Summary and key findings

The United States has a massive trade deficit with China. The growth of the U.S. annual trade deficit with China, which has increased by more than $150 billion since the beginning of the Great Recession, is a significant reason why manufacturing employment has not fully recovered along with the rest of the economy. And the growing trade deficit with China isn’t just a post-recession phenomenon hitting manufacturing: it has cost the U.S. millions of jobs throughout the economy since China entered the World Trade Organization (WTO) in 2001, a finding validated by numerous studies.

This report underscores the ongoing trade and jobs crisis by updating EPI’s research series on the jobs impact of the U.S.–China trade deficit. The most recent of these reports (Kimball and Scott 2014; Scott 2017a; and Scott and Mokhiber 2018) look at the effect of the U.S. trade deficit with China since China entered the WTO in 2001. Our model examines the job impacts of trade by subtracting the job opportunities lost to imports from those gained through exports. As with our previous analyses, we find that because imports from China have soared while exports to China have increased much less, the United States is both losing jobs in manufacturing (in electronics and high tech, apparel, textiles, and a range of heavier durable goods industries) and missing opportunities to add jobs in manufacturing (in exporting industries such as transportation equipment, agricultural products, computer and electronic parts, chemicals, machinery, and food and beverages).

The growing trade deficit with China since China entered the WTO affects different regions in different ways. Some regions are devastated by layoffs and factory closings while others are surviving but not growing the way they could be if new factories were opening and existing plants were hiring more workers. This slowdown in manufacturing job generation is also contributing to stagnating wages of typical workers and widening income inequality.

Following are the key highlights of this report:

U.S. jobs lost are spread throughout the country but are concentrated in manufacturing, including in industries in which the United States has traditionally held a competitive advantage.

The growth of the U.S. trade deficit with China between 2001 and 2018 was responsible for the loss of 3.7 million U.S. jobs, including 1.7 million jobs lost since 2008 (the first full year of the Great Recession, which technically began at the end of 2007). Three-fourths (75.4%) of the jobs lost between 2001 and 2018 were in manufacturing (2.8 million manufacturing jobs lost due to the growth in the trade deficit with China).

including 1.7 million jobs lost since 2008 (the first full year of the Great Recession, which technically began at the end of 2007). Three-fourths (75.4%) of the jobs lost between 2001 and 2018 were in manufacturing (2.8 million manufacturing jobs lost due to the growth in the trade deficit with China). Trade deficits with China and resulting jobs losses continued to grow during the first two years of the Trump administration —despite the administration’s heated rhetoric and imposition of tariffs. The U.S. trade deficit with China rose from $347 billion in 2016 to $420 billion in 2018, an increase of 21.0%. U.S. jobs displaced by China trade deficits increased from nearly 3.0 million in 2016 to 3.7 million in 2018,—a total of more than 700,000 jobs lost or displaced in the first two years of the Trump administration.

—despite the administration’s heated rhetoric and imposition of tariffs. The U.S. trade deficit with China rose from $347 billion in 2016 to $420 billion in 2018, an increase of 21.0%. U.S. jobs displaced by China trade deficits increased from nearly 3.0 million in 2016 to 3.7 million in 2018,—a total of more than 700,000 jobs lost or displaced in the first two years of the Trump administration. The growing trade deficit with China has cost jobs in all 50 states and in every congressional district in the United States. The 10 hardest-hit states, when looking at job loss as a share of total state employment, were New Hampshire, Oregon, California, North Carolina, Minnesota, Massachusetts, Wisconsin, Vermont, and Indiana, and Idaho. Job losses in these states ranged from 2.82% (in Idaho) to 3.66% (in New Hampshire) of total state employment. The five hardest-hit states based on total jobs lost were California (654,100 jobs lost), Texas (334,800), New York (185,100), Illinois (162,400), and Florida (150,700).

The 10 hardest-hit states, when looking at job loss as a share of total state employment, were New Hampshire, Oregon, California, North Carolina, Minnesota, Massachusetts, Wisconsin, Vermont, and Indiana, and Idaho. Job losses in these states ranged from 2.82% (in Idaho) to 3.66% (in New Hampshire) of total state employment. The five hardest-hit states based on total jobs lost were California (654,100 jobs lost), Texas (334,800), New York (185,100), Illinois (162,400), and Florida (150,700). The trade deficit in the computer and electronic parts industry grew the most, and that is reflected in job losses: 1,340,600 jobs were lost in that industry, accounting for 36.2% of the 2001–2018 total jobs lost. Not surprisingly, the hardest-hit congressional districts (those ranking in the top 20 districts in terms of jobs lost as a share of all jobs in the district) included districts in California, Massachusetts, Minnesota, Oregon, and Texas, where jobs in that industry are concentrated. One district in Georgia and two others in North Carolina were also especially hard hit by trade-related job displacement in a variety of manufacturing industries, including computer and electronic parts, textiles and apparel, and furniture.

1,340,600 jobs were lost in that industry, accounting for 36.2% of the 2001–2018 total jobs lost. Not surprisingly, the hardest-hit congressional districts (those ranking in the top 20 districts in terms of jobs lost as a share of all jobs in the district) included districts in California, Massachusetts, Minnesota, Oregon, and Texas, where jobs in that industry are concentrated. One district in Georgia and two others in North Carolina were also especially hard hit by trade-related job displacement in a variety of manufacturing industries, including computer and electronic parts, textiles and apparel, and furniture. Surging imports of steel, aluminum, and other capital-intensive products threaten hundreds of thousands of jobs in key industries such as primary metals, machinery, and fabricated metal products as well. These three sectors, alone, have already lost 372,700 jobs due to growing trade deficits with China between 2001 and 2018.

in key industries such as primary metals, machinery, and fabricated metal products as well. These three sectors, alone, have already lost 372,700 jobs due to growing trade deficits with China between 2001 and 2018. Global trade in advanced technology products—often discussed as a source of comparative advantage for the United States—is instead dominated by China. This broad category of high-end technology products includes the more advanced elements of the computer and electronic parts industry as well as other sectors such as biotechnology, life sciences, aerospace, and nuclear technology. In 2018, the United States had a $134.6 billion trade deficit in advanced technology products with China, and this deficit was responsible for 32.1% of the total U.S.–China goods trade deficit that year. In contrast, the United States had a $6.5 billion trade surplus in advanced technology products with the rest of the world in 2018. The U.S. balance of trade in advanced technology products declined by $132.7 billion between 2001 and 2018.

This broad category of high-end technology products includes the more advanced elements of the computer and electronic parts industry as well as other sectors such as biotechnology, life sciences, aerospace, and nuclear technology. In 2018, the United States had a $134.6 billion trade deficit in advanced technology products with China, and this deficit was responsible for 32.1% of the total U.S.–China goods trade deficit that year. In contrast, the United States had a $6.5 billion trade surplus in advanced technology products with the rest of the world in 2018. The U.S. balance of trade in advanced technology products declined by $132.7 billion between 2001 and 2018. China is exporting goods to the U.S. through other countries. Although the bilateral trade deficit with China has declined in 2019 (through November), the overall U.S. trade deficit in non-oil goods, which is dominated by trade in manufactured and farm products, has continued to increase, as has China’s overall balance of trade with the rest of the world, suggesting that trade diversion (Chinese goods, and parts and materials being used in products, that the U.S. imports from other countries) has grown in importance. This is an important topic for future research.

Growing trade deficits are also associated with wage losses not just for manufacturing workers but for all workers economywide who don’t have a college degree.

Between 2001 and 2011 alone, growing trade deficits with China reduced the incomes of directly impacted workers by $37 billion per year, and in 2011 alone, growing competition with imports from China and other low wage-countries reduced the wages of all U.S. non–college graduates by a total of $180 billion. Most of that income was redistributed to corporations in the form of higher profits and to workers with college degrees at the very top of the income distribution through higher wages.

The U.S. trade deficit with China has increased since China entered into the WTO

U.S. proponents of admitting China into the World Trade Organization frequently claimed that letting China into the WTO would increase U.S. exports, shrink the U.S. trade deficit with China, and create jobs in the United States. In 2000, President Bill Clinton claimed that the agreement then being negotiated to allow China into the WTO would create “a win-win result for both countries.” Exports to China “now support hundreds of thousands of American jobs,” said Clinton, and these figures “can grow substantially with the new access to the Chinese market the WTO agreement creates” (Clinton 2000, 9–10).

China’s entry into the WTO in 2001 was supposed to bring it into compliance with an enforceable, rules-based regime that would require China to open its markets to imports from the United States and other nations by reducing Chinese tariffs and addressing nontariff barriers to trade. Promoters of liberalized U.S.–China trade argued that the United States would benefit because of increased exports to a large and growing consumer market in China. The United States also negotiated a series of temporary special safeguard measures designed to limit the disruptive effects of surging imports from China on domestic producers.

However, China’s trade-distorting practices, aided by China’s currency manipulation and misalignment and its suppression of wages and labor rights, resulted in a flood of dumped and subsidized imports that greatly exceeded the growth of U.S. exports to China. These trade-distorting practices included extending large subsidies to industries such as steel, glass, paper, concrete, and renewable energy industries and rapidly growing its state-owned enterprises, both of which generated a massive buildup of excess capacity in a range of these sectors. This excess capacity created a supply of goods far exceeding Chinese consumer demand, and China dealt with the oversupply by dumping the exports elsewhere, primarily in the United States (Scott 2017a, Scott and Mokhiber 2018).

The promised surge of U.S. exports to China was also hampered by China’s failure to implement certain policies to increase domestic demand for goods, including goods produced by trading partners. Specifically, for China to become a better market for U.S. exports, it needed to stimulate the growth of domestic consumption through policies that would allow workers to organize and bargain collectively, thus raising wages. China also needed to increase domestic consumption through increased social spending and reductions to the country’s massive savings rate (Scott 2017a). Such policies are all elements of a program of domestic, demand-led growth that the United States, other advanced countries, and international agencies have called on China to implement for many years. But none of these policies have been implemented at anywhere near a large enough scale, and China’s national savings rate has actually increased significantly over the past 15 years (Setser 2016; IMF 2019), which has contributed to the growth of U.S. trade deficits (Bernstein 2016).

In addition, China’s policies spurred foreign direct investment (FDI) in Chinese enterprises and the outsourcing of U.S. manufacturing plants, which has expanded China’s manufacturing sector at the expense of the U. S. manufacturing sector, thereby affecting the trade balance between the two countries. Finally, the core of the WTO agreement failed to include any protections to maintain or improve labor or environmental standards or to prohibit currency manipulation. (The descriptions in this paragraph derive from Scott 2017a.)

As a result of these forces, the U.S. trade deficit with China soared after China entered the WTO.

Table 1 displays changes in the U.S.–China goods trade deficit and job displacement from 2001 to 2018 (when the term “trade deficit” is used in this report, it always refers to the goods trade deficit). As the table shows, imports from China increased dramatically in this period, rising from $102.3 billion in 2001 to $539.2 billion in 2018. U.S. exports to China rose at a rapid rate from 2001 to 2018, but from a much smaller base, from $19.2 billion in 2001 to $120.1 billion in 2018. As a result, China’s exports to the United States in 2018 (“U.S. general imports”) were more than four times greater than U.S. exports to China. These trade figures make the China trade relationship the United States’ most imbalanced trade relationship by far (authors’ analysis of USITC 2019).

