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Working 9 to 5: Women make up more of the workforce

A look at women in the workforce by sector

“Tumble out of bed and I stumble to the kitchen. Pour myself a cup of ambition…”  —Dolly Parton

The song and the movie 9 to 5 were released in 1980, back when women made up only 41% of employed workers in the U.S.

The Bureau of Labor Statistics has continued to collect the data and recently announced that women have broken through the 50/50 threshold in the U.S. workforce: That is, more women are employed than men. As the FRED graph above shows, this is the second time in U.S. history this threshold has been crossed. (Note that the December 2019 number is 50.0% in this graph, but a more exact value is 50.04%.)

FRED has more-specific data to help illuminate this event. To begin with, there have been more employed women than employed men in the private service-providing industry since 1985; and this share has grown over time. In the goods-producing industry, the percentage of women peaked at the end of 1991, at 27.8%.

The trends within the service-providing industry show a shifting landscape:

  • The ratio of women employees to all employees has recently increased in professional & business services and in leisure & hospitality.
  • It has remained effectively constant in education & health services and in trade, transportation & utilities.
  • And it has decreased in financial activities and information over the past 20 years or so.

Thus, it looks like the employment share of women has increased or at least persisted in sectors where women have achieved a strong presence. But it doesn’t seem like women are increasing their share in every given sector. Economists call this the composition effect. In this case, one single figure—or ratio—doesn’t tell the whole story: For example, Dolly Parton’s movie character, when heading to her job in professional & business services, is still entering a workplace where less than half of her coworkers are women.

How these graphs were created: All the data series are part of Table B-5. Employment of women on nonfarm payrolls by industry sector, Seasonally adjusted. For the first graph: Select “Total nonfarm,” click “Add to Graph,” open the “Format” tab, select “Add Line” and “Create user-defined line,” then enter 50 as the “value start/end.” The two other graphs are similar, but select “Goods producing” and “Private service-producing” and then the six different industries within the private service-providing sector. Adjust line colors and shapes in the “Format” tab so that the trends across individual industries are easier to see.

Suggested by Diego Mendez-Carbajo.

View on FRED, series used in this post: CES0000000039, CES0600000039, CES0800000039, CES4000000039, CES5000000039, CES5500000039, CES6000000039, CES6500000039, CES7000000039

Native Americans in the U.S.

A look at county-level Native American population data

FRED has added a great deal of county-level data recently, including population estimates, which include various race and ethnicity categories. These new data are especially well-suited for viewing on a map. So, of course, we look to GeoFRED: The map above shows non-Hispanic Native Americans. Note that the color scheme relates to the total number of Native Americans in each county, not in proportion to the county’s overall population.

Clearly, large numbers of Native Americans live in the West, especially the Southwest, and in Oklahoma—for obvious historical reasons that involve displacement of Native peoples. There are also quite a few counties where hardly any Native Americans live: For example, there are none in the Kansas counties of Comanche and Cheyenne. On the other side of the range, the Arizona counties of Navajo and Apache are in the top five most-populated. Unfortunately, we have no data for Oglala Lakota County in South Dakota.

How this map was created: The original post referenced an interactive map from our now discontinued GeoFRED site. The revised post provides a replacement map from FRED’s new mapping tool. To create FRED maps, go to the data series page in question and look for the green “VIEW MAP” button at the top right of the graph. See this post for instructions to edit a FRED map. Only series with a green map button can be mapped.

Suggested by Christian Zimmermann.

Globalization affects India, too

A look at trade openness and the labor income share in India

A previous post discussed the recent decrease in the labor share and increase in the capital share in GDP for many nations. Reasons for this decline in payments to labor include capital-augmenting technology growth, globalization, and changing skill composition of the labor force. In this post, we focus solely on India’s story.

In the 1990s, India began to implement a series of economic reforms that, among other things, helped open up the economy to trade and foreign investment. These policy changes reduced import tariffs and regulations, with the aim of making the economy more market-oriented. As a result, the labor share of India’s income decreased significantly.

The graph above shows that India’s trade openness, measured as the ratio of the sum of India’s exports and imports to India’s GDP, increased by nearly 124% between 1980 and 2017. (In comparison, U.S. trade openness increased by only 19% over the same period.) India’s labor share of income fell by 30%, with a 24% fall from 1990 to 2017.

So, how are trade openness and labor shares related? Trade openness often goes hand-in-hand with reforms that allow greater international mobility of capital but not labor. If higher wages would lead capital to relocate abroad, domestic labor’s bargaining power and wage increases may be limited. Also, domestic firms may face greater foreign competition and respond accordingly. If they increase the use of labor-saving technologies, that can dampen domestic wages.

Is this bad news for India’s workers? Not necessarily. All the graph shows is that trade openness and the labor share in India have been negatively correlated. This analysis doesn’t suggest labor is worse off. A declining labor share simply means that labor income growth is slower than GDP growth; it does not mean that labor income has declined. Finally, the graph doesn’t capture the effects of the tax and transfer policies of the Indian government. If trade openness increases GDP growth, it also increases tax revenues at given tax rates; this can be good for labor if they benefit more from the government’s tax and expenditure policies.

How this graph was created: Search for the series “Goods, Value of Exports for India” (FRED series ID VALEXPINM052N) and change the frequency to “Annual” with the aggregation method as “Sum.” Use the “Customize data” search bar to search for and add “Good, Value of Imports for India” (VALIMPINM052N) and “Gross Domestic Product for India, current U.S. Dollars” (MKTGDPINA646NWDB). In the formula bar, enter (a+b)/c. Then add a second line to the graph: “Share of Labor Compensation in GDP at Current National Prices for India” (LABSHPINA156NRUG). Finally, change the start date of the graph to 1980-01-01. 

Suggested by Subhayu Bandyopadhyay and Asha Bharadwaj.

View on FRED, series used in this post: LABSHPINA156NRUG, MKTGDPINA646NWDB, VALEXPINM052N, VALIMPINM052N


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