Labor Productivity: 2015Q1 vis-à-vis 2014Q1.

Labor Productivity increased 0.2 percent when comparing 2014 first quarter and 2015 first quarter. Hours worked by all persons in the labor market increased 0.3 percent in 2015Q1 vis-à-vis 2014Q1. Compensation per hour changed 0.18 percent, whereas the Unit Labor Cost increased 0.16%. It is important to note that, while Output per person, working in nondurable goods manufacturing industry, augmented by 2.2%, the Unite Labor Cost in the same sector decreased by 0.3 percent. Compensation instead changed by 1.9%.


Industry specifics:

By looking at industry specifics, Labor Productivity in Manufacturing of Durable Goods increased by 1.2%, while Output in the industry did so by 4.1%. The number of hours worked in this sector increased by 2.8%, whereas real compensation did so by only 1.6%.

These data may help economist in understanding why GDP slowed down in the first quarter 2015, while unemployment rate stuck to 5.4%. On one side, Discourage Workers seem to be back in the labor market as more hours worked are being demanded by producers. On the other side, although workers are working longer workweeks, compensation has changed significantly, which gets reflected in low levels of inflation.

Perhaps the only conclusion for now clear is that the economy might be being driven by expectations. Based on the current figures and the optimism revealed in the Beige Book, it is possible to assert that most of the increase in level of employment is being generated solely by positive, and perhaps “risky” expectation of the close future.

On the other hand, Nonfarm Business Sector Labor Productivity decline for second quarter in a raw during the past year. BLS Revised data, released on June 4th 2015, confirmed labor productivity declined by 3.1 percent at an annual rate, which complements data on GDP negative growth for 2015Q1. Data released along the current week on the contraction of GDP, Construction, Labor Market and Productivity, reveal expectations and future outlooks of Business Leaders ought to be driven the economy and especially the labor market.

Finally, the U.S. Bureau of Labor Statistics noted that “from the first quarter of 2014 to the first quarter of 2015 productivity increased 0.3 percent”. One of the key labor indicators derived from productivity measures is Unit Labor Cost, which has actually increased 1.8 percent in the last year. For the first quarter of 2015, BLS asserted Unit Labor Cost increased by 6.7 percent. Such a statistic reflects the mentioned 3.1 percent decline in productivity, and a 3.3 percent increase in compensation by the hour.


“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.


Did the housing market affect negatively economic growth in 2015Q1?

Recent news on GDP 2015Q1 have many economists wondering about the possible domestic causes for such a negative growth (-.7%). The U.S. Bureau of Economic Analysis (BEA) did not hesitate in pointing out towards Investment in non-residential structures, which decrease 20%. Perhaps, data on housing market from both Construction Spending and Existing Home Sales might advance clues on what is going on in the U.S. economy currently. First, preliminary data on Construction Put in Place might shed light into what BEA signaled earlier, and data on Existing Housing Sales may complement an explanation, at least for as far as to the domestic economic dynamic concerns.


First, the Total Value of Residential Construction Put in Place in the U.S. economy decreased by 1.8% when comparing April 2014 to the most recent estimated statistics from the U.S. Census Bureau for April 2015. The estimated value for Private Residential Construction in April 2015 was roughly 353,086 million dollars, which totals 7,740 million less put in place than in April 2014. In spite of the decrease during April, official at the U.S. Census Bureau stated that “during the first 4 months of this year, construction spending amounted to $288.7 Billion, 4.1 percent (+/-1.5) above $277.3 Billion for the same period 2014”.


Perhaps the deceleration for the sector is being brought by Residential and Power sectors. The preliminary value of construction put in place for Residential and Power -type of constructions- went down during April 2015 inasmuch of -6,417 and -11,657 million dollars correspondingly, much of which came from a decrease of roughly 7,850 million dollars less pertaining the private sector and -3,808 million dollars less from the public sector. Though, the overall account got offset by increases in Manufacturing, Transportation and Commercial.


Since most of Construction Spending indicators went up in April 2015p, the question to ask economists is to whether or not the housing market actually slowed down economic growth during the first quarter of 2015; at least for the domestic side of the U.S. economy. Construction growth in Lodging and Commercial industries went up both by 17%, while Offices and Recreation related constructions did so by roughly 20% (April 2014 compared to April 2015p).


