Rent Prices Stickiness and the Latest CPI Data.

Fear of increasing inflation in the U.S. appear to be the trigger behind the market volatility of previous weeks. Recent gains in hourly compensation to workers have had analysts measuring the effect of wages on inflation. In turn, analysts began pondering changes in Fed’s monetary policy due to the apparent overheating path of the economy; which is believed to be mostly led by low unemployment rate and tight labor markets. Thus, within the broad measure of inflation, the piece that will help to complete the puzzle comes from housing market data. Although the item “Shelter” in Consumer Price Index was among the biggest increases for the month of January 2018, for technical definitions, its estimation does weight down the effect of housing prices over the CPI. Despite the strong argument on BLS’ imputation of Owner-Occupied Equivalent Rent, I consider relevant to take a closer look at the Shelter component of the CPI from a different perspective. That is, despite the apparent farfetched correlation between housing prices and market rents, it is worth visualizing how such correlation might hypothetically work and affect inflation. The first step in doing so is identifying the likely magnitude of the effect of house prices over the estimates and calculation of rent prices.

Given what we know so far about rent prices stickiness, Shelter cost estimation, and interest rates, the challenge in completing the puzzle consists of understanding the linking element between housing prices (which are considered capital goods instead of consumables) and inflation. Such link can be traced by looking at the relation between home prices and the price-to-rent ratio. In bridging the conceptual differences between capital goods (not measured in CPI) and consumables (measured in CPI) the Bureau of Labor Statistics forged a proxy for the amount a homeowner ought to pay if the house was rented instead: Owner-Occupied Equivalent Rent. This proxy hides the market value of the house by simply equaling nearby rent prices without controlling by house quality. Perhaps, Real Estate professional can shed light onto this matter.

The Setting Rent Prices by Brokers.

It is often said that rental prices do not move in the same direction as housing prices. Indeed, in an interview with Real Estate professional Hamilton Rodrigues from, he claimed that there is not such a relationship. Nonetheless, when asked about how he sets prices for newly rent properties, his answer hints at a link between housing prices and rent prices. Mr. Rodrigues’ estimates for rent prices equal either the average or the median of at least five “comparable” properties within a mile radius. The key word in Mr. Rodrigues statement is comparable. As a broker, he knows that rent prices go up if the value of the house goes up because of house improvements and remodeling. Those home improvements represent a deal-breaker from the observed stickiness of rent prices.

For the same reason, when a house gets an overhaul, one may expect a bump in rent price. That bump must reflect in CPI and inflation. I took Zillow’s data for December of 2017 for the fifty U.S. States, and run a simple linear OLS model. By modeling the Log of Price-to-Rent Ratio Index as a dependent outcome of housing prices -I believe- it will be feasible to infer an evident spillover of increasing house prices over current inflation expectations. The two independent variables are the Logs of House Price Index bottom tier and the Logs of House Prices Index top tier. I assume here that when a house gets an overhaul, it will switch from the bottom tier data set to the top tier data set.

Results and Conclusion.

The result table below shows the beta coefficients are consistent with what one might expect: the top tier index has a more substantial impact in the variation of the Price-to-Rent variable (estimated β₂= .12, and standardized β=.24, versus β=.06 for the Bottom tier). Hence, I would infer that overhauls might signal the link through which houses as a capital goods could affect consumption indexes (CPI and CEI). Once one has figured the effect of house prices on inflation, the picture of rising inflation nowadays will get clearer and more precise. By this means predictions on Fed tightening and accommodating policies will become more evident as well.

U.S. economic slowdown? Look at Real Estate labor market.

One month of weak payroll data does not make a crisis. The US economy appears to have added only 160,000 new jobs during the month of April in 2016, the Bureau of Labor Statistics reported on Friday. A similar number was published earlier in that week by the payroll firm ADP. Although the slowdown in hiring came from local and federal government (-11,000) as well as from mining (8,000), the sector that should get more attention is Real Estate and Leasing Services. Indeed, this sector could be revealing what is happening in the current economic conditions.

