Report | Trade and Globalization

China Trade, Outsourcing and Jobs: Growing U.S. trade deficit with China cost 3.2 million jobs between 2001 and 2013, with job losses in every state

Briefing Paper #385

Download PDF

Press release

Since China entered the World Trade Organization in 2001, the massive growth of trade between China and the United States has had a dramatic and negative effect on U.S. workers and the domestic economy. Specifically, a growing U.S. goods trade deficit with China has the United States piling up foreign debt, losing export capacity, and losing jobs, especially in the vital but under-siege manufacturing sector. Growth in the U.S. goods trade deficit with China between 2001 and 2013 eliminated or displaced 3.2 million U.S. jobs, 2.4 million (three-fourths) of which were in manufacturing. These lost manufacturing jobs account for about two-thirds of all U.S. manufacturing jobs lost or displaced between December, 2001 and December 2013.

Among specific industries, the trade deficit in the computer and electronic parts industry grew the most, and 1,249,100 jobs were lost or displaced, 39.6 percent of the 2001–2013 total. As a result, many of the hardest-hit congressional districts were in California, Texas, Oregon, Massachusetts, and Minnesota, where jobs in that industry are concentrated. Some districts in New York, Georgia, and Illinois 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.

The growing trade deficit with China has cost jobs in all 50 states and the District of Columbia. Using a new model and new congressional district data to estimate the job impacts of trade for the 113th Congress, this study also finds that job losses occurred in every congressional district but one.1

This summary of the jobs impact of trade with China arise from the following specific findings of this study:

  • Most of the jobs lost or displaced by trade with China between 2001 and 2013 were in manufacturing industries (2.4 million jobs, or 75.7 percent).
  • Within manufacturing, rapidly growing imports of computer and electronic parts (including computers, parts, semiconductors, and audio and video equipment) accounted for 56.0 percent of the $240.1 billion increase in the U.S. goods trade deficit with China between 2001 and 2013. The growth of this deficit eliminated 1,249,100 U.S. jobs in computer and electronic parts in this period. Indeed, in 2013, the total U.S. trade deficit with China was $324.2 billion—$154.4 billion of which was in computer and electronic parts.
  • 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 2013, the United States had a $116.9 billion deficit in advanced technology products with China, and this deficit was responsible for 36.0 percent of the total U.S.-China goods trade deficit. In contrast, the United States had a $35.6 billion surplus in advanced technology products with the rest of the world in 2013.
  • Other industrial sectors hit hard by the growing trade deficit with China between 2001 and 2013 include apparel (203,900 jobs); textile mills and textile product mills (106,800); fabricated metal products (141,200); electrical equipment, appliances, and components (96,700); furniture and related products (94,700); plastics and rubber products (72,800); motor vehicles and parts (34,800); and miscellaneous manufactured goods (107,600). Several service sectors were also hit hard, by indirect job losses, including administrative and support and waste management and remediation services (196,900) and professional, scientific, and technical services (169,900).
  • The 3.2 million U.S. jobs lost or displaced by the goods trade deficit with China between 2001 and 2013 were distributed among all 50 states and the District of Columbia, with the biggest net losses occurring in California (564,200 jobs), Texas (304,700), New York (179,200), Illinois (132,500), Pennsylvania (122,600), North Carolina (119,600), Florida (115,700), Ohio (106,400), Massachusetts (97,200), and Georgia (93,700).
  • In percentage terms, the jobs lost or displaced due to the growing goods trade deficit with China in the 10 hardest-hit states ranged from 2.44 percent to 3.67 percent of the total state employment: Oregon (62,700 jobs lost or displaced, equal to 3.67 percent of total state employment), California (564,200 jobs, 3.43 percent), New Hampshire (22,700 jobs, 3.31 percent), Minnesota (83,300 jobs, 3.05 percent), Massachusetts (97,200 jobs, 2.96 percent), North Carolina (119,600 jobs, 2.85 percent), Texas (304,700 jobs, 2.66 percent), Rhode Island (13,200 jobs, 2.58 percent), Vermont (8,200 jobs, 2.51 percent), and Idaho (16,700 jobs, 2.44 percent).
  • The hardest-hit congressional districts were concentrated in states that were heavily exposed to the growing U.S.-China trade deficit in computer and electronic parts and other durable goods industries such as furniture as well nondurable industries such as textiles and apparel. The three hardest-hit congressional districts were all located in Silicon Valley in California, including the 17th (South Bay, encompassing Sunnyvale, Cupertino, Santa Clara, Fremont, Newark, North San Jose, and Miltpitas2), which lost 61,500 jobs, equal to 17.77 percent of all jobs in the district), the 18th Congressional District (including parts of San Jose, Palo Alto, Redwood City, Mountain View, and Los Gatos), which lost 50,700 jobs, 14.72 percent), and the 19th Congressional District (most of San Jose and other parts of Santa Clara County, which lost 39,900 jobs, 12.31 percent of all jobs). Of the top 20 hardest-hit districts, eight were in California (in rank order, the 17th, 18th, 19th, 15th, 40th, 34th, 52nd, and 45th), six were in Texas (31st, 3rd, 10th, 18th, 17th, and 2nd), and one each in Oregon (1st), Massachusetts (3rd), Georgia (14th), Minnesota (1st), New York (18th), and Illinois (6th). Job losses in these districts ranged from 13,900 jobs to 61,500 jobs, and 4.28 percent to 17.77 percent of total district jobs.

The job displacement estimates in this study are conservative. They include only the jobs directly or indirectly displaced by trade, and exclude jobs in domestic wholesale and retail trade or advertising; they also exclude respending employment.3 They also do not account for the fact that during the Great Recession of 2007–2009, and continuing through 2013, jobs displaced by China trade reduced wages and spending, which led to further job losses.

Further, the jobs impact of the U.S. trade deficit with China is not limited to job loss and displacement and the associated direct wages losses. Competition with low-wage workers from less-developed countries such as China has driven down wages for workers in U.S. manufacturing and reduced the wages and bargaining power of similar, non-college-educated workers throughout the economy, as previous EPI research has shown. The affected population includes essentially all workers with less than a four-year college degree—roughly 70 percent of the workforce, or about 100 million workers (U.S. Census Bureau 2012).

As earlier EPI research has shown, trade with China between 2001 and 2011 displaced 2.7 million workers, who suffered a direct loss of $37.0 billion in reduced wages alone in 2011 (Scott 2013a). The nation’s 100 million non-college educated workers suffered a total loss of roughly $180 billion due to increased trade with low-wage countries (Bivens 2013). These indirect wage losses were nearly five times greater than the direct losses suffered by workers displaced by China trade, and the pool of affected workers was nearly 40 times larger (100 million non-college-educated workers versus 2.7 million displaced workers).

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

Proponents of China’s entry into the World Trade Organization (WTO) frequently claimed that it would create jobs in the United States, increase U.S. exports, and improve the trade deficit with China.4 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,” and these figures “can grow substantially with the new access to the Chinese market the WTO agreement creates,” he said (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 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 special safeguard measures designed to limit the disruptive effects of surging imports from China on domestic producers.

However, as a result of China’s currency manipulation and other trade-distorting practices, including extensive subsidies, legal and illegal barriers to imports, dumping, and suppression of wages and labor rights, the envisioned flow of U.S. exports to China did not occur. Further, the agreement spurred foreign direct investment (FDI) in Chinese enterprises, which has expanded China’s manufacturing sector at the expense of the United States. Finally, the core of the agreement failed to include any protections to maintain or improve labor or environmental standards or to prohibit currency manipulation.

In retrospect, the promises about jobs and exports misrepresented the real effects of trade on the U.S. economy: Trade leads to both job creation and job loss or displacement. (This paper describes the net effect of trade on employment as jobs “lost or displaced,” with the terms “lost” and “displaced” used interchangeably.) Increases in U.S. exports tend to create jobs in the United States, but increases in imports lead to job loss—by destroying existing jobs and preventing new job creation—as imports displace goods that otherwise would have been made in the United States by domestic workers. This is what has occurred with China since it entered the WTO; the United States’ widening trade deficit with China is costing U.S. jobs.

From 2001 to 2013, imports from China increased dramatically, rising from $102.1 billion in 2001 to $438.2 billion in 2013, as shown in Table 1.5 U.S. exports to China rose rapidly from 2001 to 2013, but from a much smaller base, from $18.0 billion in 2001 to $114.0 billion in 2013. As a result, China’s exports to the United States in 2013 were almost four times greater than U.S. exports to China. These trade figures make the China trade relationship the United States’ most imbalanced by far (authors’ analysis of USITC 2014).

Table 1

U.S.-China goods trade and job displacement, 2001–2013

Change ($billions) Percent change
2001 2008 2013 2001–2013 2008–2013 2001–2013
U.S. goods trade with China ($ billions, nominal)
U.S. domestic exports* $18.0 $67.2 $114.0 $96.1 $46.9 534.9%
U.S. imports for consumption $102.1 $337.5 $438.2 $336.1 $100.7 329.3%
U.S. trade balance -$84.1 -$270.3 -$324.2 -$240.1 -$53.8 285.4%
Average annual change in the trade balance -$21.8 -$10.8 11.9%
Change (thousands of jobs) Percent change
U.S. trade-related jobs supported and displaced (thousands of jobs)
U.S. domestic exports–jobs supported 161.4 499.2 767.5 606.2 268.4 375.6%
U.S. imports for consumption–jobs displaced 1,127.7 3,620.1 4,890.9 3,763.3 1,270.8 333.7%
U.S. trade deficit–net jobs displaced 966.3 3,121.0 4,123.4 3,157.1 1,002.4 326.7%
Average annual change in net jobs displaced 287.0 200.5 12.9%

*Domestic exports are goods produced in the United States and exclude foreign exports (re-exports), i.e., goods produced in other countries and shipped through the United States. Total exports as reported by the U.S. International Trade Commission include re-exports. Total exports were estimated to be $121.7 billion in 2013, and U.S. re-exports to China represent 6.33 percent of total exports. The employment estimates shown here are based on domestic exports only. See endnotes 5 and 6 for additional details.

Source: Authors' analysis of U.S. Census Bureau (2013), U.S. International Trade Commission (USITC 2014), Bureau of Labor Statistics (BLS 2014b), and BLS Employment Projections program (BLS-EP 2014a and 2014b). For a more detailed explanation of data sources and computations, see the appendix.

Copy the code below to embed this chart on your website.

Overall, the U.S. goods trade deficit with China rose from $84.1 billion in 2001, the year China entered the WTO, to $324.2 billion in 2013, an increase of $240.1 billion, as shown in Table 1. Put another way, since China entered the WTO in 2001, the U.S. trade deficit with China has increased annually by $21.8 billion, or 11.9 percent, on average. Between 2008 and 2013, the U.S. goods trade deficit with China increased 53.8 billion, as shown in Table 1. This 19.9 percent increase occurred despite the collapse in world trade between 2008 and 2009 caused by the Great Recession and a decline in the U.S. trade deficit with the rest of the world of 16.1 percent between 2008 and 2013 (according to the authors’ analysis of U.S. ITC 2014). As a result, China’s share of the overall U.S. goods trade deficit increased from 29.4 percent in 2008 to 37.3 percent in 2013.6

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

Each $1 billion in exports to China from the United States supports some American jobs. However, each $1 billion in imports from China displaces the American workers who would have been employed making these products in the United States. 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.

Trade and employment models

The Economic Policy Institute and other researchers have examined the job impacts of trade in recent years by subtracting the job opportunities lost to imports from those gained through exports. This report uses standard input-output models and data to estimate the jobs displaced by trade. Many reports by 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 used input-output and “employment requirements” tables from the Bureau of Labor Statistics Employment Projections program (BLS-EP 2014a), 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, including Mark Doms, then chief economist at the U.S. Department of Commerce (now undersecretary of commerce for economic affairs), and David Walters, chief economist at the Office of the U.S. Trade Representative.

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 195 U.S. industries, 77 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 December 2013 (accessed by EPI in 2014; see BLS-EP 2014a and 2014b). Our models have been completely revised and updated using the newest, best available data for this report.

The model 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.7 The difference between these two numbers is essentially the jobs displaced by the growing trade deficit, holding all else equal.

Jobs displaced by the growing China trade deficit 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 and home health care pay lower wages and have less comprehensive benefits than traded-goods industries (Scott 2013a).

As shown in the bottom half of Table 1, U.S. exports to China in 2001 supported 161,400 jobs, but U.S. imports displaced production that would have supported 1,127,700 jobs. Therefore, the $84.1 billion trade deficit in 2001 displaced 966,300 jobs in that year. Net job displacement rose to 3,121,000 jobs in 2008 and 4,123,400 jobs in 2013.

That means that since China’s entry into the WTO in 2001 and through 2013, the increase in the U.S.-China trade deficit eliminated or displaced 3,157,100 U.S. jobs. Also shown in Table 1, the U.S. trade deficit with China increased by $53.8 billion (or 19.9 percent) between 2008 and 2013. During that period, the number of jobs displaced increased by 32.1 percent.

Figure A

Cumulative U.S. jobs displaced by growing goods trade deficit with China, 2001–2013 (in thousands of jobs)

Year  Jobs displaced (thousands)
2001 0
2002 223.3
2003 464.7
2004 882.4
2005 1,359.6
2006 1,726.8
2007 2,087.4
2008 2,154.7
2009 1,861.6
2010 2,568.1
2011 2,911.4
2012 3,109.0
2013 3,157.1 
ChartData Download data

The data below can be saved or copied directly into Excel.

Source: Authors' analysis of U.S. Census Bureau (2013), U.S. International Trade Commission (USITC 2014), Bureau of Labor Statistics (BLS 2014b), and BLS Employment Projections program (BLS-EP 2014a and 2014b).  For a more detailed explanation of data sources and computations, see the appendix.

Copy the code below to embed this chart on your website.

Total jobs lost or displaced between 2008 and 2013 alone amounted to 1,002,400, either by the elimination of existing jobs or by the prevention of new job creation. 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). On average, 287,000 jobs per year have been lost or displaced since China’s entry into the WTO (as shown in Table 1, last row, data column four). The continuing growth of job displacement between 2008 and 2013 despite the relatively small increase in the bilateral trade deficit in this period reflects the relatively rapid growth of U.S. imports of computer and electronics parts from China, and the fact that the price index for most of these products fell continuously throughout the study period, as noted later in this paper. The share of U.S. imports from China accounted for by computer and electronic products (in current, nominal dollars) increased from 32.9 percent in 2008 to 37.8 percent in 2013 (according to the authors’ analysis of USITC 2014).

The trade deficit and job losses, by industry

Table 2

Change in U.S. goods trade with China, by industry, 2001–2013

Imports 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
Agriculture, forestry, fishing, and hunting $2.3 0.7% $20.2 21.0% $17.9 -7.5%
Mining $0.0 0.0% $2.6 2.7% $2.7 -1.1%
    Oil and gas $0.0 0.0% $0.1 0.1% $0.2 -0.1%
    Minerals and ores $0.0 0.0% $2.5 2.6% $2.5 -1.0%
Manufacturing $333.6 99.2% $65.5 68.2% $-268.1 111.7%
    Nondurable goods $49.0 14.6% $6.1 6.4% $-42.8 17.8%
Food $3.1 0.9% $3.9 4.0% $0.8 -0.3%
Beverage and tobacco products $0.0 0.0% $1.5 1.5% $1.5 -0.6%
Textile mills and textile product mills $9.3 2.8% $0.5 0.5% $-8.8 3.7%
Apparel $24.1 7.2% $0.0 0.0% $-24.1 10.0%
Leather and allied products $12.4 3.7% $0.3 0.3% $-12.2 5.1%
    Industrial supplies $34.0 10.1% $17.4 18.1% $-16.6 6.9%
Wood products $2.5 0.7% $1.1 1.1% $-1.4 0.6%
Paper $2.7 0.8% $2.0 2.0% $-0.7 0.3%
Printed matter and related products $1.6 0.5% $0.1 0.1% $-1.5 0.6%
Petroleum and coal products $0.1 0.0% $1.4 1.4% $1.3 -0.5%
Chemicals $11.2 3.3% $11.5 11.9% $0.2 -0.1%
Plastics and rubber products $12.2 3.6% $1.0 1.0% $-11.2 4.7%
Nonmetallic mineral products $3.8 1.1% $0.4 0.4% $-3.4 1.4%
    Durable goods $250.6 74.6% $42.0 43.7% $-208.7 86.9%
Primary metal $3.4 1.0% $2.6 2.7% $-0.8 0.3%
Fabricated metal products $14.5 4.3% $1.6 1.7% $-12.9 5.4%
Machinery $20.4 6.1% $7.3 7.6% $-13.2 5.5%
Computer and electronic parts $141.3 42.0% $6.8 7.0% $-134.5 56.0%
Computer and peripheral equipment $60.0 17.8% $-0.4 -0.4% $-60.3 25.1%
Communications, audio, and video equipment $62.5 18.6% $-0.2 -0.2% $-62.7 26.1%
Navigational, measuring, electromedical, and control instruments $5.0 1.5% $4.5 4.7% $-0.5 0.2%
Semiconductor and other electronic components, and reproducing magnetic and optical media $13.8 4.1% $2.8 2.9% $-11.0 4.6%
Electrical equipment, appliances, and components $23.3 6.9% $1.5 1.6% $-21.7 9.1%
Transportation equipment $11.4 3.4% $19.6 20.4% $8.2 -3.4%
Motor vehicles and motor vehicle parts $9.8 2.9% $10.0 10.4% $0.2 -0.1%
Aerospace products and parts $0.7 0.2% $9.5 9.9% $8.8 -3.7%
Railroad, ship, and other transportation equipment $0.9 0.3% $0.1 0.1% $-0.8 0.3%
Furniture and related products $12.3 3.6% $0.1 0.1% $-12.2 5.1%
Miscellaneous manufactured commodities $24.1 7.2% $2.4 2.5% $-21.7 9.0%
Information** $0.0 0.0% $0.1 0.1% $0.1 0.0%
Scrap and second-hand goods $0.3 0.1% $7.7 8.0% $7.4 -3.1%
Total change $336.1 100.0% $96.1 100.0% $-240.1 100.0%

* Excludes utilities, construction, and service sectors, which reported no goods trade in this period.
** Includes publishing industries (excluding Internet); goods trade in this sector is concentrated in NAICS 5111, Newspaper, periodical, book, and directory publishers.

Source: Authors' analysis of U.S. International Trade Commission (USITC 2014). For a more detailed explanation of the data sources and computations, see the appendix.

Copy the code below to embed this chart on your website.

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. China is moving rapidly “upscale,” from low-tech, low-skilled, 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 also developed a rapidly growing trade surplus in high-technology products.

Table 2 provides a snapshot of the changes in goods trade flows between 2001 and 2013, by industry, for exports, imports, and the trade balance. The rapid growth of the bilateral trade deficit in computer and electronic parts (including computers, parts, semiconductors, and audio and video equipment) accounted for 56.0 percent of the $240.1 billion increase in the U.S. trade deficit with China between 2001 and 2013. In 2013, the total U.S. trade deficit with China was $324.2 billion—$154.4 billion of which was in computer and electronic parts (trade flows by industry in 2001 and 2013 are shown in Supplemental Table 5, available at the end of this document.

Table 2 shows that the growth in manufactured imports explained 99.2 percent of total growth in imports from China between 2001 and 2013, and included a wide array of products. Computer and electronic parts were responsible for 42.0 percent of the growth in imports in this period, including computer equipment ($60.0 billion, or 17.8 percent of the overall growth in imports) and communications, audio, and video equipment ($62.5 billion, or 18.6 percent). Other major importing sectors included apparel ($24.1 billion, or 7.2 percent) and miscellaneous manufactured commodities ($24.1 billion, or 7.2 percent).

As Table 2 shows, manufacturing was the top sector exporting to China—68.2 percent of the growth in exports to China between 2001 and 2013 was in manufactured goods, totaling $65.5 billion. Within manufacturing, key export-growth industries included chemicals ($11.5 billion, or 11.9 percent of the growth in exports), aerospace products and parts ($9.5 billion, or 9.9 percent), motor vehicles and parts ($10.0 billion, or 10.4 percent), and machinery ($7.3 billion, or 7.6 percent). Scrap and second-hand goods industries (which support no jobs, according to BLS–EP 2014a models8) accounted for 8.0 percent ($7.7 billion) of the growth in exports. Agricultural exports, which were dominated by corn, soybeans, and other cash grains, grew faster than any individual manufacturing industry, increasing $20.2 billion (21.0 percent of the total increase) between 2001 and 2013. Nonetheless, the overall scale of U.S. exports to China in 2013 was dwarfed by imports from China in that year, which exceeded the value of exports by nearly 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 37.8 percent ($165.6 billion) of U.S. imports from China in 2013 (as shown in Supplemental Table 5. 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).9 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 now one of the top suppliers of auto parts to the United States, having recently surpassed Germany (Scott and Wething 2012).

