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Colorado income growth continues to outpace nation

The US Bureau of Economic Analysis recently issued its quarterly state-level personal income estimates. Total personal income increased 0.92 percent in the first quarter of 2012, compared to 0.8 percent for the US overall. Over the past 5 years, Colorado has tended to outperform the nation. (In 2011, Colorado’s per capita income was $44,088, 5.8 percent higher than the US ($41,633)). 


Amongst all states, Colorado’s growth ranked 16th. 



the value of a certificate

I have written much about the value of a college degree. On average, those with a 4-year degree make more money than those with a 2-year degree who make more money than those with a college degree. Education pays.

Yet I have not written much about certificates, another way people can boost their earnings via education, but without getting a degree.

A recent study from Georgetown University’s  Center on Education and the Workforce shows that certificates, in many instances, are a good alternative for people seeking to raise their incomes. Here is the punchline:

“Certificates count when it comes to leveraging gainful employment in a variety of ways. On average, certificate holders earn 20 percent more than high school-educated workers – about $240,000 over a high school diploma in lifetime earnings. More than 60 percent of certificates have a clearly demonstrated economic payoff over high school diplomas—i.e., earnings 10 percent higher than the median high school graduate. Moreover, even when certificates don’t provide much of an earnings boost, they can make individuals more employable, giving them access to valuable learning on the job.”

The report also looks at differences in return by sex:

“Certificates provide more bang for the buck for men than women. Men who earn certificates earn 27 percent more than high school- educated men. Women with a certificate, by comparison, only receive an average 16 percent increase in earnings over women with a high school diploma.”

Finally, the authors recognize that the field in which the certificate earns matters:

“The earnings of the median certificate holder who works outside his or her field of study are only 1 percent higher than the median high school-educated worker. Certificate holders who work in field, by contrast, earn 37 percent more than those who work out of field—only 4 percent less than the median worker with an Associate’s degree.”

Bottom line: A carefully chosen certificate program can be a good alternative path to higher earnings.




increases in unemployment by age and sex

Although the impacts of the Great Recession continue to be widespread, there have been important differences by sex and age. In particular, males and the young were harder hit by unemployment. With respect to male-female differences, much is attributed to the declines in male dominated professions such as manufacturing and construction; whereas the economy’s main growth sector (health care), has more women than men.

In the following charts I show unemployment data from the US Census Bureau’s Current Population Survey to demonstrate these varied impacts.

In the first chart I show unemployment rates in 2007 (right before the recession) and 2011 (the most recent available), by sex (blue for males; red for females). It shows that unemployment has gone up for each cohort and both sexes; typically with higher male rates in both years. It also shows that unemployment rates decline as age cohorts increase.

In the following chart I present the same data somewhat differently. Here I show the percentage point increase in unemployment rate by sex and age cohort.

This picture provides more clear evidence that males have been slightly harder hit than females.

Lastly, I show changes in labor force participation rates over this time frame, once again by age and sex. (The labor force participation rate (LFPR) is the percentage of the civilian population that is employed or actively looking for a job). A decline in the labor force participation rate is usually seen as “bad.”

Here, we see once again that males were more impacted, with the LFPR dropping most for younger people.


A closer look at the recent increase in the Colorado unemployment rate

The Colorado Department of Labor and Employment reported today the state’s unemployment rate climber to 8.1 percent, up 0.2 percentage points from last month, yet down from 8.4 percent in May 2011. The national rate is 8.2 percent, up slightly from a month ago.

In this post I want to look a bit closer at the underlying mechanisms, as the unemployment rate is a more complicated indicator than many realize.

Let’s start with a few definitions. First, there are the “employed.” These are people with a job. It does not matter if it is full-time or part-time. It does not matter if it fully utilizes their skills and experience or not. Then there are the “unemployed.” These are people who are not currently employed, but who are actively looking for a job. They may or may not be collecting unemployment compensation benefits.

The size of the “labor force” is determined by adding up the employed and the unemployed. The “unemployment rate” is calculated by dividing the number of unemployed by the labor force.

Because both the number of employed and unemployed varies from month to month, and because the unemployed are included in both the numerator and denominator of the unemployment rate, understanding changes in the rate is trickier than understanding simply changes in employment or unemployment. As a result, we sometimes see the unemployment rate behave in ways that are counter-intuitive.

For example, as I noted above, last month’s unemployment rate increased in Colorado. But so, too, did the number of employed people. So, the unemployment rate went up despite the fact that more people are working. Why? Because the labor force–which includes the unemployed–grew even faster.

Before looking closer at Colorado, I will provide an example.

Suppose an economy has 10 unemployed workers and 90 employed workers. The unemployment rate is 10 percent (=10/(10+90).

Now suppose that the number of employed grows to 95, but the labor force adds 10 workers, increasing to 110. This means there are now 15 unemployed workers. (This could happen because people move into the state, or more people in the state begin looking for work…remember, to be unemployed you have to be actively looking for work). In this case the unemployment rate is 13.6 percent (-15/110).

