Summary

The Trans-Pacific Partnership (TPP) agreement between the United States and 11 other Pacific Rim countries lacks an absolutely key component to keep it from doing potential damage to the U.S. economy. The missing piece of this trade and investment deal is a set of restrictions and/or enforceable penalties against member countries that engage in currency manipulation. Currency manipulation is one of the key driving forces behind the high and rapidly rising U.S. trade deficit with the 11 other members of the TPP. In 2015, the U.S. deficit with TPP countries translated into 2 million U.S. jobs lost, more than half (1.1 million) of which were in manufacturing. Without such provisions against currency manipulation, the TPP could well follow other trade agreements and leave even greater U.S. trade deficits in its wake.

Currency manipulation occurs when a country artificially depresses the value of its currency. Currency manipulation acts like a subsidy to the exports of the manipulating country, and a tax on U.S. exports to every country where U.S. exports compete with the currency manipulator’s exports. In this way, currency manipulation increases U.S. imports, suppresses U.S. exports, and inflates U.S. trade deficits. As past EPI research has shown, currency-manipulation-fueled trade deficits have reduced U.S. gross domestic product (GDP), eliminated millions of U.S. jobs, driven down U.S. wages, and propelled the outsourcing of U.S. jobs to currency manipulators.

Many members of the proposed TPP, including Malaysia, Singapore, and Japan, are known currency manipulators. Others, namely Vietnam, appear to be following the lead of currency manipulators by, for example, acquiring excess foreign exchange reserves to depress the value of their currency. Currency manipulation explains a substantial share of the large, persistent U.S. trade deficit with the 11 other TPP countries that has not only cost millions of U.S. jobs but also increased income inequality and put downward pressure on American wages. We can’t afford a trade agreement that not only allows but would intensify these harmful trends:

The $177.9 billion U.S. goods trade deficit with the 11 other TPP countries reduced U.S. GDP by $284.6 billion (1.6 percent) and eliminated 2 million jobs in 2015.

The 2 million jobs lost due to the U.S. goods trade deficit with TPP member countries in 2015 included 418,900 direct jobs in commodity and manufacturing industries that competed with unfairly traded goods from TPP member countries.

The currency-manipulation-fueled trade deficit with TPP countries in 2015 was also responsible for the loss of 847,200 indirect jobs in supplier industries, and an additional 759,700 “respending” jobs. These lost respending jobs are jobs that—in a U.S. economy still suffering from low demand—would have been supported by the wages of workers who would have had jobs were trade with the TPP member countries balanced.

The U.S. trade deficit with TPP member countries in 2015 cost 1,057,200 manufacturing jobs (52.2 percent of the jobs lost due to the U.S. trade deficit with TPP member countries). Within manufacturing, by far the largest losses occurred in motor vehicles and parts, which lost 738,300 jobs (36.4 percent of total jobs lost). Other manufacturing industries with large losses include apparel (181,900 jobs lost or displaced, equal to 9 percent of total jobs lost) and computer and electronic parts (163,900 jobs, or 8.1 percent).

The U.S. trade deficit with TPP member countries was also responsible for significant job losses outside of manufacturing in 2015. Industries that lost jobs include health care and social assistance (204,200 jobs, 10.1 percent); retail trade (142,800 jobs, 7 percent); accommodation and food services (101,800 jobs, 5 percent); finance and insurance (42,700 jobs, 2.1 percent); agricultural industries (41,600 jobs, 2.1 percent), and education services (37,300 jobs, 1.8 percent).

Each of the 50 states and the District of Columbia lost jobs due to the U.S. trade deficit with TPP member countries in 2015. Net job losses were greatest in California, which lost 227,500 jobs (constituting 1.38 percent of total state employment). Michigan experienced the greatest jobs lost as a share of state employment (5.12 percent).

In the 10 hardest-hit states (jobs lost as a share of all state jobs), the share of jobs lost due to the U.S. trade deficit with the TPP countries in 2015 ranged from 1.83 percent to 5.12 percent of total state employment.

Seven of the 10 states with the highest job losses (as a share of total employment) are in the Midwest or Southeast, in states where manufacturing (especially of motor vehicles and parts) predominates: Michigan (214,600 jobs lost, equal to 5.12 percent), Indiana (103,800 jobs, 3.54 percent), Kentucky (53,700 jobs, 2.92 percent), Alabama (46,000 jobs, 2.32 percent), Tennessee (61,000 jobs, 2.19 percent), Ohio (112,500 jobs, 2.16 percent), and Mississippi (22,000 jobs, 1.86 percent). Other hard-hit states in the top 10 were Oklahoma (35,300 jobs, 2.10 percent), Wyoming (6,800 jobs, 2.34 percent), and Alaska (6,300 jobs, 1.83 percent), all of which have been hard hit by the collapse of the oil industry and related sectors.

The U.S. trade deficit with TPP member countries in 2015 produced net job losses in all but two U.S. congressional districts. The 11th Congressional District in Michigan was the hardest-hit district in the country, ranked in terms of jobs eliminated as a share of total district employment, losing 26,200 jobs (7.66 percent of total employment). In the 20 congressional districts with the largest shares of jobs lost, net losses ranged from 11,400 to 26,200 jobs, and jobs lost as a share of overall employment ranged from 3.89 percent to 7.66 percent. Michigan had 10 districts in the top 20 job-losing districts, followed by Indiana (five districts); California (two districts); and Ohio, Alabama, and Tennessee (one district each).

These stark figures highlight how much damage the U.S. economy and American workers have already suffered from growing trade deficits with TPP member countries.

And we have seen this picture before—it’s similar to the economic impact that followed the North American Free Trade Agreement (NAFTA), but this time the stakes are higher and the costs more severe (Scott 2013). Prior to NAFTA, the United States sustained balanced trade with Mexico (Scott 2011). The U.S. trade deficit with Mexico took off only after NAFTA was adopted, further demonstrating the degree to which these unfair trade and investment agreements negatively affect the U.S. economy. With the TPP countries, the United States is already starting behind with a trade deficit of $177.9 billion. As a result, the TPP trade and investment deal is likely to be significantly more costly to the U.S. economy.

In this context the United States should insist that currency manipulation be directly addressed in the core of the TPP agreement. Member governments of the TPP should also agree to rebalance trade and currency markets, including by divesting excess foreign assets in their 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, wages, and job loss

A considerable body of trade policy research has established connections between currency manipulation, trade deficits, job losses, and wages. These connections are heightened in an era of incomplete recovery from the Great Recession. This section provides a broad overview of the connections and introduces proposed new approaches for intervening when currency management unfairly threatens U.S. jobs and wages.

The effect of exchange rates on imports and exports

Exchange rates measure the value of a country’s currency relative to other currencies (Nelson 2013). The nominal exchange rate is simply the rate at which one currency can be exchanged for another. Exchange rates are used to calculate the value of foreign goods, services, and assets in terms of U.S. dollars. Thus, consumers and businesses in the United States use exchange rates to compute the cost of Japanese and Korean cars in terms of U.S. dollars. In the same way, consumers and businesses in Japan and South Korea use exchange rates to calculate the cost of U.S. cars in Japanese yen or Korean dollars.

Exchange rates are determined by the relative supply and demand for currencies in foreign exchange (FX) markets. The current constellation of trade imbalances is primarily the result of governments that use intentional policies, especially official purchases of foreign assets (public financial flows), to influence exchange rates (Gagnon 2013). This is the basic tool of currency manipulators. They purchase foreign assets such as U.S. treasuries to increase demand for the U.S. dollar, which increases the value of the dollar relative to their own currency.

The price of all of a country’s exports and imports are strongly influenced by the exchange rate; it is one of the most fundamental prices in the economy. Therefore, changes in the exchange rate can have a large impact on the level of imports and exports, and on the trade balance. When a country’s exchange rate declines, relative to other currencies (a depreciation or devaluation), its exports become cheaper in foreign markets, and imports from other countries become more expensive. Over time, devaluation will increase the level of exports and reduce the level of imports. (More on how currency manipulation affects employment levels can be found in Scott 2014c).

How trade deficits affect jobs

In turn, the levels of exports and imports have an effect on employment. 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 changes in the trade balance. An improving trade balance will, all else equal, support job creation, while growing trade deficits will result in growing net U.S. job displacement.

How trade deficits affect wages

For example EPI research has shown that growing U.S. trade deficits with China pushed American workers out of good jobs with excellent wages, primarily in manufacturing industries, into lower-paying jobs in nontraded industries, or into unemployment. Growing trade deficits with China between 2001 and 2011 resulted in the net loss of at least $13,505 per displaced worker in 2011 alone. For all displaced workers, using education group averages, net wage losses totaled $37 billion (Scott 2013).

Direct trade, job, and wage losses are just the tip of the iceberg when it comes to the cost of trade deficits, and globalization more broadly, for American workers. Using standard models to benchmark the cost of globalization for American workers without a college degree, Bivens (2013) estimated that in 2011, trade with low-wage countries lowered wages by 5.5 percent—roughly $1,800 for a full-time, full-year worker without a college degree. These losses were experienced by all American workers without a college degree, who make up about two-thirds of the labor force or roughly 100 million U.S. workers.

Adding the TPP to a low-demand economy would aggravate chronic trade deficits

The United States has run chronic trade deficits for well over a decade. Since 2002, these deficits have been overwhelmingly driven by the conscious policy choices made by several of our major trading partners to manage the value of their currency for competitive advantage in U.S. and global markets. (Gagnon 2013; Bayoumi, Gagnon, and Saborowski 2014; Bergsten and Gagnon 2012; Krugman 2009; Scott 2014c). They buy dollar-denominated assets to boost the value of the dollar and depress the value of their own currencies.

More than 20 countries, led by China, have, together, been spending about $1 trillion per year buying foreign assets in order to artificially suppress the value of their currencies (Scott 2014c). Several of this group—including Malaysia, Singapore, and Japan—are currently members of the TPP and several others—including South Korea, Taiwan, and China—have expressed interest in joining. In addition, Vietnam, which is part of the proposed TPP, has been accumulating foreign-exchange reserves over the past decade. Vietnam has seen its current account surplus, the broadest measure of its trade surplus with the world, rise to an estimated 4.9 percent of GDP in 2014 (IMF 2015 and 2016); in short it is behaving like the other currency manipulators in the TPP.

As Bivens (2016) notes, the threat posed by allowing currency manipulation to go unchecked is heightened in the current context of a U.S. economy not fully recovered from the Great Recession. Despite efforts by the Federal Reserve to bring the economy back to full employment, the U.S. economy has been stuck well below potential for more than eight years. Worse yet, there is widespread evidence that the shortfall in demand that has delayed a full recovery from the Great Recession could last for years to come (Bivens 2016 citing Krugman 2013; Summers 2014).

Economic history shows that such prolonged downturns are quite possible in advanced economies: Japan has been stuck below potential output for decades, and Western Europe is experiencing a double-dip recession because it failed to adequately boost aggregate demand. In the United States, fiscal policy has been notably contractionary since 2011, and the Fed has just raised short-term interest rates for the first time in more than a decade. The Fed’s mistaken rate increase in the face of chronic demand shortfalls means that we are now going in the wrong direction on both fiscal and monetary policy. Thus, a prolonged period of policy-induced, chronic demand shortfall or “secular stagnation” now seems likely in the United States and much of the developed world. For these reasons, more sensible exchange rate policies are needed now more than ever. (Bivens 2016)

Given that the economy is not at full employment and that there is no automatic mechanism that can return it there quickly due to our fiscal and monetary choices, trade flows can have a powerful influence on aggregate demand. Thus, ending the currency manipulation that has thrown U.S. trade flows out of whack is a crucially important goal for macroeconomic stabilization in coming years.

Responses to currency manipulation are gaining traction

Several policy alternatives for ending currency manipulation have already been proposed in Congress, including the Ryan-Murphy Currency Reform and Fair Trade Act (H.R. 2378), which would “clearly define currency manipulation as an illegal subsidy and authorize the Commerce Department to address currency manipulation in countervailing duty (CVD) complaints” (Scott 2014c, 16). In 2010, the House of Representatives approved the Ryan-Murphy act, but the Senate failed to pass a complementary measure (S.1027) in the 111th Congress (OpenCongress.org 2009). In 2011, the Senate was successful in passing the Currency Exchange Rate Oversight Reform Act (S.1619). Together, these bills would address currency manipulation by imposing tariffs on countries with undervalued currency.

Similar legislation was introduced in both houses of Congress in 2013, including the Currency Reform and Fair Trade Act (H.R. 1276) in the House and the Currency Exchange Rate Oversight Reform Act (S. 1114) in the Senate. Both bills have gained considerable bipartisan support in both houses of Congress and would produce the economic and political pressure needed to hold currency manipulators accountable.

In addition to legislative action, taxing or offsetting the acquisition of foreign assets and foreign exchange by currency manipulators is an effective policy tool for stopping currency manipulation. In the case of China, the world’s biggest currency manipulator and possible future member of the TPP, Gagnon and Hufbauer (2011) suggest that “the U.S. government should tax the income (the interest payments) earned on Chinese holding of U.S. financial assets.” This form of taxation is especially potent considering that China was holding $3.4 trillion in foreign exchange assets as of the end of December 2015 (Bloomberg 2016), about two-thirds of which are made up of U.S. Treasury bonds and other U.S. government assets.

Finally, trade agreements such as the TPP have the capacity to address currency manipulation by setting important precedents for international trade and financial regulations. Bipartisan majorities in both houses of Congress recognized the opportunity to make progress on currency manipulation through the Trans-Pacific Partnership. In June 2013, more than half of the U.S. House of Representatives signed a letter to President Obama urging that the TPP agreement include “currency disciplines” that would “bolster our ongoing efforts to respond to these trade-distorting policies” (Congress of the United States 2013). In September 2013, 60 senators signed a similar letter (United States Senate 2013) calling for “strong and enforceable foreign currency manipulation disciplines” in the “TPP and all future trade agreements.” Despite these clearly expressed desires on the part of majorities in both houses of Congress, enforceable currency disciplines where not included in the core of the proposed TPP agreement. The only progress made on addressing currency manipulation was a side pact among finance officials from the 12 countries that included promises to avoid “unfair currency practices and refrain from competitive devaluation,” and to provide a range of data on foreign-exchange holdings. But that agreement will not be subject to the TPP’s enforcement mechanisms. By not pushing to include penalties against currency manipulators in the core of the agreement, the United States has missed an opportunity to establish fair trade standards, protect American workers, and address the high and rapidly growing trade deficit.

In addition to including high standards designed to prohibit currency manipulation by TPP member countries in the future, more should have been done to eliminate existing currency manipulation as a precondition for membership, as noted by Scott (2015). Members of the TPP should have also agreed 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 have forsworn the use of currency manipulation in the future, while also agreeing to submit to strong, binding currency disciplines in the event that these commitments are violated.

To quantify that missed opportunity, this report adds to the research on the costs of currency manipulation. Bergsten and Gagnon (2012) have estimated that currency manipulation by more than 20 countries had increased global trade (current account) surpluses of intervening countries by between $400 billion and $800 billion per year. They have also estimated that the “largest loser is the United States, whose trade and current account deficits have been $200 to $500 billion per year larger as a result” (Bergsten and Gagnon 2012, 2). Building on this research, Scott (2014c) found that eliminating currency manipulation could create between 1.0 million and 5.8 million U.S. jobs.

What role could rewriting the terms of the TPP to end currency manipulation by TPP members play? Consider that Bergsten and Gagnon’s list of currency manipulators includes several important members of the TPP (Japan, Malaysia, and Singapore), and several countries that have expressed interest in joining the agreement at some point (China, South Korea, and Taiwan). In addition, although TPP member Vietnam is a low-income country (and was thus excluded from Bergsten and Gagnon’s list), it is following the lead of other currency manipulators and acquiring excess foreign exchange reserves and achieving a large trade surplus. While some have argued that China and other countries are not presently manipulating their currencies, nothing in the agreement would prevent these countries from engaging in massive interventions again in the future, thereby nullifying any potential benefits of tariff and nontariff trade barrier reductions and other provisions included in the TPP.

Also, there are significant risks that currency manipulation by China and other TPP neighbors would increase pressure on many of the TPP countries to either initiate or increase the degree to which they engage in currency manipulation, and thereby nullify the benefits of the TPP to the United States. Artificial reductions in the values of the currencies of our TPP trading partners would increase U.S. trade deficits and job losses, and reduce GDP growth.

Estimating the impact of currency manipulation by TPP member countries on the United States

Currency manipulation is the most important cause of the large and growing U.S. goods trade deficit with the group of countries in the Trans-Pacific Partnership. Coupled with the fact that the United States is the largest and most reliable trading partner for many of the TPP countries, this is a recipe for U.S. pain at others’ gain. But for the subsidies provided by currency manipulation, Japanese automakers, for example, 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 the markets of currency manipulators for many products, due to the effective tax imposed on U.S. products by currency manipulation.

As shown in Table 1, the U.S. goods trade deficit with TPP member countries reached $177.9 billion in 2015. Using a simple macroeconomic model developed by Bivens (2014), we estimate the effects of this trade deficit on U.S. GDP and employment, including respending effects. (The approach is also based, in part, on the models developed in Scott 2014b.) The macroeconomic model estimates the amount of labor (i.e., 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. Within that model, we use an input-output (IO) model to determine the distribution of jobs supported by exports and the jobs eliminated by imports in the U.S. economy. (See the appendix methodology section below for further details on the model structure and data sources used in this study). By providing estimates of the direct and indirect labor requirements of producing output in a given domestic industry, the model tells us each industry’s share of the overall jobs lost. The IO employment model is based on the Bureau of Labor Statistics’ (BLS) employment requirements matrix (ERM), which includes 195 U.S. industries, 77 of which are in the manufacturing sector (see the appendix for details on model structure and data sources). This paper assumes that currency manipulation is the primary cause of the U.S. goods trade deficit with Japan, Malaysia, Singapore, and Vietnam; deficits with these four countries make up the majority of the U.S. trade deficit with the TPP countries overall.

Table 1 U.S. trade with TPP countries and effect on GDP, 2015 (billions of dollars) Exports $680.1 Imports $858.0 Trade balance -$177.9 U.S. GDP $17,937.8 U.S. GDP lost as a result of TPP trade deficit -$284.6 U.S. GDP lost to TPP as a share of GDP -1.6% Note: The GDP effect of the trade deficit is estimated by multiplying the trade deficit by 1.6 because, following Bivens (2014), $1 billion in gained/lost GDP will result in $1.6 billion of economic activity. Source: Authors’ analysis of Bivens (2014), U.S. Census Bureau (2013), U.S. International Trade Commission (USITC 2016), Bureau of Labor Statistics (BLS 2016a and 2016b), and BLS Employment Projections program (BLS-EP 2014a, 2014b, and 2015). For a more detailed explanation of data sources and computations, see the appendix. Share on Facebook Tweet this chart Embed Copy the code below to embed this chart on your website. Download image

Jobs eliminated by the U.S. goods trade deficit with TPP member countries directly decrease total employment in trade-related industries, especially those in manufacturing. The IO model also estimates the number of “indirect” jobs supported or eliminated in supplier industries, including those in manufacturing, and in related service sectors. Finally, wages that would have been earned by the jobs people would have held had trade with the TPP member countries been balanced would have supported additional rounds of “respending,” which would have a multiplier effect on output (GDP) and employment.

A breakdown of the jobs eliminated by the U.S. trade deficit with the TPP countries

Note: Tables 3 through 8 are available at the end of this report.

Using data on U.S. imports from and exports to the 11 other TPP member countries in 2015, coupled with the models developed in this paper, we estimate the total impact of TPP trade on U.S. GDP and the total number of jobs lost. The $177.9 billion U.S. goods trade deficit with the 11 other TPP countries reduced U.S. GDP by $284.6 billion (1.6 percent) in 2015, as shown in Table 1. This analysis includes both the direct effect of the trade deficit on U.S. GDP (-$177.9 billion), and the multiplier or respending effect ($106.7 billion or 60 percent, not shown in Table 1).

The U.S. trade deficit with the 11 other TPP countries eliminated 2 million jobs, as shown in Table 2, which reports the number of direct, indirect, and respending jobs lost (aggregated over all industries). The trade deficit between the United States and the 11 other TPP member countries in 2015 directly eliminated 418,900 jobs. In addition to the direct jobs lost, the U.S. trade deficit with the TPP country group eliminated an additional 847,200 indirect jobs in supplier industries, including jobs in manufacturing, commodity, and service industries. Finally, wages lost because of direct and indirect job cuts from the trade deficits with the TPP member countries would have supported an additional 759,700 respending jobs. The direct, indirect, and respending jobs displaced by the U.S. trade deficit with TPP member countries totals 2,025,800 jobs lost.

Table 2 Number of jobs eliminated by U.S. goods trade deficit with TPP countries, 2015 Direct + indirect jobs 1,266,100 Direct jobs 418,900 Indirect jobs 847,200 Respending jobs 759,700 Total jobs lost 2,025,800 Source: Authors’ analysis of Bivens (2014), U.S. Census Bureau (2013), U.S. International Trade Commission (USITC 2016), Bureau of Labor Statistics (BLS 2016a and 2016b), and BLS Employment Projections program (BLS-EP 2014a, 2014b, and 2015). For a more detailed explanation of data sources and computations, see the appendix. Share on Facebook Tweet this chart Embed Copy the code below to embed this chart on your website. Download image

Job losses and gains by industry

U.S. imports from and exports to the 11 other TPP member countries in 2015 were used to estimate the distribution of net jobs (direct, indirect, and respending) eliminated by the U.S. trade deficit with the TPP member countries 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 lost or gained as a share of industry employment (U.S. Census Bureau 2013).

Table 3 provides a snapshot of the U.S. goods trade balance with the TPP countries in 2015. The United States had trade surpluses with the TPP countries in some industries and deficits in most others. Most of the surpluses occurred in the category of manufactured products referred to as “industrial supplies,” in which the United States had an overall surplus of $46.7 billion in 2015. The largest surpluses occurred in chemicals ($25.3 billion), petroleum and coal products ($19.1 billion, predominantly refined petroleum products), and plastics and rubber products ($5.9 billion). On the other hand, the United States was a net importer of crude oil and gas (-$58.0 billion) from the TPP in 2015. Thus, the United States has become a specialist in the production of basic chemical products and refined petroleum products that are used in other countries to make final products (for example, toys and tires) that are then re-exported back to the United States.

Completing the picture of manufacturing trade with the TPP, the United States has a small trade deficit in nondurable goods of -$16.2 billion, which includes trade surpluses in textiles ($3.3 billion) and food products ($1.8 billion), and a sizeable deficit in apparel, -$13.3 billion. The surplus in textiles reflects, in part, the NAFTA rules of origin, which favor fabric that originates within North America in NAFTA apparel trade. Rules of origin have been substantially weakened in many sectors in the TPP, and it is unclear if the United States will retain its net trade advantage in textile products if the TPP is approved and implemented.

By far the vast majority of the U.S. trade deficit with the TPP countries is in the durable goods industries (-$151.3 billion, 85.0 percent of the total TPP deficit). This deficit, in turn, is dominated by the trade deficit in motor vehicles and parts (-$118.7 billion), computer and electronic parts (-$27.8 billion), and primary metals (-$13.0 billion). Large trade deficits in these sectors explain a large share of the jobs lost due to trade with the TPP countries, as shown in Table 4. It is important to note that durable goods industries such as motor vehicles, computer and electronic parts (including communications, audio, and video equipment), and primary metals industries (including basic steel and steel products) provide large numbers of good jobs with high wages and excellent benefits, especially for workers without a college education. These are the sectors that have been hardest hit by the TPP trade deficit, as shown below.

Overall, the U.S. trade deficit with the 11 other TPP members eliminated 1,057,200 jobs in manufacturing (52.2 percent of jobs lost across all industries), by far the largest number of jobs lost in any major industry, as shown in Table 4. Within manufacturing, the largest losses occurred in motor vehicles and parts, which lost 738,300 jobs (36.4 percent of total jobs lost). Other manufacturing industries with large losses include apparel (181,900 jobs, 9 percent) and computer and electronic parts (163,900 jobs, 8.1 percent). Trade with TPP member countries did contribute to employment in a few manufacturing industries including chemicals (105,400 jobs created); machinery (66,900 jobs); fabricated metal products (55,700 jobs); plastics and rubber products (40,200 jobs); printed matter and related products (21,800 jobs); and petroleum and coal products (20,900 jobs).

In the case of petroleum and coal products, chemicals, plastics, and rubber, while high-wage jobs were created in these industries, the products derived from petroleum and natural gas are also associated with the generation of large amounts of toxic byproducts which have resulted in increased air and water pollution that is most concentrated at domestic production sites. Over the last 10 years, the United States has, in effect, imported pollution and exported chemical products for the production of manufactured goods in other countries. These developments are a byproduct of the rapid development of oil and gas fracking in the United States, which has dramatically increased the supply and reduced the prices of natural gas and related petroleum byproducts.

The U.S.–TPP trade deficit was also responsible for significant job losses outside of manufacturing, in agricultural industries (41,600 jobs); mining (182,800 jobs); utilities (8,400 jobs); wholesale trade (26,700 jobs); retail trade (142,800 jobs); transportation and warehousing (17,900 jobs); information (19,000 jobs); finance and insurance (42,700 jobs); real estate and rental and leasing (16,500 jobs); professional, scientific, and technical services (10,700 jobs); administrative and support and waste management and remediation services (6,900 jobs); education services (37,300 jobs); health care and social services (204,200 jobs); arts, entertainment, and recreation (23,000 jobs); accommodation and food services (101,800 jobs); other services (except public administration) (70,700 jobs); and public administration (15,700 jobs). These jobs losses reflect the combined effects of both indirect jobs loss and respending effects, which reduced the demand for services.

Job losses and gains by state and congressional district

Estimates of job losses by industry form the foundation for the estimates 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 used as the baseline for estimating jobs lost as a share of total state or district employment. Thus, states and congressional districts that have high shares of employment in industries with a large exposure to trade with the TPP member countries (such as motor vehicles and equipment, apparel, or computer and electric parts) were the biggest losers from the trade deficit between the United States and TPP member countries in 2015.

The U.S. trade deficit with the 11 other TPP member countries in 2015 produced net job losses in all 50 states and the District of Columbia. Jobs lost by state, ranked by jobs lost as a share of total state employment, are reported in Table 5a. (Table 5b ranks the states by net jobs displaced and Table 5c lists them alphabetically). Michigan lost the most jobs as a share of total state employment, with 214,600 jobs lost (5.12 percent of total state employment in 2011). Seven of the 10 states with the highest job loss shares are in the Midwest or Southeast census regions, all states where manufacturing predominates. After Michigan they include Indiana (103,800 jobs or 3.54 percent ), Kentucky (53,700 jobs, or 2.92 percent), Alabama (46,000 jobs, 2.32 percent), Tennessee (61,000 job, 2.19 percent), Ohio (112,500 jobs, 2.16 percent), and Mississippi (22,000 jobs, 1.86 percent). Rounding out the top 10 states losing the largest shares of jobs were Wyoming (6,800 jobs, 2.34 percent), Oklahoma (35,300 jobs, 2.1 percent), and Alaska (6,300 jobs, 1.83 percent). The distribution of job losses in the 50 states and the District of Columbia is shown in the map in Figure A. 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 the 11 other TPP member countries in 2015.

This study also estimates trade-related employment changes by congressional district for the 114th Congress (elected in 2014), using 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 B. 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 TPP countries.

