First, the Huffington Post’s assessment:
For Some Who Are Back At Work, Positive Jobs Report Doesn’t Tell The Full Story
By Arthur Delaney
WASHINGTON — Every weeknight Bridget Krueger and her husband catch up with an 8 p.m. phone call because his new job is far away and he works long hours, so he has to spend the night in a hotel.
“It’s almost like being a single parent during the week,” Krueger, 46, said in an interview. But it’s better now than it was before, when her husband, Brian, was out of work. “Sometimes you have to do what you have to do.”
Since May, Brian Krueger, 48, has been working as a steamfitter at a power plant in Nelson, Illinois, about two hours from his home in Mount Horeb, Wisconsin. He drives out on Monday, works five or six 10-hour shifts, and heads home on Friday or Saturday.
“I get to see my family maybe one or two days of the week,” he said. “It’s tough.”
When Krueger got this job, he escaped a nasty brush with longterm unemployment. The Labor Department announced Friday morning that the number of longterm jobless has dwindled to 2.8 million, nearly 1 million fewer than a year ago. Economists have been debating whether the decline in longterm unemployment reflects improving job prospects for the jobless or simply fewer of them seeking work, since people who don’t look don’t count as officially unemployed. Either way, the decline is part of a happy story about the economy — the national unemployment rate stood at 5.7 percent in January, down from 6.6 percent a year ago.
Krueger had been unemployed for about a year before getting this job. About halfway through his unemployment spell, in December 2013, his benefits stopped. He would have been eligible for another six months of checks, but Congress declined to renew federal longterm benefits. Last January his congressman, Rep. Mark Pocan (D), invited him to attend the president’s State of the Union address as part of a Democratic effort to dramatize the plight of the jobless.
Republicans favored cutting the benefits, and some conservatives have claimed the cutoff helped spur the economy, but in Krueger’s case only hardship ensued. His family went on food stamps, struggled to pay bills, and feared foreclosure. Now that he’s back to working, Krueger doesn’t see himself as part of the happy story.
“It’s just a coincidence that I have work,” he said. “If the economy was better, I would have been working a lot sooner.”
Still, he and his wife are much happier now that they’re both working again, even though his job ends in the spring. (She works as a branch manager of a bank.)
“It’s way better,” Brian Krueger said. “We paid off a majority of our bills, we’ve been able to save a lot of money so when the job down here does end in the middle of April, we’ll have at least a cushion of savings.”
Click here to read the rest of Arthur Delaney’s article on Huffington Post.
And, Jared Bernstein here, and over at WaPo (see link in the text):
With all the labor market improvement, surely wage growth is accelerating…nuh-uh.
Over at PostEverything, but here’s the figure–five wage/comp series ably smashed together by my colleague Ben Spielberg using principal components analysis (a useful way to avoid cherry-picking the series that tells the story you like, PC analysis pulls out the common, underlying trend on the combined series).
Source: BLS, see data note in WaPo post.
The figure above is a weighted average of year-over-year wage growth for five different data series:
–Employment cost index: hourly compensation
–Employment cost index: hourly wages
–Productivity series: hourly compensation
–Median weekly earnings: full-time workers
–Average hourly earnings: production, non-supervisory workers
The data all come from the BLS and are non-seasonally adjusted, except for the productivity series.
To derive the figure, we:
–take yearly changes in the data (e.g., q1/q1/, q2/q2, etc.) and run a principal components analysis on the yearly changes.
–using the first principal component, we divide each coefficient by the standard deviation of the series that corresponds to that coefficient.
–obtain weights by dividing the resultant value for each series by the sum of all the resultant values.
–multiply the matrix of series data by a vector of weights from the previous step to obtain the plot.
All of the above is necessary just to generate a series of percent changes that is a weighted average of the underlying data. But this plot just shifts the scale of the first PC series that any statistical software package will generate (i.e., it is perfectly correlated with that series (r=1)).
For further info or for an E-views program that automates the above, write to Ben Spielberg (firstname.lastname@example.org). We thank Jesse Rothstein for helping with this scaling transformation.
Data in Excel are here.
Reprinted with permission from Jared Bernstein – originally posted here.