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.


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.

Real US GDP increased 5.0 percent in the third quarter of 2014: BEA.


Real Gross Domestic Product increased 5.0 percent in the third quarter of 2014, the US Bureau of Economic Analysis reported today January 22 of 2015. The largest contributor for its expansion was the Finance, insurance, real state, rental and leasing Industry with a significant 20% of the total value added to GDP during the third quarter of 2014. Real State and leasing industry contributed 13 percent while the Finance and Insurance contributed 7.4 percent. The actual change in Value Added of the Finance industry was 21.2 percent when compared to the second quarter 2014, from which it had grown previously only 6.0 percent. Real Value Added is a measure of an Industry’s contribution to GDP given in constant prices (2005) rather than current prices.

Value Added by Industry group as a Percentage of GDP during the third quarter of 2014 was largely driven by the Finance and Insurance Industry. The second contributors for total GDP Value Added were both manufacturing Industry as well as Professional and Business Services Industry, which both contributed with 12 percent each. The public sector contributed with 9 percent of the GDP Value Added for the third quarter 2014. Education and health care also bolstered GDP Value Added largely with 8 percent.

These data point out toward a more convincing signals of a solid path of United States GDP expansion. First quarter of 2014 posed many question about the strength of the economic recovery from the Great Recession.

Take a look at Real Value Added by Industry:

Real Value Added by Mining industry augmented by 25.6 percent, which meant its largest increase since the fourth quarter of 2008. It contributed 3 percent of the 5% GDP increase.

Utility Industry which contributed 2 percent out of the 5 percent GDP growth, showed a 18.2 percent change from the preceding period.

Real Value Added by Construction industry registered a small 2.3 percent change during the third quarter of 2014. Construction as a whole industry enlarged by 4 percent the total GDP Value Added for the same period.

Manufacturing barely changed with a small 0.5 percent from the second quarter of 2014, though it still made up 12 percent of the total Value Added to GDP for the third quarter 2014.

Real Value Added by the Wholesale trade industry registered a 7.3 percent change from period before. Wholesale industry made up 6 percent of the third 2014 quarter change. (Learn more details on Wholesale trade industry during 2014)

Retail trade industry changed 1.1 percent and contributed the 5 percent GDP change by 6 percent. (See more details on Retail trade Industry).

Real Value Added by the Transportation and Warehousing Industry changed 6.7 from preceding period. Such increase represents 3 percent of the total GDP change of third quarter 2014 (Read more on industries related to oil).

Information Industry contributed 5 percent to GDP growth during the third quarter 2014, which came out of a 6.4 percent change of Real Value Added from preceding period.

Real State, Rental and Leasing also grew its Value by 4.4 percent from the preceding period. The entire industry, which includes Finance and Insurance contributed 21 percent.

Real Value Added by the Professional and Business services Industry experienced a 5.3 percent change during the third quarter of 2014. Professional Services Industry’s Value Added as a percentage of the total GDP represented 12 percent.

Education services and Health care industries accounted for 8 percent change of the total 5 percent GDP Value Added during third quarter 2014.

Real Value Added by Arts, recreation, Food Services, Entertainment added value at 5.1 percent when compared to the period before the third quarter 2014. This Industry as a group made up 4 percent of the total value added to GDP for the same period.



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

Where 0.8 percent US’ Inflation came from?

The Consumer Price Index increased 0.8 percent from December 2013 through December 2014, the US Bureau of Labor Statistics reported on January 16th 2015. This small increase represents the second-smallest December-December increase since 1965. Energy prices in general dragged the average down with a sharp drop in Gasoline prices of about -21 percent change over the year, though Energy in general increased a little more than 10 percent. Indexes for both Food and Rent rose 3.4 percent during 2014. The Consumer Price Index is a measure of the average change in prices of goods and services purchased on a daily basis by US households. Prices are collected monthly in 87 urban areas from roughly 26,000 retail stores and 4,000 housing units. The US Federal Reserve Bank aims at a 2 percent increase in the Consumer Price Index.

Although Consumer Price Index is a good statistical measure, its own aggregation as an average may lead to wrong conclusions. A more detailed approach on price changes can be done by looking at each good’s and service’s average price change. The graph below shows average price changes for the major 30 goods and services, which were selected by their weight relevance in the CPI calculation (higher than 1.0). These are the goods and services that matter the most for a day-to-day household tracking expenses.

Major 30 CPI

As the graph shows the biggest drop in prices were on energy related goods and services. Gasoline prices dropped roughly -21 percent through the year. Wireless telephone services also decreased its price by -4 percent approximately. Women’s and girl’s apparel fell -3.6 percent along 2014, whereas Apparel in general did so by -2 percent. Read the entire BLS’ report.

