** 10 next-least-affected states
*** 10 middle affected states
**** 10 next-most-affected states
***** 10 most-affected states

Totals may vary slightly due to rounding.

Note: Percentages are calculated using rounded totals.

Source: Authors’ analysis of American Community Survey (ACS) data (U.S. Census Bureau 2019a and 2020a), USITC 2019, and Bureau of Labor Statistics Employment Projections program 2019a and 2019b. For a more detailed explanation of data sources and computations, see the appendix.

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Table 4

Net U.S. jobs displaced due to goods trade deficit with China, by state, 2001–2018 (ranked by jobs displaced as a share of total state employment)

Rank State Net jobs displaced State employment (2013–2017 5-year ACS estimate) Jobs displaced as a share of employment
1 New Hampshire 26,100 713,400 3.66%
2 Oregon 68,700 1,886,000 3.64%
3 California 654,100 17,993,900 3.64%
4 North Carolina 147,700 4,571,000 3.23%
5 Minnesota 92,400 2,904,100 3.18%
6 Massachusetts 109,300 3,525,700 3.10%
7 Wisconsin 88,900 2,939,900 3.02%
8 Vermont 9,600 327,300 2.93%
9 Indiana 85,800 2,934,500 2.92%
10 Idaho 21,100 748,700 2.82%
11 Georgia 123,200 4,606,300 2.67%
12 South Carolina 57,900 2,181,100 2.65%
13 Texas 334,800 12,689,000 2.64%
14 Illinois 162,400 6,181,700 2.63%
15 Rhode Island 13,400 526,100 2.55%
16 Ohio 136,700 5,488,200 2.49%
17 Michigan 112,400 4,524,900 2.48%
18 Alabama 50,700 2,055,500 2.47%
19 Tennessee 73,800 2,996,600 2.46%
20 Colorado 67,700 2,760,100 2.45%
21 Mississippi 29,700 1,221,800 2.43%
22 Kentucky 46,900 1,938,200 2.42%
23 Utah 33,900 1,412,200 2.40%
24 Pennsylvania 137,300 6,097,000 2.25%
25 Arizona 65,800 2,953,900 2.23%
26 New Jersey 95,000 4,388,000 2.16%
27 Iowa 33,900 1,599,700 2.12%
28 Connecticut 38,100 1,805,100 2.11%
29 Washington 67,400 3,418,100 1.97%
30 Missouri 56,200 2,867,400 1.96%
31 New York 185,100 9,467,600 1.96%
32 Arkansas 24,400 1,276,500 1.91%
33 Maine 12,200 658,700 1.85%
34 Oklahoma 31,500 1,746,400 1.80%
35 South Dakota 7,800 438,300 1.78%
36 Virginia 70,500 4,084,000 1.73%
37 Kansas 24,200 1,420,100 1.70%
38 Florida 150,700 9,018,600 1.67%
39 Nebraska 16,200 987,200 1.64%
40 Maryland 44,800 3,040,800 1.47%
41 Delaware 6,200 441,500 1.40%
42 West Virginia 10,000 747,000 1.34%
43 Nevada 17,900 1,341,400 1.33%
44 New Mexico 11,500 879,200 1.31%
45 North Dakota 5,100 400,500 1.27%
46 Louisiana 24,900 2,031,200 1.23%
47 Montana 5,500 498,000 1.10%
48 Hawaii 6,600 671,800 0.98%
49 DC 3,300 357,700 0.92%
50 Wyoming 2,500 293,600 0.85%
51 Alaska 3,000 354,000 0.85%
Total* 3,704,700 150,409,500 2.46%

* Totals may vary slightly due to rounding.

Note: Percentages are calculated using rounded totals

Source:?Authors’ analysis of American Community Survey (ACS) data (U.S. Census Bureau 2019a), USITC 2019, and Bureau of Labor Statistics Employment Projections program 2019a and 2019b. For a more detailed explanation of data sources and computations, see the appendix.

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As shown in Supplemental Table 2, the top 10 states in terms of total jobs lost were California (654,100 jobs lost), Texas (334,800), New York (185,100), Illinois (162,400), Florida (150,700), North Carolina (147,700), Pennsylvania (137,300), Ohio (136,700), Georgia (123,200), and Michigan (112,400).

The map in Figure B shows the broad impact of the growing trade deficit with China across the United States, with no areas exempt from job displacement. The 3.7 million U.S. jobs displaced due to the growing trade deficit with China between 2001 and 2018 represented 2.46% of total U.S. employment.

Job losses by congressional?district

This study also reports the employment impacts of the growing U.S. goods trade deficit with China in every congressional district and in the District of Columbia. Table 5?lists the top 20 hardest-hit congressional districts (those with the largest job losses as a share of overall district employment). Figure C shows job displacement as a share of total district employment in all 435 congressional districts plus the District of Columbia. (Supplemental Table 4?shows net job displacement and displacement as a share of total district employment, ranked by net jobs displaced;?Supplemental Table 5 provides the same data sorted alphabetically by state; and Supplemental Table 6 ranks the districts by jobs displaced as a share of total district employment.) Because the largest growth in the goods trade deficits with China from 2001 to 2018 occurred in the computer and electronic parts industry, 17 of the 20 hardest-hit districts were in California, Massachusetts, Minnesota, Oregon, and Texas, where remaining jobs in that industry are concentrated. Georgia (one district) and North Carolina (two districts), which suffered considerable job displacement in a variety of manufacturing industries, also each have at least one district in the top 20 hardest-hit districts.17

Table 5

Twenty congressional districts hardest hit by U.S. goods trade deficit with China, 2001–2018 (ranked by jobs displaced as a share of district employment)

Rank State District ?Net jobs displaced District employment (2013–2017 5-year ACS estimate) Jobs displaced as a share of employment
1 California 17 78,700 390,700 20.14%
2 California 18 48,400 372,700 12.99%
3 California 19 45,400 381,300 11.91%
4 Texas 31 30,600 376,600 8.13%
5 Oregon 1 31,800 408,300 7.79%
6 California 15 27,900 382,500 7.29%
7 Georgia 14 21,000 314,900 6.67%
8 California 52 21,000 377,300 5.57%
9 Texas 10 22,000 406,600 5.41%
10 California 40 16,600 306,800 5.41%
11 Massachusetts 3 20,800 387,100 5.37%
12 Texas 3 22,000 425,900 5.17%
13 California 34 17,500 354,700 4.93%
14 California 45 18,300 383,300 4.77%
15 North Carolina 10 15,400 339,200 4.54%
16 North Carolina 6 15,500 344,900 4.49%
17 Massachusetts 2 16,500 378,000 4.37%
18 Minnesota 3 16,400 381,900 4.29%
19 California 49 14,500 338,400 4.28%
20 Minnesota 2 16,000 381,700 4.19%

* Totals may vary slightly due to rounding.

Note: Percentages are calculated using rounded totals.

Source:?Authors’ analysis of American Community Survey (ACS) data (U.S. Census Bureau 2019a and 2020a), USITC 2019, and Bureau of Labor Statistics Employment Projections program 2019a and 2019b. For a more detailed explanation of data sources and computations, see the appendix.

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Figure C

Net U.S. jobs displaced due to the goods trade deficit with China as a share of total congressional district employment, 2001–2018

Rank (by jobs displaced as a share of total) State District Net jobs displaced District employment (2013-2017 5-year ACS estimate) Jobs displaced as a share of employment
331 Alabama 1 4,900 289,900 1.69%
211 Alabama 2 6,200 283,200 2.19%
106 Alabama 3 8,200 295,600 2.77%
65 Alabama 4 9,000 273,400 3.29%
44 Alabama 5 11,500 319,300 3.60%
307 Alabama 6 5,900 327,900 1.80%
280 Alabama 7 5,100 266,300 1.92%
435 Alaska Statewide 3,000 354,000 0.85%
376 Arizona 1 3,900 274,800 1.42%
402 Arizona 2 3,800 304,800 1.25%
326 Arizona 3 5,400 312,700 1.73%
330 Arizona 4 4,500 265,500 1.69%
31 Arizona 5 14,200 360,100 3.94%
230 Arizona 6 7,900 377,500 2.09%
202 Arizona 7 7,200 323,700 2.22%
203 Arizona 8 7,300 328,700 2.22%
91 Arizona 9 11,600 406,100 2.86%
262 Arkansas 1 5,800 289,200 2.01%
332 Arkansas 2 5,800 345,900 1.68%
271 Arkansas 3 7,000 355,500 1.97%
242 Arkansas 4 5,900 285,900 2.06%
321 California 1 4,900 281,000 1.74%
258 California 2 6,800 337,400 2.02%
358 California 3 4,700 309,100 1.52%
50 California 4 11,000 313,800 3.51%
265 California 5 7,100 355,500 2.00%
277 California 6 6,300 327,200 1.93%
67 California 7 10,800 335,700 3.22%
365 California 8 3,900 262,500 1.49%
221 California 9 6,600 310,500 2.13%
119 California 10 8,400 309,600 2.71%
234 California 11 7,400 356,100 2.08%
53 California 12 15,400 446,300 3.45%
137 California 13 9,900 384,300 2.58%
51 California 14 13,900 400,000 3.48%
6 California 15 27,900 382,500 7.29%
337 California 16 4,400 265,300 1.66%
1 California 17 78,700 390,700 20.14%
2 California 18 48,400 372,700 12.99%
3 California 19 45,400 381,300 11.91%
167 California 20 7,900 327,700 2.41%
436 California 21 2,000 253,900 0.79%
395 California 22 4,100 315,500 1.30%
428 California 23 3,100 290,200 1.07%
318 California 24 6,100 346,200 1.76%
225 California 25 6,700 316,900 2.11%
107 California 26 9,600 346,300 2.77%
114 California 27 9,500 348,200 2.73%
222 California 28 8,300 390,800 2.12%
149 California 29 8,700 347,300 2.51%
244 California 30 8,100 393,400 2.06%
241 California 31 6,500 314,800 2.06%
68 California 32 10,700 334,100 3.20%
152 California 33 9,200 368,300 2.50%
13 California 34 17,500 354,700 4.93%
136 California 35 8,300 320,800 2.59%
344 California 36 4,500 281,400 1.60%
171 California 37 8,600 360,800 2.38%
84 California 38 9,700 328,100 2.96%
59 California 39 12,000 353,500 3.39%
10 California 40 16,600 306,800 5.41%
194 California 41 7,300 320,900 2.27%
120 California 42 9,100 335,800 2.71%
93 California 43 9,600 338,100 2.84%
47 California 44 10,800 304,500 3.55%
14 California 45 18,300 383,300 4.77%
34 California 46 13,700 351,600 3.90%
87 California 47 10,100 346,300 2.92%
32 California 48 14,600 371,500 3.93%
19 California 49 14,500 338,400 4.28%
76 California 50 10,600 343,200 3.09%
270 California 51 5,600 283,900 1.97%
8 California 52 21,000 377,300 5.57%
153 California 53 9,400 378,300 2.48%
302 Colorado 1 8,000 442,100 1.81%
23 Colorado 2 17,600 426,500 4.13%
423 Colorado 3 3,900 343,100 1.14%
71 Colorado 4 12,200 385,200 3.17%
147 Colorado 5 8,600 342,900 2.51%
279 Colorado 6 7,900 412,200 1.92%
185 Colorado 7 9,500 408,000 2.33%
295 Connecticut 1 6,600 358,600 1.84%
288 Connecticut 2 6,700 360,200 1.86%
218 Connecticut 3 7,800 364,200 2.14%
251 Connecticut 4 7,400 364,700 2.03%
123 Connecticut 5 9,600 357,400 2.69%
432 DC Statewide 3,300 357,700 0.92%
380 Delaware Statewide 6,200 441,500 1.40%
410 Florida 1 3,900 322,600 1.21%
420 Florida 2 3,300 285,600 1.16%
393 Florida 3 4,000 304,300 1.31%
342 Florida 4 6,000 374,600 1.60%
416 Florida 5 3,700 309,500 1.20%
308 Florida 6 5,400 300,400 1.80%
334 Florida 7 6,200 371,700 1.67%
26 Florida 8 12,300 301,800 4.08%
408 Florida 9 4,300 353,200 1.22%
340 Florida 10 6,400 392,200 1.63%
384 Florida 11 3,300 239,200 1.38%
293 Florida 12 5,800 314,200 1.85%
228 Florida 13 7,000 333,300 2.10%
338 Florida 14 6,100 372,100 1.64%
346 Florida 15 5,500 345,500 1.59%
352 Florida 16 5,100 330,200 1.54%
409 Florida 17 3,200 264,500 1.21%
360 Florida 18 5,000 330,200 1.51%
397 Florida 19 4,100 316,600 1.30%
345 Florida 20 5,600 350,900 1.60%
310 Florida 21 6,100 339,800 1.80%
223 Florida 22 7,900 372,800 2.12%
269 Florida 23 7,300 369,700 1.97%
370 Florida 24 4,800 329,900 1.45%
169 Florida 25 8,400 350,500 2.40%
407 Florida 26 4,400 361,200 1.22%
378 Florida 27 5,400 382,100 1.41%
396 Georgia 1 4,000 308,300 1.30%
232 Georgia 2 5,300 253,500 2.09%
70 Georgia 3 10,100 318,400 3.17%
263 Georgia 4 7,000 349,200 2.00%
312 Georgia 5 6,500 362,600 1.79%
88 Georgia 6 11,300 389,600 2.90%
58 Georgia 7 13,100 384,500 3.41%
328 Georgia 8 4,900 285,800 1.71%
80 Georgia 9 9,500 313,600 3.03%
213 Georgia 10 6,900 316,900 2.18%
94 Georgia 11 10,800 380,700 2.84%
261 Georgia 12 5,700 284,200 2.01%
243 Georgia 13 7,100 344,300 2.06%
7 Georgia 14 21,000 314,900 6.67%
429 Hawaii 1 3,600 348,600 1.03%
433 Hawaii 2 2,900 323,100 0.90%
69 Idaho 1 11,900 373,400 3.19%
165 Idaho 2 9,100 375,200 2.43%
299 Illinois 1 5,600 306,600 1.83%
289 Illinois 2 5,400 290,900 1.86%
179 Illinois 3 8,000 339,100 2.36%
81 Illinois 4 10,200 338,700 3.01%
215 Illinois 5 9,200 426,100 2.16%
35 Illinois 6 14,500 378,100 3.83%
216 Illinois 7 7,200 334,500 2.15%
24 Illinois 8 15,500 377,300 4.11%
181 Illinois 9 8,500 361,000 2.35%
37 Illinois 10 13,200 349,900 3.77%
97 Illinois 11 10,300 364,900 2.82%
268 Illinois 12 6,000 303,800 1.97%
348 Illinois 13 5,200 332,400 1.56%
40 Illinois 14 13,700 375,400 3.65%
193 Illinois 15 7,200 316,300 2.28%
159 Illinois 16 8,100 331,300 2.44%
146 Illinois 17 7,800 311,000 2.51%
274 Illinois 18 6,700 344,400 1.95%
187 Indiana 1 7,200 310,600 2.32%
45 Indiana 2 11,400 317,800 3.59%
25 Indiana 3 13,400 327,000 4.10%
141 Indiana 4 8,400 328,500 2.56%
212 Indiana 5 7,800 357,700 2.18%
75 Indiana 6 9,700 311,900 3.11%
154 Indiana 7 7,700 312,200 2.47%
49 Indiana 8 11,600 329,300 3.52%
142 Indiana 9 8,600 339,400 2.53%
115 Iowa 1 10,900 399,700 2.73%
151 Iowa 2 9,700 387,900 2.50%
356 Iowa 3 6,500 426,400 1.52%
322 Iowa 4 6,700 385,700 1.74%
364 Kansas 1 5,100 343,000 1.49%
314 Kansas 2 6,100 341,700 1.79%
236 Kansas 3 8,100 391,500 2.07%
375 Kansas 4 4,900 344,000 1.42%
145 Kentucky 1 7,300 290,900 2.51%
148 Kentucky 2 8,400 335,300 2.51%
186 Kentucky 3 8,500 365,700 2.32%
196 Kentucky 4 8,000 352,100 2.27%
323 Kentucky 5 4,000 231,100 1.73%
82 Kentucky 6 10,800 363,000 2.98%
401 Louisiana 1 4,700 376,500 1.25%
426 Louisiana 2 3,800 343,800 1.11%
404 Louisiana 3 4,300 351,600 1.22%
411 Louisiana 4 3,600 297,800 1.21%
414 Louisiana 5 3,400 283,700 1.20%
389 Louisiana 6 5,100 377,800 1.35%
278 Maine 1 6,800 353,300 1.92%
317 Maine 2 5,400 305,300 1.77%
350 Maryland 1 5,600 361,700 1.55%
362 Maryland 2 5,600 375,600 1.49%
369 Maryland 3 5,800 398,300 1.46%
421 Maryland 4 4,500 394,100 1.14%
424 Maryland 5 4,400 389,700 1.13%
240 Maryland 6 7,900 382,400 2.07%
363 Maryland 7 5,000 335,600 1.49%
354 Maryland 8 6,200 403,600 1.54%
208 Massachusetts 1 7,700 350,200 2.20%
17 Massachusetts 2 16,500 378,000 4.37%
11 Massachusetts 3 20,800 387,100 5.37%
43 Massachusetts 4 14,300 393,300 3.64%
73 Massachusetts 5 13,100 415,100 3.16%
77 Massachusetts 6 12,300 401,000 3.07%
276 Massachusetts 7 8,200 424,900 1.93%
227 Massachusetts 8 8,700 413,500 2.10%
238 Massachusetts 9 7,500 362,600 2.07%
294 Michigan 1 5,500 298,800 1.84%
46 Michigan 2 12,400 347,700 3.57%
110 Michigan 3 9,600 350,000 2.74%
214 Michigan 4 6,500 300,200 2.17%
235 Michigan 5 5,800 279,200 2.08%
83 Michigan 6 10,000 336,700 2.97%
140 Michigan 7 8,200 320,400 2.56%
166 Michigan 8 8,700 359,600 2.42%
155 Michigan 9 8,600 350,500 2.45%
133 Michigan 10 8,800 336,000 2.62%
112 Michigan 11 10,100 369,200 2.74%
275 Michigan 12 6,500 335,300 1.94%
253 Michigan 13 5,200 256,800 2.02%
199 Michigan 14 6,400 284,400 2.25%
36 Minnesota 1 13,500 352,900 3.83%
20 Minnesota 2 16,000 381,700 4.19%
18 Minnesota 3 16,400 381,900 4.29%
129 Minnesota 4 9,600 364,200 2.64%
