Report | Trade and Globalization

Currency Manipulation and the 896,600 U.S. Jobs Lost Due to the U.S.-Japan Trade Deficit

Briefing Paper #387

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Executive summary

U.S. trade and investment agreements have almost always resulted in growing trade deficits and job losses. Under the 1993 North American Free Trade Agreement, growing trade deficits with Mexico cost 682,900 U.S. jobs as of 2010, and U.S.-Mexico trade deficits and job displacement have increased since then. President Obama promised that the U.S.-Korea Free Trade Agreement would increase U.S. goods exports by between $10 billion and $11 billion, supporting 70,000 American jobs from increased exports alone. However, in the first two years after that deal went into effect, U.S. exports actually declined, and growing trade deficits with South Korea cost nearly 60,000 U.S. jobs. The U.S. trade deficit with South Korea continues to rise.

This is important to keep in mind as secret negotiations for the Trans-Pacific Partnership (TPP) continue, most recently in Washington and New York. The United States has a large and growing trade deficit with Japan and the 10 other countries in the proposed TPP. This deficit has increased from $110.3 billion in 1997 to an estimated $261.7 billion in 2014.

Additionally, several members of the proposed TPP deal are well known currency manipulators, including Malaysia, Singapore, and Japan. In fact, Japan is the world’s second largest currency manipulator, behind China. The United States should not sign a trade and investment deal with these countries that does not include strong prohibitions on currency manipulation. Yet U.S. Trade Representative Michael Froman has testified that currency manipulation has not been discussed in the TPP negotiations (McCormack 2014).

As one of the world’s largest currency manipulators, Japan is responsible for a substantial share of the bloated U.S. global trade deficit. Eliminating currency manipulation by about 20 developing and developed countries (including Japan) could reduce the U.S. global trade deficit by between $200 billion and $500 billion each year, which could increase overall U.S. GDP by between $288 billion and $720 billion and create between 2.3 million and 5.8 million U.S. jobs. This report evaluates the impacts of Japan’s currency manipulation, specifically as manifested in the U.S. trade deficit with Japan, on the U.S. economy and jobs. It finds that currency manipulation by Japan has resulted in a large, persistent U.S. trade deficit with Japan that has displaced hundreds of thousands of U.S. jobs:

  • The U.S.-Japan goods trade deficit reached $78.3 billion in 2013, reducing U.S. GDP by $125.3 billion or nearly 0.75 percent of actual GDP in that year. Japan’s currency manipulation was the most important cause of this deficit, which displaced 896,600 U.S. jobs in 2013, with job losses in every state and nearly all U.S. congressional districts.
  • The 896,600 jobs eliminated by the U.S. goods trade deficit with Japan included 148,400 direct jobs in commodity and manufacturing industries that competed with unfairly traded imports and exports from Japan. The currency-manipulation-fueled trade deficit was also responsible for the loss of 412,000 indirect jobs in supplier industries, and an additional 336,200 “respending” jobs—jobs that would have been supported by the wages of workers displaced by trade with Japan.
  • The nearly 900,000 direct, indirect, and respending jobs displaced by the U.S.-Japan trade deficit in 2013 affected multiple sectors and industries. Job losses include 466,000 manufacturing jobs (52 percent of the jobs lost due to the U.S.-Japan trade deficit). Within manufacturing, by far the largest losses occurred in motor vehicles and parts, which lost 118,800 jobs (13.3 percent of total jobs lost). Other manufacturing industries with large losses include machinery (96,600 jobs), fabricated metal products (80,800 jobs), and computer and electronic parts (66,100 jobs). The U.S.-Japan trade deficit was also responsible for significant job losses outside of manufacturing in administrative and support industries (61,800 jobs); health care and social assistance (60,500 jobs); retail trade (51,800); professional, scientific, and technical services (50,000 jobs), and accommodation and food services (48,500 jobs). Net trade with Japan also created a total of 63,600 jobs in U.S. agricultural industries.
  • The U.S.-Japan trade deficit also reduced tax revenues and increased safety net expenditures in 2013, increasing the federal budget deficit by $46.4 billion, 7.4 percent of the federal budget deficit in that year. If the U.S. trade deficit with Japan were to persist at the 2013 level for the next 10 years, the loss of jobs and wages would add $460 billion to the total federal deficit over the next decade. The U.S.-Japan trade deficit also reduced net state and local resources by $17.5 billion in 2013, alone.
  • Each of the 50 states and the District of Columbia lost jobs due to the U.S. trade deficit with Japan in 2013. Job losses were greatest in Michigan, where they constituted 1.34 percent of total state employment.
  • Eight of the 10 states with the highest job losses (as a share of total employment) are in the Midwest or the East South Central census regions, all states where manufacturing predominates: Michigan (56,200 jobs), Indiana (33,700 jobs), Ohio (50,900 jobs), Kentucky (16,400 jobs), Wisconsin (24,300 jobs), Tennessee (23,200 jobs), Alabama (16,000 jobs) and Illinois (45,500 jobs). Rounding out the top 10 states losing the largest shares of jobs  were South Carolina (16,800 jobs) in the South Atlantic region, and New Hampshire (5,300 jobs) in New England.
  • The U.S. trade deficit with Japan resulted in net job losses in all but three U.S. Congressional Districts, and has displaced up to 6,000 jobs in a single U.S. congressional district. In the 20 congressional districts with the largest shares of jobs lost, losses ranged from 3,100 to 6,000 jobs. The 10th Congressional District in Michigan was the hardest hit district in the country, ranked in terms of jobs displaced as a share of total district employment, losing 5,500 jobs (1.78 percent of total employment). Among these top 20 U.S. congressional districts, job losses as a share of district employment ranged from 1.17 percent to 1.78 percent. Of the states with top-20 job-losing districts, the hardest hit state was Michigan (with 10 districts in the top 20, followed by Indiana (four districts); Ohio and South Carolina (two districts each); and California and Wisconsin (one each).

Currency manipulation is the most important cause of the large and growing U.S. trade deficit with Japan. In the past two years, Japan has driven down the value of the yen primarily through large purchases of foreign assets, and also and by announcing its intention to reduce the yen’s value.

Purchases and holdings of foreign exchange reserves by the Bank of Japan and of other foreign assets by Japan’s Government Pension Investment Fund (GPIF) are an indispensable element of Japan’s currency policy. Without its massive government holdings of foreign assets, and its continuing and periodic massive purchases of new foreign assets, the government of Japan would have been unable to prevent the yen from adjusting to levels consistent with large trade and current account surpluses.

It is important to distinguish the effects of quantitative easing (defined as central bank purchases of assets denominated in its own currency) from currency intervention (defined as government purchases of assets denominated in foreign currencies). All countries should be free to engage in quantitative easing and other elements of domestic monetary policy, subject only to their own domestic policy goals and constraints (such as excessive inflationary pressure, as perceived by domestic authorities, as well as domestic employment and wage targets). Domestic monetary policies should not be labeled as part of currency manipulation, and such policies should not be constrained by international agreements. Prudential measures are appropriate to deal with short-term economic problems.

In short, all countries should be free to print money to purchase their own domestic assets. On the other hand, countries should be strongly discouraged from purchasing and holding assets denominated in foreign currencies, which is the central, defining tool of currency manipulation.

In this context the United States should insist that currency manipulation be directly addressed in the proposed Trans-Pacific Partnership. Members of the TPP should also agree to rebalance trade and currency markets, including through divestiture of excess foreign assets in government portfolios, before any trade and investment agreement takes effect. They should also forswear the use of currency manipulation in the future, and submit to strong, binding currency disciplines in the event these commitments are violated.

Background: Currency manipulation, trade, and job loss

Growing trade deficits have cost U.S. workers millions of jobs over the past two decades. Most of the lost jobs were good jobs in manufacturing industries. Under the 1993 North American Free Trade Agreement (NAFTA), growing trade deficits with Mexico cost 682,900 U.S. jobs through 2010, and U.S.-Mexico trade deficits and job displacement have increased since then (Scott 2011, 2014c). President Obama promised that the U.S.-Korea Free Trade Agreement would increase U.S. goods exports by between $10 billion and $11 billion, supporting 70,000 American jobs from increased exports alone (White House 2010). However, in the first two years after that deal went into effect, U.S. exports actually declined, and growing trade deficits with Korea cost nearly 60,000 U.S. jobs (Scott 2014d).

The job losses stemming from past trade deals must inform continuing negotiations for the Trans-Pacific Partnership, which have proceeded in secret, most recently in Washington and New York (Arirang News 2014, and Brunnstrom 2015). The United States has a large and growing trade deficit with Japan and the 10 other countries in the proposed TPP; this deficit with the TPP countries increased from $110.3 billion in 1997 to an estimated $261.7 billion in 2014 (Scott 2014a).

Currency manipulation by more than 20 countries is the most important reason why U.S. trade deficits have not decisively reversed (Bergsten and Gagnon 2012). Currency manipulation by other counties lowers the value of the countries’ currencies relative to the U.S. dollar, which acts as a subsidy to those countries’ exports, and a tax on U.S. exports to every country where the U.S. competes with the exports of currency manipulators. After China, Japan is the world’s largest currency manipulator and thus responsible for a substantial share of the bloated U.S. global trade deficit.1 Laffer (2014, 2) also concludes that currency manipulation has cost millions of U.S. jobs, and that by falling back into old patterns of currency manipulation, Japan is “foisting the burden of its flawed policies onto its trading partners.”

Elimination of currency manipulation by about 20 developed and developing countries could reduce the United States’ global trade deficit by between $200 billion and $500 billion (Bergsten and Gagnon 2012). This reduction could increase U.S. GDP by between $288 billion and $720 billion, and create between 2.3 million and 5.8 million U.S. jobs (Scott 2014b).

The biggest tool of currency manipulation is the purchase of assets denominated in the currencies of other countries, which is known as currency intervention. Purchases of foreign assets by central banks and other government agencies in Japan, China, and other countries directly increase the demand for foreign currencies, especially the U.S. dollar. This increases the value of the dollar (the exchange rate), and drives down the value of the currency of the country purchasing foreign assets. Foreign assets include Treasury bills, other government assets (which are held as foreign exchange reserves by central banks), and foreign stocks and bonds (purchased by other government agencies, such as the Japanese pension fund, discussed below).

The importance of exchange-rate manipulation in driving global trade imbalances is clear. There is a near perfect correlation between official purchases of foreign exchange reserves and other foreign assets and the global current account surpluses of currency manipulators. Recent research has shown that causation runs from currency manipulation to trade surpluses among the manipulators, and not the other way around. Gagnon (2013) estimated that a “country’s current account balance increases between 60 and 100 cents for each dollar spent on intervention.” Importantly, his data include asset purchases by government-owned “sovereign wealth funds” (also known as SWFs) which now control over $7.0 trillion dollars in assets (SWFI 2015). For example, in November 2014, Japan’s gigantic Government Pension Investment Fund, whose assets totaled over $1.2 trillion in 2013, announced that it intended to raise the target share of its assets held in foreign stocks and bonds from 23 percent in 2013 to 40 percent (approximately $480 billion) in the near future (Warnock and Narioka 2014). This will have a significant impact on Japan’s expected future trade surplus because it will directly suppress the value of the yen, in ways that are described below.

Currency manipulation, trade, and Japan

Japan has a long history of currency manipulation. Between 2000 and November 2014 its holdings of foreign exchange reserves alone nearly quadrupled, rising from $347 billion in 2000 to $1,208 billion in November 2014, an increase of $861 billion (IMF 2015). Furthermore, the holdings of foreign assets in Japan’s GPIF increased steadily in this period, reaching $308.8 billion in 2013, and are projected to increase to $480 billion or more in the near future (GPIF 2015).

Japan’s real effective exchange rate index declined steadily from 131.4 in 2000 to 74.5 in 2007, a decline of 43.3 percent (International Monetary Fund 2015).2 During this period its current account balance, the broadest measure of Japan’s trade in goods, services, and income, increased from $130.7 billion (2.8 percent of its GDP) to $212.1 billion, or 4.9 percent of GDP (IMF 2014). Market forces and the Great Recession combined to push the yen up to a recent peak of 109.0 in 2012, a 46.2 percent increase since 2007. The rise of the yen and the 2011 Tōhoku earthquake and tsunami combined to push up Japan’s imports and suppress exports, creating a crisis in Japan’s trade and current accounts. Japan’s current account surplus shrank to $58.7 billion in 2012 and Japan developed its first global goods trade deficit in more than a decade, which reached $53.5 billion in that year (IMF 2015).

Japan’s trade and economic crises set the stage for the election of Prime Minister Shinzo Abe in December 2013 and subsequently gave his Liberal Democratic Party control of both houses of the Japanese Diet (parliament) in 2012 and 2013 (The Economist 2013). Abe is widely recognized for adopting a three-part plan for revitalizing Japan’s economy. The widely recognized parts of this plan included a substantial increase in government spending, liberalization of monetary policy, and deregulation of the Japanese economy.

Abe also stated his intentions to reduce the value of the yen shortly after his election. As noted in the Wall Street Journal at the time:

Mr. Abe … called on Japan’s central bank to resist what he described as moves by the U.S. and Europe to cheapen their currencies and noted that a yen level of around ¥90 to the dollar—it was at ¥84.38 in early Asian trading Monday, down from ¥84.26 late Friday—would support the profit of Japanese exporters. … “Central banks around the world are printing money, supporting their economies and increasing exports. … If it goes on like this, the yen will inevitably strengthen. It is vital to resist this,” said Mr. Abe. (Ito and Mallard 2012)

And resist it they did. The yen fell sharply as a direct result of Abe’s currency policies. Between the third quarter of 2012 and the end of 2014, the market value of the yen declined by 35.3 percent.3 Japan’s real effective exchange rate index declined to 74.6 by the end of 2014, essentially the level that prevailed in 2007 when Japan’s current account reached a peak of $212.1 billion (4.9 percent of GDP).4

Japan’s current account and trade balance remained suppressed in 2013 by several temporary factors, including the hangover from the Tōhoku earthquake and tsunami, increased demand for imports in anticipation of value-added tax increases taking effect in 2015, and the short-run impacts of the fall of the yen, which increased the cost of Japanese imports. Over the next few years the fall in the yen is expected to stimulate exports and suppress imports, resulting in growing trade and current account balances (as shown below).

Foundations of Japan’s currency policy

There are two key elements of Japan’s currency policy:

  1. Maintain and increase foreign exchange reserves and government purchases of other foreign assets. In 2011, prior to Abe’s election, the Bank of Japan engaged in a massive, $185 billion purchase of foreign exchange reserves. This had no immediate impact on the value of the yen, which gained slightly against the dollar between the end of 2011 and the third quarter of 2012 (IMF 2015). However, maintaining a large stock of government-controlled foreign assets will have a strong, positive effect on Japanese trade accounts, due to portfolio balance effects. For Japan, which has a large and open private capital market, changes in the stock of foreign assets affect Japan’s trade flows with a lag, as shown below.
  2. Increase holdings of foreign assets by Japan’s Government Pension Investment Fund. In November 2014 the GPIF announced its plan to increase target holdings of foreign stocks and bonds from 23 percent of its total $1.2 trillion dollars plus in assets in 2013 to 40 percent in the near future (Warnock and Narioka 2014). GPIF data show that the shift was already in progress in 2012 and 2013, and that actual foreign holdings exceeded even the 2013 target. Between 2012 and 2013 the GPIF increased its actual holdings of foreign assets from 21.4 percent ($244.2 billion) to 25.7 percent ($308.8 billion). While billed as a financial diversification effort, the GPIF announcement was also a public commitment to increase Japan’s total government holdings of foreign assets, which will have long-term impacts on Japan’s expected trade and current account surpluses.

Purchases and holdings of foreign exchange reserves by the Bank of Japan and of other foreign assets by the GPIF are the sine qua non of Japan’s currency policy. Without its massive holdings of foreign assets, and its continuing and periodic massive purchases of new foreign assets, the government of Japan would have been unable to prevent the yen from adjusting to levels consistent with trade and current account balances.

