Online Appendix for “Workers beneath the Floodgates: Low-Wage Import Competition and Workers’ Adjustment” Hˆale Utar∗ Bielefeld University, and CESIfo

March 12, 2017

∗

Department of Economics, Bielefeld University, Universitaetsstr. 25, 33615, Bielefeld, Germany. Correspondence to electronic address: [email protected]

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Contents 1 Data Appendix 1.1

2

Variable Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

7

2 Import competition through quota removals

11

3 Labor Market

12

4 Robustness and Additional Analysis

15

4.1

Supplemental Analysis for Section 4 “Effects of Import Competition from China and Workers’ Adjustment” . . . . . . . . . . . . . . . . . . . . . .

5 Supplemental Analysis: Heterogeneity of Workers’ Adjustment

1

15 21

5.1

Age . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

21

5.2

Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

24

5.3

Occupation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

27

5.4

The Triple Difference Analysis . . . . . . . . . . . . . . . . . . . . . . . .

32

5.5

Trade-induced Skill Upgrading at the Worker Level . . . . . . . . . . . .

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Data Appendix

The main database employed in this study is the Integrated Database for Labour Market Research (abbreviated IDA), which is compiled from person (IDA- personer ), establishment (IDA-arbejdssteder ), and job files (IDA-ansættelser ) by Statistics Denmark. They are supplemented with the domestic production dataset (VARES), a dataset on business statistics (FIRE), and the dataset on customs transactions (UHDI). These datasets are accessed through the servers sponsored by the Labor Market Development and Growth (LDMG) project. Information on import quotas for the European Union textile and clothing sector comes from the Syst`eme Int´egr´e de Gestion de Licenses (SIGL) database, 2

which is available online at http://trade.ec.europa.eu/sigl/index.html. Below I provide a brief description of this data. More detailed information regarding the Danish data can be accessed at http://www.dst.dk/da/Statistik/dokumentation/Times. Integrated Database for Labor Market Research (IDA): The IDA Database is the main source of information on workers. It provides a snapshot of the labor market for each year at the end of November.1 It contains demographic and education information on every resident in Denmark between the age of 15 and 74 with a unique personal identification number. Compiled from separate establishment and job files, it provides the labor market status of an individual and their annual earnings, annual hours worked, hourly wages, occupational position and the industry code for their primary employment as of November. Information on annual hours worked is constructed by information slips sent by employers’ to the tax authorities. Hours worked does not include time spent in leaves or holidays. It is the annual hours spent at the work. Hourly wages are estimated by Statistics Denmark based on annual hours worked and annual earnings information for a given job. Statistics Denmark also includes information regarding the quality of hourly wage estimates, the quality information is taken into account when constructing variables containing hourly wage or hours worked information so that lowquality information are dropped from the analysis. This, at the end, explains a lower number of observations for the hours worked and hourly wage analysis in the study. The information on industry, education, and occupation is described in greater detail below. Production Database (VARES): This dataset is collected as part of the industrial commodity production statistics (PRODCOM) collected by Statistics Denmark. Production is reported following the Combined Nomenclature (CN) classification at the eight-digit level for all firms with ten or more employees. I employ the VARES database to identify firms that domestically manufacture products subject to the removal of the Multi-fiber Arrangement quotas on Chinese goods after 2001. While some manufacturing firms have less than ten employees, such firms typically do not have in house production. Manufacturing firms with domestic production capacity are generally employ more than ten employees, and consequently virtually all firms that domestically produce quota products can be identified using VARES. The reporting unit is the “Kind of Activity Unit” (KAU), which is the sum of a company’s workplaces in the same main industry. Reporting units provide their company identification code (CVR), allowing us 1

See Bunzel (2008) and Timmermans (2010) for more information on the structure of IDA-database.

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to match the eight-digit production information with other firm-level information. International trade data (UHDI):The international trade data set is compiled from the Danish Customs records as well as monthly reports to Statistics Denmark from about 8,000 companies in Denmark in which their trade with other EU countries is reported. This is supplemented with information on EU trade from VAT returns, which are mandatory for virtually all firms in Denmark. The information of each record gives shipment date, value, and weight, and if applicable the shipment’s quantity. It also provides information on the 8-digit product classification according to the Combined Nomenclature (CN) system, as well as a unique firm identifier. Statistics Denmark aggregates this data into annual information for each triplet of product-firm-country. The underlying data in Figure 1 of “Workers beneath the Floodgates: The Impact of Low-Wage Import Competition and Workers’ Adjustment” is this data-set. Textile and clothing quota data (SIGL): The Syst`eme Int´egr´e de Gestion de Licenses (SIGL) database provides categories of textile and clothing products imported from a particular country that are subject to quotas in the European Union for a particular year. I employ this data to identify firms in Denmark that will be affected by the quota removals for China following that country’s entry into the WTO. I also employ this data to obtain quotas imposed on other developing countries. The textile and clothing license database is classified according to 163 grouped quota categories defined by the EU. The quota categories are administrative descriptions of quota products that do not follow standard statistical product classifications. I first match these categories with the corresponding CN-1999 product classifications manually by going over the description of each quota category as well as each CN-1999 8-digit product description. Annex I of the “Council Regulation (EEC) No 3030/93 of 12 October 1993 on common rules for imports of certain textile products from third countries” that provides category matches with the CN-2009 codes and the CN concordances across years as provided by EU-RAMON are used to double-check the manual matching and used as a main reference for the concordance between quota categories and the CN 8-digit products. The annex is available at the SIGL. The quotas have varying degrees of coverage in terms of CN products; for example, the category “gloves, mittens and mitts, knitted or crocheted” corresponds to 9 products at the 8-digit level and “Woven fabrics of synthetic filament yarn obtained from strip or the like of polyethylene or polypropylene, less than 3 m wide” corresponds to a single 8-digit CN product. Quota products include both