So, we see that despite having more people employed, the unemployment rate has gone up. Weird.

And this is what is happening in Colorado. In the next chart I show the state’s unemployment rate since May 2008. here we see a sharp increase early only, a gradual decline, and the uptick last month.

Much of the dynamic can be attributed to changes in the number of employed. In the following chart I show how Colorado’s total employment changed from 12 months earlier. The job losses in Fall 2008 correspond well with the increase in the unemployment rate. More recently, employment growth has helped knock down the unemployment rate.

But what complicates this is the common transition that many people make between being unemployed and IN the labor force (actively looking for work) and being unemployed and OUT of the labor force. In the next chart I show changes in Colorado’s number of unemployed from 12 months earlier.

Here we see that the number of unemployed grew dramatically in 2008 and 2009, and has only declined somewhat since then. Here is another view of that.

Now, let’s pull this all together. In the following chart I show recent changes in Colorado’s labor force from 12 months earlier.

The slight increases over time are often–but not always–indicative of an improving economy.

Three things affect this over time: First, as I not above, people transition often transition between “unemployed” status and “unemployed and not in the labor force status.” Perhaps people give up looking for work (the number of unemployed an labor force decline in size, knocking down the rate). Or, perhaps they feel confident that there might be opportunities, so they start looking for work, even if they don’t have a job offer in hand. This optimism can cause the unemployment rate to increase in the short-run, as both the number of unemployed and labor force grow by the same amount.

Second, the labor force can grow due to demographic trends. Perhaps there are more people aging into the labor force than aging out of it (ie, retiring). This tends to be true in “younger” states, such as Colorado. (It is also affected if peoples retirement plans are put on hold due to financial circumstances).

Third, the labor force can grow if more working age people move into the state than out of it. This is true for Colorado, which tends to have net in-migration because the economy is relatively healthy, and because of its natural amenities.

Bottom line: the unemployment rate is a tricky indicator to interpret. Although a declining rate is generally “good,” the underlying definitions and mechanisms require a more careful analysis of both economic and demographic trends.

a closer look at changes in Colorado GDP

I wrote the other day about the state’s recent economic growth from 2010-2011, relative to the rest of the country. Today I break the analysis down within the state to identify the composition of the 1.9 percent growth in state GSP. This data is from the US Bureau of Economic Analysis.

Over the past year, the state’s largest growth contributors were Information, Professional, Scientific and Technical Services and Durable Goods Manufacturing, with the three sectors accounting for 1.16% growth in the state economy. Real estate, rental and leasing, AFF and Utilities were the industries with the largest drag on the state economy.

Colorado income growth exceeded US average in 2011

The US Bureau of Economic Analysis released the 2011 state income report today. Colorado’s inflation-adjusted real gross domestic product (real GDP) was up 1.9 percent from the previous year, higher than the US gain of 1.5 percent.

In the Rocky Mountain region, Colorado’s growth trailed only Utah. and substantially outpaced Wyoming.

Decomposing Colorado’s growth by industry shows the driving sectors were Information (0.47 percentage points), Professional, Scientific and Technical Services (0.41 percentage points), and Durable Goods Manufacturing (0.38 percentage points). Together these three sectors accounted for more than two thirds of the net change in state real GDP.

How has the Great Recession impacted income distribution in Colorado?

In my last post I wrote about the fact that Colorado’s “poor counties” are no longer catching-up to rich ones. This suggests that income distribution between counties should be relatively unchanged over the course of the last decade. In this post I take a closer look at this idea.

The coefficient of variation (CV) is one statistic that is used to look at how equal the distribution is within a sample. If there is a relatively high average and little variation between places, the CV will be relatively low. Conversely, if the average is fairly low, and the variation between places is fairly high, then the CV will be high. Simply put, aa low CV suggests less inequality in per capita income between places.

In the following chart I use BEA data to show the CV for Colorado counties from 1969-2010. Up until the late 1980s, the CV was flat (though volatile). Beginning in the late 1980s, Colorado’s CV steadily increased, up until the start of the Great Recession (depicted by the green line). This shows that income inequality between counties was growing over this time.

Interestingly, a dramatic reduction in the CV was an important outcome of the Great Recession (shown by the dramatic reduction over the past 2 years). Why did this happen?

Let’s think back to yesterday’s post, where I looked at the relationship between a county’s initial income per person and its subsequent growth for the period 2000-2010. Recall that there was no real relationship.

However, when we shorten the time period, looking only back to 2008, the graph below shows a much stronger negative correlation between initial per capita income and subsequent growth rates. In particular, it shows most of the poorer counties had positive per person income growth over this time frame, while most of the richer counties actually had negative growth in per person income.

A likely explanation is that poorer counties are more reliant on government transfer payments as a share of income, and those have remained steady or increased since the recession’s start. Conversely, the richer counties see more of their income generated by the stock market and individual and corporate profits, which were hard hit by the recession.

So, one impact of the recession has been to reduce income inequality between rich and poor counties in Colorado. But this is due more to declines in the richest counties than any great progress by most of the poorer places.