Report | Trade and Globalization

Stop Currency Manipulation in the Trans-Pacific Partnership: Millions of Jobs at Stake

Policy Memo #206

Download PDF

Press release

Currency manipulation distorts trade flows by artificially lowering the cost of U.S. imports and raising the cost of U.S. exports, and is the leading cause of stubbornly high U.S. trade deficits over the past 15 years. More than 20 countries, led by China, have, together, been spending about $1 trillion per year buying foreign assets to artificially suppress the value of their currencies. Several members of the proposed Trans-Pacific Partnership (TPP)—including Japan, Malaysia, and Singapore—are well known currency manipulators, and others—including South Korea, Taiwan, and China—have expressed interest in joining the agreement.

A growing number of economists, and a bipartisan majority of members of Congress, have called for the inclusion of “strong and enforceable foreign currency manipulation disciplines” in the TPP. This policy memorandum describes currency manipulation, outlines standards that should be used to define currency manipulation for enforcement purposes, and reviews enforcement tools that can be used to counter currency manipulation in the future. It also estimates jobs that would be gained by eliminating currency manipulation. As this research shows, ending currency manipulation could significantly reduce U.S. trade deficits and create millions of jobs, with job gains in every state and most or all U.S. congressional districts.

This policy memorandum, which draws heavily from research findings in a 2014 EPI report (Scott 2014), makes the following key points about currency manipulation:

  • Government purchases of foreign exchange reserves and other financial assets denominated in foreign currencies are the principal tool of currency manipulation. Large-scale purchases of such assets keep the currencies of interveners undervalued, artificially subsidizing the cost of their exports and taxing their imports, and increasing their trade surpluses.
  • Official (government) holdings of foreign exchange reserves and other foreign assets increased by roughly $1 trillion per year between 2008 and 2014. Although official holdings of foreign exchange reserves by currency manipulators fell slightly in 2014, largely due to the Russian financial crisis, total government holdings of other foreign assets increased by more than $600 billion. Currency manipulation kept the currencies of most interveners substantially undervalued in 2014.
  • There is a strong correlation between the trade (current account) surpluses of interveners and their purchases of foreign assets. Both averaged approximately $1 trillion per year between 2008 and 2014.
  • Quantitative easing (QE) is easily distinguishable from currency manipulation; thus action to end currency manipulation won’t infringe on a nation’s right to engage in quantitative easing.
  • Although currency manipulation is prohibited by both the International Monetary Fund (IMF) and the World Trade Organization (WTO), neither has been able to stop it. The IMF, in particular, has no enforcement tools to compel countries to abide by their obligations to avoid manipulating exchange rates for commercial gain.
  • Eliminating currency manipulation would reduce the annual U.S. trade deficit by $200 billion under a low-impact scenario and $500 billion under a high-impact scenario. This would increase U.S. GDP by between $288 billion and $720 billion per year (between 2.0 percent and 4.9 percent) and create between 2.3 and 5.8 million jobs.
  • Each of the 50 states and the District of Columbia would gain jobs under both the low- and high-impact scenarios. Job gains under the low-impact scenario would range from 1.06 percent of employment in Washington, D.C., to 2.29 percent of state employment in Wisconsin. Job gains under the high-impact scenario would range from 2.64 percent of employment in Washington, D.C., to 5.55 percent of employment in Wisconsin.
  • Nine of the top 10 states gaining the most jobs (as a share of total employment) in both scenarios are in the Midwest. They are Wisconsin (64,700 to 156,600 jobs), Indiana (61,000 to 152,600 jobs), Iowa (34,000 to 79,600 jobs), Minnesota (55,900 to 135,300 jobs), Michigan (82,800 to 207,200 jobs), Ohio (103,200 to 254,600 jobs), South Dakota (9,200 to 21,100 jobs), Kansas (28,900 to 67,000 jobs), and Nebraska (19,000 to 44,200 jobs). In the West, Idaho (13,900 to 32,700 jobs) rounds out the top 10 states gaining the most jobs.
  • Jobs are gained in all but two congressional districts under the low-impact scenario, and in all congressional districts under the high-impact scenario. Under the high-impact scenario, each of the top 20 districts by jobs created as a share of district employment would gain at least 14,700 jobs and as many as 24,400 jobs (gains representing between 5.79 percent and 8.65 percent of total district employment). Of the top 20 congressional districts, five are in California; three are in Wisconsin; two each in Indiana, Ohio, and Michigan; and one each in Kansas, Nebraska, Illinois, Minnesota, Washington, and Iowa. Under the high-impact scenario, among all districts, net job gains range from a low of 6,300 jobs in the 34th Congressional District in California to a high of 24,400 jobs in the 17th Congressional District in California.

Currency manipulation is a growing problem that has vexed policymakers for more than two decades. Currency manipulation has shifted production and jobs from deficit countries (principally the United States, and to a lesser extent, the European Union) to the surplus countries (China and other currency manipulators). In the current economic environment, this has contributed to critical trends bedeviling the U.S. economy: the slow recovery from the recent recession, the persistence of high levels of un- and under-employment, and the suppression of wage growth. In this context, it would be unconscionable for the administration to negotiate, or for Congress to approve, a trade agreement that does not include strong and enforceable tools to end currency manipulation.

Describing currency manipulation

The United States has run chronic trade deficits for well over a decade. For at least 15 years, these deficits have been largely driven by the decision made by several of our major trading partners to manage the value of their currency for competitive advantage in U.S. and global markets (Bergsten and Gagnon 2012; Bayoumi, Gagnon, and Saborowski 2014). They buy dollar-denominated financial assets to boost the value of the dollar and depress the value of their own currencies. This results in cheaper imports for the United States and makes U.S. exports more expensive in global markets. More than 20 countries, led by China, have been spending about $1 trillion per year buying foreign assets to artificially suppress the value of their currencies (Bergsten and Gagnon 2012). Ending this currency manipulation by our trading partners is thus crucially important for reducing U.S. trade deficits and stabilizing the global economy in coming years.

Given this, a trade agreement that includes several countries that are obvious currency manipulators would seem like a good place to start addressing the problem. Several members of the proposed TPP—including Japan, Malaysia, and Singapore—are well known currency manipulators, and others—including South Korea, Taiwan, and China—have expressed interest in joining the agreement. Yet U.S. Trade Representative Michael Froman has testified that currency manipulation has not been discussed in the TPP negotiations (McCormack 2014). The arguments against such provisions are weak. Particularly unconvincing is the argument that any such currency provision would somehow bar the Federal Reserve from undertaking expansionary monetary policy that included purchasing bonds to help the U.S. economy through a recession, also known as quantitative easing (Bergsten 2014).

All monetary policy tools, including quantitative easing, affect exchange rates. However, direct intervention in currency markets directly affects exchange rates through the purchase and sale of foreign currency assets. Quantitative easing, on the other hand, consists of Fed purchases of domestic assets (such as U.S. Treasuries and mortgage-backed securities), without affecting foreign currency assets (Bivens 2015). All relevant international rules and standards, including those of the International Monetary Fund and the G-7, acknowledge this distinction and clearly exempt QE policies from responsibility for currency manipulation (Bergsten 2014, 5). Other countries may complain about QE policies, but those objections have no merit and should be ignored (Bivens 2015).

Conversely, when people describe mercantilist currency management, they universally mean the purchase by a nation’s monetary and financial authorities of foreign assets. For example, the Chinese central bank buys not Chinese bonds, but U.S. Treasury bonds and mortgage-backed securities . This has a direct effect on the relative demand for Chinese versus U.S. assets, which moves the U.S.–China exchange rate.

Many commentators narrowly define currency manipulation to include only official holdings of foreign exchange reserves by central banks. However, a growing number of countries have created sovereign wealth funds (SWFs) to invest in private companies, land and commodities, and other foreign financial assets. SWF investments also directly affect the demand for foreign currencies. Bergsten and Gagnon (2012, note 1) define “intervention to include all net purchases of foreign assets by the public sector, including in sovereign wealth funds.” The same definition is used here.

Figure A reports the change in foreign exchange reserves by the 22 currency manipulators identified by Bergsten and Gagnon and the change in sovereign wealth fund assets held by a subset of nine of those countries.1 Since 2008, holdings of foreign exchange reserves (principally U.S. Treasury bonds, mortgage-backed securities, and other government financial assets) have increased by $590 billion per year, on average, but this includes a decline of $128 billion in 2014 (largely due to the Russian financial crisis). Over the same period, holdings of SWF investments in nine countries identified by Bergsten and Gagnon increased by an average of $384 billion per year, but this average includes a $642 billion increase in 2013 and a $673 billion increase in 2014. Thus, as official holdings of foreign exchange reserves by central banks leveled off or declined in 2014, holdings of SWFs skyrocketed. Foreign governments have shifted the composition of their acquisitions, but the levels of net official purchases remained high, in excess of $500 billion in 2014, as shown in Figure A. Overall, holdings of foreign exchange reserves and SWFs increased $974 billion per year, as the figure shows.2 Currency manipulation remains a serious problem today.

Figure A

Change in net holdings of foreign assets of currency manipulators, by type, 2008–2014

Year Foreign exchange reserves Sovereign wealth funds
2008 $591 $629
2009 $732 -$83
2010 $883 $273
2011 $783 $300
2012 $610 $252
2013 $658 $642
2014 -$128 $673
ChartData Download data

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

Source: Author's analysis of data from the International Monetary Fund, Sovereign Wealth Fund Institute, and Central Bank of the Republic of China

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

There is a near one-to-one correlation between the net increase in foreign asset holdings of currency manipulators between 1980 and 2010, and their total current account surplus (the broadest measure of trade in goods, services, and income), as shown by Bergsten and Gagnon (2012, Figure A). This strong correlation continued between 2008 and 2014, as shown in Figure B.3 Figure B also includes the latest (April 2015) IMF projections of the total current account surplus of the 22 currency manipulators for 2015–2020; the surplus is conservatively projected to increase from $855 billion in 2015 to $1.1 trillion in 2020, an increase of 31 percent. The United States will likely absorb the largest share of this increase, because it is the largest trading country, and because the dollar still makes up the majority of the foreign exchange reserves held by most countries.4

Figure B

Change in foreign asset holdings, and current account surplus, of currency manipulators, 2008–2020

Year Current account Current account (projected) Change in foreign assets
2008 $1,240 $1,221
2009 $792 $649
2010 $1,040 $1,156
2011 $1,058 $1,083
2012 $1,105 $862
2013 $973 $1,300
2014 $921 $921 $544
2015 $855
2016 $923
2017 $1,004
2018 $1,047
2019 $1,100
2020 $1,120
ChartData Download data

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

Source: Author's analysis of International Monetary Fund 2015a, 2015b; Sovereign Wealth Fund Institute 2015; and Central Bank of the Republic of China 2015

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

Gagnon (2013) estimates that a country’s current account increases by between 60 and 100 cents for each dollar spent on intervention, confirming the strong correlation shown in Figure B. In more recent research, Bayoumi, Gagnon, and Saborowski (2014) find a somewhat smaller direct effect of intervention on the current account (42 cents on the dollar). However, they also find “that there is an important positive effect of lagged net official flows on current accounts,” which implies that for countries with open capital markets, both stocks and flows of net foreign official assets have positive effects on the current account. This research confirms that currency manipulation is a direct cause of current account imbalances.

Why hasn’t more been done to end currency manipulation?

Currency manipulation “to gain unfair comparative advantage” is prohibited in the charters of both the International Monetary Fund and the World Trade Organization (IMF 2015c, Bergsten 2014). The United States, which has suffered from large and growing trade deficits for 30 years, also has substantial incentives for ending currency manipulation. Yet neither the United States nor the IMF nor the WTO has been able to thwart currency manipulation. A variety of explanations have been advanced to explain why persistent currency manipulation and sustained current account imbalances have been tolerated, globally and by the United States.

Currency policies and trade policies are generally managed by different branches of government. Finance ministries and central banks are typically responsible for currency issues, while trade and commerce officials are responsible for trade policy. Likewise, at the international level, the WTO handles trade while the IMF is responsible for exchange rates. Coordination problems between different agencies at the national and international levels have made it difficult to reconcile trade and exchange rate imbalances (Bergsten 2014).