In 2013, the United States had a $116.9 billion trade deficit with China in ATP, reflecting a ten-fold increase from $11.8 billion in 2002.10 This ATP deficit was responsible for 36.0 percent of the total U.S.-China trade deficit in 2013. It dwarfs the $35.6 billion surplus in ATP that the United States had with the rest of the world in 2013, the result of a 5.0 percent annual increase in U.S. ATP exports to the rest of the world between 2002 and 2013. As a result of the U.S. ATP deficit with China, the United States ran an overall deficit in ATP products in 2013 (of $81.3 billion), as it has in every year since 2002 (U.S. Census Bureau 2014b).

Table 3

Net U.S. jobs created or displaced by goods trade with China, by industry, 2001–2013

Industry Total Share of total jobs displaced
Agriculture, forestry, fishing, and hunting 87,900 -2.8%
Mining -1,600 0.1%
Oil and gas -1,200 0.0%
Minerals and ores -400 0.0%
Utilities -10,900 0.3%
Construction -14,700 0.5%
Manufacturing -2,391,500 75.7%
Nondurable goods -375,700 11.9%
Food -7,500 0.2%
Beverage and tobacco products 2,700 -0.1%
Textile mills and textile product mills -106,800 3.4%
Apparel -203,900 6.5%
Leather and allied products -60,100 1.9%
Industrial supplies -200,300 6.3%
Wood products -22,100 0.7%
Paper -24,300 0.8%
Printed matter and related products -30,700 1.0%
Petroleum and coal products -900 0.0%
Chemicals -22,100 0.7%
Plastics and rubber products -72,800 2.3%
Nonmetallic mineral products -27,400 0.9%
Durable goods -1,815,600 57.5%
Primary metal -42,300 1.3%
Fabricated metal products -141,200 4.5%
Machinery -70,100 2.2%
Computer and electronic parts -1,249,100 39.6%
Computer and peripheral equipment -732,900 23.2%
Communications, audio, and video equipment -234,700 7.4%
Navigational, measuring, electromedical, and control instruments -11,900 0.4%
Semiconductors and other electronic components, and reproducing magnetic and optical media -269,600 8.5%
Electrical equipment, appliances, and components -96,700 3.1%
Transportation equipment -14,000 0.4%
Motor vehicles and motor vehicle parts -34,800 1.1%
Aerospace products and parts 23,500 -0.7%
Railroad, ship, and other transportation equipment -2,800 0.1%
Furniture and related products -94,700 3.0%
Miscellaneous manufactured commodities -107,600 3.4%
Wholesale trade 0 0.0%
Retail trade 0 0.0%
Transportation and warehousing -94,200 3.0%
Information -81,400 2.6%
Finance and insurance -40,100 1.3%
Real estate and rental and leasing -24,300 0.8%
Professional, scientific, and technical services -169,900 5.4%
Management of companies and enterprises -113,400 3.6%
Administrative and support and waste management and remediation services -196,900 6.2%
Education services -2,200 0.1%
Healthcare and social assistance -1,500 0.0%
Arts, entertainment, and recreation -12,100 0.4%
Accommodation and food services -46,700 1.5%
Other services (except public administration) -27,500 0.9%
Public administration -16,100 0.5%
Subtotal, nonmanufacturing -765,600 24.3%
Total* -3,157,100

*Subcategory and overall totals may vary slightly due to rounding.

Source: Authors' analysis of U.S. Census Bureau (2013), U.S. International Trade Commission (USITC 2014), Bureau of Labor Statistics (BLS 2014b), and BLS Employment Projections program (BLS-EP 2014a and 2014b).  For a more detailed explanation of data sources and computations, see the appendix.

Copy the code below to embed this chart on your website.

Trade deficits are highly correlated with job loss or displacement by industry, as shown in Table 3. The growing trade deficit with China eliminated 2,391,500 manufacturing jobs between 2001 and 2013, more than three-fourths (75.7 percent) of the total. By far the largest job displacements occurred in the computer and electronic parts industry, which lost 1,249,100 jobs (39.6 percent of the 3.2 million jobs displaced overall). This industry includes computer and peripheral equipment (732,900 jobs, or 23.2 percent of the overall jobs displaced), semiconductors and components (269,600 jobs, or 8.5 percent), and communications, audio, and video equipment (234,700 jobs, or 7.4 percent). Other hard-hit industries included apparel (203,900 jobs displaced, equal to 6.5 percent of the total), textile mills and textile product mills (106,800, or 3.4 percent), fabricated metal products (141,200, or 4.5 percent), furniture and related products (94,700, or 3.0 percent), plastics and rubber products (72,800,or 2.3 percent), motor vehicles and motor vehicle parts (34,800, or 1.1 percent), and miscellaneous manufactured commodities (107,600 jobs, or 3.4 percent). 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 (196,900 jobs, or 6.2 percent) and professional, scientific, and technical services (169,900 jobs, or 5.4 percent).

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 23.8 percent in 2001 to 37.8 percent in 2013 (to $165.6 billion, as shown in Supplemental Table 5), the average price indexes (deflators) for most of these products fell sharply between 2001 and 2013—40.5 percent on a trade-weighted basis. Thus, the real value of computer and electronic imports increased more than twelvefold in this period, rising from $19.5 billion to $236.2 billion in 2013 in constant 2005 dollars (authors’ analysis of real trade flows—see methodology appendix for data sources and computational details).11

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 led to job displacement in all 50 states and the District of Columbia, as shown in Table 4 and Figure B. (Supplemental Table 1 ranks the states by the number of net jobs displaced, while Supplemental Table 2 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 2013 due to the growing goods trade deficit with China ranged from 2.44 percent to 3.67 percent of total state employment in the 10 hardest-hit states ranked by job shares displaced: Oregon, California, New Hampshire, Minnesota, Massachusetts, North Carolina, Texas, Rhode Island, Vermont, and Idaho. As shown in Supplemental Table 1, 564,200 jobs were lost in California, compared with 304,700 in Texas, 179,200 in New York, and 132,500 in Illinois. The 3.2 million U.S. jobs displaced due to the growing trade deficit with China between 2001 and 2013 represented 2.25 percent of total U.S. employment, as shown in Table 4.

Figure B

Net U.S. jobs displaced due to goods trade deficit with China as a share of total state employment, 2001–2013

State Jobs displaced as share
of state employment
Alabama 2.13%
Alaska 0.76%
Arizona 2.28%
Arkansas 1.98%
California 3.43%
Colorado 2.38%
Connecticut 2.04%
Delaware 1.31%
District of Columbia 0.90%
Florida 1.43%
Georgia 2.23%
Hawaii 0.97%
Idaho 2.44%
Illinois 2.24%
Indiana 2.31%
Iowa 1.60%
Kansas 1.38%
Kentucky 2.24%
Louisiana 0.93%
Maine 1.77%
Maryland 1.47%
Massachusetts 2.96%
Michigan 1.91%
Minnesota 3.05%
Mississippi 1.71%
Missouri 1.61%
Montana 0.75%
Nebraska 1.29%
Nevada 1.26%
New Hampshire 3.31%
New Jersey 2.22%
New Mexico 1.44%
New York 2.00%
North Carolina 2.85%
North Dakota 0.65%
Ohio 2.04%
Oklahoma 1.74%
Oregon 3.67%
Pennsylvania 2.09%
Rhode Island 2.58%
South Carolina 2.27%
South Dakota 1.20%
Tennessee 2.24%
Texas 2.66%
Utah 2.14%
Vermont 2.51%
Virginia 1.65%
Washington 1.79%
West Virginia 1.26%
Wisconsin 2.43%
Wyoming 0.59%
Total 2.25%
ChartData Download data

The data below can be saved or copied directly into Excel.

* 10 least-impacted states, plus D.C.
** 10 next-least-impacted states
*** 10 midde-impacted states
**** 10 next-most-impacted states
***** 10 most-impacted states

Source: Authors' analysis of U.S. Census Bureau (2013), U.S. International Trade Commission (USITC 2014), Bureau of Labor Statistics (BLS 2014b), and BLS Employment Projections program (BLS-EP 2014a and 2014b). For a more detailed explanation of data sources and computations, see the appendix.

Copy the code below to embed this chart on your website.

Table 4

Net U.S. jobs displaced due to goods trade deficit with China, by state, 2001–2013 (ranked by jobs displaced as a share of total state employment)

Rank State Net jobs displaced State employment (in 2011) Jobs displaced as share
of state employment
1 Oregon 62,700 1,710,300 3.67%
2 California 564,200 16,426,700 3.43%
3 New Hampshire 22,700 684,800 3.31%
4 Minnesota 83,300 2,728,900 3.05%
5 Massachusetts 97,200 3,284,700 2.96%
6 North Carolina 119,600 4,195,800 2.85%
7 Texas 304,700 11,455,100 2.66%
8 Rhode Island 13,200 511,200 2.58%
9 Vermont 8,200 327,300 2.51%
10 Idaho 16,700 684,900 2.44%
11 Wisconsin 68,600 2,819,500 2.43%
12 Colorado 59,400 2,492,400 2.38%
13 Indiana 67,800 2,934,500 2.31%
14 Arizona 61,200 2,688,000 2.28%
15 South Carolina 44,700 1,968,900 2.27%
16 Tennessee 62,500 2,784,500 2.24%
17 Kentucky 41,100 1,838,400 2.24%
18 Illinois 132,500 5,926,900 2.24%
19 Georgia 93,700 4,193,800 2.23%
20 New Jersey 92,000 4,152,500 2.22%
21 Utah 27,000 1,260,800 2.14%
22 Alabama 42,100 1,981,100 2.13%
23 Pennsylvania 122,600 5,853,300 2.09%
24 Ohio 106,400 5,213,500 2.04%
25 Connecticut 35,500 1,742,500 2.04%
26 New York 179,200 8,959,000 2.00%
27 Arkansas 24,500 1,235,800 1.98%
28 Michigan 80,100 4,191,900 1.91%
29 Washington 55,900 3,118,000 1.79%
30 Maine 11,400 643,100 1.77%
31 Oklahoma 29,300 1,681,800 1.74%
32 Mississippi 20,200 1,181,300 1.71%
33 Virginia 63,500 3,860,100 1.65%
34 Missouri 44,200 2,742,100 1.61%
35 Iowa 24,600 1,538,800 1.60%
36 Maryland 42,600 2,894,600 1.47%
37 New Mexico 12,500 869,800 1.44%
38 Florida 115,700 8,101,900 1.43%
39 Kansas 19,100 1,389,000 1.38%
40 Delaware 5,500 420,400 1.31%
41 Nebraska 12,200 943,600 1.29%
42 Nevada 15,200 1,204,900 1.26%
43 West Virginia 9,400 748,600 1.26%
44 South Dakota 5,000 415,600 1.20%
45 Hawaii 6,100 629,500 0.97%
46 Louisiana 18,300 1,973,900 0.93%
47 District of Columbia 2,800 310,600 0.90%
48 Alaska 2,600 344,300 0.76%
49 Montana 3,600 480,000 0.75%
50 North Dakota 2,400 370,800 0.65%
51 Wyoming 1,700 290,000 0.59%
Total* 3,154,300 140,399,600 2.25%

*Subcategory and overall totals may vary slightly due to rounding.

Source: Authors' analysis of U.S. Census Bureau (2013), U.S. International Trade Commission (USITC 2014), Bureau of Labor Statistics (BLS 2014b), and BLS Employment Projections program (BLS-EP 2014a and 2014b). For a more detailed explanation of data sources and computations, see the appendix.

Copy the code below to embed this chart on your website.

Figure B shows the broad impact of the growing trade deficit with China across the United States, with no areas exempt. Job losses have been most concentrated in states with high-tech industries, such as California, Massachusetts, Oregon, Minnesota, Idaho, Texas, Arizona, and Colorado, and in manufacturing states, including New Hampshire, North Carolina, and Vermont. Other hard-hit states include traditional manufacturing powers such as Rhode Island, South Carolina, Georgia, Tennessee, Wisconsin, Kentucky, Indiana, Illinois, and New Jersey.

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, including the District of Columbia. The top 20 hardest-hit congressional districts are shown in Table 5. Figure C shows job displacement in all 535 congressional districts plus the district of Columbia, as a share of total district employment. (Data for all 435 districts plus the District of Columbia are also provided in Supplemental Tables 3 and 4 at the end of this report.) Because the largest growth in the goods trade deficits with China occurred in the computer and electronic parts industry, many hard-hit congressional districts were in California, Texas, Oregon, Massachusetts, and Minnesota, where remaining jobs in that industry are concentrated. Other states with hard-hit districts include Georgia, New York and Illinois, which suffered considerable job displacement in a variety of manufacturing industries.12

Specifically, of the top 20 hardest-hit districts, eight were in California (in rank order, the 17th, 18th, 19th, 15th, 40th, 34th, 52nd, and 45th), six were in Texas (31st, 3rd, 10th, 18th, 17th, and 2nd), and one each in Oregon (1st), Massachusetts (3rd), Georgia (14th), Minnesota (1st), New York (18th), and Illinois (6th). Job losses in these districts ranged from 13,900 jobs to 61,500 jobs, and 4.28 percent to 17.77 percent of total district jobs.These distributions reflect both the size of some states (e.g., California and Texas) and also the concentration of the industries hardest-hit by the growing U.S.-China trade deficit, such as computer and electronic parts and other industries including furniture, textiles, apparel, and other manufactured products. Overall, manufacturing job losses were responsible for 2.4 million (75.7 percent) of the 3.2 million jobs displaced by the growing U.S.-China trade deficit between 2001 and 2013 (Table 3).

Figure C

Net U.S. jobs displaced due to goods trade deficit with China as a share of total congressional district employment, 2001–2013