4

textile and clothing products and does not, within each of these, cover a (technologically or materially) homogeneous group of products. For example, “shawls and scarves of silk or silk waste” was part of a quota restriction for China while “shawls and scarves of wool and fine animal hair” was not. “brasseries of all types of textile material” was under quota but not “corselettes of all types of textile materials”. Similarly “knotted netting of twine, cordage or rope” was protected under quota but not “twine, cordage, ropes and cables”. Since firms producing both materially and technologically similar products may have or not been exposed to the increased competition, utilizing MFA quotas at a detailed product-level is important. For example, within the Textile Weaving, 54 % of workers were employed in quota producing firms, while 46 % of workers were among the control group. Similarly within the Manufacturing of Made-up Furnishing Articles, 32 % of workers were exposed to the competition via their quota product producing employers. Labor Market Positions Labor market position information used in the paper is obtained from the IDA variable that codes primary labor market affiliation of an individual. Every individual in IDA has a primary affiliation code regardless of whether they are employed, unemployed, in education, or in retirement. This code indicates if an individual is an employed employee (if so a position as an employee), employed employer, unemployed, in a sick leave or other types of leaves, in an early or normal retirement program, in school, etc. The primary affiliation with the labor market is determined by first identifying all different ways in which the individual is affiliated with the labor market. If the respective individual is affiliated with the labor market in more than one way, the primary affiliation is determined according to a set of priority rules that are based on recommendations of ILO regarding the labor statistics. Occupation Classifications The information on worker occupation in the IDA database is provided in terms of the Danish version of the United Nation’s occupational classification system, called DISCO; here, ISCO stands for International Standard Classification of Occupations. The Danish classification follows the four-digit ISCO-88 system between the years 1999 and 2002, and from 2003 the Danish system employs a six-digit classification, where the first four digits are identical to the international ISCO system. In 2010 a revision of this

5

classification which follows ISCO-08 was adapted. Since the occupation classifications used throughout the paper are based on workers’ initial occupations, this break in the occupational codes does not have any relevance for this study. Occupational groups used in section titled “Heterogeneity in Workers’ Adjustment to Trade Shock” follow ISCO-88 major groupings: Managers (ISCO-88=1), Professionals and Technicians (ISCO-88=2 & ISCO-88=3), Clerks and Service Workers (ISCO-88=4 & ISCO-88=5), Craft Workers (ISCO-88=7), Operators and Assemblers (ISCO-88=8) and Manufacturing Labourers (ISCO-88=9). Industry Classifications The IDA database provides industry codes for each wage earner based on administrative sources rather than surveys. For persons who work at a specific workplace, typically a firm, the personal industry code is equal to the industry code of the workplace following the Danish Industrial Classification (detailed below). If a person does not have a specific workplace, for example the person works from home or performs duties at several different locations, such as day care providers, the personal industry code is assigned according to the person’s work performed. Similarly if a person’s workplace is not a particular physical location, for example a nurse employed by the municipality to provide care for elderly people in their residences, the person’s workplace (employer) is the municipality while the person’s personal industry code is defined by the work performed, in this case the ‘nursing homes’ industry. Throughout the sample period three different Danish Industrial Classification systems apply, these are DB93, DB03 and DB07. Dansk Branchekode 1993 (DB93) is a 6-digits industry nomenclature that follows NACE Rev. 1 classification. It was replaced with DB03 in 2003. DB03 follows NACE Rev. 1.1. Finally in 2008 DB03 was replaced with DB07 which follows NACE Rev. 2. The first four digits of the Danish Industrial Classifications are identical to the corresponding NACE revision. Education Classifications The education variable in IDA is the highest completed education of an individual. This variable shows the individual’s 8-digit classification of education, where the first two digits describe the level for the highest completed program, such as primary education, lower secondary education, general upper secondary education, vocational and technical upper secondary education, higher education, etc..Throughout the paper education vari6

ables refer to workers’ highest attained education as of the initial year, 1999. In terms of education levels three education categories are used: workers with a (non-technical) high school degree as the highest attained education, workers with vocational education and workers with at least some college education. Vocational education in Denmark is provided by the technical high schools (after 9 years of mandatory schooling) and involves several years of formalized training which contains periods of formal schooling and apprenticeships. For example being a tailor requires between 3 years and 3 years and 4 months skill education or being an industry operator requires between 2 years and 2 years and 8 months education depending on additional qualifications. To distinguish different forms of vocational training I have examined the roughly 3,000 education titles and classified them broadly into service, transportation, agriculture, and manufacturing orientation. Those with a service focus include office workers and decorators, IT-technicians while vocational training with a manufacturing focus includes dyeing machine operator, garment technician, or textile operator for example.

1.1

Variable Definitions

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Table 1: Variable Definitions Variable Name Female Immigrant Age Experience Past Unemployment Spells Union Membership UI Membership Negative Employment Trend College Vocational Ed. Manufacturing Focused Vocational Ed.

8

Service Focused Vocational Ed. High School ManuSpec TexSpec ManExp TexExp OccExp RTI Continued on next page

Variable definition Equal to 1 if worker is female, 0 otherwise. Equal to 1 if worker is first or second generation immigrant, 0 otherwise. Worker’s age in years as of 1999. Worker i’s labor market experience in years as of 1999. Summation of duration of past unemployment spells of worker i, expressed in years. Equal to 1 if worker i is a member of a union, 0 otherwise. Equal to 1 if worker i is a member of Unemployment Insurance (UI) , 0 otherwise. Equal to 1 if employment in worker i’s workplace decreased more than 5 percent in 1999 relative to year before. Equal to 1 if worker attended a college as of 1999, 0 otherwise. Equal to 1 if highest attained education of worker is vocational school as of 1999, 0 otherwise. Equal to 1 if highest attained education of worker is manufacturing activity oriented vocational school as of 1999, 0 otherwise. Equal to 1 if highest attained education of worker is service activity oriented vocational school as of 1999, 0 otherwise. Equal to 1 if highest attained education of worker is a non-technical high school as of 1999, 0 otherwise. The ratio of the number of workers with four-digit occupation j in the manufacturing industry to the total number of workers with occupation j in the overall economy in the initial year, 1999. The ratio of the number of workers with four-digit occupation j in the textile and clothing industry to the total number of workers with occupation j in the overall economy in the initial year, 1999. Worker i’s experience in years within the manufacturing sector as of 1999. The industry of employment information can be traced back until 1985. Worker i’s experience in years within the textile and clothing sector as of 1999. The industry of employment information can be traced back until 1985. Worker i’s experience in his/her two-digit occupation in years as of 1999. The two-digit occupation information can be traced back until 1991. Worker i’s two-digit occupation’s intensity in routine tasks as defined by

