Real Earnings: Bureau of Labor Statistics, January 2016.

Press Release by the Bureau of Labor Statistics.

All employees Real average hourly earnings for all employees increased 0.4 percent from December to January, seasonally adjusted, the U.S. Bureau of Labor Statistics reported today. This result stems from a 0.5-percent increase in average hourly earnings combined with no change in the Consumer Price Index for All Urban Consumers (CPI-U).

Real average weekly earnings increased 0.7 percent over the month due to the increase in real average hourly earnings combined with a 0.3-percent increase in the average workweek.

Real average hourly earnings increased 1.1 percent, seasonally adjusted, from January 2015 to January 2016. This increase in real average hourly earnings combined with no change in the average workweek resulted in a 1.2-percent increase in real average weekly earnings over this period.

Production and nonsupervisory employees.

Real average hourly earnings for production and nonsupervisory employees increased 0.3 percent from December to January, seasonally adjusted. This result stems from a 0.3-percent increase in average hourly earnings combined with no change in the Consumer Price Index for Urban Wage Earners and Clerical Workers (CPI-W).

Real average weekly earnings increased 0.3 percent over the month due to the increase in real average hourly earnings combined with no change in average weekly hours. From January 2015 to January 2016, real average hourly earnings increased 1.3 percent, seasonally adjusted. The increase in real average hourly earnings combined with no change in the average workweek resulted in a 1.3-percent increase in real average weekly earnings over this period.

 

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Real Earnings Technical Note:

The earnings series presented in this release are derived from the Bureau of Labor Statistics’ Current Employment Statistics (CES) survey, a monthly establishment survey of employment, payroll, and hours. The deflators used for constant- dollar earnings series presented in this release come from the Consumer Price Indexes Programs. The Consumer Price Index for All Urban Consumers (CPI- U) is used to deflate the all employees series, while the Consumer Price Index for Urban Wage Earners and Clerical Workers (CPI-W) is used to deflate the production employees series.

Seasonally adjusted data are used for estimates of percent change from the same month a year ago for current and constant average hourly and weekly earnings. Special techniques are applied to the CES hours and earnings data in the seasonal adjustment process to mitigate the effect of certain calendar-related fluctuations. Thus, over-the-year changes of these hours and earnings are best measured using seasonally adjusted series. A discussion of the calendar-related fluctuations in the hours and earnings data and the special techniques to remove them is available in the February 2004 issue of Employment and Earnings or on the Internet under ‘Technical Notes’ (http://www.bls.gov/ces/).

Earnings series from the monthly establishment series are estimated arithmetic averages (means) of the hourly and weekly earnings of all jobs in the private nonfarm sector of the economy, as well as of all production and nonsupervisory jobs in the private nonfarm sector of the economy. Average hourly earnings estimates are derived by dividing the estimated industry payroll by the corresponding paid hours. Average weekly hours estimates are similarly derived by dividing estimated aggregate hours by the corresponding number of jobs. Average weekly earnings estimates are derived by multiplying the average hourly earnings and the average weekly hours estimates. This is equivalent to dividing the estimated payroll by the corresponding number of jobs The weekly and hourly earnings estimates for aggregate industries, such as the major industry sector and the total private sector averages printed in this release, are derived by summing the corresponding payroll, hours, and employment estimates of the component industries. As a result, each industry receives a “weight” in the published averages that corresponds to its current level of activity (employment or total hours). This further implies that fluctuations and varying trends in employment in high-wage versus low- wage industries as well as wage rate changes influence the earnings averages.

There are several characteristics of the series presented in this release that limit their suitability for some types of economic analyses. (1) The denominator for the all employee weekly earnings series is the number of private nonfarm jobs. Similarly, the denominator of the production employee weekly earnings series is the number of private nonfarm production and nonsupervisory employee jobs. This number includes full-time and part-time jobs as well as the jobs held by multiple jobholders in the private nonfarm sector. These factors tend to result in weekly earnings averages significantly lower than the corresponding numbers for full-time jobs. (2) Annual earnings averages can differ significantly from the result obtained by multiplying average weekly earnings times 52 weeks. The difference may be due to factors such as turnovers and layoffs. (3) The series are the average earnings of all employees or all production and nonsupervisory jobs, not the earnings average of “typical” jobs or jobs held by “typical” workers. Specifically, there are no adjustments for occupational, age, or schooling variations or for household type or location. Many studies have established the significance of these factors and that their impact varies over time.

