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 .

Real Earnings: Bureau of Labor Statistics, January 2016.

Press Release by the Bureau of Labor Statistics.

All employees Real average hourly earnings for all employees increased 0.4 percent from December to January, seasonally adjusted, the U.S. Bureau of Labor Statistics reported today. This result stems from a 0.5-percent increase in average hourly earnings combined with no change in the Consumer Price Index for All Urban Consumers (CPI-U).

Real average weekly earnings increased 0.7 percent over the month due to the increase in real average hourly earnings combined with a 0.3-percent increase in the average workweek.

Real average hourly earnings increased 1.1 percent, seasonally adjusted, from January 2015 to January 2016. This increase in real average hourly earnings combined with no change in the average workweek resulted in a 1.2-percent increase in real average weekly earnings over this period.

Production and nonsupervisory employees.

Real average hourly earnings for production and nonsupervisory employees increased 0.3 percent from December to January, seasonally adjusted. This result stems from a 0.3-percent increase in average hourly earnings combined with no change in the Consumer Price Index for Urban Wage Earners and Clerical Workers (CPI-W).

Real average weekly earnings increased 0.3 percent over the month due to the increase in real average hourly earnings combined with no change in average weekly hours. From January 2015 to January 2016, real average hourly earnings increased 1.3 percent, seasonally adjusted. The increase in real average hourly earnings combined with no change in the average workweek resulted in a 1.3-percent increase in real average weekly earnings over this period.



Real Earnings Technical Note:

The earnings series presented in this release are derived from the Bureau of Labor Statistics’ Current Employment Statistics (CES) survey, a monthly establishment survey of employment, payroll, and hours. The deflators used for constant- dollar earnings series presented in this release come from the Consumer Price Indexes Programs. The Consumer Price Index for All Urban Consumers (CPI- U) is used to deflate the all employees series, while the Consumer Price Index for Urban Wage Earners and Clerical Workers (CPI-W) is used to deflate the production employees series.

Seasonally adjusted data are used for estimates of percent change from the same month a year ago for current and constant average hourly and weekly earnings. Special techniques are applied to the CES hours and earnings data in the seasonal adjustment process to mitigate the effect of certain calendar-related fluctuations. Thus, over-the-year changes of these hours and earnings are best measured using seasonally adjusted series. A discussion of the calendar-related fluctuations in the hours and earnings data and the special techniques to remove them is available in the February 2004 issue of Employment and Earnings or on the Internet under ‘Technical Notes’ (http://www.bls.gov/ces/).

Earnings series from the monthly establishment series are estimated arithmetic averages (means) of the hourly and weekly earnings of all jobs in the private nonfarm sector of the economy, as well as of all production and nonsupervisory jobs in the private nonfarm sector of the economy. Average hourly earnings estimates are derived by dividing the estimated industry payroll by the corresponding paid hours. Average weekly hours estimates are similarly derived by dividing estimated aggregate hours by the corresponding number of jobs. Average weekly earnings estimates are derived by multiplying the average hourly earnings and the average weekly hours estimates. This is equivalent to dividing the estimated payroll by the corresponding number of jobs The weekly and hourly earnings estimates for aggregate industries, such as the major industry sector and the total private sector averages printed in this release, are derived by summing the corresponding payroll, hours, and employment estimates of the component industries. As a result, each industry receives a “weight” in the published averages that corresponds to its current level of activity (employment or total hours). This further implies that fluctuations and varying trends in employment in high-wage versus low- wage industries as well as wage rate changes influence the earnings averages.

There are several characteristics of the series presented in this release that limit their suitability for some types of economic analyses. (1) The denominator for the all employee weekly earnings series is the number of private nonfarm jobs. Similarly, the denominator of the production employee weekly earnings series is the number of private nonfarm production and nonsupervisory employee jobs. This number includes full-time and part-time jobs as well as the jobs held by multiple jobholders in the private nonfarm sector. These factors tend to result in weekly earnings averages significantly lower than the corresponding numbers for full-time jobs. (2) Annual earnings averages can differ significantly from the result obtained by multiplying average weekly earnings times 52 weeks. The difference may be due to factors such as turnovers and layoffs. (3) The series are the average earnings of all employees or all production and nonsupervisory jobs, not the earnings average of “typical” jobs or jobs held by “typical” workers. Specifically, there are no adjustments for occupational, age, or schooling variations or for household type or location. Many studies have established the significance of these factors and that their impact varies over time.