Rank (by jobs displaced as share of total) State District Net jobs displaced District employment (in 2011) Jobs displaced as a share of employment
373 Alabama 1 3,500 283,000 1.24%
243 Alabama 2 5,100 276,900 1.84%
139 Alabama 3 6,400 274,600 2.33%
71 Alabama 4 7,600 262,900 2.89%
50 Alabama 5 10,100 311,900 3.24%
265 Alabama 6 5,600 318,400 1.76%
324 Alabama 7 3,800 253,500 1.50%
424 Alaska Statewide 2,600 344,300 0.76%
362 Arizona 1 3,400 264,900 1.28%
279 Arizona 2 5,100 299,200 1.70%
385 Arizona 3 3,100 262,200 1.18%
352 Arizona 4 3,100 233,500 1.33%
21 Arizona 5 13,600 317,900 4.28%
134 Arizona 6 8,600 366,000 2.35%
148 Arizona 7 6,400 282,300 2.27%
201 Arizona 8 6,100 301,700 2.02%
49 Arizona 9 11,700 360,300 3.25%
322 Arkansas 1 4,200 277,400 1.51%
190 Arkansas 2 7,000 336,300 2.08%
122 Arkansas 3 7,900 327,000 2.42%
246 Arkansas 4 5,400 295,100 1.83%
233 California 1 4,900 260,300 1.88%
329 California 2 4,700 323,100 1.45%
423 California 3 2,200 286,600 0.77%
61 California 4 8,900 294,200 3.03%
206 California 5 6,500 326,800 1.99%
223 California 6 5,500 288,300 1.91%
42 California 7 10,800 313,200 3.45%
378 California 8 2,900 235,500 1.23%
376 California 9 3,400 275,300 1.24%
280 California 10 4,700 277,200 1.70%
250 California 11 5,900 324,200 1.82%
75 California 12 11,300 399,400 2.83%
47 California 13 11,100 340,200 3.26%
22 California 14 15,200 364,000 4.18%
6 California 15 28,000 336,400 8.32%
434 California 16 1,100 244,900 0.45%
1 California 17 61,500 346,100 17.77%
2 California 18 50,700 344,500 14.72%
3 California 19 39,900 324,000 12.31%
221 California 20 5,800 302,500 1.92%
436 California 21 -1,100 243,800 -0.45%
430 California 22 1,900 289,600 0.66%
429 California 23 1,800 274,100 0.66%
395 California 24 3,600 323,500 1.11%
128 California 25 7,200 302,700 2.38%
188 California 26 6,800 325,900 2.09%
99 California 27 8,600 332,200 2.59%
166 California 28 7,800 359,900 2.17%
119 California 29 7,400 303,700 2.44%
91 California 30 9,600 358,200 2.68%
208 California 31 5,800 292,200 1.98%
77 California 32 8,300 293,800 2.83%
127 California 33 8,700 364,200 2.39%
11 California 34 16,200 309,400 5.24%
30 California 35 11,100 284,800 3.90%
415 California 36 2,300 251,900 0.91%
72 California 37 9,700 335,600 2.89%
64 California 38 9,300 313,300 2.97%
37 California 39 12,000 332,000 3.61%
8 California 40 15,600 280,500 5.56%
222 California 41 5,200 271,900 1.91%
102 California 42 7,800 307,000 2.54%
97 California 43 7,900 302,800 2.61%
52 California 44 8,700 270,600 3.22%
16 California 45 16,200 354,400 4.57%
36 California 46 11,400 314,400 3.63%
140 California 47 7,600 327,600 2.32%
27 California 48 14,100 352,600 4.00%
26 California 49 12,000 299,700 4.00%
132 California 50 7,000 296,200 2.36%
303 California 51 4,100 258,600 1.59%
13 California 52 17,000 350,100 4.86%
163 California 53 7,500 342,700 2.19%
229 Colorado 1 7,300 384,400 1.90%
39 Colorado 2 13,800 384,600 3.59%
394 Colorado 3 3,700 331,400 1.12%
40 Colorado 4 12,300 344,100 3.57%
142 Colorado 5 7,300 315,900 2.31%
162 Colorado 6 8,100 369,600 2.19%
235 Colorado 7 6,800 362,500 1.88%
262 Connecticut 1 6,200 349,800 1.77%
275 Connecticut 2 6,000 348,600 1.72%
169 Connecticut 3 7,600 352,700 2.15%
143 Connecticut 4 7,900 343,000 2.30%
153 Connecticut 5 7,800 348,300 2.24%
356 Delaware Statewide 5,500 420,400 1.31%
417 DC Statewide 2,800 310,600 0.90%
418 Florida 1 2,700 303,900 0.89%
390 Florida 2 3,400 301,500 1.13%
419 Florida 3 2,400 277,000 0.87%
366 Florida 4 4,200 329,900 1.27%
347 Florida 5 3,800 284,000 1.34%
357 Florida 6 3,700 283,200 1.31%
289 Florida 7 5,300 322,500 1.64%
90 Florida 8 7,600 283,400 2.68%
405 Florida 9 3,200 317,200 1.01%
386 Florida 10 3,900 331,500 1.18%
360 Florida 11 2,800 217,400 1.29%
170 Florida 12 6,100 283,200 2.15%
167 Florida 13 6,700 309,200 2.17%
336 Florida 14 4,600 320,700 1.43%
354 Florida 15 4,000 304,200 1.31%
320 Florida 16 4,200 276,100 1.52%
435 Florida 17 1,100 248,700 0.44%
367 Florida 18 3,600 284,000 1.27%
407 Florida 19 2,600 265,200 0.98%
338 Florida 20 4,300 302,100 1.42%
353 Florida 21 4,200 316,800 1.33%
212 Florida 22 6,500 332,000 1.96%
186 Florida 23 7,100 339,900 2.09%
350 Florida 24 3,900 293,400 1.33%
307 Florida 25 5,100 326,000 1.56%
388 Florida 26 3,800 335,600 1.13%
317 Florida 27 4,800 313,600 1.53%
404 Georgia 1 2,900 286,100 1.01%
292 Georgia 2 4,100 251,200 1.63%
88 Georgia 3 7,700 285,800 2.69%
273 Georgia 4 5,400 311,700 1.73%
264 Georgia 5 5,600 318,100 1.76%
104 Georgia 6 9,100 361,200 2.52%
55 Georgia 7 9,800 312,500 3.14%
363 Georgia 8 3,500 272,700 1.28%
86 Georgia 9 7,700 284,600 2.71%
255 Georgia 10 5,200 287,400 1.81%
174 Georgia 11 7,300 340,900 2.14%
282 Georgia 12 4,700 278,200 1.69%
306 Georgia 13 4,900 312,800 1.57%
10 Georgia 14 15,700 290,700 5.40%
380 Hawaii 1 4,000 330,100 1.21%
426 Hawaii 2 2,100 299,400 0.70%
74 Idaho 1 9,400 329,900 2.85%
195 Idaho 2 7,300 355,000 2.06%
321 Illinois 1 4,400 290,200 1.52%
263 Illinois 2 4,900 278,200 1.76%
259 Illinois 3 5,700 319,500 1.78%
110 Illinois 4 8,100 326,600 2.48%
155 Illinois 5 8,900 397,600 2.24%
18 Illinois 6 15,700 355,600 4.42%
315 Illinois 7 4,600 298,500 1.54%
56 Illinois 8 11,400 366,300 3.11%
95 Illinois 9 9,100 347,200 2.62%
58 Illinois 10 9,900 324,800 3.05%
65 Illinois 11 10,300 347,300 2.97%
345 Illinois 12 4,100 301,000 1.36%
387 Illinois 13 3,700 326,600 1.13%
85 Illinois 14 9,500 351,000 2.71%
309 Illinois 15 4,900 316,500 1.55%
260 Illinois 16 5,900 330,800 1.78%
213 Illinois 17 6,100 311,700 1.96%
316 Illinois 18 5,200 337,500 1.54%
257 Indiana 1 5,600 310,600 1.80%
84 Indiana 2 8,700 317,800 2.74%
53 Indiana 3 10,400 327,000 3.18%
210 Indiana 4 6,500 328,500 1.98%
242 Indiana 5 6,600 357,700 1.85%
118 Indiana 6 7,600 311,900 2.44%
171 Indiana 7 6,700 312,200 2.15%
87 Indiana 8 8,900 329,300 2.70%
211 Indiana 9 6,700 339,400 1.97%
185 Iowa 1 8,200 392,300 2.09%
236 Iowa 2 7,000 373,400 1.87%
349 Iowa 3 5,200 390,800 1.33%
398 Iowa 4 4,200 382,300 1.10%
422 Kansas 1 2,900 345,900 0.84%
335 Kansas 2 4,900 339,900 1.44%
176 Kansas 3 7,900 370,300 2.13%
402 Kansas 4 3,500 332,900 1.05%
182 Kentucky 1 6,000 284,800 2.11%
131 Kentucky 2 7,500 317,100 2.37%
137 Kentucky 3 7,800 333,300 2.34%
272 Kentucky 4 5,800 333,500 1.74%
365 Kentucky 5 3,000 234,300 1.28%
46 Kentucky 6 11,000 335,400 3.28%
416 Louisiana 1 3,200 354,000 0.90%
420 Louisiana 2 2,800 329,000 0.85%
414 Louisiana 3 3,000 328,100 0.91%
400 Louisiana 4 3,400 311,100 1.09%
421 Louisiana 5 2,400 283,900 0.85%
410 Louisiana 6 3,500 367,800 0.95%
285 Maine 1 5,700 340,400 1.67%
241 Maine 2 5,600 302,700 1.85%
342 Maryland 1 4,800 342,300 1.40%
371 Maryland 2 4,400 351,700 1.25%
346 Maryland 3 5,000 369,500 1.35%
327 Maryland 4 5,700 384,100 1.48%
372 Maryland 5 4,600 368,200 1.25%
251 Maryland 6 6,600 363,200 1.82%
343 Maryland 7 4,400 315,700 1.39%
261 Maryland 8 7,100 400,100 1.77%
308 Massachusetts 1 5,300 341,000 1.55%
24 Massachusetts 2 14,600 356,500 4.10%
7 Massachusetts 3 20,000 355,400 5.63%
35 Massachusetts 4 13,600 374,800 3.63%
43 Massachusetts 5 13,200 387,400 3.41%
107 Massachusetts 6 9,300 372,000 2.50%
300 Massachusetts 7 5,900 369,800 1.60%
192 Massachusetts 8 7,800 375,600 2.08%
184 Massachusetts 9 7,400 352,300 2.10%
339 Michigan 1 4,100 290,200 1.41%
117 Michigan 2 7,700 315,900 2.44%
168 Michigan 3 6,800 315,300 2.16%
218 Michigan 4 5,500 286,300 1.92%
294 Michigan 5 4,300 264,800 1.62%
203 Michigan 6 6,200 310,400 2.00%
278 Michigan 7 5,100 299,100 1.71%
200 Michigan 8 6,700 330,800 2.03%
217 Michigan 9 6,300 326,100 1.93%
144 Michigan 10 7,100 308,700 2.30%
161 Michigan 11 7,500 342,100 2.19%
337 Michigan 12 4,500 313,800 1.43%
281 Michigan 13 3,900 230,700 1.69%
277 Michigan 14 4,400 257,700 1.71%
14 Minnesota 1 16,300 348,200 4.68%
23 Minnesota 2 14,900 358,300 4.16%
25 Minnesota 3 14,200 353,800 4.01%
156 Minnesota 4 7,500 336,000 2.23%
111 Minnesota 5 8,700 352,000 2.47%
69 Minnesota 6 10,100 348,700 2.90%
249 Minnesota 7 6,000 328,700 1.83%
252 Minnesota 8 5,500 303,400 1.81%
78 Mississippi 1 8,600 305,600 2.81%
374 Mississippi 2 3,300 266,900 1.24%
331 Mississippi 3 4,400 303,900 1.45%
355 Mississippi 4 4,000 304,900 1.31%
425 Montana Statewide 3,600 480,000 0.75%
330 Missouri 1 4,800 331,500 1.45%
202 Missouri 2 7,600 378,600 2.01%
326 Missouri 3 5,500 370,000 1.49%
369 Missouri 4 4,100 324,900 1.26%
302 Missouri 5 5,500 345,300 1.59%
344 Missouri 6 4,900 355,900 1.38%
239 Missouri 7 6,300 337,400 1.87%
256 Missouri 8 5,400 298,500 1.81%
325 Nebraska 1 4,800 321,700 1.49%
288 Nebraska 2 5,200 316,300 1.64%
428 Nebraska 3 2,100 305,600 0.69%
401 Nevada 1 3,100 284,700 1.09%
283 Nevada 2 5,200 309,400 1.68%
389 Nevada 3 3,800 336,500 1.13%
399 Nevada 4 3,000 274,300 1.09%
63 New Hampshire 1 10,500 352,600 2.98%
33 New Hampshire 2 12,200 332,200 3.67%
247 New Jersey 1 6,200 339,200 1.83%
377 New Jersey 2 4,000 324,400 1.23%
293 New Jersey 3 5,600 344,200 1.63%
228 New Jersey 4 6,200 326,400 1.90%
79 New Jersey 5 10,000 356,100 2.81%
129 New Jersey 6 8,400 353,600 2.38%
45 New Jersey 7 12,400 377,100 3.29%
103 New Jersey 8 9,400 371,000 2.53%
126 New Jersey 9 8,100 338,500 2.39%
244 New Jersey 10 5,700 310,700 1.83%
96 New Jersey 11 9,400 358,800 2.62%
237 New Jersey 12 6,600 352,400 1.87%
196 New Mexico 1 6,400 311,900 2.05%
427 New Mexico 2 1,900 273,100 0.70%
323 New Mexico 3 4,300 284,800 1.51%
113 New York 1 8,400 343,300 2.45%
135 New York 2 8,400 357,800 2.35%
268 New York 3 5,900 336,700 1.75%
361 New York 4 4,400 342,500 1.28%
286 New York 5 5,600 336,200 1.67%
276 New York 6 5,600 327,000 1.71%
98 New York 7 8,400 322,200 2.61%
305 New York 8 4,600 292,700 1.57%
328 New York 9 4,800 324,900 1.48%
304 New York 10 5,700 360,300 1.58%
314 New York 11 4,900 317,500 1.54%
209 New York 12 8,300 418,800 1.98%
313 New York 13 4,900 317,200 1.54%
319 New York 14 5,200 341,800 1.52%
351 New York 15 3,400 255,900 1.33%
298 New York 16 5,200 323,600 1.61%
204 New York 17 6,800 341,400 1.99%
15 New York 18 15,200 332,100 4.58%
68 New York 19 9,600 327,300 2.93%
301 New York 20 5,700 357,600 1.59%
253 New York 21 5,600 309,200 1.81%
130 New York 22 7,600 320,200 2.37%
199 New York 23 6,600 324,600 2.03%
181 New York 24 6,900 327,300 2.11%
70 New York 25 9,700 335,400 2.89%
296 New York 26 5,300 327,700 1.62%
215 New York 27 6,600 337,800 1.95%
207 North Carolina 1 5,800 291,800 1.99%
28 North Carolina 2 12,100 303,800 3.98%
403 North Carolina 3 3,100 305,600 1.01%
82 North Carolina 4 9,800 350,900 2.79%
54 North Carolina 5 10,200 324,500 3.14%
41 North Carolina 6 12,200 341,800 3.57%
348 North Carolina 7 4,200 315,400 1.33%
31 North Carolina 8 11,400 301,700 3.78%
125 North Carolina 9 8,900 371,400 2.40%
38 North Carolina 10 11,700 324,000 3.61%
112 North Carolina 11 7,300 295,400 2.47%
67 North Carolina 12 9,400 319,800 2.94%
32 North Carolina 13 13,200 349,900 3.77%
431 North Dakota Statewide 2,400 370,800 0.65%
270 Ohio 1 5,800 332,300 1.75%
298 Ohio 2 5,200 323,600 1.61%
271 Ohio 3 5,800 333,000 1.74%
121 Ohio 4 7,700 317,900 2.42%
151 Ohio 5 7,500 334,200 2.24%
216 Ohio 6 5,700 292,300 1.95%
101 Ohio 7 8,400 326,800 2.57%
159 Ohio 8 7,300 328,800 2.22%
254 Ohio 9 5,700 315,000 1.81%
197 Ohio 10 6,400 312,800 2.05%
291 Ohio 11 4,500 275,200 1.64%
290 Ohio 12 5,900 359,500 1.64%
120 Ohio 13 7,800 320,400 2.43%
100 Ohio 14 9,000 349,700 2.57%
274 Ohio 15 5,800 336,400 1.72%
158 Ohio 16 7,900 355,600 2.22%
123 Oklahoma 1 8,700 361,900 2.40%
333 Oklahoma 2 4,200 290,300 1.45%
392 Oklahoma 3 3,700 329,900 1.12%
191 Oklahoma 4 7,300 350,900 2.08%
310 Oklahoma 5 5,400 348,800 1.55%
5 Oregon 1 32,500 377,200 8.62%
318 Oregon 2 4,800 314,200 1.53%
66 Oregon 3 11,300 383,300 2.95%
149 Oregon 4 7,000 309,000 2.27%
165 Oregon 5 7,100 326,700 2.17%
266 Pennsylvania 1 4,800 273,300 1.76%
364 Pennsylvania 2 3,500 273,100 1.28%
108 Pennsylvania 3 7,900 317,700 2.49%
178 Pennsylvania 4 7,300 342,900 2.13%
189 Pennsylvania 5 6,600 316,800 2.08%
136 Pennsylvania 6 8,500 362,300 2.35%
194 Pennsylvania 7 7,000 339,700 2.06%
124 Pennsylvania 8 8,600 357,800 2.40%
226 Pennsylvania 9 5,800 304,800 1.90%
172 Pennsylvania 10 6,700 312,500 2.14%
240 Pennsylvania 11 6,100 329,300 1.85%
146 Pennsylvania 12 7,600 331,900 2.29%
220 Pennsylvania 13 6,500 339,000 1.92%
248 Pennsylvania 14 5,900 323,200 1.83%
115 Pennsylvania 15 8,400 343,800 2.44%
198 Pennsylvania 16 6,700 327,700 2.04%
154 Pennsylvania 17 7,000 312,600 2.24%
157 Pennsylvania 18 7,700 345,000 2.23%
105 Rhode Island 1 6,300 250,900 2.51%
93 Rhode Island 2 6,900 260,300 2.65%
358 South Carolina 1 3,900 299,800 1.30%
245 South Carolina 2 5,600 305,600 1.83%
48 South Carolina 3 8,600 264,500 3.25%
83 South Carolina 4 8,400 301,000 2.79%
62 South Carolina 5 8,200 275,200 2.98%
287 South Carolina 6 4,200 253,500 1.66%
179 South Carolina 7 5,700 269,400 2.12%
382 South Dakota 1 5,000 415,600 1.20%
160 Tennessee 1 6,600 297,600 2.22%
295 Tennessee 2 5,300 327,200 1.62%
133 Tennessee 3 7,000 297,000 2.36%
94 Tennessee 4 8,300 314,500 2.64%
92 Tennessee 5 9,400 353,400 2.66%
183 Tennessee 6 6,400 304,500 2.10%
81 Tennessee 7 8,000 285,800 2.80%
224 Tennessee 8 5,700 299,200 1.91%
227 Tennessee 9 5,800 305,300 1.90%
334 Texas 1 4,300 297,700 1.44%
20 Texas 2 15,600 364,600 4.28%
9 Texas 3 20,600 371,200 5.55%
238 Texas 4 5,600 299,300 1.87%
232 Texas 5 5,700 300,800 1.89%
145 Texas 6 8,000 348,800 2.29%
60 Texas 7 11,400 376,300 3.03%
269 Texas 8 5,400 309,200 1.75%
180 Texas 9 6,900 326,400 2.11%
12 Texas 10 17,300 342,600 5.05%
408 Texas 11 3,000 308,800 0.97%
73 Texas 12 9,700 337,500 2.87%
397 Texas 13 3,400 309,000 1.10%
393 Texas 14 3,400 303,300 1.12%
406 Texas 15 2,800 280,900 1.00%
214 Texas 16 5,500 281,300 1.96%
19 Texas 17 14,100 329,300 4.28%
17 Texas 18 13,900 306,400 4.54%
409 Texas 19 3,000 310,700 0.97%
383 Texas 20 3,700 311,400 1.19%
164 Texas 21 7,900 361,200 2.19%
258 Texas 22 6,300 352,500 1.79%
381 Texas 23 3,500 289,700 1.21%
51 Texas 24 12,500 388,600 3.22%
29 Texas 25 11,800 302,200 3.90%
138 Texas 26 8,600 368,300 2.34%
340 Texas 27 4,300 305,600 1.41%
413 Texas 28 2,500 266,300 0.94%
234 Texas 29 5,500 292,900 1.88%
187 Texas 30 6,100 292,300 2.09%
4 Texas 31 36,800 323,000 11.39%
34 Texas 32 13,200 360,900 3.66%
44 Texas 33 9,500 283,900 3.35%
411 Texas 34 2,300 242,200 0.95%
175 Texas 35 6,800 318,200 2.14%
368 Texas 36 3,700 291,900 1.27%
219 Utah 1 6,000 312,400 1.92%
193 Utah 2 6,300 305,700 2.06%
116 Utah 3 7,600 311,200 2.44%
173 Utah 4 7,100 331,500 2.14%
106 Vermont Statewide 8,200 327,300 2.51%
332 Virginia 1 5,100 352,400 1.45%
359 Virginia 2 4,400 339,800 1.29%
384 Virginia 3 3,800 320,100 1.19%
297 Virginia 4 5,300 327,900 1.62%
230 Virginia 5 6,000 316,100 1.90%
284 Virginia 6 5,700 339,900 1.68%
267 Virginia 7 6,400 364,600 1.76%
396 Virginia 8 4,700 423,700 1.11%
114 Virginia 9 7,300 298,400 2.45%
141 Virginia 10 8,700 376,400 2.31%
311 Virginia 11 6,200 400,900 1.55%
59 Washington 1 10,100 332,300 3.04%
379 Washington 2 3,900 318,900 1.22%
57 Washington 3 8,700 284,500 3.06%
433 Washington 4 1,500 284,500 0.53%
341 Washington 5 4,100 291,500 1.41%
391 Washington 6 3,100 275,500 1.13%
177 Washington 7 8,100 380,000 2.13%
370 Washington 8 4,000 318,000 1.26%
225 Washington 9 6,500 341,400 1.90%
205 Washington 10 5,800 291,300 1.99%
312 West Virginia 1 4,000 258,700 1.55%
374 West Virginia 2 3,300 266,900 1.24%
412 West Virginia 3 2,100 223,000 0.94%
109 Wisconsin 1 8,500 342,500 2.48%
231 Wisconsin 2 7,400 390,000 1.90%
76 Wisconsin 3 10,000 353,500 2.83%
152 Wisconsin 4 6,900 308,000 2.24%
80 Wisconsin 5 10,400 370,600 2.81%
89 Wisconsin 6 9,500 353,600 2.69%
150 Wisconsin 7 7,600 338,400 2.25%
147 Wisconsin 8 8,300 362,800 2.29%
432 Wyoming Statewide 1,700 290,000 0.59%
Total* 3,157,100 140,399,600 2.25%
ChartData Download data

The data below can be saved or copied directly into Excel.

*Subcategory and overall totals may vary slightly due to rounding.

Source: Authors' analysis of U.S. Census Bureau (2013), U.S. International Trade Commission (USITC 2014), Bureau of Labor Statistics (BLS 2014b), and BLS Employment Projections program (BLS-EP 2014a and 2014b). For a more detailed explanation of data sources and computations, see the appendix.

Copy the code below to embed this chart on your website.


Table 5

20 congressional districts hardest hit by U.S. goods trade deficit with China, 2001–2013 (ranked by jobs displaced as share of district employment)

Rank State District Net jobs displaced District employment (in 2011) Jobs displaced as a share of district employment
1 California 17 61,500 346,100 17.77%
2 California 18 50,700 344,500 14.72%
3 California 19 39,900 324,000 12.31%
4 Texas 31 36,800 323,000 11.39%
5 Oregon 1 32,500 377,200 8.62%
6 California 15 28,000 336,400 8.32%
7 Massachusetts 3 20,000 355,400 5.63%
8 California 40 15,600 280,500 5.56%
9 Texas 3 20,600 371,200 5.55%
10 Georgia 14 15,700 290,700 5.40%
11 California 34 16,200 309,400 5.24%
12 Texas 10 17,300 342,600 5.05%
13 California 52 17,000 350,100 4.86%
14 Minnesota 1 16,300 348,200 4.68%
15 New York 18 15,200 332,100 4.58%
16 California 45 16,200 354,400 4.57%
17 Texas 18 13,900 306,400 4.54%
18 Illinois 6 15,700 355,600 4.42%
19 Texas 17 14,100 329,300 4.28%
20 Texas 2 15,600 364,600 4.28%

Source: Authors' analysis of U.S. Census Bureau (2013), U.S. International Trade Commission (USITC 2014), Bureau of Labor Statistics (BLS 2014b), and BLS Employment Projections program (BLS-EP 2014a and 2014b). For a more detailed explanation of data sources and computations, see the appendix.

Copy the code below to embed this chart on your website.

The three hardest-hit congressional districts were all located in Silicon Valley in California, including the 17th (South Bay, encompassing Sunnyvale, Cupertino, Santa Clara, Fremont, Newark, North San Jose, and Miltpitas13), which lost 61,500 jobs, equal to 17.77 percent of all jobs in the district), the 18th Congressional District (including parts of San Jose, Palo Alto, Redwood City, Mountain View, and Los Gatos), which lost 50,700 jobs, 14.72 percent), and the 19th Congressional District (most of San Jose and other parts of Santa Clara County), which lost 39,900 jobs, 12.31 percent of all jobs.

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

Recent academic research has confirmed findings in this and earlier EPI research (e.g., Scott 2005) that the growing U.S.-China trade deficit has caused significant loss of U.S. jobs, especially in manufacturing. Autor, Dorn, and Hanson “conservatively estimated” that growing “Chinese import competition … imply a supply-shock driven net reduction in U.S. manufacturing employment of 548 thousand workers between 1990 and 2000, and a further reduction of 982 thousand workers between 2000 and 2007.” They note further that these results are based on microeconomic research “exploiting cross-market variation in import exposure.” (Autor, Dorn, and Hanson 2012, 19–20, abstract)

These estimates are conservative, for several reasons, as noted by the authors. They fail to account for the overall macroeconomic impacts of growing U.S. trade deficits with China, including the direct and indirect effects of growing China trade deficits on U.S. employment. As shown in Table 3, earlier, the growing U.S. goods trade deficit with China displaced 2.4 million total manufacturing jobs between 2001 and 2013, and an additional 765,600 nonmanufacturing jobs. Thus, approximately 0.32 nonmanufacturing jobs were displaced for each manufacturing job displaced.

Our macroeconomic estimate of manufacturing jobs displaced from 2001 to 2013 is about 2.4 times as large as the Autor, Dorn, and Hanson estimate of the 982,000 manufacturing jobs displaced between 2000 and 2007, though the time period our estimate covers is not twice as long. Further, our estimate would exceed theirs by an even greater amount if we factored in the 765,600 nonmanufacturing jobs displaced between 2001 and 2013. Differences in parameter estimates notwithstanding, it is important to note that Autor, Dorn, and Hanson confirm that growing Chinese import competition is responsible for the displacement of approximately 1.5 million U.S. manufacturing jobs from 1990 to 2007, generally confirming the results of current and earlier EPI research.