For the last economic quarter, analysts have seen employment growth being incongruent when compared to GDP growth. And now that the employment payroll looks weak, many analysts would like to rush and call out an economic recession. However, it is too soon for asserting anything akin a crisis mainly because the slowdown in hiring came from local and federal government (-11,000) as well as from mining (8,000). Those two sectors were expected not to grow given that oil prices are still low, and the electoral cycle continues. Retail trade also failed to add jobs at the same pace the sector was adding during the past three months, but the -3,000 jobs slowdown is not alarming since the industry’s previous growth was strong.

Otherwise, the sector that should get more attention is Real Estate and Leasing Services as the spring season brings business to their stores. Establishing how busy real estate agents are around this time of the year could shed light onto how the economy is running actually for two reasons. Not only because weather season affects their business cycle, but also because their business depends highly on the interest rate. In fact, Real Estate labor market seems anemic lately. The sector’s change over the month of April seems to have added about 600 new jobs, which is certainly poor for what the season should have demanded.

The fact that housing sales depend on interest rates allows for inferences on how expectations on Federal Reserve bonds influence the job market. In other words, the anemic employment growth in Real Estate appears not to derive from a sluggish demand for housing as it does from interest rate expectations. Thus, persistent market speculations on rising interest rates could have had an effect on current consumer expectation on both housing and consumption. Therefore, it seems logical to think that because of that companies halted hiring in April, especially the Real Estate ones. Only time will unveil the outcome though.

The focus right now is on the next meeting of the Federal Open Market Committee in which monetary policy maker will decide again whether to increase the rates or leave them unchanged.

Internal demand strengthens as external conditions weaken.

Main national economic indicators reveal a solidifying moment of the American economy. In spite of job losses in mining and oil-related sectors, total nonfarm payroll employment increased by 242,000 in February; and although the unemployment rate kept unmovingly, the economy shows signs of very good standing relative to past winter season data. The biggest risk, though, is probably to come from outside the United States. In that regard, the latest data on international trade in goods and services confirm that the economic momentum in being built on the internal demand for goods, whereas the international market weakens. In other words, foreign trade is not adding much to the current economic expansion given that both imports and exports decreased in January. With the dollar as it stands currently, what analysts expect to see is a big inflow of trade, which has not realized yet. That could be somewhat worrisome.

By Catherine De Las Salas

By Catherine De Las Salas

Countries have not found their way in:

Most of the accounts of trade balance declined in January. Countries have not found the way in for commodities even when the US Dollar remains high. In fact, the US Balance of Trade in 2015 exhibited a positive trend with a net gain of U$851 million of dollars (Graph 1 below). In January, imports of goods declined U$2.9 billion as a result of a noticeable drop in the value of Crude Oil imports, and a decrease in Capital Goods. On the other hand, exports of Goods fell U$4.0 billion mainly as a result of small international sales of Capital Goods and Industrial Supplies Materials. Nevertheless, those decreases, exports of services increased especially on Travel for all purposes and transportation.

No analyst expects to see US Exports to grow considerably currently. Otherwise, economists expect overseas countries to take advantage of the current dollar rate, which has not happened yet. US Exports deteriorate due to strong dollar abroad. The deficit in the Balance of Trade continues to grow negatively for the United States in spite of 2015 being a good year. The deficit increased by $U2.1 billion over the year, which correspond to 4.8 percent when compared January 2016 and January 2016. Exports decreased U$12.5 billion over the year as Imports did so too by U$10.5 billion. Over the month changes in the Balance of Trade registered only positive increases in exports of Services. All these data beg the question about international markets. Why countries overseas are not selling to the United States?