125 Minnesota 5 10,500 391,800 2.68%
86 Minnesota 6 11,100 380,100 2.92%
131 Minnesota 7 8,800 334,900 2.63%
246 Minnesota 8 6,500 316,600 2.05%
29 Mississippi 1 12,900 326,500 3.95%
324 Mississippi 2 4,600 265,900 1.73%
287 Mississippi 3 5,900 316,200 1.87%
248 Mississippi 4 6,400 313,200 2.04%
303 Missouri 1 6,400 353,700 1.81%
290 Missouri 2 7,300 394,300 1.85%
206 Missouri 3 8,400 380,700 2.21%
282 Missouri 4 6,400 337,400 1.90%
316 Missouri 5 6,600 372,400 1.77%
286 Missouri 6 6,900 369,100 1.87%
233 Missouri 7 7,400 355,500 2.08%
207 Missouri 8 6,700 304,400 2.20%
427 Montana Statewide 5,500 498,000 1.10%
301 Nebraska 1 6,100 334,900 1.82%
339 Nebraska 2 5,600 342,100 1.64%
366 Nebraska 3 4,600 310,200 1.48%
418 Nevada 1 3,800 319,500 1.19%
309 Nevada 2 6,000 334,100 1.80%
405 Nevada 3 4,600 376,500 1.22%
425 Nevada 4 3,500 311,300 1.12%
41 New Hampshire 1 13,300 364,800 3.65%
39 New Hampshire 2 12,800 348,700 3.67%
319 New Jersey 1 6,300 358,100 1.76%
387 New Jersey 2 4,500 331,000 1.36%
327 New Jersey 3 6,100 355,500 1.72%
291 New Jersey 4 6,400 345,700 1.85%
134 New Jersey 5 9,900 378,700 2.61%
180 New Jersey 6 8,600 365,000 2.36%
135 New Jersey 7 10,000 384,300 2.60%
176 New Jersey 8 9,500 401,500 2.37%
99 New Jersey 9 10,200 363,600 2.81%
347 New Jersey 10 5,500 345,900 1.59%
184 New Jersey 11 9,000 386,100 2.33%
170 New Jersey 12 8,900 372,700 2.39%
351 New Mexico 1 4,900 317,200 1.54%
431 New Mexico 2 2,600 275,300 0.94%
381 New Mexico 3 4,000 286,700 1.40%
231 New York 1 7,400 353,800 2.09%
172 New York 2 8,800 369,400 2.38%
285 New York 3 6,600 352,800 1.87%
361 New York 4 5,500 363,400 1.51%
406 New York 5 4,500 369,400 1.22%
273 New York 6 7,000 359,800 1.95%
209 New York 7 7,800 355,000 2.20%
383 New York 8 4,800 346,600 1.38%
413 New York 9 4,200 349,600 1.20%
304 New York 10 7,000 387,200 1.81%
359 New York 11 5,000 329,800 1.52%
329 New York 12 7,700 450,100 1.71%
399 New York 13 4,600 360,600 1.28%
333 New York 14 5,900 352,300 1.67%
415 New York 15 3,400 283,700 1.20%
390 New York 16 4,700 350,500 1.34%
343 New York 17 5,800 362,600 1.60%
96 New York 18 9,800 345,700 2.83%
64 New York 19 11,100 331,900 3.34%
272 New York 20 7,100 364,600 1.95%
311 New York 21 5,600 312,100 1.79%
150 New York 22 8,000 319,400 2.50%
200 New York 23 7,100 316,100 2.25%
160 New York 24 8,200 336,400 2.44%
56 New York 25 12,100 353,300 3.42%
296 New York 26 6,200 337,500 1.84%
132 New York 27 9,300 354,200 2.63%
162 North Carolina 1 8,000 328,600 2.43%
28 North Carolina 2 15,000 374,700 4.00%
284 North Carolina 3 5,700 304,200 1.87%
21 North Carolina 4 18,500 442,300 4.18%
62 North Carolina 5 11,100 331,600 3.35%
16 North Carolina 6 15,500 344,900 4.49%
281 North Carolina 7 6,400 336,800 1.90%
113 North Carolina 8 8,800 322,000 2.73%
143 North Carolina 9 8,500 337,700 2.52%
15 North Carolina 10 15,400 339,200 4.54%
60 North Carolina 11 10,800 319,500 3.38%
191 North Carolina 12 10,000 435,100 2.30%
30 North Carolina 13 14,000 354,500 3.95%
400 North Dakota Statewide 5,100 400,500 1.27%
210 Ohio 1 7,600 346,300 2.19%
219 Ohio 2 7,400 345,600 2.14%
335 Ohio 3 6,300 378,500 1.66%
57 Ohio 4 11,300 331,000 3.41%
74 Ohio 5 11,300 360,400 3.14%
217 Ohio 6 6,400 298,000 2.15%
72 Ohio 7 10,800 341,000 3.17%
102 Ohio 8 9,600 344,300 2.79%
183 Ohio 9 7,600 325,900 2.33%
188 Ohio 10 7,600 328,800 2.31%
264 Ohio 11 5,900 295,100 2.00%
250 Ohio 12 7,800 384,000 2.03%
108 Ohio 13 9,100 329,400 2.76%
78 Ohio 14 10,800 354,500 3.05%
283 Ohio 15 6,800 358,700 1.90%
95 Ohio 16 10,400 366,800 2.84%
247 Oklahoma 1 7,800 380,500 2.05%
306 Oklahoma 2 5,300 293,700 1.80%
371 Oklahoma 3 5,000 344,400 1.45%
298 Oklahoma 4 6,500 354,700 1.83%
300 Oklahoma 5 6,800 373,200 1.82%
5 Oregon 1 31,800 408,300 7.79%
325 Oregon 2 5,900 341,400 1.73%
104 Oregon 3 11,900 427,200 2.79%
116 Oregon 4 9,200 338,000 2.72%
126 Oregon 5 9,900 371,200 2.67%
349 Pennsylvania 1 4,900 316,300 1.55%
422 Pennsylvania 2 3,400 298,500 1.14%
144 Pennsylvania 3 8,000 318,100 2.51%
124 Pennsylvania 4 9,600 357,800 2.68%
130 Pennsylvania 5 8,300 315,300 2.63%
138 Pennsylvania 6 9,700 378,000 2.57%
320 Pennsylvania 7 6,300 360,600 1.75%
157 Pennsylvania 8 9,100 371,100 2.45%
259 Pennsylvania 9 6,200 308,300 2.01%
195 Pennsylvania 10 7,200 316,800 2.27%
190 Pennsylvania 11 7,800 338,200 2.31%
174 Pennsylvania 12 8,000 336,300 2.38%
266 Pennsylvania 13 7,000 352,600 1.99%
297 Pennsylvania 14 6,400 349,200 1.83%
101 Pennsylvania 15 9,900 355,000 2.79%
98 Pennsylvania 16 9,900 352,300 2.81%
177 Pennsylvania 17 7,600 321,600 2.36%
192 Pennsylvania 18 8,000 350,800 2.28%
127 Rhode Island 1 6,900 258,800 2.67%
163 Rhode Island 2 6,500 267,300 2.43%
367 South Carolina 1 5,300 360,800 1.47%
197 South Carolina 2 7,400 327,400 2.26%
27 South Carolina 3 11,800 289,700 4.07%
55 South Carolina 4 11,400 332,500 3.43%
54 South Carolina 5 10,400 302,300 3.44%
267 South Carolina 6 5,400 273,400 1.98%
220 South Carolina 7 6,300 294,900 2.14%
315 South Dakota 1 7,800 438,300 1.78%
175 Tennessee 1 7,200 304,000 2.37%
249 Tennessee 2 7,000 343,200 2.04%
105 Tennessee 3 8,700 313,600 2.77%
79 Tennessee 4 10,700 352,000 3.04%
229 Tennessee 5 8,400 400,600 2.10%
89 Tennessee 6 9,700 335,500 2.89%
178 Tennessee 7 7,500 317,500 2.36%
198 Tennessee 8 7,100 315,200 2.25%
173 Tennessee 9 7,500 315,000 2.38%
313 Texas 1 5,400 301,300 1.79%
33 Texas 2 15,700 401,700 3.91%
12 Texas 3 22,000 425,900 5.17%
161 Texas 4 7,700 316,100 2.44%
156 Texas 5 8,000 326,200 2.45%
224 Texas 6 8,000 377,900 2.12%
118 Texas 7 10,700 394,000 2.72%
128 Texas 8 9,500 357,000 2.66%
255 Texas 9 7,600 376,000 2.02%
9 Texas 10 22,000 406,600 5.41%
382 Texas 11 4,700 338,700 1.39%
237 Texas 12 7,800 377,100 2.07%
379 Texas 13 4,500 319,100 1.41%
353 Texas 14 5,000 324,700 1.54%
374 Texas 15 4,300 300,600 1.43%
205 Texas 16 6,700 303,100 2.21%
42 Texas 17 13,100 359,500 3.64%
164 Texas 18 8,500 350,200 2.43%
419 Texas 19 3,800 324,200 1.17%
368 Texas 20 5,200 356,200 1.46%
103 Texas 21 11,100 398,100 2.79%
189 Texas 22 9,400 406,800 2.31%
377 Texas 23 4,400 310,600 1.42%
90 Texas 24 12,400 433,100 2.86%
22 Texas 25 14,200 341,900 4.15%
111 Texas 26 11,800 431,200 2.74%
388 Texas 27 4,500 331,000 1.36%
417 Texas 28 3,500 294,000 1.19%
254 Texas 29 6,500 321,100 2.02%
257 Texas 30 6,800 336,700 2.02%
4 Texas 31 30,600 376,600 8.13%
38 Texas 32 14,600 397,300 3.67%
117 Texas 33 8,600 316,400 2.72%
412 Texas 34 3,200 265,100 1.21%
252 Texas 35 7,700 379,900 2.03%
336 Texas 36 5,200 312,900 1.66%
158 Utah 1 8,400 343,000 2.45%
245 Utah 2 7,100 345,700 2.05%
168 Utah 3 8,300 346,200 2.40%
122 Utah 4 10,200 377,400 2.70%
85 Vermont Statewide 9,600 327,300 2.93%
305 Virginia 1 6,900 381,800 1.81%
386 Virginia 2 4,700 341,300 1.38%
398 Virginia 3 4,300 336,900 1.28%
355 Virginia 4 5,400 352,500 1.53%
182 Virginia 5 7,800 333,800 2.34%
239 Virginia 6 7,300 353,200 2.07%
357 Virginia 7 5,900 387,300 1.52%
403 Virginia 8 5,600 454,000 1.23%
139 Virginia 9 7,600 296,600 2.56%
226 Virginia 10 8,900 422,500 2.11%
373 Virginia 11 6,100 424,000 1.44%
121 Washington 1 9,900 366,000 2.70%
341 Washington 2 5,600 348,300 1.61%
66 Washington 3 10,200 313,000 3.26%
430 Washington 4 3,100 305,200 1.02%
292 Washington 5 5,700 308,000 1.85%
391 Washington 6 3,900 292,900 1.33%
260 Washington 7 8,800 438,000 2.01%
201 Washington 8 7,900 351,900 2.24%
256 Washington 9 7,600 376,100 2.02%
372 Washington 10 4,600 318,700 1.44%
392 West Virginia 1 3,500 265,200 1.32%
385 West Virginia 2 3,700 268,500 1.38%
394 West Virginia 3 2,800 213,300 1.31%
61 Wisconsin 1 12,100 358,300 3.38%
204 Wisconsin 2 9,100 410,700 2.22%
48 Wisconsin 3 12,900 366,000 3.52%
100 Wisconsin 4 9,200 329,700 2.79%
63 Wisconsin 5 12,900 385,600 3.35%
52 Wisconsin 6 12,600 364,900 3.45%
109 Wisconsin 7 9,600 348,900 2.75%
92 Wisconsin 8 10,700 375,800 2.85%
434 Wyoming Statewide 2,500 293,600 0.85%
Total* 3,704,700 150,410,200 2.46%
ChartData Download data