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Table 1 – continued from previous page Variable Name Variable definition Autor, Levy, Murnane (2003), the measure is taken from Goos, Manning and Salomon (2014)). Annual Variables (Table 2 of Section 4) Annual Unemployment Fraction of working time unemployed within a year in per mille. Hourly Wage Hourly wage of worker i in the primary job. Annual hours worked The total number of hours worked in the primary job within a year. Annual wage Annual labor earnings in the primary job. Personal Income Annual personal income. It includes all labor earnings, unemployment insurance and all other government transfers. Cumulative Variables Cumulative Labor Earnings Sum of annual wages over pre- and post-shock sample years expressed in multiples of 1996-1999 average annual wage Cumulative Employment Number of years with a primary employment over pre- and post-shock sample years Cumulative Hours Worked Sum of annual hours worked over pre- and post-shock sample years expressed in multiples of 1996-1999 average annual hours worked Cumulative Total Labor earnings Sum of total annual wages over pre- and post-shock sample years, expressed in multiples of 1996-1999 average total annual wages. Total annual wages are obtained from potentially multiple employment relationships within a year. Cumulative Unemployment Spells Sum of duration of unemployment spells expressed in months. Cumulative Personal Income Sum of annual personal income expressed in multiples of 1996-1999 average annual personal income. Number of Years with School Enrollment Number of years with education allowance.

Table 2: Worker Characteristics in 1999

Mean Std N

Mean Std N

Age

Female

Immigrant

38.783 10.264 10511

0.571 0.495 10511

0.062 0.241 10511

Union UnemploymentExposure to Membership Insurance Competition Membership CompExpD 0.800 0.896 0.468 0.400 0.305 0.499 10511 10511 10511

College Vocational Experience Education Education (in years)

Machine Operators

Professionals & Techs

0.114 0.318 10511

0.350 0.477 10511

0.140 0.347 10511

0.350 0.477 10511

14.417 5.879 10511

Exposure to Competition CompExpC 0.120 0.142 10511

Table 3: Summary Statistics Mean Panel A. Cumulative Worker Variables Cumulative Personal Income (in multiples of initial annual per- 7.314

Std

N

8.072

21,022

6.834 4.934 5.513 0.196 1.119

9.853 2.845 5.134 0.616 0.609

21,022 21,022 20,860 21,022 20,384

worked) Earnings Per Year of Employment (in multiples of initial annual 1.298

1.229

20,602

4.195

8.103

21,022

12.152 12.278 12.406 7.190 5.029 0.959

0.735 0.593 0.409 0.455 0.355 1.916

109,487 109,487 109,487 105,709 105,709 109,487

sonal income) Cumulative Earnings (in multiples of initial annual earnings) Cumulative Employment Cumulative Hours (in multiples of initial annual hours worked) Cumulative Number of Years with Education Allowance Hours Per Year of Employment (in multiples of initial annual hours

earnings) Cumulative Unemployment Spells Panel B. Annual Variables (in log) Annual Wage Total Labor Earnings Personal Income Annual Hours Worked Hourly Wage Annual Unemployment

In Panel A, earnings, employment, hours worked, and hourly wages are all associated with workers’ primary employment. Initial variables are the average across 1996-1999. In Panel B, all variables are in logarithmic form and values are expressed in year 2000 Danish Kroner.

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2

Import competition through quota removals

Due to its political sensitivity as a traditionally labor intensive industry, world trade in textiles was excluded from the agreement when GATT was signed in 1948 and continued to be governed by bilateral agreements. As the number of agreements grew, The Multi-fibre Arrangement was introduced in 1974 to govern the world trade in textiles and clothing. The original purpose of the Multi-Fibre Arrangement (MFA) of the year 1974 was to provide comprehensive protection against competition from low-wage country exports of textiles and clothing through quantitative restrictions. After years of multilateral negotiations, it was agreed in the year 1995 (Agreement on Textile and Clothing (ATC)) that the MFA would gradually be lifted (so-called Phases of liberalization). China’s non-WTO status rendered it ineligible to benefit from these trade liberalizations, which changed only once China had joined the WTO in the year 2001. The subsequent dramatic surge of Chinese textiles and clothing exports and the resulting increase in competition is the plausibly exogenous source of shifts in employment trajectories among Danish workers.2 As one of the smaller members of the EU, the coverage of quotas was largely exogenous to Denmark’s industrial structure. It was determined throughout the 1960s and 1970s with a set of negotiations held at the EU level. While quotas covered a wide range of both textile and clothing products ranging from bed linens over synthetic filament yarns to shirts, their coverage within each broad product category varied, making it important to utilize MFA quotas at a detailed product-level. For example, “Shawls and scarves of silk or silk waste” were part of a quota restriction for China while “Shawls and scarves of wool and fine animal hair” were not. The empirical strategy focuses on the removal of import quotas for China because although the removal of the import quotas started in 1995, Phase I and II removals did not effectively trigger increased competition. First, this is because the law allowed the EU to choose the products to be integrated into the normal system, and the EU started with inactive, non-binding quotas.3 Then, among the exporting countries subject to 2

See Harrigan and Barrows (2009), and Brambilla, Khandelwal, and Schott (2008) for the US, Utar (2014) for Denmark in examining the price and quantity effect of the quota lifting experience. 3 Under ATC the selection of MFA products to be integrated into the normal WTO system was left to the importing countries/legislatures and the EU started its phasing out processes by integrating products with no quotas vis-` a-vis WTO members. During the first two phases, the EU integrated 34 MFA categories, but removed only a few existing quotas vis-`a-vis WTO members (OETH, (2000)).