In the United States, the Omnibus Foreign Trade and Competitiveness Act of 1988 requires the Treasury secretary to make semiannual reports on economic and exchange rate policies (Scott 2010). Since 1988, Treasury has identified three countries as currency manipulators: Taiwan, South Korea, and China, with Taiwan cited in 1988 and again in 1992. Each citation lasted for at least two six-month reporting periods, while China’s lasted for five periods, ending in 1994 (GAO 2005, 13). In each case, Treasury entered into negotiations with the offending country. Each ultimately made “substantial reforms to their foreign exchange regimes” (GAO 2005, 14).

The Treasury has not identified any countries as currency manipulators since 1994. Prior to the formation of the World Trade Organization, the United States had the authority to impose unilateral trade sanctions under Section 301 of the Trade Act of 1974 (as amended), to address unfair trade practices. When the United States joined the WTO in 1994, it gave up Section 301 enforcement actions and agreed to resolve trade disputes through the WTO dispute settlement mechanism (Dunn and Fennell 2004). This eliminated effective tools for enforcement actions against currency manipulation under the 1988 trade act.

Since 1994, Treasury has consistently expressed a preference for quiet diplomacy over open confrontation, and conducting negotiations about exchange rate policies, for example, through the annual Strategic and Economic Dialogues with Chinese officials. Yet despite these efforts, China and other countries continue to manipulate their exchange rates, as shown above. Treasury’s position appears to reflect the mixed effects of exchange rate policy on U.S. interest groups. While large trade deficits and currency manipulation are bad for export- and import-competing industries, implicit currency subsidies are good for U.S. industries that get access to artificially cheap components. Chinese capital inflows (the counterpart of China’s large trade surpluses) helped finance the U.S. federal deficit and the housing boom of the early 2000s. But these same capital flows also contributed to the housing bubble, which caused the financial collapse and Great Recession (Bernanke 2010).

In the WTO, the General Agreement on Tariffs and Trade (GATT) agreement (Article XV.4) states that “contracting parties shall not, by exchange action, frustrate the intent … of this agreement” (GATT 2015). However, Article XV also directs parties to consult with the IMF in the event of problems regarding “exchange arrangements.” Hence, the WTO relies on the IMF for resolution and enforcement of currency manipulation problems.

Article IV (Section 1.iii) of the IMF articles of incorporation clearly states that “each member shall … avoid manipulating exchange rates … to gain an unfair competitive advantage over other members.” However, the IMF has failed to resolve outstanding currency issues (Bergsten 2014). One reason for this is that the IMF has no national enforcement instruments to rely on to enforce its rules (Henning 2008).

Thus, both the United States and the IMF suffer from a common problem when it comes to dealing with currency manipulation. Both lack adequate enforcement mechanisms to compel countries to abandon sustained purchases of foreign exchange reserves which are causing substantial, sustained imbalances in global current account flows.

There is now widespread agreement among economists and elected officials that new tools and institutions are needed to address currency manipulation. A bipartisan majority of members of Congress have called for the inclusion of “strong and enforceable foreign currency manipulation disciplines” in the TPP. Experts such as Peterson Institute for International Economics director emeritus C. Fred Bergsten (2014) and former Treasury secretary Larry Summers (Summers and Balls 2015, 22) have recommended that new trade agreements address currency manipulation. Currency manipulation can and should be addressed through trade agreements such as the TPP (Bivens 2015).

How should currency manipulation be defined and regulated?

The objectives for new policies to end currency manipulation should be based on Article IV of the IMF articles of incorporation. Recent experience suggests that some clarification is needed on the criteria to be used to define currency manipulation. Also, a variety of enforcement tools should be developed, both within the proposed TPP agreement and in related trade legislation.

Bergsten and Gagnon (2012) have identified a clear set of criteria that can be used to define currency manipulation. There are three key elements5:

  1. Sustained net official (government-owned or controlled) purchases of foreign assets. This category must be clearly defined to include both traditional foreign exchange reserves as well as sovereign wealth funds and other government-controlled investments, including foreign investment by state-owned enterprises.
  2. Sustained current account surpluses in excess of some minimum threshold. The 22 countries identified by Bergsten and Gagnon had, on average, a surplus between 2001 and 2011.
  3. Foreign exchange reserves with a value in excess of three months of goods and services imports, or one year of short-term financial liabilities (whichever is larger).

The goal of enforcement actions should be to encourage currency manipulators to move toward current account balance (having neither a surplus nor deficit) within two to three years. For some countries with large stocks of foreign assets, divestiture may be necessary to achieve trade balance within a reasonable time period, due to the hangover impact of large stocks of reserves on current account balances.

Within the TPP, enforcement of currency manipulation standards could be achieved through the dispute settlement process used to enforce other trade, labor, and environmental disciplines in the agreement. Bergsten (2014) recommends that a currency manipulation chapter be included within the TPP. He identifies five different types of penalties which could be included as enforcement tools in the dispute settlement process. These include: a) a “snapback” provision which would withdraw the benefits of the agreement; b) imposition of countervailing duties; c) tariffs; d) other monetary penalties (fines); and e) countervailing currency intervention. The fifth penalty is in the form of a proposal for governments of deficit countries to purchase assets denominated in the currency of interveners to offset their purchases of foreign assets.

Inclusion of a currency manipulation clause in the TPP is an important first step—a way to get the “camel’s nose under the tent”—to begin to create a regime of enforceable currency manipulation disciplines. However, the long-run goal must be to end currency manipulation by all countries, not just those few who join the TPP (Japan, Singapore, and Malaysia), or others that could join in the near future (Korea, Taiwan, and ultimately China). The United States needs other tools to end currency manipulation, and new enforcement tools are the key.

Trade Promotion Authority, which the Obama administration has asked for to help it complete negotiations on the TPP, is being packaged with a number of related measures, such as trade adjustment assistance. Separate legislation to authorize the United States to impose countervailing duties on imports from any country engaging in currency manipulation should be included in that package. Currency manipulation is a direct subsidy to exports, and businesses in the United States should be able to obtain relief from it in the form of countervailing duties. A mechanism for countervailing duties might affect only a small share of total U.S. imports but would be an important signal that currency manipulation will no longer be tolerated.

The employment impacts of ending currency manipulation

Ending currency manipulation could significantly reduce U.S. trade deficits and create millions of U.S. jobs, with job gains in every state and most or all U.S. congressional districts. This section summarizes jobs that could be created by eliminating currency manipulation. as estimated in Scott 2014. (The findings estimate effects at the end of a three-year timeline for implementation; see Scott 2014 for methodology.)

Table 1

Impact of ending currency manipulation on U.S. economy

Scenario*
Change Low impact High impact
Trade deficit (billions of dollars) -$200 -$500
Gross domestic product
in annual billions of dollars +$288 +$720
as a share of GDP** +2.0% +4.9%
Number of jobs +2,300,000 +5,800,000

*The low-impact scenario assumes ending currency manipulation would reduce the trade deficit by $200 billion; the high-impact scenario assumes a $500 billion reduction in the trade deficit. The table shows the hypothetical change in 2015 three years after implementation.

**Percentages shown are relative to baseline forecasts for 2015.

Note: Dollar calculations are in 2005 dollars.

Source: Scott 2014

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

Eliminating currency manipulation would reduce the U.S. trade deficit by $200 billion under a low-impact scenario and $500 billion under a high-impact scenario. This would increase annual U.S. GDP by between $288 billion and $720 billion (between 2.0 percent and 4.9 percent), as shown in Table 1.

Each of the 50 states and the District of Columbia would gain jobs under both the low- and high-impact scenarios. As shown in Table 2 at the end of this memorandum, job gains under the low-impact scenario, expressed as a share of total jobs in the state, would range from 1.06 percent in Washington, D.C., to 2.29 percent in Wisconsin. Job gains under the high-impact scenario would range from 2.64 percent in Washington, D.C., to 5.55 percent in Wisconsin.

Nine of the top 10 states gaining the most jobs (as a share of total employment) in both scenarios are in the Midwest. They are Wisconsin (64,700 to 156,600 jobs), Indiana (61,000 to 152,600 jobs), Iowa (34,000 to 79,600 jobs), Minnesota (55,900 to 135,300 jobs), Michigan (82,800 to 207,200 jobs), Ohio (103,200 to 254,600 jobs), South Dakota (9,200 to 21,100 jobs), Kansas, (28,900 to 67,000 jobs), and Nebraska (19,000 to 44,200 jobs). In the West, Idaho (13,900 to 32,700 jobs) rounds out the top 10 states gaining the most jobs.

Jobs are gained in all but two congressional districts under the low-impact scenario, and in all congressional districts under the high-impact scenario, as shown in Figure C.

Figure C

Jobs created as a share of state employment from ending currency manipulation in high-impact scenario

