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The prices of caffeine and education Which is more jittery?

As many students prepare to face the costs of education, they find themselves wondering what, if any, nonessential items they’ll be able to afford with the added burden of paying for their schooling. Whether hot beverages, especially coffee, are “nonessential” to students remains debatable, yet their price changes relative to price changes in education is a concept worth exploring.

We use two data series found in FRED to construct the price ratio of caffeine to education: the Harmonized Index of Consumer Prices for Coffee, Tea, and Cocoa and the Harmonized Index of Consumer Prices for education. The graph shows this ratio over the past 13 years for the EU as a whole, the EU nations with the highest and lowest GDP in 2016—Germany and Malta—and those with the highest and lowest GDP per capita in 2016—Luxembourg and Bulgaria, respectively. The Harmonized Index of Consumer Prices is an inflation indicator that measures the changes in price of the goods it describes. By creating a ratio of HICP for hot drinks to HICP for education, we can examine how much the price of the former changes in comparison with price of the latter. The data are indexed using 2015 as the base year, explaining the convergence of the trend lines over time until they are all equal in 2015.

As expected, the EU indicator appears to fall near the mean of the four nations, except for the period from April 2011 to September 2012, when the ratio of HICP for hot drinks to education was highest in the EU as a whole (which had 27 countries at the time). In the two relatively better-off countries, Luxembourg and Germany, the ratio of the HICP for hot drinks to education generally remains lower than the others, meaning that the relative price of education has been increasing (as the index year is toward the end of the period) while in poorer countries the decrease in price of hot drinks is comparatively greater when compared with that of education. Note also the sudden drop for the ratio in Germany in April 2007. According to the IMF, several German states introduced new educational fees during that time period. The sudden rise in the price of education increased the HICP for education, thus causing the indicator we constructed to fall.

We can also examine the two indices separately across the five regions. While the HICP for hot drinks (above) remains similar for the EU and the four countries being investigated, the HICP for education (below) varies far more drastically, with Germany and Luxembourg seeing lower inflation for education and Malta and Bulgaria experiencing the opposite. Thus, we can attribute the differences in the ratio of HICP for hot drinks to education more to variability in education than in coffee, tea, and cocoa.

Education costs differ among nations likely because education is a good that is very specific to the country in question and its education policies. The prices of consumer goods such as coffee, tea, and cocoa are dictated by factors that span the international market, like input availability and technology. Thus, the inflation of the prices of foods and beverages is more homogeneous within the European Union, while that of education includes a higher degree of variability between nations.

How these graphs were created: Graph 1: Search for “HICP coffee EU,” select the relevant series, then click on “Add to Graph.” Under “Edit Line 1” and “Customize Data,” search for “HICP education EU” and click “Add.” In the formula box, type “a/b” and click “Apply.” Add a line and repeat the process for Germany, then Malta in Line 3, Luxembourg in Line 4, and Bulgaria in Line 5.
Graph 2: Search for “HICP coffee EU,” select the relevant series, then click on “Add to Graph.” Add a line and repeat the process for Germany, then Malta, Luxembourg, and Bulgaria. Graph 3: Follow the same process as for Graph 2, using “HICP education” as the search term.

Suggested by Maria Hyrc.

View on FRED, series used in this post: CP0121BGM086NEST, CP0121DEM086NEST, CP0121EU28M086NEST, CP0121LUM086NEST, CP0121MTM086NEST, CP1000BGM086NEST, CP1000DEM086NEST, CP1000EU28M086NEST, CP1000LUM086NEST, CP1000MTM086NEST

The rise (and fall?) of the cost of education Education inflation appears to be converging with general inflation, at least for now

For many years, the cost of education has risen steadily and significantly more than the general level of prices. This trend has led to numerous complaints that education is out of reach; it has also led to a boom in student loans. The graph clearly shows how education inflation (blue line) has been above general inflation (red line) every year since 1994. And, again, quite significantly so. The past few observations, however, exhibit a marked reversal, with one observation even showing CPI inflation higher than education inflation. Does this mean education will become relatively more affordable now? It’s difficult to say from current data, especially since there have been two other episodes, in 2008 and 2011, when the two series converged only to diverge again. Time will tell if this latest development is pomp or circumstance.

How this graph was created: Search for “CPI Education” and create the graph. From the “Edit Graph” section, under “the Add a Line” option, search for and select CPI. Choose units “Percent Change from Year Ago” and click on “Copy to All.”

Suggested by Christian Zimmermann.

View on FRED, series used in this post: CPIAUCSL, CUUR0000SAE1

Gas prices and transportation habits When gas prices rise, do Americans park their cars and take the train?

Millions of Americans purchase gasoline each day and make choices about how much to drive, if at all. A basic premise, found in any high school economics textbook, suggests that a rise in the price of something increases the demand for its substitutes. So we might expect the use of public transit to be correlated with gas prices: When gasoline gets more expensive, car travel does too; so, shouldn’t consumers opt for a cheaper alternative, such as public transit? The data shown in the graph, however, don’t reveal anything so clear: When gas prices increase, we might expect a corresponding decrease in vehicle miles traveled and an increase in public transit usage. But that isn’t the case.

