1. Where Did All The U. S. Manufacturing Workers Go. Evidence from U. S
Where Did All The U.S. Manufacturing Workers Go? Evidence from U.S. Matched Employer-Employee Data (in progress)
Cristina Tello-Trillo U.S. Census Bureau
Justin Pierce Board of Governors, Federal Reserve System
Peter K. Schott Yale School of Management & NBER

2. Motivation
Large literatures examine worker reallocation across industries, firms and regions in response to economic shocks
This paper analyzes the earnings and employment trajectories of workers that are directly (industry) and indirectly (county) exposed to a large negative shock induced by a change in U.S. trade policy
Manufacturing workers with greater direct exposure experience
Lower cumulative earnings
Fewer years of employment
Non-manufacturing workers with greater indirect exposure exhibit similar outcomes
So, why does this matter?
Isn’t what we find obvious?
Are we trying to recover frictions?

3. Existing Research “China” Shock
Imports: Autor et al (2013,2014,2016: direct and indirect impacts of increase in Chinese imports on U.S. employment, earnigns, etc.
Displacement/Mass Layoff
Jacobson, LaLonde and Sullivan (1993)
Hummels, Jorgenson, Munch and Xiang (2014)
Von Wachter and Davis (2011)
Acemoglu et al. (2016) look at three effects: direct industry effect; local commuting zone effect; reduction in demand effect. Here, we can look directly at the impact on different groups of workers, i.e., those that are affected by the shock directly via their initial industry of employment and those that are only affected indirectly.
Autor et al. (2014): observe age, gender, labor market experience, race, foreign-born but not education. Their region data is not so good, so they don’t have that in the main specs. Look at labor churning and earnings outcomes, but not exposure via region. They can look at self employment

4. Outline
Background
Data
Sector transitions
Earnings

5. Source: Pierce and Schott (2012)
Background
Source: Pierce and Schott (2012)

6. US NTR and Non-NTR Tariffs
NTR = Normal Trade Relations
Synonym for “Most Favored Nation” (MFN)
The US has two basic tariff schedules
NTR tariffs : for WTO members; generally low
Non-NTR tariffs : for non-market economies; generally high; set by Smoot-Hawley (1930)
How does China fit into these categories?

7. Non-NTR g Renewable NTR g Permanent NTR
2001 (December)
China enters WTO
Non-NTR
PNTR
Annual renewals of MFN status were uncertain, particularly in the 1990s, creating uncertainty for U.S. and Chinese firms
1980 (February)
China granted temporary NTR status
Requires annual approval by President and Congress
2000 (October)
U.S. Congress grants China Permanent NTR status, U.S. and Chinese firms have incentive to move production to China

8. Plausible Mechanism?
“U.S. companies expect to benefit from billions of dollars in new business and an end to years of uncertainty in which they had put off major decisions about investing in China.
NYT
“[P]rivate sector officials cited uncertainty surrounding the annual renewal of China’s most-favored-nation trade status as the single most important issue affecting U.S. trade relations with China….[T]he great majority of the U.S. business associations and companies… contacted told us that the annual uncertainty surrounding China’s MFN status potentially hinders their business activities in China.”
US GAO
“While the risk that the United States would withdraw NTR status from China may be small, if it did occur the consequences would be catastrophic for U.S. toy companies given the 70 percent non-MFN U.S. rate of duty applicable to toys.”
Testimony of Thomas F. St. Maxens (Mattel)
Ways and Means Committee
“In the months since the enactment of [PNTR] there has been an escalation of production shifts out of the U.S. and into China. According to our media-tracking data, between October 1, 2000 and April 30, 2001 more than eighty corporations announced their intentions to shift production to China, with the number of announced production shifts increasing each month from two per month in October to November to nineteen per month by April.”
Bronfenbrenner et al. ( )
U.S.-China Security Review Commission

9. Exposure to PNTR via the “NTR Gap” i=industry
Define the difference between the non-NTR and NTR rates as
NTR Gapi = Non-NTR Tariffi – NTR Tariffi
Calculated using tariff-line (HS8) data from Feenstra et al. (2003)
Use tariffs for 1999, the year before PNTR
Gap for industry i is the mean across tariff lines in industry i

10. Distribution of NTR Gap
89 percent of the variation in the NTR gap across industries arises from variation in non-NTR rates, which were set in 1930
The higher the NTR gap, the greater the industry’s exposure to potential tariff increases before PNTR
Mean: 0.32
Std:

11. Simple View of the Data
Divide U.S. import and manufacturing industries into two groups:
Most-exposed: NTR gap above median
Least-exposed: NTR gap below median
Plot their trajectory before and after PNTR