More recent research by Acemoglu et al. (2014) finds that import competition with China from 1999 to 2011 was responsible for 2.0 million to 2.4 million net job losses. Their results more closely match the findings in this paper.

Further academic confirmation of the impacts of China trade on manufacturing employment is provided by Pierce and Schott (2012). The authors 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 status (PNTR), which the U.S. 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 United States lost 2.9 million manufacturing jobs between December 2001 and December 2008, a decline of 18.2 percent in total manufacturing employment (BLS 2014a). If 15.6 percentage points of this 18.2 percent decline can be attributed the growth of the U.S. trade deficit with China, this implies that about 85.7 percent (or 2.5 million) of the manufacturing jobs lost in this period were lost due to the growing trade deficit with China. This estimate is even larger than this study’s estimated total manufacturing jobs displaced by the growing U.S.-China trade deficit (2.4 million net jobs displaced). Thus, two other recent academic studies have confirmed that the growing U.S.-China trade deficit is responsible for the displacement of more than 1 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, which is documented in this report, has several direct and indirect effects on workers’ wages and incomes. The direct wage effects are a function of the wages foregone in jobs displaced by growing U.S. imports from China minus potential gains from the growth of jobs supported in export-producing industries and the wages available in alternative jobs in nontraded industries. (U.S. workers displaced from traded-goods production in manufacturing industries who are lucky enough to find jobs in nontraded goods experience permanent wage losses, as discussed below). Scott (2013a) estimates the gains and losses associated with direct changes in employment caused by growing U.S.-China trade deficits between 2001 and 2011.14 The key finding in that study is that jobs displaced by imports from China actually paid 17.0 percent more than jobs exporting to China: $1,021.66 per week in import-competing industries versus $872.89 per week in exporting industries (Scott 2013a, 24, Table 9a). 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 (2013a), which showed that import-competing jobs pay better than alternative jobs in export-producing industries. Therefore, simple trade expansion which 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.15

Furthermore, jobs in both import-competing and exporting industries paid substantially more than jobs in nontraded industries, which pay $791.14 per week (Scott 2013a, 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 2013a, Table 5, 13). Thus, not only did workers lose wages moving from import to export industries, but 2.7 million workers were displaced from jobs making $1,021.66 per week on average, and (if they were lucky enough to find jobs) were pushed into jobs in nontraded industries paying an average of only $791.14 per week (a decline of 22.6 percent). In total, U.S. workers suffered a direct net wage loss of $37 billion per year (Scott 2013a, 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 (2008a, results updated in Bivens 2013), growing trade with China 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 percent 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). The growth of China trade 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 percent—roughly $1,800 for all full-time, full-year workers without a college degree. To provide comparable economy-wide impact estimates, assume that 100 million workers without a college degree suffered total losses of $1,800 per year, which yields a total national loss of $180 billion.16 Therefore, the indirect, macroeconomic losses to U.S. workers caused by growing Chinese imports are about five times as large as the direct impact of $37 billion, and about 40 times as many workers were affected (2.7 million versus 100 million).

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, 27). This confirms the findings of Bivens (2008a, 2013). They 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 non-participation, relative to 71% among the prime-age group and 68% among the younger group.” Thus, more than two-thirds of all workers displaced by growing competition with Chinese imports dropped out of the labor force in this study. 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 or 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 [Trade Adjustment Assistance] TAA 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.”

Summing up the overall impact of the growing U.S.-China trade deficit on jobs and wages

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 2010), nearly two-thirds (61.3 percent) of manufacturing workers displaced from January 2007 to December 2009 remained unemployed, including 16.7 percent who were not in the labor force. The average wage decline for those who were reemployed was 17.5 percent (Farber 2011, 21). 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 (Scott 2013a). 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. 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. Trade adjustment assistance is a Bureau of Labor Statistics program to provide retraining and unemployment benefits to certain workers who were displaced by growing imports. However, new research suggests that a significant share of displaced workers are signing up for permanent disability payments, rather than for temporary trade adjustment assistance, thereby increasing the number of permanent labor force dropouts. Autor and Hanson (2014) find that increases in disability payments are more than 30 times larger than the estimated dollar increases in trade-adjustment assistance payments for trade-displaced workers.

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.17 However, while the trade balance is but one of many variables affecting aggregate job creation, it plays a much larger role in explaining structural change in employment, especially in the manufacturing sector. Between December 2001 and December 2013, 3.7 million U.S. manufacturing jobs were lost (Bureau of Labor Statistics 2014a). The growth of the U.S. trade deficit with China was responsible for the displacement of 2.4 million manufacturing jobs in this period, or about 65 percent of manufacturing jobs lost. Thus, manufacturing job loss due to the growing trade deficit with China accounts for nearly two-thirds of all U.S. manufacturing jobs lost or displaced in this period.

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.

Many of the mechanisms that could offset employment losses caused by growing trade deficits are not operating in the current economic climate. The Federal Reserve cannot cut interest rates any further than it already has, and interest-rate-sensitive industries such as residential construction are not experiencing employment gains from lower rates. In short, in today’s economy with its high unemployment rate, jobs displaced due to the trade deficit with China are much more likely to be actual economy-wide losses than simply job reallocations.

It’s not an accident: Addressing the causes of trade-related job losses

The job and wage losses from the growing U.S. trade deficit with China should be unacceptable to U.S. policymakers. Especially since this is a solvable problem: The increase in the U.S.-China trade deficit is caused by specific Chinese policies that U.S. policy can address.

Currency manipulation is a major cause of the trade deficit

A major cause of the rapidly growing U.S. trade deficit with China is currency manipulation. Unlike other currencies, the Chinese yuan does not fluctuate freely against the dollar.18 Instead, China has tightly pegged its currency to the U.S. dollar at a rate that encourages a large bilateral trade surplus with the United States.

How do we know China has manipulated its currency? As China’s productivity has soared, its currency should have adjusted, increasing in value to maintain balanced trade. But the yuan has instead remained artificially low as China has aggressively acquired dollar-denominated assets such as U.S. Treasury bills and other foreign exchange reserves to further depress the value of its own currency. (To depress the value of its own currency, a government can sell its own currency and buy foreign government securities, which increases its foreign reserves.) Just between December 2012 and June 2014, China purchased $681 billion in U.S. Treasury bills and other securities to suppress its currency against the U.S. dollar and other major currencies (IMF 2014a). As of June 30, 2014, China held nearly $4 trillion in foreign exchange reserves (IMF 2014a), about 70 percent of which were held in U.S. dollars. This intervention makes the yuan artificially cheap relative to the dollar, effectively subsidizing Chinese exports.

Recent economic research has shown that the purchase of Treasury bills and other foreign assets increases a country’s current account by between 60 and 100 cents for each dollar spent on foreign exchange reserve purchases (Gagnon 2013). (A country’s current account balance is the broadest measure of its trade balance.) Between 2001 and 2013, China acquired $3.6 trillion in new foreign exchange reserves (excluding gold) (IMF 2014a). In the same period, China’s cumulative current account surplus totaled $2.3 trillion, or 64.0 percent of its total purchases of foreign exchange reserves. Thus, as shown by Gagnon’s estimates and country data, there is a clear, cause-and-effect relationship between China’s foreign exchange intervention and its large, sustained, current account surplus. For each dollar of foreign exchange reserves purchased, China’s current account increased by 64 cents, well within the range estimated by Gagnon (2013).

Although the yuan has appreciated significantly since 2005, economist H.W. Brock (2012) estimates that the Chinese currency is still massively undervalued, and, as quoted in Miller (2012), is “arguably one-sixth of what it should be.”19 Gagnon (2012a, 3) estimates that massive currency manipulation, especially by countries in Asia, has raised “the current account of the developing economies by roughly $700 billion [per year], relative to what it would have been.” Gagnon also notes that this “amount is roughly equivalent to the large output gaps in the United States and euro area. In other words, millions more Americans and Europeans would be employed if other countries did not manipulate their currencies…” (Gagnon 2012a, 1). China is the most important currency manipulator, based on both its massive currency intervention over the past decade and its share of global current account surpluses.20 Currency intervention by China artificially raises the cost of U.S. exports to China and the rest of the world by an amount similar to the artificial depression of the yuan, making U.S. goods less competitive in China and in every country where U.S. exports compete with Chinese goods. And China is the most important competitor for the United States in all other third-country markets, even more important than Germany and all other members of the European Union combined.

By manipulating its currency, China has compelled other countries to follow similar policies in order to protect their relative competitiveness and to promote their own exports. This widespread currency manipulation has also contributed to the growth of very large global current account imbalances; there are currently many countries with large surpluses or deficits.

Unless China raises the real value of the yuan by at least a third and eliminates other trade distortions (enumerated later in this section), the U.S. trade deficit and related job losses will continue to grow rapidly. Although China did respond to international pressure between 2005 and 2008 and allowed some appreciation in the yuan, it was too little and too late to help arrest the widening U.S.-China trade gap.21 Recently, China reversed course, purchasing $681 billion in total foreign exchange reserves between December 2012 and June 2014, and more than $2 trillion since the Obama Administration took office in 2009 (IMF 2014a). The Chinese yuan actually fell 1.3 percent against the U.S. dollar between December 2013 and October 10, 2014, as a consequence of China’s massive purchases of foreign exchange reserves over the past 18 months (Board of Governors of the Federal Reserve System 2014, and Scott 2014c.

Policy remedies to address currency manipulation

Gagnon recommends that the rules of the WTO be changed to allow countries to impose tariffs on imports from currency manipulators. Since changing the rules of the WTO requires unanimous consent of all members, Gagnon observes that “the main targets of currency manipulation—the United States and euro area—may have to play tough. One strategy would be to tax or otherwise restrict purchases of U.S. and euro area financial assets by currency manipulators” (Gagnon 2012a, 1). Such financial taxes would be “consistent with international law” (Gagnon 2011).

In addition, Congress can help end currency manipulation by passing pending legislation (H.R. 1276 and S. 1114) that would allow the Commerce Department to treat currency manipulation as a subsidy in countervailing duty trade cases (OpenCongress.org 2014a and 2014b). In addition, the president and federal agencies possess the tools needed to end currency manipulation with the stroke of a pen (Scott 2013b). The Treasury and Federal Reserve have the authority needed to offset purchases of foreign assets by foreign governments by engaging in countervailing currency intervention (Bergsten and Gagnon 2012). By taking these steps, the U.S. government could make efforts by foreign governments to manipulate their currencies costly and/or ineffective.

A recent report from EPI shows that full revaluation of the yuan and other undervalued Asian currencies would reduce the U.S. trade deficit by between $200 billion and $500 billion within three years, thereby increasing U.S. GDP by as much as $720 billion, adding up to 5.8 million U.S. jobs, and reducing the federal budget deficit by up to $266 billion per year and increasing net state and local fiscal resources by up to $101 billion per year (Scott 2014a). Revaluation would also help workers in China and other Asian countries by reducing inflationary overheating and increasing workers’ purchasing power.

It would also benefit other countries. The undervaluation of the yuan has put the burden of global current account realignment pressures on other countries such as Australia, New Zealand, South Africa, and Brazil, whose currencies have also become overvalued with respect to those of China and other currency manipulators.

Other illegal laws, regulations, and policies are also responsible for the large U.S. trade deficit with China

Currency manipulation is one practice that violates the rules of the international trading system set out in the GATT (General Agreement on Tariffs and Trade) and WTO agreements (Stewart and Drake 2010). Other Chinese government policies also illegally encourage exports. China extensively suppresses labor rights, which lowers production costs within China. A 2006 AFL-CIO study estimated that repression of labor rights by the Chinese government had lowered manufacturing wages of Chinese workers by between 47 percent and 86 percent (AFL-CIO, Cardin, and Smith 2006, 138). China also provides massive direct export subsidies to many key industries (see, for example, Haley 2008, 2009, 2012). Finally, it maintains strict, nontariff barriers to imports.

Police remedies to address other violations

Partly because the agreement accepting China into the WTO failed to include any protections to maintain or improve labor or environmental standards, China’s entry has further tilted the international economic playing field against U.S. domestic workers and firms and in favor of multinational companies from the United States and other countries, as well as state-owned and privately owned exporters in China. This shift has accelerated the global “race to the bottom” in wages and environmental quality and closed thousands of U.S. factories, decimating employment in a wide range of communities, states, and entire regions of the United States. U.S. national interests have suffered while U.S. multinationals have enjoyed record profits on their foreign direct investments (Scott 2007, 2011).

Some actions have been taken in response. In September 2009, the Obama administration announced that it would take action to restrict imports of Chinese tires for three years under special safeguard measures, the first time since 2001 that these measures had been utilized (USTR 2009, and Alliance for American Manufacturing 2010).

In September 2010, the United Steelworkers (USW) filed a Section 301 petition with the U.S. Trade Representative, accusing China of illegally stimulating and protecting producers of green technology exports, ranging from wind and solar energy products to advanced batteries and energy-efficient vehicles. Indeed, the U.S. trade deficit in clean energy products had more than doubled between 2008 and 2010, displacing more than 8,000 U.S. jobs in 2010 alone (Scott 2010). The 2010 USW petition details more than 80 Chinese laws, regulations, and practices that violate international trade agreements and have hurt U.S. clean energy manufacturing and green-technology industries.

In July 2012, the Obama administration filed a WTO complaint against China over its tariffs on large vehicles exported from the United States to China. This was the seventh complaint filed by the administration against China, and the previous six had all been successful (Scott 2012b).

Another crucial missing link: Foreign direct investment and outsourcing

Proponents of trade deals such as the agreement to endorse China’s admission to the World Trade Organization usually focus on the impacts of these deals on tariff and nontariff barriers to trade.22 China agreed to make major tariff reductions as a condition of entry into the WTO. President Clinton and many others argued that since U.S. tariff barriers were already low, the agreement would have a much larger effect on U.S. exports to China than on U.S. imports (Clinton 2000).

But proponents failed to anticipate the effect of China’s entry on foreign direct investment (FDI) and outsourcing. FDI has played a key role in the growth of China’s manufacturing sector. China is the largest recipient of FDI of all developing countries (Xing 2010) and is the third-largest recipient of FDI over the past three decades, trailing only the United States and the United Kingdom. Foreign-invested enterprises (both joint ventures and wholly owned subsidiaries) were responsible for nearly two-thirds of China’s global trade surplus in 2013 (Ministry of Commerce, China 2014; IMF 2014a). Outsourcing—through foreign direct investment in factories that make goods for export to the United States—has played a key role in the shift of manufacturing production and jobs from the United States to China since China entered the WTO in 2001.

Failed expectations of a growing Chinese market for U.S. goods

Another critically important promise made by the promoters of liberalized U.S.-China trade was that the United States would benefit because of increased exports to a large and growing consumer market in China. However, despite widespread reports of the rapid growth of the Chinese middle class, this growth has not resulted in a significant increase in U.S. consumer exports to China. The most rapidly growing exports to China are bulk commodities such as grains, scrap, and chemicals; intermediate products such as semiconductors; and producer durables such as aircraft and non-electrical machinery (see the discussion of Table 2 earlier in this paper, and Supplemental Table 5 at the end of this report. Furthermore, the increase in U.S. exports to China since 2001 has been overwhelmed by the growth of U.S. imports, as shown earlier in Table 1.

Conclusion

The growing U.S. goods trade deficit with China has displaced millions of jobs in the United States and 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.

Is America’s loss China’s gain? The answer is not clearly affirmative. China has become dependent on the U.S. consumer market for employment generation, suppressed the purchasing power of its own middle class with a weak currency, and, most importantly, now holds nearly $4 trillion in foreign exchange reserves instead of investing them in public goods that could benefit Chinese households. Although economic growth in China has been rapid, it is unbalanced and unsustainable. China’s vast purchases of foreign assets, intended to depress the value of its currency, have led to the overheating of its domestic economy, and inflation in China has accelerated rapidly. Recently, growth in China slowed to 7.3 percent in the third quarter of 2014, and it is projected to slow further over the next five years (Schuman 2014). Its repression of labor rights has suppressed wages, thereby artificially subsidizing exports. 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.

The U.S.-China trade relationship needs to undergo a fundamental change. Addressing the exchange rate policies and labor standards issues in the Chinese economy are elements of an important first step. It is time for the administration to respond to the growing chorus of calls from economists, workers, businesses, and Congress and take action to stop illegal currency manipulation by China and other countries (Scott 2014c).

About the authors

Robert E. Scott joined the Economic Policy Institute in 1996 and is currently director of trade and manufacturing policy research. His areas of research include international economics, trade and manufacturing policies and their impacts 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. He has published widely in academic journals and the popular press, including The Journal of Policy Analysis and ManagementThe International Review of Applied Economics, and The Stanford Law and Policy Review, as well as The Los Angeles TimesNewsdayUSA TodayThe Baltimore SunThe Washington Times, 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.

Will Kimball joined EPI in 2013. As a research assistant, he supports the research of EPI’s economists on topics such as wages, labor markets, macroeconomics, international trade, and health insurance. Prior to joining EPI, Will worked at the Center on Budget and Policy Priorities and the Center for Economic and Policy Research. He has a B.A. in economics and political Science from the University of Connecticut.

Acknowledgments

The authors thank Ross Eisenbrey and Josh Bivens for comments. 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 195 individual industries of the U.S. economy, specially grouped into 45 custom sectors23 and using the North American Industry Classification System (NAICS) with data obtained from the U.S. Census Bureau (2013) and the U.S. International Trade Commission (USITC 2014).

This study separates exports produced domestically from foreign exports—which are goods produced in other countries, exported to the United States, and then re-exported from the United States. Because only domestically produced exports generate jobs in the United States, employment calculations here are based only on domestic exports. The measure of the net impact of trade used here to calculate the employment content of trade is the difference between domestic exports and consumption imports.

The number of jobs supported by $1 million of exports or imports for each of 195 different U.S. industries is estimated using a labor requirements model derived from an input-output table developed by the BLS–EP (2014a).24 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, and computer programming 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 were converted to constant 2005 dollars using industry-specific deflators (see next section for further details). This was necessary because the labor requirements table was estimated using price levels in that year. Data on real trade flows were converted to constant 2005 dollars using industry-specific price deflators from the BLS–EP (2013b). These price deflators were updated using Bureau of Labor Statistics producer price indexes (industry and commodity data; BLS 2014b). Use of constant 2005 dollars was 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 (U.S. ITC 2014) in four-digit, three-digit, and two-digit NAICS formats. Consumption imports and domestic exports are downloaded for each year.

Step 2. To conform to the BLS Employment Requirements tables (BLS–EP 2014a), trade data must be converted into the BLS industry classifications system. For NAICS-based data, there are 195 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 2005 dollars using published price deflators from the BLS–EP (2014b) and the Bureau of Labor Statistics (2014b).

Step 3. Real domestic employment requirements tables are downloaded from the BLS–EP (2014a). These matrices are input-output industry-by-industry tables that show the employment requirements for $1 million in outputs in 2005 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 [T2001] be the 195×2 matrix made up of a column of imports and a column of exports for 2001. [T2013] is defined as the 195×2 matrix of 2013 trade data. Finally, [T2008] is defined as the 195×2 matrix of 2008 trade data. Define [E2001] as the 195×195 matrix consisting of the real 2001 domestic employment requirements tables. To estimate the jobs displaced by trade, perform the following matrix operations:

[J2001]=[T2001]×[E2001]

[J2008]=[T2008]×[E2001]

[J2013]=[T2013]×[E2001]

[J2001] is a 195×2 matrix of job displacement by imports and jobs supported by exports for each of 195 industries in 2001. Similarly, [J2008] and [J2013] are 195×2 matrices of jobs displaced or supported by imports and exports (respectively) for each of 195 industries in 2008 and 2013, respectively.

The employment estimates for retail trade, wholesale trade, and advertising were set to zero for this analysis. We assume that goods must be sold and advertised whether they are produced in the United States or imported for consumption.

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

[Jnx01-13]=[J2013]-[J2001]

[Jnx01-08]=[J2008]-[J2001]

[Jnx08-13]=[J2013]-[J2008]

Step 2. State-by-state analysis. For states, employment-by-industry data are obtained from the Census Bureau’s American Community Survey (U.S. Census Bureau 2013) data for 2011 and are mapped into 45 unique census industries and eight aggregated total and subtotals for a total of 53 sectors.25 We look at job displacement from 2001 to 2013, so from this point, we use [Jnx01-13]. In order to work with 45 sectors, we group the 195 BLS industries into a new matrix, defined as [Jnew01-13], a 45×2 matrix of job displacement numbers. Define [St2011] as the 45×51 matrix of state employment shares (with the addition of the District of Columbia) of employment in each industry. Calculate:

[Stjnx01-13]=[St2011]T [Jnew01-13]

where [Stjnx01-13] is the 45×51 matrix of job displacement/support by state 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, by state is obtained from the ACS data from 2011, which for the first time use geographic codings which match the boundaries of the 113th Congress (elected in 2012). In order to calculate job displacement in each congressional district, we use each column in [Stjnx01-13], which represent individual state job-displacement-by-industry estimates, and define them as [Stj01], [Stj02], [Stji]…[Stj51], with i representing the state number and each matrix being 45×1.

