Tag Archives: unemployment

Good News – The Unemployment Rate Increased

The Bureau of Labor Statistics (BLS) had some good news for us yesterday. The unemployment rate increased from 7.8 percent to 7.9 percent.

You read that right: it’s good news that the unemployment rate increased. In fact, economists have been waiting for it to increase, and have been a little disappointed that it didn’t increase sooner.

This might sound a little balmy, but there’s method to this madness. It’s all in the reason the unemployment rate increased.

The unemployment rate is the number of unemployed people divided by the total number of unemployed plus employed. The BLS collects these numbers by contacting 60,000 households every month.

To be “unemployed”, you have to be actively looking for work. People who are aren’t actively looking aren’t included, even if they’d like to be working.

During a severe recession like we just had, lots of people get so discouraged about finding work that they give up and stop looking – or, as economists say, they exit the workforce. They’re no longer considered either employed or unemployed if they’re not actively looking.

When job markets improve after the downturn, many of these discouraged workers again start to look for work. This is a good sign for the economy, and typically indicates that things will continue to improve.

In October, the number of unemployed in the U.S. increased by 170,000, to 12.3 million. At the same time, the number of employed increased by 410,000, to 143.4 million. As a result, the unemployment rate increased from 7.8 to 7.9 percent.

I’ll be anxious to see the JOLT (Job Openings, Layoffs, and Turnover) data that will be coming out on Tuesday, November 6. It will provide more details about exactly what is going on in the job market, and what is (likely) causing it.


Voting with Your Wallet II: Education, Liberalism, and Unemployment

Last time I talked about an apparent positive relationship between state unemployment rates and state support for President Obama in political polls. This despite the conventional wisdom that Americans vote with their wallets. That would suggest that states with higher unemployment rates should be less likely to support an incumbent President.

I have to be honest. I started this thinking that the positive relationship was just a fluke. That it was really just a sort of anomaly that would go away if I included the right other variables. So I did what any good data analyst would do in the situation. I played with my data. That is to say, I added other variables, tried this and that, checked correlations here, ran regressions there.

And it didn’t turn out the way I expected.


Again, my Economical Oath requires me to point out in advance that there are assorted technical issues with approaching this problem this way. But that doesn’t mean we can’t learn some interesting things from it. Virtually every economic analysis has technical problems with it; it’s mostly a matter of degree.

“Regression” is an intimidating term to many people. It sounds all complicated and statistical. All it really means is trying to find out how a some numbers affect other numbers. Practically speaking, it’s just using a computer to do calculations – you can even do it in Excel.

In addition to correlations, I used simple regression analysis to look at the relationships here. In the end, you come out with an equation that represents the relationships, along with numbers that tell you how good the relationships are, statistically-speaking.


Bbefore I tell you about the relationships that I found, let me first tell you about the variables that didn’t work.

First, the percentage of the states’ populations that are black. Virtually no relationship to speak of – a correlation of -0.002. Likewise percentage Latino had a correlation of 0.18, but this went away after taking account of other factors.

Percentage urban versus rural, percentage with a Bachelor’s degree, population, population density, even acres of farmland – all of these have some correlation with support for Obama, but all of these apparent relationships proved small or insignificant when other variables were included.


Including the state median family income looked really good at first. And in fact it made the state unemployment rate look even more important, statistically. A $1000 increase in a state’s median family income  was associated with a 0.5 percent increase in support for Obama.

But it turns out that family income was just capturing something else: the effects of advanced college degrees. States with a larger percentage of people with masters and doctor’s degrees have higher family incomes. And it’s those advanced degrees that really are associated with more support for Obama.

Education really does make you liberal. Or, properly speaking, appears to make you more likely to support the more liberal candidate. In this case, anyhow.


So shut up and cut to the chase. What’s the bottom line?

After looking at more than a dozen different variables, here’s the equation that turned out to explain the most differences in state support for Obama in the best statistical way:

By “religiosity”, I mean the percentage of the state population who say that religion is very important in their lives. I used state data from 2007 Pew Research survey for this. My thinking was that many people base their political views on their religious faith, and that this might explain some of the differences in support for Obama.

And it does. For every 5 percent increase in state religiosity, support for Obama decreases by 1 percent, and that relationship is statistically significant. In other words it isn’t just be caused by random factors.

