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The International Monetary Fund compiles a financial access survey
for most countries in the world. This survey allows us to compare metrics on how households and businesses in different countries participate in financial markets—as either borrowers or lenders. For this map, we chose one particular measure that determines how much households deposit in banks, with the latest numbers from 2015 as a share of that country's GDP. The map highlights stark differences: The general tendency is that poorer countries have smaller shares. The lowest are Malta (0.2%), Chad (8%), and Sudan (11%). But Argentina has only 16% (in 2014, since 2015 data aren't available) and Germany has only 28%. If you're looking for the highest, you'll need to zoom in a lot: The tiny republic of San Marino has 280%, followed by Lebanon (254%) and Venezuela (178%).
How can we make sense of this? In countries with sophisticated financial markets, households have more options and thus may choose to put their savings in other assets. For example, it's very popular in Germany to save by investing in building societies, which promote homeownership. Conversely, the lack of options beyond savings and deposit accounts may induce households to concentrate all their wealth there or to look for assets outside the financial sector. In other words, every country should probably have some sort of footnote attached to its numbers to explain its unique context.
How this map was created
: From GeoFRED
, click on "Build map"; open the cogwheel in the upper left corner, open the "Choose data" panel, select "Nation" as region type, and find the series you want.
Suggested by Christian Zimmermann
How this graph was created
: Search for “cpi” and select “Consumer Price Index for All Urban Consumers: All Items.” From the "Edit Graph" menu, click "Add Line" and enter “chained cpi” in the search box. Select “Chained Consumer Price Index for all Urban Consumers: All items” and click “Add Data Series.” Change the units to “Index (Scale value to 100 for chosen date),” change the date to 2000-01-01, and click “Copy to all.” Finally, adjust the date range to start at 2000-01-01.
Suggested by Daniel Eubanks and Don Schlagenhauf
The new Tax Cuts and Jobs Act changed both personal and corporate income taxes. Much of the discussion has focused on the changes in the tax rates, but there's another change in this law that has an effect on personal income tax. A household's tax obligation depends on the income bracket for their earned income. For example, a married couple earning $77,000 faces a tax rate of 12% on all income over $19,050. The next bracket starts at $77,400. While the new tax code decreased the tax rate, another change that has received less attention deals with how the tax brackets are adjusted over time. Economists argue that the income brackets should be adjusted when prices change. If the household in this example received a 3 percent cost-of-living increase, their new income level would be $79,310. Despite their inflation-adjusted income level not changing, this household would now fall in the next income bracket and face a 22% tax rate on income over $77,400. If the tax brackets were adjusted for the increased inflation, the marginal rate would not change. This is referred to as indexing the tax brackets.
The Act continues to adjust the tax brackets for price changes, as did the prior personal income tax legislation. The difference is the price index used to adjust those tax brackets. The previous tax code used the CPI-U measure to adjust the brackets, while the new code uses the C-CPI-U measure. The first measure, the consumer price index for urban consumers, assumes you purchase the same quantity of each good in the CPI basket over time. In contrast, the C-CPI-U, or chained consumer price index for urban consumers, recognizes that individuals can shift their expenditure patterns toward cheaper goods in the same expenditure category. For example, suppose chicken prices increase while turkey prices remain unchanged. The CPI-U implicitly assumes individuals continue to buy the same quantity of chicken. In contrast, the C-CPI-U recognizes that consumers will see the price change and substitute turkey for chicken. As a result of this expenditure shift, the price change is less. In the graph, these two price indexes are shown for the period 2000 to 2017 and indexed so that both equal 100 in 2000. The prices in the C-CPI-U index increase at a slower rate because individuals can shift their purchases toward cheaper substitute goods.
The choice of CPI measure has consequences. As shown in the graph, C-CPI-U implies smaller price increases than the CPI-U. This means the tax brackets will have smaller adjustments and more individuals will fall into the higher tax brackets and pay more taxes. If social security payments are linked to the C-CPI-U, then social security payments will increase at a slower rate. In the popular press, some have pointed out that this change in the price index has resulted in an implicit increase in tax revenue, which is true. But from an economic perspective, the primary concern is which index more accurately measures the change in the prices of goods that individuals purchase.
View on FRED, series used in this post:
How this graph was created:
For the first two lines: Search for "passenger miles" and choose "Revenue Passenger Miles for U.S. Air Carrier Domestic and International, Scheduled Passenger Flights." From the "Edit Graph" panel, use the "Add Line" feature to search for "seat miles" and choose "Available Seat Miles for U.S. Air Carrier Domestic and International, Scheduled Passenger Flights." For the third line: Use the "Add Line" feature to search for and select the "Available Seat Miles..." series again. Then use the "Add data series" feature: Under "Customize data," search for and add the "Revenue Passenger Miles..." series again; in the formula box, use the formula a/b. From the "Format" menu, select "Right" for the y-axis position for the third line, then choose the widths and colors you prefer for all the lines.
Suggested by Alexander Monge-Naranjo
Business or pleasure. Domestic or international. Air travel is a frequent fact of life for more and more people, and booking a flight is much easier than it used to be: A couple of clicks and you have your ticket. Unfortunately, airline logistics and operations are much more costly and complicated. Air travel technology and airport capacity have not progressed as quickly as online commerce. Crowds, delays, cancellations, and long layovers in airports are still part of the travel experience.
Deciding when or whether to fly is not always up to us, but some air travel decisions are entirely under our control. For psychological insights into the travel experience, consider the cinematic namesake of this blog post. But for some economic insights, let's see what FRED data can tell us.
The blue line in the graph shows the number of scheduled “revenue passenger miles” each month for commercial U.S. air carriers, both domestic and international, from January 2000 through August 2017. A revenue passenger mile is equal to one paying passenger carried one mile. The red line shows the number of "available seat miles," which is a measure of capacity. An available seat mile is equal to one available seat, occupied or not, carried one mile. The green line, which uses the right scale, shows the ratio of available seat miles to revenue passenger miles.
The graph reveals a number of interesting facts. First, albeit not too rapidly, the volume of air passenger transportation has been trending up. For businesses, online communication such as email, Skype, and Zoom have not been able to replace face-to-face meetings. For families, enhanced electronic entertainment has not replaced the full experience of visiting relatives or getting to know a new place. Second, business cycles do not appear to be a major factor of air passenger transportation: The behavior of all three series is similar in recessionary and non-recessionary periods. Although only two recessions are recorded during the sample period in the graph, one was major. Third, seasonality is a key factor. Americans travel much more during the summer than during the winter. Indeed, the volume of travel is very high in July and very low in February. Fourth, the airlines are clearly anticipating and responding to these seasonal fluctuations, as capacity (available seats) is highly synchronized with scheduled passengers, especially since 2005.
Despite this synchronization, airplanes are more crowded when there's more travel occurring—that is, there are fewer available seats per passenger in the summer than in the winter. So, overbooked airplanes and overworked airline employees can disrupt summer travel, weather can disrupt winter travel, and all sorts of other disruptions can occur any time of year.
Last, but not least, there's a clear trend in the seat-to-passenger ratios. The chances of having an overbooked flight have increased over time, consistent with recent and well-known unpleasant incidents.
View on FRED, series used in this post:
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Last week the St. Louis Fed updated its estimates of economic growth through the third quarter of 2017 for 68 of the U.S.'s most populous metropolitan statistical areas (MSAs). Average growth across these MSAs was 2.7 percent, which is consistent with the 2.3 percent year-over-year growth in U.S. real GDP during the same period.
The data are summarized in a FRED release table called Economic Conditions Index by BEA Region
(under the main category of Metro Area Economic Conditions Indexes). Growth was the fastest in the Southwest and the slowest in the Plains. Given the geographical aspects here, we can use GeoFRED to map the economic growth for these MSAs in September 2017: For example, activity declined in Detroit, Hartford, Houston, and Miami, while growth was relatively strong in San Antonio, Louisville, and San Jose.
