Some good news on inflation: The CPI fell in December for the second straight month and is up only 0.16% over the last 6 months

Here is the Consumer Price Index for each of the last three months:

Oct. 298.012

Nov. 297.711

Dec. 296.797

 

The 296.797 means that what cost $100 in 1983 would cost $296.797 today. To see all the monthly CPI figures going back over 100 years, see Consumer Price Index Data from 1913 to 2022. 

 

The Dec. CPI is about 0.3% less than the Nov. figure since 296.797/297.711 = .997. The news reports are saying it was only down 0.1%. Why?

The Bureau of Labor Statistics makes seasonal adjustments and that is why prices were reported to have gone down less than the first set of numbers that I show above indicate. See Consumer Price Index Summary. Here is what it says:

"Use of Seasonally Adjusted and Unadjusted 

 

Data The Consumer Price Index (CPI) produces both unadjusted and seasonally adjusted data. Seasonally adjusted data are computed using seasonal factors derived by the X-13ARIMA-SEATS seasonal adjustment method. These factors are updated each February, and the new factors are used to revise the previous 5 years of seasonally adjusted data. The factors are available at www.bls.gov/cpi/tables/seasonal-adjustment/seasonal-factors-2022.xlsx. For more information on data revision scheduling, please see the Factsheet on Seasonal Adjustment at www.bls.gov/cpi/seasonal-adjustment/questions-and-answers.htm and the Timeline of Seasonal Adjustment Methodological Changes at www.bls.gov/cpi/seasonal-adjustment/timeline-seasonal-adjustment-methodology-changes.htm. 

 

For analyzing short-term price trends in the economy, seasonally adjusted changes are usually preferred since they eliminate the effect of changes that normally occur at the same time and in about the same magnitude every year-such as price movements resulting from weather events, production cycles, model changeovers, holidays, and sales. This allows data users to focus on changes that are not typical for the time of year. The unadjusted data are of primary interest to consumers concerned about the prices they actually pay. Unadjusted data are also used extensively for escalation purposes. Many collective bargaining contract agreements and pension plans, for example, tie compensation changes to the Consumer Price Index before adjustment for seasonal variation. BLS advises against the use of seasonally adjusted data in escalation agreements because seasonally adjusted series are revised annually."

The CPI in was 296.311 in June. So it was up only 0.16% in the last six months since 296.797/296.311 = 1.0016. If it went up that much again for the next six months then we would have a 12 month inflation rate of about 0.32%. This is not seasonally adjusted.

 

The CPI in Dec. 2022 was 6.5% higher than it was in Dec. 2021. That gives us an official inflation rate for 2022 of 6.5%. It was 7.0% for 2021. From 1983-2020, the average annual compound inflation rate was about 2.62%. The highest single year in that time was 6.1% and it got above 4% only four other years. We saw a long term trend of falling inflation rates, too. See Four Decades Of Disinflation. The average annual inflation rate for the 2010s ended up being 1.76%

 

To see the current inflation report from the BLS, go to Consumer Price Index Summary.

Will Studying Economics Make You Rich? A Regression Discontinuity Analysis of the Returns to College Major

See By Zachary Bleemer and Aashish Mehta.

People who majored in economics might earn more money than the average college major. But that does not necessarily mean that majoring in econ causes higher incomes. It could be that those with above average intelligence choose to major in economics and they would have made higher incomes no matter what subject they studied.

In economics we use the Latin phrase "ceteris paribus" (people who know Latin tell me you are supposed to pronounce the C like a K). It means "all other things being equal" or "holding all other factors constant." Ideally, we would have two identical groups of students, then randomly assign them to a major. If the econ majors made more than, say, the sociology majors, we could say that studying econ led to the higher incomes. 

A study used the ceteris paribus idea (almost). The authors looked at students who just barely qualified to major in economics."  

"Abstract

We investigate the wage return to studying economics by leveraging a policy that prevented students with low introductory grades from declaring a major. Students who barely met the grade point average threshold to major in economics earned $22,000 (46 percent) higher annual early-career wages than they would have with their second-choice majors. Access to the economics major shifts students’ preferences toward business/finance careers, and about half of the wage return is explained by economics majors working in higher-paying industries. The causal return to majoring in economics is very similar to observational earnings differences in nationally representative data."