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

* Totals may vary slightly due to rounding.

Note: Percentages are calculated using rounded totals.

Source:?Authors’ analysis of U.S. Census Bureau 2019a and 2020a, USITC 2019, and Bureau of Labor Statistics Employment Projections program 2019a and 2019b. For a more detailed explanation of data sources and computations, see the appendix.

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Specifically, of the 20 hardest-hit districts, nine were in California (in rank order, the 17th, 18th, 19th, 15th, 52nd, 40th, 34th, 45th, and 49th); three were in Texas (31st, 10th, and 3rd,); two each were in Massachusetts (the 3rd and 2nd), Minnesota (3rd and 2nd), and North Carolina (10th and 6th); and one each were in Oregon (1st), and Georgia (14th). Job losses in these districts ranged from 14,500 jobs to 78,700 jobs, and from 4.19% to 20.14% of total district jobs. These distributions reflect both the size of some states (e.g., California and Texas) and the concentration of the industries hardest hit by the growing U.S.–China trade deficit. We have already mentioned the prevalence of the computer and electronic parts industry in certain states; other industries with a presence in these districts include furniture, textiles, apparel, and other manufactured products.

The three hardest-hit congressional districts were all located in Silicon Valley (South Bay Area) in California, including the 17th Congressional District (encompassing Sunnyvale, Cupertino, Santa Clara, Fremont, Newark, North San Jose, and Milpitas), which lost 78,700 jobs, equal to 20.14% of all jobs in the district; the 18th Congressional District (including parts of San Jose, Palo Alto, Redwood City, Menlo Park, Stanford, Los Altos, Campbell, Saratoga, Mountain View, and Los Gatos), which lost 48,400 jobs, or 12.99%; and the 19th Congressional District (most of San Jose and other parts of Santa Clara County), which lost 45,400 jobs, or 11.91%.18

Although the San Francisco Bay Area has experienced rapid growth over the past decade in software and related industries, this growth has come at the expense of direct employment in the production of computer and electronic parts. The computer and electronic parts manufacturing sector has experienced more actual job losses than any other major manufacturing industry has since China joined the WTO.19 There are substantial questions about the long-run ability of firms in the high-tech sectors to continue to innovate while offshoring most or all of the production in their industries (Shi 2010).

Other research confirms job losses from U.S.–China trade

Academic research has confirmed findings in this and earlier EPI research (e.g., Kimball and Scott 2014, Scott 2017a, Scott and Mokhiber 2018) that the growing U.S.–China trade deficit has caused significant losses of U.S. jobs, especially in manufacturing. (For a further analysis of EPI research on trade and globalization, see Bivens 2017.)

For example, Acemoglu et al. (2014) find that import competition with China from 1999 to 2011 was responsible for up to 2.4 million net job losses (including direct, indirect, and respending effects).20 This result compares with the finding in this paper that 2.6 million jobs were lost due to growing trade deficits with China between 2001 and 2011, as shown in Figure A. Thus, over a roughly comparable period, Acemoglu et al. estimate an employment impact that is roughly 90% as large as the estimate found in this study.21

Further academic confirmation of the impacts of China trade on manufacturing employment is provided by Pierce and Schott (2016). Pierce and Schott use an entirely different estimation technique based on differences in the pre- and post-China WTO entry maximum tariff rates, with and without permanent normal trade relations (PNTR) status, which the United States granted to China in the China–WTO implementing legislation. Pierce and Schott estimate the impacts of changes in U.S. international transactions between 1992 and 2008. They find that the grant of PNTR status to China “reduced relative employment growth of the average industry by 3.4 percentage points…after one year [and] 15.6 percentage points after 6 years” (following the grant of PNTR status to China in 2001). They do not translate percentage-point changes in employment into total jobs displaced, but data on changes in total manufacturing employment in this period provide a base of comparison.