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the MFA quotas, China stood out as it was facing the largest number of quotas with the highest quota utilization rates (SIGL). Except for China, the exporting countries with the highest quota utilization were India, Pakistan and Indonesia. None of these countries had any active quotas removed in Phase I or II.4 The empirical strategy further utilizes China’s accession to the WTO as a shock because while the ATC provided a schedule for gradual dismantling of MFA quotas already in 1995, removal of MFA quotas for China depended on whether and when it would join the WTO.5 Furthermore, there is significant overlap between firms producing quota products subject to 2002 or 2005 phases, making it difficult to separate the effects of different phases. Finally, due to a surge of Chinese imports in the first few months of 2005 at the EU ports in response to the final phase of the quota removal, the EU retained a few of the Phase IV quota categories until 2008. The selection of the retained categories were clearly endogenous to the EU-wide industrial structure as it was due to the pressure of the European industrialist. Workers’ subsequent exposure to competition is determined by all CN-8 digit goods that were subject to the removal of quotas for China (and were removed subsequently over the period).

3

Labor Market

The labor market in Denmark is characterized by liberal hiring and firing regulations. Firms are not burdened by monetary compensation when firing. In case of lay-offs they are not required to give advance notification to hourly paid workers regardless of 4

For Indonesia all active quotas imposed were subject to Phase IV removal except two quotas (category 21 and category 33) which were subject to Phase III removal and were removed in 2002. For India there were only two quota categories that were subject to Phase III removal in 2002 (category 24 and category 27). The remaining fifteen categories for India were removed in 2005. Only one quota category regulating imports from Pakistan was removed in Phase I, but it was an inactive quota with a 0 percent utilization. 5 During the long period of China’s negotiation for WTO membership, mainly with the US and EU, there was a great deal of uncertainty about the membership and its timing. “China’s entry into the WTO is far from a foregone conclusion. It has been trying to join the multilateral trading system since 1986. Its hopes have been disappointed many times before.”–quoted from an article titled “China and WTO” published in the Economist on April 1, 1999. This uncertainty was a recurring theme in articles in the Economist from 1999 until the end of 2001. See also The Economist (2000a) and The Economist (2000b).

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their tenure.6 In the Global Competitiveness Report 2013-2014 Denmark is one of a few countries in the world with estimated redundancy/firing costs of zero. This high level of flexibility of firing and hiring practices is combined with a high level of publicly provided social protection. The system is generally referred to as a ‘flexicurity’ system. In 2006 the Danish employment rate was 77.4 percent (highest among the EU), and the unemployment rate was 3.9 percent (Madsen (2008)). There is no minimum wage in Denmark, but reference wages are to a great extent determined by collective wage bargaining. The coverage of collective wage bargaining agreements over all wage and salary earners was 85% in 2004 (Visser, 2013). The Danish labor market has a high union density with a ratio of union members to all wage and salary earners of 72% in 2004 in the overall economy (Visser, 2013). Reflecting a higher unionization rate in manufacturing than the service sector, the unionization rate in the sample was 80% in 1999 (Table 2). Thus the Danish labor market exhibits relatively rigid wage determination and highly flexible and liberal firing and hiring regulations. Denmark has a very comprehensive and large scale ALMP which started in the late 1970s and underwent a major reform in 1994. Any unemployed worker is subject to the ALMP measures.7 The type of programs ranges from job search assistance to employment and training programs.8 As a result, the long term unemployment rate (in total unemployment) is generally low in Denmark compared to the OECD average. In 2008, it was 13.5 %, compared to, for example 52.5 and 10.6 % for Germany and the US respectively (OECD Employment Database 2013).

References [1] Autor, David, Levy, Frank, and Richard Murnane 2003. “The Skill-Content of Recent Technological Change: An Empirical Investigation”, Quarterly Journal of Economics, 118, 1279-1333. 6

For hourly paid workers employed under collective bargaining agreements, in principle such agreements may contain provisions for tenure dependent advance notification. 7 Although the participation in the UI fund and hence with ALMP voluntary, if one is not part of UI, receives welfare benefits while unemployed and both welfare benefit receivers and UI benefits receivers are subject to the same active social and labor policies. 8 Employment programs are designed for unemployed people to gain work experience in either the private or the public sector.

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[2] Brambilla, Irene, Amit Khandelwal and Peter Schott 2010. “China’s Experience Under the Multifiber Arrangement (MFA) and the Agreement on Textile and Clothing (ATC)”, Robert Feenstra and Shang-Jin Wei (Eds), China’s Growing Role in World Trade, NBER, Cambridge: MA. [3] Bunzel, Henning 2008. “The LMDG Data Sets”, mimeo, Univeristy of Aarhus. [4] Harrigan, James, and Geoffrey Barrows 2009. “Testing the Theory of Trade Policy: Evidence from the Abrupt End of the Multifiber Arrangement”, The Review of Economics and Statistics, 91(2), 282-294. [5] Goos, Maarten, Alan Manning, and Anna Salomons 2014. “Explaining Job Polarization: Routine-Biased Technological Change and Offshoring”, American Economic Review, 104(8), 2509-2526. [6] Madsen, Per Kongshøj 2008. “Flexicurity in Danish-A Model for Labour Market Reform in Europe?”, Intereconomics, 43(2): 74-78. [7] OETH 2000. Phase III Of The Agreement On Textiles And Clothing- Identifying Areas For Reform, Final Report, L’Observatoire Europeen Du Textile Et De L’Habillement. [8] Timmermans, Bram 2010. “The Danish Integrated Database for Labor Market Research: Towards Demystification for the English Speaking Audience”, DRUID Working Papers 10-16. [9] The Economist (1999), “China and The WTO”, The Economist, April 1 1999. [10] The Economist (2000a), “China and The WTO: Dire Straights?”, The Economist, May 23 2000. [11] The Economist (2000b), “Chinese WTO Torture”, The Economist, Nov 5 2000. [12] Utar, Hale 2014. “When the Floodgates Open : Northern Firms’ Response to Removal of Trade Quotas on Chinese Goods”, American Economic Journal: Applied Economics, 6(4): 226-250. [13] Visser, Jelle 2013. “Data Base on Institutional Characteristics of Trade Unions, Wage Setting, State Intervention and Social Pacts, 1960-2011 (ICTWSS)-Version 14