For the curious, the R-squared for this equation was 0.54, meaning these variables explain 54 percent of the differences in state support for Obama — not too shabby for a back-of-the-cocktail-napkin analysis. All three variables were significant at the 0.05 level.


In the end, including percentage with advanced degrees and religiosity in the equation made the relationship between state unemployment rate and support for Obama look even better than it did without them. In fact, no matter what variables I included, there still was a positive relationship between unemployment and Obama support.

So the relationship that I didn’t think existed, the one that I thought would go away if I included the right variables, turns out to be pretty solid. Higher unemployment rates do seem to be associated with more support for President Obama.

Maybe Americans do vote with the wallets, only not the way you might think.

Do Americans Vote With Their Wallets? State Unemployment Rates and Support for the Incumbent

Americans vote with their wallets, or at least that’s the conventional wisdom. Economic issues are supposed to be of primary concern to American voters. Or, as Bill Clinton’s presidential campaign famously put it in 1992, “It’s the Economy, Stupid.”

I avoid politics here – it’s not that kind of blog – but sometimes it’s impossible to separate economy from political economy, particularly in an election year. So I decided to take a look at this piece of conventional wisdom. In a non-partisan sort of way.

If the economy really is key to American voters, then one might expect that – all else being equal – states with high unemployment rates should be less likely to support the incumbent (Obama), and states with low unemployment rates should be more like to support him.

Put another way, there should be a negative correlation between support for the incumbent and state unemployment rate. A graph of states with support for Obama on one axis and state unemployment rate on the other should tend to slope downward.


The economists’ union requires that I preface this with caveats: There are various technical issues with analyzing aggregate data this way, and this is not intended to be a scientific study. That said, back-of-the-cocktail-napkin analyses can still be interesting and enlightening.


Correlations are measures of how much two sets of numbers tend to move in the same or opposite directions. A correlation of 0 means there’s no relationship, close to -1 means the two move in opposite directions, and close to 1 means there’s a strong positive relationship. In this case, we’re expecting a negative number.

The correlation between state support for Obama and state unemployment rate actually turns out to be positive 0.27. This means that – all else being equal – the higher the unemployment rate in a given state, the higher the support for President Obama. The opposite of what we’d expect, if economic issues are driving voter preferences.

(Note: I used FiveThirtyEight’s unadjusted average poll numbers by state, simply because they were available in an easy-to-use format. The numbers were as of 3:00 pm on 28 October. The unemployment rates are those for September 2012, from the Bureau of Labor Statistics, bls.gov.)


Correlations are rather unexciting to many people. After all, they’re just numbers. A mentor of mine used to recommend an alternative technique that he called, “Look at your data”. So here’s a – yes, I am an economist – graph of state preference for Obama versus state unemployment rate.

As suggested by the positive correlation, the data points do tend to slope upward. The best fitting line is shown, and it has a positive slope too.

States such as Rhode Island, California, New Jersey, and Nevada have relatively high unemployment rates and yet have relatively large percentages supporting Obama. States such as North Dakota, Nebraska, and Oklahoma have low unemployment rate yet have smaller percentages supporting Obama.


What gives? Is the conventional wisdom about Americans voting with their wallets wrong? Is it not the economy, stupid? Does high unemployment actually cause people to prefer the incumbent?

That’s hard to say. Political preferences are complex, and many factors combine and interact to determine an individual’s political views. It gets even more complicated when you consider things at the state level.

It’s possible that higher unemployment rates really do cause people to support this incumbant. Perhaps people who are unemployed are more likely to prefer Obama because they believe, for whatever reason, that they personally will be better off if he is re-elected. On the other hand, it’s also possible that other factors are affecting both unemployment rates and political preferences, making it appear that higher unemployment rates are causing people to support Obama.

Whatever the details, it’s clear that saying money is all that matters to American voters vote is too simplistic. Maybe Americans do vote with their wallets, but that could mean voting for the candidate they believe is most likely to fill them.

More on this Topic: Voting With Your Wallet II: Education, Liberalism, and Unemployment

What Happened to the Unemployed?

According to the numbers released Friday, the number of people unemployed in the U.S. decreased from 12.5 million August to 12.1 million September, and the unemployment rate decreased from 8.1 percent to 7.8 percent.