Table 1 U.S.–China goods trade and job displacement, 2001–2018 Change ($billions) Percent change 2001 2008 2018 2001–2018 2008–2018 2001–2018 2008–2018 U.S. goods trade with China ($billions, nominal) U.S. total exports* $19.2 $71.5 $120.1 $100.9 $48.7 524.6% 68.1% U.S. general imports $102.3 $337.8 $539.7 $437.4 $201.9 427.6% 59.8% U.S. trade balance ‑$83.0 ‑$266.3 -$419.5 -$336.5 -$153.2 405.2% 57.5% Average annual change in the trade balance -$19.8 -$15.3 10.0% Change (thousands of jobs) Percent change U.S. trade-related jobs supported and displaced (thousands of jobs) U.S. total exports—jobs supported 175.8 561.9 885.9 710.1 324.0 403.9% 57.7% U.S. general imports—jobs displaced 1,132.5 3,548.9 5,547.2 4,414.8 1,998.4 389.8% 56.3% U.S. trade deficit—net jobs displaced 956.7 2,987.0 4,661.4 3,704.7 1,674.3 387.3% 56.1% Average annual change in net jobs displaced 217.9 167.4 9.8% * Total exports as reported by the U.S. International Trade Commission include re-exports. The employment estimates shown here are based on total exports. See note 2 for additional details Source: Authors’ analysis of American Community Survey (ACS) data (U.S. Census Bureau 2019a and 2020a), USITC 2019, and Bureau of Labor Statistics Employment Projections program 2019a and 2019b. For a more detailed explanation of data sources and computations, see the appendix. Share on Facebook Tweet this chart Embed Copy the code below to embed this chart on your website. Download image

Overall, the U.S. goods trade deficit with China grew from $83.0 billion in 2001 to $419.5 billion in 2018, an increase of $336.5 billion. Put another way, since China entered the WTO in 2001, the U.S. trade deficit with China has increased annually by $19.8 billion, or 10.0%, on average. Although not shown in the table, we can also examine the trade deficit in another way—not by how much it grew annually, but by adding up what the total deficit was each year to produce a cumulative figure. The data reveal that the cumulative U.S. trade deficit with China over the 2002–2018 (post-WTO) era was $4.7 trillion (USITC 2019 and authors’ calculations).

Between 2008 and 2018, the U.S. goods trade deficit with China increased $153.2 billion. This 57.5% increase occurred despite the Great Recession–induced collapse in world trade between 2008 and 2009 and the 17.7% decline in the U.S. trade deficit with the rest of the world between 2008 and 2018. As a result, China’s share of the overall U.S. goods trade deficit increased from 31.6% in 2008 to 47.2% in 2018. (The figures in this paragraph derive from the authors’ analysis of USITC 2019 and U.S. Census Bureau 2019d.)

Although the bilateral trade deficit with China declined in 2019 (through November), the overall U.S. trade deficit in non-oil goods, which is dominated by trade in manufactured and farm products, has continued to increase (U.S. Census Bureau 2020c), as has China’s overall balance of trade with the rest of the world (Setser 2019), suggesting that trade diversion—Chinese goods, and parts and materials being used in products, that the U.S. imports from other countries—has grown in importance. This is an important topic for future research.

The growing trade deficit with China has led to U.S. job losses

Each $1 billion in exports to another country from the United States supports some American jobs. However, each $1 billion in imports from another country leads to job loss—by eliminating existing jobs and preventing new job creation—as imports displace goods that otherwise would have been made in the United States by domestic workers. The net employment effect of trade depends on the changes in the trade balance. An improving trade balance can support job creation, but a growing trade deficit usually results in growing net U.S. job displacement.

The model used in this report (described in the box below, and Methodology Appendix) estimates the amount of labor (number of jobs) required to produce a given volume of exports and the labor displaced when a given volume of imports is substituted for domestic output. The difference between these two numbers is essentially the jobs displaced by the growing trade deficit, holding all else equal.

The net change in the U.S.–China trade balance between 2001 and 2018 also reflects the effect of trade in intermediate products between the two countries on net trade flows and job losses (intermediate products are parts and materials used to assemble final products). This is what has occurred with China since it entered the WTO; the United States’ widening trade deficit with China has been costing U.S. jobs. While some imports of parts and components from China have gone into the production of final goods, some of which have then been exported to China and the rest of the world, the overall U.S. trade deficit in manufactured products with China and with the world as a whole has grown substantially since China entered the WTO.

This paper describes the net effect of the growing U.S.–China goods trade deficit (hereafter referred to as the U.S.–China trade deficit) on employment as jobs “lost or displaced,” with the terms “lost” and “displaced” used interchangeably. The employment impacts of the growing U.S. trade deficit with China are estimated in this paper using an input-output model that estimates the direct and indirect labor requirements of producing output in a given domestic industry. The model includes 205 U.S. industries, 76 of which are in the manufacturing sector (see the box titled “Trade and employment models,” as well as the appendix, for details on model structure and data sources). The Bureau of Labor Statistics Employment Projections program (BLS-EP) revised and updated its labor requirements model and related data in October 2019 (BLS-EP 2019a, 2019b). Our models have been revised and updated for this report using the latest available data.

We have also updated all Census data used to allocated jobs by industry and congressional district, as described in the Methodology Appendix. The new estimates reflect the congressional districts in effect for the 115th Congress (which met between January 3, 2017 and January 3, 2019).

Trade and employment models The Economic Policy Institute and other research entities have examined the job impacts of trade in recent years by subtracting the job opportunities lost to imports from those gained through exports. That general approach is used in this report. Specifically, this report uses standard input-output models and data to estimate the jobs displaced by trade. Many economists in the public and private sectors have used this type of all-but-identical methodology to estimate jobs gained or displaced by trade, including Groshen, Hobijn, and McConnell 2005 of the Federal Reserve Bank of New York and Bailey and Lawrence 2004 in the Brookings Papers on Economic Activity. The U.S. Department of Commerce has published estimates of the jobs supported by U.S. exports (Tschetter 2010). That study uses input-output and “employment requirements” tables from the Bureau of Labor Statistics Employment Projections program (earlier editions of BLS-EP 2019a), the same source used to develop job displacement estimates in this report. The Tschetter report represents the work of a panel of experts from 20 federal agencies.

Jobs displaced by the United States’ growing trade deficit with China are a net drain on employment in trade-related industries, especially those in manufacturing. Even if increases in demand in other sectors absorb all the workers displaced by trade (which is unlikely), job quality will likely suffer because many nontraded industries such as retail trade and home health care pay lower wages and have less comprehensive benefits than traded-goods industries (Scott 2013, Scott and Mokhiber 2018).

As shown in the bottom panel of Table 1, U.S. exports to China in 2001 supported 175,800 jobs, but U.S. imports displaced production that would have supported 1,132,500 jobs. Therefore, the $83.0 billion trade deficit in 2001 displaced 956,700 jobs in that year. Net job displacement rose to 2,987,000 jobs in 2008 and 4,661,400 jobs in 2018. As a result, since China’s entry into the WTO in 2001 and through 2018, the increase in the U.S.–China trade deficit eliminated or displaced 3,704,700 U.S. jobs. Also shown in Table 1, the U.S. trade deficit with China increased by $153.2 billion (or 57.5%) between 2008 and 2018. During that period, the number of jobs displaced increased by 1,674,300 (or 56.1%).

For comparative purposes, the growth of the U.S.–China trade deficit between 2001 and 2018 represents a direct loss of 1.6% of U.S. GDP in 2018 (authors’ analysis of BEA 2019). Using a macroeconomic model with standard economic multipliers (see Appendix: Methodology in Scott and Glass 2016 for further details) yields an estimate of 3.4 million jobs displaced by a trade deficit of this magnitude, providing further support for the job displacement estimates shown in Table 1.

Total jobs lost or displaced between 2008 and 2018 alone increased by 1,674,300 either by the elimination of existing jobs or by the prevention of new job creation through the displacement of domestic production by imports.

The total number of jobs displaced by the growing U.S.–China trade deficit, as estimated here, is thus directly proportional to the size of the total bilateral deficit, which has increased steadily throughout the 2001–2018 period, except for a sharp decline in the recession year of 2009 and a much smaller drop in 2016. Figure A shows visually how rising trade deficits have displaced a growing number of jobs every year since China joined the WTO, with the exception of 2009 (during the Great Recession) and 2016 (during a brief lull in imports from China). On average, 232,000 jobs per year have been lost or displaced since China’s entry into the WTO (as shown in Table 1, last row, and data column four).

Figure A U.S. jobs displaced by the growing goods trade deficit with China since 2001 (in thousands of jobs) Year Jobs displaced (thousands) 2001 0.0 2002 218.1 2003 445.7 2004 852.1 2005 1,306.1 2006 1,651.5 2007 1,964.5 2008 2,030.4 2009 1,686.2 2010 2,295.0 2011 2,616.8 2012 2,764.6 2013 2,812.3 2014 2,993.2 2015 3,197.9 2016 2,965.2 2017 3,339.8 2018 3,704.7 Chart Data Download data The data below can be saved or copied directly into Excel. The data underlying the figure. Source: Authors’ analysis of USITC 2019, and Bureau of Labor Statistics Employment Projections program 2019a and 2019b. For a more detailed explanation of data sources and computations, see the appendix. Share on Facebook Tweet this chart Embed Copy the code below to embed this chart on your website. Download image

Trade deficits with China and the resulting jobs losses continued to grow during the first two years of the Trump administration—despite the administration’s heated rhetoric and imposition of tariffs. The U.S. trade deficit with China rose from $347 billion in 2016 to $420 billion in 2018, an increase of 21.0%. U.S. jobs displaced by trade deficits with China increased from nearly 3.0 million in 2016 to 3.7 million in 2018, resulting in more than 700,000 jobs lost or displaced in the first two years of the Trump administration, as shown in Figure A (Scott 2020).

The continuing growth of job displacement between 2008 and 2018 slightly outpaced the increase in the bilateral trade deficit in this period because of the relatively rapid growth of U.S. imports of computer and electronic parts from China, discussed below, and the fact that the price index for most of these products fell continuously throughout the study period. The share of U.S. imports from China accounted for by computer and electronic parts (in current, nominal dollars) increased from 32.0% in 2008 to 34.5% in 2018 (according to the authors’ analysis of USITC 2019).

Unfortunately, growing job losses due to outsourcing and growing trade deficits with China are only part of the story.

Next we turn to analysis of direct China trade and job loss in more detail.

The trade deficit and job losses, by industry

The composition of imports from China is changing in fundamental ways, with significant, negative implications for certain kinds of high-skill, high-wage jobs once thought to be the hallmark of the U.S. economy. Since it entered the WTO in 2001, China has moved rapidly “upscale,” from low-tech, low-skill, labor-intensive industries such as apparel, footwear, and basic electronics to more capital- and skills-intensive industries such as computers, electrical machinery, and motor vehicle parts. It has developed a rapidly growing trade surplus in these specific industries and in high-technology products in general.

Table 2 provides a snapshot of the changes in U.S.–China goods trade flows between 2001 and 2018, by industry, for imports, exports, and the trade balance. The rapid growth of the bilateral trade deficit in computer and electronic parts (including computer and peripheral equipment, semiconductors, and audio and video equipment) accounted for 44.3% of the $336.5 billion increase in the U.S. trade deficit with China between 2001 and 2018. In 2018, the total U.S. trade deficit with China was $419.5 billion—$168.2 billion of which was in computer and electronic parts (trade flows by industry in 2001 and 2018 are shown in Supplemental Table 1, available at the end of this document).

Table 2 Change in U.S. goods trade with China, by industry, 2001–2018 U.S. imports U.S. exports Trade balance Industry* Change ($billions, nominal) Share of total change Change ($billions, nominal) Share of total change Change ($billions, nominal) Share of total change Total change 437.4 100.0% 100.9 100.0% -336.5 100.0% Agriculture, forestry, fishing, and hunting 2.4 0.6% 7.0 6.9% 4.6 -1.4% Mining -0.1 0.0% 8.3 8.3% 8.4 -2.5% Oil and gas -0.1 0.0% 7.1 7.0% 7.2 -2.1% Minerals and ores 0.1 0.0% 1.3 1.3% 1.2 -0.4% Manufacturing 434.8 99.4% 82.6 81.8% -352.2 104.7% Nondurable goods 88.9 20.3% 21.8 21.6% -67.2 20.0% Food and kindred products 3.6 0.8% 2.2 2.2% -1.4 0.4% Beverage and tobacco products 0.1 0.0% 0.1 0.1% 0.0 0.0% Textile mills and textile product mills 13.1 3.0% 0.4 0.4% -12.7 3.8% Apparel, leather, and allied product manufacturing 29.3 6.7% 0.5 0.5% -28.8 8.6% Paper 3.0 0.7% 2.2 2.2% -0.8 0.2% Printed matter and related products 2.3 0.5% 0.1 0.1% -2.2 0.6% Petroleum and coal products 0.5 0.1% 1.0 1.0% 0.5 -0.1% Chemicals 19.6 4.5% 13.9 13.8% -5.6 1.7% Plastics and rubber products 17.5 4.0% 1.4 1.4% -16.1 4.8% Durable goods 345.8 79.1% 60.8 60.3% -285.0 84.7% Wood products 3.2 0.7% 1.5 1.5% -1.7 0.5% Nonmetallic mineral products 6.7 1.5% 0.7 0.7% -5.9 1.8% Primary metal 3.2 0.7% 2.9 2.8% -0.3 0.1% Fabricated metal products 22.5 5.1% 2.1 2.1% -20.4 6.1% Machinery 34.2 7.8% 8.6 8.5% -25.7 7.6% Computer and electronic parts 161.8 37.0% 12.7 12.6% -149.1 44.3% Computer and peripheral equipment 51.6 11.8% 0.7 0.7% -50.9 15.1% Communications, audio and video equipment 81.7 18.7% 0.5 0.5% -81.2 24.1% Navigational, measuring, electromedical, and control instruments 5.4 1.2% 5.0 4.9% -0.5 0.1% Semiconductor and other electronic components, and reproducing magnetic and optical media 23.1 5.3% 6.6 6.5% -16.5 4.9% Electrical equipment, appliances, and components 41.0 9.4% 2.9 2.9% -38.1 11.3% Transportation equipment 20.2 4.6% 24.9 24.7% 4.7 -1.4% Motor vehicles and motor vehicle parts 17.2 3.9% 9.2 9.1% -8.0 2.4% Aerospace products and parts 0.9 0.2% 15.6 15.5% 14.7 -4.4% Railroad, ship, and other transportation equipment 2.1 0.5% 0.1 0.1% -2.1 0.6% Furniture and related products 20.8 4.8% 0.2 0.2% -20.7 6.1% Miscellaneous manufactured commodities 32.1 7.3% 4.4 4.3% -27.7 8.2% Scrap and secondhand goods 0.2 0.1% 3.0 3.0% 2.7 -0.8% * Excludes utilities, construction, and service sectors, which reported no goods trade in this period, and information, which reported negligible goods trade in this period. Source: Authors’ analysis of USITC 2019. For a more detailed explanation of the data sources and computations, see the appendix. Share on Facebook Tweet this chart Embed Copy the code below to embed this chart on your website. Download image

As evident in the increasing trade deficit and also shown in Table 2, imports from China far exceeded exports to China between 2001 and 2018. Table 2 further shows that the growth in manufactured goods imports explained virtually all (99.4%) of total growth in imports from China between 2001 and 2018 and included a wide array of products. Computer and electronic parts were responsible for 37.0% of the growth in imports in this period, including computer equipment ($51.6 billion, or 11.8% of the overall growth in imports) and communications, audio, and video equipment ($81.7 billion, or 18.7%). Other major importing sectors included electrical equipment ($41.0 billion, or 9.4%), machinery ($34.2 billion, or 7.8%), apparel ($29.3 billion, or 6.7%) and miscellaneous manufactured commodities ($32.1 billion, or 7.3%).

As Table 2 shows, manufacturing was also the top sector exporting to China—81.8% of the growth in exports to China between 2001 and 2018 was in manufactured goods, totaling $82.6 billion. Within manufacturing, key export-growth industries included chemicals ($13.9 billion, or 13.8% of the growth in exports), aerospace products and parts ($15.6 billion, or 15.5%), motor vehicles and parts ($9.2 billion, or 9.1%), computer and electronic parts ($12.7 billion, or 12.6%), and machinery ($8.6 billion, or 8.5%). Scrap and secondhand goods industries—which support no production jobs, according to the models used in this report (BLS-EP 2019a)—accounted for 3.0% ($3.0 billion) of the growth in exports.

Agricultural exports—dominated by corn, soybeans, and other cash grains—dropped sharply in 2018 (down $10.2 billion or 55.0% ) but increased $7.0 billion (6.9% of the total increase) between 2001 and 2018. Nonetheless, the overall scale of U.S. total exports to China in 2018 was dwarfed by imports from China in that year, which exceeded the value of exports by more than 4 to 1, as shown in Table 1.

The import data in Table 2 reflect China’s rapid expansion into higher-value-added commodities once considered strengths of the United States, such as computer and electronic parts, which accounted for 34.5% ($186.2 billion) of U.S. imports from China in 2018 (as shown in Supplemental Table 1). This growth is apparent in the shifting trade balance in advanced technology products (ATP), a broad category of high-end technology goods trade tracked by the U.S. Census Bureau (but not broken out in Table 2, which uses U.S. International Trade Commission data). ATP includes the more advanced elements of the computer and electronic parts industry as well as other sectors such as biotechnology, life sciences, aerospace, nuclear technology, and flexible manufacturing. The ATP sector includes some auto parts; China is one of the top suppliers of auto parts to the United States, having surpassed Germany (Scott and Wething 2012).

In 2018, the United States had a $134.6 billion trade deficit with China in ATP, reflecting more than a tenfold increase from $11.8 billion in 2002. This ATP deficit was responsible for 32.1% of the total U.S.–China trade deficit in 2018. It dwarfs the $6.5 billion surplus in ATP that the United States had with the rest of the world in 2018. As a result of the U.S. ATP deficit with China, the United States ran an overall deficit in ATP products in 2018 (of $128.2 billion), as it has in every year since 2002 (U.S. Census Bureau 2019b). The U.S. balance of trade in advanced technology products declined by $132.7 billion between 2001 and 2018 (U.S. Census Bureau 2020b).

Job loss or displacement by industry is directly related to trade flows by industry, as shown in Table 3. The growing trade deficit with China eliminated 2,793,200 manufacturing jobs between 2001 and 2018, nearly three-fourths (75.4%) of the total. By far the largest job displacements occurred in the computer and electronic parts industry, which lost 1,340,600 jobs (36.2% of the 3.7 million jobs displaced overall). This industry includes computer and peripheral equipment (688,600 jobs lost, or 18.6% of the overall jobs displaced), semiconductors and components (337,300 jobs, or 9.1%), and communications, audio, and video equipment (293,700 jobs, or 7.9%).

Table 3 Net U.S. jobs created or displaced by goods trade with China, by industry, 2001–2018 Total Share of total jobs displaced Total* -3,704,700 100% Subtotal, nonmanufacturing -911,300 24.6% Subtotal, manufacturing -2,793,200 75.4% Agriculture, forestry, fishing, and hunting 15,900 -0.4% Mining 0 0.0% Oil and gas 3,600 -0.1% Minerals and ores –3,600 0.1% Utilities –13.900 0.4% Construction -13,700 0.4% Manufacturing -2,793,200 75.4% Nondurable goods -536,800 14.5% Food -9,800 0.3% Beverage and tobacco products -100 0.0% Textile mills and textile product mills -125,400 3.4% Apparel, leather, and allied products -205,700 5.6% Paper -28,600 0.8% Printed matter and related products -33,600 0.9% Petroleum and coal products -1,300 0.0% Chemicals -39,100 1.1% Plastics and rubber products -93,200 2.5% Durable goods -2,256,400 60.9% Wood products -31,300 0.8% Nonmetallic mineral products -37,900 1.0% Primary metals -59,200 1.6% Fabricated metal products -193,800 5.2% Machinery, except electronics -119,700 3.2% Computer and electronic parts -1,340,600 36.2% Computer and peripheral equipment -688,600 18.6% Communications, audio, and video equipment -293,700 7.9% Navigational, measuring, electromedical, and control instruments -21,100 0.6% Semiconductors and other electronic components, and reproducing magnetic and optical media -337,300 9.1% Electrical equipment, appliances, and components -165,200 4.5% Transportation equipment -33,900 0.9% Motor vehicles and motor vehicle parts -66,000 1.8% Aerospace products and parts 39,300 -1.1% Railroad, ship, and other transportation equipment -7,200 0.2% Furniture and related products -146,400 4.0% Miscellaneous manufactured commodities -128,400 3.5% Wholesale trade -176,600 4.8% Retail trade -57,500 1.6% Transportation and warehousing -76,000 2.1% Information -45,900 1.2% Finance and insurance -51,600 1.4% Real estate and rental and leasing -19,000 0.5% Professional, scientific, and technical services -118,000 3.2% Management of companies and enterprises -57,400 1.6% Administrative and support and waste management and remediation services -173,400 4.7% Education services -1,600 0.0% Healthcare and social assistance -1,700 0.0% Arts, entertainment, and recreation -10,400 0.3% Accommodation and food services -42,600 1.2% Other services (except public administration) -33,400 0.9% Government -34,500 0.9% * Subcategory and overall totals may vary slightly due to rounding. Note: Percentages are calculated using rounded totals. Source: Authors’ analysis of USITC 2019, and Bureau of Labor Statistics Employment Projections program 2019a and 2019b. For a more detailed explanation of data sources and computations, see the appendix. Share on Facebook Tweet this chart Embed Copy the code below to embed this chart on your website. Download image

Other hard-hit industries include apparel and leather products (205,700 jobs displaced, equal to 5.6% of the total), electrical equipment, appliances, and components (165,200 jobs, or 4.5%), fabricated metal products (193,800 jobs, or 5.2%), furniture and related products (146,400 jobs, or 4.0%), miscellaneous manufactured commodities (128,400 jobs, or 3.5%), textile mills and textile product mills (125,400 jobs, or 3.4%), plastics and rubber products (93,200 jobs, or 2.5%), and motor vehicles and motor vehicle parts (66,000 jobs, or 1.8%). In addition, surging imports of steel, aluminum, and other capital-intensive products threaten hundreds of thousands of jobs in key metal-using industries such as primary metals, machinery, and fabricated metal products (Stewart et al. 2014).

Several service industries, which provide key inputs to traded-goods production, experienced significant job displacement, including administrative and support and waste management and remediation services (173,400 jobs, or 4.7% of overall jobs displaced) and professional, scientific, and technical services (118,000 jobs, or 3.2%).

These job displacement estimates are based on changes in the real value of exports and imports. For example, while the share of U.S. imports accounted for by computer and electronic parts from China rose from 23.9% in 2001 to 34.5% in 2018 (to $186.2 billion, as shown in Supplemental Table 1), the average price indexes (deflators) for most of these products fell sharply between 2001 and 2018—47.4% on a trade-weighted basis. Thus, the real value of computer and electronic parts imports increased more than twelvefold in this period, rising from $15.2 billion in 2001 to $200.4 billion in 2018 in constant 2012 dollars (authors’ analysis of real trade flows; see the methodology appendix for data sources and computational details).

Missed opportunities to create more jobs through fair trade with China

The trade and jobs analysis in this report is focused on the actual jobs gained and lost due to increased trade with China over the past 17 years. This raises the question of what trade and employment could have looked like but for the massive growth of the U.S. trade deficit with China between 2001 and 2018. A full analysis of such scenarios at the level of employment impacts by industry and geographic area is beyond the scope of this report. But the broad outlines of one such scenario can be quickly sketched from the trade data in Table 2.

To have maintained a stable trade balance with China between 2001 and 2018, imports would have had to grow less rapidly or exports would have had to grow more rapidly—or some combination of the two. For example, had U.S. export growth to China matched the growth of China’s exports to the United States dollar for dollar between 2001 and 2018, balanced trade would have required more than a fourfold increase in U.S. exports to China in 2018. If actual 2018 exports in each industry (shown in Supplemental Table 1) had increased by this ratio (the specific ratio is 4.49-to-1), then the largest growth in exports would have occurred in transportation equipment ($111.9 billion), agricultural products ($31.4 billion), computer and electronic parts ($57.0 billion), chemicals ($62.5 billion), machinery ($38.5 billion), and food ($9.8 billion). In total, U.S. exports to China would have increased by $554.0 billion, $453.1 billion more than they actually did.

If exports to China had increased at this pace, it would have supported the creation of millions of U.S. manufacturing jobs and prevented much of the collapse of overall U.S. manufacturing employment between December 2001 and December 2018, when 2.9 million U.S. manufacturing jobs were lost (BLS 2019a). This level of growth in U.S. exports to China could not have taken place without major structural changes in China’s trade, industrial, macroeconomic, and labor policies. This analysis does illustrate the potential gains had China trade delivered on the promises made by China trade proponents when China entered the WTO in 2001.

Job losses by state

Growing U.S. trade deficits with China have reduced demand for goods produced in every region of the United States and have led to job displacement in all 50 states and the District of Columbia, as shown in Table 4 and Figure B. (Supplemental Table 2 ranks the states by the number of net jobs displaced, while Supplemental Table 3 ranks the states by jobs displaced as a share of total state jobs and presents the states alphabetically.) Table 4 shows that jobs displaced from 2001 to 2018 due to the growing goods trade deficit with China ranged from 0.85% to 3.66% of total state employment.

The 10 hardest-hit states ranked by job shares displaced were New Hampshire, Oregon, California, North Carolina, Minnesota, Massachusetts, Wisconsin, Vermont, Indiana, and Idaho. This list includes states with high-tech industries (California, Massachusetts, Minnesota, Oregon, and Idaho) and manufacturing states (New Hampshire, North Carolina, Wisconsin, Vermont and Indiana). Job losses in these states ranged from 2.82% to 3.66% of total state employment. Other traditional manufacturing powers—such as Georgia, South Carolina, Illinois, Rhode Island, Ohio, Michigan, Alabama, and Tennessee—are among the top 20 hardest-hit states, as are Texas and Colorado, also high-tech hubs.

Table 4 Net U.S. jobs displaced due to goods trade deficit with China, by state, 2001–2018 (ranked by jobs displaced as a share of total state employment) Rank State Net jobs displaced State employment (2013–2017 5-year ACS estimate) Jobs displaced as a share of employment 1 New Hampshire 26,100 713,400 3.66% 2 Oregon 68,700 1,886,000 3.64% 3 California 654,100 17,993,900 3.64% 4 North Carolina 147,700 4,571,000 3.23% 5 Minnesota 92,400 2,904,100 3.18% 6 Massachusetts 109,300 3,525,700 3.10% 7 Wisconsin 88,900 2,939,900 3.02% 8 Vermont 9,600 327,300 2.93% 9 Indiana 85,800 2,934,500 2.92% 10 Idaho 21,100 748,700 2.82% 11 Georgia 123,200 4,606,300 2.67% 12 South Carolina 57,900 2,181,100 2.65% 13 Texas 334,800 12,689,000 2.64% 14 Illinois 162,400 6,181,700 2.63% 15 Rhode Island 13,400 526,100 2.55% 16 Ohio 136,700 5,488,200 2.49% 17 Michigan 112,400 4,524,900 2.48% 18 Alabama 50,700 2,055,500 2.47% 19 Tennessee 73,800 2,996,600 2.46% 20 Colorado 67,700 2,760,100 2.45% 21 Mississippi 29,700 1,221,800 2.43% 22 Kentucky 46,900 1,938,200 2.42% 23 Utah 33,900 1,412,200 2.40% 24 Pennsylvania 137,300 6,097,000 2.25% 25 Arizona 65,800 2,953,900 2.23% 26 New Jersey 95,000 4,388,000 2.16% 27 Iowa 33,900 1,599,700 2.12% 28 Connecticut 38,100 1,805,100 2.11% 29 Washington 67,400 3,418,100 1.97% 30 Missouri 56,200 2,867,400 1.96% 31 New York 185,100 9,467,600 1.96% 32 Arkansas 24,400 1,276,500 1.91% 33 Maine 12,200 658,700 1.85% 34 Oklahoma 31,500 1,746,400 1.80% 35 South Dakota 7,800 438,300 1.78% 36 Virginia 70,500 4,084,000 1.73% 37 Kansas 24,200 1,420,100 1.70% 38 Florida 150,700 9,018,600 1.67% 39 Nebraska 16,200 987,200 1.64% 40 Maryland 44,800 3,040,800 1.47% 41 Delaware 6,200 441,500 1.40% 42 West Virginia 10,000 747,000 1.34% 43 Nevada 17,900 1,341,400 1.33% 44 New Mexico 11,500 879,200 1.31% 45 North Dakota 5,100 400,500 1.27% 46 Louisiana 24,900 2,031,200 1.23% 47 Montana 5,500 498,000 1.10% 48 Hawaii 6,600 671,800 0.98% 49 DC 3,300 357,700 0.92% 50 Wyoming 2,500 293,600 0.85% 51 Alaska 3,000 354,000 0.85% Total* 3,704,700 150,409,500 2.46% * Totals may vary slightly due to rounding. Note: Percentages are calculated using rounded totals Source: Authors’ analysis of American Community Survey (ACS) data (U.S. Census Bureau 2019a), USITC 2019, and Bureau of Labor Statistics Employment Projections program 2019a and 2019b. For a more detailed explanation of data sources and computations, see the appendix. Share on Facebook Tweet this chart Embed Copy the code below to embed this chart on your website. Download image

As shown in Supplemental Table 2, the top 10 states in terms of total jobs lost were California (654,100 jobs lost), Texas (334,800), New York (185,100), Illinois (162,400), Florida (150,700), North Carolina (147,700), Pennsylvania (137,300), Ohio (136,700), Georgia (123,200), and Michigan (112,400).

The map in Figure B shows the broad impact of the growing trade deficit with China across the United States, with no areas exempt from job displacement. The 3.7 million U.S. jobs displaced due to the growing trade deficit with China between 2001 and 2018 represented 2.46% of total U.S. employment.

Job losses by congressional district

This study also reports the employment impacts of the growing U.S. goods trade deficit with China in every congressional district and in the District of Columbia. Table 5 lists the top 20 hardest-hit congressional districts (those with the largest job losses as a share of overall district employment). Figure C shows job displacement as a share of total district employment in all 435 congressional districts plus the District of Columbia. (Supplemental Table 4 shows net job displacement and displacement as a share of total district employment, ranked by net jobs displaced; Supplemental Table 5 provides the same data sorted alphabetically by state; and Supplemental Table 6 ranks the districts by jobs displaced as a share of total district employment.) Because the largest growth in the goods trade deficits with China from 2001 to 2018 occurred in the computer and electronic parts industry, 17 of the 20 hardest-hit districts were in California, Massachusetts, Minnesota, Oregon, and Texas, where remaining jobs in that industry are concentrated. Georgia (one district) and North Carolina (two districts), which suffered considerable job displacement in a variety of manufacturing industries, also each have at least one district in the top 20 hardest-hit districts.

Table 5 Twenty congressional districts hardest hit by U.S. goods trade deficit with China, 2001–2018 (ranked by jobs displaced as a share of district employment) Rank State District Net jobs displaced District employment (2013–2017 5-year ACS estimate) Jobs displaced as a share of employment 1 California 17 78,700 390,700 20.14% 2 California 18 48,400 372,700 12.99% 3 California 19 45,400 381,300 11.91% 4 Texas 31 30,600 376,600 8.13% 5 Oregon 1 31,800 408,300 7.79% 6 California 15 27,900 382,500 7.29% 7 Georgia 14 21,000 314,900 6.67% 8 California 52 21,000 377,300 5.57% 9 Texas 10 22,000 406,600 5.41% 10 California 40 16,600 306,800 5.41% 11 Massachusetts 3 20,800 387,100 5.37% 12 Texas 3 22,000 425,900 5.17% 13 California 34 17,500 354,700 4.93% 14 California 45 18,300 383,300 4.77% 15 North Carolina 10 15,400 339,200 4.54% 16 North Carolina 6 15,500 344,900 4.49% 17 Massachusetts 2 16,500 378,000 4.37% 18 Minnesota 3 16,400 381,900 4.29% 19 California 49 14,500 338,400 4.28% 20 Minnesota 2 16,000 381,700 4.19% * Totals may vary slightly due to rounding. Note: Percentages are calculated using rounded totals. Source: Authors’ analysis of American Community Survey (ACS) data (U.S. Census Bureau 2019a and 2020a), USITC 2019, and Bureau of Labor Statistics Employment Projections program 2019a and 2019b. For a more detailed explanation of data sources and computations, see the appendix. Share on Facebook Tweet this chart Embed Copy the code below to embed this chart on your website. Download image

Figure C Net U.S. jobs displaced due to the goods trade deficit with China as a share of total congressional district employment, 2001–2018 Rank (by jobs displaced as a share of total) State District Net jobs displaced District employment (2013-2017 5-year ACS estimate) Jobs displaced as a share of employment 331 Alabama 1 4,900 289,900 1.69% 211 Alabama 2 6,200 283,200 2.19% 106 Alabama 3 8,200 295,600 2.77% 65 Alabama 4 9,000 273,400 3.29% 44 Alabama 5 11,500 319,300 3.60% 307 Alabama 6 5,900 327,900 1.80% 280 Alabama 7 5,100 266,300 1.92% 435 Alaska Statewide 3,000 354,000 0.85% 376 Arizona 1 3,900 274,800 1.42% 402 Arizona 2 3,800 304,800 1.25% 326 Arizona 3 5,400 312,700 1.73% 330 Arizona 4 4,500 265,500 1.69% 31 Arizona 5 14,200 360,100 3.94% 230 Arizona 6 7,900 377,500 2.09% 202 Arizona 7 7,200 323,700 2.22% 203 Arizona 8 7,300 328,700 2.22% 91 Arizona 9 11,600 406,100 2.86% 262 Arkansas 1 5,800 289,200 2.01% 332 Arkansas 2 5,800 345,900 1.68% 271 Arkansas 3 7,000 355,500 1.97% 242 Arkansas 4 5,900 285,900 2.06% 321 California 1 4,900 281,000 1.74% 258 California 2 6,800 337,400 2.02% 358 California 3 4,700 309,100 1.52% 50 California 4 11,000 313,800 3.51% 265 California 5 7,100 355,500 2.00% 277 California 6 6,300 327,200 1.93% 67 California 7 10,800 335,700 3.22% 365 California 8 3,900 262,500 1.49% 221 California 9 6,600 310,500 2.13% 119 California 10 8,400 309,600 2.71% 234 California 11 7,400 356,100 2.08% 53 California 12 15,400 446,300 3.45% 137 California 13 9,900 384,300 2.58% 51 California 14 13,900 400,000 3.48% 6 California 15 27,900 382,500 7.29% 337 California 16 4,400 265,300 1.66% 1 California 17 78,700 390,700 20.14% 2 California 18 48,400 372,700 12.99% 3 California 19 45,400 381,300 11.91% 167 California 20 7,900 327,700 2.41% 436 California 21 2,000 253,900 0.79% 395 California 22 4,100 315,500 1.30% 428 California 23 3,100 290,200 1.07% 318 California 24 6,100 346,200 1.76% 225 California 25 6,700 316,900 2.11% 107 California 26 9,600 346,300 2.77% 114 California 27 9,500 348,200 2.73% 222 California 28 8,300 390,800 2.12% 149 California 29 8,700 347,300 2.51% 244 California 30 8,100 393,400 2.06% 241 California 31 6,500 314,800 2.06% 68 California 32 10,700 334,100 3.20% 152 California 33 9,200 368,300 2.50% 13 California 34 17,500 354,700 4.93% 136 California 35 8,300 320,800 2.59% 344 California 36 4,500 281,400 1.60% 171 California 37 8,600 360,800 2.38% 84 California 38 9,700 328,100 2.96% 59 California 39 12,000 353,500 3.39% 10 California 40 16,600 306,800 5.41% 194 California 41 7,300 320,900 2.27% 120 California 42 9,100 335,800 2.71% 93 California 43 9,600 338,100 2.84% 47 California 44 10,800 304,500 3.55% 14 California 45 18,300 383,300 4.77% 34 California 46 13,700 351,600 3.90% 87 California 47 10,100 346,300 2.92% 32 California 48 14,600 371,500 3.93% 19 California 49 14,500 338,400 4.28% 76 California 50 10,600 343,200 3.09% 270 California 51 5,600 283,900 1.97% 8 California 52 21,000 377,300 5.57% 153 California 53 9,400 378,300 2.48% 302 Colorado 1 8,000 442,100 1.81% 23 Colorado 2 17,600 426,500 4.13% 423 Colorado 3 3,900 343,100 1.14% 71 Colorado 4 12,200 385,200 3.17% 147 Colorado 5 8,600 342,900 2.51% 279 Colorado 6 7,900 412,200 1.92% 185 Colorado 7 9,500 408,000 2.33% 295 Connecticut 1 6,600 358,600 1.84% 288 Connecticut 2 6,700 360,200 1.86% 218 Connecticut 3 7,800 364,200 2.14% 251 Connecticut 4 7,400 364,700 2.03% 123 Connecticut 5 9,600 357,400 2.69% 432 DC Statewide 3,300 357,700 0.92% 380 Delaware Statewide 6,200 441,500 1.40% 410 Florida 1 3,900 322,600 1.21% 420 Florida 2 3,300 285,600 1.16% 393 Florida 3 4,000 304,300 1.31% 342 Florida 4 6,000 374,600 1.60% 416 Florida 5 3,700 309,500 1.20% 308 Florida 6 5,400 300,400 1.80% 334 Florida 7 6,200 371,700 1.67% 26 Florida 8 12,300 301,800 4.08% 408 Florida 9 4,300 353,200 1.22% 340 Florida 10 6,400 392,200 1.63% 384 Florida 11 3,300 239,200 1.38% 293 Florida 12 5,800 314,200 1.85% 228 Florida 13 7,000 333,300 2.10% 338 Florida 14 6,100 372,100 1.64% 346 Florida 15 5,500 345,500 1.59% 352 Florida 16 5,100 330,200 1.54% 409 Florida 17 3,200 264,500 1.21% 360 Florida 18 5,000 330,200 1.51% 397 Florida 19 4,100 316,600 1.30% 345 Florida 20 5,600 350,900 1.60% 310 Florida 21 6,100 339,800 1.80% 223 Florida 22 7,900 372,800 2.12% 269 Florida 23 7,300 369,700 1.97% 370 Florida 24 4,800 329,900 1.45% 169 Florida 25 8,400 350,500 2.40% 407 Florida 26 4,400 361,200 1.22% 378 Florida 27 5,400 382,100 1.41% 396 Georgia 1 4,000 308,300 1.30% 232 Georgia 2 5,300 253,500 2.09% 70 Georgia 3 10,100 318,400 3.17% 263 Georgia 4 7,000 349,200 2.00% 312 Georgia 5 6,500 362,600 1.79% 88 Georgia 6 11,300 389,600 2.90% 58 Georgia 7 13,100 384,500 3.41% 328 Georgia 8 4,900 285,800 1.71% 80 Georgia 9 9,500 313,600 3.03% 213 Georgia 10 6,900 316,900 2.18% 94 Georgia 11 10,800 380,700 2.84% 261 Georgia 12 5,700 284,200 2.01% 243 Georgia 13 7,100 344,300 2.06% 7 Georgia 14 21,000 314,900 6.67% 429 Hawaii 1 3,600 348,600 1.03% 433 Hawaii 2 2,900 323,100 0.90% 69 Idaho 1 11,900 373,400 3.19% 165 Idaho 2 9,100 375,200 2.43% 299 Illinois 1 5,600 306,600 1.83% 289 Illinois 2 5,400 290,900 1.86% 179 Illinois 3 8,000 339,100 2.36% 81 Illinois 4 10,200 338,700 3.01% 215 Illinois 5 9,200 426,100 2.16% 35 Illinois 6 14,500 378,100 3.83% 216 Illinois 7 7,200 334,500 2.15% 24 Illinois 8 15,500 377,300 4.11% 181 Illinois 9 8,500 361,000 2.35% 37 Illinois 10 13,200 349,900 3.77% 97 Illinois 11 10,300 364,900 2.82% 268 Illinois 12 6,000 303,800 1.97% 348 Illinois 13 5,200 332,400 1.56% 40 Illinois 14 13,700 375,400 3.65% 193 Illinois 15 7,200 316,300 2.28% 159 Illinois 16 8,100 331,300 2.44% 146 Illinois 17 7,800 311,000 2.51% 274 Illinois 18 6,700 344,400 1.95% 187 Indiana 1 7,200 310,600 2.32% 45 Indiana 2 11,400 317,800 3.59% 25 Indiana 3 13,400 327,000 4.10% 141 Indiana 4 8,400 328,500 2.56% 212 Indiana 5 7,800 357,700 2.18% 75 Indiana 6 9,700 311,900 3.11% 154 Indiana 7 7,700 312,200 2.47% 49 Indiana 8 11,600 329,300 3.52% 142 Indiana 9 8,600 339,400 2.53% 115 Iowa 1 10,900 399,700 2.73% 151 Iowa 2 9,700 387,900 2.50% 356 Iowa 3 6,500 426,400 1.52% 322 Iowa 4 6,700 385,700 1.74% 364 Kansas 1 5,100 343,000 1.49% 314 Kansas 2 6,100 341,700 1.79% 236 Kansas 3 8,100 391,500 2.07% 375 Kansas 4 4,900 344,000 1.42% 145 Kentucky 1 7,300 290,900 2.51% 148 Kentucky 2 8,400 335,300 2.51% 186 Kentucky 3 8,500 365,700 2.32% 196 Kentucky 4 8,000 352,100 2.27% 323 Kentucky 5 4,000 231,100 1.73% 82 Kentucky 6 10,800 363,000 2.98% 401 Louisiana 1 4,700 376,500 1.25% 426 Louisiana 2 3,800 343,800 1.11% 404 Louisiana 3 4,300 351,600 1.22% 411 Louisiana 4 3,600 297,800 1.21% 414 Louisiana 5 3,400 283,700 1.20% 389 Louisiana 6 5,100 377,800 1.35% 278 Maine 1 6,800 353,300 1.92% 317 Maine 2 5,400 305,300 1.77% 350 Maryland 1 5,600 361,700 1.55% 362 Maryland 2 5,600 375,600 1.49% 369 Maryland 3 5,800 398,300 1.46% 421 Maryland 4 4,500 394,100 1.14% 424 Maryland 5 4,400 389,700 1.13% 240 Maryland 6 7,900 382,400 2.07% 363 Maryland 7 5,000 335,600 1.49% 354 Maryland 8 6,200 403,600 1.54% 208 Massachusetts 1 7,700 350,200 2.20% 17 Massachusetts 2 16,500 378,000 4.37% 11 Massachusetts 3 20,800 387,100 5.37% 43 Massachusetts 4 14,300 393,300 3.64% 73 Massachusetts 5 13,100 415,100 3.16% 77 Massachusetts 6 12,300 401,000 3.07% 276 Massachusetts 7 8,200 424,900 1.93% 227 Massachusetts 8 8,700 413,500 2.10% 238 Massachusetts 9 7,500 362,600 2.07% 294 Michigan 1 5,500 298,800 1.84% 46 Michigan 2 12,400 347,700 3.57% 110 Michigan 3 9,600 350,000 2.74% 214 Michigan 4 6,500 300,200 2.17% 235 Michigan 5 5,800 279,200 2.08% 83 Michigan 6 10,000 336,700 2.97% 140 Michigan 7 8,200 320,400 2.56% 166 Michigan 8 8,700 359,600 2.42% 155 Michigan 9 8,600 350,500 2.45% 133 Michigan 10 8,800 336,000 2.62% 112 Michigan 11 10,100 369,200 2.74% 275 Michigan 12 6,500 335,300 1.94% 253 Michigan 13 5,200 256,800 2.02% 199 Michigan 14 6,400 284,400 2.25% 36 Minnesota 1 13,500 352,900 3.83% 20 Minnesota 2 16,000 381,700 4.19% 18 Minnesota 3 16,400 381,900 4.29% 129 Minnesota 4 9,600 364,200 2.64% 125 Minnesota 5 10,500 391,800 2.68% 86 Minnesota 6 11,100 380,100 2.92% 131 Minnesota 7 8,800 334,900 2.63% 246 Minnesota 8 6,500 316,600 2.05% 29 Mississippi 1 12,900 326,500 3.95% 324 Mississippi 2 4,600 265,900 1.73% 287 Mississippi 3 5,900 316,200 1.87% 248 Mississippi 4 6,400 313,200 2.04% 303 Missouri 1 6,400 353,700 1.81% 290 Missouri 2 7,300 394,300 1.85% 206 Missouri 3 8,400 380,700 2.21% 282 Missouri 4 6,400 337,400 1.90% 316 Missouri 5 6,600 372,400 1.77% 286 Missouri 6 6,900 369,100 1.87% 233 Missouri 7 7,400 355,500 2.08% 207 Missouri 8 6,700 304,400 2.20% 427 Montana Statewide 5,500 498,000 1.10% 301 Nebraska 1 6,100 334,900 1.82% 339 Nebraska 2 5,600 342,100 1.64% 366 Nebraska 3 4,600 310,200 1.48% 418 Nevada 1 3,800 319,500 1.19% 309 Nevada 2 6,000 334,100 1.80% 405 Nevada 3 4,600 376,500 1.22% 425 Nevada 4 3,500 311,300 1.12% 41 New Hampshire 1 13,300 364,800 3.65% 39 New Hampshire 2 12,800 348,700 3.67% 319 New Jersey 1 6,300 358,100 1.76% 387 New Jersey 2 4,500 331,000 1.36% 327 New Jersey 3 6,100 355,500 1.72% 291 New Jersey 4 6,400 345,700 1.85% 134 New Jersey 5 9,900 378,700 2.61% 180 New Jersey 6 8,600 365,000 2.36% 135 New Jersey 7 10,000 384,300 2.60% 176 New Jersey 8 9,500 401,500 2.37% 99 New Jersey 9 10,200 363,600 2.81% 347 New Jersey 10 5,500 345,900 1.59% 184 New Jersey 11 9,000 386,100 2.33% 170 New Jersey 12 8,900 372,700 2.39% 351 New Mexico 1 4,900 317,200 1.54% 431 New Mexico 2 2,600 275,300 0.94% 381 New Mexico 3 4,000 286,700 1.40% 231 New York 1 7,400 353,800 2.09% 172 New York 2 8,800 369,400 2.38% 285 New York 3 6,600 352,800 1.87% 361 New York 4 5,500 363,400 1.51% 406 New York 5 4,500 369,400 1.22% 273 New York 6 7,000 359,800 1.95% 209 New York 7 7,800 355,000 2.20% 383 New York 8 4,800 346,600 1.38% 413 New York 9 4,200 349,600 1.20% 304 New York 10 7,000 387,200 1.81% 359 New York 11 5,000 329,800 1.52% 329 New York 12 7,700 450,100 1.71% 399 New York 13 4,600 360,600 1.28% 333 New York 14 5,900 352,300 1.67% 415 New York 15 3,400 283,700 1.20% 390 New York 16 4,700 350,500 1.34% 343 New York 17 5,800 362,600 1.60% 96 New York 18 9,800 345,700 2.83% 64 New York 19 11,100 331,900 3.34% 272 New York 20 7,100 364,600 1.95% 311 New York 21 5,600 312,100 1.79% 150 New York 22 8,000 319,400 2.50% 200 New York 23 7,100 316,100 2.25% 160 New York 24 8,200 336,400 2.44% 56 New York 25 12,100 353,300 3.42% 296 New York 26 6,200 337,500 1.84% 132 New York 27 9,300 354,200 2.63% 162 North Carolina 1 8,000 328,600 2.43% 28 North Carolina 2 15,000 374,700 4.00% 284 North Carolina 3 5,700 304,200 1.87% 21 North Carolina 4 18,500 442,300 4.18% 62 North Carolina 5 11,100 331,600 3.35% 16 North Carolina 6 15,500 344,900 4.49% 281 North Carolina 7 6,400 336,800 1.90% 113 North Carolina 8 8,800 322,000 2.73% 143 North Carolina 9 8,500 337,700 2.52% 15 North Carolina 10 15,400 339,200 4.54% 60 North Carolina 11 10,800 319,500 3.38% 191 North Carolina 12 10,000 435,100 2.30% 30 North Carolina 13 14,000 354,500 3.95% 400 North Dakota Statewide 5,100 400,500 1.27% 210 Ohio 1 7,600 346,300 2.19% 219 Ohio 2 7,400 345,600 2.14% 335 Ohio 3 6,300 378,500 1.66% 57 Ohio 4 11,300 331,000 3.41% 74 Ohio 5 11,300 360,400 3.14% 217 Ohio 6 6,400 298,000 2.15% 72 Ohio 7 10,800 341,000 3.17% 102 Ohio 8 9,600 344,300 2.79% 183 Ohio 9 7,600 325,900 2.33% 188 Ohio 10 7,600 328,800 2.31% 264 Ohio 11 5,900 295,100 2.00% 250 Ohio 12 7,800 384,000 2.03% 108 Ohio 13 9,100 329,400 2.76% 78 Ohio 14 10,800 354,500 3.05% 283 Ohio 15 6,800 358,700 1.90% 95 Ohio 16 10,400 366,800 2.84% 247 Oklahoma 1 7,800 380,500 2.05% 306 Oklahoma 2 5,300 293,700 1.80% 371 Oklahoma 3 5,000 344,400 1.45% 298 Oklahoma 4 6,500 354,700 1.83% 300 Oklahoma 5 6,800 373,200 1.82% 5 Oregon 1 31,800 408,300 7.79% 325 Oregon 2 5,900 341,400 1.73% 104 Oregon 3 11,900 427,200 2.79% 116 Oregon 4 9,200 338,000 2.72% 126 Oregon 5 9,900 371,200 2.67% 349 Pennsylvania 1 4,900 316,300 1.55% 422 Pennsylvania 2 3,400 298,500 1.14% 144 Pennsylvania 3 8,000 318,100 2.51% 124 Pennsylvania 4 9,600 357,800 2.68% 130 Pennsylvania 5 8,300 315,300 2.63% 138 Pennsylvania 6 9,700 378,000 2.57% 320 Pennsylvania 7 6,300 360,600 1.75% 157 Pennsylvania 8 9,100 371,100 2.45% 259 Pennsylvania 9 6,200 308,300 2.01% 195 Pennsylvania 10 7,200 316,800 2.27% 190 Pennsylvania 11 7,800 338,200 2.31% 174 Pennsylvania 12 8,000 336,300 2.38% 266 Pennsylvania 13 7,000 352,600 1.99% 297 Pennsylvania 14 6,400 349,200 1.83% 101 Pennsylvania 15 9,900 355,000 2.79% 98 Pennsylvania 16 9,900 352,300 2.81% 177 Pennsylvania 17 7,600 321,600 2.36% 192 Pennsylvania 18 8,000 350,800 2.28% 127 Rhode Island 1 6,900 258,800 2.67% 163 Rhode Island 2 6,500 267,300 2.43% 367 South Carolina 1 5,300 360,800 1.47% 197 South Carolina 2 7,400 327,400 2.26% 27 South Carolina 3 11,800 289,700 4.07% 55 South Carolina 4 11,400 332,500 3.43% 54 South Carolina 5 10,400 302,300 3.44% 267 South Carolina 6 5,400 273,400 1.98% 220 South Carolina 7 6,300 294,900 2.14% 315 South Dakota 1 7,800 438,300 1.78% 175 Tennessee 1 7,200 304,000 2.37% 249 Tennessee 2 7,000 343,200 2.04% 105 Tennessee 3 8,700 313,600 2.77% 79 Tennessee 4 10,700 352,000 3.04% 229 Tennessee 5 8,400 400,600 2.10% 89 Tennessee 6 9,700 335,500 2.89% 178 Tennessee 7 7,500 317,500 2.36% 198 Tennessee 8 7,100 315,200 2.25% 173 Tennessee 9 7,500 315,000 2.38% 313 Texas 1 5,400 301,300 1.79% 33 Texas 2 15,700 401,700 3.91% 12 Texas 3 22,000 425,900 5.17% 161 Texas 4 7,700 316,100 2.44% 156 Texas 5 8,000 326,200 2.45% 224 Texas 6 8,000 377,900 2.12% 118 Texas 7 10,700 394,000 2.72% 128 Texas 8 9,500 357,000 2.66% 255 Texas 9 7,600 376,000 2.02% 9 Texas 10 22,000 406,600 5.41% 382 Texas 11 4,700 338,700 1.39% 237 Texas 12 7,800 377,100 2.07% 379 Texas 13 4,500 319,100 1.41% 353 Texas 14 5,000 324,700 1.54% 374 Texas 15 4,300 300,600 1.43% 205 Texas 16 6,700 303,100 2.21% 42 Texas 17 13,100 359,500 3.64% 164 Texas 18 8,500 350,200 2.43% 419 Texas 19 3,800 324,200 1.17% 368 Texas 20 5,200 356,200 1.46% 103 Texas 21 11,100 398,100 2.79% 189 Texas 22 9,400 406,800 2.31% 377 Texas 23 4,400 310,600 1.42% 90 Texas 24 12,400 433,100 2.86% 22 Texas 25 14,200 341,900 4.15% 111 Texas 26 11,800 431,200 2.74% 388 Texas 27 4,500 331,000 1.36% 417 Texas 28 3,500 294,000 1.19% 254 Texas 29 6,500 321,100 2.02% 257 Texas 30 6,800 336,700 2.02% 4 Texas 31 30,600 376,600 8.13% 38 Texas 32 14,600 397,300 3.67% 117 Texas 33 8,600 316,400 2.72% 412 Texas 34 3,200 265,100 1.21% 252 Texas 35 7,700 379,900 2.03% 336 Texas 36 5,200 312,900 1.66% 158 Utah 1 8,400 343,000 2.45% 245 Utah 2 7,100 345,700 2.05% 168 Utah 3 8,300 346,200 2.40% 122 Utah 4 10,200 377,400 2.70% 85 Vermont Statewide 9,600 327,300 2.93% 305 Virginia 1 6,900 381,800 1.81% 386 Virginia 2 4,700 341,300 1.38% 398 Virginia 3 4,300 336,900 1.28% 355 Virginia 4 5,400 352,500 1.53% 182 Virginia 5 7,800 333,800 2.34% 239 Virginia 6 7,300 353,200 2.07% 357 Virginia 7 5,900 387,300 1.52% 403 Virginia 8 5,600 454,000 1.23% 139 Virginia 9 7,600 296,600 2.56% 226 Virginia 10 8,900 422,500 2.11% 373 Virginia 11 6,100 424,000 1.44% 121 Washington 1 9,900 366,000 2.70% 341 Washington 2 5,600 348,300 1.61% 66 Washington 3 10,200 313,000 3.26% 430 Washington 4 3,100 305,200 1.02% 292 Washington 5 5,700 308,000 1.85% 391 Washington 6 3,900 292,900 1.33% 260 Washington 7 8,800 438,000 2.01% 201 Washington 8 7,900 351,900 2.24% 256 Washington 9 7,600 376,100 2.02% 372 Washington 10 4,600 318,700 1.44% 392 West Virginia 1 3,500 265,200 1.32% 385 West Virginia 2 3,700 268,500 1.38% 394 West Virginia 3 2,800 213,300 1.31% 61 Wisconsin 1 12,100 358,300 3.38% 204 Wisconsin 2 9,100 410,700 2.22% 48 Wisconsin 3 12,900 366,000 3.52% 100 Wisconsin 4 9,200 329,700 2.79% 63 Wisconsin 5 12,900 385,600 3.35% 52 Wisconsin 6 12,600 364,900 3.45% 109 Wisconsin 7 9,600 348,900 2.75% 92 Wisconsin 8 10,700 375,800 2.85% 434 Wyoming Statewide 2,500 293,600 0.85% Total* 3,704,700 150,410,200 2.46% Chart Data Download data The data below can be saved or copied directly into Excel. The data underlying the figure. * Totals may vary slightly due to rounding. Note: Percentages are calculated using rounded totals. Source: Authors’ analysis of U.S. Census Bureau 2019a and 2020a, USITC 2019, and Bureau of Labor Statistics Employment Projections program 2019a and 2019b. For a more detailed explanation of data sources and computations, see the appendix. Share on Facebook Tweet this chart Embed Copy the code below to embed this chart on your website. Download image