4”, Amsterdam Institute for Advanced Labour Studies (AIAS), available at: http://www.uva-aias.net/207

4

Robustness and Additional Analysis

4.1

Supplemental Analysis for Section 4 “Effects of Import Competition from China and Workers’ Adjustment” ln Xit = α0 + α1 CompExpZi ∗ P ost02t + δi + τt + it

(1)

where P ost02t = 1 when year > 2002 and 0 otherwise. CompExpZi is the measure of exposure to import competition from China for each worker, i, where superscript Z = D, C indicates whether it is defined as a discrete, D, or a continuous, C, variable. The discrete treatment variable CompExpD i takes the value of one if in the year 1999 worker i is employed in a firm that domestically manufactures a product that with China’s entry into the WTO is subject to the abolishment of the MFA quotas for China, and zero otherwise. The continuous treatment variable CompExpC i is the revenue share of these goods in the year 1999 of worker i’s employer. Table 4 presents estimation of equation 1 conducted separately among college, vocational and high-school educated workers.

Workers’ Adjustment by Moving across Jobs within and between Sectors This section contains the following tables. • Table 5 presents an analysis of workers’ adjustment with the dependent variables being the earnings and hours worked per year of calendar employment.

˜ is = β0 + β1 CompExpZi ∗ P ost02s + β2 P ost02s + δi + is , s = 0, 1 , X

(2)

where s = 0 indicates the pre-shock and s = 1 refers to the post-shock period. In this ˜ is is the cumulative outcome variable, say wage earnings of worker i over regression X the period of 1999-2001 (s = 0) and 2002-2010 (s = 1) period. 15

16

C

D

C

D

(5) C

(6)

Personal Income

D

(7)

D

(9)

C

(10)

Hourly Wage

D

(11)

0.333* (0.164) 55,127

Notes: Estimation of equation 1. All regressions include year and worker fixed effects. All dependent variables are in logarithmic form and are listed in the table. A constant is included but not reported. Robust standard errors clustered at worker-level are reported in parentheses. ∗ , ∗∗ and ∗∗∗ indicate significance at the 5 %, 1% and 0.1% levels respectively.

School Degree -0.124** -0.026** -0.051 -0.052*** -0.140*** 0.013* 0.058* 0.108* (0.046) (0.009) (0.033) (0.010) (0.037) (0.006) (0.023) (0.045) 55,127 55,127 55,127 52,706 52,706 52,706 52,706 55,127

Workers with Vocational Education -0.028 -0.089 -0.008 -0.016 0.009 0.035 -0.036*** -0.115 0.004 0.017 0.172*** 0.652*** (0.015) (0.055) (0.012) (0.043) (0.007) (0.026) (0.011) (0.038) (0.007) (0.024) (0.048) (0.170) 38,780 38,780 38,780 38,780 38,780 38,780 37,845 37,845 37,845 37,845 38,780 38,780

-0.100 (0.280) 13,175

C

(12)

Annual Unemployment

-0.088 0.012 0.086 -0.030 (0.063) (0.014) (0.048) (0.081) 12,840 12,840 12,840 13,175

C

(8)

Annual Hours Worked

Dependent Variable

Workers with College Education -0.046 -0.096 -0.023 -0.028 -0.000 0.039 -0.038* (0.030) (0.106) (0.024) (0.081) (0.017) (0.057) (0.018) 13,175 13,175 13,175 13,175 13,175 13,175 12,840

D

(4)

(3)

(1)

(2)

Total Annual Earnings

Annual Wage

Workers with at most a High CompExpZ ∗ Dum02 -0.065*** -0.148** -0.049*** s i (0.016) (0.057) (0.013) N 55,127 55,127 55,127

N Panel C.

CompExpZ i ∗ Dum02s

N Panel B.

CompExpZ i ∗ Dum02s

Panel A.

Z=

Sample Period 1999-2009

Table 4: Impact of the Chinese Import Shock on Earnings, Income, Employment, and Unemployment

Table 5: Workers’ Recovery across Jobs within and between Sectors (a) Overall Effect

CompExpD

∗ P ost02

P ost02 CompExpC ∗ P ost02 P ost02

CompExpD

∗ P ost02

P ost02 CompExpC ∗ P ost02 P ost02

CompExpD

∗ P ost02

P ost02 CompExpC ∗ P ost02 P ost02

(b) (c) (d) (e) Initial Firm other T&C other Manuf Service

A. Earnings per year of employment (in initial -0.109*** -0.006 -0.085* -0.070 (0.026) (0.014) (0.039) (0.108) 0.295*** 0.067*** 0.233*** 0.576*** (0.022) (0.011) (0.031) (0.091) -0.358*** (0.086) 0.286*** (0.021)

-0.017 (0.047) 0.066*** (0.011)

-0.334** (0.128) 0.236*** (0.030)

-0.103 (0.362) 0.556*** (0.083)

(f) Other

annual wage) -0.268* 0.173 (0.116) (0.566) 1.134*** 0.994*** (0.102) (0.276) -1.137** (0.368) 1.154*** (0.095)

-0.255 (1.396) 1.062*** (0.294)

B. Hours Worked per year of employment (in initial annual hours) -0.063*** -0.013 -0.062* 0.019 -0.138** 0.106 (0.011) (0.007) (0.032) (0.043) (0.043) (0.112) 0.093*** 0.018*** 0.179*** 0.287*** 0.497*** 0.274*** (0.008) (0.005) (0.025) (0.030) (0.037) (0.069) -0.197*** (0.037) 0.087*** (0.008) C. Log Hourly 0.007 (0.005) 0.038*** (0.003) 0.037* (0.017) 0.037*** (0.003)

-0.056* (0.024) 0.018*** (0.005)

-0.211* (0.104) 0.176*** (0.025)

0.190 (0.154) 0.274*** (0.029)

-0.494*** (0.140) 0.495*** (0.035)

0.595 (0.435) 0.264*** (0.066)

Wage per year of employment 0.007 0.001 -0.002 (0.004) (0.015) (0.016) 0.020*** 0.001 0.067*** (0.003) (0.012) (0.011)

-0.013 (0.018) 0.120*** (0.013)

0.034 (0.080) 0.164** (0.050)

0.037* (0.016) 0.019*** (0.003)

-0.094 (0.059) 0.127*** (0.013)

0.201 (0.291) 0.160*** (0.047)

-0.014 (0.052) 0.003 (0.012)

-0.017 (0.056) 0.068*** (0.011)

Notes: Estimations of Equation 2. All regressions include worker fixed effects and a constant. The number of observations is decreasing in columns (a) through (f) from 21,022, 19,908, and 20,064 respectively in panels A, B, and C. Robust standard errors are clustered at worker-level and are reported in parentheses. ∗ , ∗∗ and ∗∗∗ indicate significance at the 5 %, 1% and 0.1% levels respectively.

17

18

-0.028 (0.033)

0.020 (0.062)

0.009 (0.035)

0.229** (0.085)

-0.046 0.190*** (0.036) (0.057)

0.194 (0.106)

0.020 (0.050)

-0.000 (0.006)

-0.032 (0.143)

0.049 0.039 0.041 (0.065) (0.023) (0.042)

-0.052* (0.024)

-0.079+ (0.044)

-0.095** 0.002 0.028 (0.031) (0.054) (0.047)

-0.009 (0.013)

0.015 (0.026)

0.089 (0.050)

Notes: Dependent variables is the cumulative employment across the specified industries as indicated in column headings. Estimations of Equation 2 with Trade=CompExpC ∗ P ost02. In addition to reported coefficients all regressions include worker fixed effects, the post WTO accession period indicator, P ost02, and a constant. The number of observations in columns is 21,022. Robust standard errors are clustered at worker-level and are reported in parentheses. ∗ , ∗∗ and ∗∗∗ indicate significance at the 5 %, 1% and 0.1% levels respectively.

Trade

Manufacturing Food Leather Wood Publishing Petroleum Glass Metals Machines Measuring Transport Misc. & & & & & Shoes Paper Printing Chemicals Minerals Equip Equip Manuf.

Table 7: Trade-induced Workers’ Movement across Jobs within Manufacturing

Notes: Dependent variables is the cumulative employment across the specified industries as indicated in column headings. Estimations of Equation 2 with Trade=CompExpC ∗ P ost02. In addition to reported coefficients all regressions include worker fixed effects, the post WTO accession period indicator, P ost02, and a constant. The number of observations in columns is 21,022. Robust standard errors are clustered at worker-level and are reported in parentheses. ∗ , ∗∗ and ∗∗∗ indicate significance at the 5 %, 1% and 0.1% levels respectively.

Trade 3.213*** 2.636*** (0.200) (0.152)

All Wholesale Hotels & Transportation, Finance RealEstate Defense Education Health & Personal Household Services & Retail Restaurants Communication Renting, Business Social Work Services Activities

Table 6: Trade-induced Workers’ Movement across Jobs within Services

Workers’ Reallocation within the Sectors

Dynamics of Workers’ Adjustment-Average Annual Changes Dep. Var.: Yearly Average of Cumulative Years of Employment 0.5 Overall at Initial Workplace at T&C Jobs at other Manufacturing at Service Sector Jobs

0.4

0.3

DID Coefficient Values

0.2

0.1

0

−0.1

−0.2

−0.3

−0.4

−0.5

2002

2003

2004

2005

2006

2007

2008

2009

2010

Year

Figure 1: Effect of Import Competition on Years with Employment All regressions include worker fixed effects and the post-WTO period indicator.

Dep. Var.: Yearly Average of Cumulative Hours Worked 0.5 Overall at Initial Workplace at T&C Jobs at other Manufacturing at Service Sector Jobs

0.4

0.3

DID Coefficient Values

0.2

0.1

0

−0.1

−0.2

−0.3

−0.4

−0.5

2002

2003

2004

2005

2006

2007

2008

2009

2010

Year

Figure 2: Effect of Import Competition on Hours Worked All regressions include worker fixed effects and the post-WTO period indicator.

19

Dep. Var.: Yearly Average of Cumulative Unemployment Spells 0.8 0.7 0.6

DID Coefficient Values

0.5 0.4 0.3 0.2 0.1 0 −0.1

Overall T&C other Manufacturing Service Sector

−0.2

2002

2003

2004

2005

2006

2007

2008

2009

2010

Year

Figure 3: Effect of Import Competition on Unemployment Spells All regressions include worker fixed effects and the post-WTO period indicator. Dep. Var.: Yearly Average of Cumulative Unemployment Spells 0.8

Last Sector of Employment: T&C Service Sector

0.7

0.6

DID Coefficient Values

0.5

0.4

0.3

0.2

0.1

0

−0.1

−0.2

2002

2003

2004

2005

2006

2007

2008

2009

2010

Year

Figure 4: Effect of Import Competition on Unemployment Spells All regressions include worker fixed effects and the post-WTO period indicator.

