Federal Reserve Economic Data

The FRED® Blog

Reflections on net migration

The (almost) mirror image behavior of net migration for the United States and Mexico

Some people move in, and some people move out. The difference, for each country, is its net migration. This FRED graph shows the 5-year estimates of net migration for the U.S. (solid line) and Mexico (dashed line), which include both citizens and noncitizens. Notice anything about the pattern? Net migration for the U.S. increased steadily from the mid-1960s to its peak in 1997: The largest increase was between 1992 and 1997, when it started to decrease. Net migration for Mexico is the mirror image of net migration for the U.S., albeit with some lags.

During the period 1962-2012, there were more immigrants coming into the U.S. than emigrants leaving the U.S. for other countries: i.e., positive net migration for the U.S. Moreover, during this period, U.S. net migration increased (i.e., more people immigrating to the U.S. and/or less people leaving the U.S.): It was around 959,000 people in 1962 and around 4.6 million people in 1992. In those 30 years, net migration experienced an annual increase of 5.3% per year. Between 1992 to 1997, however, the annual growth was more than twice as large, around 13%.

In Mexico, net migration was negative during 1962-2012, with more people leaving Mexico than migrating into Mexico. The evolution of Mexico’s net migration has behaved as the (almost) mirror image as that for the U.S. In Mexico, there was a steady decrease in net migration between the mid-1960s to the 2000s—the biggest drop during the 1990s—and it started recovering after 2000. However, as of 2012, net migration from Mexico was still below the value of 1962.

Most immigrants arriving in the U.S. come from Mexico, so it’s not surprising that the trends in net migration for those two countries behave similarly. A new analysis by the Pew Hispanic Center has documented a sharp decrease in net migration flow from Mexico to the U.S. As a result, net migration from Mexico has fallen to almost zero in 2011. This change has been partly driven by weaker job and housing markets in the U.S. in 2010, together with stronger border adjustment.

However, the timing of changes in net migration for Mexico and the U.S. was not exactly identical. U.S. net migration started increasing before Mexican net migration started recovering. During the 1990s, there was a sharp increase of immigrants from Asia into the U.S. Indeed, according to the Pew Research Center Projections, the share of Asians among total immigrants to the U.S. has been rising above the share of Hispanic immigrants and that is expected to increase further.

How this graph was created: Search for “Net migration for the United States,” and click on the series you want to create the first line. To add line 2 to the existing graph, click “Edit Graph” and use the “Add Line” tab to search “Net migration for Mexico.” Finally, use the “Format” tab within “Edit Graph” and select “Dash” under Line 2 line style.

Suggested by Ana Maria Santacreu.

View on FRED, series used in this post: SMPOPNETMMEX, SMPOPNETMUSA

The Great Recession’s regional effects

How have different industrial compositions affected unemployment for Census regions?

Every corner of the U.S. was hard-hit by the Great Recession, but the varying makeup of local economies resulted in different effects on individual cities, states, and regions. Areas that are reliant on economic necessity—say, healthcare and waste management—may fare better during economic downturns than areas that are reliant on economic prosperity—say, tourism and construction. In 2008, the recession particularly disrupted the construction industry. According to the Bureau of Labor Statistics, the percentage of jobs lost in that industry, 19.8%, exceeded those of all other nonfarm industry supersectors.

FRED can help us see the recession’s effects on the four U.S. Census regions: Midwest, Northeast, West, and South. The annual percent change in unemployment varied most in 2008, where unemployment increased nearly 40% in the West but increased only 19.6% in the Midwest. The map below shows that some of this variation may have been the result of job losses in construction. Western states experienced declines of over 8% in the number of construction employees from 2007 to 2008. The rest of the U.S. fared better, with some Midwestern and Southern states even seeing increases in the number of construction employees.

The relatively small increase in unemployment in the Midwest may be a result of its more agriculturally based economy, which wasn’t initially hit as drastically as the shock-prone industries of tourism and construction, which are more prominent in the West. The years 2009 and 2010 saw less-significant differences in unemployment changes among regions. But data from 2011 show the continued comparative resilience of the Midwest, where the decrease in unemployment was over 11%, compared with the other regions’ 4% to 6% range. This variation in rates of recovery may also be attributable to industrial makeup: Research from the St. Louis Fed has analyzed the construction industry’s contribution to the slow overall recovery and compared it with the manufacturing industry. Research from the Minneapolis Fed notes that the construction industry took longer to recover from the recession than other industries in part because of workers’ reluctance to return to construction jobs.

How this graph was created: In FRED, search “unemployment rate census region.” Check the boxes next to the seasonally adjusted series for each of the four regions and click “Add to Graph.” Next, click “Edit Graph” and change the units to “Percent Change.” Click “Copy to all.” Modify the frequency of each line to “Annual.” Next, click the “Format” tab and change the graph type to “Bar.” Finally, adjust the dates on the graph to show from 2007-01-01 to 2011-01-01.

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 Maria Hyrc.

View on FRED, series used in this post: CMWRUR, CNERUR, CSOUUR, CWSTUR

Cost of living and per capita incomes in U.S. cities

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.



Back to Top