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

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.

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.

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.

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.

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

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.

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:

[J_nx19-24] = [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 [J_nx19-24]. In order to work with 44 sectors, we group the 205 BLS industries into a new matrix, defined as [Jnew_19-24], 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 [St_2013-2017] 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:

[Stj_nx19-24] = [St_2013-2017]T [Jnew_19-24]

where [Stj_nx19-24] 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.

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.

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