education – Economic Thinking In Action

Younger workers today have slightly less attachment to the workforce than younger workers in the past

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.

Table 4. Comparsion of Worklife Expectancy for 1992-2000 and 2001-2013 Time Periods
	1992-2000		2001-2013
Age	Less than High School	High School	Less than High School	High School
18	31.469	38.410	30.569	37.314
19	30.926	37.846	30.128	36.833
20	30.306	37.180	29.603	36.237
21	29.670	36.493	29.021	35.590
22	29.027	35.787	28.419	34.917
23	28.365	35.054	27.809	34.231
24	27.685	34.293	27.205	33.539
25	27.007	33.518	26.588	32.830
26	26.319	32.728	25.964	32.108
27	25.643	31.939	25.357	31.387
28	24.958	31.123	24.736	30.646
29	24.271	30.304	24.110	29.892
30	23.590	29.481	23.491	29.136
31	22.892	28.640	22.866	28.371
32	22.191	27.796	22.237	27.599
33	21.487	26.944	21.606	26.819
34	20.783	26.097	20.970	26.034
35	20.095	25.254	20.327	25.239
36	19.400	24.408	19.685	24.446
37	18.707	23.560	19.039	23.648
38	18.018	22.714	18.392	22.850
39	17.324	21.864	17.737	22.044
40	16.627	21.014	17.085	21.242
41	15.944	20.169	16.421	20.432
42	15.264	19.328	15.764	19.627
43	14.595	18.494	15.110	18.825
44	13.931	17.664	14.456	18.024
45	13.272	16.840	13.798	17.220
46	12.616	16.018	13.154	16.429
47	11.972	15.204	12.520	15.641
48	11.328	14.398	11.886	14.859
49	10.682	13.593	11.259	14.081
50	10.053	12.803	10.642	13.311
51	9.432	12.020	10.030	12.550
52	8.802	11.239	9.429	11.798
53	8.199	10.477	8.843	11.057
54	7.593	9.723	8.270	10.333
55	6.996	8.980	7.709	9.618
56	6.422	8.263	7.152	8.912
57	5.872	7.564	6.618	8.230
58	5.339	6.883	6.095	7.560
59	4.812	6.216	5.587	6.908
60	4.307	5.578	5.097	6.280
61	3.840	4.979	4.624	5.677
62	3.400	4.415	4.181	5.112
63	3.024	3.918	3.782	4.593
64	2.708	3.485	3.428	4.128
65	2.422	3.093	3.109	3.700
66	2.180	2.756	2.819	3.312
67	1.970	2.461	2.556	2.960
68	1.787	2.200	2.323	2.646
69	1.624	1.967	2.102	2.359
70	1.471	1.756	1.905	2.101
71	1.348	1.584	1.728	1.869
72	1.238	1.430	1.577	1.670
73	1.134	1.289	1.427	1.484
74	1.042	1.167	1.296	1.322
75	0.965	1.065	1.184	1.181
76	0.904	0.983	1.077	1.054
77	0.834	0.899	0.980	0.942
78	0.784	0.836	0.894	0.843
79	0.735	0.778	0.807	0.750
80	0.694	0.735	0.675	0.636

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.

Return on education, student loan interest and repayment periods

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.

Just move already…geographic mobility and economic opportunity

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.

What is the true value of placing your kids in a high performing school?

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

Going to Harvard from the comfort of your couch? It is a good idea that many start but few finish

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.