My new publication in November’s IR Applications, which is published by the Association for Institutional Research:

This paper explores the relationship between student major and industry of employment and its application to higher education accountability. Data provided by statewide longitudinal data systems (SLDS) have enabled state educational agencies and colleges to follow students into the workforce. While most studies have focused on wage outcomes, this study shows how to use SLDS data to understand the correlation between major and industry. The transition into the workforce is an important outcome since it is an assessment of a college’s ability to develop specific, targeted sectors of the economy. We use SLDS data from Iowa to follow community college alumni from 2002 through 2008.

Find it here.

Darker areas represent a higher proportion of kids in poverty. Pockets of high poverty, 30%+, are predominately in southern Iowa. Not surprisingly, educational attainment for adults in these same school districts also tend to be lower.

Even though recessions are becoming less frequent (e.g., great moderation), state revenue estimates are increasing inaccurate. I have covered Iowa revenue estimates before and it turns out the same pattern is being exhibited nationwide:

In 2009, the median error among states was an overestimate of 10.2 percent. That equated to an unexpected revenue shortfall of nearly $50 billion in person income, corporate income and sales tax revenues. In a year when state policy makers faced $63 billion in midyear budget gaps–coming atop $47 billion they had already closed when crafting their budgets–this misestimate posed a significant challenge.

The similar pattern between Iowa and the nation is remarkable. Here is a recent graph that shows the relationship between Iowa revenue estimates and actual revenues for December:

State net revenue in millions

Project and actual are the same curves, but project is a bit lagged. A similar graph for all states shows a similar pattern:

Pew Center on the States/Nelson A. Rockefeller Institute for Government, Cracks in the Crystal Ball, Exhibit 4

Again, revenues are overestimated at the beginning of a recession, underestimated during recoveries.

Pew also suggests these estimates are becoming more inaccurate:

During the 1990-92 revenue crisis, 25 percent of all state forecasts fell short by 5 percent or more. During the 2001-03 downturn, 45 percent of all state forecasts were off by 5 percent or more. In 2009, 70 percent of all forecasts overestimated revenues by 5 percent or more

Much of the problem stems from a basic statistical issue, it’s rare that the economy turns. Since 1991, the economy has only made six “turns”–we’ve come out of three recessions and recovered from three.* Even very sophisticated statistical models will likely miss these “turns”. So what can we do to ameliorate these difficulties?

The report highlights four promising approaches:

1. Analyzing Errors and refining assumptions
2. Making frequent estimates
3. Ensuring independence from the political process
4. Adding expertise

Each are a good suggestion. I really can’t fathom providing accurate estimates without #3 and #4 being met.

A fifth and sixth recommendation should be:

• Incorporating estimated errors
• Improving recent economic data

My fifth recommendation is somewhat technical, but important. To illustrate the importance, consider projecting a path of a hurricane. Below is an example of such projections. The projections show the hurricane will hit New Orleans (this is 2008′s Hurricane Gustav). But notice the margin of error around the projections? These are the estimated errors, and they are very important to forecasting.

Projected path of Hurricane Gustav, via watercrunch.com

There are equally important to budget forecasts. If state revenue estimates are truly becoming more inaccurate, then that means these errors are becoming more important. If revenue projections aren’t an exact science, then perhaps we should better present the data as an inexact science. Gustav? If you recall, didn’t land in New Orleans, but in nearby Cocodrie–within the range of the hurricane’s predicted path.

My sixth recommendation is also important. There are tremendous resources at the federal level that permit fairly accurate projections. As economies change, we may need to look at different sets of data to forecast future economic fluctuations. Some recent innovative practices is using road tolls to “nowcast” the business cycle.

We’ve tried a few other methods to display this same data at the Iowa Department of Education. Below is a the standard image that shows up in our Tuition and Fees Report and Condition of Iowa’s Community Colleges. We use a similar, static plot, with one major difference is we plot on dollar amounts, not percentage.

I’ve previously highlighted a Tufte scatterplot of the same data. Below we use shading to represent the approximate decade of data. The lighest points represent the 1970s, which grows darker until the black points represent the 2000s and the red point is the most recent data:

I like the interactive nature of the Chronicle data, but I don’t like that you cannot see all of the data without having to scroll. I’m not sure why the Chronicle chose to especially highlight Iowa data–it was accompanied by an article about funding in general which talked with a lot of Iowa leaders–but it is always nice to see someone else using our data.

“I honor and appreciate the effort and work that has been put into it up to this point. There are a lot of people who have poured [sic] their heart and soul into trying to develop a really quality set of curriculum standards for Iowa … but it has room to evolve and grow,” [Jason Glass] said

More than any other context, it is tough to stomach an even slight type-o (I make these all the time, unfortunately). It is even tougher to stomach a grammatical error–this was intentional, not a slip of the finger. Homophones usually are sorted in classroom learning or through a steady reading habit. So, whatever the state does, let us make sure there is still lots of reading, so we don’t rely solely on “writing” the way we speak or using daily idiosyncratic writing. K? Thx.

As expected, the Iowa Revenue Estimating Conference are estimating higher state tax revenues for the current and next year than previously thought.

Last year, I predicted these revenue estimates would be underestimated. These models tend not to predict changes in the economy, so they tend to overestimate revenues during recessions and underestimate revenues during economic expansion.

Above is an updated graph comparing net tax revenues with the December projections. Notice that project tax revenues almost appear to be the exact copy of actual revenues–just shifted to the right. Projected revenues seem to be a better lagging indicator than predictor.

A record total of 106,597 full- and part-time students enrolled in community-college courses this school year, a 4.8 percent increase that marked the 14th consecutive yearly rise in the overall student population.

Tom Schenk Jr., educational program consultant with the state Department of Education, said this year’s jump followed a 14 percent increase last year that was the second-largest rise since the community college system was started in 1965…

Because community college enrollments tend to be cyclical with economic conditions, Schenk said projections call for the number of enrolled students to drop by as much as 9 percent in the next school year.”That’s our best guesstimate,” he said. “Community colleges have been cautious about this huge enrollment increase knowing that this is not something that necessarily is going to be sustainable.”

The projected enrollment for next year stood at 103,290, which would be a 9.3 percent drop from this year’s expected total but a 3.1 percent decline from the actual student count.

I want to clarify, I didn’t say “guesstimate”.