GE’s Jeffrey Immelt, Now Slamming Sanders, Once Said It Was His “Task to Outsource”

BACK IN 2014, in an interview with the magazine Chief Executive, General Electric Co. CEO Jeffrey Immelt explained that starting in the 1980s, “most of us” — i.e. GE executives — “saw it as our task to outsource manufacturing, to move it to low-cost countries. This continued through the 1990s and into the very early 2000s.”

Immelt’s statement of the obvious is relevant because Democratic presidential candidate Bernie Sanders said essentially the same thing about GE this week, which triggered an angry response from Immelt.

In a meeting on Monday with the New York Daily News editorial board, Sanders was asked to name a corporation that he believed was “destroying the fabric of our nation.” Sanders said that GE was a “good example” because it had shut down “many major plants in this country. Sending jobs to low-wage countries. … That is saying that I don’t care that the workers, here have worked for decades. … The only thing that matters is that I can make a little bit more money. That the dollar is all that is almighty.”

Immelt (or, more likely, his ghostwriter) replied in a Washington Post op-ed that “Sanders says that he is upset about GE’s operations abroad — as though a company that has customers in more than 180 countries should have no presence in any of them.”

This is, of course, intentionally misleading language: Sanders’s criticism was not that GE has established a “presence” in other countries, but that it has moved many of its factories there in order to save money by paying workers less.

As Immelt himself said in 2014, outsourcing became his task because “U.S. labor was expensive and materials were cheap.”

In other words, there’s no actual argument between Sanders and Immelt about the reality of GE’s outsourcing. It’s simply that Immelt believes that a U.S.-based multinational corporation should do everything possible to maximize its profits, even at the cost of its American workers’ jobs, while Sanders does not.

Photo: Immelt addresses the Economic Club of Washington, D.C., at the Mandarin Oriental Hotel in 2011.

Source: GE’s Jeffrey Immelt, Now Slamming Sanders, Once Said It Was His “Task to Outsource”

American Academy of Pediatrics recommends middle schools and high schools start classes at 8:30 a.m. or later to “align school schedules to the biological sleep rhythms of adolescents.” 

2016 study in The Review of Economics and Statistics that examines whether adolescents have higher grades, test scores if their math and English classes are scheduled at the start of the school day.

Source: Time of day and student productivity in middle school and high school – Journalist’s Resource Journalist’s Resource


Public school administrators are continually looking for ways to boost student achievement.

In recent decades, some school districts have lengthened school days and others have experimented with school start and end times to try to improve student learning. In various parts of the country, high schools have begun implementing later start times, a response to complaints from parents and educators who say teenagers need more sleep and have trouble getting to class on time.

In late 2014, the American Academy of Pediatrics took a stance on the issue, recommending that middle schools and high schools start classes at 8:30 a.m. or later to “align school schedules to the biological sleep rhythms of adolescents.”

Academic scholars have studied the relationship between time of day and student learning to better understand the opportune time for teaching core subjects such as reading and math or administering high-stakes standardized tests.

A growing body of research examines the issue from multiple angles. A 2016 study published in PNAS, for example, suggests that students aged 8 to 15 years are more likely to do better on standardized exams in the morning because, over the course of a school day, children may experience cognitive fatigue.

Meanwhile, a 2011 study led by scholars at the University of California, Davis indicates that college freshmen’s grades tend to be lower when they study certain subjects — chemistry and computer science, for instance — very early in the morning.

A 2016 study published in The Review of Economics and Statistics sought to determine whether scheduling math and English courses at the beginning of the day or at the end of the day would result in higher grades and test scores for adolescents.

For the study, “How the Time of Day Affects Productivity: Evidence from School Schedules,” Nolan G. Pope of the University of Chicago analyzed the grade-point averages and standardized test scores of nearly 2 million students enrolled in grades 6 through 11 in the Los Angeles Unified School District.

He examined data collected between 2003 and 2009, including scores from the math and English sections of the annual California Standards Test (CST).

For the middle school students and high school students in the study sample, the school day typically started around 8 a.m. and ended around 3:10 p.m.

The study’s key findings include:

  • Students who had a math class during the first two periods of the school day earned higher scores on the CST math section than students who had math class during the last two periods of the day. The average math CST score of students who had math during periods 1 and 2 was 309.8. The average score of students who had math during periods 5 and 6 was 304.5. Students who had math class early in the day also had slightly higher grades in their math courses.
  • Students who had English during first or second period had slightly higher grades in the subject than students who had English class late in the day. There was no significant difference in English CST scores.
  • Advanced Placement (AP) courses — advanced-level courses that high school students can take for college credit — were almost three times more likely to be scheduled during the first or second period of the school day than during the last two periods of the day.

This study suggests that students tend to be more productive in the morning than they are in the afternoon, especially in math.

While the author cannot say for certain why, he identifies three possible causes or contributing factors: changes in the quality of instruction over the course of the school day, changes in students’ learning ability during the school day and differences in student attendance at the start and end of the school day.

The author states that “rearranging school schedules can lead to increased academic performance,” but notes there are constraints to how much school administrators can alter those schedules.