The graph tracks the percent change in the price of gas, public transit ridership, and vehicle miles traveled over the past five years. When gas prices have increased, so have public transit ridership and vehicle miles traveled: Americans were both driving more and making greater use of public transit. Seasonally adjusted data on gas prices aren’t available, so that lack of adjustment may explain some of this variation. Consider that Americans travel more in the spring and summer, which is shown by the tendency of all three indicators to spike in March and for transit ridership and vehicle miles both to increase throughout the summer months.

What happens when gas prices decrease? During those times, the relationship between gas price, vehicle miles, and transit use is even less clear, serving as a reminder of the other factors at play in the everyday decisions of Americans. For example, in December 2014, gas prices decreased over 13% while public transit ridership and vehicle miles traveled continued to increase. The price of gas fluctuates often, but the daily obligations of Americans are pretty steady: Commuting to work or school, visiting family, or shipping goods may not change significantly. The convenience of travel options, availability of infrastructure, employment and income levels, and seasonal opportunities are far more influential on transportation habits than the price of gas alone.

How this graph was created: Search for “conventional gas price” and choose the monthly series. From the “Edit Graph” tab / “Add Line” option, search for “public transit” and add the “not seasonally adjusted” series. Add the third line by searching for “vehicle miles” and choosing “Vehicle Miles Traveled (not seasonally adjusted).” From the “Edit Line” option, change the units to “Percent Change” and click “Copy to all.” Adjust the view to the five years starting with January 1, 2012.

Suggested by Maria Hyrc.

View on FRED, series used in this post: GASREGCOVM, TRANSIT, TRFVOLUSM227NFWA

The recent evolution of labor force participation Small movements from a lot of labor market churn

Since the early 2000s, labor force participation has been declining in the U.S. After peaking at 67.3 percent in March of 2000, the labor force participation rate declined consistently to 62.4 percent in September 2015 and has since flattened out. The first graph shows the period of decline in the labor force participation rate, which started in early 2000, flattened out in mid-2005, and then declined again from the onset of the Great Recession to 2015.

Several variables in FRED can illustrate the labor force dynamics at play behind the declining labor force participation rate. The next graph shows the annual change in the labor force (employment plus unemployment). While the labor force has mostly been increasing since 2000, it has not been increasing fast enough to keep up with population growth. Starting in 2014, however, the pace of growth in the labor force picked up, which led to the flattening out of the participation rate.

The last graph shows monthly flows into (red line) and out of (blue line) the labor force. These gross flows are very close to each other, with the net changes (green line) always close to zero. It is the net changes that explain the evolution of aggregate labor force participation. From 2009 to 2016, the positive values are not enough to offset the more negative values and more people flowed out of the labor force. More recently, however, the positive contributions more than offset the negative values, leading to an increase in participation. Despite this recent evolution, the graph does not seem to point to any particular new trend that’s different from the past. This suggests that more research is needed to understand the observed decline in the participation rate.

How these graphs were created:
Graph 1: Search for “Labor Force Participation.” Graph the first result and limit the date range from 2000 to current.
Graph 2: Search for “Unemployment.” Graph the series titled “Unemployment Level.” From the Edit Graph tab, type “Employment Level” in the customize data section search box. Click the series titled “Civilian Employment Level” and then click Add. Finally, type a+b in the formula box and change the units to “Change, Thousands of Persons.”
Graph 3: Search for “Labor Force Flows.” Graph the series titled “Labor Force Flows Employed to Not in Labor Force.” Repeat the process outlined in Graph 2 to modify the line by adding “Labor Force Flows Unemployed to Not in Labor Force” to the graphed series. Now, select the middle menu and search for “Labor Force Flows Not in Labor Force to Unemployed” and add this series as a new line. Repeat the process to modify the line by adding “Labor Force Flows Not in Labor Force to Employed.” Once again, use the middle menu to add “Labor Force Flows Not in Labor Force to Employed” as a new line and then modify the line by adding the remaining three flows as additional series on the new line. Use the letters assigned to each series to calculate the difference of the sum of those flowing into the labor force less those flowing out of the labor force (e.g., consider (a+b)-(c+d)).

Suggested by Maximiliano Dvorkin and Hannah Shell.

View on FRED, series used in this post: CE16OV, CIVPART, LNS17200000, LNS17600000, LNS17800000, LNS17900000, UNEMPLOY

How much has China grown? Uncertainty about the numbers

There’s some debate on how reliable the GDP growth rates from China are. In part, this worry comes from the pre-reform era in which all levels of production had to reach targets and may not have been entirely truthful. Also, the string of very high growth rates over the past two decades is unprecedented. Can FRED shed some light? Well, it has seven different series that measure GDP in different ways. One is from the World Bank, one is from the OECD, and five are from the Penn World Tables. Differences pertain to how currency conversions are treated. For example, there’s the issue that exchange rates may drift away from so-called purchasing power parity (PPP) and which side of the GDP equation is used. Indeed, technically, there are three ways to measure GDP: add up all output, all expenditures, or all incomes. All three should get to the same number, but in practice there are some residual errors. So what does this graph show? These different measures essentially come to the same conclusions, the differences being relatively small and not systemically biased in one direction. However, these statistics are based on information that is coming out of China. And, at least in the past, there was plenty of uncertainty arising from that.

How this graph was created: Search for “real GDP China” and select the relevant series, then click on “Add to Graph.” Click the “Edit Graph” button, then change the units to “Percent Change from Year Ago” for the lines that are not yet in growth rates.

Suggested by Christian Zimmermann.


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