State District Employment Jobs (high impact) Jobs (low impact) Jobs added as a share of district employment (low impact) Jobs added as a share of district employment (high impact)
Alabama 1 283,000 10,700 4,500 1.59% 3.78%
Alabama 2 276,900 10,900 4,200 1.52% 3.94%
Alabama 3 274,600 12,200 4,500 1.64% 4.44%
Alabama 4 262,900 13,700 5,200 1.98% 5.21%
Alabama 5 311,900 15,600 6,400 2.05% 5.00%
Alabama 6 318,400 12,100 4,600 1.44% 3.80%
Alabama 7 253,500 9,600 3,700 1.46% 3.79%
Alaska Statewide 344,300 10,300 3,900 1.13% 2.99%
Arizona 1 264,900 10,300 4,500 1.70% 3.89%
Arizona 2 299,200 11,400 4,900 1.64% 3.81%
Arizona 3 262,200 10,300 4,300 1.64% 3.93%
Arizona 4 233,500 8,800 3,700 1.58% 3.77%
Arizona 5 317,900 14,200 5,700 1.79% 4.47%
Arizona 6 366,000 13,300 5,400 1.48% 3.63%
Arizona 7 282,300 11,100 4,400 1.56% 3.93%
Arizona 8 301,700 10,900 4,500 1.49% 3.61%
Arizona 9 360,300 14,700 6,100 1.69% 4.08%
Arkansas 1 277,400 14,800 6,100 2.20% 5.34%
Arkansas 2 336,300 11,300 4,400 1.31% 3.36%
Arkansas 3 327,000 15,200 6,000 1.83% 4.65%
Arkansas 4 295,100 15,000 6,000 2.03% 5.08%
California 1 260,300 9,400 3,900 1.50% 3.61%
California 2 323,100 11,900 4,700 1.45% 3.68%
California 3 286,600 10,700 4,500 1.57% 3.73%
California 4 294,200 10,400 4,200 1.43% 3.54%
California 5 326,800 13,300 5,500 1.68% 4.07%
California 6 288,300 8,900 3,500 1.21% 3.09%
California 7 313,200 9,900 3,900 1.25% 3.16%
California 8 235,500 7,600 3,000 1.27% 3.23%
California 9 275,300 11,000 4,600 1.67% 4.00%
California 10 277,200 12,200 5,300 1.91% 4.40%
California 11 324,200 11,600 4,600 1.42% 3.58%
California 12 399,400 13,200 4,700 1.18% 3.30%
California 13 340,200 12,000 4,300 1.26% 3.53%
California 14 364,000 13,800 5,100 1.40% 3.79%
California 15 336,400 15,700 5,900 1.75% 4.67%
California 16 244,900 14,700 6,700 2.74% 6.00%
California 17 346,100 24,400 9,300 2.69% 7.05%
California 18 344,500 19,900 7,700 2.24% 5.78%
California 19 324,000 19,300 7,500 2.31% 5.96%
California 20 302,500 17,700 8,000 2.64% 5.85%
California 21 243,800 21,100 10,100 4.14% 8.65%
California 22 289,600 14,100 6,300 2.18% 4.87%
California 23 274,100 11,700 4,700 1.71% 4.27%
California 24 323,500 16,000 7,000 2.16% 4.95%
California 25 302,700 13,700 5,700 1.88% 4.53%
California 26 325,900 17,900 7,700 2.36% 5.49%
California 27 332,200 10,300 3,100 0.93% 3.10%
California 28 359,900 10,900 3,400 0.94% 3.03%
California 29 303,700 12,300 4,400 1.45% 4.05%
California 30 358,200 12,300 4,200 1.17% 3.43%
California 31 292,200 10,300 3,900 1.33% 3.52%
California 32 293,800 11,900 4,000 1.36% 4.05%
California 33 364,200 14,200 5,200 1.43% 3.90%
California 34 309,400 6,300 -2,100 -0.68% 2.04%
California 35 284,800 11,700 3,900 1.37% 4.11%
California 36 251,900 9,700 4,100 1.63% 3.85%
California 37 335,600 10,600 2,900 0.86% 3.16%
California 38 313,300 12,100 4,000 1.28% 3.86%
California 39 332,000 14,000 5,300 1.60% 4.22%
California 40 280,500 7,900 -800 -0.29% 2.82%
California 41 271,900 10,600 4,100 1.51% 3.90%
California 42 307,000 12,800 5,100 1.66% 4.17%
California 43 302,800 12,300 4,400 1.45% 4.06%
California 44 270,600 11,200 3,300 1.22% 4.14%
California 45 354,400 16,100 6,400 1.81% 4.54%
California 46 314,400 14,600 5,100 1.62% 4.64%
California 47 327,600 12,400 4,500 1.37% 3.79%
California 48 352,600 16,000 5,900 1.67% 4.54%
California 49 299,700 12,500 4,700 1.57% 4.17%
California 50 296,200 14,000 5,900 1.99% 4.73%
California 51 258,600 10,000 4,000 1.55% 3.87%
California 52 350,100 15,500 6,200 1.77% 4.43%
California 53 342,700 12,400 5,000 1.46% 3.62%
Colorado 1 384,400 13,700 5,400 1.40% 3.56%
Colorado 2 384,600 15,900 6,500 1.69% 4.13%
Colorado 3 331,400 12,600 5,100 1.54% 3.80%
Colorado 4 344,100 16,000 6,500 1.89% 4.65%
Colorado 5 315,900 11,600 4,800 1.52% 3.67%
Colorado 6 369,600 12,900 5,100 1.38% 3.49%
Colorado 7 362,500 12,900 5,000 1.38% 3.56%
Connecticut 1 349,800 15,000 6,300 1.80% 4.29%
Connecticut 2 348,600 15,900 6,900 1.98% 4.56%
Connecticut 3 352,700 15,700 6,600 1.87% 4.45%
Connecticut 4 343,000 13,700 5,500 1.60% 3.99%
Connecticut 5 348,300 16,800 7,100 2.04% 4.82%
Delaware Statewide 420,400 16,200 6,700 1.59% 3.85%
DC Statewide 310,600 8,200 3,300 1.06% 2.64%
Florida 1 303,900 9,500 3,900 1.28% 3.13%
Florida 2 301,500 8,600 3,400 1.13% 2.85%
Florida 3 277,000 8,900 3,700 1.34% 3.21%
Florida 4 329,900 11,600 4,700 1.42% 3.52%
Florida 5 284,000 9,800 4,000 1.41% 3.45%
Florida 6 283,200 10,300 4,300 1.52% 3.64%
Florida 7 322,500 11,100 4,500 1.40% 3.44%
Florida 8 283,400 11,600 4,900 1.73% 4.09%
Florida 9 317,200 9,700 3,800 1.20% 3.06%
Florida 10 331,500 11,600 4,700 1.42% 3.50%
Florida 11 217,400 7,000 2,800 1.29% 3.22%
Florida 12 283,200 9,500 3,800 1.34% 3.35%
Florida 13 309,200 11,800 4,600 1.49% 3.82%
Florida 14 320,700 10,700 4,200 1.31% 3.34%
Florida 15 304,200 11,100 4,600 1.51% 3.65%
Florida 16 276,100 9,300 3,700 1.34% 3.37%
Florida 17 248,700 10,800 4,800 1.93% 4.34%
Florida 18 284,000 9,200 3,700 1.30% 3.24%
Florida 19 265,200 9,100 3,700 1.40% 3.43%
Florida 20 302,100 9,900 3,900 1.29% 3.28%
Florida 21 316,800 9,800 3,900 1.23% 3.09%
Florida 22 332,000 11,000 4,300 1.30% 3.31%
Florida 23 339,900 10,800 4,200 1.24% 3.18%
Florida 24 293,400 8,500 3,200 1.09% 2.90%
Florida 25 326,000 11,500 4,400 1.35% 3.53%
Florida 26 335,600 11,200 4,600 1.37% 3.34%
Florida 27 313,600 10,000 3,800 1.21% 3.19%
Georgia 1 286,100 11,000 4,800 1.68% 3.84%
Georgia 2 251,200 10,400 4,300 1.71% 4.14%
Georgia 3 285,800 12,200 4,600 1.61% 4.27%
Georgia 4 311,700 11,400 4,500 1.44% 3.66%
Georgia 5 318,100 10,900 4,300 1.35% 3.43%
Georgia 6 361,200 14,000 5,500 1.52% 3.88%
Georgia 7 312,500 12,600 4,900 1.57% 4.03%
Georgia 8 272,700 10,600 4,300 1.58% 3.89%
Georgia 9 284,600 12,600 5,000 1.76% 4.43%
Georgia 10 287,400 11,200 4,400 1.53% 3.90%
Georgia 11 340,900 13,800 5,500 1.61% 4.05%
Georgia 12 278,200 11,100 4,300 1.55% 3.99%
Georgia 13 312,800 10,900 4,200 1.34% 3.48%
Georgia 14 290,700 14,800 5,400 1.86% 5.09%
Hawaii 1 330,100 8,700 3,200 0.97% 2.64%
Hawaii 2 299,400 9,500 4,000 1.34% 3.17%
Idaho 1 329,900 14,800 6,200 1.88% 4.49%
Idaho 2 355,000 17,900 7,800 2.20% 5.04%
Illinois 1 290,200 9,900 3,700 1.27% 3.41%
Illinois 2 278,200 11,100 4,500 1.62% 3.99%
Illinois 3 319,500 12,900 5,100 1.60% 4.04%
Illinois 4 326,600 15,200 6,000 1.84% 4.65%
Illinois 5 397,600 15,900 6,300 1.58% 4.00%
Illinois 6 355,600 17,800 6,900 1.94% 5.01%
Illinois 7 298,500 10,800 4,300 1.44% 3.62%
Illinois 8 366,300 17,900 7,200 1.97% 4.89%
Illinois 9 347,200 12,800 4,700 1.35% 3.69%
Illinois 10 324,800 16,300 6,500 2.00% 5.02%
Illinois 11 347,300 15,200 5,800 1.67% 4.38%
Illinois 12 301,000 11,800 4,800 1.59% 3.92%
Illinois 13 326,600 12,800 5,400 1.65% 3.92%
Illinois 14 351,000 17,700 7,300 2.08% 5.04%
Illinois 15 316,500 14,900 6,100 1.93% 4.71%
Illinois 16 330,800 18,100 7,700 2.33% 5.47%
Illinois 17 311,700 18,300 7,800 2.50% 5.87%
Illinois 18 337,500 17,000 7,400 2.19% 5.04%
Indiana 1 310,600 16,500 6,800 2.19% 5.31%
Indiana 2 317,800 20,000 7,900 2.49% 6.29%
Indiana 3 327,000 20,800 8,000 2.45% 6.36%
Indiana 4 328,500 17,600 7,200 2.19% 5.36%
Indiana 5 357,700 14,600 5,800 1.62% 4.08%
Indiana 6 311,900 18,000 7,400 2.37% 5.77%
Indiana 7 312,200 12,600 5,000 1.60% 4.04%
Indiana 8 329,300 16,300 6,300 1.91% 4.95%
Indiana 9 339,400 16,100 6,500 1.92% 4.74%
Iowa 1 392,300 22,700 9,700 2.47% 5.79%
Iowa 2 373,400 20,300 8,500 2.28% 5.44%
Iowa 3 390,800 15,900 6,700 1.71% 4.07%
Iowa 4 382,300 20,800 9,100 2.38% 5.44%
Kansas 1 345,900 17,900 7,800 2.25% 5.17%
Kansas 2 339,900 14,000 5,700 1.68% 4.12%
Kansas 3 370,300 14,100 5,600 1.51% 3.81%
Kansas 4 332,900 21,000 9,800 2.94% 6.31%
Kentucky 1 284,800 13,700 5,100 1.79% 4.81%
Kentucky 2 317,100 15,300 5,700 1.80% 4.82%
Kentucky 3 333,300 14,900 5,800 1.74% 4.47%
Kentucky 4 333,500 14,700 5,800 1.74% 4.41%
Kentucky 5 234,300 8,600 3,200 1.37% 3.67%
Kentucky 6 335,400 15,300 6,100 1.82% 4.56%
Louisiana 1 354,000 12,000 4,800 1.36% 3.39%
Louisiana 2 329,000 10,400 4,200 1.28% 3.16%
Louisiana 3 328,100 13,100 5,300 1.62% 3.99%
Louisiana 4 311,100 11,200 4,200 1.35% 3.60%
Louisiana 5 283,900 10,600 4,400 1.55% 3.73%
Louisiana 6 367,800 12,400 4,900 1.33% 3.37%
Maine 1 340,400 13,200 5,400 1.59% 3.88%
Maine 2 302,700 10,800 3,900 1.29% 3.57%
Maryland 1 342,300 12,900 5,300 1.55% 3.77%
Maryland 2 351,700 10,600 4,100 1.17% 3.01%
Maryland 3 369,500 11,200 4,600 1.24% 3.03%
Maryland 4 384,100 11,100 4,400 1.15% 2.89%
Maryland 5 368,200 10,100 4,000 1.09% 2.74%
Maryland 6 363,200 11,700 4,700 1.29% 3.22%
Maryland 7 315,700 9,800 3,900 1.24% 3.10%
Maryland 8 400,100 12,200 4,900 1.22% 3.05%
Massachusetts 1 341,000 13,600 5,600 1.64% 3.99%
Massachusetts 2 356,500 14,800 6,000 1.68% 4.15%
Massachusetts 3 355,400 18,200 7,100 2.00% 5.12%
Massachusetts 4 374,800 15,100 5,900 1.57% 4.03%
Massachusetts 5 387,400 14,800 5,700 1.47% 3.82%
Massachusetts 6 372,000 14,700 5,900 1.59% 3.95%
Massachusetts 7 369,800 11,500 4,500 1.22% 3.11%
Massachusetts 8 375,600 13,600 5,300 1.41% 3.62%
Massachusetts 9 352,300 12,200 4,600 1.31% 3.46%
Michigan 1 290,200 12,600 5,200 1.79% 4.34%
Michigan 2 315,900 17,900 7,200 2.28% 5.67%
Michigan 3 315,300 16,300 6,500 2.06% 5.17%
Michigan 4 286,300 14,200 5,700 1.99% 4.96%
Michigan 5 264,800 11,400 4,600 1.74% 4.31%
Michigan 6 310,400 18,400 7,700 2.48% 5.93%
Michigan 7 299,100 15,500 6,200 2.07% 5.18%
Michigan 8 330,800 15,000 5,800 1.75% 4.53%
Michigan 9 326,100 16,200 6,300 1.93% 4.97%
Michigan 10 308,700 18,100 7,400 2.40% 5.86%
Michigan 11 342,100 17,100 6,600 1.93% 5.00%
Michigan 12 313,800 13,500 5,300 1.69% 4.30%
Michigan 13 230,700 10,300 4,000 1.73% 4.46%
Michigan 14 257,700 10,700 4,200 1.63% 4.15%
Minnesota 1 348,200 18,500 7,800 2.24% 5.31%
Minnesota 2 358,300 17,500 6,800 1.90% 4.88%
Minnesota 3 353,800 19,300 7,800 2.20% 5.46%
Minnesota 4 336,000 14,600 5,900 1.76% 4.35%
