Timorous evidence of “Contagious Effect” after Dow Sell-Off.

The stock market seems to be returning to the old normal of higher levels of volatility. I suggested on Tuesday that former Fed Chairman Alan Greenspan’s comments could have brought back volatility by triggering the Dow Sell-off on Monday, February 5th. As I wrote early in the week, I believe that we should observe some panic manifestation of economic anxiety because of Mr. Greenspan’s comments. In this Blog Post, I will show two Person Correlation tests that may allow inferences as to how investors’ fear has grown since Mr. Alan Greenspan stated that the American Economy has both a Stock Market Bubble and a Bond Market Bubble. The Pearson correlation tests show that the correlation has strengthened during the last seven days (First week of February 2018), suggesting there might be symptoms of Contagious effect.

I correlate two variables taken from two different time frames for the fifty states. First, the natural logs of the search term “Inflation”; and, the natural logs of the search term “VIX” (Volatility Index). Second, I correlate the natural logs of the same searches for both the last seven days period and the previous twelve months period. By looking at the corresponding coefficients, one may infer that the correlation increased its strengthen after Mr. Greenspan Statements -which reflects on the last seven days data. The primary goal of this analysis is to gather enough information so that analysts can conclude whether there is a Contagious Effect that could make things go worst. Understanding the dynamics of economic crisis starts by identifying the triggers of them.

What is Contagious Effect?

I should say that the best way to explain the Contagious Effect is by citing Paul Krugman’s quote of Robert Shiller (see also Narrative Economics), “when stocks crashed in 1987, the economist Robert Shiller carried out a real-time survey of investor motivations; it turned out that the crash was essentially a pure self-fulfilling panic. People weren’t selling because some news item caused them to revise their views about stock values; they sold because they saw that other people were selling”.

Thus, the correlation that would help infer a link between both expectations is inflation and the index of investors’ fear VIX. As I mentioned above, I took data from Google Trends that show interest in both terms and topics. Then I took the logs of the data to normalize all metrics. The Pearson correlation tests show that the correlation has strengthened during the last seven days, suggesting there might be symptoms of Contagious effect. The over the year Person correlation coefficient is approximate to .49, which is indicative of a medium positive correlation. The over the week Person correlation test showed a stronger correlation coefficient of .74 which is indicative of a stronger correlation. Both p-values support evidence to reject the null hypothesis.

The following is the results table:

February 1st – February 8th correlation (50 U.S. States):

February 2017 – February 2017 (50 U.S. States):

It is worth noting the sequence of the events that led to these series of blog posts. On January 31st, 2018 Alan Greenspan told Bloomberg News: “There are two bubbles: We have a stock market bubble, and we have a bond market bubble.” And, on February 5th, 2018, Dow Jones index falls 1,175 points after the trading day on Monday. As of the afternoon of Friday 9th, the Dow still struggle to recover, and it is considered to be in correction territory.

Raising economic expectations with the “after-tax” reckon: President Trump’s corporate tax cut plan.

The series of documents published by the White House Council of Economic Advisers indicate that President Donald Trump’s Tax Reform will end up being his economic growth policy. The most persuasive pitch behind the corporate tax cut is that lowering taxes to corporations will foster economic investment thereby economic growth. Further, the political rhetoric refers to GDP growth estimates of a tax-cut-boosted 3 to 5 percent growth in the long run. In supporting the corporate tax cut, the White House Council of Economic Advisers presented both a theoretical framework and some empirical evidence of the effects of tax cuts on economic growth. Even though the evidence presented by the CEA is sound and right, after reading the document, any analyst would promptly notice that the story is incomplete and biased. In this blog post, I will briefly point to the incompleteness of White House CEA’s tax cut policy justification. Then, I will show that the alleged “substantial” empirical evidence meant to support the corporate tax-cut policy is insufficient as well as flawed. In third place, I will make some remarks on the relevance of the tax-cut as a fiscal policy tool in balance to the current limitation of monetary policy. Finally, I conclude that despite the short-term benefits of the corporate tax cut, such benefits are temporal as the new normal rate settles, and at the end of the day, given that tax policy cannot be optimized, setting expectations from the administration is a policy waste of time.

The very first policy instance that CEA stresses in its document is the fact that corporate tax cut does affect economic growth. Following CEA’s rationale of current economic conditions, the main obstacle to GDP growth rates above 2 percent is low rates of private fixed investment. CEA infers implicitly that the user cost of capital far exceeds profit rates. In other words, profit rates do not add up enough to cover for depreciation and wear off capital investments. Thus, if private investment depends on expected profit as well as depreciation, simply put I_t=I(π_t/(r_t+ δ)) where the numerator is profit, and the denominator is the user cost of capital (Real Interest rate plus depreciation), the quickest strategy to alter the equation is by increasing profit through lowering on fixed cost such as taxes. CEA’s rationale assumes correctly that no one can control depreciation of capital goods, and wrongly thinks that no one (including the Federal Reserve which faces serious limitations) can control real interest rate, currently.

