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Leaves fall, prices rise Tracing the timing of school supply price hikes

It’s backpack season, and many Americans are starting a new school year. A recent FRED Blog post covered the impact of seasonality. This post covers education-related data—specifically, the cost of books and supplies. The top graph shows the monthly changes in the price of educational books and supplies over the past ten years, using the consumer price index, which measures inflation by reporting the percent change over time, on average, of a variety of goods and services paid for by U.S. urban consumers. In the past decade, educational supply costs have tended to rise most significantly in August and January, coinciding with the beginning of academic semesters for most students.

Seasonal adjustment accounts for these variations by using data from previous years to smooth seasonal eccentricities. The graph above shows the difference between the raw and adjusted CPI indices, showing that level-differences have increased over the past several decades. However, the difference between indices may not show the whole story, as CPI is used to report inflation as a percent change. The next graph shows the difference in the monthly percent change in the price of books and supplies between the raw and adjusted data, showing that the seasonal difference in the monthly price change has actually decreased over time.

The seasonal changes, besides decreasing, have also scattered across the school year. In the early 1970s, prices consistently rose each October and remained fairly stable during the remaining months. In the next decade, spikes emerged at the beginning of the calendar year as well, presumably as consumers spent more on supplies for the second semester of the school year. In recent years, the autumn spike in prices has begun earlier, shifting from October to August and September, likely reflecting changing market strategies and school schedules.

The crucial question remains: When can I find the best deals on school supplies? You may not want to ask your parents for the answer. Older folks may advise against buying in October, based on their experiences of cost increases, and propose an August shopping trip instead. However, today’s students may want to make the most of the price decreases of the early summer and late fall, with back-to-school shopping trips in July and December.

How these graphs were created: For the first graph, search for “CPI US Books and Supplies” and select the not seasonally adjusted, monthly series. From the “Edit Graph” section, adjust the units to “Percent Change.” From the “Format” tab, change the graph type to “Bar.” Finally, adjust the graph to display data from the last 10 years using the “10Y” button in the top right corner of the graph page. For the second graph, search for the same series: “CPI US Books and Supplies,” not seasonally adjusted, monthly. From the “Edit Graph” section, add the seasonally adjusted data to line 1 by searching for “CPI US Books and Supplies adjusted” in the box below “Customize Data.” Click “Add.” In the formula tab, type “a-b” and click “Apply.” For the last graph, follow the steps for the previous graph, but change the units for both series to “Percent Change.” In the format tab, select “Bar” as the graph type.

Suggested by Maria Hyrc.

View on FRED, series used in this post: CUSR0000SEEA, CUUR0000SEEA

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

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: In GeoFRED, select “Build New Map.” Change the year to 2015, and select “Population Growth” from the data menu. To create the subsequent graphs, follow the same process, but search for “Population under 14,” “Population over 65,” and “GDP per capita” in the data menu.

Suggested by Maria Hyrc.

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