Minnesota 5 352,000 15,700 6,400 1.82% 4.46%
Minnesota 6 348,700 17,700 7,400 2.12% 5.08%
Minnesota 7 328,700 19,100 8,400 2.56% 5.81%
Minnesota 8 303,400 12,900 5,300 1.75% 4.25%
Mississippi 1 305,600 13,300 4,900 1.60% 4.35%
Mississippi 2 266,900 11,400 4,800 1.80% 4.27%
Mississippi 3 303,900 11,700 4,600 1.51% 3.85%
Mississippi 4 304,900 11,500 4,700 1.54% 3.77%
Montana Statewide 480,000 19,200 8,200 1.71% 4.00%
Missouri 1 331,500 11,900 4,600 1.39% 3.59%
Missouri 2 378,600 17,100 7,000 1.85% 4.52%
Missouri 3 370,000 16,400 6,800 1.84% 4.43%
Missouri 4 324,900 13,000 5,300 1.63% 4.00%
Missouri 5 345,300 13,400 5,400 1.56% 3.88%
Missouri 6 355,900 15,700 6,500 1.83% 4.41%
Missouri 7 337,400 14,900 5,900 1.75% 4.42%
Missouri 8 298,500 14,400 5,700 1.91% 4.82%
Nebraska 1 321,700 14,400 6,100 1.90% 4.48%
Nebraska 2 316,300 11,000 4,300 1.36% 3.48%
Nebraska 3 305,600 18,800 8,500 2.78% 6.15%
Nevada 1 284,700 9,100 3,500 1.23% 3.20%
Nevada 2 309,400 12,300 5,100 1.65% 3.98%
Nevada 3 336,500 10,000 3,900 1.16% 2.97%
Nevada 4 274,300 8,400 3,400 1.24% 3.06%
New Hampshire 1 352,600 15,600 6,300 1.79% 4.42%
New Hampshire 2 332,200 15,700 6,400 1.93% 4.73%
New Jersey 1 339,200 11,900 4,600 1.36% 3.51%
New Jersey 2 324,400 11,100 4,500 1.39% 3.42%
New Jersey 3 344,200 12,000 4,900 1.42% 3.49%
New Jersey 4 326,400 10,300 3,900 1.19% 3.16%
New Jersey 5 356,100 14,600 5,600 1.57% 4.10%
New Jersey 6 353,600 13,000 4,700 1.33% 3.68%
New Jersey 7 377,100 15,900 6,300 1.67% 4.22%
New Jersey 8 371,000 12,700 3,900 1.05% 3.42%
New Jersey 9 338,500 12,400 4,500 1.33% 3.66%
New Jersey 10 310,700 9,600 3,500 1.13% 3.09%
New Jersey 11 358,800 13,900 5,400 1.51% 3.87%
New Jersey 12 352,400 13,500 5,500 1.56% 3.83%
New Mexico 1 311,900 10,600 4,400 1.41% 3.40%
New Mexico 2 273,100 10,500 4,400 1.61% 3.84%
New Mexico 3 284,800 9,700 3,700 1.30% 3.41%
New York 1 343,300 11,400 4,500 1.31% 3.32%
New York 2 357,800 12,600 4,800 1.34% 3.52%
New York 3 336,700 10,100 3,700 1.10% 3.00%
New York 4 342,500 9,600 3,600 1.05% 2.80%
New York 5 336,200 9,300 3,200 0.95% 2.77%
New York 6 327,000 9,100 3,000 0.92% 2.78%
New York 7 322,200 8,600 1,900 0.59% 2.67%
New York 8 292,700 8,000 2,700 0.92% 2.73%
New York 9 324,900 8,900 3,100 0.95% 2.74%
New York 10 360,300 10,300 3,600 1.00% 2.86%
New York 11 317,500 8,400 2,700 0.85% 2.65%
New York 12 418,800 13,000 4,400 1.05% 3.10%
New York 13 317,200 9,400 3,300 1.04% 2.96%
New York 14 341,800 9,600 3,200 0.94% 2.81%
New York 15 255,900 7,500 2,600 1.02% 2.93%
New York 16 323,600 9,800 3,600 1.11% 3.03%
New York 17 341,400 10,100 3,600 1.05% 2.96%
New York 18 332,100 11,500 4,500 1.36% 3.46%
New York 19 327,300 12,200 4,900 1.50% 3.73%
New York 20 357,600 11,200 4,500 1.26% 3.13%
New York 21 309,200 11,100 4,100 1.33% 3.59%
New York 22 320,200 13,900 5,800 1.81% 4.34%
New York 23 324,600 15,100 6,100 1.88% 4.65%
New York 24 327,300 14,100 5,800 1.77% 4.31%
New York 25 335,400 14,200 5,700 1.70% 4.23%
New York 26 327,700 11,900 4,700 1.43% 3.63%
New York 27 337,800 15,500 6,400 1.89% 4.59%
North Carolina 1 291,800 12,900 5,300 1.82% 4.42%
North Carolina 2 303,800 12,600 4,200 1.38% 4.15%
North Carolina 3 305,600 11,100 4,700 1.54% 3.63%
North Carolina 4 350,900 11,800 4,600 1.31% 3.36%
North Carolina 5 324,500 13,900 4,900 1.51% 4.28%
North Carolina 6 341,800 12,900 3,900 1.14% 3.77%
North Carolina 7 315,400 12,300 5,000 1.59% 3.90%
North Carolina 8 301,700 12,400 4,100 1.36% 4.11%
North Carolina 9 371,400 16,300 6,300 1.70% 4.39%
North Carolina 10 324,000 14,800 5,400 1.67% 4.57%
North Carolina 11 295,400 12,400 4,700 1.59% 4.20%
North Carolina 12 319,800 11,800 4,400 1.38% 3.69%
North Carolina 13 349,900 14,900 5,900 1.69% 4.26%
North Dakota Statewide 370,800 17,000 7,400 2.00% 4.58%
Ohio 1 332,300 14,700 6,000 1.81% 4.42%
Ohio 2 323,600 14,600 6,000 1.85% 4.51%
Ohio 3 333,000 11,400 4,300 1.29% 3.42%
Ohio 4 317,900 19,700 8,000 2.52% 6.20%
Ohio 5 334,200 18,700 7,600 2.27% 5.60%
Ohio 6 292,300 14,900 6,100 2.09% 5.10%
Ohio 7 326,800 18,800 7,500 2.29% 5.75%
Ohio 8 328,800 19,100 8,000 2.43% 5.81%
Ohio 9 315,000 15,200 6,200 1.97% 4.83%
Ohio 10 312,800 13,500 5,400 1.73% 4.32%
Ohio 11 275,200 11,900 4,800 1.74% 4.32%
Ohio 12 359,500 14,600 6,000 1.67% 4.06%
Ohio 13 320,400 16,000 6,200 1.94% 4.99%
Ohio 14 349,700 19,800 8,300 2.37% 5.66%
Ohio 15 336,400 13,600 5,400 1.61% 4.04%
Ohio 16 355,600 18,200 7,400 2.08% 5.12%
Oklahoma 1 361,900 17,200 6,700 1.85% 4.75%
Oklahoma 2 290,300 13,600 5,500 1.89% 4.68%
Oklahoma 3 329,900 15,200 6,300 1.91% 4.61%
Oklahoma 4 350,900 13,100 5,000 1.42% 3.73%
Oklahoma 5 348,800 12,100 4,400 1.26% 3.47%
Oregon 1 377,200 20,800 7,800 2.07% 5.51%
Oregon 2 314,200 15,500 6,500 2.07% 4.93%
Oregon 3 383,300 15,200 5,600 1.46% 3.97%
Oregon 4 309,000 13,300 5,600 1.81% 4.30%
Oregon 5 326,700 13,900 5,900 1.81% 4.25%
Pennsylvania 1 273,300 9,200 3,500 1.28% 3.37%
Pennsylvania 2 273,100 8,500 3,400 1.24% 3.11%
Pennsylvania 3 317,700 16,700 6,800 2.14% 5.26%
Pennsylvania 4 342,900 15,200 6,200 1.81% 4.43%
Pennsylvania 5 316,800 15,500 6,200 1.96% 4.89%
Pennsylvania 6 362,300 16,500 6,700 1.85% 4.55%
Pennsylvania 7 339,700 15,500 6,400 1.88% 4.56%
Pennsylvania 8 357,800 15,200 6,200 1.73% 4.25%
Pennsylvania 9 304,800 13,500 5,200 1.71% 4.43%
Pennsylvania 10 312,500 13,800 5,500 1.76% 4.42%
Pennsylvania 11 329,300 13,700 5,600 1.70% 4.16%
Pennsylvania 12 331,900 14,900 6,000 1.81% 4.49%
Pennsylvania 13 339,000 13,300 5,100 1.50% 3.92%
Pennsylvania 14 323,200 11,500 4,500 1.39% 3.56%
Pennsylvania 15 343,800 16,000 6,500 1.89% 4.65%
Pennsylvania 16 327,700 15,800 6,400 1.95% 4.82%
Pennsylvania 17 312,600 12,600 4,800 1.54% 4.03%
Pennsylvania 18 345,000 15,800 6,400 1.86% 4.58%
Rhode Island 1 250,900 10,800 4,300 1.71% 4.30%
Rhode Island 2 260,300 9,900 4,000 1.54% 3.80%
South Carolina 1 299,800 11,900 4,900 1.63% 3.97%
South Carolina 2 305,600 12,300 5,000 1.64% 4.02%
South Carolina 3 264,500 14,900 5,900 2.23% 5.63%
South Carolina 4 301,000 14,700 5,600 1.86% 4.88%
South Carolina 5 275,200 13,400 5,300 1.93% 4.87%
South Carolina 6 253,500 10,800 4,500 1.78% 4.26%
South Carolina 7 269,400 11,200 4,400 1.63% 4.16%
South Dakota Statewide 415,600 21,100 9,200 2.21% 5.08%
Tennessee 1 297,600 14,100 5,700 1.92% 4.74%
Tennessee 2 327,200 12,300 4,800 1.47% 3.76%
Tennessee 3 297,000 12,900 4,700 1.58% 4.34%
Tennessee 4 314,500 13,900 5,100 1.62% 4.42%
Tennessee 5 353,400 12,500 4,900 1.39% 3.54%
Tennessee 6 304,500 13,400 5,200 1.71% 4.40%
Tennessee 7 285,800 12,900 4,900 1.71% 4.51%
Tennessee 8 299,200 14,600 6,100 2.04% 4.88%
Tennessee 9 305,300 11,400 4,400 1.44% 3.73%
Texas 1 297,700 12,500 5,100 1.71% 4.20%
Texas 2 364,600 16,100 5,600 1.54% 4.42%
Texas 3 371,200 16,800 6,300 1.70% 4.53%
Texas 4 299,300 12,600 5,100 1.70% 4.21%
Texas 5 300,800 12,000 4,800 1.60% 3.99%
Texas 6 348,800 14,800 5,600 1.61% 4.24%
Texas 7 376,300 16,200 5,800 1.54% 4.31%
Texas 8 309,200 13,600 5,300 1.71% 4.40%
Texas 9 326,400 11,700 4,400 1.35% 3.58%
Texas 10 342,600 14,400 5,700 1.66% 4.20%
Texas 11 308,800 12,800 4,700 1.52% 4.15%
Texas 12 337,500 16,900 6,800 2.01% 5.01%
Texas 13 309,000 13,700 5,700 1.84% 4.43%
Texas 14 303,300 12,900 5,600 1.85% 4.25%
Texas 15 280,900 9,200 3,700 1.32% 3.28%
Texas 16 281,300 8,200 2,600 0.92% 2.92%
Texas 17 329,300 12,400 4,700 1.43% 3.77%
Texas 18 306,400 12,800 4,900 1.60% 4.18%
Texas 19 310,700 12,100 4,900 1.58% 3.89%
Texas 20 311,400 9,600 3,700 1.19% 3.08%
Texas 21 361,200 11,700 4,600 1.27% 3.24%
Texas 22 352,500 14,000 5,400 1.53% 3.97%
Texas 23 289,700 9,800 3,500 1.21% 3.38%
Texas 24 388,600 16,700 6,600 1.70% 4.30%
Texas 25 302,200 12,200 4,800 1.59% 4.04%
Texas 26 368,300 16,400 6,500 1.76% 4.45%
Texas 27 305,600 12,700 5,200 1.70% 4.16%
Texas 28 266,300 9,300 3,900 1.46% 3.49%
Texas 29 292,900 13,000 5,200 1.78% 4.44%
Texas 30 292,300 10,300 4,000 1.37% 3.52%
Texas 31 323,000 12,600 4,700 1.46% 3.90%
Texas 32 360,900 15,700 5,900 1.63% 4.35%
Texas 33 283,900 12,200 4,500 1.59% 4.30%
Texas 34 242,200 8,400 3,400 1.40% 3.47%
Texas 35 318,200 10,100 3,900 1.23% 3.17%
Texas 36 291,900 14,000 5,900 2.02% 4.80%
Utah 1 312,400 13,900 5,700 1.82% 4.45%
Utah 2 305,700 12,200 4,800 1.57% 3.99%
Utah 3 311,200 11,500 4,600 1.48% 3.70%
Utah 4 331,500 14,000 5,600 1.69% 4.22%
Vermont Statewide 327,300 13,600 5,600 1.71% 4.16%
Virginia 1 352,400 10,800 4,500 1.28% 3.06%
Virginia 2 339,800 10,800 4,400 1.29% 3.18%
Virginia 3 320,100 10,200 4,200 1.31% 3.19%
Virginia 4 327,900 12,100 5,000 1.52% 3.69%
Virginia 5 316,100 12,000 4,600 1.46% 3.80%
Virginia 6 339,900 13,000 5,200 1.53% 3.82%
Virginia 7 364,600 12,000 4,900 1.34% 3.29%
Virginia 8 423,700 12,100 4,800 1.13% 2.86%
Virginia 9 298,400 13,400 5,200 1.74% 4.49%
Virginia 10 376,400 12,800 5,000 1.33% 3.40%
Virginia 11 400,900 11,900 4,800 1.20% 2.97%
Washington 1 332,300 16,600 7,300 2.20% 5.00%
Washington 2 318,900 15,900 7,300 2.29% 4.99%
Washington 3 284,500 13,100 5,300 1.86% 4.60%
Washington 4 284,500 16,500 7,600 2.67% 5.80%
Washington 5 291,500 11,600 4,900 1.68% 3.98%
Washington 6 275,500 9,600 4,000 1.45% 3.48%
Washington 7 380,000 15,400 6,500 1.71% 4.05%
Washington 8 318,000 16,200 7,400 2.33% 5.09%
Washington 9 341,400 15,800 6,900 2.02% 4.63%
Washington 10 291,300 9,600 4,000 1.37% 3.30%
West Virginia 1 258,700 10,500 4,300 1.66% 4.06%
West Virginia 2 266,900 9,500 3,700 1.39% 3.56%
West Virginia 3 223,000 8,800 3,800 1.70% 3.95%
Wisconsin 1 342,500 19,800 8,200 2.39% 5.78%
Wisconsin 2 390,000 17,500 7,300 1.87% 4.49%
Wisconsin 3 353,500 17,800 7,300 2.07% 5.04%
Wisconsin 4 308,000 14,800 5,800 1.88% 4.81%
Wisconsin 5 370,600 22,500 9,300 2.51% 6.07%
Wisconsin 6 353,600 23,800 9,900 2.80% 6.73%
Wisconsin 7 338,400 19,100 7,900 2.33% 5.64%
Wisconsin 8 362,800 21,200 9,000 2.48% 5.84%
Wyoming Statewide 290,000 10,900 4,200 1.45% 3.76%
ChartData Download data