Each state has Y congressional districts, so [Cdi] is defined as the 45xY matrix of congressional district employment shares for each state. Congressional district shares are calculated thus:

[Cdj01]=[Stj01]T [Cd01]

[Cdji]=[Stji]T [Cdi]

[Cdj51]=[Stj52]T [Cd51]

where [Cdji] is defined as the 45xY job displacement in state i by congressional district by industry.

Congressional districts are estimated for the 113th Congress, which was elected in 2012.

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

Endnotes

1. The 113th Congress met in 2013 and 2014. The seats in the House were apportioned based on the 2010 United States Census (Wikipedia 2014b).

2. California’s 17th Congressional District includes Apple Inc., Intel Corp, Yahoo, and eBay (Wikipedia 2014a).

3. Direct jobs displaced refer to jobs displaced within a given industry, such as motor vehicles and parts. Indirect jobs displaced are those displaced in industries that supply inputs to that industry, such as primary metal (e.g., steel), plastics and rubber products (e.g., tires and hoses), transportation, and information. Respending employment results from the spending of wages by employed workers. It is one form of a macroeconomic multiplier.

4. 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 2011).

5. Tables 1 and 2 report U.S. imports for consumption and domestic exports to China. These flows were chosen to emphasize goods produced and consumed in the United States. News reports from the Census Bureau and the Commerce Department usually emphasize general imports and total exports. Total exports as reported by the Census Bureau include foreign exports (re-exports), i.e., goods produced in other countries and shipped through the United States. For 2013, general imports from China were $440.4 billion, total exports were $121.7 billion, and the reported trade balance was $318.7 billion (USITC 2014).

6. This analysis estimates the U.S. bilateral and total trade balances with China and the world (respectively), with the trade balances calculated as domestic exports less imports for consumption (authors’ analysis of USITC 2014).

7. The analysis in this report is based on domestic exports, as shown in Table 1, and excludes foreign exports (re-exports)—which are goods produced in other countries, imported into the United States, and then re-exported to China. Since foreign exports are not produced domestically, their production does not support domestic employment, and they are excluded from the model used here.

8. Scrap and used or second-hand goods are industries 192 and 193, respectively, in the BLS model, and there are no jobs supported or displaced by trade in these sectors, according to the BLS model.

9. 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, aerospace, weapons, and nuclear technology (U.S. Census Bureau 2014a). In total ATP trade with the world, the United States had exports of $319.8 billion and imports of $401.1 billion in 2013, and a trade deficit of $81.3 billion. The United States had total ATP exports to China in 2013 of $29.0 billion and imports of $145.9 billion, and a trade deficit of $116.9 billion. This exceeded the overall U.S. ATP deficit of $81.3 billion. Thus, the United States had an ATP trade surplus with the rest of the world in 2011 of $35.6 billion (U.S. Census Bureau 2014b).

10. Data for trade in Advanced Technology Products (ATP) by country are not available before 2002.

11. Deflators for many sectors in the computer and electronics parts industry fell sharply between 2001 and 2013 due to rapid productivity growth in those sectors. For example, the price index for computer and peripheral equipment manufacturing fell from 1,712.6 in 2001 to 525.1 in 2013, a decline of 69.3 percent (the price index is set at 1,000 in 2005, the base year). In order to convert from nominal to real values for 2013, for example, the nominal value is multiplied by 1,000/525.1 (the price index in year 2013) = 1.90. Thus, the real value of computers and peripheral products is nearly twice as large as the nominal value in 2013. The real value of all computer and electronic parts imports in 2013 exceeded nominal values in that year by 42.7 percent. See appendix, “Methodology,” for source notes and deflation procedures used.

12. Five of the eight states that were home to the hardest hit Congressional districts shown in Table 5 were especially hard-hit in the computer and electronic parts industries: California, Texas, Oregon, Massachusetts, and Minnesota. The industry’s share of all jobs lost ranged from 51.0 percent in Minnesota to 63.9 percent in Oregon, compared with the national average of 39.6 percent of jobs (Table 3). The 20 hardest-hit congressional districts in Table 5 also include districts in New York and Illinois that were also very hard hit in computers and electronic parts. In New York’s 18th Congressional District, computers and electronic parts accounted for 58.3 percent of all jobs lost; and in Illinois’ 6th Congressional District, the share was 75.5 percent. Georgia is also one of the states host to one of the 20 hardest-hit congressional districts; Georgia’s 14th Congressional District lost a relatively small share of jobs in computers and parts, 4.5 percent of all jobs lost, while suffering very large share of job losses in manufacturing, overall, 85.6 percent of all jobs lost, according to unpublished data available upon request. Nationally, manufacturing accounted for a smaller, 75.7 share, of all jobs lost (Table 3).

13. California’s 17th Congressional District includes Apple Inc., Intel Corp, Yahoo, and eBay (Wikipedia 2014a).

14. These estimates are not updated in this report.

15. Even if U.S.-China trade is bad for all types of labor—that is for both high and low skill and wage levels—it can still boost national income in the United States if it increases the return to capital sufficiently. In the United States, recent research has shown that the labor share of national income declined 6.4 percentage points between the 1947–1987 average and the 2010–2012 period (Elsby, Hobjin, and Sahin 2013, Table 1 at 7), and that the residual capital share of income (rent, interest, depreciation, and profits) rose by the same amount. The authors found that “offshoring of the labor-intensive component of the U.S. supply chain” was the “leading potential explanation of the decline in the U.S. labor share over the past 25 years,” and the concomitant rise in the capital share of national income.

16. This macroeconomic estimate is developed here, and is not included in Bivens (2013).

17. One frequently repeated criticism of trade and employment studies is that the growth of imports does not displace domestic production. Some assert that if imports from China fell, they would be replaced by imports from some other low-wage country (see, for example, U.S.-China Business Council 2011). However, important new empirical research by Autor, Dorn, and Hanson (2012, 4) has shown that “increased exposure to low-income country imports is associated with rising unemployment, decreased labor-force participation, and increased use of disability and other transfer benefits, as well as with lower wages.” The bottom line is that “trade creates new jobs in exporting industries and destroys jobs when imports replace the output of domestic firms. Because trade deficits have risen over the past decade, more jobs have been displaced by imports than created by exports” (Bivens 2008b, 1).

18. The official name of the Chinese currency is the renminbi (RMB) and the units of value are yuan, the term used to describe the currency throughout this paper.

19. Over the past two decades, China first massively devalued its currency (in 1994) and then gradually increased its value, especially after 2005. While the yuan gained 31.2 percent in nominal terms between December 2005 and June 2014 (IMF 2014a), its nominal, June 2014 value remained 15.1 percent below the par value in December 1990 (Board of Governors of the Federal Reserve System 2014). As Gagnon notes, “In many developing countries, manipulation prevented the normal trend appreciation associated with rapid economic growth rather than causing any outright depreciation. The point is that without trend appreciation, such countries experience growing trade and currency account surpluses” (Gagnon 2012a, 3, note 2). Such countries would normally experience trend appreciation due to rapid productivity growth, especially in manufacturing (the sector that generates most exports). Between 1995 and 2009 China experienced manufacturing productivity growth that ranged between 6.7 percent and 9.6 percent per year (FutureofUSChinaTrade.com 2012). Over the same period, productivity growth in U.S. manufacturing averaged only 2.4 percent per year (BLS 2014c).

20. China’s accumulation of total foreign exchange reserves (minus gold) between 2001 and 2013 accounted for more than one-third (37.6 percent) of total world accumulation of reserves, exceeding the next largest accumulator (Japan) by 475 percent (IMF 2014a). In his “Ranking of Currency Manipulators,” Gagnon (2012b) noted that China had by far the largest foreign exchange reserves of currency manipulators, “as of the end of 2011.” In 2013 China’s current account surplus was a self-reported $182.8 billion, which was 24.1 percent of the current account surpluses of the top 20 currency manipulators (IMF 2014b). The IMF conservatively projects that China’s share of the global current account surplus of these 20 countries will rise to 44.1 percent in 2019, assuming continuation of current policies (IMF 2014b). It is also important to note that analysis of matched international trade data from trade data reported by China shows that China’s current account surplus was underestimated by approximately 50 percent, or more, in each year between 2005 and 2012 (Scott 2014b).

21. Beginning in 2002, the dollar declined more than 30 percent against several major currencies such as the euro and the Canadian dollar. However, yuan appreciation was largely delayed until late 2007 and 2008—too little to be of any help in slowing the growth of the U.S.-China trade deficit to date. The appreciation of the yuan has had little effect on the prices of U.S. imports from China, which rose only 2.5 percent between July 2005 (when the yuan was first adjusted) and May 2008, much less than the 19 percent appreciation of the yuan in that period (Congressional Budget Office 2008, 2). Furthermore, given the continuing rapid growth in manufacturing labor productivity in China relative to the United States and other developed countries, there must be trend appreciation in the yuan for China to simply maintain its global trade surplus, as noted by Gagnon (2012a, 3, note 2).

22. China’s admission to the WTO was endorsed by the United States in domestic legislation that offered China permanent normal trade relations status.

23. A previous edition of this research used data for 56 industries provided by the ACS (Scott 2012a). The BLS–EP consolidated several industries, including textiles and apparel, which required us to consolidate data for these industries in our ACS state and congressional district models. Other “not elsewhere classified” industries were consolidated with other industries (e.g., “miscellaneous manufacturing”) or deleted (e.g., in the case of “not specified metal industries”) to update and refine the crosswalk from BLS–EP to ACS industries. As a result of these consolidations, there are 45 industries in the ACS dataset used for this study.

24. The model includes 195 NAICS industries. The trade data include only goods trade. Goods trade data are available for 85 commodity-based industries, plus software, waste and scrap, used or second-hand merchandise, and goods traded under special classification provisions (e.g., goods imported from and returned to Canada; small, unclassified shipments). Trade in scrap, used, and second-hand goods has no impacts on employment in the BLS model. Some special classification provision goods are assigned to miscellaneous manufacturing.

25. The Census Bureau uses its own table of definitions of industries. These are similar to NAICS-based industry definitions, but at a somewhat higher level of aggregation. For this study, we developed a crosswalk from NAICS to Census industries, and used population estimates from the ACS for each cell in this matrix.

References

AFL-CIO, Benjamin L. Cardin, and Christopher H. Smith. 2006. “Section 301 Petition [on China’s repression of workers’ rights].” June 8.

Acemoglu, Daron, David Autor, David Dorn, Gordon H. Hanson, and Brendan Price. 2014. Import Competition and the Great U.S. Employment Sag of the 2000s. National Bureau of Economic Research, Working Paper #20395.

Alliance for American Manufacturing. 2010. “Obama’s Bold Economic Move on Chinese Tire Imports is Paying Off.” September 1. Alliance for American Manufacturing, Research.

Autor, David H., David Dorn, and Gordon H. Hanson. 2012. The China Syndrome: Local Labor Market Effects of Import Competition in the United States. National Bureau of Economic Research, Working Paper #18054.

Autor, David, and Gordon Hanson. 2014. “Labor Market Adjustment to International Trade.NBER Reporter, 2014, no. 2.

Bailey, Martin N., and Robert Z. Lawrence. 2004. “What Happened to the Great U.S. Jobs Machine? The Role of Trade and Electronic Offshoring.Brookings Papers on Economic Activity, vol. 35, no. 2, 211–284.

Bergsten, C. Fred, and Joseph E. Gagnon. 2012. Currency Manipulation, the US Economy, and the Global Economic Order. Peterson Institute for International Economics, Policy Brief 12-25.

Bivens, L. Josh. 2008a. Everybody Wins, Except for Most of Us: What Economics Teaches About Globalization. Washington, D.C.: Economic Policy Institute.

Bivens, Josh. 2008b. Trade, Jobs, and Wages: Are the Public’s Worries About Globalization Justified? Economic Policy Institute, Issue Brief #244.

Bivens, Josh. 2013. Using Standard Models to Benchmark the Costs of Globalization for American Workers Without a College Degree. Economic Policy Institute, Briefing Paper #354.

Board of Governors of the Federal Reserve System. 2014. “Foreign Exchange Rates – H.10, Country data (China).”

Brock, H. Woody. 2012. American Gridlock: Why the Right and Left Are Both Wrong: Commonsense 101 Solutions to the Economic Crises. Hoboken, N.J.: John Wiley & Sons.

Bronfenbrenner, Kate, and Stephanie Luce. 2004. The Changing Nature of Corporate Global Restructuring: The Impact of Production Shifts on Jobs in the U.S., China, and Around the Globe. Commissioned research paper for the U.S. Trade Deficit Review Commission.

Bureau of Labor Statistics (BLS). 2010. “Displaced Workers Summary: Worker Displacement: 2007–2009.”

Bureau of Labor Statistics (BLS). 2014a. “Employment, Hours, and Earnings from the Current Employment Statistics Survey (National): Manufacturing Employment” [Excel file].

Bureau of Labor Statistics (BLS). 2014b. “Producer Price Indexes: Industry and Commodity Data” [Excel files].

Bureau of Labor Statistics (BLS). 2014c. “Major Sector Productivity and Costs: Manufacturing-(Output Per Hour), Present Change From Previous Quarter” [Excel file].

Bureau of Labor Statistics, Employment Projections program (BLS–EP). 2014a. “Special Purpose Files—Employment Requirements; Chain-Weighted (2005 dollars) Real Domestic Employment Requirements Table for 2001” [Excel sheet, converted to Stata data file].

Bureau of Labor Statistics, Employment Projections program (BLS–EP). 2014b. “Special Purpose Files—Industry Output and Employment – Industry Output.” [CSV File, converted to Excel sheet and Stata data file].

Clinton, Bill. 2000. “Expanding Trade, Protecting Values: Why I’ll Fight to Make China’s Trade Status Permanent.” New Democrat, vol. 12, no. 1, 9–11.

Congressional Budget Office (CBO). 2008. How Changes in the Value of the Chinese Currency Affect U.S. Imports.

Elsby, Michael W.L., Bart Hobjin, and Ayşegül Şahin.2013. “The decline of the U.S. Labor Share.Brookings Papers on Economic Activity, Fall, 1–52.

Farber, Henry S. 2011. Job Loss in the Great Recession: Historical Perspective from the Displaced Workers Survey, 1984–2010. National Bureau of Economic Research, Working Paper #17040.

FutureofUSChinaTrade.com. 2012. “Labor Productivity Growth: Emerging Economies” [graph], accessed July 31.

Gagnon, Joseph E. 2011. “For a Serious Impact, Tax Chinese Assets in the United States.” U.S. News and World Report, October 13.

Gagnon, Joseph E. 2012a. Combating Widespread Currency Manipulation. Peterson Institute for International Economics, Policy Brief #12-19.

Gagnon, Joseph. 2012b. “A Ranking of Currency Manipulators.” RealTime Economic Issues Watch (Peterson Institute for International Economics blog), July 13.

Gagnon. 2013. The Elephant Hiding in the Room: Currency Intervention and Trade Imbalances. Peterson Institute for International Economics, Working Paper 13-2.

Groshen, Erica L., Bart Hobijn, and Margaret M. McConnell. 2005. “U.S. Jobs Gained and Lost Through Trade: A Net Measure.” Current Issues in Economics and Finance, vol. 11, no. 8, 1–7.

Haley, Usha C.V. 2008. Shedding Light on Energy Subsidies in China: An Analysis of China’s Steel Industry From 2000–2007. Alliance for American Manufacturing.

Haley, Usha C.V. 2009. Through China’s Looking Glass: Subsidies to the Chinese Glass Industry From 2004-08. Economic Policy Institute, Briefing Paper #242.

Haley, Usha C.V. 2012. Putting the Pedal to the Metal: Subsidies to China’s Auto-Parts Industry from 2001 to 2011. Economic Policy Institute, Briefing Paper #316.

International Monetary Fund (IMF). 2014a. “International Financial Statistics CD-ROM.” Washington, D.C.: IMF.

International Monetary Fund (IMF). 2014b. “World Economic Outlook Database.” October 2014 Edition [Excel file].

Miller, Matt. 2012. “Who Will Get Tough on China?Washington Post, July 25.

Ministry of Commerce, China, Foreign Investment Department—Invest in China. 2014. “Express on Imports and Exports by FIEs from Jan to Dec. 2013.”

OpenCongress.org. 2014a. “H.R. 1276—Currency Reform for Fair Trade Act.”

OpenCongress.org. 2014b.“S.1114 – Currency Exchange Rate Oversight Reform Act of 2013.”

Pierce, Justin R., and Peter K. Schott. 2012. The Surprisingly Swift Decline of U.S. Manufacturing Employment. National Bureau of Economic Research, Working Paper #18655.

Schuman, Michael. 2014. “Anyone Expecting a Rebound in Chinese Growth Won’t Like the New GDP Figures.” Time, Oct 21.

Scott, Robert E. 2005. U.S. – China Trade, 1989-2003: Impact on Jobs and Industries, Nationally and State-by-State. Economic Policy Institute, Briefing Paper #270.

Scott, Robert E. 2007. The Hidden Costs of Insourcing: Higher Trade Deficits and Job Losses for U.S. Workers. Economic Policy Institute, Issue Brief #236.

Scott, Robert E. 2010. China’s Subsidies to Green Industries Lead to Growing Trade Deficits in Clean Energy Products. Economic Policy Institute, Issue Brief #287.

Scott, Robert E. 2011. “Hearing on State-Owned Enterprises and U.S.-China Bilateral Investment: U.S. Investments in China.” Testimony before the U.S.-China Economic and Security Review Commission, March 30.

Scott, Robert E. 2012a. The China Toll: Growing U.S. Trade Deficit with China Cost More Than 2.7 Million Jobs Between 2001 and 2011, with Job Losses in Every State. Economic Policy Institute, Briefing Paper #345.

Scott, Robert E. 2012b. “Obama Gets Tough on China’s Unfair Tariffs on U.S. Auto Exports.” Working Economics (Economic Policy Institute blog), July 6.

Scott, Robert E. 2013a. Trading Away the Manufacturing Advantage: China Trade Drives Down U.S. Wages and Benefits and Eliminates Good Jobs for U.S. Workers. Economic Policy Institute, Briefing Paper #367.

Scott, Robert E. 2013b. “The President Can End Currency Manipulation with the Stroke of a Pen, Halving the U.S. Trade Deficit and Creating Millions of Jobs.” Working Economics (Economic Policy Institute blog), February 11.

Scott, Robert E. 2014a. Stop Currency Manipulation and Create Millions of Jobs: With Gains across States and Congressional Districts.  Economic Policy Institute, Briefing Paper #372.

Scott, Robert E. 2014b. “Not a Chimera: Global Economic Trends Tend to Have Global Economic Causes.” Working Economics (Economic Policy Institute Blog), May 29.

Scott, Robert E. 2014c. “Jack Lew Sees No Evil: Treasury Fails to Name China as a Currency Manipulator for the 12th Time.” Working Economics (Economic Policy Institute Blog), October 16.

Scott, Robert E., and Hilary Wething. 2012. Jobs in the U.S. Auto Parts Industry Are at Risk Due to Subsidized and Unfairly Traded Chinese Auto Parts. Economic Policy Institute, Briefing Paper #336.

Stewart, Terence P., and Elizabeth J. Drake. 2010. Written testimony for Committee on Ways and Means hearing on China’s Exchange Rate Policy, U.S. House of Representatives, March 24.

Tschetter, John. 2010. Exports Support American Jobs: Updated Measure Will Quantify Progress as Global Economy Recovers. U.S. Department of Commerce, International Trade Administration. International Trade Research Report #1, April.

U.S. Census Bureau. 2012. “Current Population Survey, Historical Time Series, Table A-2: Percent of People 25 Years and Over Who Have Completed High School or College, by Race, Hispanic Origin and Sex: Selected Years 1940 to 2011” [Excel file].

U.S. Census Bureau. 2013. “American Community Survey: Special Tabulation Over 45 industries, Covering 435 Congressional Districts and the District of Columbia (113th Congress Census Boundaries), Plus State and US Totals Based on ACS 2011 1-year file[spreadsheets received March 6].

U.S. Census Bureau. 2014a. “Advanced Technology Product Code Descriptions.”

U.S. Census Bureau. 2014b. “Exhibit 15a. Exports, Imports and Balance of Advanced Technology Products by Technology Group and Selected Countries and Areas—Annual Revision for 2013.”