One constraint is the supply of teachers at a given school who teach a particular subject.

Related research:

A 2015 study published in Learning, Media and Technology, “Synchronizing Education to Adolescent Biology: ‘Let Teens Sleep, Start School Later,’” examines the consequences of an early school start time.

A 2011 study in the American Economic Journal: Economic Policy, “A’s from Zzzz’s? The Causal Effect of School Start Time on the Academic Achievement of Adolescents,” looks at how starting the school day later influenced academic achievement among U.S. Air Force Academy students.

A 2006 study in the Review of Educational Research, “An Analysis of Research on Block Scheduling,” offers a review of 58 empirical studies on high school block schedules, including their effect on student performance.

Keywords: education, high school, middle school, learning, adolescence, class schedule, school schedule, math, reading, academic achievement

Writers: and | March 30, 2016

Citation: Pope, Nolan G. “How the Time of Day Affects Productivity: Evidence from School Schedules,” The Review of Economics and Statistics, March 2016, Vol. 98. doi: 10.1162/REST_a_00525.

– See more at:

Time of day and student productivity in middle school and high school

Richest 1% live on average 15 years longer than the poorest 1% – stunning evidence of the human toll of poverty in America

David Ludwig, M.D., PhD writes (on Facebook)…

“A critically important study published today by JAMA examined the relationship between income and life-expectancy by linking tax and death records. The richest 1% live on average 15 years longer than the poorest 1% – stunning evidence of the human toll of poverty. Of special concern, the income-related gap in lifespan increased from 1999 to 2014. These results show that income inequality, which has been growing in the US for decades, isn’t only a matter of social justice, it’s also a matter of life and death.”

This study uses deidentified US tax records to estimate race- and ethnicity-adjusted life expectancy at 40 years of age by household income percentile, sex, and geographic area, and to evaluate factors associated with differences in life expectancy.

The Association Between Income and Life Expectancy in the United States, 2001-2014 FREEONLINE FIRST

Raj Chetty, PhD1; Michael Stepner, BA2; Sarah Abraham, BA2; Shelby Lin, MPhil3; Benjamin Scuderi, BA4; Nicholas Turner, PhD5; Augustin Bergeron, MA4; David Cutler, PhD4
JAMA. Published online April 10, 2016. doi:10.1001/jama.2016.4226

Importance  The relationship between income and life expectancy is well established but remains poorly understood.

Objectives  To measure the level, time trend, and geographic variability in the association between income and life expectancy and to identify factors related to small area variation.

Design and Setting  Income data for the US population were obtained from 1.4 billion deidentified tax records between 1999 and 2014. Mortality data were obtained from Social Security Administration death records. These data were used to estimate race- and ethnicity-adjusted life expectancy at 40 years of age by household income percentile, sex, and geographic area, and to evaluate factors associated with differences in life expectancy.

Exposure  Pretax household earnings as a measure of income.

Main Outcomes and Measures  Relationship between income and life expectancy; trends in life expectancy by income group; geographic variation in life expectancy levels and trends by income group; and factors associated with differences in life expectancy across areas.

Results  The sample consisted of 1 408 287 218 person-year observations for individuals aged 40 to 76 years (mean age, 53.0 years; median household earnings among working individuals, $61 175 per year). There were 4 114 380 deaths among men (mortality rate, 596.3 per 100 000) and 2 694 808 deaths among women (mortality rate, 375.1 per 100 000). The analysis yielded 4 results. First, higher income was associated with greater longevity throughout the income distribution. The gap in life expectancy between the richest 1% and poorest 1% of individuals was 14.6 years (95% CI, 14.4 to 14.8 years) for men and 10.1 years (95% CI, 9.9 to 10.3 years) for women. Second, inequality in life expectancy increased over time. Between 2001 and 2014, life expectancy increased by 2.34 years for men and 2.91 years for women in the top 5% of the income distribution, but by only 0.32 years for men and 0.04 years for women in the bottom 5% (P < .001 for the differences for both sexes). Third, life expectancy for low-income individuals varied substantially across local areas. In the bottom income quartile, life expectancy differed by approximately 4.5 years between areas with the highest and lowest longevity. Changes in life expectancy between 2001 and 2014 ranged from gains of more than 4 years to losses of more than 2 years across areas. Fourth, geographic differences in life expectancy for individuals in the lowest income quartile were significantly correlated with health behaviors such as smoking (r = −0.69, P < .001), but were not significantly correlated with access to medical care, physical environmental factors, income inequality, or labor market conditions. Life expectancy for low-income individuals was positively correlated with the local area fraction of immigrants (r = 0.72, P < .001), fraction of college graduates (r = 0.42, P < .001), and government expenditures (r = 0.57, P < .001).

Conclusions and Relevance  In the United States between 2001 and 2014, higher income was associated with greater longevity, and differences in life expectancy across income groups increased over time. However, the association between life expectancy and income varied substantially across areas; differences in longevity across income groups decreased in some areas and increased in others. The differences in life expectancy were correlated with health behaviors and local area characteristics.

Source: JAMA Network | JAMA | The Association Between Income and Life Expectancy in the United States, 2001-2014

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