Seasonally adjusted data are preferred by some users for analyzing general earnings trends in the economy since they eliminate the effect of changes that normally occur at the same time and in about the same magnitude each year and, therefore, reveal the underlying trends and cyclical movements. Changes in average earnings may be due to seasonal changes in the proportion of workers in high-wage and low-wage industries or occupations or to seasonal changes in the amount of overtime work, and so on.

 

A set of possible negative US economic shocks.

The puzzling aspect of recent data on inflation has been the deflation trajectory forged by oil prices. The index on energy by itself has fallen 28.7 percent over the year. Just in January 2016, the energy index declined 2.8 percent as gasoline index did so by 4.8 percent during the same month. The energy index has been dragging down the computational results of inflation severely to the extent that it makes the entire index hard to interpret. The truth of the matter is that oil prices’ downward trend has started, at least, to cast doubts on whether the offset in the overall inflation measure represents a relocation of resources within industries, or the index is masking a worrisome situation of an entire economic sector. In other words, with the decline in energy prices, could energy-related companies lead the US economy toward a slowdown?

By Catherine De Las Salas

By Catherine De Las Salas

Could energy-related companies lead the US economy toward a slowdown?

Current conditions and economic outlook in the United States have economists looking for signs of economic overheating by looking into the theoretical relation between unemployment and inflation. However, following the economic theory may work as a perilous distraction under the present situation. In theory, when the unemployment rate becomes very small, employers increase their salaries which in turn augments consumer spending. Such an increase in consumer spending leads to higher level of prices as the demand for goods surges. Then, given that news of unemployment have been certainly positive for the last six months, economists are cautiously focusing on inflation to determine whether or not the economy is overheating. This logic of analysis might generate bias as it derives conclusions from an arithmetic average on the consumer price index.

We are left with Monetary shocks, oil shocks, or a deterioration of global economic conditions:

More precisely, the fact that energy index offsets currently core inflation keeps economists in their theory comfort zone by ignoring oil sector volatility. On one hand, they see households in a proper position as their liabilities have declined by 12 percent during the so-called “Great Deleveraging” period. Specifically, economists at the Federal Reserve Bank of New York claim that this very fact makes the household sector more resilient to absorb shocks, which seems reasonable. Also, they stress that the financial sector appears strong as the sector counts with larger liquidity buffer now than in preceding years. Further, Fed’s officials see good news in regards to the labor market and unemployment rate, which has dropped to a national average of 4.9 percent –also positive. On the fiscal front, it seems clear to most of the people that events such as the sequester of 2013 are unlikely to happen in the foreseeable future. Technology shock-wise, no negative shocks appear to linger in the horizon. Therefore, by discarding the set of possibilities on surprising negative economic shocks, the only ones lingering are either monetary shocks, oil shocks, or a deterioration of global economic conditions.

Now, if America trusts their monetary authorities, then the only standing threats are oil shocks and an international economic slowdown. Red flags have been waved during the last six months stressing the levels of debt of petroleum companies. Some estimates coming from MarketRealists.com point to numbers of around U$200 billion debt that may be approaching default soon. It is worth remembering that in the midst of the Great Recession in 2008 losses on mortgages were around U$300 billion. Although acknowledging the difference between housing sector’s debt and oil companies’ debt is a must for any analysis, the risk is somewhat similar at least regarding magnitude.

Have student loans outstripped mortgage debt?