U.S.-China Business Council. 2011. “USCBC: Flawed Study on China Jobs and Currency Once Again Distracts from Real Issues.” U.S.-China Business Council (press release), September 20.

U.S. International Trade Commission (USITC). 2014. “USITC Interactive Tariff and Trade DataWeb” [Excel files].

United States Trade Representative (USTR). 2009. “Information Regarding Application of Transitional Product-Specific Safeguard Measure to Chinese Tires.”

Wallach, Lori, and Patrick Woodall. 2011. Whose Trade Organization: A Comprehensive Guide to the WTO. Washington, D.C.: Public Citizen.

Wikipedia. 2014a. “California’s 17th Congressional District.” Accessed Oct. 16.

Wikipedia. 2014b. “113th United States Congress.” Accessed Oct. 22.

Xing, Yuqing. 2010. “Facts About and Impacts of FDI on China and the World Economy.”

Supplemental Table 1

Net U.S. jobs displaced due to goods trade deficit with China, by state, 2001–2013 (ranked by net jobs displaced)

Rank State Net jobs displaced State employment
(in 2011)
Jobs displaced as share
of state employment
1 California 564,200 16,426,700 3.43%
2 Texas 304,700 11,455,100 2.66%
3 New York 179,200 8,959,000 2.00%
4 Illinois 132,500 5,926,900 2.24%
5 Pennsylvania 122,600 5,853,300 2.09%
6 North Carolina 119,600 4,195,800 2.85%
7 Florida 115,700 8,101,900 1.43%
8 Ohio 106,400 5,213,500 2.04%
9 Massachusetts 97,200 3,284,700 2.96%
10 Georgia 93,700 4,193,800 2.23%
11 New Jersey 92,000 4,152,500 2.22%
12 Minnesota 83,300 2,728,900 3.05%
13 Michigan 80,100 4,191,900 1.91%
14 Wisconsin 68,600 2,819,500 2.43%
15 Indiana 67,800 2,934,500 2.31%
16 Virginia 63,500 3,860,100 1.65%
17 Oregon 62,700 1,710,300 3.67%
18 Tennessee 62,500 2,784,500 2.24%
19 Arizona 61,200 2,688,000 2.28%
20 Colorado 59,400 2,492,400 2.38%
21 Washington 55,900 3,118,000 1.79%
22 South Carolina 44,700 1,968,900 2.27%
23 Missouri 44,200 2,742,100 1.61%
24 Maryland 42,600 2,894,600 1.47%
25 Alabama 42,100 1,981,100 2.13%
26 Kentucky 41,100 1,838,400 2.24%
27 Connecticut 35,500 1,742,500 2.04%
28 Oklahoma 29,300 1,681,800 1.74%
29 Utah 27,000 1,260,800 2.14%
30 Iowa 24,600 1,538,800 1.60%
31 Arkansas 24,500 1,235,800 1.98%
32 New Hampshire 22,700 684,800 3.31%
33 Mississippi 20,200 1,181,300 1.71%
34 Kansas 19,100 1,389,000 1.38%
35 Louisiana 18,300 1,973,900 0.93%
36 Idaho 16,700 684,900 2.44%
37 Nevada 15,200 1,204,900 1.26%
38 Rhode Island 13,200 511,200 2.58%
39 New Mexico 12,500 869,800 1.44%
40 Nebraska 12,200 943,600 1.29%
41 Maine 11,400 643,100 1.77%
42 West Virginia 9,400 748,600 1.26%
43 Vermont 8,200 327,300 2.51%
44 Hawaii 6,100 629,500 0.97%
45 Delaware 5,500 420,400 1.31%
46 South Dakota 5,000 415,600 1.20%
47 Montana 3,600 480,000 0.75%
48 District of Columbia 2,800 310,600 0.90%
49 Alaska 2,600 344,300 0.76%
50 North Dakota 2,400 370,800 0.65%
51 Wyoming 1,700 290,000 0.59%
Total* 3,157,100 140,399,600 2.25%

*Subcategory and overall totals may vary slightly due to rounding.

Source: Authors' analysis of U.S. Census Bureau (2013), U.S. International Trade Commission (USITC 2014), Bureau of Labor Statistics (BLS 2014b), and BLS Employment Projections program (BLS-EP 2014a and 2014b). For a more detailed explanation of data sources  and computations, see the appendix.

Copy the code below to embed this chart on your website.

Supplemental Table 2

Net U.S. jobs displaced due to goods trade deficit with China, by state, 2001–2013 (sorted alphabetically)

Rank (by jobs
displaced as
share of total)
State Net jobs displaced State employment (in 2011) Jobs displaced as share
of state employment
22 Alabama 42,100 1,981,100 2.13%
48 Alaska 2,600 344,300 0.76%
14 Arizona 61,200 2,688,000 2.28%
27 Arkansas 24,500 1,235,800 1.98%
2 California 564,200 16,426,700 3.43%
12 Colorado 59,400 2,492,400 2.38%
25 Connecticut 35,500 1,742,500 2.04%
40 Delaware 5,500 420,400 1.31%
47 District of Columbia 2,800 310,600 0.90%
38 Florida 115,700 8,101,900 1.43%
19 Georgia 93,700 4,193,800 2.23%
45 Hawaii 6,100 629,500 0.97%
10 Idaho 16,700 684,900 2.44%
18 Illinois 132,500 5,926,900 2.24%
13 Indiana 67,800 2,934,500 2.31%
35 Iowa 24,600 1,538,800 1.60%
39 Kansas 19,100 1,389,000 1.38%
17 Kentucky 41,100 1,838,400 2.24%
46 Louisiana 18,300 1,973,900 0.93%
30 Maine 11,400 643,100 1.77%
36 Maryland 42,600 2,894,600 1.47%
5 Massachusetts 97,200 3,284,700 2.96%
28 Michigan 80,100 4,191,900 1.91%
4 Minnesota 83,300 2,728,900 3.05%
32 Mississippi 20,200 1,181,300 1.71%
34 Missouri 44,200 2,742,100 1.61%
49 Montana 3,600 480,000 0.75%
41 Nebraska 12,200 943,600 1.29%
42 Nevada 15,200 1,204,900 1.26%
3 New Hampshire 22,700 684,800 3.31%
20 New Jersey 92,000 4,152,500 2.22%
37 New Mexico 12,500 869,800 1.44%
26 New York 179,200 8,959,000 2.00%
6 North Carolina 119,600 4,195,800 2.85%
50 North Dakota 2,400 370,800 0.65%
24 Ohio 106,400 5,213,500 2.04%
31 Oklahoma 29,300 1,681,800 1.74%
1 Oregon 62,700 1,710,300 3.67%
23 Pennsylvania 122,600 5,853,300 2.09%
8 Rhode Island 13,200 511,200 2.58%
15 South Carolina 44,700 1,968,900 2.27%
44 South Dakota 5,000 415,600 1.20%
16 Tennessee 62,500 2,784,500 2.24%
7 Texas 304,700 11,455,100 2.66%
21 Utah 27,000 1,260,800 2.14%
9 Vermont 8,200 327,300 2.51%
33 Virginia 63,500 3,860,100 1.65%
29 Washington 55,900 3,118,000 1.79%
43 West Virginia 9,400 748,600 1.26%
11 Wisconsin 68,600 2,819,500 2.43%
51 Wyoming 1,700 290,000 0.59%
Total* 3,157,100 140,399,600 2.25%

*Subcategory and overall totals may vary slightly due to rounding.

Source: Authors' analysis of U.S. Census Bureau (2013), U.S. International Trade Commission (USITC 2014), Bureau of Labor Statistics (BLS 2014b), and BLS Employment Projections program (BLS-EP 2014a and 2014b). For a more detailed explanation of data sources and computations, see the appendix.

Copy the code below to embed this chart on your website.

Supplemental Table 3

Net U.S. jobs displaced due to goods trade deficit with China, by congressional district, 2001–2013 (ranked by jobs displaced as share of total district employment)

Rank State District Net jobs displaced District employment (in 2011) Jobs displaced as a share of employment
1 California 17 61,500 346,100 17.77%
2 California 18 50,700 344,500 14.72%
3 California 19 39,900 324,000 12.31%
4 Texas 31 36,800 323,000 11.39%
5 Oregon 1 32,500 377,200 8.62%
6 California 15 28,000 336,400 8.32%
7 Massachusetts 3 20,000 355,400 5.63%
8 California 40 15,600 280,500 5.56%
9 Texas 3 20,600 371,200 5.55%
10 Georgia 14 15,700 290,700 5.40%
11 California 34 16,200 309,400 5.24%
12 Texas 10 17,300 342,600 5.05%
13 California 52 17,000 350,100 4.86%
14 Minnesota 1 16,300 348,200 4.68%
15 New York 18 15,200 332,100 4.58%
16 California 45 16,200 354,400 4.57%
17 Texas 18 13,900 306,400 4.54%
18 Illinois 6 15,700 355,600 4.42%
19 Texas 17 14,100 329,300 4.28%
20 Texas 2 15,600 364,600 4.28%
21 Arizona 5 13,600 317,900 4.28%
22 California 14 15,200 364,000 4.18%
23 Minnesota 2 14,900 358,300 4.16%
24 Massachusetts 2 14,600 356,500 4.10%
25 Minnesota 3 14,200 353,800 4.01%
26 California 49 12,000 299,700 4.00%
27 California 48 14,100 352,600 4.00%
28 North Carolina 2 12,100 303,800 3.98%
29 Texas 25 11,800 302,200 3.90%
30 California 35 11,100 284,800 3.90%
31 North Carolina 8 11,400 301,700 3.78%
32 North Carolina 13 13,200 349,900 3.77%
33 New Hampshire 2 12,200 332,200 3.67%
34 Texas 32 13,200 360,900 3.66%
35 Massachusetts 4 13,600 374,800 3.63%
36 California 46 11,400 314,400 3.63%
37 California 39 12,000 332,000 3.61%
38 North Carolina 10 11,700 324,000 3.61%
39 Colorado 2 13,800 384,600 3.59%
40 Colorado 4 12,300 344,100 3.57%
41 North Carolina 6 12,200 341,800 3.57%
42 California 7 10,800 313,200 3.45%
43 Massachusetts 5 13,200 387,400 3.41%
44 Texas 33 9,500 283,900 3.35%
45 New Jersey 7 12,400 377,100 3.29%
46 Kentucky 6 11,000 335,400 3.28%
47 California 13 11,100 340,200 3.26%
48 South Carolina 3 8,600 264,500 3.25%
49 Arizona 9 11,700 360,300 3.25%
50 Alabama 5 10,100 311,900 3.24%
51 Texas 24 12,500 388,600 3.22%
52 California 44 8,700 270,600 3.22%
53 Indiana 3 10,400 327,000 3.18%
54 North Carolina 5 10,200 324,500 3.14%
55 Georgia 7 9,800 312,500 3.14%
56 Illinois 8 11,400 366,300 3.11%
57 Washington 3 8,700 284,500 3.06%
58 Illinois 10 9,900 324,800 3.05%
59 Washington 1 10,100 332,300 3.04%
60 Texas 7 11,400 376,300 3.03%
61 California 4 8,900 294,200 3.03%
62 South Carolina 5 8,200 275,200 2.98%
63 New Hampshire 1 10,500 352,600 2.98%
64 California 38 9,300 313,300 2.97%
65 Illinois 11 10,300 347,300 2.97%
66 Oregon 3 11,300 383,300 2.95%
67 North Carolina 12 9,400 319,800 2.94%
68 New York 19 9,600 327,300 2.93%
69 Minnesota 6 10,100 348,700 2.90%
70 New York 25 9,700 335,400 2.89%
71 Alabama 4 7,600 262,900 2.89%
72 California 37 9,700 335,600 2.89%
73 Texas 12 9,700 337,500 2.87%
74 Idaho 1 9,400 329,900 2.85%
75 California 12 11,300 399,400 2.83%
76 Wisconsin 3 10,000 353,500 2.83%
77 California 32 8,300 293,800 2.83%
78 Mississippi 1 8,600 305,600 2.81%
79 New Jersey 5 10,000 356,100 2.81%
80 Wisconsin 5 10,400 370,600 2.81%
81 Tennessee 7 8,000 285,800 2.80%
82 North Carolina 4 9,800 350,900 2.79%
83 South Carolina 4 8,400 301,000 2.79%
84 Indiana 2 8,700 317,800 2.74%
85 Illinois 14 9,500 351,000 2.71%
86 Georgia 9 7,700 284,600 2.71%
87 Indiana 8 8,900 329,300 2.70%
88 Georgia 3 7,700 285,800 2.69%
89 Wisconsin 6 9,500 353,600 2.69%
90 Florida 8 7,600 283,400 2.68%
91 California 30 9,600 358,200 2.68%
92 Tennessee 5 9,400 353,400 2.66%
93 Rhode Island 2 6,900 260,300 2.65%
94 Tennessee 4 8,300 314,500 2.64%
95 Illinois 9 9,100 347,200 2.62%
96 New Jersey 11 9,400 358,800 2.62%
97 California 43 7,900 302,800 2.61%
98 New York 7 8,400 322,200 2.61%
99 California 27 8,600 332,200 2.59%
100 Ohio 14 9,000 349,700 2.57%
101 Ohio 7 8,400 326,800 2.57%
102 California 42 7,800 307,000 2.54%
103 New Jersey 8 9,400 371,000 2.53%
104 Georgia 6 9,100 361,200 2.52%
105 Rhode Island 1 6,300 250,900 2.51%
106 Vermont Statewide 8,200 327,300 2.51%
107 Massachusetts 6 9,300 372,000 2.50%
108 Pennsylvania 3 7,900 317,700 2.49%
109 Wisconsin 1 8,500 342,500 2.48%
110 Illinois 4 8,100 326,600 2.48%
111 Minnesota 5 8,700 352,000 2.47%
112 North Carolina 11 7,300 295,400 2.47%
113 New York 1 8,400 343,300 2.45%
114 Virginia 9 7,300 298,400 2.45%
115 Pennsylvania 15 8,400 343,800 2.44%
116 Utah 3 7,600 311,200 2.44%
117 Michigan 2 7,700 315,900 2.44%
118 Indiana 6 7,600 311,900 2.44%
119 California 29 7,400 303,700 2.44%
120 Ohio 13 7,800 320,400 2.43%
121 Ohio 4 7,700 317,900 2.42%
122 Arkansas 3 7,900 327,000 2.42%
123 Oklahoma 1 8,700 361,900 2.40%
124 Pennsylvania 8 8,600 357,800 2.40%
125 North Carolina 9 8,900 371,400 2.40%
126 New Jersey 9 8,100 338,500 2.39%
127 California 33 8,700 364,200 2.39%
128 California 25 7,200 302,700 2.38%
129 New Jersey 6 8,400 353,600 2.38%
130 New York 22 7,600 320,200 2.37%
131 Kentucky 2 7,500 317,100 2.37%
132 California 50 7,000 296,200 2.36%