20

5

Supplemental Analysis: Heterogeneity of Workers’ Adjustment

5.1

Age

Industry and occupation specific human capital is likely to increase with workers’ age and the ability to learn new skills to decrease. This would make older workers more vulnerable to potential loss of human capital caused by the trade induced broad sector switch. Examining the age aspect of human capital loss, Table 8 and Table 9 present the analysis of workers’ adjustment to the low-wage import shock separately for workers with different age groups. Here the early career group consists of workers who in 1999 were between 22 and 35 years old. The mid career group is defined as workers who in 1999 were between 36 and 49 years old and finally the late career group consists of workers who in 1999 were between 50 and 56 years old.9 Results in Table 8 show that the Chinese import shock causes cumulative earnings of late-career (50+) workers to decrease significantly by about 95% of a pre-MFA abolishment annual salary. For mid-career workers (36-49) the impact is around 40% of a pre-MFA abolishment annual salary and the impact on the cumulative earnings of the younger cohort (22-35) is not found to be statistically significant. Overall effects contain differences in the ability to adjust to the initial shock by age cohorts. Results presented in column b of Panel A show that the impact on the cumulative earnings at the exposed workplace was strongest for mid career workers ( 1.5 initial annual salary), compared to early and late career workers ( 1 initial annual salary). The relatively strong initial shock on mid-career workers can be thought to be due to a combination of firms’ lack of consideration regarding employees’ tenure when laying off as they downsize, and the fact that mid-career workers should have been experiencing the most stable increase in the cumulative earnings/employment at the initial workplace compared to other age groups, had there not been a negative shock.10 9

The age group ’youth’ who were between 17 and 22 years old in 1999 are not included in this analysis as the number of observations were too low to make a meaningful decomposition analysis. 10 In Denmark employment can be based on hourly wages which is the most typical form of employment for production workers or on monthly or annual salaries no matter the number of hours worked. The former is exempt from advance notification while employers are still required to give advance notifications for the latter.

21

For all age groups, the likelihood of subsequent employment in the service sector increases due to the low-wage import shock (column 5 in Table 9). Yet younger workers have better earnings potentials in service sector jobs, and are able to compensate for all of their initial earnings losses in subsequent service sector jobs, while mid- and late career workers are not able to fully compensate for their initial earnings losses. Comparing the results in Panel A and B show that mid- and late career workers experience significant reductions in their cumulative hours worked and hours worked per year of employment. The significant reductions in hours worked are due to a combination of shortened tenure at the initial firms and also reductions in hours worked throughout the adjustment process. The reductions in hours worked per year of employment, on the other hand, are driven solely by reductions in hours worked per year of subsequent service employment. This shows that the pattern of job instability after the broad sector switch, shown to be characteristic of a difficult adjustment process, is more pronounced the older workers are, in line with the idea that younger workers are less sensitive to loss of human capital.

Table 8: Workers’ Adjustment by Age: Earnings Dep. Var. Cumulative Earnings (in initial annual wage) All Employers Initial Firm other T&C other Manuf Service Other (1) (2) (3) (4) (5) (6) The early career group: Age 22-35 (N=7,734) CompExpC *Post02

-0.496 (1.070)

-4.089*** (0.557)

0.226 (0.525)

-1.073 (0.574)

4.823*** -0.383 (0.943) (0.205)

0.854** (0.323)

0.364 (0.371)

2.687*** 0.040 (0.644) (0.119)

0.003 (0.294)

-0.184 (0.343)

1.137** -0.143 (0.368) (0.202)

The mid career group: Age 36-49 (N=8,140) CompExpC *Post02

-1.406* (0.699)

-5.351*** (0.442)

The late career group: Age 50-56 (N=4,238) CompExpC *Post02

-3.328*** (0.614)

-4.141*** (0.424)

Notes: In addition to reported coefficients, all regressions include worker fixed effects, the post WTO accession period indicator, P ost02, and a constant. Robust standard errors reported in parentheses are clustered at worker-level. ∗ , ∗∗ and ∗∗∗ indicate significance at the 5%, 1% and 0.1% levels respectively.

22

Table 9: Workers’ Adjustment by Age: Years of Employment and Hours Worked (1) (2) (3) (4) (5) All Employers Initial Firm other T&C other Manuf Service Young Workers’ Sample: Age 22-35 (N=7,734) A. Cumulative Employment C CompExp *Post02 0.541* -2.957*** 0.372 -0.286 3.711*** (0.231) (0.283) (0.216) (0.255) (0.336) B. Cumulative Hours Worked (in initial annual hours CompExpC *Post02 -0.680 -3.530*** 0.080 -0.545 3.669*** (0.641) (0.362) (0.374) (0.416) (0.594)

(6) Other

-0.299* (0.118) worked) -0.354 (0.190)

Mid-age Workers’ Sample: Age 36-49 (N=8,140) A. Cumulative Employment C CompExp *Post02 0.123 -4.819*** 0.884*** 0.265 3.744*** 0.049 (0.235) (0.315) (0.241) (0.241) (0.320) (0.098) B. Cumulative Hours Worked (in initial annual hours worked) CompExpC *Post02 -1.318** -5.183*** 0.872** 0.074 2.908*** 0.011 (0.442) (0.376) (0.287) (0.283) (0.429) (0.108) Older Workers’ Sample: Age 50-56 (N=4,238) A. Cumulative Employment C CompExp *Post02 -1.966*** -3.924*** 0.163 0.034 1.742*** (0.444) (0.383) (0.257) (0.207) (0.334) B. Cumulative Hours Worked (in initial annual hours CompExpC *Post02 -3.371*** -3.985*** 0.043 -0.321 1.049** (0.585) (0.409) (0.267) (0.344) (0.360)

0.019 (0.093) worked) -0.157 (0.194)

Notes: All regressions include worker fixed effects, the post WTO accession period indicator, P ost02, and a constant. Robust standard errors reported in parentheses are clustered at worker-level. ∗ , ∗∗ and ∗∗∗ indicate significance at the 5%, 1% and 0.1% levels respectively.