Recent data released by the Federal Reserve Bank of New York show mortgage credit has not expanded much since the beginning of the current economic expansion. Unlike many other loans products, Mortgage and home equity line of credit have not grown at the same pace that they used to before the Great Recession. Economists at the New York Fed expected mortgage debt to increase as fast as house prices do, which is a trend they observed during the expansion right before the Great Recession. However, mortgage debt has not done so. Instead, researchers at the bank found plausible that student loans might have outstripped mortgages loans over the last three years. This article takes on the issue and concludes that it is too premature to say that such is the case.

The Fed’s analysis:

The Fed’s analysis goes like the following. William C Dudley, CEO of the Bank, starts by flagging the situation. In his words, “there are other difficult challenges that many households face, particularly with respect to a subject we’ve discussed on previous occasions – student loans”. Andrew Haughwout, Head of Microeconomics Studies at the bank, seconds him by noting that this time around houses prices are up more than one third, whereas mortgages debt has barely grown by one percent since early 2012. Haughwout focuses in explaining data on mortgages, for which he claims there is “a stark contrast to last expansion” in which “both prices and debt roughly doubled” between the years of 2000 and 2006. Both economists pointed towards student loans to explain partially the current situation of the household balance sheet. In other words, the fact that mortgages are not adding debt into the Household balance sheets, begs the question of what is indeed doing it.

Google’s search terms may help out in complementing:

This article takes on the issue by looking at a similar but higher frequency data. In order to expand what economists at the New York’s Fed found, this article uses a time series of the Google’s search terms “mortgage calculator” and “student loans”. I assume both terms reveal the willingness of the American population to at least apply for either of the two lines of credit. In other words, I believe Google’s search terms unveil the interest random people have on such a products over time. Working with these two search terms implies that households face some leisure-labor model constraint. This constraint means that given the deterioration of economic conditions under the Great Recession, households were forced into the school and had to choose to study rather than work. Thus, technically, those two choices became “exclusive” during the recovery from the Great Recession.

That being said, I split the data in two to show how this time around the situation is different. First, a period right before the Great Recession stretching from 2004 until 2009; and a second period right after the Great Recession spanning from 2009 towards 2016. The outcome of splitting the data on those two cycles works for showing how the relationship has changed since 2009.

The data.

Graph 1 shows the two search terms over time. It is clear how “mortgage calculator” has declined from about the half of the length period. The term “student loans” instead has kept up over time, even while the economy entered the Great Recession.

Graph 1.

Over tiem

Graph 2 presents us with the behavior of the data during the first period ranging from 2004 towards 2009. The term “mortgage calculator” surpasses the term “student loans” by the end of the period length. Otherwise, Graph 3 shows how the term “student loans” outstripped “mortgage calculator” apparently by the end of the period.

Graph 2.

2004

Graph 3.

2009

Results:

When I run the regression, the results are somewhat similar to the graphical analysis. Table 1 summarizes the model and the mentioned two breakdowns of the data. The “all time” model covers data starting on 2004 until what has forgone of 2016. The first breakdown covers 2004 until 2009 while the second breakdown covers 2009-2016. The data on this first regression are the natural logarithms of the Google’s search terms, for which the first difference was applied. The estimated beta for the “all time” 2004-2016 model is .66. On contrast, the estimated coefficient for the first break-down of the data is .97; whereas the second breakdown of the data shows a coefficient of .81.

Table 1.

Table 1

The length period 2004-2009 shows an almost parallel growth between both terms. On the other hand, the length period 2009-2016 shows a slower rate of change of roughly four-fifths in the relationship. Apparently, there appears to be a deceleration of the “mortgage calculator” term relative to the “student loans” term. However, although the data show some contrasts across periods, it is still too premature to conclude that “student loans” have outstripped “mortgage calculator”, which in our theory equals to say that student loans have outperformed mortgage loans. The reason for stating cautiously this is the fact that the “all-time” estimated beta is considerable lower (.66) than the estimated beta of the second period 2009-2016. Therefore, as of today and by using these “Big data” sources, it is hard to conclude that student loans have surpassed mortgage loans in the balance sheets of American households.

Los Angeles’ Homelessness Crisis and the abuse of the term ‘chronic homelessness’.