On the other hand, the index that increased the most during 2014 was the one related to protein food. Meats, poultry, fish and eggs increased their prices by 9.2 percent nearly, whereas Food in general increased 3.4 percent. Prescription drugs were up 6.4 percent. Rent of primary residencies were augmented by 3.4 percent.

An even more detailed information about Price changes by goods and services can be found in the list below. The list is organized by alphabetical order:

Expenditure category followed by CPI percent change.
Admission to movies, theaters, and concerts 0.4
Admission to sporting events 2.7
Admissions 0.7
Airline fare -4.7
Alcoholic beverages 1.3
Alcoholic beverages at home 0.7
Alcoholic beverages away from home 2.2
All items 0.8
All items less food and energy 1.6
Apparel -2
Apparel services other than laundry and dry cleaning 1.8
Apples -2.3
Appliances -5.2
Audio discs, tapes and other media -3.6
Audio equipment -7.3
Automobile service clubs -0.4
Baby food 2.1
Bacon and related products -1
Bacon, breakfast sausage, and related products 2.4
Bakery products 0.9
Bananas -0.7
Bedroom furniture -2.4
Beef and veal 18.7
Beer, ale, and other malt beverages at home 0.7
Beer, ale, and other malt beverages away from home 2.1
Beverage materials including coffee and tea 2.6
Boys’ and girls’ footwear 6.1
Boys’ apparel -2.7
Bread 1.2
Bread other than white 0.8
Breakfast cereal 1.3
Breakfast sausage and related products 7.3
Butter 22.5
Butter and margarine 11.6
Cable and satellite television and radio service 2.2
Cakes, cupcakes, and cookies 0.6
Candy and chewing gum 1.8
Canned fruits 0.5
Canned fruits and vegetables -0.2
Canned vegetables 0
Car and truck rental 0
Carbonated drinks 1.4
Care of invalids and elderly at home 1.8
Cereals and bakery products 0.5
Cereals and cereal products -0.3
Checking account and other bank services 0.1
Cheese and related products 8.2
Chicken 2.1
Child care and nursery school 2.2
Cigarettes 3.1
Citrus fruits 5.4
Clocks, lamps, and decorator items -5.8
Club dues and fees for participant sports and group exercises 0.4
Coffee 3.6
College textbooks 5
College tuition and fees 3.4
Commodities less food and energy commodities -0.8
Computer software and accessories -1.2
Cookies -0.2
Cosmetics, perfume, bath, nail preparations and implements 1
Crackers, bread, and cracker products 1
Dairy and related products 5.3
Delivery services 1.1
Dental services 1.8
Dishes and flatware -6.7
Distilled spirits at home 0.9
Distilled spirits away from home 2.2
Distilled spirits, excluding whiskey, at home 0.8
Domestic services 1.2
Dried beans, peas, and lentils 4.6
Education and communication commodities -4.9
Education and communication services 0.9
Educational books and supplies 4.6
Eggs 10.7
Electricity 3.1
Elementary and high school tuition and fees 4
Energy -10.6
Energy commodities -20.5
Energy services 3.7
Eyeglasses and eye care 2.6
Fats and oils 1
Fees for lessons or instructions 2
Film and photographic supplies 23.4
Film processing 3.8
Financial services 3.5
Fish and seafood 4.3
Floor coverings 0.8
Flour and prepared flour mixes -1.9
Food 3.4
Food at elementary and secondary schools 2.3
Food at employee sites and schools 1.8
Food at home 3.7
Food away from home 3
Food from vending machines and mobile vendors 0.5
Footwear 2.8
Frankfurters 12.1
Fresh and frozen chicken parts 1.6
Fresh biscuits, rolls, muffins 1.9
Fresh cakes and cupcakes 1.5
Fresh fish and seafood 5.6
Fresh fruits 3.6
Fresh fruits and vegetables 4.1
Fresh milk other than whole 4.1
Fresh sweetrolls, coffeecakes, doughnuts 0.6
Fresh vegetables 4.6
Fresh whole chicken 3
Fresh whole milk 5.2
Frozen and freeze dried prepared foods 1.9
Frozen and refrigerated bakery products, pies, tarts, turnovers -0.5
Frozen fish and seafood 5.2
Frozen fruits and vegetables 1.5
Frozen noncarbonated juices and drinks 2.3
Frozen vegetables 0.9
Fruits and vegetables 3.2
Fuel oil -19.1
Fuel oil and other fuels -13.7
Full service meals and snacks 3.1
Funeral expenses 1.2
Furniture and bedding -1.6
Garbage and trash collection 1.4
Gardening and lawncare services 4.4