Figure B Net U.S. jobs eliminated by U.S. trade deficit with TPP countries, by congressional district, 2015 (as a share of total district employment) Rank (by share of employment displaced) State District Net jobs displaced District employment (in 2011) Jobs displaced as a share of district employment 308 Alabama 1 2,600 283,000 0.92% 60 Alabama 2 6,000 276,900 2.17% 13 Alabama 3 12,100 274,600 4.41% 30 Alabama 4 7,700 262,900 2.93% 100 Alabama 5 5,300 311,900 1.70% 82 Alabama 6 5,900 318,400 1.85% 41 Alabama 7 6,400 253,500 2.52% 86 Alaska Statewide 6,300 344,300 1.83% 419 Arizona 1 1,500 264,900 0.57% 421 Arizona 2 1,600 299,200 0.53% 337 Arizona 3 2,200 262,200 0.84% 354 Arizona 4 1,900 233,500 0.81% 323 Arizona 5 2,800 317,900 0.88% 315 Arizona 6 3,300 366,000 0.90% 294 Arizona 7 2,700 282,300 0.96% 386 Arizona 8 2,200 301,700 0.73% 369 Arizona 9 2,800 360,300 0.78% 66 Arkansas 1 5,800 277,400 2.09% 140 Arkansas 2 4,800 336,300 1.43% 210 Arkansas 3 3,800 327,000 1.16% 78 Arkansas 4 5,600 295,100 1.90% 267 California 1 2,700 260,300 1.04% 204 California 2 3,800 323,100 1.18% 328 California 3 2,500 286,600 0.87% 321 California 4 2,600 294,200 0.88% 241 California 5 3,600 326,800 1.10% 372 California 6 2,200 288,300 0.76% 340 California 7 2,600 313,200 0.83% 286 California 8 2,300 235,500 0.98% 275 California 9 2,800 275,300 1.02% 341 California 10 2,300 277,200 0.83% 305 California 11 3,000 324,200 0.93% 271 California 12 4,100 399,400 1.03% 235 California 13 3,800 340,200 1.12% 233 California 14 4,100 364,000 1.13% 142 California 15 4,800 336,400 1.43% 201 California 16 2,900 244,900 1.18% 69 California 17 7,000 346,100 2.02% 138 California 18 5,000 344,500 1.45% 125 California 19 5,000 324,000 1.54% 220 California 20 3,500 302,500 1.16% 172 California 21 3,200 243,800 1.31% 278 California 22 2,900 289,600 1.00% 49 California 23 6,600 274,100 2.41% 282 California 24 3,200 323,500 0.99% 260 California 25 3,200 302,700 1.06% 208 California 26 3,800 325,900 1.17% 120 California 27 5,200 332,200 1.57% 159 California 28 4,900 359,900 1.36% 192 California 29 3,700 303,700 1.22% 179 California 30 4,600 358,200 1.28% 148 California 31 4,100 292,200 1.40% 127 California 32 4,500 293,800 1.53% 203 California 33 4,300 364,200 1.18% 17 California 34 12,800 309,400 4.14% 64 California 35 6,000 284,800 2.11% 361 California 36 2,000 251,900 0.79% 107 California 37 5,500 335,600 1.64% 103 California 38 5,200 313,300 1.66% 167 California 39 4,400 332,000 1.33% 15 California 40 12,100 280,500 4.31% 116 California 41 4,300 271,900 1.58% 189 California 42 3,800 307,000 1.24% 123 California 43 4,700 302,800 1.55% 47 California 44 6,600 270,600 2.44% 221 California 45 4,100 354,400 1.16% 121 California 46 4,900 314,400 1.56% 171 California 47 4,300 327,600 1.31% 145 California 48 5,000 352,600 1.42% 119 California 49 4,700 299,700 1.57% 335 California 50 2,500 296,200 0.84% 214 California 51 3,000 258,600 1.16% 207 California 52 4,100 350,100 1.17% 334 California 53 2,900 342,700 0.85% 257 Colorado 1 4,100 384,400 1.07% 258 Colorado 2 4,100 384,600 1.07% 144 Colorado 3 4,700 331,400 1.42% 97 Colorado 4 6,000 344,100 1.74% 402 Colorado 5 2,100 315,900 0.66% 307 Colorado 6 3,400 369,600 0.92% 218 Colorado 7 4,200 362,500 1.16% 420 Connecticut 1 1,900 349,800 0.54% 429 Connecticut 2 1,500 348,600 0.43% 400 Connecticut 3 2,400 352,700 0.68% 387 Connecticut 4 2,500 343,000 0.73% 416 Connecticut 5 2,000 348,300 0.57% 430 Delaware Statewide 1,800 420,400 0.43% 423 DC Statewide 1,500 310,600 0.48% 363 Florida 1 2,400 303,900 0.79% 292 Florida 2 2,900 301,500 0.96% 388 Florida 3 2,000 277,000 0.72% 324 Florida 4 2,900 329,900 0.88% 356 Florida 5 2,300 284,000 0.81% 370 Florida 6 2,200 283,200 0.78% 391 Florida 7 2,300 322,500 0.71% 296 Florida 8 2,700 283,400 0.95% 364 Florida 9 2,500 317,200 0.79% 409 Florida 10 2,100 331,500 0.63% 288 Florida 11 2,100 217,400 0.97% 355 Florida 12 2,300 283,200 0.81% 256 Florida 13 3,300 309,200 1.07% 336 Florida 14 2,700 320,700 0.84% 378 Florida 15 2,300 304,200 0.76% 374 Florida 16 2,100 276,100 0.76% 371 Florida 17 1,900 248,700 0.76% 273 Florida 18 2,900 284,000 1.02% 379 Florida 19 2,000 265,200 0.75% 293 Florida 20 2,900 302,100 0.96% 376 Florida 21 2,400 316,800 0.76% 291 Florida 22 3,200 332,000 0.96% 311 Florida 23 3,100 339,900 0.91% 295 Florida 24 2,800 293,400 0.95% 298 Florida 25 3,100 326,000 0.95% 380 Florida 26 2,500 335,600 0.74% 358 Florida 27 2,500 313,600 0.80% 432 Georgia 1 1,000 286,100 0.35% 254 Georgia 2 2,700 251,200 1.07% 90 Georgia 3 5,200 285,800 1.82% 316 Georgia 4 2,800 311,700 0.90% 365 Georgia 5 2,500 318,100 0.79% 287 Georgia 6 3,500 361,200 0.97% 153 Georgia 7 4,300 312,500 1.38% 270 Georgia 8 2,800 272,700 1.03% 139 Georgia 9 4,100 284,600 1.44% 169 Georgia 10 3,800 287,400 1.32% 348 Georgia 11 2,800 340,900 0.82% 223 Georgia 12 3,200 278,200 1.15% 280 Georgia 13 3,100 312,800 0.99% 427 Georgia 14 1,300 290,700 0.45% 312 Hawaii 1 3,000 330,100 0.91% 395 Hawaii 2 2,100 299,400 0.70% 194 Idaho 1 4,000 329,900 1.21% 327 Idaho 2 3,100 355,000 0.87% 206 Illinois 1 3,400 290,200 1.17% 124 Illinois 2 4,300 278,200 1.55% 268 Illinois 3 3,300 319,500 1.03% 152 Illinois 4 4,500 326,600 1.38% 357 Illinois 5 3,200 397,600 0.80% 88 Illinois 6 6,500 355,600 1.83% 302 Illinois 7 2,800 298,500 0.94% 143 Illinois 8 5,200 366,300 1.42% 122 Illinois 9 5,400 347,200 1.56% 238 Illinois 10 3,600 324,800 1.11% 131 Illinois 11 5,200 347,300 1.50% 158 Illinois 12 4,100 301,000 1.36% 359 Illinois 13 2,600 326,600 0.80% 156 Illinois 14 4,800 351,000 1.37% 40 Illinois 15 8,100 316,500 2.56% 150 Illinois 16 4,600 330,800 1.39% 188 Illinois 17 3,900 311,700 1.25% 277 Illinois 18 3,400 337,500 1.01% 51 Indiana 1 7,300 310,600 2.35% 7 Indiana 2 17,900 317,800 5.63% 10 Indiana 3 16,900 327,000 5.17% 18 Indiana 4 13,300 328,500 4.05% 80 Indiana 5 6,700 357,700 1.87% 12 Indiana 6 14,700 311,900 4.71% 111 Indiana 7 5,100 312,200 1.63% 20 Indiana 8 12,800 329,300 3.89% 36 Indiana 9 9,100 339,400 2.68% 174 Iowa 1 5,100 392,300 1.30% 165 Iowa 2 5,000 373,400 1.34% 300 Iowa 3 3,700 390,800 0.95% 118 Iowa 4 6,000 382,300 1.57% 134 Kansas 1 5,100 345,900 1.47% 246 Kansas 2 3,700 339,900 1.09% 168 Kansas 3 4,900 370,300 1.32% 436 Kansas 4 -200 332,900 -0.06% 24 Kentucky 1 10,200 284,800 3.58% 21 Kentucky 2 12,200 317,100 3.85% 81 Kentucky 3 6,200 333,300 1.86% 48 Kentucky 4 8,100 333,500 2.43% 31 Kentucky 5 6,800 234,300 2.90% 29 Kentucky 6 10,200 335,400 3.04% 219 Louisiana 1 4,100 354,000 1.16% 411 Louisiana 2 2,000 329,000 0.61% 154 Louisiana 3 4,500 328,100 1.37% 39 Louisiana 4 8,000 311,100 2.57% 272 Louisiana 5 2,900 283,900 1.02% 329 Louisiana 6 3,200 367,800 0.87% 322 Maine 1 3,000 340,400 0.88% 105 Maine 2 5,000 302,700 1.65% 314 Maryland 1 3,100 342,300 0.91% 345 Maryland 2 2,900 351,700 0.82% 417 Maryland 3 2,100 369,500 0.57% 412 Maryland 4 2,300 384,100 0.60% 404 Maryland 5 2,400 368,200 0.65% 390 Maryland 6 2,600 363,200 0.72% 401 Maryland 7 2,100 315,700 0.67% 405 Maryland 8 2,600 400,100 0.65% 414 Massachusetts 1 2,000 341,000 0.59% 366 Massachusetts 2 2,800 356,500 0.79% 176 Massachusetts 3 4,600 355,400 1.29% 265 Massachusetts 4 3,900 374,800 1.04% 343 Massachusetts 5 3,200 387,400 0.83% 253 Massachusetts 6 4,000 372,000 1.08% 385 Massachusetts 7 2,700 369,800 0.73% 344 Massachusetts 8 3,100 375,600 0.83% 262 Massachusetts 9 3,700 352,300 1.05% 94 Michigan 1 5,100 290,200 1.76% 14 Michigan 2 13,900 315,900 4.40% 22 Michigan 3 11,800 315,300 3.74% 27 Michigan 4 9,100 286,300 3.18% 16 Michigan 5 11,400 264,800 4.31% 28 Michigan 6 9,700 310,400 3.13% 5 Michigan 7 17,500 299,100 5.85% 4 Michigan 8 20,400 330,800 6.17% 3 Michigan 9 21,900 326,100 6.72% 2 Michigan 10 22,400 308,700 7.26% 1 Michigan 11 26,200 342,100 7.66% 6 Michigan 12 18,000 313,800 5.74% 9 Michigan 13 12,900 230,700 5.59% 8 Michigan 14 14,500 257,700 5.63% 259 Minnesota 1 3,700 348,200 1.06% 155 Minnesota 2 4,900 358,300 1.37% 281 Minnesota 3 3,500 353,800 0.99% 339 Minnesota 4 2,800 336,000 0.83% 360 Minnesota 5 2,800 352,000 0.80% 245 Minnesota 6 3,800 348,700 1.09% 180 Minnesota 7 4,200 328,700 1.28% 247 Minnesota 8 3,300 303,400 1.09% 43 Mississippi 1 7,600 305,600 2.49% 76 Mississippi 2 5,100 266,900 1.91% 74 Mississippi 3 6,000 303,900 1.97% 250 Mississippi 4 3,300 304,900 1.08% 274 Montana Statewide 4,900 480,000 1.02% 248 Missouri 1 3,600 331,500 1.09% 362 Missouri 2 3,000 378,600 0.79% 200 Missouri 3 4,400 370,000 1.19% 185 Missouri 4 4,100 324,900 1.26% 151 Missouri 5 4,800 345,300 1.39% 58 Missouri 6 7,800 355,900 2.19% 117 Missouri 7 5,300 337,400 1.57% 68 Missouri 8 6,100 298,500 2.04% 202 Nebraska 1 3,800 321,700 1.18% 333 Nebraska 2 2,700 316,300 0.85% 224 Nebraska 3 3,500 305,600 1.15% 326 Nevada 1 2,500 284,700 0.88% 392 Nevada 2 2,200 309,400 0.71% 381 Nevada 3 2,500 336,500 0.74% 398 Nevada 4 1,900 274,300 0.69% 290 New Hampshire 1 3,400 352,600 0.96% 196 New Hampshire 2 4,000 332,200 1.20% 320 New Jersey 1 3,000 339,200 0.88% 383 New Jersey 2 2,400 324,400 0.74% 367 New Jersey 3 2,700 344,200 0.78% 331 New Jersey 4 2,800 326,400 0.86% 284 New Jersey 5 3,500 356,100 0.98% 264 New Jersey 6 3,700 353,600 1.05% 303 New Jersey 7 3,500 377,100 0.93% 177 New Jersey 8 4,800 371,000 1.29% 244 New Jersey 9 3,700 338,500 1.09% 289 New Jersey 10 3,000 310,700 0.97% 338 New Jersey 11 3,000 358,800 0.84% 426 New Jersey 12 1,600 352,400 0.45% 384 New Mexico 1 2,300 311,900 0.74% 130 New Mexico 2 4,100 273,100 1.50% 89 New Mexico 3 5,200 284,800 1.83% 352 New York 1 2,800 343,300 0.82% 306 New York 2 3,300 357,800 0.92% 318 New York 3 3,000 336,700 0.89% 350 New York 4 2,800 342,500 0.82% 242 New York 5 3,700 336,200 1.10% 210 New York 6 3,800 327,000 1.16% 92 New York 7 5,800 322,200 1.80% 212 New York 8 3,400 292,700 1.16% 283 New York 9 3,200 324,900 0.98% 310 New York 10 3,300 360,300 0.92% 229 New York 11 3,600 317,500 1.13% 285 New York 12 4,100 418,800 0.98% 266 New York 13 3,300 317,200 1.04% 226 New York 14 3,900 341,800 1.14% 205 New York 15 3,000 255,900 1.17% 304 New York 16 3,000 323,600 0.93% 313 New York 17 3,100 341,400 0.91% 368 New York 18 2,600 332,100 0.78% 332 New York 19 2,800 327,300 0.86% 396 New York 20 2,500 357,600 0.70% 186 New York 21 3,900 309,200 1.26% 243 New York 22 3,500 320,200 1.09% 77 New York 23 6,200 324,600 1.91% 213 New York 24 3,800 327,300 1.16% 191 New York 25 4,100 335,400 1.22% 216 New York 26 3,800 327,700 1.16% 128 New York 27 5,100 337,800 1.51% 231 North Carolina 1 3,300 291,800 1.13% 113 North Carolina 2 4,900 303,800 1.61% 349 North Carolina 3 2,500 305,600 0.82% 342 North Carolina 4 2,900 350,900 0.83% 56 North Carolina 5 7,300 324,500 2.25% 62 North Carolina 6 7,300 341,800 2.14% 373 North Carolina 7 2,400 315,400 0.76% 71 North Carolina 8 6,000 301,700 1.99% 263 North Carolina 9 3,900 371,400 1.05% 67 North Carolina 10 6,700 324,000 2.07% 87 North Carolina 11 5,400 295,400 1.83% 170 North Carolina 12 4,200 319,800 1.31% 236 North Carolina 13 3,900 349,900 1.11% 215 North Dakota Statewide 4,300 370,800 1.16% 197 Ohio 1 4,000 332,300 1.20% 149 Ohio 2 4,500 323,600 1.39% 129 Ohio 3 5,000 333,000 1.50% 11 Ohio 4 16,300 317,900 5.13% 23 Ohio 5 12,400 334,200 3.71% 72 Ohio 6 5,800 292,300 1.98% 37 Ohio 7 8,600 326,800 2.63% 50 Ohio 8 7,900 328,800 2.40% 45 Ohio 9 7,800 315,000 2.48% 61 Ohio 10 6,700 312,800 2.14% 173 Ohio 11 3,600 275,200 1.31% 91 Ohio 12 6,500 359,500 1.81% 35 Ohio 13 8,600 320,400 2.68% 330 Ohio 14 3,000 349,700 0.86% 70 Ohio 15 6,800 336,400 2.02% 146 Ohio 16 5,000 355,600 1.41% 65 Oklahoma 1 7,600 361,900 2.10% 63 Oklahoma 2 6,200 290,300 2.14% 84 Oklahoma 3 6,100 329,900 1.85% 83 Oklahoma 4 6,500 350,900 1.85% 42 Oklahoma 5 8,800 348,800 2.52% 102 Oregon 1 6,300 377,200 1.67% 96 Oregon 2 5,500 314,200 1.75% 137 Oregon 3 5,600 383,300 1.46% 175 Oregon 4 4,000 309,000 1.29% 240 Oregon 5 3,600 326,700 1.10% 297 Pennsylvania 1 2,600 273,300 0.95% 397 Pennsylvania 2 1,900 273,100 0.70% 160 Pennsylvania 3 4,300 317,700 1.35% 279 Pennsylvania 4 3,400 342,900 0.99% 98 Pennsylvania 5 5,400 316,800 1.70% 301 Pennsylvania 6 3,400 362,300 0.94% 407 Pennsylvania 7 2,200 339,700 0.65% 422 Pennsylvania 8 1,900 357,800 0.53% 55 Pennsylvania 9 6,900 304,800 2.26% 75 Pennsylvania 10 6,100 312,500 1.95% 375 Pennsylvania 11 2,500 329,300 0.76% 195 Pennsylvania 12 4,000 331,900 1.21% 234 Pennsylvania 13 3,800 339,000 1.12% 261 Pennsylvania 14 3,400 323,200 1.05% 157 Pennsylvania 15 4,700 343,800 1.37% 164 Pennsylvania 16 4,400 327,700 1.34% 193 Pennsylvania 17 3,800 312,600 1.22% 217 Pennsylvania 18 4,000 345,000 1.16% 377 Rhode Island 1 1,900 250,900 0.76% 410 Rhode Island 2 1,600 260,300 0.61% 99 South Carolina 1 5,100 299,800 1.70% 299 South Carolina 2 2,900 305,600 0.95% 57 South Carolina 3 5,900 264,500 2.23% 26 South Carolina 4 9,800 301,000 3.26% 163 South Carolina 5 3,700 275,200 1.34% 190 South Carolina 6 3,100 253,500 1.22% 222 South Carolina 7 3,100 269,400 1.15% 181 South Dakota 1 5,300 415,600 1.28% 73 Tennessee 1 5,900 297,600 1.98% 53 Tennessee 2 7,600 327,200 2.32% 46 Tennessee 3 7,300 297,000 2.46% 19 Tennessee 4 12,400 314,500 3.94% 239 Tennessee 5 3,900 353,400 1.10% 32 Tennessee 6 8,700 304,500 2.86% 44 Tennessee 7 7,100 285,800 2.48% 108 Tennessee 8 4,900 299,200 1.64% 251 Tennessee 9 3,300 305,300 1.08% 133 Texas 1 4,400 297,700 1.48% 33 Texas 2 10,400 364,600 2.85% 54 Texas 3 8,500 371,200 2.29% 147 Texas 4 4,200 299,300 1.40% 166 Texas 5 4,000 300,800 1.33% 110 Texas 6 5,700 348,800 1.63% 34 Texas 7 10,500 376,300 2.79% 85 Texas 8 5,700 309,200 1.84% 101 Texas 9 5,500 326,400 1.69% 237 Texas 10 3,800 342,600 1.11% 25 Texas 11 10,100 308,800 3.27% 115 Texas 12 5,400 337,500 1.60% 132 Texas 13 4,600 309,000 1.49% 435 Texas 14 -100 303,300 -0.03% 227 Texas 15 3,200 280,900 1.14% 79 Texas 16 5,300 281,300 1.88% 106 Texas 17 5,400 329,300 1.64% 276 Texas 18 3,100 306,400 1.01% 114 Texas 19 5,000 310,700 1.61% 162 Texas 20 4,200 311,400 1.35% 209 Texas 21 4,200 361,200 1.16% 126 Texas 22 5,400 352,500 1.53% 38 Texas 23 7,500 289,700 2.59% 230 Texas 24 4,400 388,600 1.13% 178 Texas 25 3,900 302,200 1.29% 136 Texas 26 5,400 368,300 1.47% 309 Texas 27 2,800 305,600 0.92% 141 Texas 28 3,800 266,300 1.43% 199 Texas 29 3,500 292,900 1.19% 232 Texas 30 3,300 292,300 1.13% 399 Texas 31 2,200 323,000 0.68% 109 Texas 32 5,900 360,900 1.63% 104 Texas 33 4,700 283,900 1.66% 255 Texas 34 2,600 242,200 1.07% 351 Texas 35 2,600 318,200 0.82% 424 Texas 36 1,400 291,900 0.48% 135 Utah 1 4,600 312,400 1.47% 225 Utah 2 3,500 305,700 1.14% 393 Utah 3 2,200 311,200 0.71% 353 Utah 4 2,700 331,500 0.81% 319 Vermont Statewide 2,900 327,300 0.89% 418 Virginia 1 2,000 352,400 0.57% 394 Virginia 2 2,400 339,800 0.71% 403 Virginia 3 2,100 320,100 0.66% 408 Virginia 4 2,100 327,900 0.64% 228 Virginia 5 3,600 316,100 1.14% 269 Virginia 6 3,500 339,900 1.03% 382 Virginia 7 2,700 364,600 0.74% 413 Virginia 8 2,500 423,700 0.59% 59 Virginia 9 6,500 298,400 2.18% 346 Virginia 10 3,100 376,400 0.82% 406 Virginia 11 2,600 400,900 0.65% 431 Washington 1 1,200 332,300 0.36% 434 Washington 2 200 318,900 0.06% 184 Washington 3 3,600 284,500 1.27% 325 Washington 4 2,500 284,500 0.88% 347 Washington 5 2,400 291,500 0.82% 415 Washington 6 1,600 275,500 0.58% 425 Washington 7 1,800 380,000 0.47% 433 Washington 8 800 318,000 0.25% 428 Washington 9 1,500 341,400 0.44% 389 Washington 10 2,100 291,300 0.72% 249 West Virginia 1 2,800 258,700 1.08% 161 West Virginia 2 3,600 266,900 1.35% 317 West Virginia 3 2,000 223,000 0.90% 198 Wisconsin 1 4,100 342,500 1.20% 187 Wisconsin 2 4,900 390,000 1.26% 95 Wisconsin 3 6,200 353,500 1.75% 183 Wisconsin 4 3,900 308,000 1.27% 252 Wisconsin 5 4,000 370,600 1.08% 93 Wisconsin 6 6,300 353,600 1.78% 112 Wisconsin 7 5,500 338,400 1.63% 182 Wisconsin 8 4,600 362,800 1.27% 52 Wyoming Statewide 6,800 290,000 2.34% Chart Data Download data The data below can be saved or copied directly into Excel. The data underlying the figure. * Subcategory and overall totals may vary slightly due to rounding. Source: Authors’ analysis of Bivens (2014), U.S. Census Bureau (2013), U.S. International Trade Commission (USITC 2016), Bureau of Labor Statistics (BLS 2016a and 2016b), and BLS Employment Projections program (BLS-EP 2014a, 2014b, and 2015). For a more detailed explanation of data sources and computations, see the appendix. Share on Facebook Tweet this chart Embed Copy the code below to embed this chart on your website. Download image

