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Women worldwide in the labor force

How does the participation of women relate to a nation’s overall employment ratio?

A nation’s employment-to-population ratio can provide an indicator of the health of its labor markets—specifically, how much of the workforce participates in the formal economy. The map shows worldwide employment-to-population ratios in 2016, where lighter-colored countries have higher employment ratios. The data reflect the proportion of the working age population in various countries employed during the reference period. However, the World Bank advises that nations vary in their definitions of working age, whether they include armed services personnel and the institutionalized in their counts, and how women view their employment status based on cultural norms. The United Nations reports that, globally, women’s involvement in the labor force is only 50%, whereas men’s is 77%; yet, women work longer hours when unpaid work is accounted for—which it is not in employment-to-population ratio data. Given all of these complexities, comparisons of employment ratios between nations have their limitations.

Overall, the nations with the highest ratios of employed individuals to the overall population tend to be smaller, such as those in Southeast Asia and Central Africa, with many reporting ratios over 70%. Nations surrounding the Mediterranean Sea have some of the lowest employment ratios, most below or near 40%. While purely economic factors may explain some of the discrepancies, a look at other employment-related indicators may shed some light on the factors at play.

Indeed, nations with high employment ratios also have some of the highest female labor force participation rates (as a percentage of the total female population), according to the World Bank. For example, in Uganda, where the employment-to-population ratio stood at 83.05% in 2016, the third highest worldwide, the female labor force participation rate was 82.33%, the 5th highest worldwide. The reverse also appears to be true: Many nations with low ratios, especially in North Africa and the Middle East, have far lower labor force participation rates for females than the rest of the world.

Comparisons of the GeoFRED and World Bank maps illuminate a clear correlation, which can be analyzed using a linear regression. For all nations with 2016 data on both the employment ratio and the female labor force participation rate, the correlation coefficient between the two variables is 0.82, meaning if one is high in a country, the other is very likely to be high as well. While the relationship may seem obvious, it has important implications for developing economies seeking to increase their overall employment ratio.

How these maps were 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 Maria Hyrc.

A review of labor market conditions

The U.S. unemployment rate stands at 4.3 percent, a value slightly lower than at the peak of the expansion in 2007. This is a sign of a very healthy labor market. The question is to what extent do other indicators of labor market health paint a similar picture.

FRED has recently added several data series that capture various measures of labor market tightness. A very tight labor market means that employers have a harder time filling open positions because most workers are employed and fewer are looking for jobs. There are several ways to capture labor market tightness: In the following graphs, we present a few of them and compare their evolution before and after the previous recession.

The first graph shows the vacancy-to-unemployment ratio and the quits rate. The blue line (left axis) is the number of vacancies per unemployed worker. When the economy enters recession, this measure declines as the number of unemployed workers increases and the vacancies per unemployed worker decrease. A low number of vacancies per unemployed worker is a sign of slack in the labor market. After the 2007-09 recession, this ratio increased at a slow pace until 2014, when it increased sharply and surpassed its pre-recession high. The red line (right axis) is the number of quits per employed worker. Similar to the vacancy ratio, this indicator declines in recessionary periods. Within the past few months the quit rate has recovered to pre-recession levels.

The second graph shows the mean level of vacancy duration and an index of recruiting intensity per vacancy. In a tight labor market, employers will have to look harder, or more intensely, to fill open positions as the number of unemployed candidates is reduced. Similarly, vacancy durations will be higher as recruiting efforts take longer in a tight labor market. Since the 2007-09 recession, vacancy durations have surpassed pre-recession levels, reaching a series high of 29.6 business days per vacancy in April 2016. The recruiting intensity index is close to its pre-recession level, but has not increased as quickly as vacancy durations.

Overall, the different indicators of labor market conditions analyzed here point to a healthy recovery of the U.S. labor market.

How these graphs were created: Top graph: Search for “Vacancy to Unemployment Ratio” in FRED and graph the series with the copyright symbol in the title (copyrighted by DHI Group Inc. and Dr. Steven J. Davis). Then click the orange “Edit Graph” button and add a line using the middle button on the top of the menu that appears to the right. Search for “Quits Rate” in the box and add the series with the copyright symbol. Finally, click the “Format” button on the menu and below Line 2 select the option to change the y-axis position to the right. Bottom graph: Repeat these steps, but use DHI-DFH Mean Vacancy Duration and DHI-DFH Index of Recruiting Intensity per Vacancy.

Suggested by Maximiliano Dvorkin and Hannah Shell.

View on FRED, series used in this post: DHIDFHIRIPV, DHIDFHMVDM, DHIDFHQTRT, DHIDFHVTUR

Regional price parities

How the cost of living differs across states

A well-known fact in the ordinary business of life is that the value of money doesn’t stay the same: The amount of goods and services you can buy with $100 today is far less than what you could buy 30 years ago. A somewhat similar comparison can be made about the purchasing power of $100 in different places. The amount of goods and services you can buy in a developed country, such as the United States, is far less than what you can buy in a developing country, such as India. Here, we analyze this latter phenomenon but within the United States.

Regional price parities (RPPs) measure the differences in the price levels of goods and services across states and metropolitan areas for a given year. RPPs are expressed as a percentage of the overall national price level for each year, so RPPs higher than 100 represent state prices higher than the national average and vice versa. The map shows the price parities in 2015 for each U.S. state. In general, price levels are lower in the middle section of the country and get higher on the east and west coasts.

In 2015, Hawaii’s RPP (118.8) was higher than that of any state. The other locations with the highest RPPs were District of Columbia (117.0), New York (115.3), California (113.4), and New Jersey (113.4). Kentucky (88.6), South Dakota (88.2), Arkansas (87.4), Alabama (86.8), and Mississippi (86.2) had the lowest RPPs among the states. States with RPPs closest to the national average price level were Vermont (101.6), Delaware (100.4), Illinois (99.7), Florida (99.5), and Oregon (99.2).

RPPs are important because they help inform the purchasing power behind a person’s income in different areas of the country. For example, an income of $47,520 in Hawaii has the equivalent purchasing power of an income of $36,480 in Mississippi because both of these incomes divided by the state’s RPP equal $40,000.

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 Maximiliano Dvorkin and Hannah Shell.



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