I recognized Heteroscedasticity by running this flawed regression.

In a previous post, I covered how heteroscedasticity “happened” to me. The anecdote I mentioned mostly pertains to time series data. Given the purpose of the research that I was developing back then, change over time played a key factor in the variables I analyzed. The fact that the rate of change manifested over time made my post limited to heteroscedasticity in time series analysis. However, we all know heteroscedasticity is also present in cross-sectional data. So, I decided to write something about it. Not only because did not I include cross-sectional data, but also because I believe I finally understood what heteroscedasticity was about when I identified it in cross-sectional data. In this post, I will try to depict, literally, heteroscedasticity so that we can share some opinions about it here.

As I mentioned before, my research project at the moment was not very sophisticated. I had said that I aimed at identifying the effects of the Great Recession in the Massachusetts economy. So, one of the obvious comparisons was to match U.S. states regarding employment levels. I use employment levels as an example given that employment by itself creates many econometric troubles, being heteroscedasticity one of them.

The place to start looking for data was U.S. Labor Bureau of Statistics, which is a nice place to find high quality economic and employment data. I downloaded all the fifty states and their jobs level statistics. Here in this post, I am going to restrict the number of states to the first seventeen in alphabetical order in the data set below. At first glance, the reader should notice that variance in the alphabetical array looks close to random. Perhaps, if the researcher has no other information -as I often do- about the states listed in the data set, she may conclude that there could be an association between the alphabetical order of States and their level of employment.

Heteroscedasticity 1

I could take any other variable (check these data sources on U.S. housing market) and set it alongside employment level and regress on it for me to explain the effect of the Great Recession on employment levels or vice versa. I could find also any coefficients for the number of patents per employment level and states, or whatever I could imagine. However, my estimated coefficients will always be biased because of heteroscedasticity. Well, I am going to pick a given variable randomly. Today, I happen to think that there is a strong correlation between Household’s Pounds of meat eaten per month and level of employment. Please do not take wrong, I believe that just for today. I have to caution the reader; I may change my mind after I am done with the example. So, please allow me to assume such a relation does exist.

Thus, if you look the table below you will find interesting the fact that employment levels are strongly correlated to the number of Household’s pound of meat eaten per month.

Heteroscedasticity 2

Okay, it is clear that when we array the data set by alphabetical order the correlation between employment level and Household’s Pounds of meat eaten per month is not as clear as I would like it to be. Then, let me re-array the data set below by employment level from lowest to the highest value. When I sort out the data by employment level, the correlation becomes self-evident. The reader can see now that employment drives data on Household’s Pounds of meat eaten per month up. Thus, the higher the number of employment level, the greater the number of Household’s Pounds of meat consumed per month. For those of us who appreciate protein –with all due respect for vegans and vegetarians- it makes sense that when people have access to employment, they also have access to better food and protein, right?

Heteroscedasticity 3

In this case, given that I have a small data set I can re-array the columns and visually identify the correlation. If you look at the table above, you will see how both growth together. It is possible to see the trend clearly, even without a graph.

But, let us now be a bit more rigorous. When I regressed Employment levels on Household’s Pounds of meat eaten per month, I got the following results:

Heteroscedasticity 4

After running the regression (Ordinary Least Squares), I found that there is a small effect of employment on consumption of meat indeed; nonetheless, it is statistically significant. Indeed, the regression R-squared is very high (.99) to the extent that it becomes suspicious. And, to be honest, there are in fact reasons for the R-squared to be suspicious. All I have done was tricking the reader with a fake data on meat consumption. The real data behind meat consumption used in the regression is the corresponding state population. The actual effect in the variance of employment level stems from the fact that states do vary in population size. In other words, it is clear that the scale of the states affects the variance of the level of employment. So, if I do not remove size effect from the data, heteroscedasticity will taint every single regression I could make when comparing different states, cities, households, firms, companies, schools, universities, towns, regions, son on and so forth. All this example means that if the researcher does not test for heteroscedasticity as well as the other six core assumptions, the coefficients will always be biased.

Heteroscedasticity 5

For some smart people, this thing is self-explanatory. For others like me, it takes a bit of time before we can grasp the real concept of the variance of the error term. Heteroscedasticity-related mistakes occur most of the time because social scientists look directly onto the relation among variables. Regardless of the research topic, we tend to forget to factor in how population affects the subject of our analysis. So, we tend to believe that it is enough to find the coefficient of the relation between, for instance, milk intake in children and household income without considering size effect. A social scientist surveying such a relation would regress the number of litters of milk drunk by the household on income by family.

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.

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.



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:


31 Data Sources, Surveys and Metrics for Doing Research on U.S. Labor Market.

If your research project encompasses facts on U.S. Labor Market, here are some useful data sources and metrics that might illuminate insights for your research. Although there might be some discrepancies between what you narrowed as your research question and the data sources showed below, chances are you will find a set of metrics that might capture a good proxy for your research topic.

Look through the list and then identify a possible match between your research question and the data source:

1. Employment and Unemployment (Regional, County, National and Metropolitan Area). Data source: U.S. Bureau of Labor Statistics.
2. Unemployment Insurance Claimants. Data source: U.S. Department of Labor.
3. Real Earnings. Data source: U.S. Bureau of Labor Statistics.
4. Labor Force Characteristics of Foreign Born Workers. Data source: U.S. Bureau of Labor Statistics.
5. Job Opening and Labor Turn Over. Data source: U.S. Bureau of Labor Statistics.
6. Employment Situation. Data source: U.S. Bureau of Labor Statistics.
7. ADP Employment. Data source: ADP.
8. Productivity and Cost. Data source: U.S. Bureau of Labor Statistics.
9. Employment Cost. Data source: U.S. Bureau of Labor Statistics.
10. Personal Income and Outlays. Data source: U.S. Bureau of Economic Analysis.
11. Business Employment Dynamics. Data source: U.S. Bureau of Labor Statistics.
12. Employment Characteristics of Families. Data source: U.S. Bureau of Labor Statistics.
13. Usual Weekly Earnings of Wages and Salaries of Workers. Data source: U.S. Bureau of Labor Statistics.
14. College Enrollment and Work Activity of High School Graduates. Data source: U.S. Bureau of Labor Statistics.
15. Number of Jobs, labor market experience (Longitudinal Survey). Data source: Bureau of Labor Statistics.
16. Occupational Employment and Wages. Data source: U.S. Bureau of Labor Statistics.
17. State and Local Personal Income and Real Personal Income. Data source: U.S. Bureau of Economic Research.
18. Employment Situation of the Veterans. Data source: U.S. Bureau of Labor Statistics.
19. Employer Cost for Employee Compensation. Data source: U.S. Bureau of Labor Statistics.
20. Volunteering in the U.S. Data source: U.S. Bureau of Labor Statistics.
21. Major Work Stoppages. Data source: U.S. Bureau of Labor Statistics.
22. Mass Layoffs. Data source: U.S. Bureau of Labor Statistics.
23. Union Members. Data source: U.S. Bureau of Labor Statistics.
24. Employee tenure (2014). Data source: Bureau of Labor Statistics.
25. Consumer Expenditure (2013). Data source: U.S. Bureau of Labor Statistics.
26. Summer Youth Labor Force. Data source: U.S. Bureau of Labor Statistics.
27. Employee Benefits (Private sector). Data source: U.S. Bureau of Labor Statistics.
28. Persons with Disabilities Characteristics. Data source: U.S. Bureau of Labor Statistics.
29. Employment Projections 2012-2022. Data source: U.S. Bureau of Labor Statistics.
30. Income of the 55 and older. Data source: U.S. Social Security Administration.
31. Women in Labor Force (2012). Data source: U.S. Bureau of Labor Statistics.

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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

Is Construction Investment Holding Back Job Creation?

Employment level statistics for the month of June 2015 looked a bit worrisome for some economists. At a glance, Construction was one of the missing sector in the list of industries significantly contributing to job creation. Indeed, Construction was the last sector in joining job creation after the Great Recession. Though ADP, the payroll company, reported that the sector added an estimated figure of 19,000 jobs during the month of June 2015 -which reflects a slight decline from the month of April-, establishment survey data from the U.S. Bureau of Labor Statistics (BLS) showed no addition for the Construction payroll data. More in detail, BLS employment data on Construction sector showed that it contracted at several specific specialties. The table below shows awful figures for a season which is said to be appropriate for outdoor works.
Employees on Construction Nonfarm Pay Roll
Specifically, activities that cut back in employment were nonresidential specialty trade contractors (-5.6K estimated employees), specialty trade contractor (-1.9K estimated employees), Residential Building (-6.1K estimated employees). Although there is much of a mix in the employment data for the sector, the aggregate figures suggest that a brief revision is worth doing in order to see whether there is an industry slow down, or just a deferred process due to weather conditions.

Well, the latest data on Construction put in place –May 2015- in the United States show no change in construction investment on month-to-month basis. Estimated change in construction spending for May 2015 was about 0.8% ($1,035.8 billion), with a margin error of +/- 1.5%. Furthermore, most of the estimated values do not support alternative hypothesis in order to reject the null hypothesis. In other words, there is no statistical evidence to claim that construction spending was different than zero (0) in the United States from April to May 2015. At the least, we could say that weather has not played a deferring factor for Construction activities, thereby affecting employment levels for June 2015.

Construction put in place Adjusted

Although data released by the U.S. Census Bureau is subject to constant revision, it seems unlikely that those figures change given the data on employment level. That is, employment levels data are sort of “confirming” that current investment in Construction is not enough for the sector to keep up with economic growth, at least for the summer season.

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

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

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


Discouraged Workers:

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


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



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


Despite GDP estimates, U.S. industries experienced growth on level of employment in April to April comparison.

Despite news showing negative growth in Gross Domestic Product for the first quarter 2015, most of the U.S. industries experienced growth on level of employment in April 2014 to April 2015 comparison. Besides Construction, which tends to grow faster as weather allows for outdoor activities, Leisure and hospitality industry experienced the highest average growth rate in level of employment, 2.8%. Education and Health Services seconded Hospitality with an average of 2.3%. Professional Business had 2.2% increase, while Trade and Transportation and Utilities recorded 1.8% increase.

The lowest rate of change showed up unsurprisingly in Manufacturing. Aggregate data for the industry exhibited an anemic .9% change in job creation when comparing April 2014 to April 2015. Indeed, several surveys are showing May might not have made any better difference for the sector. For instance, the Texas Manufacturing Outlook Survey revealed its main Index fell to -13.5. Moreover, the employment Index declined to -8.2, which translates into shorter workweeks for employees in Texas Manufacturing Industry. On the other hand, the Federal Reserve Bank of Richmond reported the employment gauge in their survey decreased from 7 to 3, though the average workweek actually increased.

In General, manufacturing conditions in Texas reflected continuing contraction during May 2015. The Federal Reserve Bank of Dallas claims that these readings are the lowest in the recent six years. On the other hand, the composite manufacturing index in Richmond’s survey moved a bit up to 1, from a reading of -3 in the previous month. Manufacturing Activity “flattened in May” Richmond reported.