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

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

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

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

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

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

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

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

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

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

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

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.

Unemployment √. Inflation √. So… what is the Fed worrying about?

Although the Federal Open Market Committee (hereafter FOMC) March’s meeting on monetary policy focused on what apparently was a disagreement over the timing for modifying the Federal Bonds interest rates, the minutes indicate that the disagreement is not only on timing issues but also on exchange rate challenges. Not only does the Fed struggle with when the best moment is to raise the rate, but also it grapples with the extent to which its policy decisions can reach. The FOMC current economic outlook and their consensus on the state of the U.S. economy have no room for doubts on domestic issues as it does for uncertainties on foreign markets. Thus, the minutes of the meeting held in Washington on March 15th – 16th 2016 unveils an understated intent for influencing global markets by stabilizing the U.S. currency. On one hand, both objectives of monetary policy seem accomplished regarding labor markets and inflation. On the other, the global deceleration is the only factor that concerns the Fed since it could have adverse spillovers on America. The most recent monetary policy meeting reveals a subtle attempt to stabilize the U.S dollar exchange rate at some level, thereby favoring American exports.

Unemployment rate √. Inflation rate √.

The institutional objective of the Federal Reserve Bank seems uncompromised these days. Economic activity is picking up overall, the labor market is at desired levels, and inflation seems somewhat under control. The confidence economists have right now starts by the U.S. Household Sector. Household spending looks healthy, and officials at the Bank are confident such spending will keep on buoying labor markets. As stated in the minutes, “strong expansion of household demand could result in rapid employment growth and overly tight resource utilization, particularly if productivity gains remained sluggish” (Page 6). Indeed, the labor market is showing strong gains in employment level which has made the unemployment rate to decrease down to 5.0 percent by the end of the first quarter of 2016.

Furthermore, FOMC understands the high levels of consumer confidence as a warranty for a sustained path for growth. The committee also pointed out that low gasoline prices are stimulating not only higher level of consumption but also motor vehicles sales. They know of the excellent situation of the relative high household wealth to income ratio. Otherwise, members of the Committee recognize that regions affected by oil prices are starting to struggle while business fixed investment shows signs of weakening. Nevertheless, the consensus among members of the Committee reflects an overall optimism in the resilience of the economy rather than a worrisome situation about the outlook.

By Catherine De Las Salas

By Catherine De Las Salas

The fear comes from overseas.

The transcripts, which were released on April 6th, 2016, show that  Fed officials the concerns stem from global economic and financial developments. The FOMC “saw foreign economic growth as likely to run at a somewhat slower pace than previously expected, a development that probably would further restrain growth in U.S. exports and tend to damp overall aggregate demand” (Pag. 8). They also flagged warnings on wider credit spreads on riskier corporate bonds. In sum, policymakers at the FOMC interpret the current lackluster global situation as a threat to the economic growth of the United States.

To discard choices.

Therefore, the fact that those two conditions overlap has made the Committee anxious to intervene in an arena that perhaps could be out of its reach. By keeping unmoved the interest rate of the federal bonds during March -and perhaps doing so until June-, the FOMC does not aim at stimulating investment domestically. Nor does it at controlling inflation. In fact, the policy choice reveals a subtle attempt for keeping the U.S dollar exchange rate stable overseas, thereby favoring American exports. The latter statement could be inferred from the minutes based on the Committee’s consensus on the state of the economy. First, U.S. labor markets are strong, and the Fed considers that the actual unemployment rate corresponds to the longer-run estimated rate. Second, inflation –either headline or core- are projected and expected to be on target. And third, domestic conditions are in general satisfactory. The only factor that remains risky is the rest of the world. Therefore, whatever action they took last March meeting could be interpreted as intended for influencing global markets.


Why is America’s center of gravity shifting South and West?

Ever since Florida surpassed New York as the third most populous state in the nation, journalists started to document the ways in which the South region of the United States began attracting young sun-lovers enthusiasts. Two factors have been identified as drivers of an apparent migration from the north towards the south. On one hand, real estate prices have been arguably one of the major causes for people heading south. On the other, employment growth and better job opportunities allegedly support decisions on moving out regionally. This article checks empirical data on those two factors to determine the effect on population growth of major cities in the United States. The conclusion, in spite of the statistical model limits, indicates that employment dynamic seems to drive a slightly higher level of influence in population growth when compared to housing costs.

Is it because of real estate prices?

The first factor some prominent people have identified is real estate prices. Professor Paul Krugman highlighted in his NYTimes commentary of August 24th, 2014 that the most probable reason for people heading south is housing costs, even over employment opportunities. From his perspective, employment has little effect on such a change given that wages and salaries are substantially lower in southern states when compared to the north. Whereas, housing costs are significantly lower in southern regions of the country. Professor Krugman asserts that “America’s center of gravity is shifting South and West.” He furthers his argument “by suggesting that the places Americans are leaving actually have higher productivity and more job opportunities than the places they’re going”.

By Catherine De Las Salas

By Catherine De Las Salas

Is it because of employment opportunities?

Otherwise, Patricia Cohen –also from the NYtimes- stresses the relevance of employment opportunities in cities like Denver in Colorado. In her article, the journalist unfolds the story of promising entrepreneurs immersed in an economically fertile environment. The opposite situation to that prosperous environment happens to locate northeast of the United States. Cohen writes that not only “in the Mountain West — but also in places as varied as Seattle and Portland, Ore., in the Northwest, and Atlanta and Orlando, Fla., in the Southeast — employers are hiring at a steady clip, housing prices are up, and consumers are spending more freely”. Her article focuses on contrasting the development of urban-like amenities and how those attractions lure entrepreneurs.