133 Tennessee 3 7,000 297,000 2.36%
134 Arizona 6 8,600 366,000 2.35%
135 New York 2 8,400 357,800 2.35%
136 Pennsylvania 6 8,500 362,300 2.35%
137 Kentucky 3 7,800 333,300 2.34%
138 Texas 26 8,600 368,300 2.34%
139 Alabama 3 6,400 274,600 2.33%
140 California 47 7,600 327,600 2.32%
141 Virginia 10 8,700 376,400 2.31%
142 Colorado 5 7,300 315,900 2.31%
143 Connecticut 4 7,900 343,000 2.30%
144 Michigan 10 7,100 308,700 2.30%
145 Texas 6 8,000 348,800 2.29%
146 Pennsylvania 12 7,600 331,900 2.29%
147 Wisconsin 8 8,300 362,800 2.29%
148 Arizona 7 6,400 282,300 2.27%
149 Oregon 4 7,000 309,000 2.27%
150 Wisconsin 7 7,600 338,400 2.25%
151 Ohio 5 7,500 334,200 2.24%
152 Wisconsin 4 6,900 308,000 2.24%
153 Connecticut 5 7,800 348,300 2.24%
154 Pennsylvania 17 7,000 312,600 2.24%
155 Illinois 5 8,900 397,600 2.24%
156 Minnesota 4 7,500 336,000 2.23%
157 Pennsylvania 18 7,700 345,000 2.23%
158 Ohio 16 7,900 355,600 2.22%
159 Ohio 8 7,300 328,800 2.22%
160 Tennessee 1 6,600 297,600 2.22%
161 Michigan 11 7,500 342,100 2.19%
162 Colorado 6 8,100 369,600 2.19%
163 California 53 7,500 342,700 2.19%
164 Texas 21 7,900 361,200 2.19%
165 Oregon 5 7,100 326,700 2.17%
166 California 28 7,800 359,900 2.17%
167 Florida 13 6,700 309,200 2.17%
168 Michigan 3 6,800 315,300 2.16%
169 Connecticut 3 7,600 352,700 2.15%
170 Florida 12 6,100 283,200 2.15%
171 Indiana 7 6,700 312,200 2.15%
172 Pennsylvania 10 6,700 312,500 2.14%
173 Utah 4 7,100 331,500 2.14%
174 Georgia 11 7,300 340,900 2.14%
175 Texas 35 6,800 318,200 2.14%
176 Kansas 3 7,900 370,300 2.13%
177 Washington 7 8,100 380,000 2.13%
178 Pennsylvania 4 7,300 342,900 2.13%
179 South Carolina 7 5,700 269,400 2.12%
180 Texas 9 6,900 326,400 2.11%
181 New York 24 6,900 327,300 2.11%
182 Kentucky 1 6,000 284,800 2.11%
183 Tennessee 6 6,400 304,500 2.10%
184 Massachusetts 9 7,400 352,300 2.10%
185 Iowa 1 8,200 392,300 2.09%
186 Florida 23 7,100 339,900 2.09%
187 Texas 30 6,100 292,300 2.09%
188 California 26 6,800 325,900 2.09%
189 Pennsylvania 5 6,600 316,800 2.08%
190 Arkansas 2 7,000 336,300 2.08%
191 Oklahoma 4 7,300 350,900 2.08%
192 Massachusetts 8 7,800 375,600 2.08%
193 Utah 2 6,300 305,700 2.06%
194 Pennsylvania 7 7,000 339,700 2.06%
195 Idaho 2 7,300 355,000 2.06%
196 New Mexico 1 6,400 311,900 2.05%
197 Ohio 10 6,400 312,800 2.05%
198 Pennsylvania 16 6,700 327,700 2.04%
199 New York 23 6,600 324,600 2.03%
200 Michigan 8 6,700 330,800 2.03%
201 Arizona 8 6,100 301,700 2.02%
202 Missouri 2 7,600 378,600 2.01%
203 Michigan 6 6,200 310,400 2.00%
204 New York 17 6,800 341,400 1.99%
205 Washington 10 5,800 291,300 1.99%
206 California 5 6,500 326,800 1.99%
207 North Carolina 1 5,800 291,800 1.99%
208 California 31 5,800 292,200 1.98%
209 New York 12 8,300 418,800 1.98%
210 Indiana 4 6,500 328,500 1.98%
211 Indiana 9 6,700 339,400 1.97%
212 Florida 22 6,500 332,000 1.96%
213 Illinois 17 6,100 311,700 1.96%
214 Texas 16 5,500 281,300 1.96%
215 New York 27 6,600 337,800 1.95%
216 Ohio 6 5,700 292,300 1.95%
217 Michigan 9 6,300 326,100 1.93%
218 Michigan 4 5,500 286,300 1.92%
219 Utah 1 6,000 312,400 1.92%
220 Pennsylvania 13 6,500 339,000 1.92%
221 California 20 5,800 302,500 1.92%
222 California 41 5,200 271,900 1.91%
223 California 6 5,500 288,300 1.91%
224 Tennessee 8 5,700 299,200 1.91%
225 Washington 9 6,500 341,400 1.90%
226 Pennsylvania 9 5,800 304,800 1.90%
227 Tennessee 9 5,800 305,300 1.90%
228 New Jersey 4 6,200 326,400 1.90%
229 Colorado 1 7,300 384,400 1.90%
230 Virginia 5 6,000 316,100 1.90%
231 Wisconsin 2 7,400 390,000 1.90%
232 Texas 5 5,700 300,800 1.89%
233 California 1 4,900 260,300 1.88%
234 Texas 29 5,500 292,900 1.88%
235 Colorado 7 6,800 362,500 1.88%
236 Iowa 2 7,000 373,400 1.87%
237 New Jersey 12 6,600 352,400 1.87%
238 Texas 4 5,600 299,300 1.87%
239 Missouri 7 6,300 337,400 1.87%
240 Pennsylvania 11 6,100 329,300 1.85%
241 Maine 2 5,600 302,700 1.85%
242 Indiana 5 6,600 357,700 1.85%
243 Alabama 2 5,100 276,900 1.84%
244 New Jersey 10 5,700 310,700 1.83%
245 South Carolina 2 5,600 305,600 1.83%
246 Arkansas 4 5,400 295,100 1.83%
247 New Jersey 1 6,200 339,200 1.83%
248 Pennsylvania 14 5,900 323,200 1.83%
249 Minnesota 7 6,000 328,700 1.83%
250 California 11 5,900 324,200 1.82%
251 Maryland 6 6,600 363,200 1.82%
252 Minnesota 8 5,500 303,400 1.81%
253 New York 21 5,600 309,200 1.81%
254 Ohio 9 5,700 315,000 1.81%
255 Georgia 10 5,200 287,400 1.81%
256 Missouri 8 5,400 298,500 1.81%
257 Indiana 1 5,600 310,600 1.80%
258 Texas 22 6,300 352,500 1.79%
259 Illinois 3 5,700 319,500 1.78%
260 Illinois 16 5,900 330,800 1.78%
261 Maryland 8 7,100 400,100 1.77%
262 Connecticut 1 6,200 349,800 1.77%
263 Illinois 2 4,900 278,200 1.76%
264 Georgia 5 5,600 318,100 1.76%
265 Alabama 6 5,600 318,400 1.76%
266 Pennsylvania 1 4,800 273,300 1.76%
267 Virginia 7 6,400 364,600 1.76%
268 New York 3 5,900 336,700 1.75%
269 Texas 8 5,400 309,200 1.75%
270 Ohio 1 5,800 332,300 1.75%
271 Ohio 3 5,800 333,000 1.74%
272 Kentucky 4 5,800 333,500 1.74%
273 Georgia 4 5,400 311,700 1.73%
274 Ohio 15 5,800 336,400 1.72%
275 Connecticut 2 6,000 348,600 1.72%
276 New York 6 5,600 327,000 1.71%
277 Michigan 14 4,400 257,700 1.71%
278 Michigan 7 5,100 299,100 1.71%
279 Arizona 2 5,100 299,200 1.70%
280 California 10 4,700 277,200 1.70%
281 Michigan 13 3,900 230,700 1.69%
282 Georgia 12 4,700 278,200 1.69%
283 Nevada 2 5,200 309,400 1.68%
284 Virginia 6 5,700 339,900 1.68%
285 Maine 1 5,700 340,400 1.67%
286 New York 5 5,600 336,200 1.67%
287 South Carolina 6 4,200 253,500 1.66%
288 Nebraska 2 5,200 316,300 1.64%
289 Florida 7 5,300 322,500 1.64%
290 Ohio 12 5,900 359,500 1.64%
291 Ohio 11 4,500 275,200 1.64%
292 Georgia 2 4,100 251,200 1.63%
293 New Jersey 3 5,600 344,200 1.63%
294 Michigan 5 4,300 264,800 1.62%
295 Tennessee 2 5,300 327,200 1.62%
296 New York 26 5,300 327,700 1.62%
297 Virginia 4 5,300 327,900 1.62%
298 New York 16 5,200 323,600 1.61%
299 Ohio 2 5,200 323,600 1.61%
300 Massachusetts 7 5,900 369,800 1.60%
301 New York 20 5,700 357,600 1.59%
302 Missouri 5 5,500 345,300 1.59%
303 California 51 4,100 258,600 1.59%
304 New York 10 5,700 360,300 1.58%
305 New York 8 4,600 292,700 1.57%
306 Georgia 13 4,900 312,800 1.57%
307 Florida 25 5,100 326,000 1.56%
308 Massachusetts 1 5,300 341,000 1.55%
309 Illinois 15 4,900 316,500 1.55%
310 Oklahoma 5 5,400 348,800 1.55%
311 Virginia 11 6,200 400,900 1.55%
312 West Virginia 1 4,000 258,700 1.55%
313 New York 13 4,900 317,200 1.54%
314 New York 11 4,900 317,500 1.54%
315 Illinois 7 4,600 298,500 1.54%
316 Illinois 18 5,200 337,500 1.54%
317 Florida 27 4,800 313,600 1.53%
318 Oregon 2 4,800 314,200 1.53%
319 New York 14 5,200 341,800 1.52%
320 Florida 16 4,200 276,100 1.52%
321 Illinois 1 4,400 290,200 1.52%
322 Arkansas 1 4,200 277,400 1.51%
323 New Mexico 3 4,300 284,800 1.51%
324 Alabama 7 3,800 253,500 1.50%
325 Nebraska 1 4,800 321,700 1.49%
326 Missouri 3 5,500 370,000 1.49%
327 Maryland 4 5,700 384,100 1.48%
328 New York 9 4,800 324,900 1.48%
329 California 2 4,700 323,100 1.45%
330 Missouri 1 4,800 331,500 1.45%
331 Mississippi 3 4,400 303,900 1.45%
332 Virginia 1 5,100 352,400 1.45%
333 Oklahoma 2 4,200 290,300 1.45%
334 Texas 1 4,300 297,700 1.44%
335 Kansas 2 4,900 339,900 1.44%
336 Florida 14 4,600 320,700 1.43%
337 Michigan 12 4,500 313,800 1.43%
338 Florida 20 4,300 302,100 1.42%
339 Michigan 1 4,100 290,200 1.41%
340 Texas 27 4,300 305,600 1.41%
341 Washington 5 4,100 291,500 1.41%
342 Maryland 1 4,800 342,300 1.40%
343 Maryland 7 4,400 315,700 1.39%
344 Missouri 6 4,900 355,900 1.38%
345 Illinois 12 4,100 301,000 1.36%
346 Maryland 3 5,000 369,500 1.35%
347 Florida 5 3,800 284,000 1.34%
348 North Carolina 7 4,200 315,400 1.33%
349 Iowa 3 5,200 390,800 1.33%
350 Florida 24 3,900 293,400 1.33%
351 New York 15 3,400 255,900 1.33%
352 Arizona 4 3,100 233,500 1.33%
353 Florida 21 4,200 316,800 1.33%
354 Florida 15 4,000 304,200 1.31%
355 Mississippi 4 4,000 304,900 1.31%
356 Delaware Statewide 5,500 420,400 1.31%
357 Florida 6 3,700 283,200 1.31%
358 South Carolina 1 3,900 299,800 1.30%
359 Virginia 2 4,400 339,800 1.29%
360 Florida 11 2,800 217,400 1.29%
361 New York 4 4,400 342,500 1.28%
362 Arizona 1 3,400 264,900 1.28%
363 Georgia 8 3,500 272,700 1.28%
364 Pennsylvania 2 3,500 273,100 1.28%
365 Kentucky 5 3,000 234,300 1.28%
366 Florida 4 4,200 329,900 1.27%
367 Florida 18 3,600 284,000 1.27%
368 Texas 36 3,700 291,900 1.27%
369 Missouri 4 4,100 324,900 1.26%
370 Washington 8 4,000 318,000 1.26%
371 Maryland 2 4,400 351,700 1.25%
372 Maryland 5 4,600 368,200 1.25%
373 Alabama 1 3,500 283,000 1.24%
374 Mississippi 2 3,300 266,900 1.24%
375 West Virginia 2 3,300 266,900 1.24%
376 California 9 3,400 275,300 1.24%
377 New Jersey 2 4,000 324,400 1.23%
378 California 8 2,900 235,500 1.23%
379 Washington 2 3,900 318,900 1.22%
380 Hawaii 1 4,000 330,100 1.21%
381 Texas 23 3,500 289,700 1.21%
382 South Dakota 1 5,000 415,600 1.20%
383 Texas 20 3,700 311,400 1.19%
384 Virginia 3 3,800 320,100 1.19%
385 Arizona 3 3,100 262,200 1.18%
386 Florida 10 3,900 331,500 1.18%
387 Illinois 13 3,700 326,600 1.13%
388 Florida 26 3,800 335,600 1.13%
389 Nevada 3 3,800 336,500 1.13%
390 Florida 2 3,400 301,500 1.13%
391 Washington 6 3,100 275,500 1.13%
392 Oklahoma 3 3,700 329,900 1.12%
393 Texas 14 3,400 303,300 1.12%
394 Colorado 3 3,700 331,400 1.12%
395 California 24 3,600 323,500 1.11%
396 Virginia 8 4,700 423,700 1.11%
397 Texas 13 3,400 309,000 1.10%
398 Iowa 4 4,200 382,300 1.10%
399 Nevada 4 3,000 274,300 1.09%
400 Louisiana 4 3,400 311,100 1.09%
401 Nevada 1 3,100 284,700 1.09%
402 Kansas 4 3,500 332,900 1.05%
403 North Carolina 3 3,100 305,600 1.01%
404 Georgia 1 2,900 286,100 1.01%
405 Florida 9 3,200 317,200 1.01%
406 Texas 15 2,800 280,900 1.00%
407 Florida 19 2,600 265,200 0.98%
408 Texas 11 3,000 308,800 0.97%
409 Texas 19 3,000 310,700 0.97%
410 Louisiana 6 3,500 367,800 0.95%
411 Texas 34 2,300 242,200 0.95%
412 West Virginia 3 2,100 223,000 0.94%
413 Texas 28 2,500 266,300 0.94%
414 Louisiana 3 3,000 328,100 0.91%
415 California 36 2,300 251,900 0.91%
416 Louisiana 1 3,200 354,000 0.90%
417 DC Statewide 2,800 310,600 0.90%
418 Florida 1 2,700 303,900 0.89%
419 Florida 3 2,400 277,000 0.87%
420 Louisiana 2 2,800 329,000 0.85%
421 Louisiana 5 2,400 283,900 0.85%
422 Kansas 1 2,900 345,900 0.84%
423 California 3 2,200 286,600 0.77%
424 Alaska Statewide 2,600 344,300 0.76%
425 Montana Statewide 3,600 480,000 0.75%
426 Hawaii 2 2,100 299,400 0.70%
427 New Mexico 2 1,900 273,100 0.70%
428 Nebraska 3 2,100 305,600 0.69%
429 California 23 1,800 274,100 0.66%
430 California 22 1,900 289,600 0.66%
431 North Dakota Statewide 2,400 370,800 0.65%
432 Wyoming Statewide 1,700 290,000 0.59%
433 Washington 4 1,500 284,500 0.53%
434 California 16 1,100 244,900 0.45%
435 Florida 17 1,100 248,700 0.44%
436 California 21 -1,100 243,800 -0.45%
Total* 3,157,100 140,399,600 2.25%