See also 33 of the Highest-Paying Majors You Can Choose in College (2022).

Relate posts: 

Studying Economics Increases Wages a Lot (2020)

What College Majors Pay The Highest? (2013)

50 College Majors With the Best Return on Investment (2015)

Will technology cost artists their job?

Structural Unemployment-unemployment caused by a mismatch between the skills of job seekers and the requirements of available jobs.

One example of this is when you are replaced by a machine. Another example is when there is a fall in demand for your product, so you get laid off, like with typewriters since people now use computers. A third example is geographical, when the jobs are not in your region of the country.

See If you can type it, you can see it: Text2image AI turns words into pictures by Dwight Silverman of The Houston Chronicle. Excerpts:

 

"On Craiyon, visitors enter words to generate images that are sometimes realistic, sometimes whimsical and occasionally disturbing."
 
[this] "creation is known as text2image. While Craiyon’s results clearly aren't realistic, other models can output images so advanced that it’s hard to tell them from photographs or human-created art, generating both excitement and anxiety.

In the art community, some worry such technology will cost artists their jobs. But others see it as an opportunity to inform and enhance their work."

 

"There are myriad AI models with common interfaces: Users enter text and the model generates images based on those words. Craiyon offers nine images in about a minute.

 

Craiyon’s model is trained and its images produced, Dayma says, using data from “hundreds of millions” of pictures, captions and metadata scraped from the web."

"GAN, or generative adversarial networks, is an older AI approach trained to output a specific image based on other images. An example of a GAN model can be found at www.thispersondoesnotexist.com. The site, which launched in 2019, generates a realistic human face each time the web page is reloaded. No human matches the face – each is fake – but the site raised alarm bells when it appeared, making it clear AI has progressed to where almost no image can be trusted to be real.

Models known as diffusion and transformer work from a text description. The words are applied to the dataset of images learned by the AI, and one matching the text is eventually constructed. Craiyon uses the transformer model."

"In August, the Colorado State Fair awarded first prize in its art contest for a work that was created using AI. The artwork, depicting a portal opening inside an ornately detailed room, was submitted in a category for digital art. Jason M. Allen told The New York Times he was open about how the piece was created, and the judges stood by their decision. But the award incensed some artists and created a social media backlash. Some artists are concerned about AI’s ability to mimic styles. An artist’s style can't be copyrighted." 

"And coming soon: Text2video. Facebook parent company Meta this month announced Make-a-Video, which generates a few seconds of video from a text prompt. Not to be outdone, Google announced Imagen Video. Neither is available to the public."

"You can see where AI-generated art is headed in the work of Mary Flanagan a Dartmouth College art professor, game designer, programmer, writer and author. Flanagan, who has a home base in Houston, has coded her own AI designed to create images, Grace:AI, and trained it on tens of thousands of pieces of art created by women. 

Though some artists are upset about AI-generated art putting some of them out of work, Flanagan isn’t among them. She says AI art will free artists and designers “from the really boring work” so they can concentrate on their own creativity."

 

Related posts:

 

Rent a robot for Christmas? Makes sense if you are a logistics company (2022)

Walgreens Turns to Prescription-Filling Robots to Free Up Pharmacists (2022)

Answering the Call of Automation: How the Labor Market Adjusted to the Mechanization of Telephone Operation (2022)

Many Jobs Lost During the Coronavirus Pandemic Just Aren’t Coming Back (2021)

Can computers write poetry?Could they replace poets? (2020)

Will computer programs replace newspaper columnists?  (2020)

McDonald’s Tests Robot Fryers and Voice-Activated Drive-Throughs: Burger giant wants to speed service as competition for fast-food diners mounts (2019)

Is Walmart adding robots to replace workers or because it is hard to find workers? (2019)

Robot Journalists-A Case Of Structural Unemployment? (2010)

Structural Unemployment In The News-Computers Can Now Tell Jokes  (2013)

WHAT do you get when you cross a fragrance with an actor?