The United States needs to include in the TPP and any future trade or investment agreements currency disciplines that would compel Japan and other currency manipulators to divest themselves of excess holdings of foreign assets, or to otherwise be penalized or incur offsets to their currency manipulation. Absent such disciplines, the United States should not complete and Congress should not approve implementing legislation for the proposed Trans-Pacific Partnership or any future agreements. Without an effective currency agreement, the United States could be locked into a trade and investment treaty with Japan that would prohibit actions that are necessary to restore equilibrium to currency markets and rebalance trade with currency manipulators.

In the context of Japan’s continuing currency intervention, other policies implemented by the Abe government and the Bank of Japan have reinforced downward pressures on the yen. This has increased the importance of directly addressing currency manipulation by Japan. For example, by announcing in 2012 his intention to drive down the value of the yen, Abe sent a strong signal to financial markets that his government would penalize markets if the yen failed to depreciate.

In addition, Japan followed the lead of the United States and other countries in the wake of the Great Recession and engaged in quantitative easing (defined as central bank purchases of assets denominated in the host country’s own currency). While quantitative easing by the U.S. central bank had no significant long-term effect on the real value of the dollar, quantitative easing in Japan was significantly more extensive than in the United States, and contributed to the subsequent fall in the yen, but this effect was incidental to its primary purpose, which was to stimulate and reflate Japan’s domestic economy.

It is important to distinguish the effects of quantitative easing from currency intervention (defined as government purchases of assets denominated in foreign currencies). All countries should be free to engage in quantitative easing and other elements of domestic monetary policy, subject only to their own domestic policy goals and constraints (such as excessive inflationary pressure, as perceived by domestic authorities, as well as domestic employment and wage targets). Domestic monetary policies should not be labeled as part of currency manipulation, and they should not be constrained by international agreements. Prudential measures are appropriate to deal with short-term economic problems.

It does appear that large-scale quantitative easing in Japan has reinforced the fall of the yen, as noted below. The U.S. Federal Reserve should monitor these effects, and it may wish to consider limited, countervailing purchases of Japanese assets. But quantitative easing alone, in the absence of Japanese purchases and stockpiles of foreign assets, would be unlikely to disturb the long-term trend value of the yen.

With this background in mind, the following sections review each of the factors considered above and their implications for U.S.-Japan trade.

Japan’s foreign asset holdings

As noted above, on a global level, there is a near perfect correlation between official purchases of foreign exchange reserves and other foreign assets and the global current account surpluses of currency manipulators (Gagnon 2013, Figure 1). Recent research has shown that the stock of foreign assets also contributes to currency suppression and sustained current account surpluses (Bayoumi, Gagnon, and Saborowski 2014).

Estimates of Japan’s total holdings of foreign exchange reserves and other foreign assets are shown in Figure A. Estimated total foreign assets rose from $362 billion in 2000 to $1.7 trillion in November 2014, including targeted holdings of $480 billion, 40 percent of the GPIF’s $1.2 trillion in pension fund assets.5 The latter is a significant increase in foreign asset holdings that will continue to suppress the value of the Japanese yen, and is expected to result in growing Japanese trade and current account surpluses, as shown below.

Figure A

Japan foreign exchange reserves and other foreign assets, 2000–2014

Foreign exchange reserves and GPIF foreign assets GDP share 
2000 $0.4 7.6%
2001 0.4 9.8%
2002 0.5 11.9%
2003 0.7 15.8%
2004 0.9 18.5%
2005 0.9 19.2%
2006 0.9 21.4%
2007 1.0 23.5%
2008 1.1 22.7%
2009 1.1 22.2%
2010 1.2 21.6%
2011 1.4 23.9%
2012 1.44 24.2%
2013 1.5 30.9%
2014 1.7 35.4%
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Note: The 2014 figure is as of November 2014.

Source: Author's analysis of Government Pension Investment Fund (GPIF) Japan (2015), International Monetary Fund (2015), and International Monetary Fund (2014).

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Taken as a share of GDP, Japan’s total foreign-asset holdings rose from 7.6 percent in 2000 to 30.9 percent in 2013 (actual) and 35.4 percent in 2014 (projected). Japan’s enormous, episodic foreign-asset purchases (such as the Bank of Japan’s purchase of $185 billion in foreign exchange reserves in 2011) has also provided a credible threat that the Japanese government will intervene any time that the yen threatens to rise. This helps to account for the failure of the yen to rise after 2012.

Certainty that the yen will not rise (above a widely perceived ceiling) has also contributed to the growth of the so-called carry trade, which has persisted in Japan for decades. The carry trade involves borrowing in a very-low-interest-rate country (such as Japan), converting the loan into a currency in a higher-interest-rate country (such as the United States) and investing the proceeds in the high-interest-rate country. Most of this trading is done by large financial firms with massive “leverage of 100 or 300 to one” (Dohmen 2014). As a result, U.S. bond and stock prices have increased, and the U.S. dollar has risen as well (versus the yen), as a result of increased private-sector demand for U.S. financial assets.

The carry trade has existed for many years, and helps account for the failure of the yen to rise. It also helps explain persistent U.S. trade deficits. But the carry trade would not exist were it not for the certainty that the yen would not rise, which is provided by Japan’s purchases of U.S. assets. The large GDP share of Japan’s foreign assets will have a significant impact on Japan’s trade and current accounts through the portfolio balance channel. These factors are explained below.

The impact of Abe’s currency policies on the market value of the yen

The yen was trading near an all-time high in the range of 76 to 79 yen per dollar throughout much of 2011 and continuing through the third quarter of 2012 (IMF 2015). The yen weakened slightly in the fourth quarter of 2012, during the Abe election campaign. It began a precipitous decline once he took office, falling to near 120 yen per dollar by the end of 2014, an increase of nearly 55 percent in the value of the dollar, comparing end-of-period values for the third quarter of 2012 (pre-election) with the last quarter of 2014 (Board of Governors of the Federal Reserve System 2015). Figure B presents data on the market value of the yen (in cents per yen terms), using an index set to 100 in the fourth quarter of 2011, one year before the Japanese general election. The market value of the yen was roughly stable between the fourth quarter of 2011 and the third quarter of 2012. The yen began to decline steadily after the Abe election in December 2012, losing 35.3 percent of its value between 2012Q3 and 2014Q4 (end of period values). This precipitous depreciation has substantially, and artificially, increased the competitiveness of Japanese exports. Currency manipulation acts as a subsidy to all Japanese exports, and a tax on U.S. exports to Japan, and all countries where U.S. firms compete with Japanese products.

Figure B

Yen-dollar exchange rate, market value index,* 2011–2014

Index, 2011Q4=100
2011Q1 93.5
2011Q2 96.3
2011Q3 101.4
2011Q4 100.0
2012Q1 94.6
2012Q2 98.0
2012Q3 100.2
2012Q4 89.8
2013Q1 82.5
2013Q2 79.2
2013Q3 79.5
2013Q4 73.8
2014Q1 75.6
2014Q2 76.7
2014Q3 71.1
2014Q4 64.8
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*End of period data; data shown are for yen-dollar exchange rate and nominal dollar-yen index.

Source: Author's analysis of IMF (2015) and Board of Governors of the Federal Reserve System (2015)

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The immediate causes of the yen’s decline include Abe’s announcement of his intent to devalue the yen (Ito and Mallard 2012) and the steady increase in Japan’s holdings of foreign exchange reserves and other foreign assets (Figure A, earlier). These factors were also reinforced by Japan’s aggressive quantitative easing regime, especially after Abe took office in 2012.

The role of quantitative easing

In order to carry out quantitative easing, central banks create money by purchasing government bonds and other assets such as mortgage-backed securities from banks (R.A. 2014). This increases the value of assets held by the central bank and the size of the monetary base. Quantitative easing has been used to stimulate bank lending when short term-interest rates controlled by central banks have been reduced to their lowest practical level (the zero lower bound). The Unites States, Japan, and other countries have engaged in extensive QE purchases in the wake of the Great Recession of 2007–2009.

The U.S. and Japan have both engaged in similar amounts of quantitative easing.6 However, the U.S. economy is roughly three times as large, so QE in Japan has been much larger, relative to GDP, throughout the recession and recovery, as shown in Figure C. Between the fourth quarter of 2007 and the second quarter of 2014, the U.S. monetary base as a share of GDP has expanded by 17.4 percentage points (from 5.5 percent to 22.9 percent), while the GDP share of Japan’s monetary base increased by 31.3 percentage points (from 18.7 percent to 50.0 percent).

Figure C

Quantitative easing, Japan and United States, 2007Q4–2014Q2

Japan United States
2007Q4 18.7% 5.5%
2008Q1 18.6% 5.5%
2008Q2 18.0% 5.6%
2008Q3 18.8% 7.1%
2008Q4 20.8% 11.6%
2009Q1 22.1% 11.8%
2009Q2 20.5% 11.3%
2009Q3 20.9% 12.3%
2009Q4 22.3% 13.3%
2010Q1 21.9% 13.4%
2010Q2 21.1% 12.8%
2010Q3 20.9% 12.6%
2010Q4 22.8% 12.9%
2011Q1 26.7% 15.9%
2011Q2 25.2% 16.9%
2011Q3 24.9% 17.0%
2011Q4 26.3% 16.9%
2012Q1 24.9% 16.6%
2012Q2 27.2% 16.1%
2012Q3 27.5% 15.8%
2012Q4 29.4% 16.4%
2013Q1 30.7% 18.1%
2013Q2 36.3% 19.4%
2013Q3 38.7% 20.8%
2013Q4 42.0% 21.7%
2014Q1 45.1% 22.7%
2014Q2 50.0% 22.9% 
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Source: Author's analysis of International Monetary Fund (2015).

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Japan’s QE purchases have expanded massively since the beginning of the Abe administration, as shown in Figure C. Between 2012Q3 and 2014Q2, Japan’s monetary base as a share of GDP increased by 22.5 percentage points, while U.S. QE purchases increased only 7.1 percentage points as a share of GDP, less than a third of the growth rate in Japan. As noted by Laffer (2014, 24), “the Bank of Japan put further depreciating pressures on the yen” through the expansion of its monetary base. As a result of all these policies (both QE and direct currency intervention), “the yen’s real effective exchange rate has depreciated to its 1982 level, which is below the 2007 level,” according to Bank of Japan data cited by Laffer (2014, 24). Chin (2013b) has also noted that Japan’s expansionary monetary policy has caused the yen to depreciate. The yen’s rapid, recent decline is expected to significantly improve Japan’s trade and current account balances in the future.

The Implications of currency manipulation for Japan’s trade and current account balances

There is a strong relationship between the real value of the yen and Japan’s current account balance, as shown in Figure D. The real value of the yen (as reflected in Japan’s real effective exchange rate index) fell steadily and rapidly between 2000 and 2007, when the real value of the index declined 7.8 percentage points per year (on average). During this period, Japan’s total current account balance increased from $130.7 billion to $212.1 billion. As a share of GDP, it increased from 2.8 percent in 2000 to 4.9 percent in 2007.

Japan’s current account balance collapsed in 2008 in the wake the Great Recession. It reached a nadir of only $33.6 billion (0.7 percent of GDP) in 2013. Its decline was reinforced by a sharp increase in the value of the yen, and by the after-effects of the 2011 Tōhoku earthquake and tsunami.7

Japan’s current account is expected to improve significantly in the long run, as shown in Figure D, which includes the IMF’s projections for Japan’s current account balance from 2014 to 2019 (IMF 2014). The IMF projects that Japan’s current account surplus will more than double in the future, rising from $33.6 billion in 2013 to $76.4 billion in 2019, and from 0.7 percent of GDP in 2013 to 1.4 percent in 2019. However, other data and analysis presented below suggest that Japan’s current account could and should improve much more rapidly over the next few years.

Figure D

Japan’s current account and real exchange rate index, 2000–2019*

Current account balance Projected account balance Real effective yen index**
2000 130.7 131.4
2001 86.2 121.3
2002 109.1 112.7
2003 139.4 102.7
2004 182.0 99.4
2005 170.1 91.1
2006 174.5 82.9
2007 212.1 74.5
2008 142.6 81.7
2009 145.3 98.5
2010 217.6 100.0
2011 126.5 108.0
2012 58.7 109.0
2013 33.6 88.1
2014 45.4  45.4 80.3
2015  54.9
2016  62.4
2017  65.1
2018  69.2
2019  76.4
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*2014–2019 projected (current account only)
**Real effective exchange rate index, adjusted for relative movements in unit labor costs (period average values), actual 2000–2013, author's estimate in 2014

Source: Author's analysis of International Monetary Fund (2014, 2015) and Board of Governors of the Federal Reserve System (2015).

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Chin has developed new estimates of Japan’s import and export elasticities and determined that for Japan, “an exchange depreciation results in an improved trade balance” (Chin 2013a, abstract). Chin’s estimates were developed in early 2013. His estimates did not include the sharp depreciation of the yen that actually occurred in 2013 and 2014 (Figure B, above), which only serves to strengthen his conclusions. In addition, recent and projected increase in Japan’s government holdings of foreign assets (Figure A, above), suggest that Japan’s current account balance will improve much more strongly in the future.

Expected effects of Japan’s foreign asset holdings on its current account balance

Recent research has shown that there is a strong correlation between increased holdings of foreign assets and current account balances. Gagnon (2013, abstract) found that “a country’s current account balance increases between 60 and 100 cents for each dollar spent on intervention.” Thus, the steady increase in Japan’s holdings of foreign exchange reserves and other foreign assets (Figure A) put upward pressure on Japan’s current account balance.

In a new paper, Bayoumi, Gagnon, and Saborowski (2014) (henceforth referred to as BGS) identified a second, and potentially more important channel through which Japan’s holdings of foreign assets will influence Japan’s current account balance. In this paper, the authors distinguish between countries with relatively closed capital markets (such as China) and those with relatively open capital markets (such as Japan). For countries with closed capital markets, there is a strong, direct relationship between purchases of foreign assets and the current account, confirming Gagnon’s earlier (2013) findings.

For countries with relatively open capital markets, such as Japan, BGS found that there is a strong relationship between the lagged stock of foreign assets and the current account balance. The authors believe that this effect is related to portfolio balance effects which have a direct effect on private capital flows and the current account balance. Specifically, they found that the current account is increased by .07 percent (of GDP) for each one-percentage-point increase in the lagged stock of net official assets BGS (2014, 12). This effect is large and significant in the case of Japan, because of its large government (official) holdings of net foreign assets (Figure A, above), which reached 30.9 percent of its GDP in 2013 and were projected to exceed 35 percent in 2014.

The specific variables used in the BGS model include the current account net of investment income (as a share of GDP, a dependent variable) and the lagged value of net official assets (relative to GDP, an independent variable).8 The estimated effect of Japan’s official holdings on its current account for 2005–2015, based on this result and the lagged values for Japan’s official holdings, are shown in Figure E. The dependent variable shown is the current account net of investment income, which is roughly equivalent to Japan’s balance of trade on goods and services.9

Figure E

Impact of currency manipulation on Japan’s nonfinancial current account balance, 2005–2015

As a share of GDP  Billions of dollars
2005 1.3% $59
2006 1.3% 58
2007 1.5% 65
2008 1.6% 80
2009 1.6% 80
2010 1.6% 85
2011 1.5% 89
2012 1.7% 100
2013 1.7% 83
2014 2.2% 103
2015 2.5% 121
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Note: Values for 2014 and 2015 are projected from lagged values of Japan's stock of foreign exchange reserves and foreign holdings of the GPIF.

Source: Author's analysis of International Monetary Fund (2015) and Bayoumi, Gagnon, and Saborowski (2014)

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The estimated impact of Japan’s official holdings on its current account net of investment income rises from $59.2 billion (1.3 percent of GDP) in 2005 to $120.9 billion (2.5 percent of GDP) in 2015, as shown in Figure E. Japan has also maintained a large and growing surplus on net investment income in its current account, which reached $160 billion in 2013 (IMF 2015). Taken together, these results suggest that currency manipulation could produce a total current account surplus of up to $280 billion (or more than 5 percent of GDP) within the next few years. This is slightly larger than Japan’s actual current account balance in 2007 (4.9 percent of GDP, as shown in Figure D). The real value of the yen at the end of 2014 was actually below the level that prevailed in 2007 (Laffer 2014), so this forecast is consistent with past experience. These results suggest that the IMF’s forecast of Japan’s total current account balance of $76.4 billion in 2019 (Figure D) is far too conservative.

