May 2020 Jobs Report and Wages

I started doing the monthly Job Report again because this is unique. A pandemic of this magnitude is new to us. But that doesn’t mean we can’t learn from it.

The first thing to learn is during a pandemic, the classification of jobs gets weird. This may or may not have influenced the numbers.

The second thing to learn is WOW. These numbers blew away expectations. Economists were using the weekly unemployment figures to forecast a loss of potentially 8 million jobs. But the report said 2.5 million jobs gained. A swing like this is historic. Everything about it is.

Here are the job market and compensation numbers for May, 2020 (based on the job report):

Net gain of 2,500,000 jobs in the month

  • Analysts expected an overall drop of 7,250,000
  • Private sector payrolls increased by 3,094,000
  • Private service producing industries gained 2,425,000
  • Goods producing industries grew by 669,000
  • April was revised to a loss 20,700,000 from an original reading of 20,500,000 loss jobs.
  • March was revised to a loss of 1,400,000 jobs from a revision of 870,000 from an original reading of 710,000
  • Payroll processor ADP reported an employment loss of 2,760,000 jobs
  • 1,200,000 people are considered long term unemployed (jobless for more than 6 months). It was 939,000 in April and 1.2 million people in March 2020
  • Employers announced plans to cut 397,016 jobs. It was a record in April, 671,129.

Unemployment rate dropped to 13.3%. It was 14.7% in April and 4.4% in March, 2020. There are some oddities with the reporting and the rate could be adjusted in the coming reporting cycles.

  • The labor participation rate is 60.8%, up from 60.2%. It was 62.7% in March, 2020
  • The employment to population ratio is 52.8%. It was 51.3% in April. It was 60.0% in March, 2020
  • The U-6 report, which is a broader group to count (workers who are part time but want to be full time and discouraged worker), moved down to 21.2% from 22.8% in April. It was 8.7% in March.
  • PMI, a measure of manufacturing pace, is 43.1%, up from 41.5%. It was 49.1% in March, 2020. Anything above 50% means the machines are running
  • Service sector activity increased to 37.5%. It was an all time low of 26.7% in April.

Specific Segment Job numbers:

  • Manufacturing increased by 225,000. It was down 1,300,000 in April.
  • Construction gained 464,000 jobs. It was a loss of 975,000 jobs April.
  • Retailers improved by 367,000 jobs. It was a loss of 2,100,000 jobs in April.
  • Leisure and Hospitality Services gained 1,239,000 jobs. It had lost 7,700,000 jobs or 47% of the industry in April.
  • Government sector declined by 585,000. This follows a loss of 980,000 in April.
  • Education and Health Services gained 424,000. It had dropped by 2,500,000 jobs in April.
    • Health Care and Social Assistance gained 390,708 jobs. It loss 2,086,900 in April.
      • Health Care grew by 312,400 after gaining 14,000 in April
  • Professional and Business Services increased by 127,000. It was down by 2,165,000 in April.
    • A gain of 39,100 jobs in Temporary Help. It was a loss of 841,900 jobs in April.

Wage (can be revised):

  • The average weekly paycheck (seasonally adjusted) is $1,032.33.
  • The average hourly earnings (seasonally adjusted) is $29.75. Down from $30.01 in April and up from $28.62 in March, 2020
  • Average weekly hours and overtime of production and nonsupervisory employees on private nonfarm payrolls by industry sector, seasonally adjusted is 34.7.

Bureau of Labor Statistics

Claude Shannon – The Father of Information Theory

Claude Shannon, born on April 30th, 1916 in Petoskey, Michigan and died February 24th, 2001 in Medford, Massachusetts, was an American mathematician and he change the world.

I’d like to explain and to pay homage.

In 1948 he published “A Mathematical Theory of Communication.” This work is the underpinnings to Information Theory is more influential than another invention in 1948, the transistor.

His work is the underpinnings of the internet – email, facebook, netflix, and videos of cats playing piano.

What is Information Theory?

While I’m not a mathematician, what the formula above is expressing is a means to probabilistically codify messages from a sender to a receiver. Let me explain: “RICE, CHICKEN, and NOOO VEGETABLES!”

The other night I was getting takeout from an Asian restaurant. As I was entering a man and a woman were talking ahead of me. The man turned and walked away. The woman, annoyed, asks “What do you want?” The man equally annoyed replies while turning around “Rice, Chicken, and no plenaballs.” Which prompted a “Huh? Just tell me what you want.” At this point we get the loud, slow, deliberate answer “RICE… CHICKEN… AND NO VEGETABLES!”

In this case the information was exchanged a few times. Initially not all was received so it was sent again. But this time with redundancy to ensure transmission.

Think of Information as the resolution of uncertainty. If the data in the message reduces the uncertainty, then its information. If there’s no change in uncertainty, then its simply data.

The ultimate example of this is Wheel of Fortune.

I’ve got a good feeling about this

From it to bit – how all this is applied?

For a coin flip there are only two probabilities: heads or tails or said another way, zero (0) substituting for heads and one (1) substituting for tails. Each of these is 0.50 likely. In this case information, heads or tails, is one bit, a 0 or a 1. Now suppose I have a deck of cards. There are four suits with each having 13 cards in it. In a full deck the likeliness I get a club is 0.25 or 25%. I represent 0.25 as two bits or 00, 10, 01, or 11. Just like if I flipped a coin twice I could have heads/heads (00), tails/heads (10), heads/tails (01) or tails/tails (11).

Now think about the alphabet again. For the sake of simplicity we’ll say there are 26 letters in the alphabet (not counting capitals, punctuation, or blank spaces between words). To represent 26 symbols (each letter is a symbol) I need 5 bits.

1 bit = two symbols (0 or 1) or heads and tails
2 bits = 4 symbols (00, 10, 01, 11) or the suits in a deck of cards: clubs, spades, diamonds, and hearts
3 bits = 8 symbols (000, 001, 010, 100, 011, 101, 110, 111)
4 bits = 16 symbols (0000, 0001, 0010, 0100, 1000, 0011, 0101, 1001, 0110, 1100, 1110, 0111, 1101, 1011, 1010, 1111)
5 bits = 32 symbols (00000, 00001, 00010, 00100, 01000, 00011, 00101, 01001, 00110, 01100, 01110, 00111, 01101, 01011, 01010, 01111, 10000, 10001, 10010, 10100, 11000, 10011, 10101, 11001, 10110, 11100, 11110, 10111, 11101, 11011, 11010, 11111)

With 5 bits you can have 00000 equal “e” since we know “e” is the most frequent letter of the alphabet (actually, the English alphabet requires 8 bits and “e” is 01100101). What this means, I know it sounds weird, is that resolving the uncertainty of “e” requires at least 5 bits of information (8 bits in reality). Because there are 26 letters in the alphabet, you need more information to distinguish which letter is which. Luckily, communicating is one of the few times in life where the past truly dictates the future. If I know “q” is part of the message or puzzle, then I know “u” is highly likely to follow.

Pat, I’d like to solve the puzzle.