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

Source: Scott 2014

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

Under the high-impact scenario, each of the top 20 districts by jobs created as a share of district employment would gain at least 14,700 jobs and as many as 24,400 jobs (gains representing between 5.79 percent and 8.65 percent of total district employment), as shown in Table 3 at the end of this memorandum. Of the top 20 congressional districts, five are in California; three are in Wisconsin; two each in Indiana, Ohio, and Michigan; and one each in Kansas, Nebraska, Illinois, Minnesota, Washington, and Iowa. Under the high-impact scenario, among all districts, net job gains range from a low of 6,300 jobs in the 34th Congressional District in California to a high of 24,400 jobs in the 17th Congressional District in California.

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

Table 2

Net U.S. jobs created by eliminating currency manipulation, by state (ranked by jobs gained as a share of total state employment under high-impact scenario)

Scenario*
Low impact High impact
Rank State State employment (2011 average) Net jobs created Jobs created as a share of state employment Net jobs created Jobs created as a share of state employment
1 Wisconsin 2,819,475 64,700 2.29% 156,600 5.55%
2 Indiana 2,934,500 61,000 2.08% 152,600 5.20%
3 Iowa 1,538,755 34,000 2.21% 79,600 5.17%
4 South Dakota 415,625 9,200 2.21% 21,100 5.08%
5 Minnesota 2,728,880 55,900 2.05% 135,300 4.96%
6 Michigan 4,191,880 82,800 1.98% 207,200 4.94%
7 Ohio 5,213,455 103,200 1.98% 254,600 4.88%
8 Kansas 1,389,040 28,900 2.08% 67,000 4.82%
9 Idaho 684,915 13,900 2.03% 32,700 4.77%
10 Nebraska 943,645 19,000 2.01% 44,200 4.68%
11 Oregon 1,710,335 31,300 1.83% 78,600 4.60%
12 North Dakota 370,830 7,400 2.00% 17,000 4.58%
13 New Hampshire 684,805 12,700 1.85% 31,300 4.57%
14 Arkansas 1,235,755 22,500 1.82% 56,300 4.56%
15 South Carolina 1,968,925 35,600 1.81% 89,300 4.54%
16 Washington 3,118,000 61,300 1.97% 140,300 4.50%
17 Illinois 5,926,850 107,500 1.81% 266,400 4.49%
18 Kentucky 1,838,400 31,800 1.73% 82,500 4.49%
19 Connecticut 1,742,495 32,400 1.86% 77,000 4.42%
20 Pennsylvania 5,853,320 101,400 1.73% 253,000 4.32%
21 Alabama 1,981,095 33,000 1.67% 85,000 4.29%
22 Missouri 2,742,055 47,200 1.72% 116,800 4.26%
23 Tennessee 2,784,460 45,800 1.64% 118,100 4.24%
24 Oklahoma 1,681,760 27,900 1.66% 71,100 4.23%
25 California 16,426,695 258,400 1.57% 687,100 4.18%
26 Vermont 327,300 5,600 1.71% 13,600 4.16%
27 Utah 1,260,805 20,800 1.65% 51,600 4.09%
28 Mississippi 1,181,295 18,900 1.60% 47,900 4.05%
29 North Carolina 4,195,810 63,400 1.51% 170,000 4.05%
30 Rhode Island 511,235 8,300 1.62% 20,700 4.05%
31 Texas 11,455,070 179,100 1.56% 460,400 4.02%
32 Montana 479,990 8,200 1.71% 19,200 4.00%
33 Georgia 4,193,775 65,900 1.57% 167,600 4.00%
34 Arizona 2,687,990 43,500 1.62% 105,100 3.91%
35 Massachusetts 3,284,720 50,600 1.54% 128,400 3.91%
36 Delaware 420,365 6,700 1.59% 16,200 3.85%
37 West Virginia 748,560 11,800 1.58% 28,800 3.85%
38 Colorado 2,492,420 38,300 1.54% 95,700 3.84%
39 Wyoming 289,975 4,200 1.45% 10,900 3.76%
40 Maine 643,105 9,300 1.45% 24,000 3.73%
41 New Jersey 4,152,515 57,200 1.38% 150,900 3.63%
42 New Mexico 869,775 12,500 1.44% 30,800 3.54%
43 Louisiana 1,973,940 27,800 1.41% 69,700 3.53%
44 Virginia 3,860,130 52,500 1.36% 131,300 3.40%
45 Florida 8,101,900 110,200 1.36% 274,000 3.38%
46 New York 8,959,015 109,900 1.23% 296,400 3.31%
47 Nevada 1,204,880 16,000 1.33% 39,800 3.30%
48 Maryland 2,894,565 35,800 1.24% 89,400 3.09%
49 Alaska 344,345 3,900 1.13% 10,300 2.99%
50 Hawaii 629,525 7,200 1.14% 18,200 2.89%
51 District of Columbia 310,605 3,300 1.06% 8,200 2.64%
Total** 140,399,600 2,300,000 1.64% 5,800,000 4.13%

*The low-impact scenario assumes ending currency manipulation would reduce the trade deficit by $200 billion; the high-impact scenario assumes a $500 billion reduction in the trade deficit. The table shows the hypothetical change in 2015 three years after implementation.

**Total may vary slightly due to rounding.

Source: Scott 2014

72.

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

Table 3

Net U.S. jobs created by eliminating currency manipulation, by congressional district (ranked by jobs gained as a share of total district employment under high-impact scenario)