So what happened to these 12.5 million unemployed people from August to September? It turns out that 7.3 million of these were still unemployed in September. That’s well more than half of them.

Another 2.8 million of the August unemployed had left the Labor Force by September. This could be because they just quit looking because they felt it was useless, or because they’d gone back to school. Or they retired, or had children and decided to stay home. Or maybe they won the lottery. Regardless, by September they were neither working nor looking for work.

Finally, 2.4 million – 19 percent – had found jobs by September.

The reason that the number of unemployed only decreased by 400,000 is that, at the same time these 2.4 million were finding jobs, another 1.9 million lost jobs they had, and 2.9 million entered the labor force and started looking for work.

So here’s the summary:

Here’s hoping that number keeps going down.

A Tale of Three Indicators: How Bad is the Labor Market Really?

It is the best of times. It is the worst of times. It it the worst of times but getting better.

It all depends on how you measure it.

There’s a lot of interest these days in alternative economic measures, particularly with regard to employment. Part of this is driven by real problems with unemployment rates. Even more is driven by a people wanting indicators to tell a different story than they do. And some is driven by a desire for measures that provide different policy incentives. The news yesterday that the unemployment rate fell from 8.1 percent to 7.8 has only exacerbated the situation.


Probably the three most closely watched macroeconomic indicators in the U.S. are the change in real GDP, the consumer price index (CPI), and the unemployment rate.

When we say “unemployment rate”, we really mean the “U-3 unemployment rate”; there are (at least) five others we could consider. They range from the narrowest U-1 to the broadest U-6 rates.

Basically the U-3 unemployment rate is the percentage of the workforce that is actively looking for work but is not employed.It’s true that the U-3 rate doesn’t capture a lot of the economic pain out there. But it’s the standard measure that policy-makers have focused on for decades.

Politicians who want things to sound worse will throw around phrases like “15 percent underemployment”. This is valid but misleading. The U-6 unemployment rate – the broadest measure that also includes most discouraged workers and others – is at 14.7 percent these days. But the U-6 is not the standard definition. If you’re going to use it as your standard in bad times, you also need to use it in good, or else you’re talking apples and crabapples.

That said, all of the unemployment rates move very much in tandem. In fact, since 1994, the correlation between the U-3 and the U-6 rates is 0.996. They’re practically the same measure. Here’s a graph of the six unemployment measures over the past four years, courtesy of the Washington Post.

But even the broad U-6 unemployment rate doesn’t capture people who have permanently left the labor force because they feel things are hopeless. This is one reason some analysts have proposed using the Labor Force Participation Rate (LFPR) as a main indicator of labor economic health.

I addressed the problems with the LFPR in an earlier post, and there are many. Even though the LFPR does capture long-term discouraged workers, it introduces many other difficulties. The LFPR is mostly driven by long-term demographic changes. It doesn’t reflect the business cycle at all, as is clear from the graph below.

The LFPR thus is pretty much useless as a “how are we doing?” sort of measure.

If you’re really desperate for an alternative labor economics indicator that captures both the long-term unemployed as well as the business cycle, I propose yet a third measure: the Employment-Population Ratio.

The Employment-Population Ratio (EPR) is the percentage of the total (noninstitutional) population aged 16 years and over that is employed. It has the advantage of capturing both shorter-term cyclical unemployment and long-term unemployment of discouraged workers.

The EPR has problems, too, of course. There’s no perfect economic indicator (I addressed problems with GDP in an earlier post as well). Specifically, the EPR is a victim of many of the same demographic realities that the LFPR is. When you have a lot of people reaching retirement age, the EPR will fall. When you have a lot of women entering the work force, the EPR will rise.

But the EPR does capture the business cycle. And it has the added benefit – for those who want the economy to look worse – of making the economy look a little worse.

To be fair, the EPR is being pulled in different directions by different factors. Baby boomers are hitting retirement age and are leaving the labor force in increasing number for legitimate reasons. At the same time, the economy is (slowly) recovering. The net effect is that the EPR has been basically flat since late 2010.

Personally I still prefer the U-3 unemployment rate as my main measure of how the economy’s doing, though I always keep in mind that it has its flaws like any other indictor.

If I were looking for a secondary labor measure to provide supplemental information, I’d choose the Employment-Population Ratio.

But all things considered, just measuring the economy really can scare the dickens out of you.