Seasonally adjusted data are preferred by some users for analyzing general earnings trends in the economy since they eliminate the effect of changes that normally occur at the same time and in about the same magnitude each year and, therefore, reveal the underlying trends and cyclical movements. Changes in average earnings may be due to seasonal changes in the proportion of workers in high-wage and low-wage industries or occupations or to seasonal changes in the amount of overtime work, and so on.


Real Earnings and the use of Dubious Statistics.

The use of the Average Statistic deceives readers very often whenever the Mean gets severely affected by outliers within the data. One of the most repeated critics to data analysts is the unaware use of average figures, which frequently leads to dubious generalizations. Social scientists, those of whom refuse to use statistics in their analysis, commonly attack this analytical tool by saying: ok, so… if you eat a chicken and I do not eat anything, in average… we both have had half chicken. Nobody would oppose that conclusion as wrong and deceiving. However, such a reasoning uses just half of the procedure statisticians and econometricians use for determining whether or not the conclusion is statistically valid. Therefore, although it is evident that none of the subject in the example ate half a chicken, it is also true that the analysis is half way done.

Outliers heavily affect the Mean statistic:

There is no question that all types of statistics have limited interpretations. In the case of the Mean (arithmetic average), outliers heavily affect the statistic, thereby –very often- the analysis. However, that does not mean arithmetic averages cannot illuminate wise conclusions. For instance Real Earnings, which is a very easy deceiving data on labor economics. Data on Real Earnings “are the estimated arithmetic averages (Means) of the hourly and weekly earnings of all jobs in the private non-farm sector in the economy”. Real Earnings are derived by the US Census Bureau of Labor Statistics from the Current Employment Statistics (CES) survey. So, any unaware reader could jump quickly on to ask if Real Earnings are the average of the hourly earnings of all Americans working in the non-farm private sector. Thus, analysts may also quick respond that in fact that is true. Then, most of the times, the follow up question would read as the following: Does Real Earnings mean that as a “typical” worker in the United States, I would make such an average? The answer is no, it does not. There is precisely where statistical analysis starts to work.

Few Examples:

First. In terms of worker’s earnings various aspects determine how much money people make per hour. Educational attainment is perhaps the greatest determinant of earnings in the American economy. One also can think of geography as a factor of income per hour; even taxes could have an effect on how much money a worker does; age clearly controls income; so on and so forth. Intuitively, it is possible to see that for Earnings and Income there might be many exogenous factors influencing its variability.

Second. For the sake of discussion, let us say that neither education nor taxes affect hourly income of workers. In such a case, and at first glance, it is naïve to believe that counting such a low number of observations could work for any type of analysis, regardless of it being qualitative or quantitative. That means basically that for both qualitative and quantitative analysis, the number of observations matters a lot. In quantitative research the threshold number of observation hovers around 30. Hence, sample size are crucial not only for debunking the cited joke above, but also for reaching valuable conclusion in both qualitative and quantitative social science research.

Taking Real Earnings as example has no pitfall of the latter kind, but it surely does on the former, which certainly bounds the set of conclusion analysts can make. As an Average statistic, Real Earnings have a numerator and a denominator, for which the number in the series is the number of nonfarm private jobs. All types of jobs are included, regardless of age, education attainment, location, taxes, and etcetera. In other words, the companies CEO’s salaries may pull up the statistic. Conversely, minimum wage earners could drag down the Average.

The Median statistic would do a better job sometimes:

At this point, it is clear that for some social science analysis, perhaps other type of statistics happen to be rather more suitable. For instance, the median would help analysts better understand income. So, why should one consider such a computation on Real Earnings? The answer is that Averages figures can be really useful as long as the analyst makes thorough caveats on what the Average really tells; and more importantly, limitations on what the Average figure does not tell.

Real Earnings. Data Source: US Bureau of Labor Statistics.

Real Earnings. Data Source: US Bureau of Labor Statistics.

Hence, changes in Real Earnings shed light onto changes in the proportion of workers in high-wages and low-wages industries or occupations. High-wages salaries will tend to, as in the CEO’s example above, pull up the average without substantial change in the number of hours worked. Conversely, as in the example of the minimum wage earners above, low-wages industries or occupations will tend to lower the outcome statistic. Furthermore, when paired with other data, Real Earnings could be useful for noticing improvements in use technology. If the number of work hours remains stagnant, but both earnings and employment levels increase, the net effect might stem from improvements in technology, which turns on increasing productivity. In other words, workers may work smarter rather than harder and longer. Lastly, Real Earnings Averages can also inform analysts about the amount of overtime work.

