Federal Reserve Economic Data

The North American Free Trade Agreement (NAFTA), implemented more than thirty years ago in 1994, eliminated import tariffs and other trade barriers between Canada, Mexico, and the US.

Today we explore two questions central to assessing the effectiveness of that trade agreement for US trade:

1. Did the trade agreement spur trade between the US and the other member nations?
2. In the longer run, did it sustain high levels of trade between the US and the other member nations?

The FRED graph above includes data on US exports to Canada and Mexico and US imports from Canada and Mexico, both as percentage shares of US GDP. It also includes two vertical lines: one in 1994 to indicate the start of NAFTA and one in 1989 to indicate the start of the Canada-US free trade agreement, which preceded Mexico’s entry in a free-trade bloc with the US.

We don’t see a remarkable effect on US-Canada trade shares from the 1989 agreement. But we do see growth in trade shares between the US and both its trade bloc partners after NAFTA, through 2000.

So, the answer to the first question seems to be yes: At least on the basis of export- and import-to-GDP ratios, NAFTA implementation seems to have spurred US trade growth with its bloc partners.

The answer to the second question is mixed. While US trade shares with both bloc partners grew in the 1990s, the pattern in the 2000s has been different. Trade shares with Canada declined between 2000 and 2023 to such an extent that the 2023 shares are lower than the corresponding 1994 shares. US-Mexico trade shares have grown modestly or declined in recent years, although 2023 US-Mexico trade shares exceed their 1994 levels substantially. So, while US-Mexico trade gains from NAFTA have been sustained (despite the post-Great Recession global trade slowdown), US-Canada trade gains from NAFTA have not been sustained.

FYI: NAFTA was replaced by the US-Mexico-Canada Agreement in 2020.

How this graph was created: Search FRED for and select the annual series of “Gross Domestic Product.” From the “Edit Graph” panel, add “U.S. Exports of Goods by F.A.S Basis to Canada”; take the annual frequency aggregated as a sum and apply the formula (b*100)/(a*1000) (to get percentages). For imports, use the same method with “U.S. Imports of Goods by Customs Basis to Canada” and repeat this process for Mexico. For the vertical lines, use the “Add Line” tab again, but this time add two “user-defined” lines with values 0.01 and 2.49 for both 1989-01-01 and 1994-01-01.

Suggested by Hoang Le and Subhayu Bandopadhyay.

The FRED Blog has discussed the Beveridge curve, which illustrates the relationship between unemployment and job openings. In short, when the rate of unemployment is high, the rate of job openings is low. And vice versa. A scatter plot showing these paired data points has a very distinctive negatively sloped shape. Today we tap into new research from the St. Louis Fed to tell the story behind the recent numbers shaping the Beveridge curve.

The FRED graph above shows a scatter plot of monthly values of the unemployment and job openings rates, as reported by the US Bureau of Labor Statistics. Between December 2000 and October 2024, the Beveridge curve is more or less shaped like a banana, but with a long tendril during the years of the COVID-19 pandemic. A closer inspection of post-pandemic labor market data, however, shows a steady decline in the rate of job openings and only a modest increase in the rate of unemployment.

Paulina Restrepo-Echevarria and her coauthor studied why the Beveridge curve may have lost some of its shape and found that a growing proportion of job vacancies are filled by workers who are employed by other firms. Hiring currently employed workers is known as “poaching.” Since 2015, the fraction of workers poached from other firms to fill vacancies has risen significantly and has been as high as 80% of advertised job openings. The proportion of unemployed workers who have been making their way into existing jobs has gradually declined. Thus, the Beveridge curve is a bit out of shape.

For more about this and other research, visit the publications page of the St. Louis Fed’s website, which offers an array of economic analysis and expertise provided by our staff.

How this graph was created: Search FRED for and select “Job Openings: Total Nonfarm.” From the “Edit Graph” panel, use the “Add Line” tab to search for and select “Unemployment Rate.” Next, use the “Format” tab to select “Graph type: Scatter” from the drop-down menu.

Suggested by Diego Mendez-Carbajo.

The FRED Blog has tapped into US Bureau of Economic Analysis data before to discuss the small regional differences in the price of goods and the much larger regional differences in the price of housing. Today, we tap into US Bureau of Labor Statistics data to compare differences in medical care consumer prices across urban areas.

The FRED graph above shows the annual inflation rate, calculated as the percent growth rate from the previous year, in medical care service prices recorded in eight core-based statistical areas (CBSAs). These geographies are urban clusters with high degrees of social and economic integration.*

Medical care prices vary quite a bit from year to year and across regions. Consider, for example, the year 2018 and the urban areas of Denver-Aurora-Lakewood, CO, and Tampa-St. Petersburg-Clearwater, FL. The former recorded 6% annual inflation and the latter 4% annual deflation.

Does this price variability indicate there are different constraints to the demand and supply of medical care across regions? Maybe. Perhaps medical services aren’t highly mobile and local changes to the quantity and use of those services result in large price changes. On the other hand, research by James Choy at the BEA reports estimates of regional price levels for health-related goods and services that are stable across years and that vary less across regions than existing estimates obtained using CPI data. Answering this question more precisely requires more research.

* To look for data on other CBSAs, navigate FRED to Consumer Price Index by Expenditure Category > CPI for Metropolitan Areas and search the alphabetical list of geographies.

How this graph was created: Search FRED for and select “Consumer Price Index for All Urban Consumers: Medical care in Boston-Cambridge-Newton, MA-NH (CBSA).” Click “Edit Graph” and select the “Add Line” tab to search for “Consumer Price Index for All Urban Consumers: Medical care in Dallas-Fort Worth-Arlington, TX.” Don’t forget to click “Add data series.” Repeat this step to add the other six data series shown in the graph. Use the “Edit Lines” tab to select any of the lines shown in the graph. Use the “Units” dropdown menu to select “Percent Change from Year Ago” and click on “Copy to all.” Last, use the “Format” tab to change the “Graph type” to “Bar.”

Suggested by Diego Mendez-Carbajo.