Answer: a smell Gibson.

Robot jockeys in camel races (2014)

Are Computer Programs Replacing Journalists? (2015)

Automation Can Actually Create More Jobs  (2016)

The Robots Are Coming And It Might Not Be A Case of Structural Unemployment  (2018)

Broncos to debut beer-pouring robot at upcoming game (2018)

Robots Are Ready to Shake (and Stir) Up Bars (2018)

Is Covid causing some structural unemployment? (2020)

Is Covid causing some structural unemployment? (Part 2)
(2020)

Warehouses Look to Robots to Fill Labor Gaps, Speed Deliveries  (2021)

Is unemployment still high because of structural unemployment?    (2021)

The Pizza Delivery Guy Will Be a Robot at Many Campuses This Fall  (2021)

Have labor markets worked recently the way the textbook says they work?

See Small Businesses Find Some Relief From Hiring Woes (has the MVP study) by Ruth Simon of The WSJ. Excerpts: 

"Small-business owners say it is getting easier to hire workers and keep them around, in what they hope is a sign that the worst of their labor problems are behind them.  

The U.S. job market remains historically tight. But December marked the first time since July where more small-business owners said in a survey for The Wall Street Journal that they found it easier—rather than harder—to find workers. 

Some entrepreneurs say steps such as raising pay, adding apprenticeship programs and rewriting job ads are starting to pay off. Others report an increase in applicants as competitors pull back on hiring or begin layoffs."

"Eighty-one percent of small-business owners reported raising wages in response to labor-market challenges, according to a survey conducted by Vistage Worldwide Inc."

We expect price to rise when there is a shortage. A shortage is when price is less than the equilibrium price and quantity demanded is greater than quantity supplied. This puts upward pressure on price and in this case the price of labor is wages.

The percentage of 25-54 year-olds employed rose 0.4 percentage points in December after 3 months with declines; average hours worked keep falling

One weakness of the unemployment rate is that if people drop out of the labor force they cannot be counted as an unemployed person and the unemployment rate goes down. They are no longer actively seeking work and it might be because they are discouraged workers. The lower unemployment rate can be misleading in this case. People dropping out of the labor force might indicate a weak labor market.

We could look at the employment to population ratio instead, since that includes those not in the labor force. But that includes everyone over 16 and that means that senior citizens are in the group but many of them have retired. The more that retire, the lower this ratio would be and that might be misleading. It would not necessarily mean the labor market is weak.

But we have this ratio for people age 25-54 (which also eliminates many college age people who might not be looking for work).

The percentage of 25-54 year olds employed was 80.1% in Dec. and 79.7% in Nov.. It was 80.2% in August. 

It was 80.6% in Jan. 2020 and 69.6% in April 2020.  Click here to see the BLS data. The unemployment rate was 3.5% in Dec. Click here to go to that data. The % of those 16 and older employed went from 59.887% in Nov. to 60.127% in Dec.

Here is a good graph from the St. Louis Fed. It shows that there are 127,205,000 people in the 25-54 year old group. So since we are 0.5 percentage point below the 80.6% of Jan. 2020 (the high point since the previous recession), that is still 636,025 fewer jobs (Hat tip: Vance Ginn of the Texas Public Policy Foundation). 

Also, we are up 10.5 percentage points since April 2020 (80.1 - 69.6). That is 95.5% of what we lost from Jan. 2020 to April 2020 (11.0 percentage points or 80.6 - 69.6) during Covid. Then 10.5/11.0 = 95.5%. So we have gotten about 95.5% of the jobs back.  

Here is the timeline graph of the percentage of 25-54 year olds employed since 2012.

Now since 1948

Now hours worked. This comes from the St. Louis FED. See Average Weekly Hours of All Employees, Total Private.

"Average weekly hours relate to the average hours per worker for which pay was received and is different from standard or scheduled hours. Factors such as unpaid absenteeism, labor turnover, part-time work, and stoppages cause average weekly hours to be lower than scheduled hours of work for an establishment. Group averages further reflect changes in the workweek of component industries. Average weekly hours are the total weekly hours divided by the employees paid for those hours."