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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

Aging, growing, and slowing

GeoFRED lends insight into global population trends

The U.N. estimates the world’s population may rise to over 11 billion by 2100. Much of this growth will occur in developing countries, while populations in nations such as Japan and Germany have already begun to decline. Current data in GeoFRED can help us visualize these changes in world population.

The first map shows global population growth as a yearly percent change. The contrasts between continents are striking: Populations in Eastern European nations declined nearly 1% in 2015, while populations grew over 3% in a substantial number of African and Middle Eastern nations. Fertility and mortality are key factors, as is migration. Lithuania, for example, saw nearly 170,000 people emigrate in 2012—a loss of over 5% of total population. Meanwhile Oman, which had the highest increase in the 2015 data, gained over 1 million immigrants in 2012. Given that the total population of Oman was under 3.5 million that year, to say that migration had a substantial effect on the population is a gross understatement.

The second map shows the percentage of the population under 14. The same European nations that saw populations shrink in 2015 had 15% or less of the population aged 14 and under. In some of the nations with high growth rates, over 9 in 20 inhabitants were below the age of 14. The inverse is also true: In nations where populations are in decline, the proportion of retired adults as a share of the overall population is substantially larger than that of faster-growing nations. This discrepancy presents a problem for governments and citizens alike: The costs of supporting the retired population, whose lifespans are lengthening, increase while the proportion of working-age citizens who pay into entitlement programs that support them decreases. For example, the share of adults aged 65 and over grew by nearly 4% in Poland last year, yet the overall population declined by 1/10 of 1%.

We can predict that, as growth slows, populations age, thanks to the principle of population momentum: Even if families have fewer children and population growth lessens, those already born continue to age, resulting in populations skewed toward the elderly. Economic development plays a role as well. The last map shows the same pattern of colors across the continents as the others, with south Asia and central Africa standing apart from Europe, North America, and parts of South America. Yet the data being represented have nothing to do with population. Rather, this map shows GDP per capita, which approximates a nation’s average standard of living. As living standards improve, individuals are more likely to have access to reliable medical services, including contraceptives, and may prioritize formal employment over parenthood as infant mortality rates decline and job opportunities grow. Thus, nations with higher GDP per capita tend to have aging populations and slower, if any, population growth as they progress through the demographic transition.

How these maps were created: The original post referenced interactive maps from our now discontinued GeoFRED site. The revised post provides replacement maps from FRED’s new mapping tool. To create FRED maps, go to the data series page in question and look for the green “VIEW MAP” button at the top right of the graph. See this post for instructions to edit a FRED map. Only series with a green map button can be mapped.

Suggested by Maria Hyrc.

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