Data Source: U.S. Census Bureau. Data Overview: “The Value of Construction Put in Place Survey (VIP) provides monthly estimates of the total dollar value of construction work done in the U.S. The United States Code, Title 13, authorizes this program. The survey covers construction work done each month on new structures or improvements to existing structures for private and public sectors. Data estimates include the cost of labor and materials, cost of architectural and engineering work, overhead costs, interest and taxes paid during construction, and contractor’s profits. Data collection and estimation activities begin on the first day after the reference month and continue for about three weeks. Reported data and estimates are for activity taking place during the previous calendar month. The survey has been conducted monthly since 1964”.

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.

Utah ranks on top in job creation for April 2015.

Perhaps the state that showed the best performance in level of employment was Utah. Industries seem to be booming there, where 52K jobs positions were added. Construction registered an increase of 7.7%, while Leisure and Hospitality did on 7.4%. Trade and Transportation, and Financial Services also increased their levels by 5.0% and 4.9%.

Utah Level of Employment
Utah ranked first on industry growth for Leisure and hospitality along with Vermon. Arkansas, Georgia and Florida also experienced increases in such industry. In education Utah was surpassed by Oregon and Colorado both with 4.9% increase in job creation for the industry in April 2015. In Financial Activities Utah also made an appearance in April 2015, though the state was surpassed by Oregon, South Carolina and Washington State. Even in the Manufacturing sector Utah made it to the fourth place in April 2015. The only two sector in which the State did not make it to the four top rank were Construction and Professional Business.

California Level of Employment

Data source: US Bureau of Labor Statistics.

Change in employment

Unemployment rate is still at 5.4%: BLS.

The US Bureau of Labor Statistics released on April 27th 2015 its preliminary data on unemployment. On the national level the unemployment rate is still at 5.4%. By regions, the Midwest had the lowest unemployment rate, 5.0% The Western region had the highest rate at 5.8%. The highest rates of unemployment were in Nevada and the District of Columbia, 7.1% and 7.5% correspondingly. Unemployment rate rose .4 percentage points to 3.1% in North Dakota, which registered a rate of 2.7% one year ago. On the other hand, largest percentage changes over the year were in Michigan which decreased its unemployment rate by -2.1 %, and both Kentucky and Rhode Island where the decrease in the unemployment rate was of -2%.

Unemployment april 2015
The largest over-the-month decrease occurred in New York, -14,700, followed by Missouri with -5,700. The largest increase from March to April 2015 happened in California which experimented +29,500 jobs gains, Pennsylvania and Florida with +27,000 and +24,500 jobs gains respectively. For the case of New York City and Los Angeles, some scholars at the Brookings Institute are suggesting that population growth in both states has slowed down in the recent years, which may be affecting level of employment and unemployment statistics for those states.
Employment level increased significantly in California where 457K new positions were created. Gains in employment over the year were in Construction, 6.4%, Leisure and hospitality 3.4%, and Education with 2.9%. Texas, where roughly 287K workers found a new job, showed the largest increase in Leisure and hospitality with 4.9% change from the previous year. Construction increased 3.9% from April 2014 in Texas. The third place in job creation went to Florida where approximately 277K jobs were added. There, the industries that pulled up job creation were Construction with 8.2%, Leisure and hospitality and Professional Business with 5.2% and 4.6% respectively.

Check your regression analysis for these 7 Assumptions. A Quantitative Research Consulting Firm.

At we help clients to get statistical data straight and accurate as much as possible. No matter whether our client is a lawyer supporting a case for economic damages, or  a graduate student writing a paper contesting the counterpart’s mishandling of data. will provide technical support for a cogent understanding of the data being used as evidence in litigation or academic processes.

Before you submit your paper for which you successfully run several simple or multiple regression analysis, we at can help you out checking everything is sound.

These are the 7 assumptions your work must comply with wherever you run the Method of Least Squares:
The overall objective of checking the 7 assumptions is to gauge how close or far your estimates are from the population data. In other words, we will tell you how “accurate” your betas are.