*Subcategory and overall totals may vary slightly due to rounding.

Source: Authors' analysis of U.S. Census Bureau (2013), U.S. International Trade Commission (USITC 2014), Bureau of Labor Statistics (BLS 2014b), and BLS Employment Projections program (BLS-EP 2014a and 2014b). For a more detailed explanation of data sources and computations, see the appendix.

Copy the code below to embed this chart on your website.

Supplemental Table 4

Net U.S. jobs displaced due to goods trade deficit with China, by congressional district, 2001–2013 (sorted alphabetically by state)

Rank (by jobs displaced as share of total) State District Net jobs displaced District employment (in 2011) Jobs displaced as a share of employment
373 Alabama 1 3,500 283,000 1.24%
243 Alabama 2 5,100 276,900 1.84%
139 Alabama 3 6,400 274,600 2.33%
71 Alabama 4 7,600 262,900 2.89%
50 Alabama 5 10,100 311,900 3.24%
265 Alabama 6 5,600 318,400 1.76%
324 Alabama 7 3,800 253,500 1.50%
424 Alaska Statewide 2,600 344,300 0.76%
362 Arizona 1 3,400 264,900 1.28%
279 Arizona 2 5,100 299,200 1.70%
385 Arizona 3 3,100 262,200 1.18%
352 Arizona 4 3,100 233,500 1.33%
21 Arizona 5 13,600 317,900 4.28%
134 Arizona 6 8,600 366,000 2.35%
148 Arizona 7 6,400 282,300 2.27%
201 Arizona 8 6,100 301,700 2.02%
49 Arizona 9 11,700 360,300 3.25%
322 Arkansas 1 4,200 277,400 1.51%
190 Arkansas 2 7,000 336,300 2.08%
122 Arkansas 3 7,900 327,000 2.42%
246 Arkansas 4 5,400 295,100 1.83%
233 California 1 4,900 260,300 1.88%
329 California 2 4,700 323,100 1.45%
423 California 3 2,200 286,600 0.77%
61 California 4 8,900 294,200 3.03%
206 California 5 6,500 326,800 1.99%
223 California 6 5,500 288,300 1.91%
42 California 7 10,800 313,200 3.45%
378 California 8 2,900 235,500 1.23%
376 California 9 3,400 275,300 1.24%
280 California 10 4,700 277,200 1.70%
250 California 11 5,900 324,200 1.82%
75 California 12 11,300 399,400 2.83%
47 California 13 11,100 340,200 3.26%
22 California 14 15,200 364,000 4.18%
6 California 15 28,000 336,400 8.32%
434 California 16 1,100 244,900 0.45%
1 California 17 61,500 346,100 17.77%
2 California 18 50,700 344,500 14.72%
3 California 19 39,900 324,000 12.31%
221 California 20 5,800 302,500 1.92%
436 California 21 -1,100 243,800 -0.45%
430 California 22 1,900 289,600 0.66%
429 California 23 1,800 274,100 0.66%
395 California 24 3,600 323,500 1.11%
128 California 25 7,200 302,700 2.38%
188 California 26 6,800 325,900 2.09%
99 California 27 8,600 332,200 2.59%
166 California 28 7,800 359,900 2.17%
119 California 29 7,400 303,700 2.44%
91 California 30 9,600 358,200 2.68%
208 California 31 5,800 292,200 1.98%
77 California 32 8,300 293,800 2.83%
127 California 33 8,700 364,200 2.39%
11 California 34 16,200 309,400 5.24%
30 California 35 11,100 284,800 3.90%
415 California 36 2,300 251,900 0.91%
72 California 37 9,700 335,600 2.89%
64 California 38 9,300 313,300 2.97%
37 California 39 12,000 332,000 3.61%
8 California 40 15,600 280,500 5.56%
222 California 41 5,200 271,900 1.91%
102 California 42 7,800 307,000 2.54%
97 California 43 7,900 302,800 2.61%
52 California 44 8,700 270,600 3.22%
16 California 45 16,200 354,400 4.57%
36 California 46 11,400 314,400 3.63%
140 California 47 7,600 327,600 2.32%
27 California 48 14,100 352,600 4.00%
26 California 49 12,000 299,700 4.00%
132 California 50 7,000 296,200 2.36%
303 California 51 4,100 258,600 1.59%
13 California 52 17,000 350,100 4.86%
163 California 53 7,500 342,700 2.19%
229 Colorado 1 7,300 384,400 1.90%
39 Colorado 2 13,800 384,600 3.59%
394 Colorado 3 3,700 331,400 1.12%
40 Colorado 4 12,300 344,100 3.57%
142 Colorado 5 7,300 315,900 2.31%
162 Colorado 6 8,100 369,600 2.19%
235 Colorado 7 6,800 362,500 1.88%
262 Connecticut 1 6,200 349,800 1.77%
275 Connecticut 2 6,000 348,600 1.72%
169 Connecticut 3 7,600 352,700 2.15%
143 Connecticut 4 7,900 343,000 2.30%
153 Connecticut 5 7,800 348,300 2.24%
356 Delaware Statewide 5,500 420,400 1.31%
417 DC Statewide 2,800 310,600 0.90%
418 Florida 1 2,700 303,900 0.89%
390 Florida 2 3,400 301,500 1.13%
419 Florida 3 2,400 277,000 0.87%
366 Florida 4 4,200 329,900 1.27%
347 Florida 5 3,800 284,000 1.34%
357 Florida 6 3,700 283,200 1.31%
289 Florida 7 5,300 322,500 1.64%
90 Florida 8 7,600 283,400 2.68%
405 Florida 9 3,200 317,200 1.01%
386 Florida 10 3,900 331,500 1.18%
360 Florida 11 2,800 217,400 1.29%
170 Florida 12 6,100 283,200 2.15%
167 Florida 13 6,700 309,200 2.17%
336 Florida 14 4,600 320,700 1.43%
354 Florida 15 4,000 304,200 1.31%
320 Florida 16 4,200 276,100 1.52%
435 Florida 17 1,100 248,700 0.44%
367 Florida 18 3,600 284,000 1.27%
407 Florida 19 2,600 265,200 0.98%
338 Florida 20 4,300 302,100 1.42%
353 Florida 21 4,200 316,800 1.33%
212 Florida 22 6,500 332,000 1.96%
186 Florida 23 7,100 339,900 2.09%
350 Florida 24 3,900 293,400 1.33%
307 Florida 25 5,100 326,000 1.56%
388 Florida 26 3,800 335,600 1.13%
317 Florida 27 4,800 313,600 1.53%
404 Georgia 1 2,900 286,100 1.01%
292 Georgia 2 4,100 251,200 1.63%
88 Georgia 3 7,700 285,800 2.69%
273 Georgia 4 5,400 311,700 1.73%
264 Georgia 5 5,600 318,100 1.76%
104 Georgia 6 9,100 361,200 2.52%
55 Georgia 7 9,800 312,500 3.14%
363 Georgia 8 3,500 272,700 1.28%
86 Georgia 9 7,700 284,600 2.71%
255 Georgia 10 5,200 287,400 1.81%
174 Georgia 11 7,300 340,900 2.14%
282 Georgia 12 4,700 278,200 1.69%
306 Georgia 13 4,900 312,800 1.57%
10 Georgia 14 15,700 290,700 5.40%
380 Hawaii 1 4,000 330,100 1.21%
426 Hawaii 2 2,100 299,400 0.70%
74 Idaho 1 9,400 329,900 2.85%
195 Idaho 2 7,300 355,000 2.06%
321 Illinois 1 4,400 290,200 1.52%
263 Illinois 2 4,900 278,200 1.76%
259 Illinois 3 5,700 319,500 1.78%
110 Illinois 4 8,100 326,600 2.48%
155 Illinois 5 8,900 397,600 2.24%
18 Illinois 6 15,700 355,600 4.42%
315 Illinois 7 4,600 298,500 1.54%
56 Illinois 8 11,400 366,300 3.11%
95 Illinois 9 9,100 347,200 2.62%
58 Illinois 10 9,900 324,800 3.05%
65 Illinois 11 10,300 347,300 2.97%
345 Illinois 12 4,100 301,000 1.36%
387 Illinois 13 3,700 326,600 1.13%
85 Illinois 14 9,500 351,000 2.71%
309 Illinois 15 4,900 316,500 1.55%
260 Illinois 16 5,900 330,800 1.78%
213 Illinois 17 6,100 311,700 1.96%
316 Illinois 18 5,200 337,500 1.54%
257 Indiana 1 5,600 310,600 1.80%
84 Indiana 2 8,700 317,800 2.74%
53 Indiana 3 10,400 327,000 3.18%
210 Indiana 4 6,500 328,500 1.98%
242 Indiana 5 6,600 357,700 1.85%
118 Indiana 6 7,600 311,900 2.44%
171 Indiana 7 6,700 312,200 2.15%
87 Indiana 8 8,900 329,300 2.70%
211 Indiana 9 6,700 339,400 1.97%
185 Iowa 1 8,200 392,300 2.09%
236 Iowa 2 7,000 373,400 1.87%
349 Iowa 3 5,200 390,800 1.33%
398 Iowa 4 4,200 382,300 1.10%
422 Kansas 1 2,900 345,900 0.84%
335 Kansas 2 4,900 339,900 1.44%
176 Kansas 3 7,900 370,300 2.13%
402 Kansas 4 3,500 332,900 1.05%
182 Kentucky 1 6,000 284,800 2.11%
131 Kentucky 2 7,500 317,100 2.37%
137 Kentucky 3 7,800 333,300 2.34%
272 Kentucky 4 5,800 333,500 1.74%
365 Kentucky 5 3,000 234,300 1.28%
46 Kentucky 6 11,000 335,400 3.28%
416 Louisiana 1 3,200 354,000 0.90%
420 Louisiana 2 2,800 329,000 0.85%
414 Louisiana 3 3,000 328,100 0.91%
400 Louisiana 4 3,400 311,100 1.09%
421 Louisiana 5 2,400 283,900 0.85%
410 Louisiana 6 3,500 367,800 0.95%
285 Maine 1 5,700 340,400 1.67%
241 Maine 2 5,600 302,700 1.85%
342 Maryland 1 4,800 342,300 1.40%
371 Maryland 2 4,400 351,700 1.25%
346 Maryland 3 5,000 369,500 1.35%
327 Maryland 4 5,700 384,100 1.48%
372 Maryland 5 4,600 368,200 1.25%
251 Maryland 6 6,600 363,200 1.82%
343 Maryland 7 4,400 315,700 1.39%
261 Maryland 8 7,100 400,100 1.77%
308 Massachusetts 1 5,300 341,000 1.55%
24 Massachusetts 2 14,600 356,500 4.10%
7 Massachusetts 3 20,000 355,400 5.63%
35 Massachusetts 4 13,600 374,800 3.63%
43 Massachusetts 5 13,200 387,400 3.41%
107 Massachusetts 6 9,300 372,000 2.50%
300 Massachusetts 7 5,900 369,800 1.60%
192 Massachusetts 8 7,800 375,600 2.08%
184 Massachusetts 9 7,400 352,300 2.10%
339 Michigan 1 4,100 290,200 1.41%
117 Michigan 2 7,700 315,900 2.44%
168 Michigan 3 6,800 315,300 2.16%
218 Michigan 4 5,500 286,300 1.92%
294 Michigan 5 4,300 264,800 1.62%
203 Michigan 6 6,200 310,400 2.00%
278 Michigan 7 5,100 299,100 1.71%
200 Michigan 8 6,700 330,800 2.03%
217 Michigan 9 6,300 326,100 1.93%
144 Michigan 10 7,100 308,700 2.30%
161 Michigan 11 7,500 342,100 2.19%
337 Michigan 12 4,500 313,800 1.43%
281 Michigan 13 3,900 230,700 1.69%
277 Michigan 14 4,400 257,700 1.71%
14 Minnesota 1 16,300 348,200 4.68%
23 Minnesota 2 14,900 358,300 4.16%
25 Minnesota 3 14,200 353,800 4.01%
156 Minnesota 4 7,500 336,000 2.23%
111 Minnesota 5 8,700 352,000 2.47%
69 Minnesota 6 10,100 348,700 2.90%
249 Minnesota 7 6,000 328,700 1.83%
252 Minnesota 8 5,500 303,400 1.81%
78 Mississippi 1 8,600 305,600 2.81%
374 Mississippi 2 3,300 266,900 1.24%
331 Mississippi 3 4,400 303,900 1.45%
355 Mississippi 4 4,000 304,900 1.31%
425 Montana Statewide 3,600 480,000 0.75%
330 Missouri 1 4,800 331,500 1.45%
202 Missouri 2 7,600 378,600 2.01%
326 Missouri 3 5,500 370,000 1.49%
369 Missouri 4 4,100 324,900 1.26%
302 Missouri 5 5,500 345,300 1.59%
344 Missouri 6 4,900 355,900 1.38%
239 Missouri 7 6,300 337,400 1.87%
256 Missouri 8 5,400 298,500 1.81%
325 Nebraska 1 4,800 321,700 1.49%
288 Nebraska 2 5,200 316,300 1.64%
428 Nebraska 3 2,100 305,600 0.69%
401 Nevada 1 3,100 284,700 1.09%
283 Nevada 2 5,200 309,400 1.68%
389 Nevada 3 3,800 336,500 1.13%
399 Nevada 4 3,000 274,300 1.09%
63 New Hampshire 1 10,500 352,600 2.98%
33 New Hampshire 2 12,200 332,200 3.67%
247 New Jersey 1 6,200 339,200 1.83%
377 New Jersey 2 4,000 324,400 1.23%
293 New Jersey 3 5,600 344,200 1.63%
228 New Jersey 4 6,200 326,400 1.90%
79 New Jersey 5 10,000 356,100 2.81%
129 New Jersey 6 8,400 353,600 2.38%
45 New Jersey 7 12,400 377,100 3.29%
103 New Jersey 8 9,400 371,000 2.53%
126 New Jersey 9 8,100 338,500 2.39%
244 New Jersey 10 5,700 310,700 1.83%
96 New Jersey 11 9,400 358,800 2.62%
237 New Jersey 12 6,600 352,400 1.87%
196 New Mexico 1 6,400 311,900 2.05%
427 New Mexico 2 1,900 273,100 0.70%
323 New Mexico 3 4,300 284,800 1.51%
113 New York 1 8,400 343,300 2.45%
135 New York 2 8,400 357,800 2.35%
268 New York 3 5,900 336,700 1.75%
361 New York 4 4,400 342,500 1.28%
286 New York 5 5,600 336,200 1.67%
276 New York 6 5,600 327,000 1.71%
98 New York 7 8,400 322,200 2.61%
305 New York 8 4,600 292,700 1.57%
328 New York 9 4,800 324,900 1.48%
304 New York 10 5,700 360,300 1.58%
314 New York 11 4,900 317,500 1.54%
209 New York 12 8,300 418,800 1.98%
313 New York 13 4,900 317,200 1.54%
319 New York 14 5,200 341,800 1.52%
351 New York 15 3,400 255,900 1.33%
298 New York 16 5,200 323,600 1.61%
204 New York 17 6,800 341,400 1.99%
15 New York 18 15,200 332,100 4.58%
68 New York 19 9,600 327,300 2.93%
301 New York 20 5,700 357,600 1.59%
253 New York 21 5,600 309,200 1.81%
130 New York 22 7,600 320,200 2.37%
199 New York 23 6,600 324,600 2.03%
181 New York 24 6,900 327,300 2.11%
70 New York 25 9,700 335,400 2.89%
296 New York 26 5,300 327,700 1.62%
215 New York 27 6,600 337,800 1.95%
207 North Carolina 1 5,800 291,800 1.99%
28 North Carolina 2 12,100 303,800 3.98%
403 North Carolina 3 3,100 305,600 1.01%
82 North Carolina 4 9,800 350,900 2.79%
54 North Carolina 5 10,200 324,500 3.14%
41 North Carolina 6 12,200 341,800 3.57%
348 North Carolina 7 4,200 315,400 1.33%
31 North Carolina 8 11,400 301,700 3.78%
125 North Carolina 9 8,900 371,400 2.40%
38 North Carolina 10 11,700 324,000 3.61%
112 North Carolina 11 7,300 295,400 2.47%
67 North Carolina 12 9,400 319,800 2.94%
32 North Carolina 13 13,200 349,900 3.77%
431 North Dakota Statewide 2,400 370,800 0.65%
270 Ohio 1 5,800 332,300 1.75%
298 Ohio 2 5,200 323,600 1.61%
271 Ohio 3 5,800 333,000 1.74%
121 Ohio 4 7,700 317,900 2.42%
151 Ohio 5 7,500 334,200 2.24%
216 Ohio 6 5,700 292,300 1.95%
101 Ohio 7 8,400 326,800 2.57%
159 Ohio 8 7,300 328,800 2.22%
254 Ohio 9 5,700 315,000 1.81%
197 Ohio 10 6,400 312,800 2.05%
291 Ohio 11 4,500 275,200 1.64%
290 Ohio 12 5,900 359,500 1.64%
120 Ohio 13 7,800 320,400 2.43%
100 Ohio 14 9,000 349,700 2.57%
274 Ohio 15 5,800 336,400 1.72%
158 Ohio 16 7,900 355,600 2.22%
123 Oklahoma 1 8,700 361,900 2.40%
333 Oklahoma 2 4,200 290,300 1.45%
392 Oklahoma 3 3,700 329,900 1.12%
191 Oklahoma 4 7,300 350,900 2.08%
310 Oklahoma 5 5,400 348,800 1.55%
5 Oregon 1 32,500 377,200 8.62%
318 Oregon 2 4,800 314,200 1.53%
66 Oregon 3 11,300 383,300 2.95%
149 Oregon 4 7,000 309,000 2.27%
165 Oregon 5 7,100 326,700 2.17%
266 Pennsylvania 1 4,800 273,300 1.76%
364 Pennsylvania 2 3,500 273,100 1.28%
108 Pennsylvania 3 7,900 317,700 2.49%
178 Pennsylvania 4 7,300 342,900 2.13%
189 Pennsylvania 5 6,600 316,800 2.08%
136 Pennsylvania 6 8,500 362,300 2.35%
194 Pennsylvania 7 7,000 339,700 2.06%
124 Pennsylvania 8 8,600 357,800 2.40%
226 Pennsylvania 9 5,800 304,800 1.90%
172 Pennsylvania 10 6,700 312,500 2.14%
240 Pennsylvania 11 6,100 329,300 1.85%
146 Pennsylvania 12 7,600 331,900 2.29%
220 Pennsylvania 13 6,500 339,000 1.92%
248 Pennsylvania 14 5,900 323,200 1.83%
115 Pennsylvania 15 8,400 343,800 2.44%
198 Pennsylvania 16 6,700 327,700 2.04%
154 Pennsylvania 17 7,000 312,600 2.24%
157 Pennsylvania 18 7,700 345,000 2.23%
105 Rhode Island 1 6,300 250,900 2.51%
93 Rhode Island 2 6,900 260,300 2.65%
358 South Carolina 1 3,900 299,800 1.30%
245 South Carolina 2 5,600 305,600 1.83%
48 South Carolina 3 8,600 264,500 3.25%
83 South Carolina 4 8,400 301,000 2.79%
62 South Carolina 5 8,200 275,200 2.98%
287 South Carolina 6 4,200 253,500 1.66%
179 South Carolina 7 5,700 269,400 2.12%
382 South Dakota 1 5,000 415,600 1.20%
160 Tennessee 1 6,600 297,600 2.22%
295 Tennessee 2 5,300 327,200 1.62%
133 Tennessee 3 7,000 297,000 2.36%
94 Tennessee 4 8,300 314,500 2.64%
92 Tennessee 5 9,400 353,400 2.66%
183 Tennessee 6 6,400 304,500 2.10%
81 Tennessee 7 8,000 285,800 2.80%
224 Tennessee 8 5,700 299,200 1.91%
227 Tennessee 9 5,800 305,300 1.90%
334 Texas 1 4,300 297,700 1.44%
20 Texas 2 15,600 364,600 4.28%
9 Texas 3 20,600 371,200 5.55%
238 Texas 4 5,600 299,300 1.87%
232 Texas 5 5,700 300,800 1.89%
145 Texas 6 8,000 348,800 2.29%
60 Texas 7 11,400 376,300 3.03%
269 Texas 8 5,400 309,200 1.75%
180 Texas 9 6,900 326,400 2.11%
12 Texas 10 17,300 342,600 5.05%
408 Texas 11 3,000 308,800 0.97%
73 Texas 12 9,700 337,500 2.87%
397 Texas 13 3,400 309,000 1.10%
393 Texas 14 3,400 303,300 1.12%
406 Texas 15 2,800 280,900 1.00%
214 Texas 16 5,500 281,300 1.96%
19 Texas 17 14,100 329,300 4.28%
17 Texas 18 13,900 306,400 4.54%
409 Texas 19 3,000 310,700 0.97%
383 Texas 20 3,700 311,400 1.19%
164 Texas 21 7,900 361,200 2.19%
258 Texas 22 6,300 352,500 1.79%
381 Texas 23 3,500 289,700 1.21%
51 Texas 24 12,500 388,600 3.22%
29 Texas 25 11,800 302,200 3.90%
138 Texas 26 8,600 368,300 2.34%
340 Texas 27 4,300 305,600 1.41%
413 Texas 28 2,500 266,300 0.94%
234 Texas 29 5,500 292,900 1.88%
187 Texas 30 6,100 292,300 2.09%
4 Texas 31 36,800 323,000 11.39%
34 Texas 32 13,200 360,900 3.66%
44 Texas 33 9,500 283,900 3.35%
411 Texas 34 2,300 242,200 0.95%
175 Texas 35 6,800 318,200 2.14%
368 Texas 36 3,700 291,900 1.27%
219 Utah 1 6,000 312,400 1.92%
193 Utah 2 6,300 305,700 2.06%
116 Utah 3 7,600 311,200 2.44%
173 Utah 4 7,100 331,500 2.14%
106 Vermont Statewide 8,200 327,300 2.51%
332 Virginia 1 5,100 352,400 1.45%
359 Virginia 2 4,400 339,800 1.29%
384 Virginia 3 3,800 320,100 1.19%
297 Virginia 4 5,300 327,900 1.62%
230 Virginia 5 6,000 316,100 1.90%
284 Virginia 6 5,700 339,900 1.68%
267 Virginia 7 6,400 364,600 1.76%
396 Virginia 8 4,700 423,700 1.11%
114 Virginia 9 7,300 298,400 2.45%
141 Virginia 10 8,700 376,400 2.31%
311 Virginia 11 6,200 400,900 1.55%
59 Washington 1 10,100 332,300 3.04%
379 Washington 2 3,900 318,900 1.22%
57 Washington 3 8,700 284,500 3.06%
433 Washington 4 1,500 284,500 0.53%
341 Washington 5 4,100 291,500 1.41%
391 Washington 6 3,100 275,500 1.13%
177 Washington 7 8,100 380,000 2.13%
370 Washington 8 4,000 318,000 1.26%
225 Washington 9 6,500 341,400 1.90%
205 Washington 10 5,800 291,300 1.99%
312 West Virginia 1 4,000 258,700 1.55%
374 West Virginia 2 3,300 266,900 1.24%
412 West Virginia 3 2,100 223,000 0.94%
109 Wisconsin 1 8,500 342,500 2.48%
231 Wisconsin 2 7,400 390,000 1.90%
76 Wisconsin 3 10,000 353,500 2.83%
152 Wisconsin 4 6,900 308,000 2.24%
80 Wisconsin 5 10,400 370,600 2.81%
89 Wisconsin 6 9,500 353,600 2.69%
150 Wisconsin 7 7,600 338,400 2.25%
147 Wisconsin 8 8,300 362,800 2.29%
432 Wyoming Statewide 1,700 290,000 0.59%
Total* 3,157,100 140,399,600 2.25%

*Subcategory and overall totals may vary slightly due to rounding.

Source: Authors' analysis of U.S. Census Bureau (2013), U.S. International Trade Commission (USITC 2014), Bureau of Labor Statistics (BLS 2014b), and BLS Employment Projections program (BLS-EP 2014a and 2014b). For a more detailed explanation of data sources and computations, see the appendix.

Copy the code below to embed this chart on your website.

Supplemental Table 5

U.S. goods trade with China, by industry, 2001–2013 (in billions of nominal dollars)

2001 2013 Change 2001–2013 Percent change 2001–2013
Industry* Imports Exports Trade balance  Imports Exports Trade balance Imports  Exports Trade balance Imports Exports  Trade Balance
Agriculture, forestry, fishing, and hunting $0.7 $1.3 $0.6 $3.0 $21.6 $18.5 $2.3 $20.2 $17.9 306% 1503% 3006%
Mining $0.3 $0.1 -$0.2 $0.2 $2.7 $2.5 $0.0 $2.6 $2.7 -14% 3280% -1564%
Oil and gas $0.1 $0.0 -$0.1 $0.1 $0.1 $0.1 $0.0 $0.1 $0.2 -42% 1738% -217%
Minerals and ores $0.2 $0.1 -$0.1 $0.2 $2.6 $2.4 $0.0 $2.5 $2.5 2% 3451% -2789%
Manufacturing $100.9 $15.5 -$85.4 $434.5 $80.9 -$353.5 $333.6 $65.5 -$268.1 331% 424% 314%
Nondurable goods $23.4 $1.0 -$22.4 $72.4 $7.1 -$65.3 $49.0 $6.1 -$42.8 209% 629% 191%
Food $0.6 $0.8 $0.2 $3.7 $4.6 $0.9 $3.1 $3.9 $0.8 521% 505% 449%
Beverage and tobacco products $0.0 $0.0 $0.0 $0.1 $1.5 $1.4 $0.0 $1.5 $1.5 67% 29760% -5872%
Textile mills and textile product mills $2.2 $0.1 -$2.1 $11.5 $0.6 -$10.9 $9.3 $0.5 -$8.8 427% 592% 420%
Apparel $8.6 $0.0 -$8.6 $32.7 $0.0 -$32.7 $24.1 $0.0 -$24.1 281% 30% 281%
Leather and allied products $12.0 $0.1 -$11.9 $24.4 $0.4 -$24.1 $12.4 $0.3 -$12.2 103% 294% 102%
Industrial supplies $9.6 $3.3 -$6.3 $43.6 $20.7 -$22.9 $34.0 $17.4 -$16.6 356% 526% 266%
Wood products $0.9 $0.1 -$0.8 $3.3 $1.2 -$2.2 $2.5 $1.1 -$1.4 276% 1119% 172%
Paper $0.7 $0.5 -$0.2 $3.4 $2.5 -$0.9 $2.7 $2.0 -$0.7 377% 393% 340%
Printed matter and related products $0.7 $0.0 -$0.7 $2.4 $0.2 -$2.2 $1.6 $0.1 -$1.5 225% 291% 220%
Petroleum and coal products $0.2 $0.1 -$0.1 $0.3 $1.5 $1.2 $0.1 $1.4 $1.3 24% 1560% -884%
Chemicals $1.8 $2.2 $0.4 $13.0 $13.6 $0.6 $11.2 $11.5 $0.2 619% 525% 63%
Plastics and rubber products $2.7 $0.2 -$2.5 $14.9 $1.2 -$13.7 $12.2 $1.0 -$11.2 449% 480% 447%
Nonmetallic mineral products $2.5 $0.2 -$2.3 $6.3 $0.6 -$5.7 $3.8 $0.4 -$3.4 154% 214% 149%
    Durable goods $67.9 $11.2 -$56.7 $318.5 $53.1 -$265.4 $250.6 $42.0 -$208.7 369% 376% 368%
Primary metal $0.8 $0.2 -$0.6 $4.2 $2.9 -$1.3 $3.4 $2.6 -$0.8 427% 1124% 135%
Fabricated metal products $3.9 $0.3 -$3.6 $18.4 $1.9 -$16.5 $14.5 $1.6 -$12.9 376% 564% 361%
Machinery $4.5 $2.4 -$2.1 $25.0 $9.7 -$15.2 $20.4 $7.3 -$13.2 452% 299% 631%
Computer and electronic parts $24.3 $4.4 -$19.9 $165.6 $11.2 -$154.4 $141.3 $6.8 -$134.5 581% 152% 677%
Computer and peripheral equipment $8.2 $1.2 -$7.0 $68.1 $0.8 -$67.3 $60.0 -$0.4 -$60.3 733% -30% 863%
Communications, audio, and video equipment $9.4 $0.8 -$8.6 $71.9 $0.6 -$71.3 $62.5 -$0.2 -$62.7 666% -23% 733%
Navigational, measuring, electromedical, and control instruments $1.2 $0.8 -$0.4 $6.2 $5.3 -$0.9 $5.0 $4.5 -$0.5 404% 550% 116%
Semiconductor and other electronic components, and reproducing magnetic and optical media $5.5 $1.6 -$3.9 $19.3 $4.4 -$14.9 $13.8 $2.8 -$11.0 251% 174% 283%
Electrical equipment, appliances, and components $9.0 $0.5 -$8.5 $32.3 $2.0 -$30.3 $23.3 $1.5 -$21.7 259% 335% 254%
Transportation equipment $1.8 $2.8 $1.0 $13.2 $22.4 $9.3 $11.4 $19.6 $8.2 626% 691% 806%
Motor vehicles and motor vehicle parts $1.0 $0.3 -$0.8 $10.9 $10.2 -$0.6 $9.8 $10.0 $0.2 939% 3779% -21%
Aerospace products and parts $0.1 $2.6 $2.5 $0.8 $12.1 $11.3 $0.7 $9.5 $8.8 765% 372% 358%
Railroad, ship, and other transportation equipment $0.7 $0.0 -$0.7 $1.6 $0.1 -$1.4 $0.9 $0.1 -$0.8 128% 706% 113%
Furniture and related products $4.9 $0.0 -$4.9 $17.2 $0.1 -$17.1 $12.3 $0.1 -$12.2 248% 450% 247%
Miscellaneous manufactured commodities $18.7 $0.5 -$18.2 $42.7 $2.9 -$39.9 $24.1 $2.4 -$21.7 129% 532% 119%
Information** $0.0 $0.0 $0.0 $0.0 $0.1 $0.1 $0.0 $0.1 $0.1 -50% N/A -1117%
Scrap and second-hand goods $0.2 $1.1 $0.9 $0.5 $8.8 $8.3 $0.3 $7.7 $7.4 139% 713% 839%
Total $102.07 $17.96 -$84.1 $438.2 $114.0 -$324.2 $336.1 $96.1 -$240.1 329% 535% 285%

* Excludes utilities, construction, and service sectors, which reported no goods trade in this period.

** Includes publishing industries (excluding Internet); goods trade in this sector is concentrated in NAICS 5111, Newspaper, periodical, book, and directory publishers.

Source: Authors' analysis of U.S. International Trade Commission (USITC 2014). For a more detailed explanation of the data sources and computations, see the appendix.

Copy the code below to embed this chart on your website.

See more work by Will Kimball and Robert E. Scott