CEA fetched some data from the Bureau of Economic Analysis to demonstrate that private sector Investment is showing concerning signals of exhaustion. The Council sees a “substantial” weakness in equipment and structures investments. More precisely, CEA remarks that both equipment and structure investment have declined since 2014. Indeed, both variables show a decline in levels of 2 and 4 percent respectively. However, and although CEA considers such decline worrisome, those decreases seem not extraordinary for the variables to develop truly policy concerns. In fairness, those variables have shown sharper decreases in the past. The adjective “substantial,” which justifies the corporate tax cut proposal, is fundamentally flawed.

The problem with the proposal is that “substantial” does not imply “significant” statistically speaking. In fact, when put in econometric perspective, one of those two declines does not appear to be statistically different from the mean. In other words, the two declines look perfectly as a natural variation within the normal business cycle. A simple one sample t-test will show the incorrectness of the “substantial” reading of the data. A negative .023 change (p=.062), in Private fixed investment in equipment (Non-Residential) from 2015 to 2016, is just on the verge of normal business (M=.027, SD=.097), when alpha level is set to .05. On the other hand, a negative .043 change (p=.013) in Private fixed investment for nonresidential structures stands out of the average change (M=.043, SD= .12), but still, it is too early to claim there is a substantial deacceleration of investments.

Thus, if the empirical data on investment do not support a change in tax policy, then the CEA tries to maneuver growth by policy expectations. Their statements and publications unveil the desire to influence agents’ economic behavior by reckoning with the “after-tax” condition of expected profit calculations. Naturally, the economic benefits of corporate tax cuts will run only in the short term as the new rate becomes the new normal. Therefore, the benefit of nominally increasing profits will just boost profit expectations in the short term while increasing the deficit in the long run. Ultimately, the problem of using tax reform as growth policy is that tax rates cannot be controlled for optimization. Unlike interest rate, for numerous reasons, governments do not utilize tax policy as a tool for influencing either markets or economic agents.


“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.


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.

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.

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.


Follow up on US Construction Industry Data.

Follow up on US Construction Industry Data.

At the beginning of the summer of 2015, both labor statistics on employment levels and US Gross Domestic Product showed a slowdown on job creation coming from construction related activities. Given that the summer represents a time window for developers to build fast thanks to good weather conditions, economists always expect summer job increases to largely stem from construction. However, it was not the case for the summer of 2015, which alerted analysts to look cautiously at construction investment. On the first week of July, Econometricus.com poked on construction investment by looking at statistics on Construction Put in Place (US Census Bureau) for the month of May of 2015, as a way to find out whether or not construction investments had slowed-down effectively. Data on such a metric revealed no statistically significant change, which accurately corresponded to data reflecting job creation from the US Bureau of Labor Statistics, and data on GDP growth. Now that the summer is almost gone, it is worth looking at Residential Construction to either dissipate or collect more concerns.
July’s Construction Data from the US Census Bureau and the US Housing Department.

On annual basis increases were significant, but on monthly basis they were not so much. For instance, projected economic activity on residential construction increased significantly in aggregate terms for Approved Building Permits, Housing Starts, and Housing Completion, for the month of July 2015. On one hand, and in spite of a decrease from the previous month of June, plans to build housing units jumped 7.5% when compared to the month of July 2014. Likewise, Housing Starts augmented by 10% in July 2015 when compared to the same month of 2014. In terms of Housing Completion, which shows how fast contractors wanted to finish their work during the summer, privately-owned completed units skyrocketed by 14.6% in July 2015 vis-à-vis July 2014.

Construction summer statistics by region.

Regionally speaking, so far this summer the South has shown decent pace of Housing Completion growth. But, it is not the same case everywhere else. In the West region, privately-owned Housing units completed has declined steadily since summer 2015 started. In the Midwest, although July represented a rebound for the statistic, the numbers dropped to winter season levels. Currently the rate of Completed units is a bit higher than it was a year before though. On the other hand, the Northeast region bounced back after a big drop in June 2015. The graph below shows the trajectory for New Privately-owned Housing units completed, in which the blue line represents the Northeast region. The region’s statistic is back at the level it was one year before.

Privately-Owned Housing Units.

Privately-Owned Housing Units.

Therefore, coming up with a set of conclusions, to determine whether or not housing is holding back economic growth and job creation, is really hard at this point of the year. Having seen what we have observed so far, it is tough to adventure hardcore statements. However, except by the South region, Construction has experienced a slow-down all over the United States during the summer of 2015, which is reflects on both indicators, jobs and GDP Growth.

United States Housing Units Completed on July 2015.

United States Housing Units Completed on July 2015.


Northeast Housing Units Completed on July 2015.

Northeast Housing Units Completed on July 2015.


Midwest Housing Units Completed on July 2015.

Midwest Housing Units Completed on July 2015.


West region Housing Units Completed on July 2015

West region Housing Units Completed on July 2015


South Region Housing Units Completed on July 2015.

South Region Housing Units Completed on July 2015.



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

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

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

Consumer Price Index and Producer Price Index

Consumer Price Index and Producer Price Index

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

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

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

Final Demand Index less Foods and Energy.

Final Demand Index less Foods and Energy.

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

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

Expect an increase in interest rates:

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