The overuse of the word “Strong” in economic news.

The US economy added 228,000 new jobs in November of 2017 and analysts rush to assess the state of the economy as “STRONG.” Although the job reports are indeed good indicators of the performance of the US economy, one should not simplify the job report as the snapshot of the economy that allows for those “strong” conclusions by and in itself. In this post, I show that despite the existence of a cointegration vector between unemployment rate data and the word-count of the word “Strong” in the Beige Book, journalists indeed overuse the word “Strong” in headlines. Although interpreting cointegration as elasticity goes beyond the scope of this post, I think that by looking at the cointegration relation it is safe to conclude that the current word count does not reflect the “strong” picture showed by the media, but somewhat more moderate economic conditions.

To start, let me go back to the first week of December of 2017. Back then, news outlets had headlines abusing the word “strong.” Some examples came from major newspapers in the US such as the New York Times, Reuters, CNN and the Washington Post. The following excerpts are just a sample of the narrative seen those days:

“The American economy continues its strong performance” (CNN Money).

“The economy’s vital signs are stronger than they have been in years” (NY Times).

“Strong US job growth in November bolsters economy’s outlook” (Reuters).

“These are really strong numbers, which is pretty exciting…” (Washington Post).

Getting to know what is happening in the economy challenges economists’ wisdom. Researchers are constrained by epistemological limits of data and reality, and so are journalists. To understand economic conditions, researchers utilize both quantitative and qualitative data while journalists focus on qualitative most of the times. Regarding qualitative data, the Beige Book collects anecdotes and qualitative assessments from the twelve regional banks of the Federal Reserve system that may help news outlets to gauge news statements and headlines. The Fed studies business leaders, bank employees, among other economic agents to gather information about the current conditions of the US economy. As a Researcher, I counted the number of times the word “strong” shows up in the Beige Book starting back in 2006. The results are plotted as follow:

If I were going to identify a correlation between the word count of “strong” and its relation to the unemployment rate, it would be very hard to do so by plotting the two lines simultaneously. Most of the times, when simple correlations are plotted, the dots show any relation between the two variables. However, in this case, cointegration goes a little deeper into the explanation. The graph below shows how the logs of both variables behave contemporarily over time. They both decrease during the Great Recession as well as they increase right after the crisis started to end. However, more recently both variables began to divert from each other, which makes it difficult to interpret, at least in the short run.

Qualitative data hold some clues in this case. Indeed, the plot shows a decreasing trend in the use of the word within the Beige Book. In other terms, as journalists increase its use in headlines and news articles, economists at the Federal Reserve Bank decrease the use of the word “strong”. If I were going to state causality from one variable to the other, first I would link the word “strong” to some optimism for expected economic outcomes. Thereby, one should expect a decrease in the unemployment rate as the use of the word “strong” increases. This is a classic Keynesian perspective of the unemployment rate. Such relation of causality might constitute the cointegration equation that the cointegration test identifies in the output tables below. In other words, the more you read “strong,” the more employers hire. By running a cointegration test, I can show that both variables are cointegrated over time. That is, there is a long-term relationship between both variables (both are I(1)). The cointegration test shows that at least there exists one linear combination of the two variables over time.

The difficulty with the overuse of the word nowadays is that the word is not being used by economists in the Federal Reserve at the same pace as journalist economists do. In fact, the word-count has decreased drastically for the last two years from its peak since 2015. Such mismatch may create false expectations about economic growth, sales and economic performance that may lead to economic crisis.

Why Is the Homeownership Rate Still Falling? An alternative explanation.