Gasoline (all types) -21
Gasoline, unleaded midgrade -19.6
Gasoline, unleaded premium -18.3
Gasoline, unleaded regular -21.6
Girls’ apparel -4
Hair, dental, shaving, and miscellaneous personal care products -0.3
Haircuts and other personal care services 1.5
Ham 13.1
Ham, excluding canned 14.4
Health insurance -0.5
Hospital and related services 4.5
Hospital services 4.9
Household cleaning products -0.9
Household furnishings and supplies -1.9
Household operations 2.8
Household paper products -0.7
Housekeeping supplies -0.8
Housing at school, excluding board 2.7
Ice cream and related products 3.5
Indoor plants and flowers 1.9
Infants’ and toddlers’ apparel 0.4
Infants’ equipment -0.7
Infants’ furniture
Information technology commodities -9
Inpatient hospital services 5.5
Instant and freeze dried coffee 0.2
Intercity bus fare
Intercity train fare 3.8
Internet services and electronic information providers 1.6
Intracity mass transit 1.1
Intracity transportation 1.1
Jewelry -5.1
Jewelry and watches -4.3
Juices and nonalcoholic drinks 0.1
Lamb and mutton 3.2
Lamb and organ meats 8.8
Land-line telephone services 1.8
Laundry and dry cleaning services 2.2
Laundry equipment -7.4
Leased cars and trucks -0.1
Legal services 1.4
Lettuce 4.4
Limited service meals and snacks 3.2
Living room, kitchen, and dining room furniture -1.9
Lodging away from home 6.3
Lunchmeats 5.8
Major appliances -6.9
Margarine 2.6
Meats 12.7
Meats, poultry, and fish 9.1
Meats, poultry, fish, and eggs 9.2
Medical care commodities 4.8
Medical care services 2.4
Medical equipment and supplies 0.9
Medicinal drugs 5
Men’s and boys’ apparel -3
Men’s apparel -3
Men’s footwear 1.8
Men’s furnishings -2.4
Men’s pants and shorts 1.1
Men’s shirts and sweaters -4.5
Men’s suits, sport coats, and outerwear -7.1
Milk 4.3
Miscellaneous household products -0.7
Miscellaneous personal goods -0.6
Miscellaneous personal services 2.1
Motor fuel -20.8
Motor oil, coolant, and fluids 2.4
Motor vehicle body work 2.1
Motor vehicle fees 0.3
Motor vehicle insurance 4.7
Motor vehicle maintenance and repair 2.1
Motor vehicle maintenance and servicing 2.2
Motor vehicle parts and equipment -0.7
Motor vehicle repair 2
Moving, storage, freight expense 2.1
Music instruments and accessories 2.4
New cars -0.1
New cars and trucks 0.6
New trucks 1.3
New vehicles 0.5
Newspapers and magazines 4.8
Nonalcoholic beverages and beverage materials 0.7
Nonelectric cookware and tableware -3.7
Nonfrozen noncarbonated juices and drinks -1
Nonprescription drugs -0.2
Nursing homes and adult day services 2.9
Olives, pickles, relishes 0.2
Oranges, including tangerines 3.7
Other appliances -3.1
Other bakery products 0.4
Other beverage materials including tea 1
Other condiments 1.8
Other dairy and related products 3.7
Other fats and oils including peanut butter -2.5
Other food at home 1.5
Other food away from home 2
Other foods 1.7
Other fresh fruits 6.2
Other fresh vegetables 2.3
Other furniture 0.8
Other goods 1.3
Other household equipment and furnishings -3.9
Other intercity transportation -0.7
Other linens -5.2
Other lodging away from home including hotels and motels 7.3
Other meats 7.4
Other miscellaneous foods 1.6
Other motor fuels -11.9
Other personal services 1.9
Other pork including roasts and picnics 12.5
Other poultry including turkey -0.5
Other processed fruits and vegetables including dried 0.2
Other recreation services 0.8
Other recreational goods -3.8
Other sweets -0.2
Other video equipment -0.8
Outdoor equipment and supplies -0.3
Outpatient hospital services 4.5
Owners’ equivalent rent of primary residence 2.6
Owners’ equivalent rent of residences 2.6
Parking and other fees 2.2
Parking fees and tolls 2.7
Peanut butter -3.6
Personal care products 0.3
Personal care services 1.5
Personal computers and peripheral equipment -10.5
Pet food 0.4
Pet services 1.8
Pet services including veterinary 2.7
Pets and pet products 0.3
Photographer fees 1.1
Photographers and film processing 2.2
Photographic equipment -6.1
Photographic equipment and supplies -2.2
Physicians’ services 1.5
Pork 8.2
Pork chops 10.1