So, uses of Arithmetic Means, such as Real Earnings, can be thought-provoking. However, much caution has to be considered whenever economic assertions are stated.


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:


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.


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.

Foreign-born workers made up 16% of the US total labor force in 2014.

As the Federal Government awaits for the final judicial ruling on the Executive Actions taken by President Obama in favor of immigrants  last Fall, the Bureau of Labor Statistics released today its most updated data on foreign nationals working in the USA, both legally and illegally. In general, there is no surprise 48% of US foreign workers are Latinos and 24% come from Asia. They make up to 25.7 million of persons, for which immigration status ranges from permanent residents, refugees or temporary residents to undocumented immigrants. Foreign-born workers made up 16% of the United States total labor force in 2014. (Read also “Zero-base budgeting for immigration reform”)

Foreign workers
BLS found foreign-born workers are more likely to be employed in services occupations such as construction, maintenance or extraction of natural resources. This very fact made them more likely to have a weekly paycheck of as much as U$664 in 2014. Notice that nationals made roughly U$820 for a week of work during the same year 2014. Obviously, occupations and compensation for nationals vary since they are more likely to be employed in management, professional services and/or sales related positions.
Nonetheless, foreign-born worker are winners as far as labor force participation respects. The labor force participation rate for foreign-born persons was as high as 78.7% in 2014, whereas for native-born Americans the same rate was 67.4%.
Regionally speaking, the West of the United States concentrates the higher share of foreign-born workers, 23.8%, followed by the Northeast with 19.2%. The South and Midwest regions registered 15.3% and 8.5% respectively.
Finally, it is worth noting that the US Labor Bureau of Statistics draws these data from the Current Population Survey, which reaches roughly 60,000 households in the United Sta


Median Wages and Earnings Continued Stagnant in 2014 for the Majority of US Occupations.

"Dollar Bills" by Catherine De Las Salas. January 2015. New York City.

“Dollar Bills” by Catherine De Las Salas. January 2015. New York City.

As President Barack Obama addressed the Nation’s Congress speaking of income inequalities among men and women, the Bureau of Labor Statistics released its quarterly report on weekly earnings focusing on such differences. The Median Weekly Earnings for full-time (more than 35 hours worked per week) American worker were U$799 before adjusting by season. Women’s Median weekly earnings were U$724 whereas men’s Median were U$882. This dollar amount represents earnings before taxes and other deductions of a person right in the middle of the income spectrum, which means that half of the 107 million full-time workers made more than U$796 weekly, and the other half made less than U$796 per week during the last four months of 2014 (See how this data compares with inflation).

On annual basis:

Obviously, the measure is a rough aggregation of the entire US full-time workers. And, in spite of 1.6 standard errors, data broken by occupation show that the greatest gains –if any, and if “greatest”- over the year were for the full-time median worker in transportation, production, and material moving occupations, who statistically speaking experienced an increase in his/her weekly earnings of about 3.38 percent when compared to the earnings of 2013 (See how this data compares with sales for the holiday season 2014). There are approximately 14 million people working full-time in Production and Transportation related occupation in United States. Services related occupation’s median worker realized –statistically speaking- 2.43 percent more in his weekly pay, whereas Sales and office median worker made 1.06 percent more per week during 2014 when compared to 2013. Finally, the Median manager’s earnings increased an imperceptible 0.44 percent over the year in his/her weekly paycheck.
Median earnings 2014
Although statistically insignificant, this is the first time since 2006 that the fourth quarter statistic declines compared to the third quarter statistic.

Number of workers by occupation 2014

Top ten of U.S. Occupational Employment Statistics for May: BLS.

Employment“Retail salespersons and cashiers were the occupations with the largest employment in May 2013”: BLS.

The top ten of U.S. Occupational Employment Statistics for May, was released today by the Bureau of Labor Statistics. Office and Administrative support related occupations counted almost 22M people in the field. Sales men and women are second with 14M. Continue reading

U.S. Personal Income increased only 14 billion in July 2013. The indicator had been adding higher amounts during the last four months of the current year.

U.S. Personal Income increased only 14 billion in July 2013, the Bureau of Economic Analysis reported today (August 30 2013). That indicator had been adding higher amounts during the last four months of the current year. On average, Personal Income had increased 32.9 billion each month since sequestration started in March 2013. Accordingly to the Department of Commerce, revisited data from June confirmed that Personal Income added 38.2 billion, while in May and March it added over 45 billion each month. June was confirmed of having had an important increase of 34 billion on top of May’s figure of 17.5 billion.