The research in this paper looks at the total loss or displacement of jobs due to the growing trade deficit with China and the number of those lost jobs that are manufacturing jobs. We can check the consistency of this finding with a different approach—looking at the total loss of manufacturing jobs and estimating the number of those job losses that are due to growing trade deficits with China. The United States lost 2.9 million manufacturing jobs between December 2001 and December 2018, a decline of 18.9% in total manufacturing employment (BLS 2019). Drawing from Pierce and Schott 2016 above, if 15.6 percentage points of this 18.9% decline can be attributed to the growth of the U.S. trade deficit with China, this implies that about 82.5% (or 2.4 million) of the manufacturing jobs lost in this period were lost due to the growing trade deficit with China. This estimate is very similar to this study’s estimated total manufacturing jobs displaced by the growing U.S.–China trade deficit (2.8 million net jobs displaced). Thus, two other recent academic studies have concluded that the growing U.S.–China trade deficit is responsible for the displacement of at least 2 million U.S. manufacturing jobs since 1990, with most jobs lost since China entered the WTO in 2001.

Lost wages from the increasing trade deficit with China

Growing trade-related job displacement has several direct and indirect effects on workers’ wages. The direct wage effects are a function of the wages forgone in jobs displaced by growing U.S. imports from China minus wage gains from jobs added in export-producing industries, and comparing this with the (lower) wages paid in alternative jobs in nontraded industries (U.S. workers displaced from traded-goods production in manufacturing industries who find jobs in nontraded goods industries experience permanent wage losses, as discussed below). Standard trade theory assumes that economic integration leads to “gains from trade” as workers move from low-productivity jobs in import-competing industries into higher-productivity jobs in export-competing industries. However, this assumption is proven incorrect in Scott 2013, which shows that import-competing jobs pay better than alternative jobs in export-producing industries. Specifically, Scott examines the gains and losses associated with direct changes in employment caused by growing U.S.–China trade deficits between 2001 and 2011, and finds that jobs displaced by imports from China actually paid 17.0% more than jobs exporting to China: $1,021.66 per week in import-competing industries versus $872.89 per week in exporting industries (Scott 2013, 24, Table 9a).22 Therefore, simple trade expansion that increases total trade with no underlying change in the trade balance will result in a net loss to workers as they move from higher-paying jobs in import-competing industries to lower-paying jobs in exporting industries.

Furthermore, jobs in both import-competing and exporting industries paid substantially more than jobs in nontraded industries, which pay $791.14 per week (Scott 2013, Table 9a, 24). Between 2001 and 2011, growing exports to China supported 538,000 U.S. jobs, but growing imports displaced 3,280,200 jobs, for a net loss of 2.7 million U.S. jobs (Scott 2013, Table 5, 13). Thus, not only did workers lose wages moving from import-competing to exporting industries, but 2.7 million workers were displaced from jobs where they earned $1,021.66 per week on average. If they were lucky enough to find jobs, these 2.7 million workers were mostly pushed into jobs in nontraded industries paying an average of only $791.14 per week (a decline of 22.6%). In total, U.S. workers suffered a direct net wage loss of $37 billion per year (Scott 2013, 26, Table 9b) due to trade with China. But the direct wage losses are just the tip of the iceberg.

As shown by Josh Bivens in Everybody Wins, Except for Most of Us (Bivens 2008a, with results updated in Bivens 2013), growing trade with China and other low-wage exporters essentially puts all American workers without a college degree (roughly 100 million workers) in direct competition with workers in China (and elsewhere) making much less. He shows that trade with low-wage countries was responsible for 90% of the growth in the college wage premium since 1995 (the college wage premium is the percent by which wages of college graduates exceed those of otherwise-equivalent high school graduates). It is important to note that there are roughly 100 million non-college-educated workers whose wages were suppressed by China trade. The growth of China trade alone was responsible for more than half of the growth in the college wage premium in that period, Bivens finds. To put these estimates in macroeconomic terms, in 2011, trade with low-wage countries lowered annual wages by 5.5%—roughly $1,800 per worker for all full-time, full-year workers without a college degree. To provide comparable economywide impact estimates, assume that 100 million workers without a college degree suffered average losses of $1,800 per year, which yields a total national loss of $180 billion (Scott 2015). Therefore, the indirect, macroeconomic losses to U.S. workers without college degrees caused by growing trade with low-wage nations were about five times as large as the $37 billion in direct wage losses in 2011 from trade with China, and about 40 times as many workers were affected indirectly due to globalization’s wage-lowering effect (100 million) as were displaced by trade with China (2.7 million).23 And China trade alone was responsible for about 51.6% of the increase in the overall college/noncollege wage gap between 1995 and 2011.24

Additionally, Autor, Dorn, and Hanson estimate that rising exposure to low-cost Chinese imports lowers labor force participation and reduces wages in local labor markets; in particular, they find that increased import competition has a statistically significant depressing effect on nonmanufacturing wages (Autor, Dorn, and Hanson 2012, abstract). This confirms the findings of Bivens (2008a, 2013). Autor, Dorn, and Hanson (2012) also find that “transfer benefits payments for unemployment, disability, retirement, and healthcare also rise sharply in exposed labor markets” and that “for the oldest group (50–64), fully 84% of the decline in [manufacturing] employment is accounted for by the rise in nonparticipation, relative to 71% among the prime-age group and 68% among the younger group” (Autor, Dorn, and Hanson 2012, abstract, 25). Thus, Autor, Dorn, and Hanson find that more than two-thirds of all workers displaced by growing competition with Chinese imports dropped out of the labor force. These results are explained, in part, by the finding that “9.9%…of those who lose employment following an import shock obtain federal disability insurance benefits [Social Security Disability Insurance (SSDI) benefits].” Additionally, “rising import exposure spurs a substantial increase in government transfer payments to citizens in the form of increased disability, medical, income assistance and unemployment benefits.” Moreover, “these transfer payments vastly exceed the expenses of the TAA [Trade Adjustment Assistance] program, which specifically targets workers who lose employment due to import competition” (Autor, Dorn, and Hanson 2012, 25, 30). In Autor and Hanson 2014, the effects are totaled, and they find that “for regions affected by Chinese imports, the estimated dollar increase in per capita SSDI payments is more than 30 times as large as the estimated dollar increases in TAA payments.”

The job and wage losses stemming from the growing U.S.–China trade deficit are real—and also increase demands on the social safety net

Some economists and others in the trade debate have argued that job loss numbers extrapolated from trade flows are uninformative because aggregate employment levels in the United States are set by a broad range of macroeconomic influences, not just by trade flows.25?However, while the trade balance is but one of many variables affecting aggregate job creation, it plays a large role in explaining structural change in employment, especially in the manufacturing sector. As noted earlier, between December 2001 and December 2018, 2.9 million U.S. manufacturing jobs were lost (BLS 2019a). The growth of the U.S. trade deficit with China was responsible for the displacement of 2.8 million manufacturing jobs in this period, or nearly all of the manufacturing jobs lost.

The employment impacts of trade identified in this paper can be interpreted as the “all else equal” effect of trade on domestic employment. The Federal Reserve, for example, may decide to cut interest rates to make up for job losses stemming from deteriorating trade balances (or any other economic influence), leaving net employment unchanged. This, however, does not change the fact that trade deficits by themselves are a net drain on employment. Even if macroeconomic policy is adjusted to offset the negative impact of the growing trade deficit with China on total employment, the structure of production and employment in the United States has been negatively affected (Scott 2017a, Scott and Mokhiber 2018).

The growing trade deficit with China has clearly reduced domestic employment in traded-goods industries, especially in the manufacturing sector, which has been pummeled by plant closings and job losses. Workers?from the manufacturing sector displaced by trade have had particular difficulty securing comparable employment elsewhere in the economy. According to the most recent Bureau of Labor Statistics survey covering displaced workers (BLS 2018, Table 4), more than one-third (35.4%) of long-tenured (employed more than three years) manufacturing workers displaced from January 2015 to December 2017 were not working in January 2018, including 21.7% of long-tenured manufacturing workers displaced who were not in the labor force, i.e., no longer even looking for work, and 13.7% who were unemployed.

As noted above, U.S. workers who were directly displaced by trade with China between 2001 and 2011 lost a collective $37.0 billion in wages as a result of accepting lower-paying jobs in nontraded industries or industries that export to China assuming, conservatively, that those workers are reemployed in nontraded goods industries (Scott 2013).26 Worse yet, growing competition with workers in China and other low-wage countries reduced the wages of all 100 million U.S. workers without a college degree, leading to cumulative losses of approximately $180 billion per year in 2011 (Bivens 2013; Scott 2015). The lost output of unemployed workers, especially that of labor force dropouts, can never be regained and is one of the larger costs of trade-related job displacement to the economy as a whole.27

The Trade Adjustment Assistance (TAA) program is a Department of Labor program to provide retraining and unemployment benefits to certain workers who have been displaced by growing imports. However, new research suggests that significant shares of displaced workers are signing up for disability and retirement benefits, other government income assistance, and government medical benefits, in addition to temporary trade adjustment assistance. Many of these workers, such as those on disability and retirement, are permanently dropping out of the labor force, resulting in permanent income losses to themselves and the economy. TAA benefits represent only a tiny share of the costs of adjustment. Examining only those costs for which workers actually qualify for government benefits, Autor, Dorn, and Hanson (2012, Figure 7 at 32) find that unemployment and TAA benefits represent only 6.3% of the total benefit costs associated with a $1,000 increase in imports per worker in “commuting zones” over the 1990–2007 period.28 Given the low level of coverage of social safety net programs in the United States versus other developed countries (such as those in the EU), actual adjustment costs for displaced workers are likely substantially larger than the actual U.S. benefits estimated by Autor, Dorn, and Hanson.

Conclusion

The growing U.S. goods trade deficit with China has displaced millions of jobs in the United States and has contributed heavily to the crisis in U.S. manufacturing employment, which has heightened over the last decade largely due to trade with China. Moreover, the United States is piling up foreign debt, losing export capacity, and facing a more fragile macroeconomic environment.

China and America are locked in destructive, interdependent economic cycles, and both can gain from rebalancing trade and capital flows. Although economic growth in China has been rapid, it is unbalanced and unsustainable. Growth in China slowed to 6.6% in 2018, and it is projected to fall to 5.5% in 2024 (IMF 2019). China’s economy is teetering on the edge between inflation and a growth slump, and a soft landing is nowhere in sight. China needs to rebalance its economy by becoming less dependent on exports and more dependent on domestic demand led by higher wages and infrastructure spending. It also needs to reduce excessive levels of domestic savings to better align savings levels with domestic investment and government borrowing. The best ways to do this are to raise wages and to increase public spending on pensions, health care, and other aspects of the safety net. This will reduce private saving and increase Chinese domestic demand for both domestic and imported goods, reducing China’s trade surplus (Scott 2017a).

The effects on the United States of China’s destructive, rapidly growing trade surplus are outlined in this report. To summarize, the growing U.S. trade deficit with China has eliminated 3.7 million U.S. jobs between 2001 and 2018, including 1.7 million jobs lost since 2008 (the first full year of the Great Recession) and more than 700,000 jobs lost or displaced in the first two years of the Trump administration. Of the total jobs lost due to the growing U.S.—China trade deficit, 2.8 million, or 75.4% of the total jobs lost, were in manufacturing. These losses were responsible for nearly all of the 2.9 million U.S. manufacturing jobs lost between December 2001 and December 2018. The growing trade deficit with China has reduced wages of those directly displaced by $37 billion through 2011 alone, and it is largely responsible for the loss of roughly $1,800 per worker per year, due to wage suppression, for all non-college-educated workers in the United States. These losses have been extremely costly for the workers and communities affected, as shown in this report.

The U.S.–China trade relationship needs to undergo a fundamental change. Addressing unfair trade, weak labor, and environmental standards in China, and ending currency manipulation and misalignment, should be our top trade and economic priorities with China (Scott 2017a, Scott and Mokhiber 2018).

About the authors

Robert E. Scott (@RobScott_EPI) joined the Economic Policy Institute in 1997 and is currently director of trade and manufacturing policy research. His areas of research include international economics, the impacts of trade and manufacturing policies 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 and exchange rates. He has published widely in academic journals and the popular press, including?in the Journal of Policy Analysis and Management,?the International Review of Applied Economics, and?the Stanford Law and Policy Review,? the Detroit News, the New York Times, Los Angeles Times,?Newsday,?USA Today,?The Baltimore Sun,?The Washington Times, The Hill, and other newspapers. He has also 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 at Berkeley.

Zane Mokhiber joined EPI in 2016. As a data analyst, he supports the research of EPI’s economists and EPI’s Economic Analysis and Research Network (EARN) on topics such 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.

Acknowledgments

The authors thank?Josh Bivens for comments, Lora Engdahl for editorial guidance and Jori Kandra and Daniel Perez for research assistance. This research was made possible by support from the?Alliance for American Manufacturing.

Appendix: Methodology

The trade and employment analyses in this report are based on a detailed, industry-based study of the relationships between changes in trade flows and employment for each of approximately 205 individual industries of the U.S. economy, specially grouped into 44 custom sectors,29 and using the North American Industry Classification System (NAICS) with data obtained from the U.S. Census Bureau (2019a and 2020a)) and the U.S. International Trade Commission (USITC 2019).

The number of jobs supported by $1 million of exports or imports 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 (2019a).30 This model includes both the direct effects of changes in output (for example, the number of jobs supported by $1 million in auto assembly) and the indirect effects on industries that supply goods (for example, goods used in the manufacture of cars). So, in the auto industry for example, the indirect impacts include jobs in auto parts, steel, and rubber, as well as service industries 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 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 2001 was used in this study) that were developed by the U.S. Department of Commerce and the BLS-EP. It is not a statistical survey of actual jobs gained or lost in individual companies, or the opening or closing of particular production facilities (Bronfenbrenner and Luce 2004 is one of the few studies based on news reports of individual plant closings).

Nominal trade data used in this analysis are converted to constant 2012 dollars using industry-specific deflators. This is necessary because the labor requirements table is estimated using price levels in that year. Data on real trade flows are converted to constant 2012 dollars using industry-specific price deflators from the BLS-EP (2019b). Use of constant 2012 dollars is required for consistency with the other BLS models used in this study.

Estimation and data?sources

Data requirements

Step 1.?U.S.–China trade data are obtained from the U.S. International Trade Commission DataWeb (USITC 2019) in four-digit, three-digit, and two-digit NAICS formats. General imports and total exports are downloaded for each year.

Step 2.?To conform to the BLS Employment Requirements tables (BLS-EP 2019a), trade data must be converted into the BLS industry classifications system. For NAICS-based data, there are 205 BLS industries. The data are then mapped from NAICS industries onto their respective BLS sectors. The trade data, which are in current dollars, are deflated into real 2012 dollars using published price deflators from the BLS-EP (2019b).

Step 3.?Real domestic employment requirements tables are downloaded from the BLS-EP (2019a). These matrices are input-output industry-by-industry tables that show the employment requirements for $1 million in outputs in 2012 dollars. So, for industry i the aij?entry is the employment indirectly supported in industry i by final sales in industry j and, where i=j, the employment directly supported.

Analysis

Step 1. Job equivalents.?BLS trade data are compiled into matrices. Let [T2001] be the 205×2 matrix made up of a column of imports and a column of exports for 2001. [T2018] is defined as the 205×2 matrix of 2018 trade data. Finally, [T2008] is defined as the 205×2 matrix of 2008 trade data. Define [E2001] as the 205×205 matrix consisting of the real 2001 domestic employment requirements tables. To estimate the jobs displaced by trade, perform the following matrix operations:

[J2001] = [T2001] × [E2001]

[J2008] = [T2008] × [E2001]

[J2018] = [T2018] × [E2001]

[J2001] is a 205×2 matrix of job displacement by imports and jobs supported by exports for each of 205 industries in 2001. Similarly, [J2008] and [J2018] are 205×2 matrices of jobs displaced or supported by imports and exports (respectively) for each of 205 industries in 2008 and 2018, respectively.

To estimate jobs created/lost over certain time periods, we perform the following operations:

[Jnx01-18] = [J2018] ? [J2001]

[Jnx01-08] = [J2008] ? [J2001]

[Jnx08-18] = [J2018] ? [J2008]

Step 2. State-by-state?analysis.?For states, pooled (5-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 2019a) 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).31

Previous versions of this report (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 ACS 5-year data referenced above. These date 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.32

We look at job displacement from 2001 to 2018 so from this point, we use [Jnx01-18]. In order to work with 44 sectors, we group the 205 BLS industries into a new matrix, defined as [Jnew01-18], a 44×2 matrix of job displacement numbers. 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 5-year employment estimates. We calculate:

[Stjnx01-18] = [St2013-2017]T?[Jnew01-18]

where [Stjnx01-18] is the 44×51 matrix of job displacement/support by state and by industry. To get state total job displacement, we add up the subsectors in each state.