A brief statistical analysis of cross-sectional data.

At first glance, both factors seem to be contributing factors for having an effect on migration within states. However, although both articles are well documented, neither of those readings goes beyond anecdotal facts. So, confirming those very plausible anecdotes deserves a brief statistical analysis of cross-sectional data. For doing so, I took data on estimated population growth for the 71 major cities in the U.S. from 2010 to 2015 (U.S. Census Bureau), and regressed it on the average unemployment rate in 2015 (U.S. Bureau of Labor Statistics), median sale price of existing houses for the same year (National Association of Realtors), and the U.S. Census Bureau’s vacancy rate for the same year and cities (Despite that the latter regressor might be multicollinear with sale price of existing houses, its inclusion in the model aims at reinforcing a proxy for housing demand). The statistical level of significance for the regression is a 90 percent confidence interval.


The results show that, for these data sets and model, the unemployment rate has a bigger effect on population growth than vacancy rate and median home sale prices altogether. The regression yielded a significant coefficient of -2.78 change in population growth as unemployment decreases. In other words, the lower the unemployment rate, the greater the population growth. A brief revision of empirical evidence shows that, once the coefficients are standardized, unemployment rate causes a higher effect on the dependent variable. If we were to decide which of the two factors affects population growth greater, then we would have to conclude that employment opportunities do it largely.

Regression Results.

Regression Results.

By using these data sets and this model, the employment dynamic seems to drive a slightly higher level of influence in population growth, when compared to housing costs. The unemployment rate has a standardized effect of negative 56 percent. On the other hand, median sale price of houses pushes a standardized change effect of 23 percent. Likewise, vacancy rate causes in the model an estimated 24 percent change in population change. Standardized coefficients are a tool meant to allow for disentangling the combined effect of variables in a model. Thus, despite that the model explains only 35 percent of population growth, standardized coefficients give insights on both competing factors.

Limits of the analysis.

These estimates are not very reliable given that population growth variable mirrors a five years lapse while the other variables do so for one year. In technical words, the delta of the regressand is longer than the delta of the regressors. For this and many other reasons, it is hard to conclude that employment constitutes the primary motivation for people moving out south and west. Nonetheless, this regression sheds light onto a dichotomy that needs to be understood .

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.


“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.

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.

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

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

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

By Catherine De Las Salas

By Catherine De Las Salas.

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

Managers play a roll either in cutting or increasing wages:

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

The model:

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

Thus, the statistical model would look like the following:


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

Data and method:

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

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


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

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

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

Regression output table:


Where are the teenager workers? An answer to The New York Times.

In a recent article published by the New York Times, Patricia Cohen and Ron Lieber made a brief inquiry on youth employment during summer 2015. In their writing piece, Cohen and Lieber open two windows for interpretation about factors affecting teenagers’ employment during summer school break. One of them is to believe that people between 16 and 19 years old are not interested in working at all, and instead they are doing “other stuff” (going to summer school, traveling or volunteering). The other window for interpretation is that the rivalry between teenagers and 20-years-older people for summer jobs has intensified in recent years. In their own words “Adults, desperate for second and third jobs to make ends meet, may be crowding out many teenagers”. The former rationale has to be ruled out from the analysis given that the BLS Household Survey barely allows for such an interpretation, which leads to only speculations. Otherwise, the latter issue about adults crowding out effect on teenagers may explain the picture better.

So, the argument goes the following way: youth summer employment is being taken by 20-years-and-older people. In other words, level of employment of 16-19-years-old gets affected by level of employment of 20-years-and-older people. If the American Economy creates certain number of jobs per month (average 221,000), those employments must be distributed among the population actively looking for jobs. Thus, variance in 20-years-and-older people must explain variance in youth employment. In addition, since the question is appropriately posed for summer jobs, the comparison must be run among comparable months of the year. Furthermore, given that gender plays a role in the number of hours worked by employees, and the hypothesis proposes 20-years-and-olders are chasing second jobs, it makes sense to look at 20-years-and-older women and men disjointedly.

Therefore, it also makes sense to regress 16-19-years-old’s level of employment on 20-years-and-older Women and Men’s level of employment for the months of January through June using BLS data from 2000 to 2015.

The results show that level of employment of teenagers get affected negatively by women level of employment. This effect can be interpreted as women competing fiercely against teenagers looking for a summer job. Data reveal that women tend to take jobs traditionally “meant” for teenagers. These results are twofold. First, data show that the crowding out effect maybe indeed happening. Second, data show that women might be the ones crowding out teenagers’ employment.

The situation exacerbates for young as the labor market reaches the summer. Generally speaking, women level employment affects teenagers’ less at the beginning of the year than by the summer. Coefficients in this regard show an increase from -0.58 in April to an estimated -0.75 in June. The meaning of the estimates is that teenagers have 75% less chances to get a job when women 20-years-and-older apply for it too in June. Thus, results on June data actually reinforce the hypothesis given that June and summer are supposed to have  jobs temporarily filled by teens.

The following table summarizes findings of regressions. Asterisk means 95% significance level.

Youth employment