Assumption 1:

Your model must be linear in the parameters (Note that if your data is not linear in the variables we can linearize it for you).

Assumption 2:

Your X’s values must be fixed and independent of the error term.

Assumption 3:

Zero mean value of the error term.

Assumption 4:

Homoscedasticity. Your regression must not suffer heteroscedasticity.

Assumption 5:

No autocorrelation between disturbances. This means no correlation between error terms.

Assumption 6:

The number of observations must exceed the number of parameter to estimate. You can do that by yourself.

Assumption 7:

Nature of the X variable. Neither outliers nor negative variance in variable X.


Contact us if you think we can be of any help.

Traditional statistics proceedings for analysis of data: simple linear regression.

Steps in traditional statistics proceedings for analysis of data:
1. Formulation of Hypothesis.
2. Description of Mathematical model.
3. Collecting and organizing data.
4. Estimation of the coefficients.
5. Hypothesis testing and confidence interval.
6. Forecasting and prediction.
7. Control and optimization.

1. Hypothesis: write down a statement that “in theory” you think happens in real life. For instance,

“Heavier labor regulation may be associated with lower labor force participation”.

2. Mathematical model: although it is not strictly necessary, it always helps to make clear whether the relationship you established, namely between “regulation” and “labor force participation” is positive or negative. In other words, do you believe that “labor regulation” has a positive or negative impact in “labor force participation”? One way to confirm your believes is by plotting a chart and see whether the trend is upward sloping or downward sloping.

3. Collecting and organizing the data: collecting data is expensive. In our case “heavier labor regulation may be associated with lower labor force participation” can be analyzed with data already collected by the World Bank and organized by Juan Botero et al (2004). In the case you do not have data you will need to design a questionnaire and get out to ask those question to at least 100 randomly chosen individuals. However, say you want to know about the relation between “the more you learn, he more you earn”, what would you ask to several random people? Well, you would ask at least two questions: what is your annual/monthly income? And, what level of education do you have, PhD, Masters, Undergraduate, High School? You will record every single answer perhaps into a Microsoft Excel spreadsheet. Do not forget to label the columns and what they mean. Those two columns which result from your survey are your variables (e.g. X and Y). Going back to our case “heavier labor regulation may be associated with lower labor force participation” the Excel Spreadsheet looks like the picture below. In the spreadsheet you can see each of the observations, which are actually data drawn from countries. What you read in column AO as “index_labor7a” is nothing else than a score researches like you gave to whatever they considered to be “labor regulation”. The adjacent column AP, which reads “rat_mal2024” is no more than an average of unemployment rate amongst male of ages ranging from 20 to 24. That is what researches in our example consider to be a proxy for “labor force participation”.

4. Estimation of the coefficients: this step is what is known as “regression analysis”. If you are working in Excel, you will have to activate the data Analysis Toolpack available on Excel Options.

Once you have set up your software, you will run the regression by selecting “Regression” after clicking the “Data Analysis” button, which usually can be found in the upper right corner in the “Data” tab as shown in the picture below.

Then, you will have to define your Y’s and X’s. These are your variables, which come from the empirical observations (e.g. the survey). In our case, as we defined above, our Y is the AP column in the picture below. That is, “rat_mal2024”, or “male labor force participation”. Complementary, our X is “index_labor7a”, which is as we stated a score of labor regulation. Do not forget to specify to Excel whether your columns do have or do not have labels and the output range. It is up to you to have Excel plotting the residuals and other relevant statistics. For now, just check on confidence level box.

Excel will generate the “Summary Output” table. This table contains the coefficients we are trying to estimate. From this point onwards you will have to be somewhat familiar with statistics in order to interpret the results.

5. Hypothesis testing and confidence interval: in this step you will have to deny and reject whatever contrary argument faces your initial thoughts on the relation between earnings and learnings. In other words, you will have to reject the possibility that such a relation does not exists.
6. Forecasting and prediction: this step is a bit slippery, but you can still say something about the next person to whom you would ask the survey questions. In this step you will be able to “guess” the answer other people would give to your questionnaire with certain level of confidence.