Specifically, of the 20 hardest-hit districts, nine were in California (in rank order, the 17th, 18th, 19th, 15th, 52nd, 40th, 34th, 45th, and 49th); three were in Texas (31st, 10th, and 3rd,); two each were in Massachusetts (the 3rd and 2nd), Minnesota (3rd and 2nd), and North Carolina (10th and 6th); and one each were in Oregon (1st), and Georgia (14th). Job losses in these districts ranged from 14,500 jobs to 78,700 jobs, and from 4.19% to 20.14% of total district jobs. These distributions reflect both the size of some states (e.g., California and Texas) and the concentration of the industries hardest hit by the growing U.S.–China trade deficit. We have already mentioned the prevalence of the computer and electronic parts industry in certain states; other industries with a presence in these districts include furniture, textiles, apparel, and other manufactured products.

The three hardest-hit congressional districts were all located in Silicon Valley (South Bay Area) in California, including the 17th Congressional District (encompassing Sunnyvale, Cupertino, Santa Clara, Fremont, Newark, North San Jose, and Milpitas), which lost 78,700 jobs, equal to 20.14% of all jobs in the district; the 18th Congressional District (including parts of San Jose, Palo Alto, Redwood City, Menlo Park, Stanford, Los Altos, Campbell, Saratoga, Mountain View, and Los Gatos), which lost 48,400 jobs, or 12.99%; and the 19th Congressional District (most of San Jose and other parts of Santa Clara County), which lost 45,400 jobs, or 11.91%.

Although the San Francisco Bay Area has experienced rapid growth over the past decade in software and related industries, this growth has come at the expense of direct employment in the production of computer and electronic parts. The computer and electronic parts manufacturing sector has experienced more actual job losses than any other major manufacturing industry has since China joined the WTO. There are substantial questions about the long-run ability of firms in the high-tech sectors to continue to innovate while offshoring most or all of the production in their industries (Shi 2010).

Other research confirms job losses from U.S.–China trade

Academic research has confirmed findings in this and earlier EPI research (e.g., Kimball and Scott 2014, Scott 2017a, Scott and Mokhiber 2018) that the growing U.S.–China trade deficit has caused significant losses of U.S. jobs, especially in manufacturing. (For a further analysis of EPI research on trade and globalization, see Bivens 2017.)

For example, Acemoglu et al. (2014) find that import competition with China from 1999 to 2011 was responsible for up to 2.4 million net job losses (including direct, indirect, and respending effects). This result compares with the finding in this paper that 2.6 million jobs were lost due to growing trade deficits with China between 2001 and 2011, as shown in Figure A. Thus, over a roughly comparable period, Acemoglu et al. estimate an employment impact that is roughly 90% as large as the estimate found in this study.

Further academic confirmation of the impacts of China trade on manufacturing employment is provided by Pierce and Schott (2016). Pierce and Schott use an entirely different estimation technique based on differences in the pre- and post-China WTO entry maximum tariff rates, with and without permanent normal trade relations (PNTR) status, which the United States granted to China in the China–WTO implementing legislation. Pierce and Schott estimate the impacts of changes in U.S. international transactions between 1992 and 2008. They find that the grant of PNTR status to China “reduced relative employment growth of the average industry by 3.4 percentage points…after one year [and] 15.6 percentage points after 6 years” (following the grant of PNTR status to China in 2001). They do not translate percentage-point changes in employment into total jobs displaced, but data on changes in total manufacturing employment in this period provide a base of comparison.

The research in this paper looks at the total loss or displacement of jobs due to the growing trade deficit with China and the number of those lost jobs that are manufacturing jobs. We can check the consistency of this finding with a different approach—looking at the total loss of manufacturing jobs and estimating the number of those job losses that are due to growing trade deficits with China. The United States lost 2.9 million manufacturing jobs between December 2001 and December 2018, a decline of 18.9% in total manufacturing employment (BLS 2019). Drawing from Pierce and Schott 2016 above, if 15.6 percentage points of this 18.9% decline can be attributed to the growth of the U.S. trade deficit with China, this implies that about 82.5% (or 2.4 million) of the manufacturing jobs lost in this period were lost due to the growing trade deficit with China. This estimate is very similar to this study’s estimated total manufacturing jobs displaced by the growing U.S.–China trade deficit (2.8 million net jobs displaced). Thus, two other recent academic studies have concluded that the growing U.S.–China trade deficit is responsible for the displacement of at least 2 million U.S. manufacturing jobs since 1990, with most jobs lost since China entered the WTO in 2001.