Impact of Hurricanes on the Houston and Florida MSAs
There's a noticeable decline in the growth rates of the MSAs affected by hurricanes Harvey and Irma last fall: The graph above shows Houston's 0.5 percent decline in August and 1.0 percent decline in September; Miami's 1.9 percent increase in August and 0.6 percent decline in September; and Tampa's near 4 percent increase in August and previous months and 2.4 percent increase in September.
Economic Activity in the Eighth Federal Reserve District MSAs
Overall growth among the four largest MSAs in the Eighth Federal Reserve District continued to improve at a modest pace during the third quarter of 2017, as can be seen from the graph below. Growth in Little Rock, St. Louis, and Louisville picked up, while growth in Memphis slowed a bit. Overall third-quarter growth was fastest in Louisville at 4.3 percent, followed by Little Rock at 3.3 percent, Memphis at 2.2 percent, and St. Louis at 1.9 percent.
How these graphs where created:
For the map: Go to GeoFRED
and click on “Build New Map.” Under “Region Type” in the Tools menu, select “Metropolitan Statistical Area”; under “Data,” select “Economic Conditions Index.” To change legend colors, select “Colors” and chose a divergent color set that uses a bold color for both high and low values. To change the ranges, select “Edit Legend” and enter the interval values 0, 2, 4, 6.
For the first graph: Go to the Releases section on FRED and select the category “Metro Area Economic Conditions Indexes.” From this page, select "Economic Conditions Index by BEA Region." Then, by checking the box next to their names, select the Houston-The Woordlands-Sugar Land, TX (MSA) and the Miami-Fort Lauderdale- West Palm Beach, FL (MSA) and click on “Add to Graph.” Use the slide bar below the graph to zoom in on the period of interest.
For the second graph: Again, go to the release tables page on FRED. Select "Economic Conditions Index by BEA Region." From here, select the St. Louis, MO-IL (MSA), the Little Rock-North Little Rock-Conway, AR (MSA), the Memphis, TN-MS-AR (MSA), and the Louisville/Jefferson County, KY-IN (MSA) and click on “Add to Graph”. Then adjust the dates to 2017-01-01 to 2017-09-01. From the “Edit Graph” menu, use the “Modify frequency” option to select “Quarterly" and the “Aggregation method” option to select “Average." Repeat this step for the other three lines. From the “Format” tab, under “Graph type,” select the option “Bar.”
Suggested by Asha Bharadwaj and Charles Gascon
GDP is a popular target for nowcasting, and FRED covers the nowcasts of several Federal Reserve Banks—with the Federal Reserve Banks of Atlanta (GDPNow) and St. Louis nowcasts shown here along with the final GDP numbers released by the Bureau of Economic Analysis. To gaze into the future, focus on the very last data point for each nowcast (Q4 2017, shown here), as this is what nowcasting is all about.
The earlier data points for the nowcasts are the last estimates before the first (early) GDP release by the BEA, which is typically revised over time to create the green line. Like the BEA's GDP numbers, the nowcasts are revised several times per month.
We see that there are disparities between the nowcasts. While they are in principle all based on the same information, estimates can differ because of different statistical methodologies and how they are revised over time. And what about the differences between the nowcasts and the final data? The BEA obviously has the advantage of access to more raw data and more time to refine the numbers.
How this graph was created
: Search for "nowcast" and all the series you want should appear. Select the relevant series and click "Add to Graph." From the "Edit Graph" menu, use the "Add Line" option to search for and select "real GDP" (use the growth rate series). Finally, start the graph in October 2011, the first data point of GDPNow.
Suggested by Christian Zimmermann.
Forecasting, as we all know, tries to predict the future. For FRED's purposes, that prediction is how a statistic will evolve. Nowcasting, a variant of forecasting, looks at the current state of a statistic that hasn't yet been released because the period of coverage is not yet over. Nowcasting is one way to examine current economic activity; another was discussed in a
View on FRED, series used in this post: