Big Data. Bureau of Labor Statistics. Survey data. Employment Big Data.  Those are all things that calculating worklife expectancy for U.S. workers requires.  Worklife expectancy is similar to life expectancy and indicates how long a person can be expected to be active in the workforce over their working life.  The worklife expectancy figure takes into account the anticipated to time out of the market due to unemployment, voluntary leaves, attrition, etc.

The goal of our recent work is to update the Millimet et al (2002) worklife expectancy paper and account for more recent CPS data. Their paper uses data from  the 1992 to 2000 time period. Our goal is to update that paper using data from 2000 to 2013 and  see if estimating the Millimet et al (2002) econometric worklife models with more recent data changes the results in the 2002 paper in any substantive way.

Finding: Overall, the worklife expectancy estimated using more recent data from 2000-2013 is shorter then in the earlier time period (1992-2000) data set. This is true for younger worker (18-early 40’s); younger workers from the more recent cohorts have a shorter expected work life then younger workers in the earlier cohorts.  Conversely, while older workers in their 40s and 50s have a slightly longer worklife expectancy in the later time period data set. We are in the process of determining the statistical significance of these differences.

Notes:

The econometric model described by Millimet  et al (2002) and logistic regression equations by gender and education are used to calculate the worklife expectancy estimates.   The worklife model iin the left panel of the table is estimated using matched CPS cohorts from 1992–2000 time period as described in the Millimet et al. (2002) paper.   The model on the right panel is estimated using data from 2001-2013.

The logistic equation includes independent variable for age, age squared, race, race by age interaction, race by age interaction squared, marital status, martial status by age, occupation dummies, year and year dummies.

The model is first estimated separately for each gender and education level combination for active persons.  The model is then estimated again for inactive persons.  The educational attainment variables used to estimate our model differ from that of Millimet et al. (2002)   In our model, only individuals whose highest level of attainment is high school are included in the high school category.  Millimet et al (2002) includes individuals with some college in the high school category.

Education remains the best investment around.  One economic question that always comes up is: are student loans worth it?  That is, should I take out a loan (if that is the only way I can attend college)?

The quick answer is usually yes for attendance at a reputable institution.  So how do you calculate the return on education investment?  Generally, the calculation subtracts the explicit cost of attendance (tuition, fees, books, and student loan costs both current and future) and the opportunity cost of attendance (that is the job you could have gotten) from the added income that the person will make over their working life due to the education investment.

See: https://studentaid.ed.gov/repay-loans/understand/plans

Student loans come in two forms: subsidized and unsubsidized.  Subsidized loans are usually financial need based; unsubsidized loans are not.  Both types of loans have requirements regarding school attendance (usually at least half time).

The type of loan also impacts the interest rate; higher the interest rate the lower the return on education.

Student loan repayment periods also vary. Nowadays, student loan time periods range from 10 to 25 years.  There are programs that allow the repayment to increase over time, change with income, and ones that are fixed over time.

Generally, the shorter the repayment time period, the higher the return on the education investment.  In short, less loan interest is paid on a shorter loan period.

In Chapter 6 of Moretti’s book ‘Geography of Jobs’,  he argues that geographic mobility is a key to economic prosperity.  The ability and willingness to move from an economically depressed area to a more robust one benefits not only the individual who moves but also the individuals and businesses in both areass.

Prof. Moretti points out that a number of countries that have low geographic mobility rates also have low economic activity measures. He uses his home country Italy as an example of a country where people do not move. Most interesting he makes the case for national mobility voucher program in the U.S.. The idea is that subsidizing a person’s move will help equalize economic opportunities across the nation. He closes the chapter discussing gentrification which he views as a good thing given all the pluses that arise from it.

by Dwight Steward

Deciding which schools their children attend is one of the biggest and anxiety producing decisions that parents make.  Often at the heart of the decision is determining how much value there is in children attending highly competitive high schools versus less competitive high schools. For example, are kids in highly competitive schools more likely to attend and graduate from college?

Recent research by Princeton economist Will Dobbie and Harvard economist Roland Fryer, suggest that in contrast to some conventional wisdom, that the typical college applicant does not gain much from attending competitive high schools.  The authors conclude;

Our results suggest that the typical applicant does not significantly benefit from attending a school with dramatically higher-achieving and more homogeneous peers….With that said, without longer-term measures such as income, health, or life satisfaction, it is difficult to fully interpret our results. To the extent that attending an [highly competitve high school] with higher-achieving peers increases social capital in ways that are important for later outcomes that are independent of college enrollment, graduation, important for later outcomes that are independent of college enrollment, graduation, or human capital, then there is reason to believe that our conclusions are premature…

 

by Dwight Steward

Want to go to Harvard or Stanford without all the travel?  Massive open online courses (MOOC) may be what you are looking for.  So far, however MOOCs are a good idea that many people start but few actually finish.  Prof. Robert Grossman of Marist discusses the MOOC movement in HR Magazine.  Prof. Grossman writes that MOCC’s are

Designed for large-scale participation and free access via the Web, a typical MOOC lecture is self-paced, short—maybe 10 or 15 minutes—and spiced with multimedia components. Professors highlight issues as well as pose and answer questions based on “crowdsourcing” of information that participants submit. After each session, students take quizzes to verify that they understand the material. They also discuss content among themselves; interaction often leads to Facebook and LinkedIn chats or even face-to-face meetings. Students take exams and a final, submit reports, and grade other students’ essays.  Anyone can sign up, and there are no prerequisites. MOOCs are free, although some require fees for certificates of completion or charge tuition for college credit

A number of big named universities as well as business leaders have signed on to the idea of online learning.  While there are clearly a number of advantages, such as cost and accessibility, to this type of learning environment there are a number of downsides to MOOCs.  Most notably, hardly any students actually finish MOOCs..

McFarland says the 3.48 percent completion rate is typical among MOOCs. Published reports claim about 10 percent of students finish such courses.