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Comparing quality of education in the United States and China

Penn World Tables reveal China's great leaps toward the U.S.

An economy’s long-term growth prospects depend on many factors, including the education of its workers. One measure of a country’s quality of education is the index of human capital from the Penn World Tables. This index combines years of schooling and returns to education, and it can be used to rank and evaluate the quality of education in each country.

The graph traces the evolution of the index of human capital per person for the United States and China from 1950 to 2014. Two patterns emerge:

  1. The index of human capital has been consistently lower in China than in the United States during the entire period.
  2. In both countries, the index has increased, reflecting an increase in the quality of the education; however, the increase has been more pronounced in China than in the United States. In particular, from 1952 to 2014, the index has increased by a factor of 1.42 in the United Sates and a factor of 2.22 in China.

We may expect this trend to continue in China, whose 13th Five-Year Plan for 2016-2020 includes the goal of promoting education by encouraging the entrepreneurship and innovation abilities of students and developing continuous education in China.

How this graph was created: Search for “Index of Human Capital per Person for United States” and choose the series you want. The add line 2 to the existing graph by clicking the “Edit Graph” button and using the “Add Line” tab to search “Index of Human Capital per Person for China.” Finally, go to the “Format” tab within “Edit Graph,” and select “Dash” under Line 1’s line style and select “Right” for line 2 Y-axis position.

Suggested by Ana Maria Santacreu.

View on FRED, series used in this post: hciyiscna066nrug, hciyisusa066nrug

A glass half full or half empty?

The evolution of full-time and part-time employment

The first graph shows the evolution of full-time and part-time employment over the past few decades. It’s no surprise that both have tended to increase, as the general population has also increased over that period. There’s a bit of a surprise, though, in January 1994: This is when the definition of full-time work was readjusted, leading to a jump in both types of employment. So, why are we looking at these series? There’s been quite a bit of discussion on whether the recent recovery of the labor market has resulted in growth of full-time jobs. This question should be easy to settle by looking at the data. And, indeed, it’s quite apparent that full-time work has increased significantly since the recent recession. The picture isn’t so clear for part-time work, though, in part because the numbers are smaller in general and changes are therefore more difficult to distinguish.

For a better look, we modify the graph: Now, each series has its own axis, on the right for full-time and the left for part-time. In the second graph, it is quite apparent that, over the long-run, part-time work has increased. But the recent history is different: Part-time work jumped as full-time employment fell during the recent recession, but then stayed at the same level even as full-time work trended up.

How these graphs were created: Search for “time employed” and select the two series. (We chose the seasonally adjusted ones.) Click on “Add to Graph” and you have the first graph. Click on “Edit Graph,” open the “Format” tab, and move the Y-axis for one series to the right side.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: LNS12500000, LNS12600000

Characterizing the decline of manufacturing employment

Are manufacturing workers being fired or quitting?

Many lament the decline of manufacturing employment. The graph above illustrates this decline since 2000; expanding the sample period reveals that manufacturing employment hasn’t increased in any notable fashion since the 1960s, despite a steady increase in the working-age population. The question we’re asking today is whether this decline is due to workers leaving the manufacturing sector voluntarily or not. The graph below shows that there’s no clear answer: Quits and layoffs are roughly of the same magnitude. They also fluctuate considerably through the year, with seasonal factors being quite important. Obviously, there are more layoffs during recessions, but this isn’t something specific to manufacturing.

The next graph tries to put these numbers into perspective: Adding the layoffs and the quits and comparing them with the hires highlights the considerable churn in the labor market. Indeed, it’s not that obvious that there’s a decline in employment or that hires are systematically lower. In other words, there’s considerable movement in the market for manufacturing workers and only a small part of it is about the sector downsizing its labor force.

How these graphs were created: For the first graph, search for “manufacturing employment” and select the series that isn’t seasonally adjusted. Start the sample period in December 2000. For the second graph, search for “manufacturing layoffs” and select the monthly rate without seasonal adjustment. From the “Edit Graph” section, use the “Add Line” option to search for “manufacturing quits” and again select the monthly rate without seasonal adjustment. For the third graph, start with manufacturing layoffs as in the second graph, from “Edit Graph” edit the existing line by adding “manufacturing quits,” and apply the formula a+b. Then use “Add Line” again to search for and add “manufacturing hires.”

Suggested by Christian Zimmermann.

View on FRED, series used in this post: CEU3000000001, JTU3000HIR, JTU3000LDR, JTU3000QUR


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