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Posts tagged with: "PAYEMS"

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Taking the pulse of the economy

Connecting the San Francisco Tech Pulse with other economic indicators

Posted on March 5, 2018

The San Francisco Tech Pulse is a measure of the overall health of the American tech sector; it’s calculated using variables such as employment and consumption in the sector and investment in technology. The graph shows the Tech Pulse as well as total U.S. employment, CPI, and GDP indexed to January 2000. These other indicators are common benchmarks of general economic health: Rising GDP, slow changes in CPI, and high employment all indicate a strong economy.

During the 2008 recession, the indicators behaved as we would expect them to during such an economic downturn: employment fell steadily, as did GDP, and CPI spiked and then fell in a spell of high inflation followed by deflation. The tech pulse also plummeted, which makes sense considering it’s the sum of the above indicators in a specific area of the economy. Yet the Pulse began to rise earlier than the general indicators. This early recovery, beginning in April 2009, could indicate that the tech sector was one of the first parts of the economy to gain strength after the recession and assisted in the overall economic recovery.

However, the overall impact of technology shouldn’t be overestimated. During the earlier recession, in 2001, the other indicators remained fairly stable compared with the Tech Pulse, which decreased substantially. This drastic fall could demonstrate the opposite of the pattern we see in 2008: that the technology sector was a major loser in that recession and it was the rest of the economy that helped maintain relative stability.

How this graph was created: Search for and select “San Francisco Tech Pulse.” From the “Edit Graph” panel and the “Add Line” tab, search for and select the other series shown here: “GDP,” “CPI,” and “Employment.” In the “Units” section, select “Index (Scale value to 100 for chosen date),” set the date as January 1, 2000, and click “Apply to all.”

Suggested by Maria Hyrc and Christian Zimmermann.

View on FRED, series used in this post: CPIAUCSL, GDPC1, PAYEMS, SFTPINDM114SFRBSF

Incomes determine house prices

An illustration for San Francisco

Posted on October 30, 2017

Ask someone in San Francisco what that area’s major problem is and they’ll likely complain about housing prices and how they keep getting worse. The first graph shows us this complaint is likely accurate. Indeed, house prices in the Bay Area have increased faster than the national average, with a significant run-up around the year 2000. Why has this been happening? Are people flocking there and has the increased demand for housing driven up the prices?

The second graph shows us that a large influx of residents is unlikely to be the reason behind high housing prices: The size of the working population in the area compared with the U.S. average or even the California average has in fact decreased. Thus, proportionally fewer people are living in the Bay Area, yet house prices have still gone up. What’s that all about?

The third graph traces the evolution of personal incomes in the Bay Area compared with the U.S. average. And here we see that the buying power in the Bay Area has increased significantly more than for the rest of the country. Assuming the housing stock has remained basically unchanged, there have been fewer people with much more money chasing the same houses. So house prices increase. Note how incomes increase pretty fast around the year 2000, precisely when houses got significantly more expensive. We can’t confirm this assumption because FRED doesn’t offer data for the inventory of houses in the Bay Area. Yet, the area is known for its aversion to new housing developments, so the assumption is at least likely to apply when comparing the area with the U.S. overall, which we’ve done throughout this post.

How these graphs were created: For the first graph, search for “San Francisco house price” and take the Case-Shiller series. Click on the “Edit Graph” button and add the U.S. national house price index. Apply formula a/b and choose as units the index scale, setting 100 at the end of the 1990-1991 recession. Proceed similarly for the other graphs.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: A792RC0A052NBEA, CANA, CSUSHPINSA, PAYEMS, SANF806NA, SANF806PCPI, SFXRSA

Fan your forecasting flame with FREDcast

FRED’s new forecasting game

Posted on January 19, 2017

On January 20th FRED’s newest data gizmo, FREDcast, is coming out of beta. FREDcast is an interactive forecasting game that allows users to enter forecasts for four different economic variables, track their forecast’s accuracy on the scoreboards, and compete with friends and other users in leagues. The game is designed for all levels of users, from high school students to professional forecasters. Just log-in to FREDcast using your FRED account and walk through the prompts to enter your forecasts for each variable. FREDcast forecasts are zero horizon, meaning users forecast economic data for the month (or quarter) in which they are in. For example, from January 1st to January 20th, users submit forecasts for the January unemployment rate, the January consumer price index (CPI), the January payroll employment, and quarter one real gross domestic product (GDP). Forecasts are due by the 20th of each month, and scores are released as the economic data come out. View exact release dates on FRED’s economic calendar.

The four FREDcast series are available in FRED. Below is a graph of each series in the appropriate units for FREDcast forecasts. All series in FREDcast are seasonally adjusted. From top to bottom: Real gross domestic product (GDP) is the only quarterly series, and the units are the percent change from the preceding period at a seasonally adjusted annual rate. Next is the unemployment rate, which is forecast as a monthly rate. Next are the consumer price index (CPI) and payroll employment. The inflation series used in FREDcast is the percent change in the CPI from one year ago, while payroll employment is the level change from the prior month measured in persons.

How these graphs were created: GDP: Search for real gross domestic product, and graph the series with the units “Percent Change from Preceding Period, Quarterly, Seasonally Adjusted Annual Rate.” Set the start date to 2006-07-01, and follow this path: Edit Graph > Format > Graph Type > Bar. Unemployment Rate: Search for unemployment rate, and graph the seasonally adjusted civilian unemployment rate. Set the start date to 2006-12-01. CPI: Search for consumer price index, and graph the series “Consumer Price Index for All Urban Consumers: All Items” with monthly, seasonally adjusted units. Set the start date to 2006-11-01, and follow this path: Edit Graph > Units > Percent Change from Year Ago. Payroll Employment: Search for payroll employment, and graph the series “All Employees: Total Nonfarm Payrolls” in seasonally adjusted units. Set the start date to 2006-12-01, and follow this path: Edit Graph > Units > Change, Thousands of Persons. Last, multiply the series by 1000 to get it in units of persons by entering a*1000 in the formula box and clicking “Apply.”

Suggested by Michael Owyang and Hannah Shell.

View on FRED, series used in this post: A191RL1Q225SBEA, CPIAUCSL, PAYEMS, UNRATE