Los Angeles’ failure to cope with homelessness has brought the issue to the light of many who believe that such a thing does not happen in the richest and powerful country on earth. Among the interesting facts, branches the distinction between both terms Homelessness and Chronic Homelessness. The nuance is relevant inasmuch as for public policy purposes Chronic Homelessness means something entirely different from just a growing crisis on the homeless population. The fact that Los Angeles tops the nation in homeless population makes regular people (including policy makers) think that since there is a crisis, every homeless is chronic. Even mainstream journalism reports both terms indistinctively. Peeling out layers on homelessness leads to a better understanding of the phenomenon thereby rising community expectations for homelessness plans and policies. This brief article stresses the essential differences that make a homeless situation chronic.

Let us start with the confusion in mainstream media. Los Angeles Times and The New York Times reported on their sites the following sentences. Note that the use of the terms, homeless and chronically homeless, is applied indistinctively.

“L.A.’s chronically homeless population has grown 55%, to 12,536, since 2013, accounting for almost 15% of all people in that category, HUD reported. More than one-third of the nation’s chronically homeless live in California, the agency added” (Los Angeles Times. November 19, 2015).

“The number of chronically homeless people nationwide remained basically flat, rising 1%, the report said”.

“The nationwide numbers came as a disappointment to HUD, which had extended a goal of ending chronic homelessness from the end of the year to 2017″.

“The government classifies disabled people who go without housing for a year, or who land in the street several times over three years, as chronically homeless“.

In spite of the last sentence in which the article on Los Angeles Times quotes the Federal Government in its definition of “chronic homelessness, the use of the term remains ambiguous for most of the readers. Plus, although both newspapers do a good job in informing by using data and quoting qualitative sources, the overall purpose gets defeated by the lack of clarity of the concepts. Then, the question worth asking is the following, what is chronic homelessness and how it differs from homelessness alone?

Chronic homelessness:

Homeless or house poor is every person who cannot afford to pay for shelter, whereas chronic homeless falls into a more complex definition. Among experts, the term “chronic homelessness” has emerged to define not only the absence of physical shelter but also shared psychological conditions among the homeless population. Piliavin et. al. (1996) points out several important factors that are often associated with causes of chronic homelessness. Among those factors is the lack of institutional support which is defined as having weak ties with institutions such as Employment, Marriage, Youth, and even Family. Another factor is Human capital deficiencies which are understood as poor relationship building skills. Personal disability is a third factor which ranges from substance abuse to mental health. Finally, acculturation is the last factor Piliavin et. al. (1996) relate.

Of primary relevance to Los Angeles’ crisis is the last cited aspect. Acculturation plays a significant role in determining the length of homelessness status. Shared stories, shared needs and shared means to survive among homeless individuals may affect the lasting permanence of a person’s homeless status. This phenomenon of acculturation evolves through a friendship building processes among homeless persons. Plain and simply put, homeless individuals with more homeless friends are more likely to remain homeless, therefore chronically homeless.

Also and regardless of ethnic groups, age, gender and marital status, chronic homelessness is portrayed as a phenomenon derived from mental illnesses such as depression, schizophrenia or psychotic disorders. Personal disabilities range from substance abuse to mental health. Risk factors for homelessness within this factor include social poverty, economic poverty, feeling unloved in childhood, mental disorder, and low level of friend support, sexual, drug or physical abuse and parental divorce.

Finally, structural pressures are to blame when it comes to chronic homelessness. Excessive Individualism among member of the society seems to lurk underneath the deterioration of social capital. In other words, our society reproduces cultural and ideological pressures that blame the homeless person as solely responsible for his situation (Lee et al., 1990). Experts believe that dysfunctional family environments, for example, homes headed by young (ages between 17 and 25) and female, in addition to social and cultural pressures, and a lack of public support, is a classic formula for chronic homelessness. Axelson and Dali (1998) state that chronic homelessness is an outcome of a lack of family and social support. In most, cases homeless women report having been physically abused.

Conclusion:

In conclusion, nuances in homelessness must be considered when dealing with public programs intended to curb down the phenomenon. Policy-wise, these differences may affect the target and the outcomes of policies being formulated by government officials. Furthermore, it is evident that the crisis comes not only from the lack of public programs and strong public organizations but also from the weakness of social bonds and links. Thus, besides these institutional and social facades of homelessness, media must also illustrate the public so that we all know there is a shared responsibility in solving chronic homelessness. Even the family, as an institution has a role that needs to be addressed in Los Angeles’ homelessness crisis.

 

References.

Lee, Barrett A., Sue Hinze Jones and David W. Lewis (1990). Public Beliefs about the Causes of Homelessness. Social Forces, Vol. 69, No. 1.

Piliavin, Irving, Bradley R. Entner Wright, Robert D. Mare and Alex H. Westerfelt (1996). Exits from and Returns to Homelessness. Social Service Review, Vol. 70, No. 1.

Axelson, Leland J and Paula W. Dail, (1988). The Changing Character of Homelessness in the United States. Family Relations, Vol. 37, No. 4.

 

 

“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.

Summer’s economic optimism vanishes with the season in the Midwest.

Summer enthusiasm in the Midwest lasted short. The season has not ended yet, as business leaders started to pose concerns in the months to come. The Tenth District Manufacturing Survey revealed that manufacturing activity declined moderately in August 2015. Not only manufacturing declined, but also optimism about future economic activity. Economic expectation in the Midwest are being tempered by the strength of the dollar versus other currencies -especially China’s- around the world, as well as oil prices. Manufacturer leaders expect no increases in most of the matters they are asked about.

By Catherine De Las Salas.

According to Chad Wilkerson, vice president and economist of the Bank, “survey respondents reported that weak oil and gas activity along with a strong dollar continued to weigh on regional factories”. In addition, when manufacturers identify strong dollar as one of their economic challenges, they also relate China’s devaluation of the renminbi. Even though it is very unlikely that the present survey had captured the real effect of such monetary effect, one survey respondent stated that “I speak to many business executives who do exporting, and all seem considerably concern about the dollar strength and the devaluation of the Chinese currency”.

Manufacturer’s opinions on this type of surveys yield insights for analysts to sort out in terms of business leader’s expectations of future profits. It is widely accepted by economists that both current and future output determine heavily investment decisions. Future profits is indeed in every CEO’s reckoning of investment plans. Thereby, investment ends up driving regional economic growth. Thus, the horizon for the fall and winter of 2015 does not look promising for some of the respondents to the survey, all of which live and conduct business in the states of Wyoming, Oklahoma, Kansas, Colorado, Nebraska, the western third of Missouri, and the northern half of New Mexico.

More in detail, the over-the-month change in the Composite Index, which features an average of all indexes, decreased after three months of solid gains. The index had gone up from -13 to -7 in July. However, it dropped again to -9 in August. One year ago the same measure hovered on 3 and was trending upwards in positive terrain.

 

Kansas Manufacturing Composite Index. August 2015.

Kansas Manufacturing Composite Index. August 2015.

Looking at individual indexes, the prospective might give some hope for those who like to see the glass half full. For instance, in terms of number of employees the index shows improvements in spite of it remaining in negative numbers. Number of employees’ index went from -19 up to -10 in the month of August. Similar changes were registered for the average of employees’ workweek. Also, new orders for exports index showed a bit of speed the period of the survey. One of the respondents commented that “…Our year to date has been up from last year and our cash flow position is better; however, the next six months appear shaky at best”.

Kansas Manufacturing Number of Employees Index. August 2015.

Kansas Manufacturing Number of Employees Index. August 2015.

Kansas Manufacturing Workweek Index. August 2015.

Kansas Manufacturing Workweek Index. August 2015.

Kansas Manufacturing Exports Index. August 2015.

Kansas Manufacturing Exports Index. August 2015.

Kansas Manufacturing Production Index. August 2015.

Kansas Manufacturing Production Index. August 2015.

