 3.1% for the week ending December 13th of 2014. New Jersey, where the Unemployment Rate for November 2014 was 6.4%, registered 3.1% of Insured Unemployment Rate. Montana’s Insured Unemployment Rate was 2.7%, and Unemployment Rate of 4.3%. Pennsylvania 2.7% for Insured Unemployment Rate, whereas Connecticut reported 2.6% of the same rate. California 2.5% of Insured Unemployment rate. Massachusetts reported 2.4% of Insured Unemployment and its Unemployment Rate is expected to be at 5.8% for December 2014. And, West Virginia’s Insured Unemployment Rate was 2.4%.

Unemployment Insurance Dec 2014
Largest increase in Initial Claims were the following:
California: 11,794
Massachusetts: 443
Utah: 277
Rhode Island: 35
Main: 25

Largest decrease in Initial Claims were in Pennsylvania which totaled 10,937 claimants less; New York with 9,787 less claimants; Wisconsin totaled 6,465 less people initially claiming; Georgia with 5,520 less; and Texas with 3,982 less claims for the week ending December 13th of 2014.
Data source: U.S. Department of Labor. Division of Employment and training Administration.