12. Simple View of the Data – Trade Public Census Trade Data
Imports from China in the more-exposed products jump after PNTR
Most Exposed
Least Exposed

13. Simple View of the Data – Trade Public Census Trade Data
Imports from China in the more-exposed products jump after PNTR
This trend is not present in imports from rest-of-world (ROW)
China
ROW

14. Simple View of the Data – Employment Public NBER-CES Data
Before PNTR: high- and low-gap industries follow roughly parallel trends

15. Simple View of the Data – Employment Public NBER-CES Data
Before PNTR: high- and low-gap industries follow roughly parallel trends
Post PNTR: employment falls more sharply among high-gap industries
Least Exposed
Most Exposed

16. Margins of Worker Adjustment
Background
Data
Sector transitions
Earnings

17. Background: Manufacturing vs Other Non-Farm Employment
Post-2000 Declines
2000-3: -17%
: -18%
Created in vox_02.do in pntr/drafts/

18. Data: Matching Workers and Firms
Longitudinal Employer-Household Dynamics (LEHD)
Based on state unemployment insurance (UI) records
Encompasses 46 states for (excluded: Alabama, Arkansas, New Hampshire, Mississippi, DC)
Omits workers not covered by states’ reporting systems, primarily agriculture and federal, military and postal workers
Observables
Employment History File (EHF)
Earnings and employment by longitudinal personal identifiers (PIKs) that map 1:1 to SSN
Links workers to establishments and firms; we assign workers to industries and counties based on the establishments where they work
Individual Characteristics File (ICF)
Age, gender, race, foreign-born
Additional links possible (SIPP?)

19. LEHD Caveats
Not possible to distinguish between unemployment vs NILF vs movement to out-of-scope state or industry
We categorize workers that do not show up in a particular year as “not employed”
Workers may have more than one job at a time
Follow the LEHD literature and assign worker to job with highest earnings

20. Attributes of Manufacturing vs Non-Manufacturing Workers 2000 and 2011
In 2000, manufacturing workers are
More male
More foreign-born
Less-educated
Older
By 2011, manufacturing workers become
Even more relatively male
Relatively younger (retirements!)
2011
2011

21. Margins of Worker Adjustment
Background
Data
Sector transitions
Earnings

22. Outline Background Data Where did the manufacturing workers go?
M = manufacturing
NM = non-manufacturing
NE = not employed
Direct and indirect impact of economic shock

23. Transitions Among M, NM and NE 2000-11

24. Transitions Among M, NM and NE 2000-11
There are 17.5 million workers in manufacturing (M) in 2011

25. Transitions Among M, NM and NE 2000-11
There are 17.5 million workers in manufacturing (M) in 2011
In 2007:
33% move to NM
33% remain in M
34% are NE

26. 2000-7 vs 2000-11 Why similar transition to NE for M and NM?
Maybe M was different before 2000s?
Maybe trade shock operates along industry and region margins?
Other info
<10 percent of transitions from MgNM take place within firms
87 percent of M workers that are NE in 2007 are NE in 2011
84 percent of NM workers that are NE in 2007 are NE in 2011

27. Transitions to NE Based on Initial Age
All Workers,
Transition to NE are higher for those >50 in 2000
Not shown:
87 percent of initial M workers that are NE in 2007 are also NE in 2011
For initial NM workers, it is 84 percent
Workers <=50,
Workers >50,

28. Where Did Manufacturing Workers Go?
First column reports the outflow of workers that were in manufacturing in 2000
Second column reports these outflows as a share of total outflows excluding “public” and “not employed”
E.g., 9 percent of year 2000 manufacturing workers that did not stay in manufacturing or go to not employed or public went into construction
Third column reports the stock of workers in 2000, net of workers that flowed in from manufacturing
E.g., 7.3 percent of workers in 2007 were in construction

29. Where Did Manufacturing Workers Go?
First column reports the outflow of workers that were in manufacturing in 2000
Second column reports these outflows as a share of total outflows excluding “public” and “not employed”
E.g., 9 percent of year 2000 manufacturing workers that did not stay in manufacturing or go to not employed or public went into construction
Third column reports the stock of workers in 2000, net of workers that flowed in from manufacturing
E.g., 7.3 percent of workers in 2007 were in construction

30. Margins of Worker Adjustment
Background
Data
Sector transitions
Earnings and employment after PNTR

31. Data: Direct and Indirect Exposure to PNTR j=industry ; c=county; L=employment
Direct exposure: NTR gap for industry j is
Indirect exposure: NTR gap for county c is the employment share weighted average NTR gap of each industry (Ljc/Lc) in 1990 (i.e., 10 years before PNTR)

32. Distribution of Direct and Indirect Exposure
Mean / SD
Industry 33 / 14
County / 6
Interquartile Shift
Industry
County (incl 0s)

33. Distribution of NTR Gapc Across Counties
51th-75th
76th-100th
0-25th
26th-50th

34. DID Panel Regression
Examine whether outcomes for worker j industry i employed at firm f change after PNTR
Sample
Panel of workers employed by the same firm
Take 5 percent random sample

35. DID Panel Regression Questions
Does county exposure matter relative to industry exposure?
Are non-manufacturing workers affected?
Caveats
Census has not released results yet
Can only present significance without magnitudes

36. Manufacturing Workers’ Employment – County Exposure
DID Panel Regression
Manufacturing Workers’ Employment – County Exposure
Quarters Employed
Ln(Quarters)
Notes: Figure displays the coefficient estimates and 95% error bands from estimation of the number of quarters worked on workers’ exposure to PNTR via their initial county of employment. Second covariate is the difference-in-differences coefficient of interest. Remaining coefficients capture worker and firm characteristics noted in text. Estimation period is 1997 to Estimation sample is a 5 percent random sample of manufacturing workers employed in the same firm from 1997 to 1999.
Initial Manufacturing Workers

37. Manufacturing Workers’ Employment – Industry Exposure
DID Panel Regression
Manufacturing Workers’ Employment – Industry Exposure
Notes: Figure displays the coefficient estimates and 95% error bands from estimation of the number of quarters worked on workers’ exposure to PNTR via their initial industry of employment. Second covariate is the difference-in-differences coefficient of interest. Remaining coefficients capture worker and firm characteristics noted in text. Estimation period is 1997 to Estimation sample is a 5 percent random sample of manufacturing workers employed in the same firm from 1997 to 1999.
Quarters Employed
Initial Manufacturing Workers

38. Need man earnings county x quantile?
DID Panel Regression
Manufacturing Workers’ Earnings – County Exposure
Notes: Figure displays the coefficient estimates and 95% error bands from estimation of log earnings on workers’ exposure to PNTR via their initial county of employment. Second covariate is the difference-in-differences coefficient of interest. Remaining coefficients capture worker and firm characteristics noted in text. Estimation period is 1997 to Estimation sample is a 5 percent random sample of manufacturing workers employed in the same firm from 1997 to 1999.
Need man earnings county x quantile?
Ln(Earnings+1)
Initial Manufacturing Workers

39. Manufacturing Workers’ Earnings – Industry Exposure
DID Panel Regression
Manufacturing Workers’ Earnings – Industry Exposure
Initial Manufacturing Workers
Ln(Earnings+1)
Ln(Earnings+1)
Ln(Earnings+1)
Notes: Figure displays the coefficient estimates and 95% error bands from estimation of log earnings on workers’ exposure to PNTR via their initial industry of employment. Second covariate is the difference-in-differences coefficient of interest. Remaining coefficients capture worker and firm characteristics noted in text. Estimation period is 1997 to Estimation sample is a 5 percent random sample of manufacturing workers employed in the same firm from 1997 to 1999.

40. DID Panel Regression Initial Manufacturing Workers
Manufacturing Workers’ Employment and Earnings – County and Industry Exposure
Initial Manufacturing Workers
Quarters Employed
Ln(Earnings+1)
Ln(Earnings+1)
Notes: Figure displays the coefficient estimates and 95% error bands from estimation of log earnings on workers’ exposure to PNTR via their initial industry of employment. Second covariate is the difference-in-differences coefficient of interest. Remaining coefficients capture worker and firm characteristics noted in text. Estimation period is 1997 to Estimation sample is a 5 percent random sample of manufacturing workers employed in the same firm from 1997 to 1999.

41. DID Panel Regression – Non-Manufacturing Workers
Initial Non-Manufacturing Workers
Ln(Earnings) ?
Notes: Figure displays the coefficient estimates and 95% error bands from estimation of log earnings on workers’ exposure to PNTR via their initial county of employment. Second covariate is the difference-in-differences coefficient of interest. Remaining coefficients capture worker and firm characteristics noted in text. Estimation period is 1997 to Estimation sample is a 5 percent random sample of manufacturing workers employed in the same firm from 1997 to 1999.

42. Conclusions So Far
Manufacturing directly affected by PNTR experience reduced employment and earnings losses after the change in policy
PNTR also affected workers outside manufacturing
Still to do
Characterize employment and earnings effects over time
Lots of other outcomes to look at

43. Outline Worker adjustment
New research using matched employer-employee data
Firm adjustment
New research using firm level data
Other adjustments
Health, politics
Overview: global firms