Scenario*
Low impact High impact
Rank State District District employment (2011 average) Net jobs created Jobs created as a share of state employment Net jobs created Jobs created as a share of state employment
1 California 21 243,800 10,100 4.1% 21,100 8.7%
2 California 17 346,100 9,300 2.7% 24,400 7.0%
3 Wisconsin 6 353,600 9,900 2.8% 23,800 6.7%
4 Indiana 3 327,000 8,000 2.4% 20,800 6.4%
5 Kansas 4 332,900 9,800 2.9% 21,000 6.3%
6 Indiana 2 317,800 7,900 2.5% 20,000 6.3%
7 Ohio 4 317,900 8,000 2.5% 19,700 6.2%
8 Nebraska 3 305,600 8,500 2.8% 18,800 6.2%
9 Wisconsin 5 370,600 9,300 2.5% 22,500 6.1%
10 California 16 244,900 6,700 2.7% 14,700 6.0%
11 California 19 324,000 7,500 2.3% 19,300 6.0%
12 Michigan 6 310,400 7,700 2.5% 18,400 5.9%
13 Illinois 17 311,700 7,800 2.5% 18,300 5.9%
14 Michigan 10 308,700 7,400 2.4% 18,100 5.9%
15 California 20 302,500 8,000 2.6% 17,700 5.9%
16 Wisconsin 8 362,800 9,000 2.5% 21,200 5.8%
17 Minnesota 7 328,700 8,400 2.6% 19,100 5.8%
18 Ohio 8 328,800 8,000 2.4% 19,100 5.8%
19 Washington 4 284,500 7,600 2.7% 16,500 5.8%
20 Iowa 1 392,300 9,700 2.5% 22,700 5.8%
21 Wisconsin 1 342,500 8,200 2.4% 19,800 5.8%
22 California 18 344,500 7,700 2.2% 19,900 5.8%
23 Indiana 6 311,900 7,400 2.4% 18,000 5.8%
24 Ohio 7 326,800 7,500 2.3% 18,800 5.8%
25 Michigan 2 315,900 7,200 2.3% 17,900 5.7%
26 Ohio 14 349,700 8,300 2.4% 19,800 5.7%
27 Wisconsin 7 338,400 7,900 2.3% 19,100 5.6%
28 South Carolina 3 264,500 5,900 2.2% 14,900 5.6%
29 Ohio 5 334,200 7,600 2.3% 18,700 5.6%
30 Oregon 1 377,200 7,800 2.1% 20,800 5.5%
31 California 26 325,900 7,700 2.4% 17,900 5.5%
32 Illinois 16 330,800 7,700 2.3% 18,100 5.5%
33 Minnesota 3 353,800 7,800 2.2% 19,300 5.5%
34 Iowa 4 382,300 9,100 2.4% 20,800 5.4%
35 Iowa 2 373,400 8,500 2.3% 20,300 5.4%
36 Indiana 4 328,500 7,200 2.2% 17,600 5.4%
37 Arkansas 1 277,400 6,100 2.2% 14,800 5.3%
38 Minnesota 1 348,200 7,800 2.2% 18,500 5.3%
39 Indiana 1 310,600 6,800 2.2% 16,500 5.3%
40 Pennsylvania 3 317,700 6,800 2.1% 16,700 5.3%
41 Alabama 4 262,900 5,200 2.0% 13,700 5.2%
42 Michigan 7 299,100 6,200 2.1% 15,500 5.2%
43 Kansas 1 345,900 7,800 2.3% 17,900 5.2%
44 Michigan 3 315,300 6,500 2.1% 16,300 5.2%
45 Massachusetts 3 355,400 7,100 2.0% 18,200 5.1%
46 Ohio 16 355,600 7,400 2.1% 18,200 5.1%
47 Ohio 6 292,300 6,100 2.1% 14,900 5.1%
48 Washington 8 318,000 7,400 2.3% 16,200 5.1%
49 Georgia 14 290,700 5,400 1.9% 14,800 5.1%
50 Arkansas 4 295,100 6,000 2.0% 15,000 5.1%
51 South Dakota Statewide 415,600 9,200 2.2% 21,100 5.1%
52 Minnesota 6 348,700 7,400 2.1% 17,700 5.1%
53 Illinois 14 351,000 7,300 2.1% 17,700 5.0%
54 Idaho 2 355,000 7,800 2.2% 17,900 5.0%
55 Illinois 18 337,500 7,400 2.2% 17,000 5.0%
56 Wisconsin 3 353,500 7,300 2.1% 17,800 5.0%
57 Illinois 10 324,800 6,500 2.0% 16,300 5.0%
58 Texas 12 337,500 6,800 2.0% 16,900 5.0%
59 Illinois 6 355,600 6,900 1.9% 17,800 5.0%
60 Alabama 5 311,900 6,400 2.1% 15,600 5.0%
61 Michigan 11 342,100 6,600 1.9% 17,100 5.0%
62 Washington 1 332,300 7,300 2.2% 16,600 5.0%
63 Ohio 13 320,400 6,200 1.9% 16,000 5.0%
64 Washington 2 318,900 7,300 2.3% 15,900 5.0%
65 Michigan 9 326,100 6,300 1.9% 16,200 5.0%
66 Michigan 4 286,300 5,700 2.0% 14,200 5.0%
67 Indiana 8 329,300 6,300 1.9% 16,300 4.9%
68 California 24 323,500 7,000 2.2% 16,000 4.9%
69 Oregon 2 314,200 6,500 2.1% 15,500 4.9%
70 Pennsylvania 5 316,800 6,200 2.0% 15,500 4.9%
71 Illinois 8 366,300 7,200 2.0% 17,900 4.9%
72 Minnesota 2 358,300 6,800 1.9% 17,500 4.9%
73 South Carolina 4 301,000 5,600 1.9% 14,700 4.9%
74 Tennessee 8 299,200 6,100 2.0% 14,600 4.9%
75 South Carolina 5 275,200 5,300 1.9% 13,400 4.9%
76 California 22 289,600 6,300 2.2% 14,100 4.9%
77 Ohio 9 315,000 6,200 2.0% 15,200 4.8%
78 Kentucky 2 317,100 5,700 1.8% 15,300 4.8%
79 Missouri 8 298,500 5,700 1.9% 14,400 4.8%
80 Connecticut 5 348,300 7,100 2.0% 16,800 4.8%
81 Pennsylvania 16 327,700 6,400 2.0% 15,800 4.8%
82 Kentucky 1 284,800 5,100 1.8% 13,700 4.8%
83 Wisconsin 4 308,000 5,800 1.9% 14,800 4.8%
84 Texas 36 291,900 5,900 2.0% 14,000 4.8%
85 Oklahoma 1 361,900 6,700 1.9% 17,200 4.8%
86 Indiana 9 339,400 6,500 1.9% 16,100 4.7%
87 Tennessee 1 297,600 5,700 1.9% 14,100 4.7%
88 California 50 296,200 5,900 2.0% 14,000 4.7%
89 New Hampshire 2 332,200 6,400 1.9% 15,700 4.7%
90 Illinois 15 316,500 6,100 1.9% 14,900 4.7%
91 Oklahoma 2 290,300 5,500 1.9% 13,600 4.7%
92 California 15 336,400 5,900 1.8% 15,700 4.7%
93 Illinois 4 326,600 6,000 1.8% 15,200 4.7%
94 Pennsylvania 15 343,800 6,500 1.9% 16,000 4.7%
95 New York 23 324,600 6,100 1.9% 15,100 4.7%
96 Colorado 4 344,100 6,500 1.9% 16,000 4.6%
97 Arkansas 3 327,000 6,000 1.8% 15,200 4.6%
98 California 46 314,400 5,100 1.6% 14,600 4.6%
99 Washington 9 341,400 6,900 2.0% 15,800 4.6%
100 Oklahoma 3 329,900 6,300 1.9% 15,200 4.6%
101 Washington 3 284,500 5,300 1.9% 13,100 4.6%
102 New York 27 337,800 6,400 1.9% 15,500 4.6%
103 North Dakota Statewide 370,800 7,400 2.0% 17,000 4.6%
104 Pennsylvania 18 345,000 6,400 1.9% 15,800 4.6%
105 North Carolina 10 324,000 5,400 1.7% 14,800 4.6%
106 Pennsylvania 7 339,700 6,400 1.9% 15,500 4.6%
107 Kentucky 6 335,400 6,100 1.8% 15,300 4.6%
108 Connecticut 2 348,600 6,900 2.0% 15,900 4.6%
109 Pennsylvania 6 362,300 6,700 1.8% 16,500 4.6%
110 California 45 354,400 6,400 1.8% 16,100 4.5%
111 California 48 352,600 5,900 1.7% 16,000 4.5%
112 Michigan 8 330,800 5,800 1.8% 15,000 4.5%
113 California 25 302,700 5,700 1.9% 13,700 4.5%
114 Texas 3 371,200 6,300 1.7% 16,800 4.5%
115 Missouri 2 378,600 7,000 1.8% 17,100 4.5%
116 Tennessee 7 285,800 4,900 1.7% 12,900 4.5%
117 Ohio 2 323,600 6,000 1.9% 14,600 4.5%
118 Virginia 9 298,400 5,200 1.7% 13,400 4.5%
119 Pennsylvania 12 331,900 6,000 1.8% 14,900 4.5%
120 Wisconsin 2 390,000 7,300 1.9% 17,500 4.5%
121 Idaho 1 329,900 6,200 1.9% 14,800 4.5%
122 Nebraska 1 321,700 6,100 1.9% 14,400 4.5%
123 Kentucky 3 333,300 5,800 1.7% 14,900 4.5%
124 Arizona 5 317,900 5,700 1.8% 14,200 4.5%
125 Michigan 13 230,700 4,000 1.7% 10,300 4.5%
126 Minnesota 5 352,000 6,400 1.8% 15,700 4.5%
127 Texas 26 368,300 6,500 1.8% 16,400 4.5%
128 Connecticut 3 352,700 6,600 1.9% 15,700 4.5%
129 Utah 1 312,400 5,700 1.8% 13,900 4.4%
130 Alabama 3 274,600 4,500 1.6% 12,200 4.4%
131 Texas 29 292,900 5,200 1.8% 13,000 4.4%
132 Texas 13 309,000 5,700 1.8% 13,700 4.4%
133 Pennsylvania 4 342,900 6,200 1.8% 15,200 4.4%
134 Missouri 3 370,000 6,800 1.8% 16,400 4.4%
135 Pennsylvania 9 304,800 5,200 1.7% 13,500 4.4%
136 California 52 350,100 6,200 1.8% 15,500 4.4%
137 Georgia 9 284,600 5,000 1.8% 12,600 4.4%
138 New Hampshire 1 352,600 6,300 1.8% 15,600 4.4%
139 Ohio 1 332,300 6,000 1.8% 14,700 4.4%
140 North Carolina 1 291,800 5,300 1.8% 12,900 4.4%
141 Tennessee 4 314,500 5,100 1.6% 13,900 4.4%
142 Missouri 7 337,400 5,900 1.7% 14,900 4.4%
143 Pennsylvania 10 312,500 5,500 1.8% 13,800 4.4%
144 Texas 2 364,600 5,600 1.5% 16,100 4.4%
145 Missouri 6 355,900 6,500 1.8% 15,700 4.4%
146 Kentucky 4 333,500 5,800 1.7% 14,700 4.4%
147 California 10 277,200 5,300 1.9% 12,200 4.4%
148 Tennessee 6 304,500 5,200 1.7% 13,400 4.4%
149 Texas 8 309,200 5,300 1.7% 13,600 4.4%
150 North Carolina 9 371,400 6,300 1.7% 16,300 4.4%
151 Illinois 11 347,300 5,800 1.7% 15,200 4.4%
152 Mississippi 1 305,600 4,900 1.6% 13,300 4.4%
153 Texas 32 360,900 5,900 1.6% 15,700 4.4%
154 Minnesota 4 336,000 5,900 1.8% 14,600 4.3%
155 Tennessee 3 297,000 4,700 1.6% 12,900 4.3%
156 Florida 17 248,700 4,800 1.9% 10,800 4.3%
157 Michigan 1 290,200 5,200 1.8% 12,600 4.3%
158 New York 22 320,200 5,800 1.8% 13,900 4.3%
159 Ohio 11 275,200 4,800 1.7% 11,900 4.3%
160 Ohio 10 312,800 5,400 1.7% 13,500 4.3%
161 New York 24 327,300 5,800 1.8% 14,100 4.3%
162 Michigan 5 264,800 4,600 1.7% 11,400 4.3%
163 Texas 7 376,300 5,800 1.5% 16,200 4.3%
164 Rhode Island 1 250,900 4,300 1.7% 10,800 4.3%
165 Oregon 4 309,000 5,600 1.8% 13,300 4.3%
166 Michigan 12 313,800 5,300 1.7% 13,500 4.3%
167 Texas 24 388,600 6,600 1.7% 16,700 4.3%
168 Texas 33 283,900 4,500 1.6% 12,200 4.3%
169 Connecticut 1 349,800 6,300 1.8% 15,000 4.3%
170 North Carolina 5 324,500 4,900 1.5% 13,900 4.3%
171 Mississippi 2 266,900 4,800 1.8% 11,400 4.3%
172 Georgia 3 285,800 4,600 1.6% 12,200 4.3%
173 California 23 274,100 4,700 1.7% 11,700 4.3%
174 South Carolina 6 253,500 4,500 1.8% 10,800 4.3%
175 North Carolina 13 349,900 5,900 1.7% 14,900 4.3%
176 Oregon 5 326,700 5,900 1.8% 13,900 4.3%
177 Texas 14 303,300 5,600 1.8% 12,900 4.3%
178 Minnesota 8 303,400 5,300 1.7% 12,900 4.3%
179 Pennsylvania 8 357,800 6,200 1.7% 15,200 4.2%
180 Texas 6 348,800 5,600 1.6% 14,800 4.2%
181 New York 25 335,400 5,700 1.7% 14,200 4.2%
182 Utah 4 331,500 5,600 1.7% 14,000 4.2%
183 California 39 332,000 5,300 1.6% 14,000 4.2%
184 New Jersey 7 377,100 6,300 1.7% 15,900 4.2%
185 Texas 4 299,300 5,100 1.7% 12,600 4.2%
186 Texas 10 342,600 5,700 1.7% 14,400 4.2%
187 Texas 1 297,700 5,100 1.7% 12,500 4.2%
188 North Carolina 11 295,400 4,700 1.6% 12,400 4.2%
189 Texas 18 306,400 4,900 1.6% 12,800 4.2%
190 California 49 299,700 4,700 1.6% 12,500 4.2%
191 California 42 307,000 5,100 1.7% 12,800 4.2%
192 Pennsylvania 11 329,300 5,600 1.7% 13,700 4.2%
193 South Carolina 7 269,400 4,400 1.6% 11,200 4.2%
194 Texas 27 305,600 5,200 1.7% 12,700 4.2%
195 Vermont Statewide 327,300 5,600 1.7% 13,600 4.2%
196 Michigan 14 257,700 4,200 1.6% 10,700 4.2%
197 Massachusetts 2 356,500 6,000 1.7% 14,800 4.2%
198 North Carolina 2 303,800 4,200 1.4% 12,600 4.1%
199 Texas 11 308,800 4,700 1.5% 12,800 4.1%
200 Georgia 2 251,200 4,300 1.7% 10,400 4.1%
201 California 44 270,600 3,300 1.2% 11,200 4.1%
202 Colorado 2 384,600 6,500 1.7% 15,900 4.1%
203 Kansas 2 339,900 5,700 1.7% 14,000 4.1%
204 North Carolina 8 301,700 4,100 1.4% 12,400 4.1%
205 California 35 284,800 3,900 1.4% 11,700 4.1%
206 New Jersey 5 356,100 5,600 1.6% 14,600 4.1%
207 Florida 8 283,400 4,900 1.7% 11,600 4.1%
208 Indiana 5 357,700 5,800 1.6% 14,600 4.1%
209 Arizona 9 360,300 6,100 1.7% 14,700 4.1%
210 California 5 326,800 5,500 1.7% 13,300 4.1%
211 Iowa 3 390,800 6,700 1.7% 15,900 4.1%
212 California 43 302,800 4,400 1.5% 12,300 4.1%
213 Ohio 12 359,500 6,000 1.7% 14,600 4.1%
214 West Virginia 1 258,700 4,300 1.7% 10,500 4.1%
215 Washington 7 380,000 6,500 1.7% 15,400 4.1%
216 California 32 293,800 4,000 1.4% 11,900 4.1%
217 California 29 303,700 4,400 1.4% 12,300 4.1%
