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Hyperinflation in Venezuela

Using exchange rates to measure out-of-hand inflation

There’s inflation and then there’s hyperinflation, which is when inflation gets out of hand. There’s no official definition, but economists tend to use the “if it looks like hyperinflation, then it is” dictum. When price changes occur rapidly—say, several times within the same day—that’s hyperinflation. When bank notes don’t have denominations large enough to make payments easy, that’s hyperinflation. And measuring hyperinflation isn’t easy, as no statistical office can keep up with the rapid changes in price. One way to track this phenomenon, though, is to look at exchange rates.

The first graph shows the exchange rate between the Venezuelan bolívar fuerte and the U.S. dollar. Quite obviously, something out of the ordinary happened. The bolívar lost value rapidly—so much so that the graph allows us to see only a few recent data points.

[ Update 8/20/2018: The Venezuelan government announced economic policies designed to stem hyperinflation. The immediate impact is to raise the exchange rate to 6 million bolívars to the U.S. dollar (up from about 200,000). Time will tell whether these policies are successful. ]

One remedy for visualizing the wide range of values is to use a log scale, as the second graph does. A graph with a log scale will show with a straight line any data that increase at a constant rate. If the data increase at an increasing rate, the line moves steeper (i.e., it becomes convex). A few things are remarkable. First, there have been long periods of constant exchange rates, owing to the government’s policy of setting those rates. (See the several straight lines.) But recently, the rise in the exchange rate has been accelerating. (See the several steps up and eventually the vertical line.) This behavior in the data is characteristic of hyperinflation, which is obviously not sustainable.

How these graphs were created: For the first graph, search for and select “Venezuela exchange rate” (the monthly series) and click on “Add to Graph.” For the second graph, adjust the first graph in the “Edit Graph” section: Use the “Format” tab to select “Log scale” on the left.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: EXVZUS

Dips in durables and nondurables

Tracking consumption dynamics since the Great Recession

Household consumption contributes about 70% of total U.S. production. So, movements in consumption over time and the make-up of products purchased are essential for understanding GDP.

In general, final consumption is procyclical. That is, in good times people spend more and in bad times they spend less. During the recent 2007-09 Great Recession, real personal consumption expenditures dropped by 2.4% from pre-recession levels. But did people cut back the same amount on spending for all goods? The first graph splits aggregate consumption into durable and nondurable goods: Durable goods include, for example, cars and furniture and nondurable goods include, for example, food and clothing. We can see that consumption of durable goods is more volatile. During the recession, durable goods expenditures dropped by 12%, while nodurable goods expenditures dropped by 3%. (This doesn’t add up to the total 2.4% drop because there’s also the consumption of services, which isn’t shown here.)

The second graph separates durable goods expenditures into finer categories.

Among these durable goods categories, expenditures on motor vehicles and parts dropped the most—by as much as 24%. Expenditures on furnishings and durable household equipment follow, with a drop of 16%, although this category hits bottom at the end of the recession. Both categories recovered to their pre-recession levels only after 2013. In contrast, expenditures on recreational goods and vehicles and on other durable goods decreased only mildly.

Why did vehicle sales drop so much? One reason could be that a car purchase requires a larger payment than other durable goods. Another explanation is that the decline in house prices deteriorated households’ credit scores, making it more costly to finance a car purchase with a loan. The sinking housing market also would have driven down demand for furnishings and durable household equipment. And, simply, credit may have generally been more difficult to obtain.

Expenditures for nondurable goods fell by 4% on average, less than they did for durable goods. The third graph shows four categories. Interestingly, expenditures on gasoline and other energy goods slowly and consistently fell, hitting bottom in late 2012 and gradually recovering by 2016, which is likely due to the falling car sales shown in the previous graph.

Overall, expenditures related to vehicles and houses account for most of the drop in consumption in the 2007-09 recession.

How these graphs were created: All these series can easily be found in FRED using the Release View. From the FRED home page, select Releases from the main search box and click the link for gross domestic product. You’ll then see the sections of the GDP release. Select “Section 2–Personal Income and Outlays” and then “Table 2.3.3. Real Personal Consumption Expenditures by Major Type of Product, Quantity Indexes” (quarterly frequency). The resulting page will be a table with all the series associated with the real personal consumption expenditures release and all graphs can be made from this page.

First graph: Select “Durable goods” and “Nondurable goods” and click “Add to Graph.” Using the scroll bar at the bottom or the date range at the top, adjust the time horizon of the graph to start in 2006:Q1. From the “Edit Graph” menu, adjust the units to be “Index (scale value to 100 for chosen date)” and set the date to be 2007-01-01. Click “Copy to all” to copy these changes to all the series.

Second graph: Select the four subcomponents under the durable goods category and click “Add to Graph.” Follow the same steps as in the first graph to adjust the time and index base value.

Third graph: Select the four subcomponents under the nondurable goods category and click “Add to Graph.” Follow the same steps as in the first graph to adjust the time and index base value.

Suggested by Sungki Hong.


Has wage growth been slower than normal in the current business cycle?

You may have read in the popular press that wage growth seems much slower since the Great Recession compared with previous business cycles. Let’s see what FRED data can tell us. The graph above shows wage growth, defined as the annualized percentage change in the average hourly earnings of private production and nonsupervisory employees. To interpret the graph, note the gray bars, which indicate recessions since 1976, and the green vertical lines, which indicate the peaks of each business cycle. A generally U-shaped pattern occurs between the starts of consecutive recessions. At the start of a recession, the rate of wage growth falls for a number of months, then the trend is reversed as wages increase until the next recession, and the cycle repeats.

To better compare how wages behave across business cycles, we graph the wage behavior observed for each of the prior three business cycles and the current business cycle together. Each cycle is centered at zero, which denotes the month with the lowest wage growth for each business cycle. The current business cycle is identified by the purple line. This cycle started at a lower level of wage increases than the prior three cycles. More importantly, the wage increase from the low point has been following a lower trend: In prior cycles, wage increases exceeded 4%; the current cycle’s wage increases still have yet to reach 3%.

In a future blog post, we’ll look into possible reasons why the current business cycle’s wages have been increasing much more slowly.

How these graphs were created (plus some background): For the first graph, search for wages and select “Average Hourly Earnings of Production and Nonsupervisory Employees: Total Private.” From the “Edit Graph” panel, change the units to “Percent Change from a Year Ago.” The business cycles can be accented by adding green lines to the graph corresponding to each peak using the “Create user-defined line” option under the “Add Line” tab. For the second graph, change the units to “Index” and enter the date “1986-12-01.” This was the lowest point in wage growth for the associated business cycle, which had begun 65 months earlier and would last 43 months longer. To capture the entire business cycle with monthly data, check the “Display integer periods…” box and set the range from -65 to 43. If the units under the “Customize data” tab are changed to “Percent Change from a Year Ago,” the resulting graph shows the section of the first graph from July 1981 to July 1990. While this same result could have been achieved more easily by changing the date range of the original graph, an advantage of this approach is that it allows the same series to be plotted from multiple separate date ranges. Use the “Add Line” tab to add this same series to the graph four times. The options for each line will be the same as those for the first line, except that the custom index date and length of the date range will be different: A second low point occurred in September of 1992, 26 months into a cycle that would last 102 months longer, and the next in January 2004, 35 months into a cycle that would last 46 months longer. The present cycle had its low point 58 months in, during October 2004, and the end date of the cycle has yet to be determined. One way to resolve this problem is to set an unnecessarily high integer end date, like 200. FRED will then automatically fill in the latest available data.

Suggested by Ryan Mather and Don Schlagenhauf.

View on FRED, series used in this post: AHETPI

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