Lost wages from the increasing trade deficit with China

Growing trade-related job displacement has several direct and indirect effects on workers’ wages. The direct wage effects are a function of the wages forgone in jobs displaced by growing U.S. imports from China minus wage gains from jobs added in export-producing industries, and comparing this with the (lower) wages paid in alternative jobs in nontraded industries (U.S. workers displaced from traded-goods production in manufacturing industries who find jobs in nontraded goods industries experience permanent wage losses, as discussed below). Standard trade theory assumes that economic integration leads to “gains from trade” as workers move from low-productivity jobs in import-competing industries into higher-productivity jobs in export-competing industries. However, this assumption is proven incorrect in Scott 2013, which shows that import-competing jobs pay better than alternative jobs in export-producing industries. Specifically, Scott examines the gains and losses associated with direct changes in employment caused by growing U.S.–China trade deficits between 2001 and 2011, and finds that jobs displaced by imports from China actually paid 17.0% more than jobs exporting to China: $1,021.66 per week in import-competing industries versus $872.89 per week in exporting industries (Scott 2013, 24, Table 9a). Therefore, simple trade expansion that increases total trade with no underlying change in the trade balance will result in a net loss to workers as they move from higher-paying jobs in import-competing industries to lower-paying jobs in exporting industries.

Furthermore, jobs in both import-competing and exporting industries paid substantially more than jobs in nontraded industries, which pay $791.14 per week (Scott 2013, Table 9a, 24). Between 2001 and 2011, growing exports to China supported 538,000 U.S. jobs, but growing imports displaced 3,280,200 jobs, for a net loss of 2.7 million U.S. jobs (Scott 2013, Table 5, 13). Thus, not only did workers lose wages moving from import-competing to exporting industries, but 2.7 million workers were displaced from jobs where they earned $1,021.66 per week on average. If they were lucky enough to find jobs, these 2.7 million workers were mostly pushed into jobs in nontraded industries paying an average of only $791.14 per week (a decline of 22.6%). In total, U.S. workers suffered a direct net wage loss of $37 billion per year (Scott 2013, 26, Table 9b) due to trade with China. But the direct wage losses are just the tip of the iceberg.

As shown by Josh Bivens in Everybody Wins, Except for Most of Us (Bivens 2008a, with results updated in Bivens 2013), growing trade with China and other low-wage exporters essentially puts all American workers without a college degree (roughly 100 million workers) in direct competition with workers in China (and elsewhere) making much less. He shows that trade with low-wage countries was responsible for 90% of the growth in the college wage premium since 1995 (the college wage premium is the percent by which wages of college graduates exceed those of otherwise-equivalent high school graduates). It is important to note that there are roughly 100 million non-college-educated workers whose wages were suppressed by China trade. The growth of China trade alone was responsible for more than half of the growth in the college wage premium in that period, Bivens finds. To put these estimates in macroeconomic terms, in 2011, trade with low-wage countries lowered annual wages by 5.5%—roughly $1,800 per worker for all full-time, full-year workers without a college degree. To provide comparable economywide impact estimates, assume that 100 million workers without a college degree suffered average losses of $1,800 per year, which yields a total national loss of $180 billion (Scott 2015). Therefore, the indirect, macroeconomic losses to U.S. workers without college degrees caused by growing trade with low-wage nations were about five times as large as the $37 billion in direct wage losses in 2011 from trade with China, and about 40 times as many workers were affected indirectly due to globalization’s wage-lowering effect (100 million) as were displaced by trade with China (2.7 million). And China trade alone was responsible for about 51.6% of the increase in the overall college/noncollege wage gap between 1995 and 2011.

Additionally, Autor, Dorn, and Hanson estimate that rising exposure to low-cost Chinese imports lowers labor force participation and reduces wages in local labor markets; in particular, they find that increased import competition has a statistically significant depressing effect on nonmanufacturing wages (Autor, Dorn, and Hanson 2012, abstract). This confirms the findings of Bivens (2008a, 2013). Autor, Dorn, and Hanson (2012) also find that “transfer benefits payments for unemployment, disability, retirement, and healthcare also rise sharply in exposed labor markets” and that “for the oldest group (50–64), fully 84% of the decline in [manufacturing] employment is accounted for by the rise in nonparticipation, relative to 71% among the prime-age group and 68% among the younger group” (Autor, Dorn, and Hanson 2012, abstract, 25). Thus, Autor, Dorn, and Hanson find that more than two-thirds of all workers displaced by growing competition with Chinese imports dropped out of the labor force. These results are explained, in part, by the finding that “9.9%…of those who lose employment following an import shock obtain federal disability insurance benefits [Social Security Disability Insurance (SSDI) benefits].” Additionally, “rising import exposure spurs a substantial increase in government transfer payments to citizens in the form of increased disability, medical, income assistance and unemployment benefits.” Moreover, “these transfer payments vastly exceed the expenses of the TAA [Trade Adjustment Assistance] program, which specifically targets workers who lose employment due to import competition” (Autor, Dorn, and Hanson 2012, 25, 30). In Autor and Hanson 2014, the effects are totaled, and they find that “for regions affected by Chinese imports, the estimated dollar increase in per capita SSDI payments is more than 30 times as large as the estimated dollar increases in TAA payments.”

The job and wage losses stemming from the growing U.S.–China trade deficit are real—and also increase demands on the social safety net

Some economists and others in the trade debate have argued that job loss numbers extrapolated from trade flows are uninformative because aggregate employment levels in the United States are set by a broad range of macroeconomic influences, not just by trade flows. However, while the trade balance is but one of many variables affecting aggregate job creation, it plays a large role in explaining structural change in employment, especially in the manufacturing sector. As noted earlier, between December 2001 and December 2018, 2.9 million U.S. manufacturing jobs were lost (BLS 2019a). The growth of the U.S. trade deficit with China was responsible for the displacement of 2.8 million manufacturing jobs in this period, or nearly all of the manufacturing jobs lost.

The employment impacts of trade identified in this paper can be interpreted as the “all else equal” effect of trade on domestic employment. The Federal Reserve, for example, may decide to cut interest rates to make up for job losses stemming from deteriorating trade balances (or any other economic influence), leaving net employment unchanged. This, however, does not change the fact that trade deficits by themselves are a net drain on employment. Even if macroeconomic policy is adjusted to offset the negative impact of the growing trade deficit with China on total employment, the structure of production and employment in the United States has been negatively affected (Scott 2017a, Scott and Mokhiber 2018).

The growing trade deficit with China has clearly reduced domestic employment in traded-goods industries, especially in the manufacturing sector, which has been pummeled by plant closings and job losses. Workers from the manufacturing sector displaced by trade have had particular difficulty securing comparable employment elsewhere in the economy. According to the most recent Bureau of Labor Statistics survey covering displaced workers (BLS 2018, Table 4), more than one-third (35.4%) of long-tenured (employed more than three years) manufacturing workers displaced from January 2015 to December 2017 were not working in January 2018, including 21.7% of long-tenured manufacturing workers displaced who were not in the labor force, i.e., no longer even looking for work, and 13.7% who were unemployed.

As noted above, U.S. workers who were directly displaced by trade with China between 2001 and 2011 lost a collective $37.0 billion in wages as a result of accepting lower-paying jobs in nontraded industries or industries that export to China assuming, conservatively, that those workers are reemployed in nontraded goods industries (Scott 2013). Worse yet, growing competition with workers in China and other low-wage countries reduced the wages of all 100 million U.S. workers without a college degree, leading to cumulative losses of approximately $180 billion per year in 2011 (Bivens 2013; Scott 2015). The lost output of unemployed workers, especially that of labor force dropouts, can never be regained and is one of the larger costs of trade-related job displacement to the economy as a whole.

The Trade Adjustment Assistance (TAA) program is a Department of Labor program to provide retraining and unemployment benefits to certain workers who have been displaced by growing imports. However, new research suggests that significant shares of displaced workers are signing up for disability and retirement benefits, other government income assistance, and government medical benefits, in addition to temporary trade adjustment assistance. Many of these workers, such as those on disability and retirement, are permanently dropping out of the labor force, resulting in permanent income losses to themselves and the economy. TAA benefits represent only a tiny share of the costs of adjustment. Examining only those costs for which workers actually qualify for government benefits, Autor, Dorn, and Hanson (2012, Figure 7 at 32) find that unemployment and TAA benefits represent only 6.3% of the total benefit costs associated with a $1,000 increase in imports per worker in “commuting zones” over the 1990–2007 period. Given the low level of coverage of social safety net programs in the United States versus other developed countries (such as those in the EU), actual adjustment costs for displaced workers are likely substantially larger than the actual U.S. benefits estimated by Autor, Dorn, and Hanson.

Conclusion

The growing U.S. goods trade deficit with China has displaced millions of jobs in the United States and has contributed heavily to the crisis in U.S. manufacturing employment, which has heightened over the last decade largely due to trade with China. Moreover, the United States is piling up foreign debt, losing export capacity, and facing a more fragile macroeconomic environment.

China and America are locked in destructive, interdependent economic cycles, and both can gain from rebalancing trade and capital flows. Although economic growth in China has been rapid, it is unbalanced and unsustainable. Growth in China slowed to 6.6% in 2018, and it is projected to fall to 5.5% in 2024 (IMF 2019). China’s economy is teetering on the edge between inflation and a growth slump, and a soft landing is nowhere in sight. China needs to rebalance its economy by becoming less dependent on exports and more dependent on domestic demand led by higher wages and infrastructure spending. It also needs to reduce excessive levels of domestic savings to better align savings levels with domestic investment and government borrowing. The best ways to do this are to raise wages and to increase public spending on pensions, health care, and other aspects of the safety net. This will reduce private saving and increase Chinese domestic demand for both domestic and imported goods, reducing China’s trade surplus (Scott 2017a).

The effects on the United States of China’s destructive, rapidly growing trade surplus are outlined in this report. To summarize, the growing U.S. trade deficit with China has eliminated 3.7 million U.S. jobs between 2001 and 2018, including 1.7 million jobs lost since 2008 (the first full year of the Great Recession) and more than 700,000 jobs lost or displaced in the first two years of the Trump administration. Of the total jobs lost due to the growing U.S.—China trade deficit, 2.8 million, or 75.4% of the total jobs lost, were in manufacturing. These losses were responsible for nearly all of the 2.9 million U.S. manufacturing jobs lost between December 2001 and December 2018. The growing trade deficit with China has reduced wages of those directly displaced by $37 billion through 2011 alone, and it is largely responsible for the loss of roughly $1,800 per worker per year, due to wage suppression, for all non-college-educated workers in the United States. These losses have been extremely costly for the workers and communities affected, as shown in this report.

The U.S.–China trade relationship needs to undergo a fundamental change. Addressing unfair trade, weak labor, and environmental standards in China, and ending currency manipulation and misalignment, should be our top trade and economic priorities with China (Scott 2017a, Scott and Mokhiber 2018).

About the authors

Robert E. Scott (@RobScott_EPI) joined the Economic Policy Institute in 1997 and is currently director of trade and manufacturing policy research. His areas of research include international economics, the impacts of trade and manufacturing policies on working people in the United States and other countries, the economic impacts of foreign investment, and the macroeconomic effects of trade and capital flows and exchange rates. He has published widely in academic journals and the popular press, including in the Journal of Policy Analysis and Management, the International Review of Applied Economics, and the Stanford Law and Policy Review, the Detroit News, the New York Times, Los Angeles Times, Newsday, USA Today, The Baltimore Sun, The Washington Times, The Hill, and other newspapers. He has also provided economic commentary for a range of electronic media, including NPR, CNN, Bloomberg, and the BBC. He has a Ph.D. in economics from the University of California at Berkeley.