Postage 4.1
Postage and delivery services 3.8
Potatoes -1.8
Poultry 1.6
Prepared salads 3.9
Prescription drugs 6.4
Processed fish and seafood 3
Processed fruits and vegetables 0.4
Professional services 1.7
Propane, kerosene, and firewood -4.6
Public transportation -2.9
Purchase of pets, pet supplies, accessories 0.4
Recreation commodities -2.6
Recreation services 1.5
Recreational books -0.9
Recreational reading materials 2.2
Rent of primary residence 3.4
Rent of shelter 2.9
Rental of video or audio discs and other media 1.4
Repair of household items 4
Rice -2.8
Rice, pasta, cornmeal -2.1
Roasted coffee 4.2
Salad dressing -4.3
Salt and other seasonings and spices 4.8
Sauces and gravies 1.7
Services by other medical professionals 2
Services less energy services 2.4
Sewing machines, fabric and supplies 0.1
Shelf stable fish and seafood 1.3
Shelter 2.9
Ship fare -1.9
Snacks 1.8
Soups -0.6
Spices, seasonings, condiments, sauces 2.2
Sporting goods -2.2
Sports equipment -3.1
Sports vehicles including bicycles -1.1
State motor vehicle registration and license fees -1
Stationery, stationery supplies, gift wrap 0
Sugar and artificial sweeteners 0.2
Sugar and sweets 1.1
Tax return preparation and other accounting fees 6.1
Technical and business school tuition and fees 1.8
Telephone hardware, calculators, and other consumer information items -9.9
Telephone services -2.1
Televisions -16.7
Tenants’ and household insurance 5.6
Tires -1.9
Tobacco and smoking products 3
Tobacco products other than cigarettes 1.4
Tomatoes 16.5
Tools, hardware and supplies 0.8
Tools, hardware, outdoor equipment and supplies 0.1
Toys -5.4
Toys, games, hobbies and playground equipment -2.9
Transportation commodities less motor fuel -0.9
Transportation services 1.7
Tuition, other school fees, and childcare 3.2
Uncooked beef roasts 20.6
Uncooked beef steaks 16
Uncooked ground beef 19.2
Uncooked other beef and veal 24
Used cars and trucks -4.2
Utility (piped) gas service 5.8
Vehicle accessories other than tires 1.7
Vehicle parts and equipment other than tires 1.5
Veterinarian services 2.9
Video and audio products -10.5
Video and audio services 1.8
Video discs and other media -6.3
Video discs and other media, including rental of video and audio -3
Watches -1
Water and sewer and trash collection services 4.6
Water and sewerage maintenance 5.6
Whiskey at home 1.5
White bread 0.9
Window and floor coverings and other linens -3.6
Window coverings -2.3
Wine at home 0.6
Wine away from home 2
Wireless telephone services -4
Women’s and girls’ apparel -3.6
Women’s apparel -3.5
Women’s dresses 1.6
Women’s footwear 1.7
Women’s outerwear 3.6
Women’s suits and separates -8.2
Women’s underwear, nightwear, sportswear and accessories -0.3

US Census Bureau’s Advance Estimates show Retail Sales increased 3.2% in December 2014.

Estimated Sales Dec 2014 - 2

Advance Estimates for Retail and Food Services show that Sales for December 2014 increased 3.2 percent compared to December 2013. Statistics were adjusted by season, but not corrected for inflation, which gives a total estimated sales volume of U$442.9 billion. Auto and other motor vehicles dealers were up 9.2 percent when compared to same month of 2013. Food and services also grew in sales roughly 8.2 percent. The Advance Estimates give a preliminary hint by sampling a smaller base of the full Retail Trade and Food services Survey at the US Census Bureau.

Estimated percenrt change
Almost all of the survey accounts experienced increases when compared to the same month in 2013. However, Gasoline Stations –as expected given low oil prices- realized 14.2 percent less dollars sales this past December 2014. Also, estimate data show that Department Stores sold 0.6 percent less. Food Services and Drinking places reported in advance the highest gains for the season with a strong 8.2% as well as Auto sales with a similar statistic. Health and Personal care stores also increased their sales level by 6.1 percent. Furniture and Electronics and Appliances sold 5.8 percent and 6.8 percent respectively, preliminary data showed.