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. trade deficit with the TPP member countries resulted in net job losses in all but two U.S. congressional districts, and displaced 26,200 jobs in a single U.S. congressional district (Michigan’s 11th Congressional District, located in Detroit’s northwest suburbs in parts of Wayne and Oakland counties). The 20 congressional districts with the largest shares of jobs lost are shown in Table 6. Each of the top 20 districts lost between 11,400 and 26,200 jobs. Job losses as a share of district employment among the top 20 U.S. congressional districts ranged from 3.89 percent to 7.66 percent (for the 11th Congressional District in Michigan). 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 (five districts); California (two districts); and Ohio, Alabama, and Tennessee (one district 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 Table 7. The table provides net jobs affected and jobs affected as a share of total district employment. The only two congressional districts that experienced net job gains as a result of trade with the 11 other TPP member countries are the 14th Congressional District in Texas (100 jobs gained) and the 4th Congressional District in Kansas (200 jobs gained). Table 8 displays this information alphabetically by congressional district.

Other problems with the TPP

Many researchers have raised concerns over the negative impacts of the Trans-Pacific Partnership. This paper does not include an exhaustive review but cites as an example Capaldo, Izurieta, and Sundaram (2016), who noted that studies claiming that the TPP would have a positive impact on the U.S. and global economy are based on unrealistic assumptions, including no change in the U.S. trade balance with the TPP countries and full employment.

For example, Capaldo, Izurieta, and Sundaram disprove the claim by Petri and Plummer (2016) that the TPP would increase real, annual income in the United States by $131 billion per year, or 0.5 percent of GDP. In fact, after incorporating more realistic assumptions into their model, Capaldo, Izurieta, and Sundaram estimate that the TPP would reduce economic growth in the United States by 0.54 percent after 10 years. They also find that though all 12 member countries would incur job losses from the TPP, the United States would be hardest hit, with 448,000 job losses. These job losses are the product of changes in the structure of trade, with the United States producing more capital-intensive goods and fewer labor-intensive goods. This changing structure, as policies to raise profits in some industries (pharmaceuticals, software, and other intellectual property) “push labor shares lower, redistributing income from labor to capital in all countries,” would increase income inequality across the member countries (Capaldo, Izurieta, and Sundaram 2016, 2).

It is important to note that Capaldo, Izurieta, and Sundaram maintain the assumption of stable trade balance among the TPP countries, for consistency with Petri and Plummer’s model. Thus, the estimate of 448,000 jobs lost is a lower bound on likely outcomes. In reality, the TPP would likely result in growing trade deficits and job losses for the United States for the reasons shown here. This would increase the downward pressure on wages in the United States as more good jobs in manufacturing are destroyed.

Conclusion

The failure to include provisions to stop currency manipulation alone casts the Trans-Pacific Partnership as a fatally flawed trade and investment deal. U.S. trade deficits with the 11 other members of the proposed agreement eliminated 2 million U.S. jobs in 2015, and reduced U.S. GDP by nearly $300 billion (1.6 percent). Even if the trade balance with the TPP remains stable, as assumed by the most optimistic proponents of the agreement, growing imports of labor-intensive products would over the next decade eliminate more than 400,000 U.S. jobs, reduce U.S. GDP by an additional one-half percent, and lead to growing income inequality in the United States and other members of the proposed agreement. Under a more likely scenario, the TPP would do that and more—fueling increased outsourcing, growing trade deficits, and even greater downward pressure on the incomes of working Americans.

Currency manipulation is the most important cause of large and growing U.S. trade deficits with the TPP countries. Majorities of both houses of Congress demanded that President Obama include in the core of the TPP “currency disciplines” that could be enforced with trade sanctions. But the president refused to even discuss those issues in the TPP negotiations. Currency manipulation by members of the TPP and by neighboring countries such as China, South Korea, and Taiwan (who all may soon be invited to join the deal) would likely nullify any benefits the U.S. might achieve from the TPP, which will reinforce the negative consequences of the deal for working families and manufacturing communities in the United States and other member countries. Congress should reject this agreement. The president can and should have done better for American workers, communities, and domestic businesses based in the United States.

About the authors

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.

Elizabeth Glass is a trade and manufacturing policy research assistant at the Economic Policy Institute. She provides research support on a variety of trade-related issues including currency manipulation, industrial policy, and employment. Prior to joining EPI in 2015, she worked with a number of international development organizations on international education policy and economic development research. She holds an M.A. in international economic development from The New School.

Acknowledgments

The authors thank Josh Bivens for comments, Jessica Schieder and Will Kimball for research assistance, Lora Engdahl for editing, and Chris Frazier and Chris Roof for layout and interactive graphics.