Kansas Manufacturing Inventories Index. August 2015.

Kansas Manufacturing Inventories Index. August 2015.

Kansas Manufacturing Inventories Index. August 2015.

Kansas Manufacturing Inventories Index. August 2015.

Kansas Manufacturing Prices Index. August 2015.

Kansas Manufacturing Prices Index. August 2015.

Kansas Manufacturing Prices paid Index. August 2015.

Kansas Manufacturing Prices paid Index. August 2015.

Kansas Manufacturing Supply delivery Index. August 2015.

Kansas Manufacturing Supply delivery Index. August 2015.

Kansas Manufacturing Shipments Index. August 2015.

Kansas Manufacturing Shipments Index. August 2015.

Kansas Manufacturing Backlogs Index. August 2015.

Kansas Manufacturing Backlogs Index. August 2015.

Kansas Manufacturing Volume of orders Index. August 2015.

Kansas Manufacturing Volume of orders Index. August 2015.

“Core” inflation rate will have huge influence on monetary policy next month.

Second Estimates for real GDP growth in the United States indicate that the economy grew at 3.7 percent during the second quarter of 2015 after correcting by price change. The report from the Bureau of Economic Analysis informs that the change mainly derived from positive contribution of consumer spending, exports, and spending of state and local governments. These increases are said to have been offset by a deceleration in private inventory investment, federal government investment, and residential fixed investment. The revised figure for first quarter of 2015 went up from -0.7 percent to 0.6 percent.

Besides real GDP calculations stand the estimates for prices changes in goods and purchases made by American residents that the Bureau of Economic Analysis (BEA) does simultaneously to the calculations made by the Bureau of Labor Statistics (BLS). In this regard, this time around the second quarter, prices had a positive growth of roughly 1.6 percent, which the BEA reports was derived from an increase in both consumer prices, and prices paid by local and state governments. Please bear in mind that, in the first quarter of 2015, prices were said to have dragged down the GDP numbers since the index decreased by roughly 1.1 percent change.

H&M Store in Broadway NYC. By Catherine De Las Salas. Summer 2015.

H&M Store in Broadway NYC. By Catherine De Las Salas. Summer 2015.

These price changes are actually good news for the Federal Reserve System for whom a moderate upswing in inflation helps them to achieve their yearly monetary goal of 2.0 percent inflation rate. And for those of whom like to make economic forecast, these figures mount onto their analysis for determining whether or not the Federal Reserve will increase interest rates in September. So, although real GDP measures are certainly corrected for price changes, the BEA’s price index will -on its own- have huge influence on monetary policy options for the months to come.

Thus, relevant data nowadays stem from BEA’s “core” inflation rate, which is to say price change without food prices and energy prices. Indeed, when figures isolate energy and foods volatility, the measure of inflation reaches 1.8 percent change from the first quarter of 2015. These changes in prices and output rightly affect the wallet of American residents. Price changes, plus increases in output -which reflect decreases in unemployment rate- may take consumer and producers to edge up their spending, which was one of the factor behind positive change in real GDP growth as mentioned above. Then, whenever spending tends to accelerate beyond its capacity the Federal Reserve reacts with an increase in interests rates. Even though one could argue that such is not currently the case, given that data on capacity utilization clearly shows that the American Economy has room to further spending, the BEA’s “core” inflation will be the measure that could possible make Federal Reserve Officials think twice about interest rates.

So, the puzzle about what the Federal Reserve will end up doing next Federal Open Market Committee meeting is fourfold, and it will derive from the different sources of data: first, price change data from BEA, which BEA claims to be way more “accurate” than BLS’. GDP growth from BEA, which is calculated by correcting price changes with their own price index. Price change from BLS, which may vary from BEA’s calculations. And capacity utilization from the Federal Reserve, which is whom finally decides on interest rates changes.

Despite job losses, New Jersey’s labor market looks vibrant rather than sclerotic.

