Takeaways from the BLS jobs report

Ben Casselman at FiveThirtyEight provides a detailed breakdown of the BLS jobs report.  248,000 jobs were added in September, and figures for July and August were revised upward by almost 70,000.  These data are the talking points you’ll hear on the news, but they’re deficient measures of labor market health on their own.  Casselman delves into the BLS report to corroborate his stance that the report was, in fact, good news – something raw numbers of jobs added can’t do.  (Side note, why is the font on BLS reports so awful?  The color sucks too – it’s like a “my printer is almost out of ink” light grey.)  Anyway, the good:

1) The number of people who gave up on looking for work because they didn’t think any was available is down considerably – less than 700,000 in September, compared to over a million back in 2010.

2) Layoffs are at a 10 year low.

3) The (slight) majority of the unemployed either voluntarily quit their job or (re)started the job search

(1) and (3) show some confidence in the labor market. Fewer people think that a desirable job is totally unattainable given current labor market conditions, and more people are willing to voluntarily quit their jobs because they think better opportunities are out there.  These are good signs.  There are bad signs, too:

1) Many of the jobs added were in Retail, which tends to be low-paying.  More desirable sectors added relatively few jobs

2) There is still no wage growth

3) Lots of people are working part time only because they can’t find full-time employment

(1) is maybe expected, and stems from an issue that has been brewing in the U.S. economy for a while – structural unemployment.  The U.S. economy needs more people with the right skills in the right geographical areas before it can add a decent number of jobs in higher-paying sectors. (Many economists have echoed this train of thought, suggesting that structural unemployment is the driving force behind persistently high unemployment post-recession. One way to investigate this is to analyze the Beveridge Curve.)

(3) shows us that while employment has accelerated, many of those working are underemployed. (Part time workers generally don’t receive benefits – recent legislation, which you can read about here, is starting to change this, however.)  As the linked article explains, part of the increase in part-time employment could reflect better incentives for part-time work, not underemployment.  Nevertheless, while incentives could have driven the work decision of a portion of part-time workers, many indicated that the only reason they are working part-time is because full-time employment is unavailable – corroborating the underemployment suggestion.

(2) is an issue I wrote about in a previous post, and, I’d argue, the most important of the three.  There will not be sustainable growth until wages grow, and the <2% of the past year simply won’t cut it.  Furthermore, the lack of wage growth implies that there’s still plenty of slack in the labor market.

As a technical aside, below is what I mean by real wage growth, i.e. the wage growth that needs to occur before consumption can rebound and support a robust economy.  When we say real anything in economics, we mean inflation adjusted.  The real rate of wage growth is thus the inflation adjusted rate of growth of wages.  The raw, or nominal, rate of wage growth simply tells us by how much wages increased, ignoring the price level.  This is not all that useful, because wages affect consumption via the purchasing power of consumers – and if we don’t know what the inflation situation is like, we don’t know if consumers’ purchasing power increased, stayed the same, or decreased.

You could easily look up real wage growth (i.e. inflation adjusted wage growth), but for the sake of completeness here is how you can calculate the real growth in wages given the nominal rate of wage growth and a measure of inflation:

Screen Shot 2014-10-08 at 1.29.47 AM

For the nominal rate of wage growth, you could use the % change of Average Hourly Earnings (reported by the fed), and for inflation you could use the CPI % change over the same period.  These aren’t, however, the only metrics that will work – there are plenty of ways to quantify wages and inflation, each suited to a slightly different scenario.

About schapshow

Math & Statistics graduate who likes gymnastics, 90s alternative music, and statistical modeling. View all posts by schapshow

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out /  Change )

Google+ photo

You are commenting using your Google+ account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )


Connecting to %s


Open source projects for neuroscience!

Systematic Investor

Systematic Investor Blog

Introduction to Data Science, Columbia University

Blog to document and reflect on Columbia Data Science Class

Heuristic Andrew

Good-enough solutions for an imperfect world


"History doesn't repeat itself but it does rhyme"

My Blog

take a minute, have a seat, look around

Data Until I Die!

Data for Life :)

R Statistics and Programming

Resources and Information About R Statistics and Programming

Models are illuminating and wrong

Data & Machine Learning & Product

Xi'an's Og

an attempt at bloggin, nothing more...

Practical Vision Science

Vision science, open science and data analysis

Big Data Econometrics

Small posts about Big Data.

Simon Ouderkirk

Remote Work, Small Data, Digital Hospitality. Work from home, see the world.


Quantitative research, trading strategy ideas, and backtesting for the FX and equity markets


I can't get no

The Optimal Casserole

No Line Is Ever Pointless

SOA Exam P / CAS Exam 1

Preparing for Exam P / Exam 1 thru Problem Solving


Mathematical statistics for the layman.

%d bloggers like this: