Report | Trade and Globalization

Rebuilding American manufacturing—potential job gains by state and industry: Analysis of trade, infrastructure, and clean energy/energy efficiency proposals

Download PDF

Press release

This report examines the economic output and employment implications of a two-pronged strategy for rebuilding the domestic economy around high-wage jobs and restoring American manufacturing. Job losses due to growing U.S. trade deficits hit manufacturing industries particularly hard, shrinking the share of middle-class jobs available to workers without a college degree (Scott 2020; Scott and Mokhiber 2020). Failure to maintain and upgrade U.S. infrastructure investment has been a chronic weakness, hindering American public safety and productivity growth (ASCE 2017; Bivens 2014).1

The essential elements of this two-pronged strategy for rebuilding the domestic economy are detailed in this report and summarized here:

  • Trade and industrial policies that dramatically boost U.S. exports and eliminate the U.S. trade deficit—now roughly $850 billion—within four years. At the heart of these policies are measures to end the overvaluation of the U.S. dollar and rebuild the competitiveness of U.S. manufacturing industries.
  • A four-year, $2 trillion program of investments in infrastructure, clean energy, and energy efficiency improvements. This would include investments of $70.2 billion per year in schools and broadband, which would have substantial social benefits. Note also that virtually all (91.6%) of clean energy and energy efficiency investments are for manufactured products.

Following are the key findings of this report:

  • Surging exports and major investment in infrastructure, clean energy, and energy efficiency would support between 6.9 and 12.9 million U.S. jobs annually by 2024. The lower-bound estimate includes direct and indirect jobs but not “respending” jobs created as consumers spend more in the economy.
  • Of the 6.9 million direct and indirect jobs, at least 471,200 would be construction jobs and 2.5 million would be U.S. manufacturing jobs. Because the jobs supported would be concentrated in high-wage manufacturing (36.4% of jobs supported) and construction industries (6.8% of jobs supported), this strategy would help rebuild U.S. manufacturing and restructure the domestic economy away from low-wage service-sector work.
  • Projections of rapid export expansion are not wishful thinking: they are based on the actual export performance in prior periods when the real value of the U.S. dollar was substantially reduced. And there is much room for the dollar to fall: its value has gained 21.4% since July 2014, stagnating U.S. exports and depressing domestic commodity prices, including farm products and incomes.
  • Rapidly growing exports in this forecast—especially for U.S. durable goods—along with substantial demand for manufactured products arising from infrastructure and clean energy and energy efficiency investments would support rapid growth in output and employment in a wide range of industries. Rapidly rising demands for fabricated metal products, industrial machinery, computer and electrical products, and transportation equipment (including both motor vehicles and parts, and aerospace products), would generate substantial increases in demand for primary metals (ferrous and nonferrous) and other industrial materials. Production of U.S. energy-based products (crude oil, refined petroleum, and chemicals) also would increase rapidly.
  • Within manufacturing, jobs supported would be in both durable and nondurable goods categories. Under the 6.9 million jobs scenario, rapidly growing sectors would include nondurable goods (367,600 jobs), and durable goods (2.1 million jobs). Within durable goods industries, the most jobs will be supported in nonelectrical machinery (436,700 jobs), fabricated metal products (383,700 jobs), transportation equipment (343,800 jobs), electrical equipment (302,700 jobs), and primary metals (248,000 jobs). Within primary metals, 69,900 jobs would be supported in the steel industry. Within transportation equipment will be substantial growth in motor vehicles and parts (188,800 jobs) and aerospace products (127,600 jobs).
  • Many sectors outside of manufacturing would experience substantial job growth: transportation (603,400 jobs); agriculture, forestry, and fisheries (588,600 jobs); administrative and support services (454,900 jobs); professional, scientific, and support services (375,300 jobs); wholesale trade (337,100 jobs); and mining (201,400 jobs).
  • Rapidly growing exports supported by trade and industrial polices combined with major public investments in infrastructure, clean energy, and energy efficiency would support rapid job creation in all 50 states and the District of Columbia. Jobs supported would be concentrated in regions that have been hardest hit by globalization and outsourcing. Six of the top 10 states in terms of jobs supported as a share of state employment are among the top 10 manufacturing states (as a share of total state employment),  including Wisconsin (6.16%, 181,000 jobs), Indiana (5.95%, 185,900 jobs), Iowa (5.91%, 94,500 jobs), Michigan (5.55%, 251,200 jobs), Ohio (5.51%, 302,400 jobs), and Kentucky (5.37%, 104,100 jobs). Other top-10 job gainers are in energy and resource-intensive states, including North Dakota (6.07%, 24,300 jobs), Wyoming (5.69%, 16,700 jobs), Oklahoma (5.62%, 98,200 jobs), and South Dakota (5.61%, 24,600 jobs).
  • Our lower-bound estimate of 6.9 million jobs supported is conservative. The Congressional Budget Office projects that it will take more than five years for employment to return to its pre-recession levels (CBO 2020). In this kind of environment, increases in exports and deficit-financed public investments would generate additional rounds of respending and job creation in the domestic economy (Bivens 2014). Thus our upper-bound estimate of 12.9 million jobs, which includes about 6.0 million respending jobs, is plausible. It is important also to note that these jobs supported are jobs, not job years.2

Introduction: Policy proposals and modeling assumptions

This report evaluates a set of trade and manufacturing policy proposals developed by the Alliance for American Manufacturing (Paul et al. 2020). It also estimates the impacts of a package of infrastructure and clean energy proposals that is based on investments made under a detailed plan developed by the Sierra Club and other civil society groups but at a slightly smaller scale, and for fewer years. That plan, which was analyzed by the University of Massachusetts Amherst’s Political Economy Research Institute (PERI) (Pollin and Chakraborty 2020), is a 10-year plan that would invest $683 billion per year in the elements considered here.3 The plan analyzed here is a four-year, $2 trillion plan.

Trade flows and investment allocations for these activities were prepared in order to project output and employment changes over the 2019–2024 period and thus estimate the increased annual output and jobs supported by 2024, as described below.4

Defining jobs: supported vs. created vs. job years

In this report we are quite careful to distinguish between net jobs “created,” and jobs “supported.” In general, we choose to use the term jobs supported here, especially when talking about changes in the labor market several years in the future.

The use of “supported” reflects the fact that it is hard to assess the net employment effects of large macroeconomic changes like those assessed in this paper, especially when undertaken over a relatively long period (more than two years), and particularly with regard to changes due to trade flows. If unemployment is high and labor markets are slack over most of the period, investments or large increases in net exports will lead to net new job creation. If instead unemployment is low and labor markets are tight, then such changes instead will mostly change the composition of jobs, not the economywide level of employment. However, even if investments and increases in net exports happen when labor markets already are tight, the increase in labor demand will boost workers’ leverage and bargaining power in labor markets and likely to lead to wage gains. Further, policymakers consistently have underestimated the amount of labor market slack in the U.S. economy for decades, so it is quite possible that net employment gains would be large from the changes assessed in this report even if headline unemployment looks low by historical levels. To account for some of this ambiguity of how the changes assessed in this paper will translate into either increased employment levels or different employment composition, we use the term “jobs supported” throughout in this paper. Note that other studies of the economic impacts of proposed infrastructure and clean energy investments estimate the “Annual Job Creation” (also referred to as “job years”) from these investments (Pollin and Chakraborty 2020, Table 1).

Jobs supported vs. job years

There is an important time dimension involved in measuring the employment impacts of the investment and spending flows examined in this report. Other researchers, in particular Pollin and Chakraborty (2020, 4), note that “an activity that generates 100 jobs for 1 year would create 100 job years. By contrast, the activity that produces 100 jobs for 10 years would generate 1,000 job years.” In this study, we use the term “jobs supported” and treat all jobs supported as though they will continue in the future. Hence, employment estimates in this report should be interpreted as “jobs” rather than “job years.”

Specifically, we estimate that the four-year, $2 trillion package of infrastructure and clean energy investments analyzed in this report would result in roughly 3.4 million direct and indirect job opportunities created—i.e., “jobs supported.” These jobs would continue as long as spending continued at that level. They likely would cease to exist if this spending were eliminated.

Trade (export promotion and currency rebalancing) projections

Trade projections in this study assume that currency realignment and an aggressive program of industrial policies for recovery result in elimination of U.S. trade deficits in 2024. Currency overvaluation makes U.S. exports more expensive (and suppresses prices of domestic commodities, including gains), while also acting like a subsidy to the cost of all imports (Scott 2020). The policies proposed here are based, in part, on proposals to prioritize industrial policy in the post-COVID-19 world (Paul 2020), which emphasize substantial investment in American-made infrastructure, the reshoring of critical supply chains, enhanced enforcement of Buy America laws, and aggressive enforcement of fair trade policies and the pursuit of high-standard trade agreements. The trade projections are based on actual market behavior in earlier periods of dollar realignment.

For exports supported by currency rebalancing and industrial policies, we examined prior periods of substantial dollar devaluation, including 1985 to 1991 (following the Plaza Accord of 1985) and 2002 to 2008 (the previous period of substantial dollar overvaluation).5 Total U.S. exports increased between 80% and 90% following each of those dollar realignments (Scott 2009 and 2017a). It is important to note that the real value of the U.S. dollar has gained 21.4% since July 2014, stagnating U.S. exports and depressing domestic commodity prices, including farm products and incomes (Federal Reserve Board 2020).

For the projections in this report, we first assumed that exports in each of the individual industries that make up the traded goods portion of the U.S. economy—technically, the detailed, four-digit North American Industry Classification System (NAICS) traded goods industries—would grow at the rate experienced in the 2002–2008 period, with two exceptions, noted here.6 We assume that imports would grow at their actual rate in the 2014–2019 period.7 The initial projections would have resulted in a substantial trade surplus.8 To bring projected trade flows into balance, initial projected exports were then reduced in each sector by 15.5%, resulting in overall trade balance in 2024, as shown in the tables in this report.9

Investment and clean energy projections

The allocation of the four-year, $2 trillion package of investments in infrastructure, clean energy, and efficiency improvement programs was based on allocations developed by Pollin and Chakraborty 2020.10 That report assumes levels of public investment that are about 36% higher than is assumed here (here we look at overall spending of $500 billion a year versus overall spending of $683 billion per year in Pollin and Chakraborty 2020). But the allocations assumed here are in roughly the same proportions as in Pollin and Chakraborty 2020.11 Details of these allocations are summarized in Table 1. (Table 3 shows the allocation of infrastructure and clean energy and efficiency spending by industry.) It is important to note that schools and broadband investments represent $70.2 billion (28.1%), or more than one-quarter of proposed infrastructure investments, which would generate substantial social benefits.

Table 1

Annual spending in year four of a four-year, $2 trillion infrastructure, clean energy, and energy efficiency program

 

Program category Annual spending (billions$)
Infrastructure program elements
Surface transportation $105.9
Water/wastewater treatment $10.1
Electricity $17.0
Airports $4.0
Inland waterways/ports $1.4
Dams $3.8
Hazardous and solid waste $0.7
Levees $6.7
Public parks and recreation $9.8
Rail $2.8
Schools $36.5
Natural gas pipelines $17.6
Broadband $33.7
Total $250
Renewable energy
Wind $84.4
Solar energy $84.4
Geothermal energy $18.8
Energy efficiency
Building retrofits $29.3
Industrial efficiency $4.9
High-efficiency autos $4.9
Land and agriculture
Land restoration $7.8
Agriculture $15.6
Total $250

Notes: This table takes the $500 billion that would be spent annually as part of a four-year, $2 trillion spending proposal and allocates it in proportion to spending assumptions in Sierra Club 2020, a report analyzed by the University of Massachusetts Amherst’s Political Economy Research Institute (PERI) (Pollin and Chakraborty 2020). Totals may not sum due to rounding.

Source: Authors’ analysis of Pollin and Chakraborty 2020 and Chakraborty 2020.

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

Overall economic and employment impacts of trade and investment proposals

Table 2 summarizes the overall impacts of all three components of the program proposed in this report. The top panel of the table shows the economic impact in billions of dollars, and the bottom panel shows the employment impact. The first set of rows in the top panel shows changes in trade flows from 2019 to 2024 resulting from the policies to end overvaluation of the U.S. dollar and rebalance trade. It is assumed that the real value of the U.S. dollar is reduced by approximately 25%, as discussed in the methodology appendix toward the end of this report. Total exports expand by 64.6% between 2019 (actual) and 2024 (projected), while imports increase by only 8.3%. As a result, goods trade balance is achieved in 2024, completely eliminating the U.S. goods trade deficit, which reached $854.3 billion in 2019.

Table 2

Annual economic and employment impacts of promoting U.S. exports and investing in infrastructure, clean energy, and energy efficiency by 2024

Economic impact (billions of dollars)
2019 2024 Change Percent
change
U.S. total exports (before and after export promotion policies) $1,643.2 $2,704.9 $1,061.7 64.6%
U.S. general imports (before and after export promotion policies) $2,497.5 $2,704.9 $207.4 8.3%
U.S. trade balance (before and after export promotion policies) -$854.3 $0.0 $854.3
Infrastructure investments (per year) $0.0 $250.0 $250.0
Clean energy and efficiency investments (per year) $0.0 $250.0 $250.0
Total new spending on domestic goods and services (per year) $1,354.3 $1,354.3
Total respending (60% of new domestic spending of $1,354.3 billion) $812.6
Employment impact
2019 2024 Change Total jobs with respending
U.S. total exports–jobs supported (before and after export promotion policies) 7,507,400 12,210,300 4,702,900
U.S. general imports–jobs displaced (before and after export promotion policies) 12,647,400 13,842,100 1,194,700
U.S. trade balance–net jobs supported (before and after export promotion policies) -5,140,000 -1,631,800 3,508,200 6,571,600
Infrastructure investments (permanent jobs supported*) 0 2,071,600 2,071,600 3,880,600
Climate, clean energy and efficiency investments (permanent jobs supported*) 0 1,315,400 1,315,400 2,464,000
Total direct and indirect jobs supported 6,895,200 12,916,200
Respending jobs 6,021,000
Range of potential jobs supported, including respending 6,895,200 to 12,916,200

 

Investments and spending at these levels will support continuing employment as long as spending is sustained at this level in future years. 

Note: Table estimates reflect changes in trade due to export promotion policies from baseline year 2019 and a four-year, $500 billion annual investment plan beginning in 2021.

Source: Authors’ analysis of employment requirements overall and by industry (BLS-EP 2020a) applied to trade data (USITC 2020), authors’ trade projections, and infrastructure and clean energy/energy efficiency investments at the allocations specified in Pollin and Chakraborty 2020. For a more detailed explanation of data sources and computations, see Table 1 and the text and the appendix in this report.

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

The second set of rows in the top panel shows the economic impacts of the fourth year of the new $2 trillion in infrastructure, clean energy, and energy efficiency spending in 2024, reflecting the assumption that public spending on infrastructure and clean energy and energy efficiency investments increases by $500 billion per year in 2021, 2022, 2023, and 2024 ($250 billion per year for each of these purposes). In 2024, the $854.3 billion in increased economic output from rebalancing trade combined with the additional $500 billion yearly spending on infrastructure and clean energy/energy efficiency yields an additional $1.354 trillion in total spending on domestic goods and services (some of which will include imported components). This represents an increase of approximately 6.8% of GDP. It is worth repeating that this increase in spending will only require $500 billion per year in new federal spending; the rest results from increased foreign purchases of U.S. products. The final element of increased demand shown in the top panel of Table 2 is $812.6 billion in induced respending: roughly, how much additional spending happens as the $1.354 trillion in spending makes its way to workers and consumers’ pockets and is respent on consumer goods and services. This figure assumes that there will be a macroeconomic multiplier of 1.6, i.e., a 60% boost to spending in the form of respending. Bivens (2014) reviews the economic literature on multipliers, and notes that infrastructure spending is found to have very high levels of economic multipliers.12 A multiplier of 1.6 is used for that study. Spending on clean energy products, and output from additional U.S. exports, also are likely to have very high multipliers, for similar reasons. Note that multipliers depend in part on the level of excess capacity (economic distress) in the economy. Thus we do not include the multiplier (induced or respending) effects in our main results (jobs supported by industry and by state), but we do include them for informative purposes in our upper-bound estimate of jobs supported and in Table 2 and Table 4.

The employment impacts of these policies are summarized in the bottom panel of Table 2. Note that the employment effects include direct jobs supported or created by a given level of output (an aggregate of all industries) and the aggregate indirect jobs in industries that supply goods to directly affected industries (think auto assembly jobs and the jobs held by those who make auto parts, steel, and rubber, or who provide accounting, finance, staffing, or other services to auto manufacturers).

The U.S. goods trade deficit in 2019 displaced 5.1 million jobs. If trade is balanced, the number of jobs displaced by trade flows is reduced to 1.6 million jobs, for a net gain of 3.5 million direct and indirect jobs supported, as shown in column 3 (see the text box, “Defining jobs: supported vs. created vs. job years”). The reason that there still are jobs displaced under balanced trade is that U.S. imports are more labor intensive, on average, than U.S. exports, as predicted by trade theory, so the U.S. experiences a net loss of jobs.

Infrastructure investments of $250 billion in 2024 would support 2.1 million direct and indirect jobs, and clean energy and energy efficiency investments would support an additional 1.3 million direct and indirect jobs. Overall, the combination of export promotion (balanced goods trade), and expanded public investments will support a total of 6.9 million direct and indirect jobs. In addition, to the extent that multiplier effects are generated by these activities as workers spend their incomes in the economy, up to 6.0 million additional jobs could be supported by these activities. (As noted earlier, multiplier effects are stronger when the economy is struggling than when it is at full employment.)

The fourth and last data column in panel two of the table shows the results of jobs supported or created per category if we break down the additional 6.0 million induced respending jobs by each of the three program areas: export promotion, infrastructure investment, and clean energy/energy efficiency investment. If induced (multiplier) effects are included, trade rebalancing could support an additional 3.1 million jobs, meaning trade rebalancing has the potential to support between 3.5 million jobs (column three) and 6.6 million total jobs (column four). If the overall adjustment in the trade balance is less, then total jobs supported would be smaller. For example, if the trade deficit falls by half, then net export growth will support between 1.8 and 3.3 million additional net jobs.

Similarly, a $250 billion annual increase in infrastructure spending could support an additional 1.8 million respending jobs, meaning the infrastructure spending has the potential to support between 2.1 million jobs (column three) and 3.9 million jobs (column four) when direct, indirect, and induced (respending) jobs are included. Finally, spending on clean energy and energy efficiency could support between 1.3 million and 2.5 million net new jobs. The overall results —roughly 6.3 million direct, indirect, and respending jobs supported—are comparable with Pollin and Chakraborty 2020, when multiplier effects are included.13

Overall, the programs summarized in Table 2 will support a grand total of between 6.9 million and 12.9 million new jobs (depending on the overall level of macroeconomic multipliers in 2024 and thus respending jobs) if the U.S. trade deficit is eliminated in that year.

Economic impacts by industry

Overall economic impacts of the three trade and investment proposals by industry are summarized in Tables 3 and 4. Table 3 reports changes in imports, exports, and the trade balance from implementing export promotion policies that eliminate the trade deficit by 2024, and Table 4 reports how the $500 billion in new spending on infrastructure, clean energy, and energy efficiency in 2024 breaks down by industry.

Table 3

How export promotion would change U.S. goods trade with the world, by industry, 2019–2024

U.S. imports U.S. exports Trade balance
Industries Change ($billions) Share of total change Change
($billions)
Share of total change Change ($billions) Share of total change
Total $207.4 100.0% $1,061.7 100.0% $854.3 100.0%
Agriculture, forestry, fisheries $6.1 3.0% $68.0 6.4% $61.9 7.2%
Mining -$42.0 -20.3% $127.5 12.0% $169.5 19.8%
Oil and gas -$41.7 -20.1% $66.0 6.2% $107.8 12.6%
Minerals and ores -$0.3 -0.1% $61.5 5.8% $61.8 7.2%
Utilities $0 0.0% $0 0.0% $0 0.0%
Construction $0 0.0% $0 0.0% $0 0.0%
Manufacturing $241.0 116.3% $846.8 79.8% $605.8 70.9%
Nondurable goods $78.3 37.7% $308.0 29.0% $229.7 26.9%
Food and kindred products $9.5 4.6% $46.2 4.3% $36.6 4.3%
Beverage and tobacco products $5.1 2.5% $7.9 0.7% $2.8 0.3%
Textile mills and textile product mills $2.8 1.4% $2.0 0.2% -$0.8 -0.1%
Apparel, leather and allied product Manufacturing $1.4 0.7% -$0.9 -0.1% -$2.3 -0.3%
Paper $0.4 0.2% $8.0 0.8% $7.6 0.9%
Printed matter and related products $0.4 0.2% $1.1 0.1% $0.7 0.1%
Petroleum and coal products -$9.2 -4.4% $64.7 6.1% $73.9 8.7%
Chemicals $59.6 28.7% $168.9 15.9% $109.4 12.8%
Plastics and rubber products $8.3 4.0% $10.2 1.0% $1.9 0.2%
Durable goods $162.8 78.5% $538.8 50.7% $376.0 44.0%
Wood products $1.3 0.6% $1.4 0.1% $0.1 0.0%
Nonmetallic mineral products $2.4 1.1% $3.7 0.4% $1.4 0.2%
Primary metal -$5.1 -2.5% $114.1 10.7% $119.2 14.0%
Fabricated metal products $8.0 3.8% $24.9 2.3% $17.0 2.0%
Machinery, except electrical $21.5 10.4% $84.9 8.0% $63.3 7.4%
Computer and electronic parts $19.5 9.4% $36.6 3.4% $17.1 2.0%
Computer and peripheral equipment $8.0 3.9% -$0.8 -0.1% -$8.8 -1.0%
Communications, audio and video equipment -$0.3 -0.2% $21.8 2.1% $22.1 2.6%
Navigational, measuring, electromedical, and control instruments $2.9 1.4% $19.4 1.8% $16.5 1.9%
Semiconductor and other electronic components & magnetic and optical media production $9.0 4.3% -$3.8 -0.4% -$12.8 -1.5%
Electrical equipment, appliances, and component $20.2 9.7% $28.9 2.7% $8.7 1.0%
Transportation equipment $42.1 20.3% $146.2 13.8% $104.1 12.2%
Motor vehicles and parts $32.5 15.7% $76.3 7.2% $43.8 5.1%
Aerospace products and parts $8.0 3.8% $59.8 5.6% $51.9 6.1%
Railroad, ship, and other transportation equipment $1.6 0.8% $10.1 0.9% $8.5 1.0%
Furniture and fixtures $7.5 3.6% $3.8 0.4% -$3.7 -0.4%
Miscellaneous manufactured commodities $45.5 21.9% $94.3 8.9% $48.9 5.7%
Scrap and secondhand goods $2.2 1.1% $19.4 1.8% $17.2 2.0%

 

Source: Authors’ analysis of trade data (USITC 2020) and authors' projections for trade in 2024 given currency realignment and an aggressive program of industrial policies. For a more detailed explanation of policies, data sources, and computations, see report, especially “Trade projections” section, discussion of Table 2, and the methodology appendix.

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

The trade model is based on actual trade behavior during the 2002–2008 period, the last time the dollar experienced a sustained declined of about 25%. During this period, total U.S. goods exports increased 87.5%. The forecast assumes that exports at the industry level increased at the rate that prevailed in the 2002–2008 period (with few exceptions, explained in the notes and methodology appendix), and that imports in each sector increase at the rate that prevailed in the most recent 2014–2019 period (8.3%, in total, as shown in Table 2).14 Finally, assumed export growth in each sector is further reduced by 15.5% so as to achieve overall balance in goods trade in 2024. In other words, the model assumes that overall U.S. goods exports increase 64.6% between 2019 and 2024, as shown in the last column of Table 2.

Table 3 also reports each industry’s share of the overall import growth, export growth, and trade balance change between 2019 and 2024. In terms of net changes in the trade balance, 70.9% of the improvement in the goods trade balance (i.e, the decrease in the goods trade deficit) takes place in the manufacturing sector, 7.2% is in agricultural products, and 19.8% is in mining (oil and gas is a big contributor, alone responsible for 12.6% of the increase in goods trade). Within manufacturing, petroleum and coal products, and chemicals—both essentially “refined energy products”—are together responsible for 21.5% of the total improvement in the trade balance. Finally, 44.0% of the improvement in the trade balance occurs in durable goods industries, which support many good, high-wage jobs, as discussed in the next section.

Table 4 reports the industry breakdown of the $500 billion in spending for infrastructure, clean energy, and energy efficiency in 2024, as noted above, as well as the economic output generated by the $812.6 billion in respending induced by the $1.354 trillion in spending from the trade rebalancing and infrastructure and clean energy/energy efficiency investments. Spending allocations for infrastructure, clean energy, and energy efficiency are scaled to proposals outlined in Pollin and Chakraborty 2020. Overall, 32.5% of planned spending for infrastructure is for construction services, as shown in the addendum at the bottom of Table 4. Less than one quarter (22.8%) of infrastructure spending is for manufactured products. On the other hand, virtually all (91.6%) of clean energy and energy efficiency investments are for manufactured products.

Table 4

Total new spending on investments in infrastructure, clean energy, and energy efficiency, and respending from combined export promotion and investment proposals, by industry, 2024

Infrastructure investments ($billions) Clean energy and energy efficiency investments ($billions) Respending (from $500 billion in investments and $854.3 billion improvement in net trade balance) ($billions)
Total $250.0 $250.0 $812.6
Agriculture, forestry, fisheries $0 $6.3 $4.6
Mining $0 $3.8 $0
Oil and gas $0 $3.8 $0
Minerals and ores $0 $0 $0
Utilities $13.6 $0 $16.2
Construction $81.2 $1.6 $0
Manufacturing $57.1 $229.1 $100.4
Nondurable goods $1.0 $12.7 $79.5
Food and kindred products $0 $0 $27.0
Beverage and tobacco products $0 $0 $9.3
Textile mills and textile product mills $0 $0 $1.4
Apparel, leather, and allied product manufacturing $0 $0 $9.0
Paper $0 $0 $1.3
Printed matter and related products $0 $0 $0.3
Petroleum and coal products $0 $0 $10.6
Chemicals $0 $12.7 $18.7
Plastics and rubber products $1.0 $0 $1.9
Durable goods $56.1 $216.4 $20.9
Wood products $0.0 $4.4 $0.3
Nonmetallic mineral products $2.1 $0 $0.7
Primary metal $1.0 $0 $0
Fabricated metal products $1.0 $31.8 $1.2
Machinery, except electrical $0.4 $86.4 $0.4
Computer and electronic parts $0 $21.1 $5.3
Computer and peripheral equipment $0 $8.4 $2.5
Communications, audio, and video equipment $0 $0 $2.2
Navigational, measuring, electromedical, and control instruments $0 $0 $0.5
Semiconductor and other electronic components, and magnetic and optical media production $0 $12.7 $0
Electrical equipment, appliances, and component $20.3 $69.1 $2.4
Transportation equipment $31.2 $3.7 $15.0
Motor vehicles and parts $28.6 $3.7 $13.6
Aerospace products and parts $1.4 $0 $0.0
Railroad, ship, and other transportation equipment $1.2 $0 $1.3
Furniture and fixtures $0 $0 $2.9
Miscellaneous manufactured commodities $0 $0 -$7.3
Wholesale trade $0 $0 $33.3
Retail trade $0 $0 $84.7
Transportation $44.7 $0 $18.7
Information $16.8 $0 $30.2
Finance and insurance $0 $0 $65.5
Real estate, and rental and leasing $0 $0 $41.4
Professional, scientific, and technical services $2.6 $3.9 $11.0
Management of companies and enterprises $0 $0 $0
Administrative and support and waste management and remediation services $5.2 $4.7 $5.3
Education services $21.9 $0 $21.5
Health care and social assistance $0 $0 $158.8
Arts, entertainment, and recreation $6.9 $0.8 $16.9
Accommodation and food services $0 $0 $54.0
Other services $0 $0 $37.8
Government $0 $0 $107.5
Scrap and secondhand goods $0 $0 $4.7
Addendum:
Construction share 32.5% 0.6% 0.0%
Manufacturing share 22.8% 91.6% 12.4%

 

Note: Totals may not sum due to rounding.

Source: Authors’ analysis of infrastructure and clean energy/energy efficiency investments at the allocations specified in Pollin and Chakraborty 2020. Respending applies multiplier from Bivens 2014 to these investments and authors' projections for trade in 2024 given currency realignment and an aggressive program of industrial policies. For a more detailed explanation of data sources and computations, see this report, especially Tables 1 and 2 and the methodology appendix.

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

The respending allocations assigned to each industry in the last data column are based on personal consumption expenditure data from the Bureau of Labor Statistics input–output tables (BLS-EP 2020b).15 Respending is heavily weighted toward service industry purchases, and manufactured products account for just 12.4% of respending. These differences between the industry composition of investment spending and the industry composition of respending have important implications for the patterns of job creation in the model results, as discussed in the next section.

Job impacts by industry

Table 5 provides the industry breakdown of direct and indirect jobs supported by export promotion (rebalancing trade), infrastructure investments, and clean energy/energy efficiency investments.16 The last two data columns in the table report the total direct and indirect jobs from all three categories combined and the total jobs supported in each industry as a share of the overall total jobs supported (it excludes jobs from respending).17

Table 5

Industry breakdown of net jobs supported by rebalancing trade and investing in infrastructure, clean energy, and energy efficiency, 2024

Total jobs supported
Rebalancing trade Infrastructure investments Clean energy/energy efficiency investments Total jobs supported Share of total jobs gained
Total 3,508,200 2,071,600 1,315,400 6,895,200 100.0%
Agriculture, forestry, fisheries 540,300 6,300 42,000 588,600 8.5%
Mining 187,800 7,700 5,900 201,400 2.9%
Oil and gas 64,000 2,200 2,300 68,500 1.0%
Minerals and ores 123,800 5,500 3,600 132,900 1.9%
Utilities 16,700 16,900 3,100 36,700 0.5%
Construction 44,400 411,900 14,900 471,200 6.8%
Manufacturing 1,386,400 298,800 822,800 2,508,000 36.4%
Nondurable goods 289,200 35,400 43,000 367,600 5.3%
Food and kindred products 73,100 1,900 1,000 76,000 1.1%
Beverage and tobacco products 8,900 200 200 9,300 0.1%
Textile mills and textile product mills 7,600 2,000 4,000 13,600 0.2%
Apparel, leather, and allied product manufacturing -14,600 900 300 -13,400 -0.2%
Paper 26,400 3,400 4,900 34,700 0.5%
Printed matter and related products 11,100 4,000 2,300 17,400 0.3%
Petroleum and coal products 17,400 2,000 600 20,000 0.3%
Chemicals 122,400 5,800 19,800 148,000 2.1%
Plastics and rubber products 37,000 15,100 10,000 62,100 0.9%
Durable goods 1,097,200 263,300 779,800 2,140,300 31.0%
Wood products 13,600 10,800 26,800 51,200 0.7%
Nonmetallic mineral products 16,800 20,900 7,100 44,800 0.6%
Primary metal 203,100 13,100 31,800 248,000 3.6%
Fabricated metal products 164,000 45,600 174,100 383,700 5.6%
Machinery, except electrical 183,500 11,000 242,200 436,700 6.3%
Computer and electronic parts 47,400 7,500 83,500 138,400 2.0%
Computer and peripheral equipment -36,200 400 27,700 -8,100 -0.1%
Communications, audio, and video equipment 49,300 900 800 51,000 0.7%
Navigational, measuring, electromedical, and control instruments 50,600 1,300 3,300 55,200 0.8%
Semiconductor and other electronic components, and magnetic and optical media production -16,300 4,900 51,700 40,300 0.6%
Electrical equipment, appliances, and components 44,000 62,600 196,100 302,700 4.4%
Transportation equipment 246,800 85,000 12,000 343,800 5.0%
Motor vehicles and parts 102,100 77,300 9,400 188,800 2.7%
Aerospace products and parts 122,100 3,400 2,100 127,600 1.9%
Railroad, ship, and other transportation equipment 22,500 4,300 500 27,300 0.4%
Furniture and fixtures -17,300 4,900 2,700 -9,700 -0.1%
Miscellaneous manufactured commodities 195,300 2,000 3,500 200,800 2.9%
Wholesale trade 218,600 54,100 64,400 337,100 4.9%
Retail trade 57,000 42,200 11,000 110,200 1.6%
Transportation 197,600 354,300 51,500 603,400 8.8%
Information 28,100 34,300 9,800 72,200 1.0%
Finance and insurance 93,300 43,500 21,500 158,300 2.3%
Real estate, and rental and leasing 42,600 16,800 9,200 68,600 1.0%
Professional, scientific, and technical services 209,400 90,400 75,500 375,300 5.4%
Management of companies and enterprises 117,400 22,800 43,300 183,500 2.7%
Administrative and support and waste management and remediation services 222,600 139,800 92,500 454,900 6.6%
Education services 4,400 287,600 2,000 294,000 4.3%
Health care and social assistance 2,400 2,000 600 5,000 0.1%
Arts, entertainment, and recreation 9,700 58,000 8,700 76,400 1.1%
Accommodation and food services 45,400 22,400 14,800 82,600 1.2%
Other services 34,000 13,500 9,700 57,200 0.8%
Government 50,100 148,200 12,300 210,600 3.1%
Scrap and secondhand goods 0 0 0 0 0.0%
Addendum:
Construction share 1.3% 19.9% 1.1% 6.8%
Manufacturing share 39.5% 14.4% 62.6% 36.4%
Jobs per million dollars 4.11 8.29 5.26 5.09
Total primary spending injection (billions of dollars) $854.4 $250.0 $250.0 $1,354.4

Note: Table estimates the employment effects of changes in trade due to export promotion policies from baseline year 2019 and a four-year, $500 billion annual investment plan beginning in 2021. Infrastructure, and clean energy and energy efficiency investments at the levels specified in Table 4 will support continuing employment at levels identified in this table as long as spending is sustained at specified levels in future years. Respending effects are excluded.

Source: Authors’ analysis of employment requirements by industry (BLS-EP 2020a) applied to trade data (USITC 2020) and authors' projections for trade in 2024 given currency realignment and an aggressive program of industrial policies, and to infrastructure and clean energy/energy efficiency investments at the allocations specified in Pollin and Chakraborty 2020. For a more detailed explanation of data sources and computations, see this report, especially Tables 1–4 and the methodology appendix.

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

Overall, 6,895,200 jobs would be supported between 2019 and 2024 as a result of these three activities. More than one-third (36.4%) of the jobs supported would be in manufacturing, or 2,508,000 total jobs. In addition, 471,200 jobs (6.8% of the total) would be in construction. An overwhelming share (87.4%) of the 471,200 construction jobs supported are jobs supported by infrastructure investments (411,900).

Manufacturing and construction offer high wages with excellent benefits (Scott 2017b). Nearly half (43.2%) of the direct and indirect jobs supported by the programs outlined in this study would be in these high-wage industries (supporting a combined 2,979,200 jobs). Manufacturing and construction employed a total of 20,491,000 workers, or 13.4% of total nonfarm employment, in February 2020 (BLS 2020a). Thus, these programs, if enacted, would create a threefold increase in the rate at which the U.S. economy is generating good jobs for non-college-educated workers. This would help restructure the labor market toward more high-wage jobs for these workers.18 Competition for these workers also would help pull up wages for all workers with similar characteristics in other industries, by tightening the labor market for non-college-educated workers.

The addendum at the bottom of Table 5 illustrates some of the differences and relative strengths of these three proposals for rebuilding the economy. Nearly two-fifths (39.5%, or 1,386,400 jobs) of the jobs supported by rebalancing trade would be in manufacturing. Roughly one-fifth of jobs supported by infrastructure investment will be in construction. And among the three programs considered here, infrastructure investment supports the smallest share of manufacturing jobs (14.4%, or 298,800 jobs). Clean energy and energy efficiency investments would support 1,315,400 jobs, nearly two-thirds of which (62.6%, or 822,800 jobs) would be in manufacturing. This is an important result for those concerned that clean energy proposals will hurt employment. Clean energy proposals substitute capital, and especially manufactured goods, as inputs instead of energy; these proposals also substitute wages for profits—traditional energy industries such as oil are among the most profitable in the United States.19 To understand the potential benefits of clean energy job creation, consider that the coal mining industry in the United States employed only 50,400 workers, in total, in February 2020 (BLS 2020a). While targeted policies that help workers transition to new industries are clearly a necessary complement to these investment proposals, many of these workers displaced by shifting energy production easily could be absorbed by growing manufacturing industries in the United States if the clean energy proposal were implemented. Overall, 2.5 million manufacturing jobs would be created by these three proposals over the next four years, more than enough to absorb all workers displaced by reduced energy consumption.

A state-by-state breakdown of job creation

Rebalancing trade, rebuilding U.S. infrastructure, and investing in clean energy and energy efficiency would generate significant job growth in all 50 states and in the District of Columbia, as shown in Table 6 and the interactive map in Figure A. Job gains would range from 6.16 % of total employment (or 181,000 jobs supported) in Wisconsin down to 2.85% of employment (or 10,200 jobs supported) in Washington, D.C., as shown in Table 6, which ranks states by jobs supported, as a share of total state employment. In general, job growth would be concentrated in the manufacturing-intensive areas of the country in the upper Midwest and the South which have been hardest hit by globalization and outsourcing, and especially by growing imports from China (Scott and Mokhiber 2020). Certain energy-producing states (i.e., North Dakota, South Dakota, Wyoming, and Oklahoma) are also in the top 10 job-gaining states.

Table 6

Net jobs supported by rebalancing trade and investing in infrastructure, clean energy, and energy efficiency in 2024, by state (ranked by jobs gained by share of total state employment)

Rank State Change in trade balance Infrastructure Clean energy / energy efficiency Total jobs supported State employment (2013–2017 5-year ACS estimate) Jobs supported as a share of employment
1 Wisconsin 93,300 40,800 46,900 181,000 2,939,900 6.16%
2 North Dakota 15,400 5,200 3,700 24,300 400,500 6.07%
3 Indiana 99,000 49,500 37,400 185,900 3,124,300 5.95%
4 Iowa 52,000 21,400 21,100 94,500 1,599,700 5.91%
5 Wyoming 11,000 4,000 1,700 16,700 293,600 5.69%
6 Oklahoma 55,600 24,700 17,900 98,200 1,746,400 5.62%
7 South Dakota 14,900 5,500 4,200 24,600 438,300 5.61%
8 Michigan 128,800 73,100 49,300 251,200 4,524,900 5.55%
9 Ohio 152,600 79,900 69,900 302,400 5,488,200 5.51%
10 Kentucky 52,600 30,400 21,000 104,100 1,938,200 5.37%
11 Kansas 43,200 18,900 13,500 75,600 1,420,000 5.32%
12 Arkansas 35,600 17,500 12,900 66,100 1,276,500 5.18%
13 Nebraska 29,100 13,200 8,500 50,800 987,200 5.15%
14 Illinois 156,600 87,600 69,800 314,000 6,181,700 5.08%
15 Mississippi 30,900 18,200 12,100 61,100 1,221,800 5.00%
16 Alabama 53,600 30,400 18,700 102,700 2,055,500 5.00%
17 Minnesota 76,200 37,300 31,400 144,900 2,904,100 4.99%
18 Idaho 21,100 9,800 6,400 37,300 748,700 4.98%
19 South Carolina 52,200 31,800 24,500 108,500 2,181,000 4.97%
20 Tennessee 70,500 46,400 32,100 149,000 2,996,600 4.97%
21 Texas 328,000 181,700 108,400 618,100 12,689,100 4.87%
22 Pennsylvania 150,700 82,600 62,400 295,700 6,097,000 4.85%
23 Montana 14,800 6,500 2,800 24,100 498,000 4.84%
24 New Hampshire 16,200 9,700 8,500 34,400 713,400 4.82%
25 Washington 93,300 45,000 25,200 163,600 3,418,100 4.79%
26 Missouri 69,200 39,600 28,100 136,900 2,867,400 4.77%
27 Oregon 47,400 23,800 18,900 90,000 1,886,000 4.77%
28 Utah 33,700 19,700 10,700 64,100 1,412,200 4.54%
29 Louisiana 51,100 27,500 12,900 91,400 2,031,200 4.50%
30 West Virginia 18,800 9,800 5,000 33,600 747,000 4.50%
31 Connecticut 40,100 23,500 16,900 80,500 1,805,100 4.46%
32 North Carolina 97,000 62,400 43,900 203,400 4,571,000 4.45%
33 Georgia 98,000 66,900 39,900 204,700 4,606,300 4.44%
34 Alaska 8,700 5,300 1,600 15,600 354,000 4.41%
35 California 411,200 234,400 144,100 789,700 17,993,900 4.39%
36 Colorado 61,100 37,400 19,900 118,400 2,760,100 4.29%
37 Vermont 6,800 4,300 2,900 14,000 327,300 4.28%
38 Rhode Island 10,700 6,600 4,500 21,800 526,100 4.14%
39 Maine 13,900 8,100 4,200 26,300 658,700 3.99%
40 Arizona 57,600 39,500 20,400 117,400 2,953,900 3.97%
41 New Mexico 18,900 11,300 4,600 34,900 879,200 3.97%
42 New Jersey 81,200 59,000 29,200 169,300 4,388,000 3.86%
43 Massachusetts 62,700 43,800 28,800 135,400 3,525,700 3.84%
44 Virginia 72,500 56,000 25,800 154,300 4,084,000 3.78%
45 Florida 158,700 119,400 53,600 331,700 9,018,600 3.68%
46 Delaware 8,000 5,600 2,600 16,100 441,500 3.65%
47 Nevada 22,400 17,800 7,300 47,500 1,341,400 3.54%
48 New York 151,200 122,800 60,300 334,300 9,467,600 3.53%
49 Maryland 45,800 42,000 15,400 103,200 3,040,800 3.39%
50 Hawaii 10,300 9,200 2,500 22,000 671,800 3.27%
51 District of Columbia 4,200 4,600 1,400 10,200 357,700 2.85%
Total 3,508,200 2,071,600 1,315,400 6,895,200 150,599,200 4.58%

Note: Totals may vary slightly due to rounding. Percentages are calculated using rounded totals.

Source: Authors’ analysis of employment requirements by industry (BLS-EP 2020a) applied to trade data (USITC 2020) and authors' projections for trade in 2024 given currency realignment and an aggressive program of industrial policies, and to infrastructure and clean energy/energy efficiency investments at the allocations specified in Pollin and Chakraborty 2020. American Community Survey (ACS) data (U.S. Census Bureau 2019) are used to estimate state jobs and shares supported. For a more detailed explanation of data sources and computations, see this report, especially Tables 1–5 and the methodology appendix.

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

Figure A

Net jobs created as a share of employment by rebalancing trade and investing in infrastructure, clean energy, and energy efficiency, by state

State Total jobs gained Jobs gained as as share of employment
Alabama        102,700 5.0%
Alaska           15,600 4.4%
Arizona        117,400 4.0%
Arkansas           66,100 5.2%
California        789,700 4.4%
Colorado        118,400 4.3%
Connecticut           80,500 4.5%
Delaware           16,100 3.6%
DC           10,200 2.9%
Florida        331,700 3.7%
Georgia        204,700 4.4%
Hawaii           22,000 3.3%
Idaho           37,300 5.0%
Illinois        314,000 5.1%
Indiana        185,900 6.0%
Iowa           94,500 5.9%
Kansas           75,600 5.3%
Kentucky        104,100 5.4%
Louisiana           91,400 4.5%
Maine           26,300 4.0%
Maryland        103,200 3.4%
Massachusetts        135,400 3.8%
Michigan        251,200 5.6%
Minnesota        144,900 5.0%
Mississippi           61,100 5.0%
Missouri        136,900 4.8%
Montana           24,100 4.8%
Nebraska           50,800 5.1%
Nevada           47,500 3.5%
New Hampshire           34,400 4.8%
New Jersey        169,300 3.9%
New Mexico           34,900 4.0%
New York        334,300 3.5%
North Carolina        203,400 4.4%
North Dakota           24,300 6.1%
Ohio        302,400 5.5%
Oklahoma           98,200 5.6%
Oregon           90,000 4.8%
Pennsylvania        295,700 4.8%
Rhode Island           21,800 4.1%
South Carolina        108,500 5.0%
South Dakota           24,600 5.6%
Tennessee        149,000 5.0%
Texas        618,100 4.9%
Utah           64,100 4.5%
Vermont           14,000 4.3%
Virginia        154,300 3.8%
Washington        163,600 4.8%
West Virginia           33,600 4.5%
Wisconsin        181,000 6.2%
Wyoming           16,700 5.7%

 

ChartData Download data

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

Source: See Table 6 in this report (Rebuilding American Manufacturing—Potential Job Gains by State and Industry by Scott, Mokhiber, and Perez, 2020).

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

The model used in this study assumes that construction spending, which is prominent in the infrastructure proposal, will be proportional to current distributions of construction and manufacturing employment by state. Actual results could vary if infrastructure and clean energy spending are allocated based on need, and if spending programs are used to redress existing patterns of racial and gender discrimination. The past is not prologue, in these cases, despite the structure of the model revealed in Table 2. Policy can change the distribution of jobs shown.

Supplemental Table A at the end of this report provides total jobs supported per state ranked by the total number of jobs supported. Jobs supported are in general proportional to total employment, so the states with the largest populations (California, Texas, New York, Florida, and Illinois) make up the top five on this list. Supplemental Table B ranks states alphabetically, and reports the same results shown in Table 6.

Conclusion

Rebalancing trade by expanding exports, and expanding public investments in infrastructure, clean energy, and energy efficiency, are the keys to generating at least 6.9 million good jobs, rebuilding American manufacturing and the U.S. economy.

Acknowledgments

The author thanks Josh Bivens, Scott Boos, Riley Olson, Scott Paul, and Michael Wessel for comments, and Lora Engdahl for editing assistance. We also thank Robert Polin and Shouvik Chakraborter of the Political Economy Research Institute at the University of Massachusetts, Amherst, for additional details about their modeling assumptions. This research was made possible by support from the Alliance for American Manufacturing.

About the authors

Robert E. Scott joined the Economic Policy Institute in 1996 and is currently director of trade and manufacturing policy research. His areas of research include international economics, trade, and manufacturing policies and their impacts on working people in the United States and other countries, the economic impacts of foreign investment, and the macroeconomic effects of trade and capital flows. He has published widely in academic journals and the popular press, including the Journal of Policy Analysis and Management, the International Review of Applied Economics, and the Stanford Law and Policy Review, as well as the The Hill, Los Angeles Times, Morning Consult, Newsday, The New York Times, USA Today, The Baltimore Sun, and other newspapers. He also has provided economic commentary for a range of electronic media, including NPR, CNN, Bloomberg, and the BBC. He has a Ph.D. in economics from the University of California, Berkeley.

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

Daniel Perez is a research assistant at the Economic Policy Institute. He joined EPI in December 2019 and supports the work of EPI economists on trade, inequality, worker power, and more. As a research assistant, he compiles and analyzes economic data for media briefings, research reports, and policy proposals. Prior to joining EPI, Perez served as a research assistant for The University of California, Santa Cruz’s Income Dynamics Lab, studying development and political economy, and worked as programmatic assistant for ROC United, where he worked to improve labor market outcomes for low-wage and tipped workers. Perez also has worked in other industries, including food, wholesale, and education.

Appendix: Methodology

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

This model was developed to analyze the employment impacts of trade flows on the domestic economy by Scott and Mokhiber (2020). It is adapted and extended here to examine the impacts of other types of spending, including infrastructure, clean energy, and induced respending (personal consumption expenditures or PCE) and multiplier effects. The underlying input-output and employment requirements models used to study trade effects are perfectly well suited to the study of domestic investment changes as well.

The number of jobs supported or displaced by $1 million of exports, imports, or other spending for each of 205 different U.S. industries is estimated using a labor requirements model derived from an input–output table developed by the BLS-EP (2020a).20 This model includes both the direct effects of changes in output (for example, the number of jobs supported by $1 million worth of auto assembly output) and the indirect effects on industries that supply goods (for example, goods used in the manufacture of cars). So, in the auto industry for example, the indirect impacts include jobs in auto parts, steel, and rubber, as well as service industries such as accounting, finance, computer programming, and staffing and temporary help agencies that provide inputs to the motor vehicle manufacturing companies. This model estimates the labor content of trade or other spending 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 2019 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).

Only nominal trade and expenditure data and nominal employment requirements tables are used in this analysis. Inflation and productivity growth were ignored, in the absence of complete price and productivity projections.

The steps followed to estimate the economic and employment impacts of investments in infrastructure, and in clean energy and energy efficiency, are similar to the steps followed to estimate the economic and employment impacts of trade.

Data requirements for trade and for investments

The text below follows the step-by-step process for developing the data for analyzing all three proposals, with Steps 1 through 3 applying only to trade flows.

Step 1. U.S. trade data are obtained from the U.S. International Trade Commission DataWeb (USITC 2020) in four-digit NAICS formats. General imports and total exports are downloaded for each year.

Step 2. Trade projections are developed based on actual market behavior in earlier periods, as described in the text, above.

Step 3. To conform to the BLS Employment Requirements tables (BLS-EP 2020a), trade data must be converted into the BLS industry classifications system. For NAICS-based data, there are 205 BLS industries. The data then are mapped from NAICS industries onto their respective BLS sectors.

Step 4. Data on expenditures for investments in infrastructure, clean energy, and energy efficiency improvements and for respending were collected as described in the text and in tables 1 and 4, above. Expenditure data were translated into four-digit NAICS industries and then mapped onto their respective BLS sectors.

Step 5. Nominal domestic employment requirements tables are downloaded from the BLS-EP (2020a). These matrices are input–output industry-by-industry tables that show the employment requirements for $1 million in outputs in nominal 2019 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 of trade and investment impacts

Step 1. Job equivalents. For the trade analysis, BLS trade data are compiled into matrices. Let [T2019] be the 205×2 matrix made up of a column of imports and a column of exports for 2019. [T2024] is defined as the 205×2 matrix of 2024 trade estimates. Define [E2019] as the 205×205 matrix consisting of the nominal 2019 domestic employment requirements tables. To estimate the jobs supported or displaced by trade, perform the following matrix operations:

[J2019] = [T2019] × [E2019]

[J2024] = [T2024] × [E2019 ]

[J2019] is a 205×2 matrix of job displacement by imports and jobs supported by exports for each of 205 industries in 2019. Similarly, [J2024] is a 205×2 matrix of jobs displaced or supported by imports and exports (respectively) for each of 205 industries in 2024.

A similar analysis is performed for infrastructure, clean energy, and energy efficiency investments, and for respending (PCE) as described above. The investments are all assumed to result in net increases in jobs supported by domestic spending.

To estimate jobs supported/displaced over certain time periods, we perform the following operations:

[Jnx19-24] = [J2019] − [J2024]

Step 2. State-by-state analysis. For states, pooled (five-year) estimates of employment-by-industry data are obtained from the Census Bureau’s American Community Survey (ACS) data for the 2013–2017 period (U.S. Census Bureau 2019) and are mapped into 44 unique census industries and seven aggregated total and subtotals, for a total of 52 sectors (including scrap, not part of the census analysis) (Data Planet 2019).21

Previous reports examining employment impacts of trade flows (Kimball and Scott 2014; Scott and Mokhiber 2018) relied on single-year estimates, based on ACS 2011 data, of employment by industry, state, and congressional district. This model has been completely reestimated in this version of the report with the newer ACS five-year data referenced above. These data provide substantially better detail, and greatly improved accuracy, in the form of much lower levels of variance for employment estimates at every level of detail in the model. The new estimates also reflect congressional district boundaries for the 115th Congress for most districts in the country. Boundaries changed in only a few districts in Pennsylvania and Colorado between the 115th Congress and the current 116th Congress.22

We look at net jobs supported from 2019 to 2024, so from this point, we use [Jnx19-24]. In order to work with 44 sectors, we group the 205 BLS industries into a new matrix, defined as [Jnew19-24], a 44×2 matrix of job support numbers.

Jobs supported by infrastructure and clean energy/energy efficiency investments are added to net jobs supported by trade for the state analysis and combined into the separate vectors shown in Table 6 and Supplemental Tables A and B.

We define [St2013-2017] as the 44×51 matrix of state employment shares (with the addition of the District of Columbia) of employment in each industry calculated from the ACS five-year employment estimates. We calculate:

[Stjnx19-24] = [St2013-2017]T [Jnew19-24]

where [Stjnx19-24] is the 44×51 matrix of job displacement/support by state and by industry. To get state total jobs supported, we add up the subsectors in each state.

Jobs supported by infrastructure and clean energy investments are added to net jobs supported by trade for the state analysis, shown separately in Table 6 and Supplemental Tables A and B, and then combined into one final vector for the calculation of total jobs gained as a share of total state employment.

Supplemental Table A

Net jobs supported by rebalancing trade and investing in infrastructure, clean energy, and energy efficiency, by state, 2024 (ranked by net jobs gained)

Rank State Change in trade balance Infrastructure Clean energy/energy efficiency Total jobs supported State employment (2013–2017 5-year ACS estimate) Jobs supported as a share of employment
1 California 411,200 234,400 144,100 789,700 17,993,900 4.39%
2 Texas 328,000 181,700 108,400 618,100 12,689,100 4.87%
3 New York 151,200 122,800 60,300 334,300 9,467,600 3.53%
4 Florida 158,700 119,400 53,600 331,700 9,018,600 3.68%
5 Illinois 156,600 87,600 69,800 314,000 6,181,700 5.08%
6 Ohio 152,600 79,900 69,900 302,400 5,488,200 5.51%
7 Pennsylvania 150,700 82,600 62,400 295,700 6,097,000 4.85%
8 Michigan 128,800 73,100 49,300  251,200 4,524,900 5.55%
9 Georgia 98,000  66,900 39,900 204,700 4,606,300 4.44%
10 North Carolina 97,000 62,400 43,900 203,400  4,571,000 4.45%
11 Indiana 99,000 49,500 37,400 185,900 3,124,300 5.95%
12 Wisconsin 93,300 40,800 46,900 181,000 2,939,900 6.16%
13 New Jersey 81,200 59,000 29,200 169,300 4,388,000 3.86%
14 Washington 93,300 45,000 25,200 163,600 3,418,100 4.79%
15 Virginia 72,500 56,000 25,800 154,300 4,084,000 3.78%
16 Tennessee 70,500 46,400 32,100 149,000 2,996,600 4.97%
17 Minnesota 76,200 37,300 31,400 144,900 2,904,100 4.99%
18 Missouri 69,200 39,600 28,100 136,900 2,867,400 4.77%
19 Massachusetts 62,700 43,800 28,800 135,400 3,525,700 3.84%
20 Colorado 61,100 37,400 19,900 118,400 2,760,100 4.29%
21 Arizona 57,600  39,500 20,400 117,400 2,953,900 3.97%
22 South Carolina 52,200 31,800 24,500 108,500 2,181,000 4.97%
23 Kentucky 52,600 30,400 21,000 104,100 1,938,200 5.37%
24 Maryland 45,800 42,000 15,400 103,200 3,040,800 3.39%
25 Alabama 53,600 30,400 18,700 102,700 2,055,500 5.00%
26 Oklahoma 55,600 24,700 17,900 98,200 1,746,400 5.62%
27 Iowa 52,000 21,400 21,100 94,500 1,599,700 5.91%
28 Louisiana 51,100 27,500 12,900 91,400 2,031,200 4.50%
29 Oregon 47,400 23,800 18,900 90,000 1,886,000 4.77%
30 Connecticut 40,100 23,500 16,900 80,500 1,805,100 4.46%
31 Kansas 43,200 18,900 13,500 75,600 1,420,000 5.32%
32 Arkansas 35,600 17,500 12,900 66,100 1,276,500 5.18%
33 Utah 33,700 19,700 10,700 64,100 1,412,200 4.54%
34 Mississippi 30,900 18,200 12,100 61,100 1,221,800 5.00%
35 Nebraska 29,100 13,200 8,500 50,800 987,200 5.15%
36 Nevada 22,400 17,800 7,300 47,500 1,341,400 3.54%
37 Idaho 21,100 9,800 6,400 37,300 748,700 4.98%
38 New Mexico 18,900 11,300 4,600 34,900 879,200 3.97%
39 New Hampshire 16,200 9,700 8,500 34,400 713,400 4.82%
40 West Virginia 18,800 9,800 5,000 33,600 747,000 4.50%
41 Maine 13,900 8,100 4,200 26,300 658,700 3.99%
42 South Dakota 14,900 5,500 4,200 24,600 438,300 5.61%
43 North Dakota 15,400 5,200 3,700 24,300 400,500 6.07%
44 Montana 14,800 6,500 2,800 24,100 498,000 4.84%
45 Hawaii 10,300 9,200 2,500 22,000 671,800 3.27%
46 Rhode Island 10,700 6,600 4,500 21,800 526,100 4.14%
47 Wyoming 11,000 4,000 1,700 16,700 293,600 5.69%
48 Delaware 8,000 5,600 2,600 16,100 441,500 3.65%
49 Alaska 8,700 5,300 1,600 15,600 354,000 4.41%
50 Vermont 6,800 4,300 2,900 14,000 327,300 4.28%
51 District of Columbia 4,200 4,600 1,400 10,200 357,700 2.85%
Total 3,508,200 2,071,600 1,315,400 6,895,200 150,599,200 4.58%

 

Note: Percentages are calculated using rounded totals. Totals may vary slightly due to rounding.