Regional and State statistics on employment and unemployment for the month of July 2015 looked motionless for the great majority of States in terms of over-the-month changes. Over-the-year though, nonfarm employment increased in 47 states and deceased in 2. In terms of employment levels, the greatest over-the-month increases were seen in California (+80,700), Texas (+31,400) and Florida (+30,500); while percentage wise, greatest increases were in Wyoming (+0.9 percent), Oklahoma (+0.7 percent), and Rhode Island (+0.7 percent). It is worth noting that a year ago, Rhode Island had an unemployment rate of 7.6 percent, while California’s was about 7.4 percent. Today, those two states reported unemployment rates of 5.8 percent (Rhode Island) while California recorded 6.2 percent.

Unemployment Rate July 2015.

Unemployment Rate July 2015.

Otherwise, declines in employment levels were statistically significant in North Dakota (-0.5 percent), Hawaii, Kansas, New Jersey, and West Virginia with -0.3 percent decline each. West Virginia noted an increase of 1 percent point and registered an unemployment rate of 7.5 percent. Both Dakotas also showed increases in Unemployment rate.

The challenging aspects for the analysis this time stem from the data coming out from New Jersey, Kansas and Louisiana. These three states showed decreases in employment level from June to July 2015. New Jersey’s level of employment decreased by -13,600 jobs, while Louisiana and Kansas did so also by -4,500 and -4,300 respectively. Given that the declines happened during the summer season, they all beg the question on whether those job losses were quits or separations.

When it comes to labor markets, employment levels can have negative variation for two reasons. First, firms may stop hiring new employees and further start firing the current ones. Second, employees may quite their jobs. In order to be accurate, it is key for the analysts to determining under what circumstances the drop in the statistic happened. The most expedited way to find out, whether the job losses were on the firm’s end or on the employee’s end, used to be by looking at Massive Layoff data from the BLS. However, the Massive Layoff program ended since the budget cuts fights in 2013 between Republicans and Democrats.

So, going back to New Jersey’s employment level data for the month of July 2015, intuitively it is hard to believe that a job drop happened in the state during the summer, which only has happened 13 times in almost 40 years –five of which happened since the Great Recession Started-, and it has done so mostly during economic recessions. So, particularly in the case of New Jersey, the question about quits versus separations begs an answer.

New Jersey's Level of Employment Change June-July 1076-2015.

New Jersey’s Level of Employment Change June-July 1976-2015.

Given that there is not Massive Layoff data available, one way to scratch the surface of what is going on in the State’s labor market is by looking at its output and current economic conditions. Indeed, the southern part of the state -Lehigh Valley and the Southern Jersey Shore- have seen a slowdown in real estate markets. The region, which is covered by the Eleven District of Philadelphia at the Federal Reserve System, has experienced moderate to positive changes in the economy through the second quarter of 2015. In particular, regionally speaking, auto-dealers have seen flat sales during the summer. Home builders also reported little change in activity for the same period. Likewise, and although manufacturing picked a bit up, food products, primary metals and electronic products have seen sales decreases. Similarly, staffing firms reported slowdowns as well as trucking activity showed signs of weakness.

Apparently, there is no drama when it comes to assess the current economic condition of the region. Besides the industries cited above, every other sector reported moderate improvements. Thus, the overall economic conditions of the state are not that bad so as to expect such a drop in employment levels. In fact, the State Unemployment Rate has declined since 2009 to 6.5 percent. Right after the Great Recession started, New Jersey’s Unemployment rate was over 9 percent. Even though the state’s labor market recovery appears to be slow, it also looks steady. Therefore, what seems feasible to interpret under the current circumstances is that New Jersey’s labor market looks vibrant rather than sclerotic. That is, workers in New Jersey quitted their job for better opportunities elsewhere.

Real Earnings and the use of Dubious Statistics.