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 the TPP countries in 2015 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 U.S.-TPP trade deficit (derived from the macroeconomic model) to industries, states, and congressional districts. This combined macroeconomic/IO model uses data from 2015 to estimate the impacts of the trade deficit in that year. This appendix identifies the specific data sources and comparisons used.

The macroeconomic model

The effect of the U.S.-TPP trade deficit on GDP and jobs is determined by economic multipliers surveyed 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-age 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. According to Bivens, that infrastructure spending is associated with 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.-TPP 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 overall number of jobs eliminated 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 would increase total employment by 0.9 percent (approximately 1.3 million jobs in the economy in 2015). Likewise, an identical reduction in GDP would eliminate the same number of jobs in the U.S. economy.

This study examines the impacts of total trade in goods with the 11 other TPP countries (that is, total exports and general imports). The trade deficit (and job loss estimates) would be even larger if we had separated 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, as we have done in earlier studies of trade and employment. The use of total exports in this study yields more conservative estimates of trade-related job displacement. New data collection efforts by the Customs Bureau and U.S. Census Bureau may be required to specifically identify the import sources (by country) of U.S. foreign exports. Further research is required on the origin of foreign exports to more accurately assess the impacts of trade on domestic product (and domestic exports only). We examine trade in total exports and general imports in order to develop the most conservative estimates of the U.S. trade deficit and jobs lost due to trade with the TPP countries.

The trade and jobs model

This section describes the IO model that is used to allocates jobs lost due to trade to individual industries, and the census data which are then used to allocated those losses to states and Congressional districts. The trade and employment analyses by industry in this report are based on a detailed, industry-based study of the relationships between changes in trade flows and employment for each of approximately 195 individual industries of the U.S. economy. For the state and congressional district analysis, these are specially grouped into 45 custom sectors using the North American Industry Classification System (NAICS) with data obtained from the U.S. Census Bureau (2013). Trade data for this analysis were obtained from the U.S. International Trade Commission (USITC 2016).

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). This model includes both the direct effects of changes in output (for example, the number of jobs supported by $1 million in auto assembly) and the indirect effects on industries that supply goods (for example, goods used in the manufacture of cars). So, in the auto industry for example, the indirect impacts include jobs in auto parts, steel, and rubber, as well as service industries that provide inputs to the motor vehicle manufacturing companies, such as accounting, finance, and computer programming. 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 (Bronfenbrenner and Luce 2004 is one of the few studies based on news reports of individual plant closings).

Nominal trade data used in this analysis were converted to constant 2005 dollars using industry-specific deflators (see next section for further details). This was necessary because the labor requirements table was estimated using price levels in that year. Data on real trade flows were converted to constant 2005 dollars using industry-specific price deflators from the BLS–EP (2014b). These price deflators were updated using Bureau of Labor Statistics producer price indexes (industry and commodity data; BLS 2016b). 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

Data requirements

Step 1. U.S.-TPP trade data are obtained from the U.S. International Trade Commission DataWeb (USITC 2016) in four-digit, three-digit, and two-digit NAICS formats. Consumption imports and domestic exports are downloaded for each year.

Step 2. To conform to the BLS Employment Requirements tables (BLS–EP 2014a), trade data must be converted into the BLS industry classifications system. For NAICS-based data, there are 195 BLS industries. The data are then mapped from NAICS industries onto their respective BLS sectors. The trade data, which are in current dollars, are deflated into real 2005 dollars using published price deflators from the BLS–EP (2014b) and the Bureau of Labor Statistics (2016b).

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 (2015). 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 [T 2015 ] be the 195×2 matrix made up of a column of imports and a column of exports for 2015. To estimate the vector of jobs displaced by trade, perform the following matrix operations:

[J 2015 ]=[T 2015 ]×[E 2010 ]

[J 2015 ] is a 195×2 matrix of jobs displaced (eliminated) by imports and jobs supported by exports for each of 195 industries in 2015. This matrix is used to create vectors of net jobs displaced by imports from and jobs supported by exports to the TPP countries, 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 for this analysis. We assume that goods must be sold and advertised whether they are produced in the United States or imported for consumption.

Similarly, for respending (multiplier) analysis, let [PCE 2010 ] 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:

[J PCE 2010 ]=[PCE 2010 ]×[E 2010 ]

Direct and indirect jobs. In order to estimate the direct jobs, the diagonal vector was extracted from the employment requirements matrix [E 2010 ]. This vector was multiplied by the trade vector to estimate direct trade-related jobs (e.g., [J DIRECT2015 ] ) for both imports and exports. Indirect jobs just equal total jobs less direct (e.g., [J INDIRECT2015 ] =[J 2015 ] – [J DIRECT2015 ]).

Step 2. Combining macroeconomic and IO jobs analyses. The IO jobs estimates in vectors [J 2015 ] and [J PCE 2015 ] 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 is multiplied by the trade jobs share vector derived from [J 2015 ], and the respending (also Table 2) jobs is multiplied by the respending jobs share vector derived from [J PCE 2015 ]. The results yield estimates of jobs gained or lost by industry in the total economy as a result of the U.S.-TPP trade deficit, which are reported in Table 4.

Step 3. State-by-state analysis. For states, employment-by-industry data are obtained from the Census Bureau’s American Community Survey (U.S. Census Bureau 2013) data for 2011 and are mapped into 45 unique census industries and eight aggregated total and subtotals for a total of 53 sectors. We look at jobs displaced in 2015, so from this point, macroeconomic jobs estimates are derived from the vectors [J 2015 ] and [J PCE 2010 ]. In order to work with 45 sectors, we group the 195 BLS industries into a new matrix, defined as [Jnew 2015 ], a 45×1 matrix of job gains and losses. Define [St 2011 ] as the 45×51 matrix of state employment shares (with the addition of the District of Columbia) of employment in each industry. Calculate:

[Stj 2015 ]=[St 2011 ] T [Jnew 2015 ]

where [Stj 2015 ] is the 45×51 matrix of job displacement/support by state by industry. To get state total job displacement, we add up the subsectors in each state.

Step 4. Congressional district analysis. Employment by congressional district, by industry, and by state is obtained from the ACS data for 2011, which for the first time use geographic codings that match the boundaries of the 113th Congress (elected in 2012) and the 114th Congress (elected in 2014). In order to calculate job gains or losses in each congressional district, we use each column in [Stj 2013 ], which represent individual state job-gain and loss-by-industry estimates, and define them as [Stj 01 ], [Stj 02 ], [Stj i ]…[Stj 51 ], with i representing the state number and each matrix being 45×1.

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

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

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

where [Cdj i ] 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

See Scott (2016) for further background on the impacts of trade on U.S. wages.

Bergsten and Gagnon (2012) established four criteria for identifying currency manipulators based on the levels of foreign-exchange reserves relative to imports of goods and services, on foreign-exchange reserves growing faster than GDP, on having a current account surplus, and on having GDP in excess of $3,000 per capita. The first three requirements had to demonstrate persistence (e.g., a current account surplus continuously between 2001 and 2011). Vietnam would be excluded under the fourth criterion alone, as its per capita GDP was less than $2,200 in 2015 (IMF 2015). In addition, Vietnam’s trade and foreign-exchange accumulation have only developed in the past few years. But the size of its current account surplus, and its likely role as a low-wage export platform in the TPP, suggest that its currency is, and may continue to be, undervalued due to manipulation.

See Scott (2016). China continues to accumulate massive reserves in its SWFs. Based on these data and recent changes in prices, relative productivity growth rates, and trade balances, we believe that the RMB is still substantially undervalued.

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 follows that approach, using standard input-output models and data to estimate the jobs displaced by trade. Many reports by economists in the public and private sectors have used this “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 Raumussen 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.

This classification is not used by the Census or in the North American Industrial Classification System (NAICS, see Census: http://www.census.gov/eos/www/naics/). Within the NAICS system, manufacturing consists of the two-digit industries in the ranges of 31, 32, and 33. Those sectors we refer to as industrial supplies are all NAICS industries in the 32 classification. Most of these industries are classified as nondurable goods in other classifications of industrial output. However, they are qualitatively different from other nondurable goods such as textiles and apparel, so we treat them separately here.

Capaldo et al. (2016) maintain the assumption of balanced trade, but include a greater number of industries than in Petri and Plummer (2016) and thereby examine changes in the structure of trade, with the U.S. producing more capital-intensive goods and fewer labor-intensive goods, which results in growing unemployment and other impacts discussed here.

See, for example, Scott (2014a). Foreign exports have become an especially large proportion of U.S. trade with Mexico after NAFTA, as shown in Scott (2011).

The United States had total domestic exports to the TPP countries of $565.8 billion in 2015, and consumption imports of $836.6 billion for a net export trade deficit of $270.8 billion, 52.2 percent larger than the overall trade deficit reported in Table 1. If this more narrowly defined trade deficit had been used, the GDP and jobs lost due to TPP trade would have been proportionately larger. However, this measure would likely include some imports that were reexported to other TPP countries, and thus would have overstated the actual deficit with the TPP. Until we can precisely identify the source of foreign exports (by country and industry or product code) we will be unable to more accurately estimate net domestic trade flows with the TPP or other trade partners.

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 secondhand 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 secondhand goods has no impact on employment in the BLS model. Some special classification provision goods are assigned to miscellaneous manufacturing.

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 4 (which includes all direct, indirect, and respending jobs supported or displaced by the trade deficit) does include some net jobs displaced in these industries.

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

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

Bayoumi, Tamim, Joseph Gagnon, and Christian Saborowski (BGS). 2014. Official Flows, Capital Mobility and Global Imbalances. Peterson Institute for International Economics, Working Paper WP 14-8.

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

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

Bivens, Josh. 2014. The Short- and Long-Term Impact of Infrastructure Investments on Employment and Economic Activity in the U.S. Economy. Economic Policy Institute, Briefing Paper #374.

Bivens, Josh. 2016. The Trans-Pacific Partnership is Unlikely to be a Good Deal for American Workers. Economic Policy Institute, Briefing Paper #397.

Bloomberg. 2016. “China Monthly Foreign Exchange Reserves.” BloombergBusiness, January 31.

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

Bureau of Labor Statistics (BLS). 2016a. “Current Employment and Establishment Statistics.”

Bureau of Labor Statistics (BLS). 2016b. “Producer Price Indexes: Industry and Commodity Data” [Excel files].

Bureau of Labor Statistics, Employment Projections program (BLS–EP). 2014a. “Special Purpose Files—Employment Requirements; Chain-Weighted (2005 dollars) Real Domestic Employment Requirements Table for 2010” [Excel sheet, converted to Stata data file].

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Congress of the United States. 2013. “June 6 2013 Congress of the United States Letter to President Barack Obama.” June 6.

Gagnon, Joseph, and Gary Hufbauer. 2011. “Taxing China’s Assets: How to Increase U.S. Employment Without Launching a Trade War.” Foreign Affairs.com.

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

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

International Monetary Fund (IMF). 2015. “World Economic Outlook Database: October 2015 Edition.”

International Monetary Fund (IMF). 2016. International Financial Statistics: Database and Browser (CD ROM). Washington, D.C.: International Monetary Fund. January.

Johnson, Martin, and Chris Rasmussen. 2013. “Jobs Supported by Exports 2012: An Update.” International Trade Administration, Department of Commerce.

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Krugman, Paul. 2013. “Secular Stagnation, Coalmines and Larry Summers.” The Conscience of a Liberal (New York Times blog), November 16.

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OpenCongress.org. 2009. S. 1027 – Currency Reform for Fair Trade Act of 2009.

