A recent post introduced regional price parities (RPPs) and their applications at the state level. These RPP data are also available for metropolitan statistical areas (MSAs), which include a principal city and its surrounding area. So we can conduct a similar analysis of price levels and adjusted incomes at the metro level. Data are indexed such that the population-weighted national average is equal to 100. Of the 349 MSAs in the data, 94 fall within 5% of the national average for cost of living.

Consistent with the state-level data, the most expensive cities are heavily concentrated in the Northeast and on the West Coast. Of the 34 MSAs with a cost of living more than 5% higher than the national average, 32 are in either of those two regions. Honolulu, Hawaii (not pictured above), is the priciest MSA, with a cost of living over 24% above the national average. Inland cities are substantially cheaper, especially in the Midwest and the South. In Rome, Georgia, the least costly MSA, the cost of living is about 20 percent lower than the national average.

One implication of these regional cost of living differences is that a dollar in one city isn’t necessarily the same as a dollar in another: Average per capita personal income nationwide is about $43,996. In terms of purchasing power, the equivalent income in St. Louis, Missouri, is below $40,000 due to the relatively low cost of living. Meanwhile, in comparatively expensive New York, New York, the equivalent income is almost $54,000. In other words, as the cost of living goes up, it takes more dollars to buy the same basket of goods and services. Hence, using the RPPs to adjust per capita personal income for cost of living yields a more accurate measure of how much the average person in a given city can consume.

As seen on the next map, real (cost-of-living-adjusted) per capita personal income is more broadly dispersed geographically than the RPPs: 40 MSAs across 32 different states have a real per capita personal income more than 10% greater than the national average. Midland, Texas, has the highest income, at almost $96,000 (adjusted dollars) per person.

Given that we derive real per capita personal income by dividing nominal income by cost of living, one might expect that the most costly cities have the lowest adjusted incomes. While that is sometimes true, it’s not always the case. For example, San Jose and San Francisco, California, and Bridgeport, Connecticut, are each in the top ten for both most expensive cities and highest-earning cities. Similarly, McAllen, Texas, has the lowest real per capita personal income of any MSA, about $27,000, despite being one of the 20 cheapest cities in the country.

How these maps were created: The original post referenced interactive maps from our now discontinued GeoFRED site. The revised post provides replacement maps 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 Andrew Spewak and Charles Gascon.

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.