Zane Mokhiber joined EPI in 2016. As a data analyst, he supports the research of EPI’s economists and EPI’s Economic Analysis and Research Network (EARN) on topics such as wages, labor markets, inequality, trade and manufacturing, and economic growth. Prior to joining EPI, Mokhiber worked for the Worker Institute at Cornell University as an undergraduate research fellow.

Acknowledgments

The authors thank Josh Bivens for comments, Lora Engdahl for editorial guidance and Jori Kandra and Daniel Perez for research assistance. This research was made possible by support from the Alliance for American Manufacturing.

Appendix: Methodology

The trade and employment analyses in this report are based on a detailed, industry-based study of the relationships between changes in trade flows and employment for each of approximately 205 individual industries of the U.S. economy, specially grouped into 44 custom sectors, and using the North American Industry Classification System (NAICS) with data obtained from the U.S. Census Bureau (2019a and 2020a)) and the U.S. International Trade Commission (USITC 2019).

The number of jobs supported by $1 million of exports or imports for each of 205 different U.S. industries is estimated using a labor requirements model derived from an input-output table developed by the BLS-EP (2019a). This model includes both the direct effects of changes in output (for example, the number of jobs supported by $1 million in auto assembly) and the indirect effects on industries that supply goods (for example, goods used in the manufacture of cars). So, in the auto industry for example, the indirect impacts include jobs in auto parts, steel, and rubber, as well as service industries such as accounting, finance, computer programming, and staffing and temporary help agencies that provide inputs to the motor vehicle manufacturing companies. This model estimates the labor content of trade using empirical estimates of labor content and goods flows between U.S. industries in a given base year (an input-output table for the year 2001 was used in this study) that were developed by the U.S. Department of Commerce and the BLS-EP. It is not a statistical survey of actual jobs gained or lost in individual companies, or the opening or closing of particular production facilities (Bronfenbrenner and Luce 2004 is one of the few studies based on news reports of individual plant closings).

Nominal trade data used in this analysis are converted to constant 2012 dollars using industry-specific deflators. This is necessary because the labor requirements table is estimated using price levels in that year. Data on real trade flows are converted to constant 2012 dollars using industry-specific price deflators from the BLS-EP (2019b). Use of constant 2012 dollars is required for consistency with the other BLS models used in this study.

Estimation and data sources

Data requirements

Step 1. U.S.–China trade data are obtained from the U.S. International Trade Commission DataWeb (USITC 2019) in four-digit, three-digit, and two-digit NAICS formats. General imports and total exports are downloaded for each year.

Step 2. To conform to the BLS Employment Requirements tables (BLS-EP 2019a), trade data must be converted into the BLS industry classifications system. For NAICS-based data, there are 205 BLS industries. The data are then mapped from NAICS industries onto their respective BLS sectors. The trade data, which are in current dollars, are deflated into real 2012 dollars using published price deflators from the BLS-EP (2019b).

Step 3. Real domestic employment requirements tables are downloaded from the BLS-EP (2019a). These matrices are input-output industry-by-industry tables that show the employment requirements for $1 million in outputs in 2012 dollars. So, for industry i the aij entry is the employment indirectly supported in industry i by final sales in industry j and, where i=j, the employment directly supported.

Analysis

Step 1. Job equivalents. BLS trade data are compiled into matrices. Let [T 2001 ] be the 205×2 matrix made up of a column of imports and a column of exports for 2001. [T 2018 ] is defined as the 205×2 matrix of 2018 trade data. Finally, [T 2008 ] is defined as the 205×2 matrix of 2008 trade data. Define [E 2001 ] as the 205×205 matrix consisting of the real 2001 domestic employment requirements tables. To estimate the jobs displaced by trade, perform the following matrix operations:

[J 2001 ] = [T 2001 ] × [E 2001 ]

[J 2008 ] = [T 2008 ] × [E 2001 ]

[J 2018 ] = [T 2018 ] × [E 2001 ]

[J 2001 ] is a 205×2 matrix of job displacement by imports and jobs supported by exports for each of 205 industries in 2001. Similarly, [J 2008 ] and [J 2018 ] are 205×2 matrices of jobs displaced or supported by imports and exports (respectively) for each of 205 industries in 2008 and 2018, respectively.

To estimate jobs created/lost over certain time periods, we perform the following operations:

[J nx01-18 ] = [J 2018 ] − [J 2001 ]

[J nx01-08 ] = [J 2008 ] − [J 2001 ]

[J nx08-18 ] = [J 2018 ] − [J 2008 ]

Step 2. State-by-state analysis. For states, pooled (5-year) estimates of employment-by-industry data are obtained from the Census Bureau’s American Community Survey (ACS) data for the 2013–2017 period (U.S. Census Bureau 2019a) and are mapped into 44 unique census industries and seven aggregated total and subtotals, for a total of 52 sectors (including scrap, not part of the census analysis) (Data Planet 2019).

Previous versions of this report (Kimball and Scott 2014, Scott and Mokhiber 2018) relied on single-year estimates, based on ACS 2011 data, of employment by industry, state and congressional district. This model has been completely reestimated in this version of the report with the ACS 5-year data referenced above. These date provide substantially better detail, and greatly improved accuracy, in the form of much lower levels of variance for employment estimates at every level of detail in the model. The new estimates also reflect congressional district boundaries for the 115th Congress for most districts in the country. Boundaries changed in only a few districts in Pennsylvania and Colorado between the 115th Congress and the current 116th Congress.

We look at job displacement from 2001 to 2018 so from this point, we use [J nx 01-18 ]. In order to work with 44 sectors, we group the 205 BLS industries into a new matrix, defined as [Jnew 01-18 ], a 44×2 matrix of job displacement numbers. We define [St 2013-2017 ] as the 44×51 matrix of state employment shares (with the addition of the District of Columbia) of employment in each industry calculated from the ACS 5-year employment estimates. We calculate:

[Stj nx01-18 ] = [St 2013-2017 ]T [Jnew 01-18 ]

where [Stj nx01-18 ] is the 44×51 matrix of job displacement/support by state and by industry. To get state total job displacement, we add up the subsectors in each state.

Step 3. Congressional district analysis. Employment by congressional district, by industry, and by state is obtained from the ACS 5-year employment estimates for the 2013–2017 period, which use geographic codings that match the district boundaries of the 115th Congress. In order to calculate job displacement in each congressional district, we use the columns in [Stjnx01-18], which represent individual state job-displacement-by-industry estimates, and define them as [Stj 01 ], [Stj 02 ], [Stj i ]…[Stj 51 ], with i representing the state number and each matrix being 44×1.

Each state has Y congressional districts, so [Cd i ] is defined as the 44×Y matrix of congressional district employment shares for each state. Congressional district shares are calculated thus:

[Cdj 01 ] = [Stj 01 ]T [Cd 01 ]

[Cdj i ] = [Stj i ] T [Cd i ]

[Cdj 51 ] = [Stj 51 ] T [Cd 51 ]

where [Cdj i ] is defined as the 44xY job displacement in state i by congressional district by industry.

To get total job displacement by congressional district, we add up the subsectors in each congressional district in each state.

Endnotes

The World Trade Organization, which was created in 1994, was empowered to engage in dispute resolution and to authorize imposition of offsetting duties if its decisions were ignored or rejected by member governments. It expanded the General Agreement on Tariffs and Trade (GATT) trading system’s coverage to include a huge array of subjects never before included in trade agreements, such as food safety standards, environmental laws, social service policies, intellectual property standards, government procurement rules, and more (Wallach and Woodall 2004).

Tables 1 and 2 report U.S. general imports from China as measured by “customs value” (the value of imports as appraised by the U.S. Customs Service) and total exports to China as measured by “free alongside” or FAS value (the value of exports at the U.S. port, including the transaction price, inland freight, insurance, and other charges) to China. News releases from the U.S. Census Bureau and the Commerce Department usually emphasize general imports and total exports. The U.S. Internal Trade Commission (USITC) often refers to this netting out of general imports and total exports as the “broad” measure of the trade balance, as opposed to the “narrow” measure, which relies on imports for consumption and domestic exports. (For an example, see USITC 2014.) For an explanation of the difference between general imports and imports for consumption, see the U.S. Census Bureau’s online trade glossary [2018b].) The key difference between these two measures is that total exports, as reported by the U.S. Census Bureau, include foreign exports (re-exports), i.e., goods produced in other countries and shipped through the United States, while domestic exports, as implied by the name, do not include re-exports. While a previous version of this report (Kimball and Scott 2014) relied on the narrow definition, using imports for consumption and domestic exports for the analysis, the broad measure was used in Scott 2017a. For 2017, imports for consumption were $504.0 billion, domestic exports were $120.0 billion, and the reported (narrow) trade balance was $384.0 billion. When we compare the trade deficit and job displacement estimates we obtained using the broad measure with the estimates we would have obtained using the narrow measure, we find the difference to be insignificant. The broad measure delivers an estimate of 3.36 million net jobs displaced in 2017, whereas the narrow measure delivers an estimate of 3.44 million net jobs displaced in 2017, as reported in Scott and Mokhiber (2018). In this report, all estimates for trade and jobs gained and lost for prior years are based on the broad measure of the trade balance. Data for individual years, and for the change in net jobs displaced, are reported in Table 1, in Figure A, and in other exhibits in this report.

Average annual change note shown in Table 1. Total change divided by number of years deal is in effect is $336.5 divided by 17, which equals $19.8 billion per year.

While some small proportion of goods imported from China represent a category of goods that may not be produced in the United States, and thus would be “noncompeting” goods, the model used in this report produces an overall estimate of the net jobs displaced by the growing trade deficit. It is, in essence, an estimate of the jobs displaced by the growth of imports in excess of the growth of exports. Since virtually all U.S. imports from China are manufactured goods, as shown in Table 2 in this report, nearly all could be produced in the United States but for China’s unfair trade and currency policies and its domestic “savings glut” (Setser 2016).

The term “displaced” would be appropriate to an economy that was at true full employment, where any displaced worker would immediately take a job in another sector of the economy. However, the workers displaced by goods trade are almost exclusively manufacturing workers, and these workers have not been successfully moving into different parts of the economy in recent years: more than one-third of manufacturing workers who were displaced between 2015 and 2017 and who had previously been employed for at least three years were either unemployed or out of the labor force in January 2018 (BLS 2018a). Thus, trade-related job displacement does result in at least some workers moving to a nonworking status, thus “lost” jobs, even if other workers are reemployed elsewhere in the economy (reemployment would result in a change in the composition, rather than the level, of employment).

The BLS updated its Employment Requirements Matrix in September 2019 (BLS-EP 2019a), as it normally does every two years. Those revisions have been taken into account in this update. There are 205 NAICS-based BLS industries in the 2019 BLS update (NAICS stands for North American Industry Classification System).

Updated in Rasmussen 2017. Employment requirements tables in that report are derived from BEA input-output data, which are the primary source of data used to estimate BLS employment requirement tables (BLS-EP 2019a).

The macroeconomic model developed in Scott and Glass 2016 assumes that a 1.6% decrease in GDP would reduce total direct and indirect U.S. employment by roughly 1.35%. There were, on average, 155.8 million people employed in the United States in 2019 (BLS 2019b), thus yielding 2.1 million direct and indirect jobs displaced. The macroeconomic model also assumes a respending multiplier of 0.6 and yields a total of 3.4 million direct and indirect and respending jobs displaced by a trade deficit of this magnitude.

Scrap and used or secondhand goods are industries 203 and 204, respectively, in the BLS model, and there are no jobs supported or displaced by the production of or trade in goods in these sectors, according to the BLS model. (The jobs supported or displaced by trade are counted in the year these goods are originally manufactured—that is, when they are new—not when they are traded in the secondhand market.)

Authors’ calculations and USITC (2019); data not published in this report, available upon request.

ATPs are an amalgamation of products from a variety of industries and subsectors within the broad NAICS-based categories shown in Table 2. They consist of 10 categories of products including biotechnology, life science, opto-electronics, information and communications, electronics, flexible manufacturing, advanced materials, ae