U.S. Balance of Trade

Internal demand is gaining momentum:

With almost every international trade indicator declining, what is feasible to infer about the economy is that the internal demand for good and manufacturing is gaining momentum. The evidence rests on employment data. Just in the past three months, payroll data has shown an average increase of 228,000 jobs created per month. Usually, employment creation in January and February are not that good because of the weather. February 2016 employment data exhibited gains in Healthcare (+38,000), Retail trade (+55,000), Food and Services (+40,000) and Construction (+19,000). Retail trade, Food and Services, and Construction usually are affected by weather conditions. This year seems to be different.


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.



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’ (

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 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.

“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? 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.


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


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


Florence-Muscle Shoals.


Lake Havasu City-Kingman.
Sierra Vista-Douglas.

Fort Smith.
Hot Springs.
Little Rock-North Little Rock-Conway.
Pine Bluff.

El Centro.
Los Angeles-Long Beach-Anaheim.
Oxnard-Thousand Oaks-Ventura.
Riverside-San Bernardino-Ontario.
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.
Yuba City.

Colorado Springs.
Fort Collins.
Grand Junction.

Hartford-West Hartford-East Hartford.
New Haven.
Norwich-New London-Westerly.

Salisbury (1).

District of Columbia.

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

Athens-Clarke County.
Atlanta-Sandy Springs-Roswell.
Augusta-Richmond County.
Warner Robins

Urban Honolulu.

Boise City.
Coeur d’Alene.
Idaho Falls.

Davenport-Moline-Rock Island (1).

Fort Wayne.
Lafayette-West Lafayette.
Michigan City-La Porte.
South Bend-Mishawaka.
Terre Haute.

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


Bowling Green.
Elizabethtown-Fort Knox.
Louisville/Jefferson County.

Baton Rouge.
Lake Charles.
New Orleans-Metairie.
Shreveport-Bossier City.

Portland-South Portland.

California-Lexington Park.

Barnstable Town.
New Bedford.

Ann Arbor.
Battle Creek.
Bay City.
Grand Rapids-Wyoming.
Lansing-East Lansing.
Niles-Benton Harbor.

Mankato-North Mankato.
Minneapolis-St. Paul-Bloomington.
St. Cloud.


Cape Girardeau.
Jefferson City
Kansas City.
St. Joseph.
St. Louis (2).

Great Falls.

Grand Island.
Omaha-Council Bluffs

Carson City.
Las Vegas-Henderson-Paradise.

New Hampshire.

New Jersey.
Atlantic City-Hammonton.
Ocean City.

New Mexico.
Las Cruces.
Santa Fe.

New York.
Buffalo-Cheektowaga-Niagara Falls.
Glens Falls.
New York-Newark-Jersey City.
Watertown-Fort Drum.

North Carolina.
Durham-Chapel Hill.
Greensboro-High Point.
New Bern.
Rocky Mount.

North Dakota.
Grand Forks.

Weirton-Steubenville (1)

Oklahoma City

Grants Pass

East Stroudsburg.
State College.

Rhode Island.

South Carolina.
Charleston-North Charleston.
Hilton Head Island-Bluffton-Beaufort.
Myrtle Beach-Conway-North Myrtle Beach

South Dakota.
Rapid City.
Sioux Falls.

Johnson City.
Nashville-Davidson–Murfreesboro– Franklin.

Austin-Round Rock.
Beaumont-Port Arthur.
College Station-Bryan.
Corpus Christi.
Dallas-Fort Worth-Arlington.
El Paso.
Houston-The Woodlands-Sugar Land.
San Angelo.
San Antonio-New Braunfels.
Wichita Falls.

St. George
Salt Lake City.

Burlington-South Burlington.

Virginia Beach-Norfolk-Newport News

Mount Vernon-Anacortes.
Spokane-Spokane Valley.
Walla Walla.

West Virginia.

Eau Claire.
Fond du Lac.
Green Bay
La Crosse-Onalaska.
Milwaukee-Waukesha-West Allis.


Puerto Rico.
San German.
San Juan-Carolina-Caguas.

“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.