Purchases and holdings of foreign assets by official government agencies in Japan (including the Bank of Japan and the GPIF) are the most significant, direct tool used to manipulate the value of the yen. Other factors considered here, including quantitative easing, the carry trade, and Abe’s 2012 announcement of his intent to reduce the value of the yen, have reinforced the effects of Japan’s holdings of foreign assets on the yen. Currency manipulation has also had a significant impact on Japan’s bilateral trade with the United States, as shown below.

The impact of currency manipulation on the U.S.-Japan balance of trade

The United States and Japan have a long and difficult history of trade disputes related to tariff and nontariff barriers to trade, including Japan’s closed agricultural markets and the effects of Japanese trade and industrial policies on a number of sectors ranging from semiconductors to motor vehicles and parts (Tyson 1992, Prestowitz 1993). However, currency manipulation is the most important cause of the large and growing U.S. trade deficit with Japan. But for the subsidies provided by currency manipulation, Japanese automakers would have found it difficult or impossible to achieve their dominance in wide segments of the U.S. market. And currency manipulation has made it difficult or impossible for U.S. firms to penetrate Japanese markets for many products, due to the effective tax imposed on U.S. products by currency manipulation.

Despite the ebbs and flows of Japan’s trade and current account balances with the world, Japan has maintained a large, stable trade surplus with the United States since 2005, as shown in Figure F. Aside from the peak recession year of 2009, between 2005 and 2013 Japan maintained a goods trade surplus with the United States that ranged between $64.5 billion and $92.5 billion (based on reported U.S. trade flows, USITC 2015) and stood at $78.3 billion in 2013. The effect of the carry trade (in setting limits on the appreciation of the yen) helps explain the persistence of Japan’s trade surplus with the United States.

Figure F

Impact of currency manipulation on Japan-U.S. trade balance, 2005–2013

Japan’s estimated nonfinancial current account balance Japan-U.S. trade surplus*
2005 59.2 86.3
2006 58.4 92.5
2007 65.4 86.8
2008 79.7 77.7
2009 79.8 48.9
2010 85.3 64.5
2011 89.4 67.0
2012 99.5 80.0
2013 83.0 78.3
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*Consumption imports less domestic exports, as reported by the United States.

Source: Author's analysis of Bayoumi, Gagnon, and Saborowski (2014), International Monetary Fund (2015), and USITC (2015)

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The U.S. is also Japan’s largest and most reliable trade partner. Japan’s trade surplus with the United States explains most of Japan’s entire estimated nonfinancial trade surplus resulting from currency manipulation (as estimated in Figure E and shown also in Figure F). Currency manipulation by Japan is the most important cause of the growing U.S. goods trade deficit with Japan. The next section estimates the effects of currency manipulation and the U.S. trade deficit with Japan on the U.S. economy, and on trade-related employment in the United States.

The economic impacts of the U.S. goods trade deficit with Japan

This analysis uses a simple macroeconomic model developed by Bivens (2014) to estimate the effects of the U.S. goods trade deficit with Japan in 2013 on U.S. GDP and employment, including respending effects (the approach is also based, in part, on the models developed in Scott 2014b). As part of our overall model, an input-output (IO) model was used to estimate the distribution of jobs lost or gained by industry. It provides estimates of the direct and indirect labor requirements of producing output in a given domestic industry.10 The model includes 195 U.S. industries as defined by the Bureau of Labor Statistics (BLS), 77 of which are in the manufacturing sector (see the appendix for details on model structure and data sources). The macroeconomic 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. The IO model is used to determine the distribution of jobs supported by exports and the jobs displaced by imports in the U.S. economy. This paper assumes that currency manipulation is the primary cause of the U.S. goods trade deficit with Japan.

Jobs displaced by the U.S.-Japan trade deficit directly decrease total employment in trade-related industries, especially those in manufacturing. The IO model also estimates the number of “indirect” jobs supported or displaced in supplier industries, including those in manufacturing, and in related service sectors such as law, accounting, managerial, and temporary help services. Finally, wages that would have been earned had trade with Japan been balanced would have supported additional rounds of “respending,” which would have a multiplier effect on output (GDP) and employment.

This paper estimates the impacts of the U.S.-Japan trade deficit with disaggregated trade data that is matched with each of the 195 BLS industries in the IO model. A vector of consumer spending in the domestic economy (from the IO final demand tables) is used to estimate the distribution of jobs that were displaced by the loss of wages in the domestic economy (the multiplier effect). Total employment effects of the Japan trade deficit, by industry, are estimated as the sum of the direct, indirect, and respending jobs.

These techniques generated estimates of direct, indirect, and respending jobs lost by industry. These results were used with data on the distribution of employment by industry and by state and congressional district (discussed below under “Job losses and gains by state and congressional district” and in the appendix) to estimate the impacts of the U.S.-Japan trade deficit on U.S. employment in these areas.

The impact of the U.S.-Japan trade deficit on the U.S. economy and state spending

Each $1 billion in U.S. exports supports some American jobs. However, each $1 billion in U.S. imports 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 size of the trade balance. A trade surplus will, all else equal, support a positive number of domestic jobs, while trade deficits result in net U.S. job displacement. The United States has run trade deficits since 1975, which have increased steadily since the early 1990s, and especially since the Asian financial crisis of 1997–1998, when many Asian currency manipulators (including Japan, as shown above) experienced sharp depreciations of their currencies. U.S. trade deficits did contract sharply in 2009, when U.S. trade with all countries collapsed due to the recession of 2007–2009, but they have grown significantly since then (USITC 2015).

As this research has shown, currency manipulation by Japan has resulted in large, persistent U.S. trade deficits which have displaced hundreds of thousands of U.S. jobs. This in turn has increased budget deficits and negatively impacted both federal and state finances.

Note: All of the tables referenced in the text are available at the end of this report.

The main macroeconomic results of this research are summarized in Table 1. This paper uses economic multipliers developed by Bivens (2014). As he notes, “the most pressing economic challenge for the U.S. economy remains the depressed labor market” (Bivens 2014, 1). Though not shown in the table, the share of prime-aged adults (age 25–54) currently employed remains barely above the level at the official end of the recession in 2009, and well below the peaks of the last two business cycles. In this economic environment, changes in spending for domestic goods have large multiplier effects on the economy. Bivens estimates that in the current economic environment, increases in infrastructure spending have a large, macroeconomic “multiplier” effect on the domestic economy through the wages earned and spent by workers employed by such spending. Bivens estimates that infrastructure spending has a multiplier effect of 1.6 on the domestic economy (Bivens 2014, Table 5 at 21). This paper assumes that changes in trade flows also have a multiplier effect of 1.6, and that reductions in domestic spending caused by the U.S.-Japan trade deficit impact the economy in a way that is symmetric with increases in spending associated with increased infrastructure investment (that is, the multiplier works the same way for both increases and decreases in domestic spending).

Thus, the $78.3 billion goods trade deficit with Japan reduced U.S. GDP by $125.3 billion in 2013, or 0.747 percent of U.S. GDP in 2013, as shown in Table 1. The overall number of jobs lost by this reduction in output (GDP) is estimated from a simple rule of thumb also developed by Bivens (2014, Table 5 at 21), based on historical relationships between output and employment. Each 1 percent increase in GDP supports 1.2 million jobs in the economy. Likewise, an identical reduction in GDP would eliminate 1.2 million jobs in the U.S. economy. Using both the macroeconomic and the jobs multipliers, we find that the $78.3 billion U.S.-Japan trade deficit eliminated 896,600 jobs in the domestic economy.

Reductions in domestic employment decrease tax revenues (through the fall in national income and wages) and increase safety-net expenditures (through increased spending for unemployment insurance, food stamps, Medicaid, and other forms of public assistance). Analysis of the effects of rising unemployment on net federal budget deficits indicates that federal deficits are decreased (or increased) by $0.37 for each dollar of increase (or decline) in GDP (Bivens and Edwards 2010). As a result, the estimated reduction in GDP caused by the U.S.-Japan trade deficit in 2013 increased the federal deficit in that year by $46.4 billion, or 7.4 percent of the total federal deficit in calendar year 2013.11 This effect on federal deficits will continue, in proportion to the bilateral deficit and GDP, as long as the U.S.-Japan goods trade deficit persists. Were the Japan trade deficit to persist at the present level, the loss of jobs and wages would increase the federal budget deficit by $460 billion over the next decade.

State and local revenues and spending are also reduced by the U.S.-Japan trade deficit. Recent empirical research has estimated that, on average, state budgets (spending minus revenues) will decrease by $0.14 for each dollar of decline in GDP (Kondo and Svec 2009, 10). Decreases in GDP associated with the Japan trade deficit have thus reduced net state and local resources by $17.5 billion in 2013 alone.

Breakdowns of the jobs displaced by the Japan trade deficit

The models developed here, coupled with data on U.S. imports from and exports to Japan in 2013, allow us to estimate total jobs lost overall and at the sector level.

Direct, indirect, and respending jobs (aggregated over all industries) are reported in Table 2. The U.S. trade deficit with Japan directly displaced 148,400 U.S. jobs in commodity and manufacturing industries that competed with unfairly traded imports and exports from Japan in 2013. Our model estimates the employment effects of the trade deficit with Japan. 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. The difference between these two numbers is essentially the jobs displaced by the growing trade deficit, holding all else equal. This estimate of the net number of jobs supported and displaced is used to allocate the total number of direct and indirect jobs displaced due to the trade deficit, as described in the appendix. In addition to the 148,400 direct jobs lost, the U.S.-Japan trade deficit is responsible for an additional 412,000 indirect jobs in supplier industries, including jobs in manufacturing, commodity, and service industries, as shown below. Finally, wages lost due to the trade deficit with Japan would have supported an additional 336,200 respending jobs. Combining direct, indirect, and respending jobs yields a total of 896,600 jobs displaced by the U.S.-Japan trade deficit.

Job losses and gains by industry

Actual U.S. imports from and exports to Japan in 2013 were used to estimate the distribution of net jobs (direct, indirect, and respending) displaced by the U.S. trade deficit with Japan, by industry for the 45 unique industries (plus eight aggregate sectors) in the U.S. Census Bureau sector plan (U.S. Census Bureau 2009). Our analysis compares jobs lost or gained with 2011 employment data as a baseline to estimate jobs gained or lost as a share of industry employment (U.S. Census Bureau 2013). The United States had a trade surplus with Japan in a few industries in 2013, including agriculture and processed food products. Trade with Japan did create some net jobs in the United States in these industries. The breakdown by industry is shown in Table 3.

Overall, the U.S. trade deficit with Japan displaced 466,000 jobs in manufacturing (52.0 percent of jobs lost across all industries), by far the largest number of jobs lost in any major industry. Within manufacturing, by far the largest losses occurred in motor vehicles and parts, which lost 118,800 jobs (13.3 percent of total jobs lost). Other manufacturing industries with large losses include machinery (96,600 jobs), fabricated metal products (80,800 jobs), and computer and electronic parts (66,100 jobs). Trade with Japan did contribute to employment in a few manufacturing industries: food and beverage and tobacco products (14,600 jobs supported, 14,400 in food and 200 in beverage and tobacco products) and leather and allied products (2,000 jobs).

The U.S.-Japan trade deficit was also responsible for significant job losses outside of manufacturing in administrative and support industries (61,800 jobs); health care and social assistance (60,500 jobs); retail trade (51,800); professional, scientific, and technical services (50,000 jobs), and accommodation and food services (48,500 jobs). Net trade with Japan also created a total of 63,600 jobs in U.S. agricultural industries. Job gains and losses in these industries are the result of net trade flows in sectors with high levels of imports from and exports to Japan, as well as respending effects, which have their largest effects on employment in service industries.

Job losses and gains by state and congressional district

Estimates of job losses by industry form the foundation for the estimation of job losses and gains by state and congressional district. Estimates of employment by state and congressional district for each of the 45 unique industries in the model were obtained from the U.S. Census Bureau (2013). These were used to estimate employment shares by state and congressional district for each industry. These shares were used to estimate total jobs lost or gained per district, with 2011 employment as the baseline for estimating jobs lost as a share of total state or district employment (see the appendix for further details). Thus, states and congressional districts that have high shares of employment in industries with a large exposure to trade with Japan (such as motor vehicles and equipment, machinery, or fabricated metal products) were the largest losers from Japan trade deficits.

The trade deficit with Japan resulted in net job losses in all 50 states. Jobs lost by state, ranked by shares of total state employment are reported in Table 4. (Supplemental Table 1 ranks the states by net jobs displaced and Supplemental Table 2 lists them alphabetically). Michigan lost the most jobs as a share of total state employment, with 56,200 jobs lost (1.34 percent of the total state employment in 2011). Eight of the 10 states with the highest job losses (as a share of total employment) are in the Midwest or the East South Central census regions, all states where manufacturing predominates: Michigan (56,200 jobs), Indiana (33,700 jobs), Ohio (50,900 jobs), Kentucky (16,400 jobs), Wisconsin (24,300 jobs), Tennessee (23,200 jobs), Alabama (16,000 jobs) and Illinois (45,500 jobs). Rounding out the top 10 states losing the largest shares of jobs were South Carolina (16,800 jobs) in the South Atlantic region, and New Hampshire (5,300 jobs) in New England. Manufacturing industries are also dominant these two states. The distribution of job losses in the 50 states and the District of Columbia is shown in the map in Figure G. In the online version of this report, the map is clickable, and contains additional data on job losses due to the U.S. trade deficit with Japan.

Figure G

Net U.S. jobs displaced by U.S. trade deficit with Japan, by state, 2013

State Net jobs displaced State employment Jobs displaced as share
of state employment
Alabama 16,000 1,981,100 0.81%
Alaska 1,100 344,300 0.32%
Arizona 14,300 2,688,000 0.53%
Arkansas 7,200 1,235,800 0.58%
California 86,800 16,426,700 0.53%
Colorado 12,700 2,492,400 0.51%
Connecticut 11,700 1,742,500 0.67%
Delaware 1,800 420,400 0.43%
District of Columbia 1,200 310,600 0.39%
Florida 38,800 8,101,900 0.48%
Georgia 25,300 4,193,800 0.60%
Hawaii 2,300 629,500 0.37%
Idaho 2,700 684,900 0.39%
Illinois 45,500 5,926,900 0.77%
Indiana 33,700 2,934,500 1.15%
Iowa 10,500 1,538,800 0.68%
Kansas 7,200 1,389,000 0.52%
Kentucky 16,400 1,838,400 0.89%
Louisiana 9,300 1,973,900 0.47%
Maine 2,900 643,100 0.45%
Maryland 13,100 2,894,600 0.45%
Massachusetts 20,100 3,284,700 0.61%
Michigan 56,200 4,191,900 1.34%
Minnesota 18,600 2,728,900 0.68%
Mississippi 7,300 1,181,300 0.62%
Missouri 17,800 2,742,100 0.65%
Montana 1,200 480,000 0.25%
Nebraska 4,200 943,600 0.45%
Nevada 5,700 1,204,900 0.47%
New Hampshire 5,300 684,800 0.77%
New Jersey 22,800 4,152,500 0.55%
New Mexico 3,400 869,800 0.39%
New York 46,700 8,959,000 0.52%
North Carolina 27,300 4,195,800 0.65%
North Dakota 1,100 370,800 0.30%
Ohio 50,900 5,213,500 0.98%
Oklahoma 10,800 1,681,800 0.64%
Oregon 9,400 1,710,300 0.55%
Pennsylvania 40,100 5,853,300 0.69%
Rhode Island 3,300 511,200 0.65%
South Carolina 16,800 1,968,900 0.85%
South Dakota 1,700 415,600 0.41%
Tennessee 23,200 2,784,500 0.83%
Texas 69,600 11,455,100 0.61%
Utah 7,600 1,260,800 0.60%
Vermont 1,600 327,300 0.49%
Virginia 20,000 3,860,100 0.52%
Washington 14,000 3,118,000 0.45%
West Virginia 4,100 748,600 0.55%
Wisconsin 24,300 2,819,500 0.86%
Wyoming 1,000 290,000 0.34%
Total 896,100 140,399,600 0.64%

 

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* 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: Author's analysis of Bivens (2014), U.S. Census Bureau (2013), U.S. International Trade Commission (USITC 2015), Bureau of Labor Statistics (BLS 2014), and BLS Employment Projections program (BLS-EP 2014a, 2014b, and 2014c). For a more detailed explanation of data sources and computations, see the appendix.