Petri, Peter A., and Michael G. Plummer. 2016. “The Economic Effects of the Trans-Pacific Partnership: New Estimates.” Peterson Institute for International Economics, Working Paper 16-2.

Scott, Robert E. 2011. Heading South: U.S.-Mexico Trade and Job Displacement after NAFTA. Economic Policy Institute, Briefing Paper #308.

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

Scott, Robert E. 2014a. China Trade, Outsourcing and Jobs: Growing U.S. Trade Deficit with China Cost 3.2 Million Jobs between 2001 and 2013, with Job Losses in Every State.” Economic Policy Institute, Briefing Paper #385.

Scott, Robert E. 2014b. “The Effects of NAFTA on US Trade, Jobs and Investment, 1993–2013.” Review of Keynesian Economics, vol. 2, no. 4, Winter 2014, pp. 429–441.

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

Scott, Robert E. 2015. Currency Manipulation and the 896,600 U.S. Jobs Lost Due to the U.S.-Japan Trade Deficit. Economic Policy Institute, Briefing Paper No. 387.

Scott, Robert E. 2016. Trans-Pacific Partnership Agreement: Currency Manipulation, Trade, Wages, and Job Loss. Economic Policy Institute.

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Table 3 U.S. goods trade balance with TPP countries, by industry, 2015 (billions of dollars) Total Share of trade deficit Agriculture, forestry, fishing, and hunting -4.0 2.2% Mining -54.4 30.6% Oil and gas -58.0 32.6% Minerals and ores 3.6 -2.0% Utilities 0.0 0.0% Construction 0.0 0.0% Manufacturing -120.8 67.9% Nondurable goods -16.2 9.1% Food 1.8 -1.0% Beverage and tobacco products -2.6 1.5% Textile mills and textile product mills 3.3 -1.9% Apparel -13.3 7.5% Leather and allied products -5.4 3.0% Industrial supplies 46.7 -26.3% Wood products -6.7 3.8% Paper 1.6 -0.9% Printed matter and related products 1.8 -1.0% Petroleum and coal products 19.1 -10.7% Chemicals 25.3 -14.2% Plastics and rubber products 5.9 -3.3% Nonmetallic mineral products -0.2 0.1% Durable goods -151.3 85.0% Primary metals -13.0 7.3% Fabricated metal products 7.2 -4.0% Machinery 10.8 -6.1% Computer and electronic parts -27.8 15.6% Computer and peripheral equipment 1.3 -0.7% Communications, audio, and video equipment -22.5 12.6% Navigational, measuring, electromedical, and control instruments -3.0 1.7% Semiconductors and other electronic components, and reproducing magnetic and optical media -3.6 2.0% Electrical equipment, appliances, and components -6.7 3.8% Transportation equipment -104.7 58.9% Motor vehicles and motor vehicle parts -118.7 66.7% Aerospace products and parts 12.7 -7.1% Railroad, ship, and other transportation equipment 1.3 -0.7% Furniture and related products -6.9 3.9% Miscellaneous manufactured commodities -10.2 5.7% Wholesale trade 0.0 0.0% Retail trade 0.0 0.0% Transportation and warehousing 0.0 0.0% Information 0.1 -0.1% Finance and insurance 0.0 0.0% Real estate and rental and leasing 0.0 0.0% Professional, scientific, and technical services 0.0 0.0% Management of companies and enterprises 0.0 0.0% Administrative and support and waste management and remediation services 0.0 0.0% Education services 0.0 0.0% Health care and social assistance 0.0 0.0% Arts, entertainment, and recreation 0.0 0.0% Accommodation and food services 0.0 0.0% Other services (except public administration) 0.0 0.0% Public administration 1.2 -0.7% Subtotal, nonmanufacturing -57.1 32.1% Total* -177.9 100.0% * Subcategory and overall totals may vary slightly due to rounding. Source: Authors’ analysis of Bivens (2014), U.S. Census Bureau (2013), U.S. International Trade Commission (USITC 2016), Bureau of Labor Statistics (BLS 2016a and 2016b), and BLS Employment Projections program (BLS-EP 2014a, 2014b, and 2015). For a more detailed explanation of data sources and computations, see the appendix. Share on Facebook Tweet this chart Embed Copy the code below to embed this chart on your website. Download image

Table 4 Net U.S. jobs created or eliminated by U.S. goods trade with TPP countries, by industry, 2015 Industry Total Share of total jobs eliminated Agriculture, forestry, fishing, and hunting -41,600 2.1% Mining -182,800 9.0% Oil and gas -194,100 9.6% Minerals and ores 11,300 -0.6% Utilities -8,400 0.4% Construction 0 0.0% Manufacturing -1,057,200 52.2% Nondurable goods -229,400 11.3% Food 16,700 -0.8% Beverage and tobacco products -20,100 1.0% Textile mills and textile product mills 28,400 -1.4% Apparel -181,900 9.0% Leather and allied products -72,400 3.6% Industrial supplies 129,200 -6.4% Wood products -71,300 3.5% Paper 14,500 -0.7% Printed matter and related products 21,800 -1.1% Petroleum and coal products 20,900 -1.0% Chemicals 105,400 -5.2% Plastics and rubber products 40,200 -2.0% Nonmetallic mineral products -2,200 0.1% Durable goods -957,000 47.2% Primary metals -64,900 3.2% Fabricated metal products 55,700 -2.7% Machinery 66,900 -3.3% Computer and electronic parts -163,900 8.1% Computer and peripheral equipment 8,300 -0.4% Communications, audio, and video equipment -132,000 6.5% Navigational, measuring, electromedical, and control instruments -17,100 0.8% Semiconductors and other electronic components, and reproducing magnetic and optical media -23,100 1.1% Electrical equipment, appliances, and components -47,100 2.3% Transportation equipment -654,500 32.3% Motor vehicles and motor vehicle parts -738,300 36.4% Aerospace products and parts 76,500 -3.8% Railroad, ship, and other transportation equipment 7,300 -0.4% Furniture and related products -75,500 3.7% Miscellaneous manufactured commodities -73,700 3.6% Wholesale trade -26,700 1.3% Retail trade -142,800 7.0% Transportation and warehousing -17,900 0.9% Information -19,000 0.9% Finance and insurance -42,700 2.1% Real estate and rental and leasing -16,500 0.8% Professional, scientific, and technical services -10,700 0.5% Management of companies and enterprises 0 0.0% Administrative and support and waste management and remediation services -6,900 0.3% Education services -37,300 1.8% Health care and social assistance -204,200 10.1% Arts, entertainment, and recreation -23,000 1.1% Accommodation and food services -101,800 5.0% Other services (except public administration) -70,700 3.5% Public administration -15,700 0.8% Subtotal, nonmanufacturing -968,600 47.8% Total* -2,025,800 100.0% * Subcategory and overall totals may vary slightly due to rounding. Source: Authors’ analysis of Bivens (2014), U.S. Census Bureau (2013), U.S. International Trade Commission (USITC 2016), Bureau of Labor Statistics (BLS 2016a and 2016b), and BLS Employment Projections program (BLS-EP 2014a, 2014b, and 2015). For a more detailed explanation of data sources and computations, see the appendix. Share on Facebook Tweet this chart Embed Copy the code below to embed this chart on your website. Download image

Table 5a Net U.S. jobs eliminated by U.S. trade deficit with TPP countries, by state, 2015 (ranked by jobs eliminated as a share of state employment) Rank State Net jobs eliminated State employment (in 2011) Jobs eliminated as share

of state employment 1 Michigan 214,600 4,191,900 5.12% 2 Indiana 103,800 2,934,500 3.54% 3 Kentucky 53,700 1,838,400 2.92% 4 Wyoming 6,800 290,000 2.34% 5 Alabama 46,000 1,981,100 2.32% 6 Tennessee 61,000 2,784,500 2.19% 7 Ohio 112,500 5,213,500 2.16% 8 Oklahoma 35,300 1,681,800 2.10% 9 Mississippi 22,000 1,181,300 1.86% 10 Alaska 6,300 344,300 1.83% 11 South Carolina 33,600 1,968,900 1.71% 12 Arkansas 20,100 1,235,800 1.63% 13 Texas 172,600 11,455,100 1.51% 14 Oregon 24,900 1,710,300 1.46% 15 North Carolina 60,700 4,195,800 1.45% 16 Missouri 39,200 2,742,100 1.43% 17 Wisconsin 39,600 2,819,500 1.40% 18 California 227,500 16,426,700 1.38% 19 New Mexico 11,600 869,800 1.33% 20 Illinois 78,800 5,926,900 1.33% 21 Iowa 19,800 1,538,800 1.29% 22 South Dakota 5,300 415,600 1.28% 23 Louisiana 24,700 1,973,900 1.25% 24 Maine 8,000 643,100 1.24% 25 Pennsylvania 68,900 5,853,300 1.18% 26 North Dakota 4,300 370,800 1.16% 27 Colorado 28,600 2,492,400 1.15% 28 West Virginia 8,400 748,600 1.12% 29 New York 97,300 8,959,000 1.09% 30 New Hampshire 7,400 684,800 1.08% 31 Minnesota 29,000 2,728,900 1.06% 32 Nebraska 10,000 943,600 1.06% 33 Idaho 7,100 684,900 1.04% 34 Utah 13,000 1,260,800 1.03% 35 Georgia 43,100 4,193,800 1.03% 36 Montana 4,900 480,000 1.02% 37 Kansas 13,500 1,389,000 0.97% 38 Massachusetts 29,900 3,284,700 0.91% 39 New Jersey 37,700 4,152,500 0.91% 40 Vermont 2,900 327,300 0.89% 41 Virginia 33,100 3,860,100 0.86% 42 Florida 68,200 8,101,900 0.84% 43 Hawaii 5,100 629,500 0.81% 44 Arizona 21,000 2,688,000 0.78% 45 Nevada 9,100 1,204,900 0.76% 46 Maryland 20,100 2,894,600 0.69% 47 Rhode Island 3,400 511,200 0.67% 48 Connecticut 10,400 1,742,500 0.60% 49 Washington 17,800 3,118,000 0.57% 50 District of Columbia 1,500 310,600 0.48% 51 Delaware 1,800 420,400 0.43% Total* 2,025,800 140,399,600 1.44% * Subcategory and overall totals may vary slightly due to rounding. Source: Authors’ analysis of Bivens (2014), U.S. Census Bureau (2013), U.S. International Trade Commission (USITC 2016), Bureau of Labor Statistics (BLS 2016a and 2016b), and BLS Employment Projections program (BLS-EP 2014a, 2014b, and 2015). For a more detailed explanation of data sources and computations, see the appendix. Share on Facebook Tweet this chart Embed Copy the code below to embed this chart on your website. Download image