218 Georgia 11 340,900 5,500 1.6% 13,800 4.0%
219 Ohio 15 336,400 5,400 1.6% 13,600 4.0%
220 Illinois 3 319,500 5,100 1.6% 12,900 4.0%
221 Texas 25 302,200 4,800 1.6% 12,200 4.0%
222 Indiana 7 312,200 5,000 1.6% 12,600 4.0%
223 Georgia 7 312,500 4,900 1.6% 12,600 4.0%
224 Pennsylvania 17 312,600 4,800 1.5% 12,600 4.0%
225 Massachusetts 4 374,800 5,900 1.6% 15,100 4.0%
226 South Carolina 2 305,600 5,000 1.6% 12,300 4.0%
227 Missouri 4 324,900 5,300 1.6% 13,000 4.0%
228 Montana Statewide 480,000 8,200 1.7% 19,200 4.0%
229 Illinois 5 397,600 6,300 1.6% 15,900 4.0%
230 California 9 275,300 4,600 1.7% 11,000 4.0%
231 Connecticut 4 343,000 5,500 1.6% 13,700 4.0%
232 Louisiana 3 328,100 5,300 1.6% 13,100 4.0%
233 Utah 2 305,700 4,800 1.6% 12,200 4.0%
234 Georgia 12 278,200 4,300 1.5% 11,100 4.0%
235 Illinois 2 278,200 4,500 1.6% 11,100 4.0%
236 Texas 5 300,800 4,800 1.6% 12,000 4.0%
237 Massachusetts 1 341,000 5,600 1.6% 13,600 4.0%
238 Washington 5 291,500 4,900 1.7% 11,600 4.0%
239 Nevada 2 309,400 5,100 1.6% 12,300 4.0%
240 Texas 22 352,500 5,400 1.5% 14,000 4.0%
241 South Carolina 1 299,800 4,900 1.6% 11,900 4.0%
242 Oregon 3 383,300 5,600 1.5% 15,200 4.0%
243 Massachusetts 6 372,000 5,900 1.6% 14,700 4.0%
244 West Virginia 3 223,000 3,800 1.7% 8,800 3.9%
245 Alabama 2 276,900 4,200 1.5% 10,900 3.9%
246 Arizona 7 282,300 4,400 1.6% 11,100 3.9%
247 Arizona 3 262,200 4,300 1.6% 10,300 3.9%
248 Pennsylvania 13 339,000 5,100 1.5% 13,300 3.9%
249 Illinois 12 301,000 4,800 1.6% 11,800 3.9%
250 Illinois 13 326,600 5,400 1.7% 12,800 3.9%
251 Texas 31 323,000 4,700 1.5% 12,600 3.9%
252 North Carolina 7 315,400 5,000 1.6% 12,300 3.9%
253 California 33 364,200 5,200 1.4% 14,200 3.9%
254 California 41 271,900 4,100 1.5% 10,600 3.9%
255 Georgia 10 287,400 4,400 1.5% 11,200 3.9%
256 Texas 19 310,700 4,900 1.6% 12,100 3.9%
257 Arizona 1 264,900 4,500 1.7% 10,300 3.9%
258 Georgia 8 272,700 4,300 1.6% 10,600 3.9%
259 Missouri 5 345,300 5,400 1.6% 13,400 3.9%
260 Maine 1 340,400 5,400 1.6% 13,200 3.9%
261 Georgia 6 361,200 5,500 1.5% 14,000 3.9%
262 New Jersey 11 358,800 5,400 1.5% 13,900 3.9%
263 California 51 258,600 4,000 1.5% 10,000 3.9%
264 California 38 313,300 4,000 1.3% 12,100 3.9%
265 Delaware Statewide 420,400 6,700 1.6% 16,200 3.9%
266 California 36 251,900 4,100 1.6% 9,700 3.9%
267 Mississippi 3 303,900 4,600 1.5% 11,700 3.8%
268 Georgia 1 286,100 4,800 1.7% 11,000 3.8%
269 New Mexico 2 273,100 4,400 1.6% 10,500 3.8%
270 New Jersey 12 352,400 5,500 1.6% 13,500 3.8%
271 Virginia 6 339,900 5,200 1.5% 13,000 3.8%
272 Massachusetts 5 387,400 5,700 1.5% 14,800 3.8%
273 Florida 13 309,200 4,600 1.5% 11,800 3.8%
274 Arizona 2 299,200 4,900 1.6% 11,400 3.8%
275 Kansas 3 370,300 5,600 1.5% 14,100 3.8%
276 Rhode Island 2 260,300 4,000 1.5% 9,900 3.8%
277 Colorado 3 331,400 5,100 1.5% 12,600 3.8%
278 Alabama 6 318,400 4,600 1.4% 12,100 3.8%
279 Virginia 5 316,100 4,600 1.5% 12,000 3.8%
280 California 14 364,000 5,100 1.4% 13,800 3.8%
281 Alabama 7 253,500 3,700 1.5% 9,600 3.8%
282 California 47 327,600 4,500 1.4% 12,400 3.8%
283 Alabama 1 283,000 4,500 1.6% 10,700 3.8%
284 North Carolina 6 341,800 3,900 1.1% 12,900 3.8%
285 Mississippi 4 304,900 4,700 1.5% 11,500 3.8%
286 Arizona 4 233,500 3,700 1.6% 8,800 3.8%
287 Maryland 1 342,300 5,300 1.5% 12,900 3.8%
288 Texas 17 329,300 4,700 1.4% 12,400 3.8%
289 Tennessee 2 327,200 4,800 1.5% 12,300 3.8%
290 Wyoming Statewide 290,000 4,200 1.4% 10,900 3.8%
291 Tennessee 9 305,300 4,400 1.4% 11,400 3.7%
292 Louisiana 5 283,900 4,400 1.5% 10,600 3.7%
293 California 3 286,600 4,500 1.6% 10,700 3.7%
294 Oklahoma 4 350,900 5,000 1.4% 13,100 3.7%
295 New York 19 327,300 4,900 1.5% 12,200 3.7%
296 Utah 3 311,200 4,600 1.5% 11,500 3.7%
297 Virginia 4 327,900 5,000 1.5% 12,100 3.7%
298 North Carolina 12 319,800 4,400 1.4% 11,800 3.7%
299 Illinois 9 347,200 4,700 1.4% 12,800 3.7%
300 California 2 323,100 4,700 1.5% 11,900 3.7%
301 New Jersey 6 353,600 4,700 1.3% 13,000 3.7%
302 Colorado 5 315,900 4,800 1.5% 11,600 3.7%
303 Kentucky 5 234,300 3,200 1.4% 8,600 3.7%
304 New Jersey 9 338,500 4,500 1.3% 12,400 3.7%
305 Georgia 4 311,700 4,500 1.4% 11,400 3.7%
306 Florida 15 304,200 4,600 1.5% 11,100 3.6%
307 Florida 6 283,200 4,300 1.5% 10,300 3.6%
308 Arizona 6 366,000 5,400 1.5% 13,300 3.6%
309 North Carolina 3 305,600 4,700 1.5% 11,100 3.6%
310 New York 26 327,700 4,700 1.4% 11,900 3.6%
311 Massachusetts 8 375,600 5,300 1.4% 13,600 3.6%
312 California 53 342,700 5,000 1.5% 12,400 3.6%
313 Illinois 7 298,500 4,300 1.4% 10,800 3.6%
314 Arizona 8 301,700 4,500 1.5% 10,900 3.6%
315 California 1 260,300 3,900 1.5% 9,400 3.6%
316 Louisiana 4 311,100 4,200 1.4% 11,200 3.6%
317 New York 21 309,200 4,100 1.3% 11,100 3.6%
318 Missouri 1 331,500 4,600 1.4% 11,900 3.6%
319 Texas 9 326,400 4,400 1.3% 11,700 3.6%
320 California 11 324,200 4,600 1.4% 11,600 3.6%
321 Maine 2 302,700 3,900 1.3% 10,800 3.6%
322 Colorado 1 384,400 5,400 1.4% 13,700 3.6%
323 West Virginia 2 266,900 3,700 1.4% 9,500 3.6%
324 Colorado 7 362,500 5,000 1.4% 12,900 3.6%
325 Pennsylvania 14 323,200 4,500 1.4% 11,500 3.6%
326 Tennessee 5 353,400 4,900 1.4% 12,500 3.5%
327 California 4 294,200 4,200 1.4% 10,400 3.5%
328 Florida 25 326,000 4,400 1.3% 11,500 3.5%
329 California 13 340,200 4,300 1.3% 12,000 3.5%
330 California 31 292,200 3,900 1.3% 10,300 3.5%
331 Texas 30 292,300 4,000 1.4% 10,300 3.5%
332 New York 2 357,800 4,800 1.3% 12,600 3.5%
333 Florida 4 329,900 4,700 1.4% 11,600 3.5%
334 New Jersey 1 339,200 4,600 1.4% 11,900 3.5%
335 Florida 10 331,500 4,700 1.4% 11,600 3.5%
336 Texas 28 266,300 3,900 1.5% 9,300 3.5%
337 Colorado 6 369,600 5,100 1.4% 12,900 3.5%
338 New Jersey 3 344,200 4,900 1.4% 12,000 3.5%
339 Georgia 13 312,800 4,200 1.3% 10,900 3.5%
340 Washington 6 275,500 4,000 1.5% 9,600 3.5%
341 Nebraska 2 316,300 4,300 1.4% 11,000 3.5%
342 Oklahoma 5 348,800 4,400 1.3% 12,100 3.5%
343 Texas 34 242,200 3,400 1.4% 8,400 3.5%
344 Massachusetts 9 352,300 4,600 1.3% 12,200 3.5%
345 New York 18 332,100 4,500 1.4% 11,500 3.5%
346 Florida 5 284,000 4,000 1.4% 9,800 3.5%
347 Florida 7 322,500 4,500 1.4% 11,100 3.4%
348 California 30 358,200 4,200 1.2% 12,300 3.4%
349 Florida 19 265,200 3,700 1.4% 9,100 3.4%
350 Georgia 5 318,100 4,300 1.4% 10,900 3.4%
351 Ohio 3 333,000 4,300 1.3% 11,400 3.4%
352 New Jersey 8 371,000 3,900 1.1% 12,700 3.4%
353 New Jersey 2 324,400 4,500 1.4% 11,100 3.4%
354 Illinois 1 290,200 3,700 1.3% 9,900 3.4%
355 New Mexico 3 284,800 3,700 1.3% 9,700 3.4%
356 Virginia 10 376,400 5,000 1.3% 12,800 3.4%
357 New Mexico 1 311,900 4,400 1.4% 10,600 3.4%
358 Louisiana 1 354,000 4,800 1.4% 12,000 3.4%
359 Texas 23 289,700 3,500 1.2% 9,800 3.4%
360 Louisiana 6 367,800 4,900 1.3% 12,400 3.4%
361 Florida 16 276,100 3,700 1.3% 9,300 3.4%
362 Pennsylvania 1 273,300 3,500 1.3% 9,200 3.4%
363 North Carolina 4 350,900 4,600 1.3% 11,800 3.4%
364 Arkansas 2 336,300 4,400 1.3% 11,300 3.4%
365 Florida 12 283,200 3,800 1.3% 9,500 3.4%
366 Florida 26 335,600 4,600 1.4% 11,200 3.3%
367 Florida 14 320,700 4,200 1.3% 10,700 3.3%
368 New York 1 343,300 4,500 1.3% 11,400 3.3%
369 Florida 22 332,000 4,300 1.3% 11,000 3.3%
370 California 12 399,400 4,700 1.2% 13,200 3.3%
371 Washington 10 291,300 4,000 1.4% 9,600 3.3%
372 Virginia 7 364,600 4,900 1.3% 12,000 3.3%
373 Florida 20 302,100 3,900 1.3% 9,900 3.3%
374 Texas 15 280,900 3,700 1.3% 9,200 3.3%
375 Florida 18 284,000 3,700 1.3% 9,200 3.2%
376 Texas 21 361,200 4,600 1.3% 11,700 3.2%
377 California 8 235,500 3,000 1.3% 7,600 3.2%
378 Maryland 6 363,200 4,700 1.3% 11,700 3.2%
379 Florida 11 217,400 2,800 1.3% 7,000 3.2%
380 Florida 3 277,000 3,700 1.3% 8,900 3.2%
381 Nevada 1 284,700 3,500 1.2% 9,100 3.2%
382 Florida 27 313,600 3,800 1.2% 10,000 3.2%
383 Virginia 3 320,100 4,200 1.3% 10,200 3.2%
384 Virginia 2 339,800 4,400 1.3% 10,800 3.2%
385 Florida 23 339,900 4,200 1.2% 10,800 3.2%
386 Texas 35 318,200 3,900 1.2% 10,100 3.2%
387 Hawaii 2 299,400 4,000 1.3% 9,500 3.2%
388 Louisiana 2 329,000 4,200 1.3% 10,400 3.2%
389 California 7 313,200 3,900 1.2% 9,900 3.2%
390 California 37 335,600 2,900 0.9% 10,600 3.2%
391 New Jersey 4 326,400 3,900 1.2% 10,300 3.2%
392 New York 20 357,600 4,500 1.3% 11,200 3.1%
393 Florida 1 303,900 3,900 1.3% 9,500 3.1%
394 Pennsylvania 2 273,100 3,400 1.2% 8,500 3.1%
395 Massachusetts 7 369,800 4,500 1.2% 11,500 3.1%
396 Maryland 7 315,700 3,900 1.2% 9,800 3.1%
397 New York 12 418,800 4,400 1.1% 13,000 3.1%
398 California 27 332,200 3,100 0.9% 10,300 3.1%
399 Florida 21 316,800 3,900 1.2% 9,800 3.1%
400 New Jersey 10 310,700 3,500 1.1% 9,600 3.1%
401 California 6 288,300 3,500 1.2% 8,900 3.1%
402 Texas 20 311,400 3,700 1.2% 9,600 3.1%
403 Virginia 1 352,400 4,500 1.3% 10,800 3.1%
404 Nevada 4 274,300 3,400 1.2% 8,400 3.1%
405 Florida 9 317,200 3,800 1.2% 9,700 3.1%
406 Maryland 8 400,100 4,900 1.2% 12,200 3.0%
407 Maryland 3 369,500 4,600 1.2% 11,200 3.0%
408 California 28 359,900 3,400 0.9% 10,900 3.0%
409 New York 16 323,600 3,600 1.1% 9,800 3.0%
410 Maryland 2 351,700 4,100 1.2% 10,600 3.0%
411 New York 3 336,700 3,700 1.1% 10,100 3.0%
412 Alaska Statewide 344,300 3,900 1.1% 10,300 3.0%
413 Nevada 3 336,500 3,900 1.2% 10,000 3.0%
414 Virginia 11 400,900 4,800 1.2% 11,900 3.0%
415 New York 13 317,200 3,300 1.0% 9,400 3.0%
416 New York 17 341,400 3,600 1.1% 10,100 3.0%
417 New York 15 255,900 2,600 1.0% 7,500 2.9%
418 Texas 16 281,300 2,600 0.9% 8,200 2.9%
419 Florida 24 293,400 3,200 1.1% 8,500 2.9%
420 Maryland 4 384,100 4,400 1.1% 11,100 2.9%
421 New York 10 360,300 3,600 1.0% 10,300 2.9%
422 Virginia 8 423,700 4,800 1.1% 12,100 2.9%
423 Florida 2 301,500 3,400 1.1% 8,600 2.9%
424 California 40 280,500 -800 -0.3% 7,900 2.8%
425 New York 14 341,800 3,200 0.9% 9,600 2.8%
426 New York 4 342,500 3,600 1.1% 9,600 2.8%
427 New York 6 327,000 3,000 0.9% 9,100 2.8%
428 New York 5 336,200 3,200 1.0% 9,300 2.8%
429 Maryland 5 368,200 4,000 1.1% 10,100 2.7%
430 New York 9 324,900 3,100 1.0% 8,900 2.7%
431 New York 8 292,700 2,700 0.9% 8,000 2.7%
432 New York 7 322,200 1,900 0.6% 8,600 2.7%
433 New York 11 317,500 2,700 0.9% 8,400 2.6%
434 District of Columbia District-wide 310,600 3,300 1.1% 8,200 2.6%
435 Hawaii 1 330,100 3,200 1.0% 8,700 2.6%
436 California 34 309,400 -2,100 -0.7% 6,300 2.0%
Total** 140,399,600 2,300,000 1.6% 5,800,000 4.1%

