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

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?

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

Outline Background Data Sector transitions Earnings

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

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?

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

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 2000.9.20 “[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 1994.5.4 “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 2000.2.16 “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. (2001.6.30) U.S.-China Security Review Commission

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

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: 0.15

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

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

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

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

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

Margins of Worker Adjustment Background Data Sector transitions Earnings

Background: Manufacturing vs Other Non-Farm Employment Post-2000 Declines 2000-3: -17% 2007-10: -18% Created in vox_02.do in pntr/drafts/

Data: Matching Workers and Firms Longitudinal Employer-Household Dynamics (LEHD) Based on state unemployment insurance (UI) records Encompasses 46 states for 2000-11 (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?)

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

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

Margins of Worker Adjustment Background Data Sector transitions Earnings

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

Transitions Among M, NM and NE 2000-11

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

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

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

Transitions to NE Based on Initial Age All Workers, 2000-11 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, 2000-11 Workers >50, 2000-11

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

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

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

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)

Distribution of Direct and Indirect Exposure Mean / SD Industry 33 / 14 County 7 / 6 Interquartile Shift Industry 8 County 22 (incl 0s)

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

DID Panel Regression Examine whether outcomes for worker j industry i employed at firm f change after PNTR Sample Panel of workers 1997-07 employed by the same firm 1997-99 Take 5 percent random sample

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

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 2007. Estimation sample is a 5 percent random sample of manufacturing workers employed in the same firm from 1997 to 1999. Initial Manufacturing Workers

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 2007. 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

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 2007. 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

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 2007. Estimation sample is a 5 percent random sample of manufacturing workers employed in the same firm from 1997 to 1999.

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 2007. Estimation sample is a 5 percent random sample of manufacturing workers employed in the same firm from 1997 to 1999.

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 2007. Estimation sample is a 5 percent random sample of manufacturing workers employed in the same firm from 1997 to 1999.

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

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