Source: Authors’ analysis of employment requirements by industry (BLS-EP 2020a) applied to trade data (USITC 2020) and authors' projections for trade in 2024 given currency realignment and an aggressive program of industrial policies, and to infrastructure and clean energy/energy efficiency investments at the allocations specified in Pollin and Chakraborty 2020. American Community Survey (ACS) data (U.S. Census Bureau 2019) are used to estimate state jobs and shares supported. For a more detailed explanation of data sources and computations, see this report, especially Tables 1–5 and the methodology appendix.

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

Supplemental Table B

Net jobs supported by rebalancing trade and investing in infrastructure, clean energy, and energy efficiency, by state, 2024 (sorted alphabetically)

State Change in trade balance Infrastructure Clean energy/efficiency Total jobs State employment (2013–2017 5-year ACS estimate) Jobs supported as a share of total employment
Alabama 53,600 30,400 18,700 102,700 2,055,500 5.00%
Alaska 8,700 5,300 1,600 15,600 354,000 4.41%
Arizona 57,600 39,500 20,400 117,400 2,953,900 3.97%
Arkansas 35,600 17,500 12,900 66,100 1,276,500 5.18%
California 411,200 234,400 144,100 789,700 17,993,900 4.39%
Colorado 61,100 37,400 19,900 118,400 2,760,100 4.29%
Connecticut 40,100 23,500 16,900 80,500 1,805,100 4.46%
Delaware 8,000 5,600 2,600 16,100 441,500 3.65%
District of Columbia 4,200 4,600 1,400 10,200 357,700 2.85%
Florida 158,700 119,400 53,600 331,700 9,018,600 3.68%
Georgia 98,000 66,900 39,900 204,700 4,606,300 4.44%
Hawaii 10,300 9,200 2,500 22,000 671,800 3.27%
Idaho 21,100 9,800 6,400 37,300 748,700 4.98%
Illinois 156,600 87,600 69,800 314,000 6,181,700 5.08%
Indiana 99,000 49,500 37,400 185,900 3,124,300 5.95%
Iowa 52,000 21,400 21,100 94,500 1,599,700 5.91%
Kansas 43,200 18,900 13,500 75,600 1,420,000 5.32%
Kentucky 52,600 30,400 21,000 104,100 1,938,200 5.37%
Louisiana 51,100 27,500 12,900 91,400 2,031,200 4.50%
Maine 13,900 8,100 4,200 26,300 658,700 3.99%
Maryland 45,800 42,000 15,400 103,200 3,040,800 3.39%
Massachusetts 62,700 43,800 28,800 135,400 3,525,700 3.84%
Michigan 128,800 73,100 49,300 251,200 4,524,900 5.55%
Minnesota 76,200 37,300 31,400 144,900 2,904,100 4.99%
Mississippi 30,900 18,200 12,100 61,100 1,221,800 5.00%
Missouri 69,200 39,600 28,100 136,900 2,867,400 4.77%
Montana 14,800 6,500 2,800 24,100 498,000 4.84%
Nebraska 29,100 13,200 8,500 50,800 987,200 5.15%
Nevada 22,400 17,800 7,300 47,500 1,341,400 3.54%
New Hampshire 16,200 9,700 8,500 34,400 713,400 4.82%
New Jersey 81,200 59,000 29,200 169,300 4,388,000 3.86%
New Mexico 18,900 11,300 4,600 34,900 879,200 3.97%
New York 151,200 122,800 60,300 334,300 9,467,600 3.53%
North Carolina 97,000 62,400 43,900 203,400 4,571,000 4.45%
North Dakota 15,400 5,200 3,700 24,300 400,500 6.07%
Ohio 152,600 79,900 69,900 302,400 5,488,200 5.51%
Oklahoma 55,600 24,700 17,900 98,200 1,746,400 5.62%
Oregon 47,400 23,800 18,900 90,000 1,886,000 4.77%
Pennsylvania 150,700 82,600 62,400 295,700 6,097,000 4.85%
Rhode Island 10,700 6,600 4,500 21,800 526,100 4.14%
South Carolina 52,200 31,800 24,500 108,500 2,181,000 4.97%
South Dakota 14,900 5,500 4,200 24,600 438,300 5.61%
Tennessee 70,500 46,400 32,100 149,000 2,996,600 4.97%
Texas 328,000 181,700 108,400 618,100 12,689,100 4.87%
Utah 33,700 19,700 10,700 64,100 1,412,200 4.54%
Vermont 6,800 4,300 2,900 14,000 327,300 4.28%
Virginia 72,500 56,000 25,800 154,300 4,084,000 3.78%
Washington 93,300 45,000 25,200 163,600 3,418,100 4.79%
West Virginia 18,800 9,800 5,000 33,600 747,000 4.50%
Wisconsin 93,300 40,800 46,900 181,000 2,939,900 6.16%
Wyoming 11,000 4,000 1,700 16,700 293,600 5.69%
Total 3,508,200 2,071,600 1,315,400 6,895,200 150,599,200 4.58%

 

Note: Percentages are calculated using rounded totals. Totals may vary slightly due to rounding.

Source: Authors’ analysis of employment requirements by industry (BLS-EP 2020a) applied to trade data (USITC 2020) and authors' projections for trade in 2024 given currency realignment and an aggressive program of industrial policies, and to infrastructure and clean energy/energy efficiency investments at the allocations specified in Pollin and Chakraborty 2020. American Community Survey (ACS) data (U.S. Census Bureau 2019) are used to estimate state jobs and shares supported. For a more detailed explanation of data sources and computations, see this report, especially Tables 1–5 and the appendix.

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

Endnotes

1. The plans examined in this report have long been needed, but would be especially effective at the present time, due to the depressed state of the U.S. labor market (BLS 2020b).

2. See text box, “Defining jobs: Supported vs. created vs. job years,” and discussion there of jobs supported versus job years.

3. The PERI group has published a number of detailed studies of the impacts of clean energy programs at the state, national, and global levels, including Green Growth (Pollin, Garrett-Peltier, Heintz, and Hendriks 2014), and Climate Crisis and the Global Green New Deal (Chomsky and Pollin 2020).

4. The year 2019 is chosen as the base period for this study because that is the last year for which we have complete trade data.

5. See Scott 2009, especially Figure A, for further review of the history of the Plaza Accord and currency realignment in the 2002–2008 period. See Bergsten and Gagnon 2012 for an analysis of the impacts of currency manipulation on the U.S. economy and global trade flows. There are several tools available to combat currency manipulation and offset dollar misalignment (Scott 2017a). One of the most effective and direct methods is to tax foreign investment. Recently, Sens. Tammy Baldwin (D-Wis.) and Josh Hawley (R-Mo.) introduced bipartisan legislation to address the twin problems of an overvalued dollar and growing trade imbalances. Their bill would empower the Federal Reserve to tax new foreign purchases of U.S. stocks, bonds, and other assets—which could return the dollar to a competitive, trade-balancing level (Hansen 2017; Scott 2019).

6. See Methodology Appendix for discussion of NAICs industries and trade data sources. Actual exports of energy products increased extremely rapidly between 2002 and 2008, from a very tiny base, including crude oil (which increased 395%) and refined petroleum products (which increased 632%). By 2019, exports of these products had increased very substantially, to $95.7 billion and $93.8 billion, respectively. Use of historical growth rates for these sectors would have overwhelmed the forecast. Therefore, the initial forecast is that exports of each of these products would double between 2019 and 2024, and then adjust downward by 15.5% in 2024, as described in the text.

7. Total U.S. goods imports increased only 6.0% between 2014 and 2019. Currency realignment will increase the prices of imports, limiting additional consumption of imported products to at most recent trend growth in imports. Note that imports increased rapidly in the 2002–2008 period due to currency manipulation by China and other Asian countries, and extensive unfair trade policies, which limited the decline of the U.S. trade deficit in that period. We assume in this forecast that the dollar falls against all major surplus currencies here, including the Chinese yuan, Japanese yen, Korean won, and the euro, and that fair-trade enforcement otherwise prevents and unwinds unfair import trade. (Authors’ analysis of USITC 2020).

8. The initial projection resulted in a 94.8% increase in total exports between 2019 and 2024, using the weighted average of actual 2002–2008 growth rates, and an 8.3% increase in imports, resulting in an initial projected surplus of $496.7 billion.

9. It should be noted that the 2019–2024 period is one year shorter than the 2002–2008 period mentioned above, so it is reasonable to assume that future export growth will be less than in the reference period.