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Figure H

Net U.S. jobs displaced by U.S. trade deficit with Japan, by congressional district, 2013

Rank State District Net jobs displaced District employment Jobs displaced as a share of district employment
235 Alabama 1 1,600 283,000 0.57%
132 Alabama 2 1,900 276,900 0.69%
24 Alabama 3 3,100 274,600 1.13%
43 Alabama 4 2,500 262,900 0.95%
66 Alabama 5 2,600 311,900 0.83%
97 Alabama 6 2,400 318,400 0.75%
113 Alabama 7 1,800 253,500 0.71%
420 Alaska Statewide 1,100 344,300 0.32%
408 Arizona 1 1,000 264,900 0.38%
312 Arizona 2 1,500 299,200 0.50%
417 Arizona 3 900 262,200 0.34%
386 Arizona 4 1,000 233,500 0.43%
153 Arizona 5 2,100 317,900 0.66%
253 Arizona 6 2,000 366,000 0.55%
201 Arizona 7 1,700 282,300 0.60%
266 Arizona 8 1,600 301,700 0.53%
178 Arizona 9 2,300 360,300 0.64%
228 Arkansas 1 1,600 277,400 0.58%
263 Arkansas 2 1,800 336,300 0.54%
143 Arkansas 3 2,200 327,000 0.67%
259 Arkansas 4 1,600 295,100 0.54%
423 California 1 800 260,300 0.31%
418 California 2 1,100 323,100 0.34%
430 California 3 700 286,600 0.24%
297 California 4 1,500 294,200 0.51%
385 California 5 1,400 326,800 0.43%
365 California 6 1,300 288,300 0.45%
295 California 7 1,600 313,200 0.51%
298 California 8 1,200 235,500 0.51%
399 California 9 1,100 275,300 0.40%
402 California 10 1,100 277,200 0.40%
277 California 11 1,700 324,200 0.52%
276 California 12 2,100 399,400 0.53%
243 California 13 1,900 340,200 0.56%
249 California 14 2,000 364,000 0.55%
77 California 15 2,700 336,400 0.80%
434 California 16 0 244,900 0.00%
13 California 17 4,500 346,100 1.30%
38 California 18 3,400 344,500 0.99%
32 California 19 3,400 324,000 1.05%
433 California 20 100 302,500 0.03%
436 California 21 -1,300 243,800 -0.53%
431 California 22 500 289,600 0.17%
427 California 23 800 274,100 0.29%
428 California 24 900 323,500 0.28%
206 California 25 1,800 302,700 0.59%
400 California 26 1,300 325,900 0.40%
292 California 27 1,700 332,200 0.51%
314 California 28 1,800 359,900 0.50%
240 California 29 1,700 303,700 0.56%
310 California 30 1,800 358,200 0.50%
287 California 31 1,500 292,200 0.51%
197 California 32 1,800 293,800 0.61%
280 California 33 1,900 364,200 0.52%
333 California 34 1,500 309,400 0.48%
148 California 35 1,900 284,800 0.67%
412 California 36 900 251,900 0.36%
300 California 37 1,700 335,600 0.51%
177 California 38 2,000 313,300 0.64%
150 California 39 2,200 332,000 0.66%
232 California 40 1,600 280,500 0.57%
187 California 41 1,700 271,900 0.63%
164 California 42 2,000 307,000 0.65%
207 California 43 1,800 302,800 0.59%
121 California 44 1,900 270,600 0.70%
166 California 45 2,300 354,400 0.65%
124 California 46 2,200 314,400 0.70%
223 California 47 1,900 327,600 0.58%
137 California 48 2,400 352,600 0.68%
203 California 49 1,800 299,700 0.60%
302 California 50 1,500 296,200 0.51%
389 California 51 1,100 258,600 0.43%
156 California 52 2,300 350,100 0.66%
244 California 53 1,900 342,700 0.55%
254 Colorado 1 2,100 384,400 0.55%
231 Colorado 2 2,200 384,600 0.57%
411 Colorado 3 1,200 331,400 0.36%
350 Colorado 4 1,600 344,100 0.46%
301 Colorado 5 1,600 315,900 0.51%
285 Colorado 6 1,900 369,600 0.51%
246 Colorado 7 2,000 362,500 0.55%
155 Connecticut 1 2,300 349,800 0.66%
183 Connecticut 2 2,200 348,600 0.63%
163 Connecticut 3 2,300 352,700 0.65%
171 Connecticut 4 2,200 343,000 0.64%
88 Connecticut 5 2,700 348,300 0.78%
387 Delaware Statewide 1,800 420,400 0.43%
405 DC Statewide 1,200 310,600 0.39%
388 Florida 1 1,300 303,900 0.43%
380 Florida 2 1,300 301,500 0.43%
401 Florida 3 1,100 277,000 0.40%
255 Florida 4 1,800 329,900 0.55%
272 Florida 5 1,500 284,000 0.53%
323 Florida 6 1,400 283,200 0.49%
273 Florida 7 1,700 322,500 0.53%
237 Florida 8 1,600 283,400 0.56%
341 Florida 9 1,500 317,200 0.47%
289 Florida 10 1,700 331,500 0.51%
304 Florida 11 1,100 217,400 0.51%
269 Florida 12 1,500 283,200 0.53%
221 Florida 13 1,800 309,200 0.58%
267 Florida 14 1,700 320,700 0.53%
353 Florida 15 1,400 304,200 0.46%
257 Florida 16 1,500 276,100 0.54%
432 Florida 17 400 248,700 0.16%
358 Florida 18 1,300 284,000 0.46%
364 Florida 19 1,200 265,200 0.45%
382 Florida 20 1,300 302,100 0.43%
340 Florida 21 1,500 316,800 0.47%
291 Florida 22 1,700 332,000 0.51%
313 Florida 23 1,700 339,900 0.50%
374 Florida 24 1,300 293,400 0.44%
383 Florida 25 1,400 326,000 0.43%
367 Florida 26 1,500 335,600 0.45%
368 Florida 27 1,400 313,600 0.45%
393 Georgia 1 1,200 286,100 0.42%
376 Georgia 2 1,100 251,200 0.44%
63 Georgia 3 2,400 285,800 0.84%
227 Georgia 4 1,800 311,700 0.58%
264 Georgia 5 1,700 318,100 0.53%
222 Georgia 6 2,100 361,200 0.58%
118 Georgia 7 2,200 312,500 0.70%
337 Georgia 8 1,300 272,700 0.48%
147 Georgia 9 1,900 284,600 0.67%
211 Georgia 10 1,700 287,400 0.59%
193 Georgia 11 2,100 340,900 0.62%
261 Georgia 12 1,500 278,200 0.54%
256 Georgia 13 1,700 312,800 0.54%
53 Georgia 14 2,600 290,700 0.89%
392 Hawaii 1 1,400 330,100 0.42%
424 Hawaii 2 900 299,400 0.30%
332 Idaho 1 1,600 329,900 0.48%
422 Idaho 2 1,100 355,000 0.31%
192 Illinois 1 1,800 290,200 0.62%
111 Illinois 2 2,000 278,200 0.72%
130 Illinois 3 2,200 319,500 0.69%
82 Illinois 4 2,600 326,600 0.80%
162 Illinois 5 2,600 397,600 0.65%
45 Illinois 6 3,300 355,600 0.93%
233 Illinois 7 1,700 298,500 0.57%
57 Illinois 8 3,200 366,300 0.87%
181 Illinois 9 2,200 347,200 0.63%
79 Illinois 10 2,600 324,800 0.80%
86 Illinois 11 2,700 347,300 0.78%
125 Illinois 12 2,100 301,000 0.70%
247 Illinois 13 1,800 326,600 0.55%
54 Illinois 14 3,100 351,000 0.88%
93 Illinois 15 2,400 316,500 0.76%
51 Illinois 16 3,000 330,800 0.91%
34 Illinois 17 3,200 311,700 1.03%
48 Illinois 18 3,100 337,500 0.92%
31 Indiana 1 3,300 310,600 1.06%
5 Indiana 2 5,000 317,800 1.57%
7 Indiana 3 4,900 327,000 1.50%
16 Indiana 4 4,000 328,500 1.22%
94 Indiana 5 2,700 357,700 0.75%
9 Indiana 6 4,400 311,900 1.41%
67 Indiana 7 2,600 312,200 0.83%
33 Indiana 8 3,400 329,300 1.03%
36 Indiana 9 3,400 339,400 1.00%
83 Iowa 1 3,100 392,300 0.79%
87 Iowa 2 2,900 373,400 0.78%
215 Iowa 3 2,300 390,800 0.59%
229 Iowa 4 2,200 382,300 0.58%
421 Kansas 1 1,100 345,900 0.32%
216 Kansas 2 2,000 339,900 0.59%
191 Kansas 3 2,300 370,300 0.62%
296 Kansas 4 1,700 332,900 0.51%
62 Kentucky 1 2,400 284,800 0.84%
28 Kentucky 2 3,500 317,100 1.10%
59 Kentucky 3 2,900 333,300 0.87%
64 Kentucky 4 2,800 333,500 0.84%
133 Kentucky 5 1,600 234,300 0.68%
41 Kentucky 6 3,200 335,400 0.95%
299 Louisiana 1 1,800 354,000 0.51%
360 Louisiana 2 1,500 329,000 0.46%
330 Louisiana 3 1,600 328,100 0.49%
284 Louisiana 4 1,600 311,100 0.51%
404 Louisiana 5 1,100 283,900 0.39%
352 Louisiana 6 1,700 367,800 0.46%
317 Maine 1 1,700 340,400 0.50%
410 Maine 2 1,100 302,700 0.36%
319 Maryland 1 1,700 342,300 0.50%
334 Maryland 2 1,700 351,700 0.48%
354 Maryland 3 1,700 369,500 0.46%
375 Maryland 4 1,700 384,100 0.44%
397 Maryland 5 1,500 368,200 0.41%
347 Maryland 6 1,700 363,200 0.47%
373 Maryland 7 1,400 315,700 0.44%
390 Maryland 8 1,700 400,100 0.42%
169 Massachusetts 1 2,200 341,000 0.65%
122 Massachusetts 2 2,500 356,500 0.70%
70 Massachusetts 3 2,900 355,400 0.82%
173 Massachusetts 4 2,400 374,800 0.64%
209 Massachusetts 5 2,300 387,400 0.59%
212 Massachusetts 6 2,200 372,000 0.59%
355 Massachusetts 7 1,700 369,800 0.46%
218 Massachusetts 8 2,200 375,600 0.59%
294 Massachusetts 9 1,800 352,300 0.51%
129 Michigan 1 2,000 290,200 0.69%
15 Michigan 2 4,000 315,900 1.27%
21 Michigan 3 3,600 315,300 1.14%
39 Michigan 4 2,800 286,300 0.98%
20 Michigan 5 3,100 264,800 1.17%
25 Michigan 6 3,500 310,400 1.13%
6 Michigan 7 4,500 299,100 1.50%
8 Michigan 8 4,900 330,800 1.48%
3 Michigan 9 5,500 326,100 1.69%
1 Michigan 10 5,500 308,700 1.78%
2 Michigan 11 6,000 342,100 1.75%
11 Michigan 12 4,200 313,800 1.34%
10 Michigan 13 3,200 230,700 1.39%
12 Michigan 14 3,400 257,700 1.32%
200 Minnesota 1 2,100 348,200 0.60%
109 Minnesota 2 2,600 358,300 0.73%
60 Minnesota 3 3,000 353,800 0.85%
159 Minnesota 4 2,200 336,000 0.65%
106 Minnesota 5 2,600 352,000 0.74%
76 Minnesota 6 2,800 348,700 0.80%
359 Minnesota 7 1,500 328,700 0.46%
210 Minnesota 8 1,800 303,400 0.59%
99 Mississippi 1 2,300 305,600 0.75%
205 Mississippi 2 1,600 266,900 0.60%
274 Mississippi 3 1,600 303,900 0.53%
213 Mississippi 4 1,800 304,900 0.59%
429 Montana Statewide 1,200 480,000 0.25%
258 Missouri 1 1,800 331,500 0.54%
154 Missouri 2 2,500 378,600 0.66%
142 Missouri 3 2,500 370,000 0.68%
245 Missouri 4 1,800 324,900 0.55%
126 Missouri 5 2,400 345,300 0.70%
167 Missouri 6 2,300 355,900 0.65%
134 Missouri 7 2,300 337,400 0.68%
119 Missouri 8 2,100 298,500 0.70%
271 Nebraska 1 1,700 321,700 0.53%
305 Nebraska 2 1,600 316,300 0.51%
419 Nebraska 3 1,000 305,600 0.33%
324 Nevada 1 1,400 284,700 0.49%
249 Nevada 2 1,700 309,400 0.55%
370 Nevada 3 1,500 336,500 0.45%
377 Nevada 4 1,200 274,300 0.44%
107 New Hampshire 1 2,600 352,600 0.74%
71 New Hampshire 2 2,700 332,200 0.81%
239 New Jersey 1 1,900 339,200 0.56%
398 New Jersey 2 1,300 324,400 0.40%
279 New Jersey 3 1,800 344,200 0.52%
326 New Jersey 4 1,600 326,400 0.49%
168 New Jersey 5 2,300 356,100 0.65%
208 New Jersey 6 2,100 353,600 0.59%
199 New Jersey 7 2,300 377,100 0.61%
234 New Jersey 8 2,100 371,000 0.57%
238 New Jersey 9 1,900 338,500 0.56%
335 New Jersey 10 1,500 310,700 0.48%
219 New Jersey 11 2,100 358,800 0.59%
262 New Jersey 12 1,900 352,400 0.54%
336 New Mexico 1 1,500 311,900 0.48%
426 New Mexico 2 800 273,100 0.29%
406 New Mexico 3 1,100 284,800 0.39%
320 New York 1 1,700 343,300 0.50%
242 New York 2 2,000 357,800 0.56%
306 New York 3 1,700 336,700 0.50%
348 New York 4 1,600 342,500 0.47%
339 New York 5 1,600 336,200 0.48%
356 New York 6 1,500 327,000 0.46%
379 New York 7 1,400 322,200 0.43%
371 New York 8 1,300 292,700 0.44%
381 New York 9 1,400 324,900 0.43%
372 New York 10 1,600 360,300 0.44%
395 New York 11 1,300 317,500 0.41%
311 New York 12 2,100 418,800 0.50%
394 New York 13 1,300 317,200 0.41%
346 New York 14 1,600 341,800 0.47%
344 New York 15 1,200 255,900 0.47%
322 New York 16 1,600 323,600 0.49%
345 New York 17 1,600 341,400 0.47%
260 New York 18 1,800 332,100 0.54%
327 New York 19 1,600 327,300 0.49%
308 New York 20 1,800 357,600 0.50%
363 New York 21 1,400 309,200 0.45%
158 New York 22 2,100 320,200 0.66%
116 New York 23 2,300 324,600 0.71%
144 New York 24 2,200 327,300 0.67%
88 New York 25 2,600 335,400 0.78%
172 New York 26 2,100 327,700 0.64%
104 New York 27 2,500 337,800 0.74%
251 North Carolina 1 1,600 291,800 0.55%
128 North Carolina 2 2,100 303,800 0.69%
357 North Carolina 3 1,400 305,600 0.46%
288 North Carolina 4 1,800 350,900 0.51%
105 North Carolina 5 2,400 324,500 0.74%
120 North Carolina 6 2,400 341,800 0.70%
415 North Carolina 7 1,100 315,400 0.35%
92 North Carolina 8 2,300 301,700 0.76%
73 North Carolina 9 3,000 371,400 0.81%
52 North Carolina 10 2,900 324,000 0.90%
141 North Carolina 11 2,000 295,400 0.68%
157 North Carolina 12 2,100 319,800 0.66%
204 North Carolina 13 2,100 349,900 0.60%
425 North Dakota Statewide 1,100 370,800 0.30%
110 Ohio 1 2,400 332,300 0.72%
90 Ohio 2 2,500 323,600 0.77%
152 Ohio 3 2,200 333,000 0.66%
4 Ohio 4 5,100 317,900 1.60%
14 Ohio 5 4,300 334,200 1.29%
40 Ohio 6 2,800 292,300 0.96%
23 Ohio 7 3,700 326,800 1.13%
29 Ohio 8 3,600 328,800 1.09%
30 Ohio 9 3,400 315,000 1.08%
47 Ohio 10 2,900 312,800 0.93%
80 Ohio 11 2,200 275,200 0.80%
74 Ohio 12 2,900 359,500 0.81%
26 Ohio 13 3,600 320,400 1.12%
37 Ohio 14 3,500 349,700 1.00%
103 Ohio 15 2,500 336,400 0.74%
45 Ohio 16 3,300 355,600 0.93%
78 Oklahoma 1 2,900 361,900 0.80%
160 Oklahoma 2 1,900 290,300 0.65%
282 Oklahoma 3 1,700 329,900 0.52%
185 Oklahoma 4 2,200 350,900 0.63%
230 Oklahoma 5 2,000 348,800 0.57%
55 Oregon 1 3,300 377,200 0.87%
414 Oregon 2 1,100 314,200 0.35%
186 Oregon 3 2,400 383,300 0.63%
403 Oregon 4 1,200 309,000 0.39%
384 Oregon 5 1,400 326,700 0.43%
290 Pennsylvania 1 1,400 273,300 0.51%
338 Pennsylvania 2 1,300 273,100 0.48%
44 Pennsylvania 3 3,000 317,700 0.94%
108 Pennsylvania 4 2,500 342,900 0.73%
69 Pennsylvania 5 2,600 316,800 0.82%
151 Pennsylvania 6 2,400 362,300 0.66%
241 Pennsylvania 7 1,900 339,700 0.56%
170 Pennsylvania 8 2,300 357,800 0.64%
95 Pennsylvania 9 2,300 304,800 0.75%
175 Pennsylvania 10 2,000 312,500 0.64%
180 Pennsylvania 11 2,100 329,300 0.64%
98 Pennsylvania 12 2,500 331,900 0.75%
139 Pennsylvania 13 2,300 339,000 0.68%
217 Pennsylvania 14 1,900 323,200 0.59%
84 Pennsylvania 15 2,700 343,800 0.79%
198 Pennsylvania 16 2,000 327,700 0.61%
176 Pennsylvania 17 2,000 312,600 0.64%
72 Pennsylvania 18 2,800 345,000 0.81%
140 Rhode Island 1 1,700 250,900 0.68%
195 Rhode Island 2 1,600 260,300 0.61%
123 South Carolina 1 2,100 299,800 0.70%
161 South Carolina 2 2,000 305,600 0.65%
19 South Carolina 3 3,100 264,500 1.17%
17 South Carolina 4 3,600 301,000 1.20%
58 South Carolina 5 2,400 275,200 0.87%
113 South Carolina 6 1,800 253,500 0.71%
146 South Carolina 7 1,800 269,400 0.67%
396 South Dakota 1 1,700 415,600 0.41%
50 Tennessee 1 2,700 297,600 0.91%
91 Tennessee 2 2,500 327,200 0.76%
49 Tennessee 3 2,700 297,000 0.91%
27 Tennessee 4 3,500 314,500 1.11%
189 Tennessee 5 2,200 353,400 0.62%
42 Tennessee 6 2,900 304,500 0.95%
56 Tennessee 7 2,500 285,800 0.87%
65 Tennessee 8 2,500 299,200 0.84%
214 Tennessee 9 1,800 305,300 0.59%
179 Texas 1 1,900 297,700 0.64%
68 Texas 2 3,000 364,600 0.82%
96 Texas 3 2,800 371,200 0.75%
202 Texas 4 1,800 299,300 0.60%
236 Texas 5 1,700 300,800 0.57%
131 Texas 6 2,400 348,800 0.69%
102 Texas 7 2,800 376,300 0.74%
112 Texas 8 2,200 309,200 0.71%
196 Texas 9 2,000 326,400 0.61%
145 Texas 10 2,300 342,600 0.67%
248 Texas 11 1,700 308,800 0.55%
135 Texas 12 2,300 337,500 0.68%
362 Texas 13 1,400 309,000 0.45%
321 Texas 14 1,500 303,300 0.49%
351 Texas 15 1,300 280,900 0.46%
265 Texas 16 1,500 281,300 0.53%
281 Texas 17 1,700 329,300 0.52%
100 Texas 18 2,300 306,400 0.75%
413 Texas 19 1,100 310,700 0.35%
286 Texas 20 1,600 311,400 0.51%
275 Texas 21 1,900 361,200 0.53%
188 Texas 22 2,200 352,500 0.62%
407 Texas 23 1,100 289,700 0.38%
127 Texas 24 2,700 388,600 0.69%
184 Texas 25 1,900 302,200 0.63%
138 Texas 26 2,500 368,300 0.68%
325 Texas 27 1,500 305,600 0.49%
329 Texas 28 1,300 266,300 0.49%
85 Texas 29 2,300 292,900 0.79%
252 Texas 30 1,600 292,300 0.55%
136 Texas 31 2,200 323,000 0.68%
101 Texas 32 2,700 360,900 0.75%
117 Texas 33 2,000 283,900 0.70%
409 Texas 34 900 242,200 0.37%
309 Texas 35 1,600 318,200 0.50%
165 Texas 36 1,900 291,900 0.65%
174 Utah 1 2,000 312,400 0.64%
278 Utah 2 1,600 305,700 0.52%
226 Utah 3 1,800 311,200 0.58%
149 Utah 4 2,200 331,500 0.66%
327 Vermont Statewide 1,600 327,300 0.49%
361 Virginia 1 1,600 352,400 0.45%
268 Virginia 2 1,800 339,800 0.53%
316 Virginia 3 1,600 320,100 0.50%
225 Virginia 4 1,900 327,900 0.58%
303 Virginia 5 1,600 316,100 0.51%
270 Virginia 6 1,800 339,900 0.53%
349 Virginia 7 1,700 364,600 0.47%
391 Virginia 8 1,800 423,700 0.42%
75 Virginia 9 2,400 298,400 0.80%
307 Virginia 10 1,900 376,400 0.50%
366 Virginia 11 1,800 400,900 0.45%
293 Washington 1 1,700 332,300 0.51%
343 Washington 2 1,500 318,900 0.47%
182 Washington 3 1,800 284,500 0.63%
435 Washington 4 -100 284,500 -0.04%
283 Washington 5 1,500 291,500 0.51%
378 Washington 6 1,200 275,500 0.44%
315 Washington 7 1,900 380,000 0.50%
342 Washington 8 1,500 318,000 0.47%
318 Washington 9 1,700 341,400 0.50%
369 Washington 10 1,300 291,300 0.45%
224 West Virginia 1 1,500 258,700 0.58%
331 West Virginia 2 1,300 266,900 0.49%
220 West Virginia 3 1,300 223,000 0.58%
35 Wisconsin 1 3,500 342,500 1.02%
194 Wisconsin 2 2,400 390,000 0.62%
190 Wisconsin 3 2,200 353,500 0.62%
61 Wisconsin 4 2,600 308,000 0.84%
22 Wisconsin 5 4,200 370,600 1.13%
18 Wisconsin 6 4,200 353,600 1.19%
115 Wisconsin 7 2,400 338,400 0.71%
81 Wisconsin 8 2,900 362,800 0.80%
416 Wyoming Statewide 1,000 290,000 0.34%
ChartData Download data

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

Source: Author's analysis of Bivens (2014), U.S. Census Bureau (2013), U.S. International Trade Commission (USITC 2015), Bureau of Labor Statistics (BLS 2014), and BLS Employment Projections program (BLS-EP 2014a, 2014b, and 2014c). For a more detailed explanation of data sources and computations, see the appendix.

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This study also estimates trade-related employment changes by congressional district for the 113th Congress (elected in 2012), using new congressional district boundaries from the 2010 Census. The distribution of job losses in the 435 congressional districts and in the District of Columbia is shown in the map in Figure H. In the online version of this report, the map is clickable, and contains additional data on job losses due to the U.S. trade deficit with Japan.

Our analysis compares jobs lost with 2011 employment data as a baseline to estimate job losses as a share of district employment. The data show that the U.S.-Japan trade deficit resulted in net job losses in all but three U.S. Congressional Districts, and has displaced up to 6,000 jobs in a single U.S. congressional district. The 20 congressional districts with the largest shares of jobs lost are shown in Table 5. Each of the top 20 districts lost between 3,100 and 6,000 jobs. The 10th Congressional District in Michigan was the hardest hit district in the country, losing 5,500 jobs (1.78 percent of total employment). Job losses as a share of district employment among the top 20 U.S. congressional districts ranged from 1.17 percent to 1.78 percent. Of the states with top-20 job-losing districts, the hardest hit state was Michigan (with 10 districts in the top 20, followed by Indiana (four districts); Ohio and South Carolina (two districts each); and California and Wisconsin (one each). Complete lists of jobs lost or gained by congressional district for all 435 congressional districts and for the District of Columbia are included in Supplemental Table 3. Only two congressional districts experienced net job gains as a result of trade with Japan in 2013. They are the 21st Congressional District in California (1,300 jobs gained) and the 4th Congressional District in Washington (100 jobs gained).

Conclusion

Currency manipulation is the most important cause of the large and growing U.S. trade deficit with Japan, which eliminated 896,600 U.S. jobs in 2013 alone. In the past two years, Japan has driven down the value of the yen through large purchases of foreign assets, massive quantitative easing, and its announced intention to reduce the yen’s value. These actions threaten to increase Japan’s trade and current account surpluses and the U.S.–Japan trade deficit.

In this context the United States should insist that currency manipulation be directly addressed in the proposed Trans-Pacific Partnership. Members of the TPP should agree to rebalance trade and currency markets, including by divesting excess foreign assets in government portfolios, before any trade and investment agreement takes effect. They should also forswear the use of currency manipulation in the future, and submit to strong, binding currency disciplines in the event these commitments are violated.

A number of prominent economists have developed specific proposals for addressing currency manipulation in the core of the agreement. Bernstein (2015) suggests that the TPP must clarify the allowable limits on holdings of foreign assets as part of the definition of currency manipulation. If these limits are violated, he recommends allowing an array of responses including taxes on the imports of currency manipulators, fines, and temporary repeal of trade privileges negotiated under the agreement.

Fred Bergsten has also called for the inclusion in the TPP and other future trade agreements of a currency chapter that includes “clear obligations to avoid currency manipulation” (Bergsten 2014, 2). He recommends that these agreements should include an effective dispute resolution mechanism and sanctions against violators to enforce these obligations. Bergsten enumerates five types of sanctions that could be included in the agreement’s proposals, including withdrawal of concessions made in the TPP agreement, imposition of countervailing duties, import surcharges, monetary fines, and countervailing currency intervention, or CCI (Bergsten 2014, 7). The CCI proposal calls for the U.S. government (and other countries injured by currency manipulation) to offset foreign government purchases of financial assets denominated in U.S. dollars and other currencies by purchasing financial assets issued in the offending country’s currency (for example, through U.S. government purchases of yen-denominated stocks and bonds). Bergsten and Gagnon (2012) have proposed CCI as a general response to currency manipulation, and Bergsten (2014) proposes authorizing such activities specifically as sanctions available in the TPP.

The findings of this study suggest somewhat stronger measures addressing currency manipulation in the TPP. Purchases and holdings of foreign exchange reserves by the Bank of Japan, and of other foreign assets by the GPIF, are an indispensable element of Japan’s currency policy. Without its massive government holdings of foreign assets, and its continuing and periodic massive purchases of new foreign assets, the government of Japan would have been unable to prevent the yen from adjusting to levels consistent with trade and current account balances.

The United States needs to negotiate currency disciplines that would compel Japan and other currency manipulators to divest themselves of excess holdings of foreign assets, or to otherwise be penalized for or take measures to offset currency manipulation. Such disciplines must be included in any future trade and/or investment agreement with Japan and other countries that are, or could be, engaged in currency manipulation now or in the future. Absent such disciplines, the United States should not complete the pending negotiations, and Congress should not approve implementing legislation for the proposed Trans-Pacific Partnership.

In addition, there are a number of steps that should be taken to address global currency manipulation by more than 20 countries. In the short run, Congress should pass legislation authorizing the Commerce Department to treat currency manipulation as a countervailable subsidy in countervailing duty (CVD) trade complaints. This would provide immediate relief to importers that have been hurt by unfair competition from imports from currency manipulators. It would also send a strong signal to these countries that the United States is willing to confront currency manipulators. (Scott 2014b, 12).

Ultimately, the United States must develop measures to prevent or offset any efforts to manipulate currency through purchases of foreign exchange reserves by all countries with significant, sustained trade surpluses and excessive reserves of government-owned foreign assets. As recommended by Gagnon and Hufbauer (2011) and Bergsten and Gagnon (2012), the administration must implement strategies that would tax and/or offset purchases of foreign assets by currency manipulating governments, which would make efforts to manipulate the dollar and other currencies costly and/or futile. The United States should also announce its intent to take these measures and encourage other countries to adopt similar measures to block or offset currency manipulation.

But the first rule for addressing currency manipulation should be to do no harm. The United States should refuse to enter into the proposed Trans-Pacific Partnership with Japan and 10 other countries unless that agreement includes strong and enforceable prohibitions on currency manipulation. Otherwise, we would be locked into an open trading relationship with Japan, a country that has demonstrated a persistent commitment to maintaining an undervalued currency that has generated a sustained, job-destroying trade deficit with the United States.

About the author

Robert E. Scott is director of trade and manufacturing policy research at the Economic Policy Institute. He joined EPI as an international economist in 1996. Before that, he was an assistant professor with the College of Business and Management of the University of Maryland at College Park. His areas of research include international economics and trade agreements 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 a Ph.D. in economics from the University of California-Berkeley.

Acknowledgments

The author thanks Ross Eisenbrey and Josh Bivens for comments, and William Kimball for research assistance.

Appendix: Methodology

This analysis uses a simple macroeconomic model developed by Bivens (2014) to estimate the effects of the U.S. goods trade deficit with Japan in 2013 on U.S. GDP and employment, including respending effects (based, in part, on the models developed in Scott 2014b). It then uses an input-output model based on Bureau of Labor Statistics (BLS) data to allocate jobs displaced by the Japan trade deficit to industries, states, and congressional districts. The research uses data from 2013 to estimate the impacts of the trade deficit in that year. This appendix identifies the specific data sources and comparisons used.

The macroeconomic model

This paper uses economic multipliers developed in by Bivens (2014). As he notes, “the most pressing economic challenge for the U.S. economy remains the depressed labor market” (Bivens 2014, 1). The share of prime-aged adults (age 25–54) remains barely above the level at the official end of the recession in 2009, and well below the peaks of the last two business cycles. In this economic environment, changes in spending for domestic goods have large multiplier effects on the economy. Bivens estimates that in the current economic environment, increases in infrastructure spending have a large, macroeconomic multiplier impact on the domestic economy through the wages earned and spent by workers employed by such spending. Bivens estimates that infrastructure spending has a multiplier impact of 1.6 on the domestic economy (Bivens 2014, Table 5 at 21). This paper assumes that changes in trade flows also have a multiplier effect of 1.6, and that reductions in domestic spending caused by the U.S.-Japan trade deficit impact the economy in a way that is symmetric with increases in spending associated with increased infrastructure investment (that is, the multiplier works the same way for both increases and decreases in domestic spending).

The Bivens model is used to estimate the impact of the U.S.-Japan trade deficit on U.S. GDP. The overall number of jobs supported by this reduction in output (GDP) is estimated from a simple rule of thumb also developed by Bivens (2014, Table 5 at 21), based on historical relationships between output and employment in which each 1 percent increase in GDP supported 1.2 million jobs in the economy. Likewise, an identical reduction in GDP would eliminate 1.2 million jobs in the U.S. economy.

Reductions in domestic employment decrease tax revenues (through the fall in national income and wages) and increase safety-net expenditures (through increased spending for unemployment insurance, food stamps, Medicaid, and other forms of public assistance). Analysis of the effects of rising unemployment on net federal budget deficits indicates that federal deficits are increased by $0.37 for each dollar of increased GDP (Bivens and Edwards 2010).

State and local revenues and spending are also reduced by the  U.S.-Japan trade deficit. Recent empirical research has estimated that, on average, state budgets (spending minus revenues) will decrease by $0.14 for each dollar of decline in GDP (Kondo and Svec 2009, 10).

The trade and jobs model

The trade and employment analysis in this report is 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 sectors 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 2015).

This study separates exports produced domestically from foreign exports—which are goods produced in other countries, exported to the United States, and then reexported 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).12 This input-output (IO) 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 2010 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.

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 (2014b). These price deflators were updated using Bureau of Labor Statistics producer price indexes (industry and commodity data; BLS 2014). Use of constant 2005 dollars was required for consistency with the other BLS models used in this study.

The IO model is used to estimate the distribution of jobs displaced by trade, and by the loss of wages and respending, as explained below.

Estimation and data sources

The findings in this paper come from a four-step data retrieval process followed by a four-step analysis.

Data requirements

Step 1. U.S. trade data were obtained from the U.S. International Trade Commission Interactive Tariff and Trade DataWeb (USITC 2015) in four-digit, three-digit, and two-digit NAICS format for total U.S. Consumption Imports and Domestic Exports.

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 (2014).

Step 3. A 1×195 vector of data for total personal consumer expenditures (PCE) in 2005 dollars for 2010 was extracted from historical input-output data assembled by the BLS-EP (2014c). These data were used to estimate total employment supported by PCE expenditures (using the job-equivalents analysis described below). The results were used to estimate the share of respending jobs supported in each of 195 BLS industries.

Step 4. 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 [T2013] be the 195×2 matrix made up of a column of imports and a column of exports for 2013. To estimate the vector of jobs displaced by trade, perform the following matrix operations:

[J2013]=[T2013]×[E2010]

[J2013] is a 195×2 matrix of job displaced by imports and jobs supported by exports for each of 195 industries in 2013. This matrix is used to create vectors of net jobs displaced by imports from Japan and jobs supported exports to Japan, as described above. The total number of direct and indirect jobs displaced by trade is estimated using the macroeconomic model described above.

The employment estimates for retail trade, wholesale trade, and advertising were set to zero in the trade portion of this analysis.13 We assume that traded goods must be sold and advertised whether they are produced in the United States or imported for consumption.

Similarly, for respending (multiplier) analysis, let [PCE2010] be the 195×1 matrix of total U.S. personal consumer expenditures by industry in 2010 (in real 2005 dollars). To estimate the distribution of jobs supported by respending, perform the following matrix operations:

[JPCE2010]=[PCE2010]×[E2010]

Direct and indirect jobs. In order to estimate the direct jobs, the diagonal vector was extracted from the employment requirements matrix [E2010]. This vector was multiplied times the trade vector to estimate direct trade-related jobs (e.g., [JDIRECT2013]) for both imports and exports. Indirect jobs just equal total jobs less direct (e.g. [JINDIRECT2013] =[J2013]- [JDIRECT2013]).

Step 2. Combining macroeconomic and IO jobs analyses. The IO jobs estimates in vectors [J2013] and [JPCE2010] are converted into share vectors, representing the share of total jobs supported in each of 195 industries by reductions in trade deficits and related respending in the domestic economy. The shares in each vector sum to 1. Share vectors are used to allocate jobs gained by industry. The sum of direct and indirect jobs gained (Table 2) in each scenario are multiplied by the trade jobs share vector derived from [J2013], and the respending (also Table 2) jobs are multiplied by respending jobs share vector derived from [JPCE2010]. The results yield estimates of jobs gained or lost by industry in the total economy as a result of the U.S.-Japan trade deficit.

Step 3. State-by-state analysis. For states, employment-by-industry data were obtained from the U.S. Census Bureau’s American Community Survey (U.S. Census Bureau 2013) data for the 2011 period and were mapped into 45 unique census industries and eight aggregated total and subtotals for a total of 53 sectors.14 We look at jobs displaced in 2013, so from this point, we use macroeconomic jobs estimates derived from the vectors [J2013] and [JPCE2010]. In order to work with 45 sectors, we group the 195 BLS industries into a new matrix, defined as [Jnew2013], a 45×1 matrix of job gains and losses. Define [St2011] as the 45×51 matrix of state shares (with the addition of the District of Columbia) of employment in each industry. Calculate:

[Stj2013]=[St2011]T [Jnew2013]

where [Stj2013] is the 45×51 matrix of job gains and losses supported by state by industry. To get state total job losses, we add up the subsectors in each state.

Step 4. Congressional district analysis. Employment by congressional district, by industry, by state is obtained from the ACS data for 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 gains or losses in each congressional district, we use each column in [Stj2013], which represent individual state job-gain and loss-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]

[Cdjy]=[Stj51]T Cdy]

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

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

Endnotes

1. Gagnon (2012) provides a ranking of currency manipulators based on total holdings of foreign exchange reserves (only).

2. ULC-Based (Unit Labor Cost) Real Effective Exchange Rate Index, 2010 = 100, (International Monetary Fund 2015). This is a market-value index such that an increase in the index represents growth in the value of the yen, and vice versa (“up is up,” as market analysts would say). In contrast, the market price of the yen (say, for example, in yen per dollar) moves in the opposite direction from the quoted price—an increase in the market price of the currency (in yen per dollar) corresponds to a decrease in the value of the yen.

3. IMF (2015), Board of Governors of the Federal Reserve System (2015), and author’s analysis, as shown later in Figure B.

4. The market value of the yen dropped significantly in the second half of 2014 (as shown later in Figure B). Thus, the end-of-year real value of yen was below the period average. This is consistent with Laffer’s observation that the end-of-year value of the yen was below its level in 2007 (Laffer 2014, 24).

5. Actual holdings of foreign assets by the GPIF rose from $244.3 billion in 2012 to $308.8 billion in 2013, according to financial statements (GPIF 2015), an increase of 26.4 percent. This figure was used to estimate the level of foreign holdings by the GPIF in prior years in Figure A. It was assumed that targeted portfolio reallocations took effect in 2014. Implementation of this reallocation could take longer, but holdings of these assets will ultimately have an impact on Japan’s trade and current account balances.

6. The U.S. monetary base reached $3.9 trillion in the second quarter of 2014, while Japan’s monetary base equaled $2.4 trillion, at end-of-period exchange rates (IMF 2015).

7. The sharp fall in the yen after 2012 also had a negative impact on the current account. In the short run, depreciation raises the yen cost of imports, while exports are only expected to improve in the medium to long term. This is the well-known J curve effect, which is based on the observation that depreciation tends to worsen the trade balance in the short run and improve it in the medium to long term.

8. Other variables included in the BGS model include net official flows (the change in official asset holdings from year to year), and dummy variables designed to measure the impact of capital mobility. The model is estimated using instrumental variables techniques with a number of instruments. Estimates show that the direct effects of official flows are small for countries with high capital mobility, so the primary channel of current account impact in such countries is through the net official assets variable, which is a measure of the stock of foreign assets held by government agencies (BGS 2014, 8–12).

9. Japan is a large net investor, and it maintained a large surplus of net investment income which increased steadily from $100 billion in 2005 to $160 billion in 2013 (IMF 2015). These amounts are in addition to the estimated surplus on the current account (net of investment income) shown in Figure E.

10. The Economic Policy Institute and other research entities have examined the job impacts of trade in recent years by netting the job opportunities lost to imports against 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 an “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 recently published estimates of the jobs supported by U.S. exports (Johnson and Rasmussen 2013) using 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.

11. The calendar year deficit was computed as a weighted average of the actual federal deficit of $680 billion in fiscal year 2013 (Irwin 2013) and the actual federal deficit of $483 billion in fiscal year 2014  (Collender 2014). The weighted average of these two estimates (using weights of 0.75 for FY 2013 and 0.25 for FY 2014) was $630.7 billion for calendar year 2013.

12. 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.

13. The respending analysis does include some impacts on employment in wholesale and retail trade, and in advertising. Thus, the net jobs analysis presented in Table 3 (which includes all direct, indirect, and respending jobs supported or displaced by the trade deficit) does include some net jobs displaced in these industries.

14. 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

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Table 1

Impact of U.S. trade deficit with Japan on U.S. economy and state spending, 2013

Change in 2013 Impact
Trade deficit (billions of dollars) $78.3
Gross Domestic Product
in annual billions of dollars -$125.3
as a share of GDP 0.747%
Number of jobs displaced 896,600
Federal budget deficit
in annual billions of dollars $46.4
as a share of federal deficit 7.4%
State and local budget funds
in annual billions of dollars -$17.5

Source: Author's analysis of Bivens (2014), U.S. Census Bureau (2013), U.S. International Trade Commission (USITC 2015), Bureau of Labor Statistics (BLS 2014a), and BLS Employment Projections program (BLS-EP 2014a, 2014b, and 2014c).  For a more detailed explanation of data sources and computations, see the appendix.

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Table 2

Number of jobs displaced by U.S. trade deficit with Japan, 2013

Direct and indirect jobs 560,400
Direct jobs 148,400
Indirect jobs 412,000
Respending jobs 336,200
Total 896,600

Source: Author's analysis of Bivens (2014), U.S. Census Bureau (2013), U.S. International Trade Commission (USITC 2015), Bureau of Labor Statistics (BLS 2014a), and BLS Employment Projections program (BLS-EP 2014a, 2014b, and 2014c).  For a more detailed explanation of data sources and computations, see the appendix.

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Table 3

Net U.S. jobs created or displaced by U.S. goods trade with Japan, by industry, 2013

Total Share of total jobs displaced
Agriculture, forestry, fishing, and hunting 63,600 -7.1%
Mining -4,100 0.5%
Oil and gas -500 0.1%
Minerals and ores -3,700 0.4%
Utilities -4,800 0.5%
Construction -8,000 0.9%
Manufacturing -466,000 52.0%
Nondurable goods 9,300 -1.0%
Food 14,400 -1.6%
Beverage and tobacco products 200 0.0%
Textile mills and textile product mills -6,200 0.7%
Apparel -1,200 0.1%
Leather and allied products 2,000 -0.2%
Industrial supplies -48,700 5.4%
Wood products -1,300 0.1%
Paper -3,000 0.3%
Printed matter and related products -2,300 0.3%
Petroleum and coal products -800 0.1%
Chemicals -5,500 0.6%
Plastics and rubber products -26,300 2.9%
Nonmetallic mineral products -9,500 1.1%
Durable goods -426,600 47.6%
Primary metal -28,100 3.1%
Fabricated metal products -80,800 9.0%
Machinery -96,600 10.8%
Computer and electronic parts -66,100 7.4%
Computer and peripheral equipment -11,600 1.3%
Communications, audio, and video equipment -7,600 0.8%
Navigational, measuring, electromedical, and control instruments -12,300 1.4%
Semiconductors and other electronic components, and reproducing magnetic and optical media -34,500 3.8%
Electrical equipment, appliances, and components -23,600 2.6%
Transportation equipment -120,100 13.4%
Motor vehicles and motor vehicle parts -118,800 13.3%
Aerospace products and parts 3,100 -0.3%
Railroad, ship, and other transportation equipment -4,400 0.5%
Furniture and related products -2,500 0.3%
Miscellaneous manufactured commodities -9,000 1.0%
Wholesale trade -12,200 1.4%
Retail trade -51,800 5.8%
Transportation and warehousing -39,700 4.4%
Information -14,300 1.6%
Finance and insurance -32,500 3.6%
Real estate and rental and leasing -11,600 1.3%
Professional, scientific, and technical services -50,000 5.6%
Management of companies and enterprises -30,300 3.4%
Administrative and support and waste management and remediation services -61,800 6.9%
Education services -12,200 1.4%
Healthcare and social assistance -60,500 6.7%
Arts, entertainment, and recreation -10,100 1.1%
Accommodation and food services -48,500 0.0%
Other services (except public administration) -30,400 3.4%
Public administration -11,400 1.3%
Subtotal, nonmanufacturing -430,600 48.0%
Total* -896,600

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

Source: Author's analysis of Bivens (2014), U.S. Census Bureau (2013), U.S. International Trade Commission (USITC 2015), Bureau of Labor Statistics (BLS 2014a), and BLS Employment Projections program (BLS-EP 2014a, 2014b, and 2014c).  For a more detailed explanation of data sources and computations, see the appendix.

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Table 4

Net U.S. jobs displaced by U.S. trade deficit with Japan, by state, 2013 (ranked by jobs displaced as a share of state employment)

Rank State Net jobs displaced State employment (in 2011) Jobs displaced as share
of state employment
1 Michigan 56,200 4,191,900 1.34%
2 Indiana 33,700 2,934,500 1.15%
3 Ohio 50,900 5,213,500 0.98%
4 Kentucky 16,400 1,838,400 0.89%
5 Wisconsin 24,300 2,819,500 0.86%
6 South Carolina 16,800 1,968,900 0.85%
7 Tennessee 23,200 2,784,500 0.83%
8 Alabama 16,000 1,981,100 0.81%
9 New Hampshire 5,300 684,800 0.77%
10 Illinois 45,500 5,926,900 0.77%
11 Pennsylvania 40,100 5,853,300 0.69%
12 Iowa 10,500 1,538,800 0.68%
13 Minnesota 18,600 2,728,900 0.68%
14 Connecticut 11,700 1,742,500 0.67%
15 North Carolina 27,300 4,195,800 0.65%
16 Missouri 17,800 2,742,100 0.65%
17 Rhode Island 3,300 511,200 0.65%
18 Oklahoma 10,800 1,681,800 0.64%
19 Mississippi 7,300 1,181,300 0.62%
20 Massachusetts 20,100 3,284,700 0.61%
21 Texas 69,600 11,455,100 0.61%
22 Georgia 25,300 4,193,800 0.60%
23 Utah 7,600 1,260,800 0.60%
24 Arkansas 7,200 1,235,800 0.58%
25 Oregon 9,400 1,710,300 0.55%
26 New Jersey 22,800 4,152,500 0.55%
27 West Virginia 4,100 748,600 0.55%
28 Arizona 14,300 2,688,000 0.53%
29 California 86,800 16,426,700 0.53%
30 New York 46,700 8,959,000 0.52%
31 Kansas 7,200 1,389,000 0.52%
32 Virginia 20,000 3,860,100 0.52%
33 Colorado 12,700 2,492,400 0.51%
34 Vermont 1,600 327,300 0.49%
35 Florida 38,800 8,101,900 0.48%
36 Nevada 5,700 1,204,900 0.47%
37 Louisiana 9,300 1,973,900 0.47%
38 Maryland 13,100 2,894,600 0.45%
39 Maine 2,900 643,100 0.45%
40 Washington 14,000 3,118,000 0.45%
41 Nebraska 4,200 943,600 0.45%
42 Delaware 1,800 420,400 0.43%
43 South Dakota 1,700 415,600 0.41%
44 Idaho 2,700 684,900 0.39%
45 New Mexico 3,400 869,800 0.39%
46 District of Columbia 1,200 310,600 0.39%
47 Hawaii 2,300 629,500 0.37%
48 Wyoming 1,000 290,000 0.34%
49 Alaska 1,100 344,300 0.32%
50 North Dakota 1,100 370,800 0.30%
51 Montana 1,200 480,000 0.25%
Total* 896,600 140,399,600 0.64%

*Totals may vary slightly due to rounding.

Source: Author's analysis of Bivens (2014), U.S. Census Bureau (2013), U.S. International Trade Commission (USITC 2015), Bureau of Labor Statistics (BLS 2014a), and BLS Employment Projections program (BLS-EP 2014a, 2014b, and 2014c).  For a more detailed explanation of data sources and computations, see the appendix.

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Table 5

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

Rank State District Net jobs displaced District employment (in 2011) Jobs displaced as a share of district employment
1 Michigan 10 5,500 308,700 1.78%
2 Michigan 11 6,000 342,100 1.75%
3 Michigan 9 5,500 326,100 1.69%
4 Ohio 4 5,100 317,900 1.60%
5 Indiana 2 5,000 317,800 1.57%
6 Michigan 7 4,500 299,100 1.50%
7 Indiana 3 4,900 327,000 1.50%
8 Michigan 8 4,900 330,800 1.48%
9 Indiana 6 4,400 311,900 1.41%
10 Michigan 13 3,200 230,700 1.39%
11 Michigan 12 4,200 313,800 1.34%
12 Michigan 14 3,400 257,700 1.32%
13 California 17 4,500 346,100 1.30%
14 Ohio 5 4,300 334,200 1.29%
15 Michigan 2 4,000 315,900 1.27%
16 Indiana 4 4,000 328,500 1.22%
17 South Carolina 4 3,600 301,000 1.20%
18 Wisconsin 6 4,200 353,600 1.19%
19 South Carolina 3 3,100 264,500 1.17%
20 Michigan 5 3,100 264,800 1.17%

Source: Author's analysis of Bivens (2014), U.S. Census Bureau (2013), U.S. International Trade Commission (USITC 2015), Bureau of Labor Statistics (BLS 2014a), and BLS Employment Projections program (BLS-EP 2014a, 2014b, and 2014c).  For a more detailed explanation of data sources and computations, see the appendix.

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Supplemental Table 1

Net U.S. jobs displaced by U.S. trade deficit with Japan, by state, 2013 (ranked by net jobs displaced)

Rank State Net jobs displaced State employment Jobs displaced as share
of state employment
1 California 86,800 16,426,700 0.53%
2 Texas 69,600 11,455,100 0.61%
3 Michigan 56,200 4,191,900 1.34%
4 Ohio 50,900 5,213,500 0.98%
5 New York 46,700 8,959,000 0.52%
6 Illinois 45,500 5,926,900 0.77%
7 Pennsylvania 40,100 5,853,300 0.69%
8 Florida 38,800 8,101,900 0.48%
9 Indiana 33,700 2,934,500 1.15%
10 North Carolina 27,300 4,195,800 0.65%
11 Georgia 25,300 4,193,800 0.60%
12 Wisconsin 24,300 2,819,500 0.86%
13 Tennessee 23,200 2,784,500 0.83%
14 New Jersey 22,800 4,152,500 0.55%
15 Massachusetts 20,100 3,284,700 0.61%
16 Virginia 20,000 3,860,100 0.52%
17 Minnesota 18,600 2,728,900 0.68%
18 Missouri 17,800 2,742,100 0.65%
19 South Carolina 16,800 1,968,900 0.85%
20 Kentucky 16,400 1,838,400 0.89%
21 Alabama 16,000 1,981,100 0.81%
22 Arizona 14,300 2,688,000 0.53%
23 Washington 14,000 3,118,000 0.45%
24 Maryland 13,100 2,894,600 0.45%
25 Colorado 12,700 2,492,400 0.51%
26 Connecticut 11,700 1,742,500 0.67%
27 Oklahoma 10,800 1,681,800 0.64%
28 Iowa 10,500 1,538,800 0.68%
29 Oregon 9,400 1,710,300 0.55%
30 Louisiana 9,300 1,973,900 0.47%
31 Utah 7,600 1,260,800 0.60%
32 Mississippi 7,300 1,181,300 0.62%
33 Arkansas 7,200 1,235,800 0.58%
33 Kansas 7,200 1,389,000 0.52%
35 Nevada 5,700 1,204,900 0.47%
36 New Hampshire 5,300 684,800 0.77%
37 Nebraska 4,200 943,600 0.45%
38 West Virginia 4,100 748,600 0.55%
39 New Mexico 3,400 869,800 0.39%
40 Rhode Island 3,300 511,200 0.65%
41 Maine 2,900 643,100 0.45%
42 Idaho 2,700 684,900 0.39%
43 Hawaii 2,300 629,500 0.37%
44 Delaware 1,800 420,400 0.43%
45 South Dakota 1,700 415,600 0.41%
46 Vermont 1,600 327,300 0.49%
47 District of Columbia 1,200 310,600 0.39%
47 Montana 1,200 480,000 0.25%
49 Alaska 1,100 344,300 0.32%
49 North Dakota 1,100 370,800 0.30%
51 Wyoming 1,000 290,000 0.34%
Total* 896,600 140,399,600 0.64%

*Totals may vary slightly due to rounding.

Source: Author's analysis of Bivens (2014), U.S. Census Bureau (2013), U.S. International Trade Commission (USITC 2015), Bureau of Labor Statistics (BLS 2014a), and BLS Employment Projections program (BLS-EP 2014a, 2014b, and 2014c).  For a more detailed explanation of data sources and computations, see the appendix.

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Supplemental Table 2

Net U.S. jobs displaced by U.S. trade deficit with Japan, by state, 2013 (listed alphabetically)

Rank (by share of employment) State Net jobs displaced State employment Jobs displaced as share
of state employment
 8 Alabama 16,000 1,981,100 0.81%
49 Alaska 1,100 344,300 0.32%
28 Arizona 14,300 2,688,000 0.53%
24 Arkansas 7,200 1,235,800 0.58%
29 California 86,800 16,426,700 0.53%
33 Colorado 12,700 2,492,400 0.51%
14 Connecticut 11,700 1,742,500 0.67%
42 Delaware 1,800 420,400 0.43%
46 District of Columbia 1,200 310,600 0.39%
35 Florida 38,800 8,101,900 0.48%
22 Georgia 25,300 4,193,800 0.60%
47 Hawaii 2,300 629,500 0.37%
44 Idaho 2,700 684,900 0.39%
10 Illinois 45,500 5,926,900 0.77%
2 Indiana 33,700 2,934,500 1.15%
12 Iowa 10,500 1,538,800 0.68%
31 Kansas 7,200 1,389,000 0.52%
4 Kentucky 16,400 1,838,400 0.89%
37 Louisiana 9,300 1,973,900 0.47%
39 Maine 2,900 643,100 0.45%
38 Maryland 13,100 2,894,600 0.45%
20 Massachusetts 20,100 3,284,700 0.61%
1 Michigan 56,200 4,191,900 1.34%
13 Minnesota 18,600 2,728,900 0.68%
19 Mississippi 7,300 1,181,300 0.62%
16 Missouri 17,800 2,742,100 0.65%
51 Montana 1,200 480,000 0.25%
41 Nebraska 4,200 943,600 0.45%
36 Nevada 5,700 1,204,900 0.47%
9 New Hampshire 5,300 684,800 0.77%
26 New Jersey 22,800 4,152,500 0.55%
45 New Mexico 3,400 869,800 0.39%
30 New York 46,700 8,959,000 0.52%
15 North Carolina 27,300 4,195,800 0.65%
50 North Dakota 1,100 370,800 0.30%
3 Ohio 50,900 5,213,500 0.98%
18 Oklahoma 10,800 1,681,800 0.64%
25 Oregon 9,400 1,710,300 0.55%
11 Pennsylvania 40,100 5,853,300 0.69%
17 Rhode Island 3,300 511,200 0.65%
6 South Carolina 16,800 1,968,900 0.85%
43 South Dakota 1,700 415,600 0.41%
7 Tennessee 23,200 2,784,500 0.83%
21 Texas 69,600 11,455,100 0.61%
23 Utah 7,600 1,260,800 0.60%
34 Vermont 1,600 327,300 0.49%
32 Virginia 20,000 3,860,100 0.52%
40 Washington 14,000 3,118,000 0.45%
27 West Virginia 4,100 748,600 0.55%
5 Wisconsin 24,300 2,819,500 0.86%
48 Wyoming 1,000 290,000 0.34%
Total* 896,600 140,399,600 0.64%

*Totals may vary slightly due to rounding.

Source: Author's analysis of Bivens (2014), U.S. Census Bureau (2013), U.S. International Trade Commission (USITC 2015), Bureau of Labor Statistics (BLS 2014a), and BLS Employment Projections program (BLS-EP 2014a, 2014b, and 2014c).  For a more detailed explanation of data sources and computations, see the appendix.

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Supplemental Table 3

Net U.S. jobs displaced by U.S. trade deficit with Japan, by congressional district, 2013 (listed alphabetically)

Rank (by share of employment) State State District # Net jobs displaced Employment Jobs displaced as a share of district employment
235 Alabama 1 1,600 283,000 0.57%
132 Alabama 2 1,900 276,900 0.69%
24 Alabama 3 3,100 274,600 1.13%
43 Alabama 4 2,500 262,900 0.95%
66 Alabama 5 2,600 311,900 0.83%
97 Alabama 6 2,400 318,400 0.75%
113 Alabama 7 1,800 253,500 0.71%
420 Alaska Statewide 1,100 344,300 0.32%
408 Arizona 1 1,000 264,900 0.38%
312 Arizona 2 1,500 299,200 0.50%
417 Arizona 3 900 262,200 0.34%
386 Arizona 4 1,000 233,500 0.43%
153 Arizona 5 2,100 317,900 0.66%
253 Arizona 6 2,000 366,000 0.55%
201 Arizona 7 1,700 282,300 0.60%
266 Arizona 8 1,600 301,700 0.53%
178 Arizona 9 2,300 360,300 0.64%
228 Arkansas 1 1,600 277,400 0.58%
263 Arkansas 2 1,800 336,300 0.54%
143 Arkansas 3 2,200 327,000 0.67%
259 Arkansas 4 1,600 295,100 0.54%
423 California 1 800 260,300 0.31%
418 California 2 1,100 323,100 0.34%
430 California 3 700 286,600 0.24%
297 California 4 1,500 294,200 0.51%
385 California 5 1,400 326,800 0.43%
365 California 6 1,300 288,300 0.45%
295 California 7 1,600 313,200 0.51%
298 California 8 1,200 235,500 0.51%
399 California 9 1,100 275,300 0.40%
402 California 10 1,100 277,200 0.40%
277 California 11 1,700 324,200 0.52%
276 California 12 2,100 399,400 0.53%
243 California 13 1,900 340,200 0.56%
249 California 14 2,000 364,000 0.55%
77 California 15 2,700 336,400 0.80%
434 California 16 0 244,900 0.00%
13 California 17 4,500 346,100 1.30%
38 California 18 3,400 344,500 0.99%
32 California 19 3,400 324,000 1.05%
433 California 20 100 302,500 0.03%
436 California 21 -1,300 243,800 -0.53%
431 California 22 500 289,600 0.17%
427 California 23 800 274,100 0.29%
428 California 24 900 323,500 0.28%
206 California 25 1,800 302,700 0.59%
400 California 26 1,300 325,900 0.40%
292 California 27 1,700 332,200 0.51%
314 California 28 1,800 359,900 0.50%
240 California 29 1,700 303,700 0.56%
310 California 30 1,800 358,200 0.50%
287 California 31 1,500 292,200 0.51%
197 California 32 1,800 293,800 0.61%
280 California 33 1,900 364,200 0.52%
333 California 34 1,500 309,400 0.48%
148 California 35 1,900 284,800 0.67%
412 California 36 900 251,900 0.36%
300 California 37 1,700 335,600 0.51%
177 California 38 2,000 313,300 0.64%
150 California 39 2,200 332,000 0.66%
232 California 40 1,600 280,500 0.57%
187 California 41 1,700 271,900 0.63%
164 California 42 2,000 307,000 0.65%
207 California 43 1,800 302,800 0.59%
121 California 44 1,900 270,600 0.70%
166 California 45 2,300 354,400 0.65%
124 California 46 2,200 314,400 0.70%
223 California 47 1,900 327,600 0.58%
137 California 48 2,400 352,600 0.68%
203 California 49 1,800 299,700 0.60%
302 California 50 1,500 296,200 0.51%
389 California 51 1,100 258,600 0.43%
156 California 52 2,300 350,100 0.66%
244 California 53 1,900 342,700 0.55%
254 Colorado 1 2,100 384,400 0.55%
231 Colorado 2 2,200 384,600 0.57%
411 Colorado 3 1,200 331,400 0.36%
350 Colorado 4 1,600 344,100 0.46%
301 Colorado 5 1,600 315,900 0.51%
285 Colorado 6 1,900 369,600 0.51%
246 Colorado 7 2,000 362,500 0.55%
155 Connecticut 1 2,300 349,800 0.66%
183 Connecticut 2 2,200 348,600 0.63%
163 Connecticut 3 2,300 352,700 0.65%
171 Connecticut 4 2,200 343,000 0.64%
88 Connecticut 5 2,700 348,300 0.78%
387 Delaware Statewide 1,800 420,400 0.43%
405 DC Statewide 1,200 310,600 0.39%
388 Florida 1 1,300 303,900 0.43%
380 Florida 2 1,300 301,500 0.43%
401 Florida 3 1,100 277,000 0.40%
255 Florida 4 1,800 329,900 0.55%
272 Florida 5 1,500 284,000 0.53%
323 Florida 6 1,400 283,200 0.49%
273 Florida 7 1,700 322,500 0.53%
237 Florida 8 1,600 283,400 0.56%
341 Florida 9 1,500 317,200 0.47%
289 Florida 10 1,700 331,500 0.51%
304 Florida 11 1,100 217,400 0.51%
269 Florida 12 1,500 283,200 0.53%
221 Florida 13 1,800 309,200 0.58%
267 Florida 14 1,700 320,700 0.53%
353 Florida 15 1,400 304,200 0.46%
257 Florida 16 1,500 276,100 0.54%
432 Florida 17 400 248,700 0.16%
358 Florida 18 1,300 284,000 0.46%
364 Florida 19 1,200 265,200 0.45%
382 Florida 20 1,300 302,100 0.43%
340 Florida 21 1,500 316,800 0.47%
291 Florida 22 1,700 332,000 0.51%
313 Florida 23 1,700 339,900 0.50%
374 Florida 24 1,300 293,400 0.44%
383 Florida 25 1,400 326,000 0.43%
367 Florida 26 1,500 335,600 0.45%
368 Florida 27 1,400 313,600 0.45%
393 Georgia 1 1,200 286,100 0.42%
376 Georgia 2 1,100 251,200 0.44%
63 Georgia 3 2,400 285,800 0.84%
227 Georgia 4 1,800 311,700 0.58%
264 Georgia 5 1,700 318,100 0.53%
222 Georgia 6 2,100 361,200 0.58%
118 Georgia 7 2,200 312,500 0.70%
337 Georgia 8 1,300 272,700 0.48%
147 Georgia 9 1,900 284,600 0.67%
211 Georgia 10 1,700 287,400 0.59%
193 Georgia 11 2,100 340,900 0.62%
261 Georgia 12 1,500 278,200 0.54%
256 Georgia 13 1,700 312,800 0.54%
53 Georgia 14 2,600 290,700 0.89%
392 Hawaii 1 1,400 330,100 0.42%
424 Hawaii 2 900 299,400 0.30%
332 Idaho 1 1,600 329,900 0.48%
422 Idaho 2 1,100 355,000 0.31%
192 Illinois 1 1,800 290,200 0.62%
111 Illinois 2 2,000 278,200 0.72%
130 Illinois 3 2,200 319,500 0.69%
82 Illinois 4 2,600 326,600 0.80%
162 Illinois 5 2,600 397,600 0.65%
45 Illinois 6 3,300 355,600 0.93%
233 Illinois 7 1,700 298,500 0.57%
57 Illinois 8 3,200 366,300 0.87%
181 Illinois 9 2,200 347,200 0.63%
79 Illinois 10 2,600 324,800 0.80%
86 Illinois 11 2,700 347,300 0.78%
125 Illinois 12 2,100 301,000 0.70%
247 Illinois 13 1,800 326,600 0.55%
54 Illinois 14 3,100 351,000 0.88%
93 Illinois 15 2,400 316,500 0.76%
51 Illinois 16 3,000 330,800 0.91%
34 Illinois 17 3,200 311,700 1.03%
48 Illinois 18 3,100 337,500 0.92%
31 Indiana 1 3,300 310,600 1.06%
5 Indiana 2 5,000 317,800 1.57%
7 Indiana 3 4,900 327,000 1.50%
16 Indiana 4 4,000 328,500 1.22%
94 Indiana 5 2,700 357,700 0.75%
9 Indiana 6 4,400 311,900 1.41%
67 Indiana 7 2,600 312,200 0.83%
33 Indiana 8 3,400 329,300 1.03%
36 Indiana 9 3,400 339,400 1.00%
83 Iowa 1 3,100 392,300 0.79%
87 Iowa 2 2,900 373,400 0.78%
215 Iowa 3 2,300 390,800 0.59%
229 Iowa 4 2,200 382,300 0.58%
421 Kansas 1 1,100 345,900 0.32%
216 Kansas 2 2,000 339,900 0.59%
191 Kansas 3 2,300 370,300 0.62%
296 Kansas 4 1,700 332,900 0.51%
62 Kentucky 1 2,400 284,800 0.84%
28 Kentucky 2 3,500 317,100 1.10%
59 Kentucky 3 2,900 333,300 0.87%
64 Kentucky 4 2,800 333,500 0.84%
133 Kentucky 5 1,600 234,300 0.68%
41 Kentucky 6 3,200 335,400 0.95%
299 Louisiana 1 1,800 354,000 0.51%
360 Louisiana 2 1,500 329,000 0.46%
330 Louisiana 3 1,600 328,100 0.49%
284 Louisiana 4 1,600 311,100 0.51%
404 Louisiana 5 1,100 283,900 0.39%
352 Louisiana 6 1,700 367,800 0.46%
317 Maine 1 1,700 340,400 0.50%
410 Maine 2 1,100 302,700 0.36%
319 Maryland 1 1,700 342,300 0.50%
334 Maryland 2 1,700 351,700 0.48%
354 Maryland 3 1,700 369,500 0.46%
375 Maryland 4 1,700 384,100 0.44%
397 Maryland 5 1,500 368,200 0.41%
347 Maryland 6 1,700 363,200 0.47%
373 Maryland 7 1,400 315,700 0.44%
390 Maryland 8 1,700 400,100 0.42%
169 Massachusetts 1 2,200 341,000 0.65%
122 Massachusetts 2 2,500 356,500 0.70%
70 Massachusetts 3 2,900 355,400 0.82%
173 Massachusetts 4 2,400 374,800 0.64%
209 Massachusetts 5 2,300 387,400 0.59%
212 Massachusetts 6 2,200 372,000 0.59%
355 Massachusetts 7 1,700 369,800 0.46%
218 Massachusetts 8 2,200 375,600 0.59%
294 Massachusetts 9 1,800 352,300 0.51%
129 Michigan 1 2,000 290,200 0.69%
15 Michigan 2 4,000 315,900 1.27%
21 Michigan 3 3,600 315,300 1.14%
39 Michigan 4 2,800 286,300 0.98%
20 Michigan 5 3,100 264,800 1.17%
25 Michigan 6 3,500 310,400 1.13%
6 Michigan 7 4,500 299,100 1.50%
8 Michigan 8 4,900 330,800 1.48%
3 Michigan 9 5,500 326,100 1.69%
1 Michigan 10 5,500 308,700 1.78%
2 Michigan 11 6,000 342,100 1.75%
11 Michigan 12 4,200 313,800 1.34%
10 Michigan 13 3,200 230,700 1.39%
12 Michigan 14 3,400 257,700 1.32%
200 Minnesota 1 2,100 348,200 0.60%
109 Minnesota 2 2,600 358,300 0.73%
60 Minnesota 3 3,000 353,800 0.85%
159 Minnesota 4 2,200 336,000 0.65%
106 Minnesota 5 2,600 352,000 0.74%
76 Minnesota 6 2,800 348,700 0.80%
359 Minnesota 7 1,500 328,700 0.46%
210 Minnesota 8 1,800 303,400 0.59%
99 Mississippi 1 2,300 305,600 0.75%
205 Mississippi 2 1,600 266,900 0.60%
274 Mississippi 3 1,600 303,900 0.53%
213 Mississippi 4 1,800 304,900 0.59%
429 Montana Statewide 1,200 480,000 0.25%
258 Missouri 1 1,800 331,500 0.54%
154 Missouri 2 2,500 378,600 0.66%
142 Missouri 3 2,500 370,000 0.68%
245 Missouri 4 1,800 324,900 0.55%
126 Missouri 5 2,400 345,300 0.70%
167 Missouri 6 2,300 355,900 0.65%
134 Missouri 7 2,300 337,400 0.68%
119 Missouri 8 2,100 298,500 0.70%
271 Nebraska 1 1,700 321,700 0.53%
305 Nebraska 2 1,600 316,300 0.51%
419 Nebraska 3 1,000 305,600 0.33%
324 Nevada 1 1,400 284,700 0.49%
249 Nevada 2 1,700 309,400 0.55%
370 Nevada 3 1,500 336,500 0.45%
377 Nevada 4 1,200 274,300 0.44%
107 New Hampshire 1 2,600 352,600 0.74%
71 New Hampshire 2 2,700 332,200 0.81%
239 New Jersey 1 1,900 339,200 0.56%
398 New Jersey 2 1,300 324,400 0.40%
279 New Jersey 3 1,800 344,200 0.52%
326 New Jersey 4 1,600 326,400 0.49%
168 New Jersey 5 2,300 356,100 0.65%
208 New Jersey 6 2,100 353,600 0.59%
199 New Jersey 7 2,300 377,100 0.61%
234 New Jersey 8 2,100 371,000 0.57%
238 New Jersey 9 1,900 338,500 0.56%
335 New Jersey 10 1,500 310,700 0.48%
219 New Jersey 11 2,100 358,800 0.59%
262 New Jersey 12 1,900 352,400 0.54%
336 New Mexico 1 1,500 311,900 0.48%
426 New Mexico 2 800 273,100 0.29%
406 New Mexico 3 1,100 284,800 0.39%
320 New York 1 1,700 343,300 0.50%
242 New York 2 2,000 357,800 0.56%
306 New York 3 1,700 336,700 0.50%
348 New York 4 1,600 342,500 0.47%
339 New York 5 1,600 336,200 0.48%
356 New York 6 1,500 327,000 0.46%
379 New York 7 1,400 322,200 0.43%
371 New York 8 1,300 292,700 0.44%
381 New York 9 1,400 324,900 0.43%
372 New York 10 1,600 360,300 0.44%
395 New York 11 1,300 317,500 0.41%
311 New York 12 2,100 418,800 0.50%
394 New York 13 1,300 317,200 0.41%
346 New York 14 1,600 341,800 0.47%
344 New York 15 1,200 255,900 0.47%
322 New York 16 1,600 323,600 0.49%
345 New York 17 1,600 341,400 0.47%
260 New York 18 1,800 332,100 0.54%
327 New York 19 1,600 327,300 0.49%
308 New York 20 1,800 357,600 0.50%
363 New York 21 1,400 309,200 0.45%
158 New York 22 2,100 320,200 0.66%
116 New York 23 2,300 324,600 0.71%
144 New York 24 2,200 327,300 0.67%
88 New York 25 2,600 335,400 0.78%
172 New York 26 2,100 327,700 0.64%
104 New York 27 2,500 337,800 0.74%
251 North Carolina 1 1,600 291,800 0.55%
128 North Carolina 2 2,100 303,800 0.69%
357 North Carolina 3 1,400 305,600 0.46%
288 North Carolina 4 1,800 350,900 0.51%
105 North Carolina 5 2,400 324,500 0.74%
120 North Carolina 6 2,400 341,800 0.70%
415 North Carolina 7 1,100 315,400 0.35%
92 North Carolina 8 2,300 301,700 0.76%
73 North Carolina 9 3,000 371,400 0.81%
52 North Carolina 10 2,900 324,000 0.90%
141 North Carolina 11 2,000 295,400 0.68%
157 North Carolina 12 2,100 319,800 0.66%
204 North Carolina 13 2,100 349,900 0.60%
425 North Dakota Statewide 1,100 370,800 0.30%
110 Ohio 1 2,400 332,300 0.72%
90 Ohio 2 2,500 323,600 0.77%
152 Ohio 3 2,200 333,000 0.66%
4 Ohio 4 5,100 317,900 1.60%
14 Ohio 5 4,300 334,200 1.29%
40 Ohio 6 2,800 292,300 0.96%
23 Ohio 7 3,700 326,800 1.13%
29 Ohio 8 3,600 328,800 1.09%
30 Ohio 9 3,400 315,000 1.08%
47 Ohio 10 2,900 312,800 0.93%
80 Ohio 11 2,200 275,200 0.80%
74 Ohio 12 2,900 359,500 0.81%
26 Ohio 13 3,600 320,400 1.12%
37 Ohio 14 3,500 349,700 1.00%
103 Ohio 15 2,500 336,400 0.74%
45 Ohio 16 3,300 355,600 0.93%
78 Oklahoma 1 2,900 361,900 0.80%
160 Oklahoma 2 1,900 290,300 0.65%
282 Oklahoma 3 1,700 329,900 0.52%
185 Oklahoma 4 2,200 350,900 0.63%
230 Oklahoma 5 2,000 348,800 0.57%
55 Oregon 1 3,300 377,200 0.87%
414 Oregon 2 1,100 314,200 0.35%
186 Oregon 3 2,400 383,300 0.63%
403 Oregon 4 1,200 309,000 0.39%
384 Oregon 5 1,400 326,700 0.43%
290 Pennsylvania 1 1,400 273,300 0.51%
338 Pennsylvania 2 1,300 273,100 0.48%
44 Pennsylvania 3 3,000 317,700 0.94%
108 Pennsylvania 4 2,500 342,900 0.73%
69 Pennsylvania 5 2,600 316,800 0.82%
151 Pennsylvania 6 2,400 362,300 0.66%
241 Pennsylvania 7 1,900 339,700 0.56%
170 Pennsylvania 8 2,300 357,800 0.64%
95 Pennsylvania 9 2,300 304,800 0.75%
175 Pennsylvania 10 2,000 312,500 0.64%
180 Pennsylvania 11 2,100 329,300 0.64%
98 Pennsylvania 12 2,500 331,900 0.75%
139 Pennsylvania 13 2,300 339,000 0.68%
217 Pennsylvania 14 1,900 323,200 0.59%
84 Pennsylvania 15 2,700 343,800 0.79%
198 Pennsylvania 16 2,000 327,700 0.61%
176 Pennsylvania 17 2,000 312,600 0.64%
72 Pennsylvania 18 2,800 345,000 0.81%
140 Rhode Island 1 1,700 250,900 0.68%
195 Rhode Island 2 1,600 260,300 0.61%
123 South Carolina 1 2,100 299,800 0.70%
161 South Carolina 2 2,000 305,600 0.65%
19 South Carolina 3 3,100 264,500 1.17%
17 South Carolina 4 3,600 301,000 1.20%
58 South Carolina 5 2,400 275,200 0.87%
113 South Carolina 6 1,800 253,500 0.71%
146 South Carolina 7 1,800 269,400 0.67%
396 South Dakota 1 1,700 415,600 0.41%
50 Tennessee 1 2,700 297,600 0.91%
91 Tennessee 2 2,500 327,200 0.76%
49 Tennessee 3 2,700 297,000 0.91%
27 Tennessee 4 3,500 314,500 1.11%
189 Tennessee 5 2,200 353,400 0.62%
42 Tennessee 6 2,900 304,500 0.95%
56 Tennessee 7 2,500 285,800 0.87%
65 Tennessee 8 2,500 299,200 0.84%
214 Tennessee 9 1,800 305,300 0.59%
179 Texas 1 1,900 297,700 0.64%
68 Texas 2 3,000 364,600 0.82%
96 Texas 3 2,800 371,200 0.75%
202 Texas 4 1,800 299,300 0.60%
236 Texas 5 1,700 300,800 0.57%
131 Texas 6 2,400 348,800 0.69%
102 Texas 7 2,800 376,300 0.74%
112 Texas 8 2,200 309,200 0.71%
196 Texas 9 2,000 326,400 0.61%
145 Texas 10 2,300 342,600 0.67%
248 Texas 11 1,700 308,800 0.55%
135 Texas 12 2,300 337,500 0.68%
362 Texas 13 1,400 309,000 0.45%
321 Texas 14 1,500 303,300 0.49%
351 Texas 15 1,300 280,900 0.46%
265 Texas 16 1,500 281,300 0.53%
281 Texas 17 1,700 329,300 0.52%
100 Texas 18 2,300 306,400 0.75%
413 Texas 19 1,100 310,700 0.35%
286 Texas 20 1,600 311,400 0.51%
275 Texas 21 1,900 361,200 0.53%
188 Texas 22 2,200 352,500 0.62%
407 Texas 23 1,100 289,700 0.38%
127 Texas 24 2,700 388,600 0.69%
184 Texas 25 1,900 302,200 0.63%
138 Texas 26 2,500 368,300 0.68%
325 Texas 27 1,500 305,600 0.49%
329 Texas 28 1,300 266,300 0.49%
85 Texas 29 2,300 292,900 0.79%
252 Texas 30 1,600 292,300 0.55%
136 Texas 31 2,200 323,000 0.68%
101 Texas 32 2,700 360,900 0.75%
117 Texas 33 2,000 283,900 0.70%
409 Texas 34 900 242,200 0.37%
309 Texas 35 1,600 318,200 0.50%
165 Texas 36 1,900 291,900 0.65%
174 Utah 1 2,000 312,400 0.64%
278 Utah 2 1,600 305,700 0.52%
226 Utah 3 1,800 311,200 0.58%
149 Utah 4 2,200 331,500 0.66%
327 Vermont Statewide 1,600 327,300 0.49%
361 Virginia 1 1,600 352,400 0.45%
268 Virginia 2 1,800 339,800 0.53%
316 Virginia 3 1,600 320,100 0.50%
225 Virginia 4 1,900 327,900 0.58%
303 Virginia 5 1,600 316,100 0.51%
270 Virginia 6 1,800 339,900 0.53%
349 Virginia 7 1,700 364,600 0.47%
391 Virginia 8 1,800 423,700 0.42%
75 Virginia 9 2,400 298,400 0.80%
307 Virginia 10 1,900 376,400 0.50%
366 Virginia 11 1,800 400,900 0.45%
293 Washington 1 1,700 332,300 0.51%
343 Washington 2 1,500 318,900 0.47%
182 Washington 3 1,800 284,500 0.63%
435 Washington 4 -100 284,500 -0.04%
283 Washington 5 1,500 291,500 0.51%
378 Washington 6 1,200 275,500 0.44%
315 Washington 7 1,900 380,000 0.50%
342 Washington 8 1,500 318,000 0.47%
318 Washington 9 1,700 341,400 0.50%
369 Washington 10 1,300 291,300 0.45%
224 West Virginia 1 1,500 258,700 0.58%
331 West Virginia 2 1,300 266,900 0.49%
220 West Virginia 3 1,300 223,000 0.58%
35 Wisconsin 1 3,500 342,500 1.02%
194 Wisconsin 2 2,400 390,000 0.62%
190 Wisconsin 3 2,200 353,500 0.62%
61 Wisconsin 4 2,600 308,000 0.84%
22 Wisconsin 5 4,200 370,600 1.13%
18 Wisconsin 6 4,200 353,600 1.19%
115 Wisconsin 7 2,400 338,400 0.71%
81 Wisconsin 8 2,900 362,800 0.80%
416 Wyoming Statewide 1,000 290,000 0.34%
Total* 896,600 140,399,600 0.64%

*Totals may vary slightly due to rounding.

Source: Author's analysis of Bivens (2014), U.S. Census Bureau (2013), U.S. International Trade Commission (USITC 2015), Bureau of Labor Statistics (BLS 2014), and BLS Employment Projections program (BLS-EP 2014a, 2014b, and 2014c).  For a more detailed explanation of data sources and computations, see the appendix.

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