Step 3. Congressional district?analysis.?Employment by congressional district, by industry, and by state is obtained from the ACS 5-year employment estimates for the 2013–2017 period, which use geographic codings that match the district boundaries of the 115th Congress.33 In order to calculate job displacement in each congressional district, we use the columns in [Stjnx01-18], which represent individual state job-displacement-by-industry estimates, and define them as [Stj01], [Stj02], [Stji]…[Stj51], with?i?representing the state number and each matrix being 44×1.

Each state has?Y?congressional districts, so [Cdi] is defined as the 44×Y?matrix of congressional district employment shares for each state. Congressional district shares are calculated thus:

[Cdj01] = [Stj01]T?[Cd01]

[Cdji] = [Stji]T?[Cdi]

[Cdj51] = [Stj51]T?[Cd51]

where [Cdji] is defined as the 44xY?job displacement in state?i?by congressional district by industry.

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

Endnotes

1. The World Trade Organization, which was created in 1994, was empowered to engage in dispute resolution and to authorize imposition of offsetting duties if its decisions were ignored or rejected by member governments. It expanded the General Agreement on Tariffs and Trade (GATT) trading system’s coverage to include a huge array of subjects never before included in trade agreements, such as food safety standards, environmental laws, social service policies, intellectual property standards, government procurement rules, and more (Wallach and Woodall 2004).

2. Tables 1 and 2 report U.S. general imports from China as measured by “customs value” (the value of imports as appraised by the U.S. Customs Service) and total exports to China as measured by “free alongside” or FAS value (the value of exports at the U.S. port, including the transaction price, inland freight, insurance, and other charges) to China. News releases from the U.S. Census Bureau and the Commerce Department usually emphasize general imports and total exports. The U.S. Internal Trade Commission (USITC) often refers to this netting out of general imports and total exports as the “broad” measure of the trade balance, as opposed to the “narrow” measure, which relies on imports for consumption and domestic exports. (For an example, see USITC 2014.) For an explanation of the difference between general imports and imports for consumption, see the U.S. Census Bureau’s online trade glossary [2018b].) The key difference between these two measures is that total exports, as reported by the U.S. Census Bureau, include foreign exports (re-exports), i.e., goods produced in other countries and shipped through the United States, while domestic exports, as implied by the name, do not include re-exports. While a previous version of this report (Kimball and Scott 2014) relied on the narrow definition, using imports for consumption and domestic exports for the analysis, the broad measure was used in Scott 2017a. For 2017, imports for consumption were $504.0 billion, domestic exports were $120.0 billion, and the reported (narrow) trade balance was $384.0 billion. When we compare the trade deficit and job displacement estimates we obtained using the broad measure with the estimates we would have obtained using the narrow measure, we find the difference to be insignificant. The broad measure delivers an estimate of 3.36 million net jobs displaced in 2017, whereas the narrow measure delivers an estimate of 3.44 million net jobs displaced in 2017, as reported in Scott and Mokhiber (2018). In this report, all estimates for trade and jobs gained and lost for prior years are based on the broad measure of the trade balance. Data for individual years, and for the change in net jobs displaced, are reported in Table 1, in Figure A, and in other exhibits in this report.

3. Average annual change note shown in Table 1. Total change divided by number of years deal is in effect is $336.5 divided by 17, which equals $19.8 billion per year.

4. While some small proportion of goods imported from China represent a category of goods that may not be produced in the United States, and thus would be “noncompeting” goods, the model used in this report produces an overall estimate of the net jobs displaced by the growing trade deficit. It is, in essence, an estimate of the jobs displaced by the growth of imports in excess of the growth of exports. Since virtually all U.S. imports from China are manufactured goods, as shown in Table 2 in this report, nearly all could?be produced in the United States but for China’s unfair trade and currency policies and its domestic “savings glut” (Setser 2016).

5. The term “displaced” would be appropriate to an economy that was at true full employment, where any displaced worker would immediately take a job in another sector of the economy. However, the workers displaced by goods trade are almost exclusively manufacturing workers, and these workers have not been successfully moving into different parts of the economy in recent years: more than one-third of manufacturing workers who were displaced between 2015 and 2017 and who had previously been employed for at least three years were either unemployed or out of the labor force in January 2018 (BLS 2018a). Thus, trade-related job displacement does result in at least some workers moving to a nonworking status, thus “lost” jobs, even if other workers are reemployed elsewhere in the economy (reemployment would result in a change in the composition, rather than the level, of employment).

6. The BLS updated its Employment Requirements Matrix in September 2019 (BLS-EP 2019a), as it normally does every two years. Those revisions have been taken into account in this update. There are 205 NAICS-based BLS industries in the 2019 BLS update (NAICS stands for North American Industry Classification System).

7. Updated in Rasmussen 2017. Employment requirements tables in that report are derived from BEA input-output data, which are the primary source of data used to estimate BLS employment requirement tables (BLS-EP 2019a).

8. The macroeconomic model developed in Scott and Glass 2016 assumes that a 1.6% decrease in GDP would reduce total direct and indirect U.S. employment by roughly 1.35%. There were, on average, 155.8 million people employed in the United States in 2019 (BLS 2019b), thus yielding 2.1 million direct and indirect jobs displaced. The macroeconomic model also assumes a respending multiplier of 0.6 and yields a total of 3.4 million direct and indirect and respending jobs displaced by a trade deficit of this magnitude.

9. Scrap and used or secondhand goods are industries 203 and 204, respectively, in the BLS model, and there are no jobs supported or displaced by the production of or trade in goods?in these sectors, according to the BLS model. (The jobs supported or displaced by trade are counted in the year these goods are originally manufactured—that is, when they are new—not when they are traded in the secondhand market.)

10. Authors’ calculations and USITC (2019); data not published in this report, available upon request.

11. ATPs are an amalgamation of products from a variety of industries and subsectors within the broad NAICS-based categories shown in Table 2. They consist of 10 categories of products including biotechnology, life science, opto-electronics, information and communications, electronics, flexible manufacturing, advanced materials, aerospace, weapons, and nuclear technology (U.S. Census Bureau 2018a).

12. In total ATP trade with the world in 2018, the United States had exports of $368.4 billion, imports of $496.5 billion, and a trade deficit of $128.2 billion. In total ATP trade with China in 2018, the United States had exports of $39.1 billion, imports of $173.8 billion, and a trade deficit of $134.6 billion. This exceeded the overall U.S. ATP deficit of $128.2 billion. Thus, the United States had an ATP trade surplus with the rest of the world in 2018 of $6.5 billion ($128.2 billion ? $134.6 billion) (U.S. Census Bureau 2019b). Data for trade in advanced technology products (ATP) by country are not available before 2002.

13. These results are derived from the trade and employment model described in the appendix to this report.

14. Deflators for many sectors in the computer and electronics parts industry fell sharply between 2001 and 2018 due to rapid productivity growth in those sectors. For example, the price index for computer and peripheral equipment fell from 3155.9 in 2001 to 850.9 in 2018, a decline of 73.0% (the price index is set at 1,000 in 2012, the base year). In order to convert exports or imports of computers and peripheral equipment from nominal to real values for 2018, the nominal value is multiplied by 1,000/850.9 (the price index adjustment 2018 = 1.175). Thus, the real value of computers and peripheral products, a subset of the computer and electronic parts industry, is 17.5% larger than the nominal value in 2018 (in constant 2012 dollars). Overall, the real value of all computer and electronic parts imports in 2018 exceeded nominal values in that year by 7.7%. See the appendix for source notes and deflation procedures used.

15. Total imports from China in 2018 exceeded exports by a factor of 4.49-to-1 (539.1/120.1, as shown in Table 1). Thus, exports to China would have had to be roughly four times larger than they actually were in 2018 to achieve balanced trade with China.

16. Data not shown in Table 2. Authors’ analysis based on the change in exports shown, by industry, and the multiplier referred to in the previous note (4.49), based on analysis of data shown in Supplemental Table 1.

17. The computer and electronic parts industry’s share of all jobs lost in Table 5 due to the growth in the U.S.–China trade deficit from 2001 to 2018 ranged from 12.1% in North Carolina’s 10th District to 93.0% in California’s 17th?District (authors’ analysis of U.S. Census Bureau 2019a and 2020b; USITC 2019; BLS-EP 2019a, 2019b), compared with the national average of 36.2% of jobs (Table 3). In these states the only exceptions—that is, districts where job losses were concentrated in industries other than computer and electronic parts—were California’s 34th?district, where jobs losses in the apparel industry were 68.6% (and computers and electronics only 6.3%) of jobs lost in that district (compared with the national average of apparel industry job losses accounting for 5.6% of jobs lost due to U.S.–China trade, as shown in Table 3). Georgia is also one of the states that are host to one of the 20 hardest-hit congressional districts; Georgia’s 14th Congressional District’s job losses due to the trade deficit include a very large share of jobs in manufacturing, overall, 91.8% of all jobs lost, according to unpublished data available upon request. Nationally, manufacturing accounted for a smaller, 75.4% share, of all jobs lost (Table 3). Overall, more than two-thirds (67.6%) of jobs lost in Georgia’s 14th district were in textile mills and textile product mills alone. North Carolina’s 6th and 10th districts also suffered large numbers of job losses in a wide range of manufacturing industries, totaling 87.3% to 88.0% of job losses in these district. These losses were spread over a large number of industries, including especially in textiles, apparel and leather products, and furniture manufacturing (47.8% to 48.1% of jobs lost in these two districts).

18. California’s 17th Congressional District is home to Santa Clara University and corporate offices for Apple, Intel, Yahoo, and eBay (Wikipedia 2018). The 18th Congressional District is home to the headquarters of Google, Netflix, and HP, among others (Eshoo 2018).

19. The term “major manufacturing sector” refers here to employment by three-digit NAICS manufacturing industries. The computer and electronic parts industry lost 1,340,600 of the 2,793,200 U.S. manufacturing jobs lost due to growing China trade deficits between 2001 and 2018 (Table 3), more than six?times as many jobs as were lost as in apparel, the next largest of the hardest-hit three-digit manufacturing industries. Trade-related job losses in these industries, shown in Table 3, reflect both potential jobs displaced by the growth of imports (which represents domestic consumption that could have been supplied by domestically produced goods) and by the failure of exports to grow, resulting in large trade deficits in these products.

20. In earlier research, Autor, Dorn, and Hanson “conservatively estimate” that growing “Chinese import competition…imply a supply-shock driven net reduction in U.S. manufacturing employment of 548 thousand workers between 1990 and 2000, and a further reduction of 982 thousand workers between 2000 and 2007.” They note further that these results are based on microeconomic research “exploiting cross-market variation in import exposure” (Autor, Dorn, and Hanson 2012, 19–20, abstract). These estimates are conservative, for several reasons, as noted by the authors. They fail to account for the overall macroeconomic impacts of growing U.S. trade deficits with China, including the direct and indirect effects of growing China trade deficits on U.S. employment, as noted by Acemoglu et al. 2014. As shown in Table 3, the growing U.S. goods trade deficit with China displaced 2.8 million total manufacturing jobs between 2001 and 2018, and an additional 911,300 nonmanufacturing jobs. Thus, approximately 0.33 nonmanufacturing jobs were displaced for each manufacturing job displaced. Differences in parameter estimates notwithstanding, it is important to note that Autor, Dorn, and Hanson (2012) confirm that growing Chinese import competition is responsible for the displacement of approximately 1.5 million U.S. manufacturing jobs from 1990 to 2007, generally confirming the results of current and earlier EPI research.

21. Acemoglu et al. (2014) examine the impacts of U.S.–China trade from 1999 to 2011. The U.S. trade deficit with China increased from $68.7 billion in 1999 to $83.0 billion in 2001 to $295.2 billion in 2011 (U.S. Census Bureau 2019d). Thus, 93.6% of the growth of the U.S. trade deficits with China in the 1999–2011 period occurred after China entered the WTO in 2001.

22. Scott 2013 estimates are based on average wages from a three-year pooled sample of workers by industry from 2009–2011. These estimates are not updated in this report.

23. The $180 billion in income is redistributed to college-educated workers in the top third of the labor force and to owners of capital. Bivens and Mishel (2015, Figure C) find that for the period of 1973–2014, the loss in the labor share of income was responsible for 8.7 percentage points of the gap between net productivity and real median hourly compensation (a measure of the growth in inequality in this period).

24. Between 1995 and 2011, growing trade with China was responsible for 51.6 percent of the increase in the college/noncollege wage gap in the United States in this period (Bivens 2013, Table 1). Bivens (2013, 2) concludes that “growing trade with less developed countries” overall, according to “a standard model estimating the impact of trade on American wages,” “lowered wages in 2011 by 5.5 percent—or roughly $1,800 for a full-time, full-year worker earning the average wage for workers without a four-year college degree. One-third of this effect is due to growing trade with just China.”

25. One frequent criticism of trade and employment studies is the claim that the growth of imports does not displace domestic production and thus that such imports do not actually cost jobs. In addition, some assert that if imports from China fell, they would be replaced by imports from some other low-wage country (see, for example, U.S.–China Business Council 2014). However, important empirical research by Autor, Dorn, and Hanson (2012, 4) has shown that “increased exposure to low-income country imports is associated with rising unemployment, decreased labor-force participation, and increased use of disability and other transfer benefits, as well as with lower wages.” The bottom line is that “trade creates new jobs in exporting industries and destroys jobs when imports replace the output of domestic firms. Because trade deficits have risen over the past decade, more jobs have been displaced by imports than created by?exports” (Bivens 2008b, 1, Bivens 2017).

26. This analysis refers to the wage impacts of net jobs lost due to the growth of the U.S.–China trade deficit between 2001 and 2011. It includes net wage gains in the 538,000 jobs supported by increased employment in export industries, less net wage losses in the 3.3 million jobs displaced by increased imports, assuming that all of the 2.7 million net displaced workers are rehired and receive average earnings in jobs in nontraded goods industries (Scott 2013, Table 9a). It is conservative in the sense that it assumes that all of the net displaced workers are rehired in jobs in nontraded goods industries; it excludes the wage losses absorbed by those displaced workers who are not reemployed (for example, the 35.4% of long-tenured workers in manufacturing who had been displaced between January 2015 and December 2017 and were not employed in January 2018, as estimated in the BLS Displaced Worker Survey [BLS 2018]).

27. These losses can never be regained in that the hours unemployed are a permanent loss to the economy, even if an individual worker later finds employment at wages equal to or higher than pre-displacement wages. Unemployment costs are a dead-weight loss to the economy, in the same way that unemployment during a recession generates a permanent loss in national economic output.

28. Autor, Dorn, and Hanson (2012) use an analytic technique that?compares employment in import-sensitive industries in various geographic areas at a fairly disaggregated level (roughly, cities or counties), referred to in their research as “commuting zones.” They use these zones and data on imports in each region over the study period to do their statistical analysis.

29. A previous edition of this research (Scott 2012) used data for 56 industries provided by the ACS. The Bureau of Labor Statistics’ Employment Projections program (BLS-EP) consolidated several industries, including textiles and apparel, which required us to consolidate data for these industries in our ACS state and congressional district models. Other “not elsewhere classified” industries were consolidated with other industries (e.g., “miscellaneous manufacturing”) or deleted (e.g., “not specified metal industries”) to update and refine the crosswalk from BLS-EP to ACS industries. As a result of these consolidations, there are 44 unique industries in the ACS data set used for this study. The current iteration of the employment requirements tables used in this study (BLS-EP 2019a) breaks the economy down into 205 industries, including 76 manufacturing industries. The previous iteration of employment requirements tables, used in Scott 2017a, broke the economy down into 195 industries, including 77 manufacturing industries.

30. 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.

31. ?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.

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

33. ACS 5-year estimates for the 2014–2018 period, which include estimates for employment by congressional district for the 116th Congress (in session from January 3, 2019, through January 3, 2020) were released by the Census Bureau on December 19, 2019. Estimates of trade-related job loss by state and congressional district in this report will be revised using 116th Congress data and posted in the first quarter of 2020.

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Supplemental tables

Supplemental Table 1: U.S. goods trade with China, by industry, 2001–2018 (in billions of nominal dollars)
Supplemental Table 2: Net U.S. jobs displaced due to the goods trade deficit with China, by state, 2001–2018 (ranked by net jobs displaced)
Supplemental Table 3: Net U.S. jobs displaced due to the goods trade with China, by state, 2001–2018 (sorted alphabetically)
Supplemental Table 4: Net U.S. jobs displaced due to the goods trade with China, by congressional district, 2001–2018 (ranked by net jobs displaced)
Supplemental Table 5: Net U.S. jobs displaced due to the goods trade with China, by congressional district, 2001–2018 (sorted alphabetically by state)
Supplemental Table 6: Net U.S. jobs displaced due to goods trade deficit with China, by congressional district, 2001–2018 (ranked by jobs displaced as a share of total state employment)

 

 


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