* The low-impact scenario assumes ending currency manipulation would reduce the trade deficit by $200 billion; the high-impact scenario assumes a $500 billion reduction in the trade deficit. The table shows the hypothetical change in 2015 three years after implementation.

** Totals may vary slightly due to rounding.

Source: Scott 2014

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

Endnotes

1. The 22 countries identified as currency manipulators are China, Hong Kong, Japan, Korea, Malaysia, Singapore, Taiwan, Thailand, Algeria, Angola, Azerbaijan, Kazakhstan, Kuwait, Libya, Norway, Qatar, Russia, Saudi Arabia, United Arab Emirates, Denmark, Israel, and Switzerland (Bergsten and Gagnon 2012, Table 1). The nine countries with SWFs included by Bergsten and Gagnon were China, Korea, Singapore, Azerbaijan, Kazakhstan, Kuwait, Norway, Qatar, and the United Arab Emirates. These countries accounted for 70.7 percent of total SWF holdings listed by the Sovereign Wealth Fund Institute (SWFI 2015). Other currency manipulators also included in the SWFI list were Malaysia, Algeria, Angola, Libya, Russia, and Saudi Arabia. Together, all currency manipulators were responsible for 92.4 percent of total SWF holdings on the SWFI list (data through March 2015). In addition, Japan’s $1.2 trillion Government Pension Investment Fund (which is not included on the SWFI list, or by Bergsten and Gagnon) has announced plans to increase holdings of foreign stocks and bonds from 23 percent to 40 percent of total holdings in the near future (Warnock and Narioka 2014). This will increase actual foreign holdings from $244.2 billion in 2012 to $480 billion in 2015, an increase of $235.8 billion (Scott 2015)

2. The increase in net holdings never fell below $500 billion per year, despite the global financial crisis in 2009, when net holdings increased by $649 billion, and the Russian financial crisis in 2014, when net holdings increased by $544 billion, as shown in Figure B.

3. SWFI (2015) provides data on total holdings for each SWF for March 2015 (or latest available), by fund and country. It also provides total holdings of all SWFs between September 2007 and March 2015. Currency manipulators were responsible for virtually all of the holdings recorded by the SWFI in March 2015 (endnote 1, above). Thus, Figure A assumes that total SWF holdings of currency manipulators are a constant share of the total stock of all SWFs, as estimated by the SWFI (there are no publicly available historical data on the holdings of individual SWFs).

4. The IMF (2015a) reports that in the 4th quarter of 2014, U.S. dollar claims made up $3.826 trillion out of a total of $6.085 trillion of allocated reserves, or 62.9 percent of the total. Note that unallocated reserves comprised $5.5 trillion out of the total of $11.6 trillion in total foreign exchange reserves, or nearly half of the total.

5. Bergsten and Gagnon (2012) recommend six months of goods and services imports, and two additional criteria not mentioned here. The first is that foreign exchange reserves grew faster than the GDP between 2001 and 2011, but this criterion is less relevant for enforcement actions. The second excludes countries with a GDP per capita of less than $3,000. That criterion may not be desirable in the context of a TPP that includes low-income countries such as Vietnam.

References

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

Bergsten, C. Fred. 2014. Addressing Currency Manipulation Through Trade Agreements. Peterson Institute for International Economics, Policy Brief 14-2.

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.

Bernanke, Ben S. 2010. “Rebalancing the Global Recovery.” Speech at the Sixth European Central Bank Central Banking Conference, Frankfurt, November 19. Washington: Board of Governors of the Federal Reserve System.

Bivens, Josh. 2015. TPP and Provisions to Stop Currency Management: Not That Hard. Working Economics (Economic Policy Institute blog). February 3.

Central Bank of the Republic of China (Taiwan). 2015. “Monthly Releases: Foreign Exchange Reserves.” Accessed April 28.

Dunn, Alan, and Bill Fennell. “Memorandum to FTAC: Brief Comparison of Section 301 and Special 301 Trade Laws,” Stewart and Stewart. April 23.

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

Henning, C. Randall. 2008. Accountability and Oversight of US Exchange Rate Policy. Washington: Peterson Institute for International Economics.

International Monetary Fund (IMF). 2015a. IMF eLibrary Data: Currency Composition of Official Foreign Exchange Reserves (COFER). Accessed May 4.

International Monetary Fund (IMF). 2015b. International Financial Statistics. Washington, D.C.: International Monetary Fund.

International Monetary Fund (IMF). 2015c. World Economic Outlook Database: April 2015 Edition.

McCormack, Richard A. 2014. “Senators Tell USTR that Asia Trade Pact Is in Jeopardy without Currency Provisions.” Manufacturing and Technology News, Vol. 21, No. 8.

Scott, Robert E. 2011. Currency Manipulation—History Shows that Sanctions Are Needed. Economic Policy Institute, Policy Memo #164.

Scott, Robert E. 2014. Stop Currency Manipulation and Create Millions of Jobs: With Gains across States and Congressional Districts. Economic Policy Institute, Briefing Paper No. 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.

Sovereign Wealth Fund Institute (SWFI). 2015. Sovereign Wealth Fund Rankings: Largest Sovereign Wealth Funds by Assets under Management. Accessed April 27.

Summers, Lawrence H., and Ed Balls. 2015. Report of the Commission on Inclusive Prosperity. Center For American Progress.

U.S. Government Accountability Office (GAO). 2005. “International Trade: Treasury Assessments Have Not Found Currency Manipulation, but Concerns about Exchange Rates Continue.” GAO-5-351.

Warnock, Eleanor, and Kosaku Narioka. 2014. “Japan Mega-Pension Shifts to Stocks: Government Makes Bold Bet on Inflation, Higher Equity Returns.” Wall Street Journal. October 31.


See related work on Trade and Globalization | Trade

See more work by Robert E. Scott