The use of the Average Statistic deceives readers very often whenever the Mean gets severely affected by outliers within the data. One of the most repeated critics to data analysts is the unaware use of average figures, which frequently leads to dubious generalizations. Social scientists, those of whom refuse to use statistics in their analysis, commonly attack this analytical tool by saying: ok, so… if you eat a chicken and I do not eat anything, in average… we both have had half chicken. Nobody would oppose that conclusion as wrong and deceiving. However, such a reasoning uses just half of the procedure statisticians and econometricians use for determining whether or not the conclusion is statistically valid. Therefore, although it is evident that none of the subject in the example ate half a chicken, it is also true that the analysis is half way done.

Outliers heavily affect the Mean statistic:

There is no question that all types of statistics have limited interpretations. In the case of the Mean (arithmetic average), outliers heavily affect the statistic, thereby –very often- the analysis. However, that does not mean arithmetic averages cannot illuminate wise conclusions. For instance Real Earnings, which is a very easy deceiving data on labor economics. Data on Real Earnings “are the estimated arithmetic averages (Means) of the hourly and weekly earnings of all jobs in the private non-farm sector in the economy”. Real Earnings are derived by the US Census Bureau of Labor Statistics from the Current Employment Statistics (CES) survey. So, any unaware reader could jump quickly on to ask if Real Earnings are the average of the hourly earnings of all Americans working in the non-farm private sector. Thus, analysts may also quick respond that in fact that is true. Then, most of the times, the follow up question would read as the following: Does Real Earnings mean that as a “typical” worker in the United States, I would make such an average? The answer is no, it does not. There is precisely where statistical analysis starts to work.

Few Examples:

First. In terms of worker’s earnings various aspects determine how much money people make per hour. Educational attainment is perhaps the greatest determinant of earnings in the American economy. One also can think of geography as a factor of income per hour; even taxes could have an effect on how much money a worker does; age clearly controls income; so on and so forth. Intuitively, it is possible to see that for Earnings and Income there might be many exogenous factors influencing its variability.

Second. For the sake of discussion, let us say that neither education nor taxes affect hourly income of workers. In such a case, and at first glance, it is naïve to believe that counting such a low number of observations could work for any type of analysis, regardless of it being qualitative or quantitative. That means basically that for both qualitative and quantitative analysis, the number of observations matters a lot. In quantitative research the threshold number of observation hovers around 30. Hence, sample size are crucial not only for debunking the cited joke above, but also for reaching valuable conclusion in both qualitative and quantitative social science research.

Taking Real Earnings as example has no pitfall of the latter kind, but it surely does on the former, which certainly bounds the set of conclusion analysts can make. As an Average statistic, Real Earnings have a numerator and a denominator, for which the number in the series is the number of nonfarm private jobs. All types of jobs are included, regardless of age, education attainment, location, taxes, and etcetera. In other words, the companies CEO’s salaries may pull up the statistic. Conversely, minimum wage earners could drag down the Average.

The Median statistic would do a better job sometimes:

At this point, it is clear that for some social science analysis, perhaps other type of statistics happen to be rather more suitable. For instance, the median would help analysts better understand income. So, why should one consider such a computation on Real Earnings? The answer is that Averages figures can be really useful as long as the analyst makes thorough caveats on what the Average really tells; and more importantly, limitations on what the Average figure does not tell.

Real Earnings. Data Source: US Bureau of Labor Statistics.

Real Earnings. Data Source: US Bureau of Labor Statistics.

Hence, changes in Real Earnings shed light onto changes in the proportion of workers in high-wages and low-wages industries or occupations. High-wages salaries will tend to, as in the CEO’s example above, pull up the average without substantial change in the number of hours worked. Conversely, as in the example of the minimum wage earners above, low-wages industries or occupations will tend to lower the outcome statistic. Furthermore, when paired with other data, Real Earnings could be useful for noticing improvements in use technology. If the number of work hours remains stagnant, but both earnings and employment levels increase, the net effect might stem from improvements in technology, which turns on increasing productivity. In other words, workers may work smarter rather than harder and longer. Lastly, Real Earnings Averages can also inform analysts about the amount of overtime work.

So, uses of Arithmetic Means, such as Real Earnings, can be thought-provoking. However, much caution has to be considered whenever economic assertions are stated.