When it comes to loan rates, the one that concerns the most regular consumers is the mortgage loan interest rate. This past February 2016 a 30-year mortgage interest rate averaged 3.66 percent accordingly to Freddie Mac, whereas the homeownership stubbornly kept its 63 percent level. So, with the mortgage interest rates averaging 3.53 percent (15-year mortgage loan), why the homeownership has not come back to 69 percent level as it was before the Great Recession? Some analysts have proposed cynically that 69 percent homeownership represents an unsustainable level, and that homeownership is no longer attractive. Neither of those explanations would look rational to a maximizing agent. Otherwise, an alternative analysis could lead to a different conclusion. That is, low-interest rates are helping investors to outbid competitors rather than prospective homeowners to get a house. The worrisome part of the problem is that this situation could lead the housing market to a crisis due to inflated home prices, as well as to higher levels of inequality.

Given that purchasing a house represents arguably the biggest investment of a lifetime of a regular person, these rates are mainly observed by monetary authorities, analysts, and homebuyers. In fact, these rates have become even more relevant since the Great Recession originated ostensibly from failures within the regulation of the housing market.

By Catherine De Las Salas

By Catherine De Las Salas

Homeownership rate has been declining.

A rapid view of real estate market indexes will show firstly that homeownership rate is flat. This rate has been flat and declining since its highest level before the Great Recession for which it reached 69 percent. Last economic quarter of 2015, homeownership registered 63.7 percent. Second, prices of both sales and rents are up to the extent that cost of shelter is among the only factors driving up inflation in the United States. Following the Case-Shiller index and the Federal Housing Finance Agency, home prices have increased at a yearly rate of 6.0 percent. Third, new residential sales, as measured by the U.S. Census Bureau, were also up by 6.1 percent in January 2016 when compared to the same month of 2015. Likewise, Pending Home Sales in January recorded 3.5 percent increase. Fourth, mortgage loan interest rate averaged 2.96 percent for a 15-year fixed loan during February 2016 (find more on housing indicators)

Investors could be outbidding prospective homeowners.

All these indexes beg the question on why homeownership has not increased due to the rising levels of sales, as well as the cost of shelter, and upturns in home prices. One of the answers available for this puzzle is that investors are taking over the market. Investors could be outbidding prospective homeowners making it harder for them to access ownership. Likewise, having investors controlling the housing market retains the risk that speculative money could inflate a bubble again in the housing sector, leading loans to go underwater at some point afterwards. A housing sector crisis could repeat under the same circumstances of the Great Recession nowadays.

The counter argument derives from the fact that the housing crisis was only the trigger for the Great Recession to start. Indeed, default in mortgage-backed loans trickled down in the form of multiple spillovers on the banking system. Securitization of banking products through the practice of bundling subprime mortgages led to the spreading of toxic assets all over the financial system (learn more of this issue here). Therefore, the fact that recent regulation within the financial system, as well as regulation governing lending practices, makes it less vulnerable for the rest of the economy. So, if the housing sector happens to be a risky position, an eventual crisis will not spread inasmuch as it did before the Great Recession.

The consequence, rising levels of inequality.

So, although a housing sector crisis could be discarded by looking at the arguments herein, the effects on inequality could not. As of March 2016, there appears to be no worrying signs or data with respect to the housing market. Nevertheless, assuming that homeownership has not increased because an alleged lack of incentives in owing seems ridiculously naïve.  And concluding that pre-Great Recession levels of homeownership were unsustainable appears not rational either. Then, an alternative explanation points at the competition of capital for seizing valuable assets. The consequence, low level of homeownership rate while rising levels of inequality.

“Core” inflation might be reflecting pressures solely generated by retailers.

Data on both unemployment and prices have monetary policy analysts wondering whether or not the US supply side of the economy is heading towards overheating. Thus far, indicators on industrial production and capacity utilization show there is still room for the economy to advance at a good pace without risking too many resources. Such indicators are produced and tracked by the monetary authority of the nation, so they have particular relevance for every analysis. However, there still are data on both unemployment and prices to help out with the diagnosis of the actual economic situation. On one hand, 92% of the metropolitan areas in the nation experienced lower unemployment rates in July 2015 than a year earlier, while only 20 metro areas showed higher rates. On the other, measure of the “core” inflation, which isolates energy and foods price volatility, reaches 1.8 percent change from the first quarter of 2015.

