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Job volatility among races

This graph traces employment over the past 43 years for three categories of people: Black, Hispanic, and White. Specifically, the graph shows the percentage of these groups who are employed. Each group’s employment follows basically the same general trend line, at different levels, but we can see some clear differences.

White employment has been the least volatile—that is, least likely to change rapidly or unpredictably from point to point. Black employment and Hispanic employment are not as steady; and, until recently, Hispanic employment has been especially volatile. These sharp upturns and downturns for Hispanic and Black workers, both in the boom-recession cycle and through the seasonal cycle, mean they are hired more quickly but are also fired more quickly.

Besides becoming less volatile, Hispanic employment has closed the gap with White employment: It had generally been between White and Black employment, but since 2000 it has most often been at the top. Black employment, however, has consistently maintained a gap of 5-10% compared with White employment.

Look to FRASER, FRED’s sibling site, for a deeper examination of historical demographics related to employment: The statistical publications “Employment and Earnings” (1954-2007) and “Women in the Labor Force: A Databook” (2004-2010) are good examples. The latter focuses mainly on differences between the sexes, but also provides statistical tables that relate to race, including one on multiple jobholders.

How this graph was created: Search for “Employment-Population Ratio” and then “Black,” “Hispanic,” and “White.”

Suggested by Emily Furlow.

View on FRED, series used in this post: LNS12300003, LNS12300006, LNU02300009

$7.25 of pay keeps the FLSA away

In 1938, the U.S. federal government passed the Fair Labor Standards Act, establishing a federal minimum wage of $0.25 per hour. Today, the federal minimum wage stands at $7.25 per hour. The FLSA doesn’t cover all workers, but it does cover those who meet certain criteria, such as those who work for businesses with annual sales or business conducted of at least $500,000. It covers those who work for hospitals and other medical or nursing care providers, schools, and government agencies. Domestic service workers and those involved in interstate commerce are covered as well.

A state minimum wage law applies to all residents within a state. If a worker isn’t covered under FLSA, they’ll receive the state minimum wage. If a worker is covered by both FLSA and state legislation, they’re generally paid the higher wage. These state standards differ across the country. Some states have no minimum wage. Some states, such as Washington, adjust their minimum wage annually according to changes in price levels.

The maps show changes in state minimum wages between 1985 (top map) and 2016 (bottom map). California and Massachusetts have the highest wage, at $10 per hour; Oregon and Connecticut also have comparatively high wages, at $9.25 and $9.60, respectively. A cluster of states, including Mississippi and Alabama, do not have a state minimum wage. In these states, if a worker meets FLSA criteria, they’re paid $7.25. If not, they’re paid whatever wage they negotiate with their employer. Finally, some states have lower minimum wages: Wyoming, for example, sets its minimum wage at $5.15, less than the FLSA rate. But one trend is consistent: Over time, the minimum wage has increased across the United States.

Many factors can contribute to the rate a state chooses to set. States’ labor forces and economies vary dramatically, so naturally the minimum wage can vary as well. Some regions, such as New England and the West Coast, have higher costs of living, which is a likely reason for higher minimum wages.

How these maps were created: From the GeoFRED site, click the “Build a new map” button at the top right. After a world map appears, use the tab on the left to customize the map. Select “State” for the region type and search for “State minimum wage rate” in the data section. To create maps for different time periods, select different years under the “Dates” section.

Suggested by Meaulnes Kenwood.

Hiring at firms, large and small

The Great Recession, with its layoffs and slow hiring, drastically decreased the employment rate. But not every firm behaved the same, and there are striking patterns across firm size. At small firms, employment fell by less and recovered to pre-recession levels more quickly than at large firms. The graph shows this consistent pattern through the firm-size distribution. (Note that the level of employment for each size category is normalized to the level in December 2007, just before the recession.)

Employment at the smallest firms (1 to 19 employees) fell by 2.7% at the nadir in June 2010 and then recovered by February 2012. Employment at the largest firms (1000+ employees) fell by 12.7% at the nadir in January 2010 and has only just recovered to pre-recession levels. The peaks are also different: At large firms, employment began declining in the spring of 2006, though it was slow at first. At the smallest firms, employment began to fall only in the autumn of 2008.

What makes small and large firms different and how does this explain the very different experiences during the Great Recession? This behavior is consistent with a job ladder across firms, a line of research explored extensively by Giuseppe Moscarini and Fabian Postel-Vinay: If larger firms tend to be more productive, they can offer higher wages and attract workers from smaller, less-productive firms. As the cycle turns downward, they no longer pursue new workers, as workers are less profitable. This means that growth at large firms slows down because they’re hiring less, but small firms stay the same size because they lose fewer workers. Yet, small firms are often thought to be more sensitive to credit conditions. While large firms can use retained earnings to fund operations to a point, small firms may require outside capital. It’s surprising, then, to see relatively robust employment levels at small firms despite difficulties in credit access associated with the Great Recession.

How this graph was created: Go to “Categories -> ADP Employment” and select “Nonfarm Private Payroll Employment” at various firm sizes: (1-19), (20-49), etc. Add the series to the graph. In the “Edit Graph” tab, change the units to “Index” and scale to December 2007. Select “Copy to All” to apply this transformation to all of the series.

Suggested by David Wiczer.

View on FRED, series used in this post: NPPTL1, NPPTL2, NPPTM, NPPTS1, NPPTS2

S’weird in Switzerland

Today is Switzerland’s national holiday, and of course FRED has Swiss data, which can be especially interesting because the Swiss economy is in many ways out of the ordinary. Previous FRED Blog posts have discussed the “peculiar” Swiss unemployment rate as well as its negative interest rates. In fact, as of today, the Swiss 50-year government bond has a negative nominal yield.

Today we look at the Swiss exchange rate. The graph shows in green the exchange rate of the Swiss franc with the euro, including a dramatic change on January 15, 2015. Unlike other countries’ exchange rate troubles, this event is actually an appreciation of the Swiss franc. Swiss franc appreciation is bad for exports, which Switzerland depends on. Because the franc has long been viewed as a refuge currency when economic trouble brews in Europe or elsewhere, it has been under a lot of appreciation pressure for some time. The Swiss National Bank has tried to cap the exchange rate at 1.20 for some time, flooding the currency markets with francs in exchange for euros and other assets. This sounds like a dream come true for any central banker: print money at will without negative consequences. Yet, this environment was unsustainable and, on January 15, 2015, the SNB decided to stop managing the exchange rate. The franc appreciated by about 20% almost immediately, and LIBOR interest rates dropped deep into negative territory. The graph shows the 3-month LIBOR in red.

How this graph was created: To create the Swiss franc/euro exchange rate, use the franc/dollar and dollar/euro exchange rates. First, search for “Franc Dollar” and graph the exchange rate. In the “Edit Graph” panel, add a series to the current line, searching for “Dollar Euro.” Apply the formula a*b. Then  search for “Franc LIBOR” and place that series on the right axis using the format tab.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: CHF3MTD156N, DEXSZUS, DEXUSEU

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