Table 5b Net U.S. jobs eliminated by U.S. trade deficit with TPP countries, by state, 2015 (ranked by jobs eliminated) Rank State Net jobs eliminated State employment (in 2011) Jobs eliminated as share

of state employment 1 California 227,500 16,426,700 1.38% 2 Michigan 214,600 4,191,900 5.12% 3 Texas 172,600 11,455,100 1.51% 4 Ohio 112,500 5,213,500 2.16% 5 Indiana 103,800 2,934,500 3.54% 6 New York 97,300 8,959,000 1.09% 7 Illinois 78,800 5,926,900 1.33% 8 Pennsylvania 68,900 5,853,300 1.18% 9 Florida 68,200 8,101,900 0.84% 10 Tennessee 61,000 2,784,500 2.19% 11 North Carolina 60,700 4,195,800 1.45% 12 Kentucky 53,700 1,838,400 2.92% 13 Alabama 46,000 1,981,100 2.32% 14 Georgia 43,100 4,193,800 1.03% 15 Wisconsin 39,600 2,819,500 1.40% 16 Missouri 39,200 2,742,100 1.43% 17 New Jersey 37,700 4,152,500 0.91% 18 Oklahoma 35,300 1,681,800 2.10% 19 South Carolina 33,600 1,968,900 1.71% 20 Virginia 33,100 3,860,100 0.86% 21 Massachusetts 29,900 3,284,700 0.91% 22 Minnesota 29,000 2,728,900 1.06% 23 Colorado 28,600 2,492,400 1.15% 24 Oregon 24,900 1,710,300 1.46% 25 Louisiana 24,700 1,973,900 1.25% 26 Mississippi 22,000 1,181,300 1.86% 27 Arizona 21,000 2,688,000 0.78% 28 Arkansas 20,100 1,235,800 1.63% 28 Maryland 20,100 2,894,600 0.69% 30 Iowa 19,800 1,538,800 1.29% 31 Washington 17,800 3,118,000 0.57% 32 Kansas 13,500 1,389,000 0.97% 33 Utah 13,000 1,260,800 1.03% 34 New Mexico 11,600 869,800 1.33% 35 Connecticut 10,400 1,742,500 0.60% 36 Nebraska 10,000 943,600 1.06% 37 Nevada 9,100 1,204,900 0.76% 38 West Virginia 8,400 748,600 1.12% 39 Maine 8,000 643,100 1.24% 40 New Hampshire 7,400 684,800 1.08% 41 Idaho 7,100 684,900 1.04% 42 Wyoming 6,800 290,000 2.34% 43 Alaska 6,300 344,300 1.83% 44 South Dakota 5,300 415,600 1.28% 45 Hawaii 5,100 629,500 0.81% 46 Montana 4,900 480,000 1.02% 47 North Dakota 4,300 370,800 1.16% 48 Rhode Island 3,400 511,200 0.67% 49 Vermont 2,900 327,300 0.89% 50 Delaware 1,800 420,400 0.43% 51 District of Columbia 1,500 310,600 0.48% Total* 2,025,800 140,399,600 1.44% * Subcategory and overall totals may vary slightly due to rounding. Source: Authors’ analysis of Bivens (2014), U.S. Census Bureau (2013), U.S. International Trade Commission (USITC 2016), Bureau of Labor Statistics (BLS 2016a and 2016b), and BLS Employment Projections program (BLS-EP 2014a, 2014b, and 2015). For a more detailed explanation of data sources and computations, see the appendix. Share on Facebook Tweet this chart Embed Copy the code below to embed this chart on your website. Download image

Table 5c Net U.S. jobs eliminated by U.S. trade deficit with TPP countries, by state, 2015 (sorted alphabetically) Rank State Net jobs eliminated State employment (in 2011) Jobs eliminated as share

of state employment 5 Alabama 46,000 1,981,100 2.32% 10 Alaska 6,300 344,300 1.83% 44 Arizona 21,000 2,688,000 0.78% 12 Arkansas 20,100 1,235,800 1.63% 18 California 227,500 16,426,700 1.38% 27 Colorado 28,600 2,492,400 1.15% 48 Connecticut 10,400 1,742,500 0.60% 51 Delaware 1,800 420,400 0.43% 50 District of Columbia 1,500 310,600 0.48% 42 Florida 68,200 8,101,900 0.84% 35 Georgia 43,100 4,193,800 1.03% 43 Hawaii 5,100 629,500 0.81% 33 Idaho 7,100 684,900 1.04% 20 Illinois 78,800 5,926,900 1.33% 2 Indiana 103,800 2,934,500 3.54% 21 Iowa 19,800 1,538,800 1.29% 37 Kansas 13,500 1,389,000 0.97% 3 Kentucky 53,700 1,838,400 2.92% 23 Louisiana 24,700 1,973,900 1.25% 24 Maine 8,000 643,100 1.24% 46 Maryland 20,100 2,894,600 0.69% 38 Massachusetts 29,900 3,284,700 0.91% 1 Michigan 214,600 4,191,900 5.12% 31 Minnesota 29,000 2,728,900 1.06% 9 Mississippi 22,000 1,181,300 1.86% 16 Missouri 39,200 2,742,100 1.43% 36 Montana 4,900 480,000 1.02% 32 Nebraska 10,000 943,600 1.06% 45 Nevada 9,100 1,204,900 0.76% 30 New Hampshire 7,400 684,800 1.08% 39 New Jersey 37,700 4,152,500 0.91% 19 New Mexico 11,600 869,800 1.33% 29 New York 97,300 8,959,000 1.09% 15 North Carolina 60,700 4,195,800 1.45% 26 North Dakota 4,300 370,800 1.16% 7 Ohio 112,500 5,213,500 2.16% 8 Oklahoma 35,300 1,681,800 2.10% 14 Oregon 24,900 1,710,300 1.46% 25 Pennsylvania 68,900 5,853,300 1.18% 47 Rhode Island 3,400 511,200 0.67% 11 South Carolina 33,600 1,968,900 1.71% 22 South Dakota 5,300 415,600 1.28% 6 Tennessee 61,000 2,784,500 2.19% 13 Texas 172,600 11,455,100 1.51% 34 Utah 13,000 1,260,800 1.03% 40 Vermont 2,900 327,300 0.89% 41 Virginia 33,100 3,860,100 0.86% 49 Washington 17,800 3,118,000 0.57% 28 West Virginia 8,400 748,600 1.12% 17 Wisconsin 39,600 2,819,500 1.40% 4 Wyoming 6,800 290,000 2.34% Total 2,025,800 140,399,600 1.44% Note: Rank is by jobs eliminated as a share of state employment. Subcategory and overall totals may vary slightly due to rounding. Source: Authors’ analysis of Bivens (2014), U.S. Census Bureau (2013), U.S. International Trade Commission (USITC 2016), Bureau of Labor Statistics (BLS 2016a and 2016b), and BLS Employment Projections program (BLS-EP 2014a, 2014b, and 2015). For a more detailed explanation of data sources and computations, see the appendix. Share on Facebook Tweet this chart Embed Copy the code below to embed this chart on your website. Download image

Table 6 20 congressional districts hardest hit by U.S. trade deficit with TPP countries, 2015 (ranked by jobs eliminated as a share of district employment) Rank State District Net jobs eliminated District employment (in 2011) Jobs eliminated as a share of district employment 1 Michigan 11 26,200 342,100 7.66% 2 Michigan 10 22,400 308,700 7.26% 3 Michigan 9 21,900 326,100 6.72% 4 Michigan 8 20,400 330,800 6.17% 5 Michigan 7 17,500 299,100 5.85% 6 Michigan 12 18,000 313,800 5.74% 7 Indiana 2 17,900 317,800 5.63% 8 Michigan 14 14,500 257,700 5.63% 9 Michigan 13 12,900 230,700 5.59% 10 Indiana 3 16,900 327,000 5.17% 11 Ohio 4 16,300 317,900 5.13% 12 Indiana 6 14,700 311,900 4.71% 13 Alabama 3 12,100 274,600 4.41% 14 Michigan 2 13,900 315,900 4.40% 15 California 40 12,100 280,500 4.31% 16 Michigan 5 11,400 264,800 4.31% 17 California 34 12,800 309,400 4.14% 18 Indiana 4 13,300 328,500 4.05% 19 Tennessee 4 12,400 314,500 3.94% 20 Indiana 8 12,800 329,300 3.89% Source: Authors’ analysis of Bivens (2014), U.S. Census Bureau (2013), U.S. International Trade Commission (USITC 2016), Bureau of Labor Statistics (BLS 2016a and 2016b), and BLS Employment Projections program (BLS-EP 2014a, 2014b, and 2015). For a more detailed explanation of data sources and computations, see the appendix. Share on Facebook Tweet this chart Embed Copy the code below to embed this chart on your website. Download image

Table 7 Net U.S. jobs eliminated by U.S. trade deficit with TPP countries, by congressional district, 2015 (ranked by jobs eliminated as a share of district employment) Rank State State District # Net jobs eliminated District employment (in 2011) Jobs eliminated as a share of district employment 1 Michigan 11 26,200 342,100 7.66% 2 Michigan 10 22,400 308,700 7.26% 3 Michigan 9 21,900 326,100 6.72% 4 Michigan 8 20,400 330,800 6.17% 5 Michigan 7 17,500 299,100 5.85% 6 Michigan 12 18,000 313,800 5.74% 7 Indiana 2 17,900 317,800 5.63% 8 Michigan 14 14,500 257,700 5.63% 9 Michigan 13 12,900 230,700 5.59% 10 Indiana 3 16,900 327,000 5.17% 11 Ohio 4 16,300 317,900 5.13% 12 Indiana 6 14,700 311,900 4.71% 13 Alabama 3 12,100 274,600 4.41% 14 Michigan 2 13,900 315,900 4.40% 15 California 40 12,100 280,500 4.31% 16 Michigan 5 11,400 264,800 4.31% 17 California 34 12,800 309,400 4.14% 18 Indiana 4 13,300 328,500 4.05% 19 Tennessee 4 12,400 314,500 3.94% 20 Indiana 8 12,800 329,300 3.89% 21 Kentucky 2 12,200 317,100 3.85% 22 Michigan 3 11,800 315,300 3.74% 23 Ohio 5 12,400 334,200 3.71% 24 Kentucky 1 10,200 284,800 3.58% 25 Texas 11 10,100 308,800 3.27% 26 South Carolina 4 9,800 301,000 3.26% 27 Michigan 4 9,100 286,300 3.18% 28 Michigan 6 9,700 310,400 3.13% 29 Kentucky 6 10,200 335,400 3.04% 30 Alabama 4 7,700 262,900 2.93% 31 Kentucky 5 6,800 234,300 2.90% 32 Tennessee 6 8,700 304,500 2.86% 33 Texas 2 10,400 364,600 2.85% 34 Texas 7 10,500 376,300 2.79% 35 Ohio 13 8,600 320,400 2.68% 36 Indiana 9 9,100 339,400 2.68% 37 Ohio 7 8,600 326,800 2.63% 38 Texas 23 7,500 289,700 2.59% 39 Louisiana 4 8,000 311,100 2.57% 40 Illinois 15 8,100 316,500 2.56% 41 Alabama 7 6,400 253,500 2.52% 42 Oklahoma 5 8,800 348,800 2.52% 43 Mississippi 1 7,600 305,600 2.49% 44 Tennessee 7 7,100 285,800 2.48% 45 Ohio 9 7,800 315,000 2.48% 46 Tennessee 3 7,300 297,000 2.46% 47 California 44 6,600 270,600 2.44% 48 Kentucky 4 8,100 333,500 2.43% 49 California 23 6,600 274,100 2.41% 50 Ohio 8 7,900 328,800 2.40% 51 Indiana 1 7,300 310,600 2.35% 52 Wyoming Statewide 6,800 290,000 2.34% 53 Tennessee 2 7,600 327,200 2.32% 54 Texas 3 8,500 371,200 2.29% 55 