So, if higher production leads to lower unemployment, and the latter in turn leads to higher prices, then the easiest way to identify whether or not an economy is overheating is by analyzing to what extent prices changes are pushed up by falling rates of unemployment. This far of 2015, both conditions are met apparently. Unemployment rates are indeed falling; therefore, it could mean production is moving up. Then, what is a stake currently is to clarify whether or not US production is exceeding its capacity. Again, by looking at capacity indexes, it seems not to be the case right now. But, it is better to make sure it is not happening and thereby ruling out any alternative possibility.

Many econometric methods will help analysts to achieve valuable conclusions.

Perhaps digging into the price setting relation through regressing real wages on profits may yield some clues about the current situation. However, econometric models would severely hide the actual magnitude of oil and energy price volatility. Therefore, a rather quicker alternative lives in qualitative data. In other words, if analysts would like to know whether or not companies would transfer increasing labor costs onto the customers via price increase, what would the answers be? Econometricus.com looked at one of the state-level surveys in which such a question was included. The Texas Manufacturing Survey, which is conducted by the Dallas Fed, inquired among 114 Texas manufactures the following question. “If the labor costs are increasing, are you passing the costs on to customers in the way of price increases?” The survey answers were collected on August 18th through the 26th.

Here is what the study showed.

By sectors, surveyed retailers appear be the only ones prompted to transfer increasing labor costs to customers via price increase. Although very tight, 43.9 percent of the answers indicated that retailers would rise price as an outcome of increasing labor costs, whereas 41.5 would not. The Texas service sector respondents indicated that they would not do so by 54.5. Likewise, manufacturers rejected the possibility by 52.4 percent and considered positively by 35.7 percent. Below are the charts of which all used Texas Manufacturing Survey Data.

Texas Manufacturing Survey. Dallas Fed Aug. 2015.

Texas Manufacturing Survey. Dallas Fed Aug. 2015.

Although it is not feasible to extrapolate survey’s results onto the entire US economy, Texas’ has a particular significance for any current economic analysis. Indeed, Texas’ economy comprises a large share of oil related business, which is precisely the industry that brought this puzzle in the first place. Thus, it seems somewhat clear to conclude that following the Dallas survey, the economy might not be overheating currently.

Texas Manufacturing Survey. Dallas Fed Aug. 2015.

Texas Manufacturing Survey. Dallas Fed Aug. 2015.

So, what does these data tell economists about the US economy?

Although some would answer it says little because of its sample size and geographic limits, and its business size aggregation, there are some hints within the survey. First, it could be said that companies are currently absorbing the cost of growing, which might indicate that they are indeed venturing and the economy is expanding. So far so good. The concerns, though, stem from the speed of such expansion, which is hard to identify by using these data. But again, it is important to check Federal Reserve Data on industrial production and capacity utilization, which would yield some confidence against overheating. Second, although business size matters for determining whether or not increasing labor costs can be transferred to the customer via prices, the fact that retailers stand out in the survey must mean something for analysts. According to these data, retail appears to be the most sensitive sector right now; therefore, the 1.8 “core” inflation might be reflecting inflationary pressures solely generated by retailers.

Texas Manufacturing Survey. Dallas Fed Aug. 2015.

Texas Manufacturing Survey. Dallas Fed Aug. 2015.

Texas Manufacturing Survey. Dallas Fed Aug. 2015.

Texas Manufacturing Survey. Dallas Fed Aug. 2015.

Note:

The Dallas Fed conducts the Texas Manufacturing Outlook Survey monthly to obtain a timely assessment of the state’s factory activity. Data were collected Aug. 18–26, and 114 Texas manufacturers responded to the survey. Firms are asked whether output, employment, orders, prices and other indicators increased, decreased or remained unchanged over the previous month.

 

 

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.

It’s time to look at price changes without accounting for oil price effect.

After a year of declining crude oil prices which forged price spillovers all over the US economy, it is time for economists to look at price changes without accounting for the petrol effect. So far, 2015 has been a year in which dropping gas prices have affected almost every index from the US Bureau of Labor Statistics. Indeed, the Consumer Price Index started to decline since summer 2014 when the price of crude oil marked roughly U$107 per barrel. Since then, the Consumer Price Index declined continuously until January 2015. Likewise, the Producer Price Index, which behaves similarly, followed the decline until the beginning of the current year. However, both indexes started to increase from negative territory to positive areas up to 0.4 percent in July 2015, which is particularly the case of Producer Price Index.

So, if economists believed that oil prices accounted vastly for the overall decrease on Inflation, then, what is going on now with the hike in Indexes since oil prices are still low? The clear answer is that inflation has begun to bounce back.

Consumer Price Index and Producer Price Index

Consumer Price Index and Producer Price Index

Price statistics have begun to move wider than they did before the summer of 2014:

Generally speaking, data in Price Indexes show that price statistics have begun to move wider than they did before the summer of 2014. This trend marks a year of some sort of stagnation in Indexes that can be traced back to the spring of 2013. This period between summer 2013 and the summer 2014 looks almost flat for both indexes. Right after such a flat period, oil prices started to drop and so did both indexes. However, oil prices are still at record lows whereas the indexes started to rebound.

Therefore, it is time to scrutinize indexes in order to establish to what extent oil prices are still dragging down arithmetically consumer prices, and at the same time looking at the origin of current monetary pressures. By isolating prices from oil effect, several conclusions on prices can be drawn. First, inflation rate without accounting for energy prices, is higher than what got reported officially. Second, prices for “guest rooms”, which is to say tourism, may indicate people are spending conspicuously. And third, almost everything else -independent from oil- is increasing.

Final Demand Index less Foods and Energy.

Final Demand Index less Foods and Energy.

For instance, “in July, a 3.1 percent advance in margins for building materials, paint, and hardware wholesaling was a major factor in the increase in prices for services for intermediate demand. Furthermore, “the indexes for processed goods and feeds and for processed materials less food and energy moved up 0.9 percent and 0.1 percent respectively”, reported the US Bureau of Labor Statistics last August 14th 2015.

More in detail and in regards to final demand services, “over 40 percent of July increase in the index for final demand services is attributable to prices for “guest room rental”, which jumped 9.9 percent”. Clearly, prices are moving up whenever oil effect gets removed from calculations.

Expect an increase in interest rates:

US monetary authorities should be aware of these recent trends for sure. Therefore, it is reasonable to expect an increase in interest rates in order to curb down excessive consumer spending, particularly whatever spending gets associated with “guest room rentals”. Nonetheless, although this conclusion is drawn exclusively from the point of view of price stability, such a thing happens to be the main mandate of central banks.

“Discouraged Workers” are coming back into the labor market.

Data on employment levels for April and May 2015 look favorable.

Both months have shown increments above 200,000 jobs. However, the unemployment rate stubbornly hovers around 5.4%. In spite of U.S.’ GDP negative growth in 2015Q1, the U.S. job market seems to be growing at desirable pace. Although there is no clear answer for the persistent unemployment rate on 5.4%, the return of “Discouraged Workers” into the labor force might hold a clue. Accordingly to the U.S. Bureau of Labor Statistics (BLS), “over the past 12 months long-term unemployment has decreased by 888,000”, which might open a window for thinking on “Discouraged Workers” as a pressure preventing the rate to decrease further 5.4%. That pressure is hard to see inasmuch as we focus on month to month analysis and especially when we focus into a specific threshold for jobs gains.


x

Discouraged Workers:

So, the attention should be brought to the current dynamics of “Discouraged Workers”. That segment of the labor market should inform economists about two connected aspects. First, it may shed light onto current expectations of workers, which also has an interesting impact on consumer spending. Second, by focusing on “Discouraged Workers” economists may explain such a persistent Unemployment rate. Some data from BLS reveal “discouraged Workers” are coming back to reenter the labor market, which constitutes an upward pressure strong enough for the Unemployment Rate to start dropping significantly. It is worth noting that “Discouraged Workers” are not count as unemployed persons since they had not looked actively for a job during the four weeks preceding the BLS’ Survey.

 

y
Data wise, level of employment increased by 223,000 jobs in April 2015, and roughly by 201,000 in May. In April Job gains went mostly to Professional and Business Services, Health Care and Construction, the U.S. Bureau of Labor Statistics reported on June 2nd. Meanwhile, ADP reported on June 3rd that their estimates for May are 201,000 job added. Losses were on Mining in April accordingly to BLS, whereas ADP reported losses on Manufacturing in May 2015.

 

z

Finally, data from the U.S. Bureau of Labor Statistics show that on April 2015 there were literally no changes in the Unemployment Rate when compared to the same month in 2014. By looking at major groups, percentages are still the same for Asian which have the lowest rate at 4.4%, followed by Whites which is at 4.7%; Hispanics are 6.9% and African Americans at 9.6% unemployment rate. Nonetheless, jobs added to the economy for the month of April 2015 were roughly 223,000. Most of those job gains went on to Professional and Business Services sector, Health Care Business, and Construction. Mining though experienced losses due to low oil prices.

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Did the housing market affect negatively economic growth in 2015Q1?

Recent news on GDP 2015Q1 have many economists wondering about the possible domestic causes for such a negative growth (-.7%). The U.S. Bureau of Economic Analysis (BEA) did not hesitate in pointing out towards Investment in non-residential structures, which decrease 20%. Perhaps, data on housing market from both Construction Spending and Existing Home Sales might advance clues on what is going on in the U.S. economy currently. First, preliminary data on Construction Put in Place might shed light into what BEA signaled earlier, and data on Existing Housing Sales may complement an explanation, at least for as far as to the domestic economic dynamic concerns.

A

First, the Total Value of Residential Construction Put in Place in the U.S. economy decreased by 1.8% when comparing April 2014 to the most recent estimated statistics from the U.S. Census Bureau for April 2015. The estimated value for Private Residential Construction in April 2015 was roughly 353,086 million dollars, which totals 7,740 million less put in place than in April 2014. In spite of the decrease during April, official at the U.S. Census Bureau stated that “during the first 4 months of this year, construction spending amounted to $288.7 Billion, 4.1 percent (+/-1.5) above $277.3 Billion for the same period 2014”.

B

Perhaps the deceleration for the sector is being brought by Residential and Power sectors. The preliminary value of construction put in place for Residential and Power -type of constructions- went down during April 2015 inasmuch of -6,417 and -11,657 million dollars correspondingly, much of which came from a decrease of roughly 7,850 million dollars less pertaining the private sector and -3,808 million dollars less from the public sector. Though, the overall account got offset by increases in Manufacturing, Transportation and Commercial.

C

Since most of Construction Spending indicators went up in April 2015p, the question to ask economists is to whether or not the housing market actually slowed down economic growth during the first quarter of 2015; at least for the domestic side of the U.S. economy. Construction growth in Lodging and Commercial industries went up both by 17%, while Offices and Recreation related constructions did so by roughly 20% (April 2014 compared to April 2015p).

D

Data Source: U.S. Census Bureau. Data Overview: “The Value of Construction Put in Place Survey (VIP) provides monthly estimates of the total dollar value of construction work done in the U.S. The United States Code, Title 13, authorizes this program. The survey covers construction work done each month on new structures or improvements to existing structures for private and public sectors. Data estimates include the cost of labor and materials, cost of architectural and engineering work, overhead costs, interest and taxes paid during construction, and contractor’s profits. Data collection and estimation activities begin on the first day after the reference month and continue for about three weeks. Reported data and estimates are for activity taking place during the previous calendar month. The survey has been conducted monthly since 1964”.

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.