10. The Sierra Club (2020) plan is detailed but is at a higher spending level and for a longer period of time than the plan considered here, which is based on a four-year, $2 trillion climate and infrastructure investment proposal.

11. The authors thanks Robert Pollin and Shouvik Chakraborty for additional details about model assumptions (Chakraborty 2020). The final version of that report also evaluates a proposed investment of $186 billion per year in agricultural and land restoration investments that are not included here. The program considered here includes a much smaller component of agricultural programs, for energy conservation, as noted below. Individual modeling elements were converted from the IMPLAN 546 modeling format to the Bureau of Labor Statistics formal modeling of 205 individual industries of the U.S. economy; this conversion was implemented using IMPLAN to NAICS crosswalks.

12. The actual level of respending achieved could be higher or lower than shown in Table 2 and elsewhere in this report. The actual size of the multiplier will depend on the level economic activity when the spending takes place. See the text box, “Defining jobs: Supported vs. created vs. job years,” for discussion of the role of labor market tightness or slack on overall job creation. See also Bivens (2014) for a review of the literature on economic multipliers.

13. Pollin and Chakraborty (2020, Tables 1b and 2b) estimate that $683.1 billion in infrastructure and clean energy spending would support a total of 9.3 million new jobs, including direct, indirect, and induced spending. Our report estimates that $500 billion in infrastructure and clean energy and energy efficiency could support a total of 6.34 million jobs (including respending, Table 2, above). Adjusting for the 36.6% higher spending levels in Pollin and Chakraborty relative to this report’s $500 billion spending package, overall projections shown in Table 2 are about 7.6% lower, in terms of jobs per billion dollars of spending, which is likely explained by small differences in multipliers (induced spending) in the two models. In addition, the BLS model used here is based on 2019 input–output tables, and the IMPLAN model used by Pollin and Chakraborty is based on 2018 input–output data. See the methodology appendix for further details.

14. The model is based on trade flows at the NAICS 4-digit level, which are aggregated into the 205-industry BLS model used for this study, as described in the appendix. These data are further aggregated into 52 sectors for presentation in Tables 2–4 (some of which with no data are omitted from Tables 2 and 3).

15. The personal consumer expenditures vector is one of the components of the Aggregate Final Demand data set that is included with the BLS input–output matrix files, as a component of “Nominal dollar input–output data for 1997–2019” (BLS-EP 2020b).

16. The table provides detailed information on jobs supported by industry and within industries. An additional fact not provided in the table but rather from unpublished analysis of the data is that within primary metals, 69,900 new jobs would be supported in the steel industry (NAICS 3311 and 3312).

17. Four industries show net jobs displaced by trade, and in three of those industries that translates into jobs displaced in the trade plus investments total.

18. Manufacturing and construction employ a substantially higher share of non-college-educated workers than other sectors of the economy. For example, in 2009–2011, 47.7% of manufacturing workers had a high school diploma or less education, compared with 37.6% of workers in all industries (Scott 2013, Table 1).

19. Profits are much lower in manufacturing industries, which produce 91.6% of products in this study. Hence, substitution of clean energy equipment for energy products will increase the labor share of energy expenditures.

20. The model includes 205 NAICS industries. The trade data include only goods trade. Goods trade data are available for 85 commodity-based industries, plus information (publishing and software, NAICS industry 51), 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. Most trade in the special classifications provisions is small package trade that enters duty free, and involves products that are not classified.

21. The U.S. 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 develop a crosswalk from NAICS to Census industries, and we use population estimates from the ACS for each cell in this matrix. The ACS data we obtain from the Census Bureau for this project includes 44 unique sectors, plus subtotals for manufacturing, and for total employment. Trade and job loss coefficients are estimated using data only for the 44 unique sectors, across states and congressional districts.

22. According to the U.S. Census Bureau, only Colorado and Pennsylvania had congressional district boundary changes for the 116th Congress.

References

American Society of Civil Engineers (ASCE). 2017. 2017 Infrastructure Report Card.

Bivens, Josh. 2012. Green ‘Sequester’ is Already Costing U.S. jobs: Job Losses from Ongoing Clean-tech Cuts Will Rival Those from Defense Cuts. Economic Policy Institute, December 2012.

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, July 2014.

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

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). 2020a. “Employment, Hours, and Earnings from the Current Employment Statistics (National)” (Excel spreadsheets). Accessed September 21, 2020.

Bureau of Labor Statistics (BLS). 2020b. “Table A-11. Unemployed Persons by Reason of Unemployment.” Accessed September 29, 2020.

Bureau of Labor Statistics, Employment Projections program (BLS-EP). 2020a. “Nominal Domestic Employment Requirements Table for 2019” [Excel sheet, converted to Stata data file]. In Historical Employment Requirements Tables, 1997–2019 [data series]. Last modified June 10, 2020.

Bureau of Labor Statistics, Employment Projections program (BLS-EP). 2020b. “Inter-industry relationships (Input Output matrix): Nominal Final Demand Aggregate Data.” [Excel sheet]. Last modified June 10, 2020.

Chakraborty, Shouvik. 2020. Personal communication with Robert Scott, August 31, 2020.

Chomsky, Noam, and Robert Pollin, with C.J. Polychroniou. 2020. Climate Crisis and the Global Green New Deal: The Political Economy of Saving the Planet. London and New York: Verso.

Congressional Budget Office (CBO). 2020. “An Update to the Economic Outlook: 2020 to 2030 (10 year Economic Projections).” (Report), Congressional Budget Office, July 2, 2020.

Data Planet. 2019. “American Community Survey, 5-year Estimates: About the ACS 5-year Estimates” (web portal for exploring ACS data). Last updated December 18, 2019.

Federal Reserve Board. 2020. “Foreign Exchange Rates – H.10: Real Broad Dollar Index – Monthly Index” (data table). Accessed September 14, 2020.

Hansen, John R. 2017. “Why the Market Access Charge is Necessary to Fix Trade Imbalances.” Coalition for a Prosperous America. September 2017.

Kimball, Will, and Robert E. Scott. 2014. 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 no. 385, December 2014.

Lee, Thea. 2020. “HEROES Act Provides Critical Relief and Recovery Measures to U.S. Workers.” (Statement). Economic Policy Institute, May 12, 2020.

Osterholm, Michael T., and Neel Kashkari. 2020. “Here’s How to Crush the Virus Until Vaccines Arrive: To Save Lives, and Save the Economy, We Need Another Lockdown.” New York Times, August 7, 2020.

Paul, Scott. 2020. Our American Manufacturing Plan Will Create 6.9 to 12.9 Million New Jobs by 2024, Alliance for American Manufacturing. October, 2020.

Pollin, Robert, James Heintz, and Heidi Garrett-Peltier. 2009. How Infrastructure Investments Support the U.S. Economy. Political Economy Research Institute, University of Massachusetts Amherst, January 2009.

Pollin, Robert, Heidi Garrett-Peltier, James Heintz, and Bracken Hendricks. 2014. Green Growth: A U.S. Program for Controlling Climate Change and Expanding Job Opportunities. Center for American Progress and Political Economy Research Institute, University of Massachusetts Amherst, September 2014.

Pollin, Robert, and Shouvik Chakraborty. 2020. Job Creation Estimates Through Proposed Economic Stimulus Measures.  Political Economy Research Institute, University of Massachusetts Amherst, September 2020.

Scott, Robert E. 2009. “Re-Balancing U.S. Trade and Capital Accounts. Economic Policy Institute, Working Paper no. 286, December 2009.

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, September 2013.

Scott, Robert E. 2017a. Growth in U.S.–China Trade Deficit between 2001 and 2015 Cost 3.4 million Jobs: Here’s How to Rebalance Trade and Rebuild American Manufacturing. Economic Policy Institute, January 2017.

Scott, Robert E. 2017b. “We Still Haven’t Recovered Well-Paying Construction and Manufacturing Jobs.” Economic Snapshot, Economic Policy Institute, August 16, 2017.

Scott, Robert E. 2019. “Trade Wars and the Over-Valued Dollar. The Hill, August 9, 2019.

Scott, Robert E. 2020. We Can Reshore Manufacturing Jobs, but Trump Hasn’t Done It: Trade Rebalancing, Infrastructure, and Climate Investments Could Create 17 Million Good Jobs and Rebuild the American Economy. Economic Policy Institute, August 2020.

Scott, Robert E., and Zane Mokhiber. 2018. The China Toll Deepens: Growth in the Bilateral Trade Deficit between 2001 and 2017 Cost 3.4 Million U.S. Jobs, with Losses in Every State and Congressional District. Economic Policy Institute, December 2018.

Scott, Robert E., and Zane Mokhiber. 2020. Growing China Trade Deficit Cost 3.7 Million American Jobs Between 2001 and 2018: Jobs Lost in Every U.S. State and Congressional District. Economic Policy Institute, January 2020.

Sierra Club. 2020. Millions of Good Jobs: A Plan for Economic Renewal. May 2020.

U.S. Census Bureau. 2019. “American Community Survey: Special Tabulation over 44 industries, Covering 435 Congressional Districts and the District of Columbia (115th Congress Census Boundaries), Plus State and US Totals Based on ACS 2013 5-year file” [custom tabulation, spreadsheets received November 26, 2019; Rhode Island data received January 14, 2020].

U.S. International Trade Commission (USITC). 2020. USITC Interactive Tariff and Trade DataWeb [database]. Accessed September 2020.


See related work on Job creation | Trade deficit | China trade | Infrastructure | Recession/stimulus | Trade and Globalization | Manufacturing | Green Economics | Stimulus/stabilization policy | Public Investment | Currency misalignment

See more work by Robert E. Scott, Zane Mokhiber, and Daniel Perez