23

5.2

Education

This section presents supplemental analysis for Section 5.1 “Education and Workers’ Adjustment” of the accompanying paper. Table 10, Table 11, and Table 12 show workers’ adjustment experience by initial education where the dependent variables are cumulative employment, earnings per year of employment, and cumulative unemployment spells.

Table 10: Adjustment of Workers by Initial Education– Cumulative Employment Dep. Var. Cumulative Employment All Employers Initial Firm other T&C other Manuf Service Other (1) (2) (3) (4) (5) (6) College, N=2,398 CompExp*Post02

0.998* (0.395)

-4.106*** (0.558)

1.313** (0.399)

-1.168** (0.427)

4.939*** 0.020 (0.571) (0.154)

-0.507 (0.285)

-3.799*** (0.320)

0.612** (0.228)

-0.087 (0.238)

2.892*** -0.125 (0.337) (0.116)

-0.421 -3.584*** (0.255) (0.241) Manufacturing-Focused Vocational, N=2,590 CompExp*Post02 -0.906 -3.987*** (0.493) (0.514) Service-Focused Vocational, N=5,366 CompExp*Post02 0.704 -3.800*** (0.767) (0.538)

0.147 (0.189)

0.218 (0.203)

2.928*** -0.129 (0.281) (0.089)

0.432 (0.417)

-0.998* (0.391)

3.750*** -0.102 (0.509) (0.169)

0.945* (0.373)

0.014 (0.455)

3.669*** -0.125 (0.736) (0.213)

Vocational, N= 7,352 CompExp*Post02 High School, N=10,774 CompExp*Post02

Notes: Each cell present a DID coefficient estimate of Equation 2 where CompExp = CompExpC and the dependent variable is the cumulative employment at respective column heading. All regressions include a constant, post-shock period indicator and worker fixed effects. Robust standard errors reported in parentheses are clustered at worker-level. ∗ , ∗∗ and ∗∗∗ indicate significance at the 5%, 1% and 0.1% levels respectively.

24

Table 11: Adjustment of Workers by Initial Education– Earnings per year of employment Dep. Var. Earnings per year of employment All Employers Initial Firm other T&C other Manuf Service Other (1) (2) (3) (4) (5) (6) College, N=2,398 CompExp*Post02 Vocational, N= 7,352 CompExp*Post02

-0.306 (0.229)

-0.167 (0.180)

0.304 (0.520)

0.025 (0.588)

-1.126 1.093 (0.802) (0.817)

-0.099 (0.061)

0.075 (0.043)

-0.656** (0.215)

0.453 (0.303)

-0.476* 0.484 (0.222) (0.963)

-0.378* (0.167)

-0.470 (0.629)

-1.377* -0.347 (0.621) (2.628)

-0.685* (0.338)

1.613*** (0.479)

-0.570 0.252 (0.659) (1.090)

-0.329 (0.256)

-0.180 (0.344)

-0.343 0.337 (0.218) (1.679)

High School, N=10,774 CompExp*Post02 -0.506*** -0.024 (0.150) (0.055) Manufacturing-Focused Vocational, N=2,590 CompExp*Post02 -0.360* -0.057 (0.169) (0.097) Service-Focused Vocational, N=5,366 CompExp*Post02 0.079 0.111* (0.075) (0.054)

Notes: Each cell present a DID coefficient estimate of Equation 2 where CompExp = CompExpC and the dependent variable is the earnings per year of employment at respective column heading. All regressions include a constant, post-shock period indicator and worker fixed effects. Robust standard errors reported in parentheses are clustered at worker-level. ∗ , ∗∗ and ∗∗∗ indicate significance at the 5%, 1% and 0.1% levels respectively.

25

Table 12: Unemployment and Education (1)

(2)

(3)

(4)

Dep. Var. Cumulative Unemployment Spells (expressed in months) depending on the sector of last employment: All U. Spells Textile Manuf. Service College, N=2,398 CompExp*Post02

1.823 -0.428 (1.512) (0.965) Vocational, N= 7,352 CompExp*Post02 3.640*** 2.463** (1.098) (0.750) High School, N=10,774 CompExp*Post02 4.020*** -0.042 (1.012) (0.681) Manufacturing-Focused Vocational, N=2,590 CompExp*Post02 7.132*** 3.917** (1.725) (1.290) Service-Focused Vocational, N=5,366 CompExp*Post02 1.065 1.515 (1.233) (0.786)

-0.134 (0.487)

2.234* (1.025)

-0.498 (0.378)

2.076** (0.681)

-0.021 (0.358)

4.134*** (0.705)

-0.544 (0.502)

4.121*** (1.018)

-0.741 (0.441)

0.489 (0.795)

Notes: All regressions include worker fixed effects, the post WTO accession period indicator, P ost02, and a constant. Robust standard errors reported in parentheses are clustered at worker-level. ∗ , ∗∗ and ∗∗∗ indicate significance at the 5%, 1% and 0.1% levels respectively.

26

5.3

Occupation

In this section Table 13, Table 14, and Table 15 present the analysis underlying the figures presented in Section 5.2 “Occupation and Workers’ Adjustment” of the accompanying paper. Table 16, and Table 17 present a supplemental analysis for Section 5.2 “Occupation and Workers’ Adjustment”.

Table 13: Adjustment of Workers by Initial Occupation– Cumulative Earnings Dep. Var. Cumulative Earnings (in pre-shock annual wage) All Employers Initial Firm other T&C other Manuf Service Other (1) (2) (3) (4) (5) (6) Managers (N=1062) CompExp*Post02

-1.649 -5.308*** (1.541) (1.206) Professionals and Technicians (N=2948) CompExp*Post02 4.372** -5.230*** (1.342) (0.771) Clerks and Service Workers (N=2730) 0.898 -4.710*** CompExp*Post02 (2.099) (0.997) Craft Workers (N=1780) CompExp*Post02 -5.892*** -2.986** (1.389) (0.942) Operators and Assemblers (N=9106) CompExp*Post02 -3.724*** -4.821*** (0.713) (0.326) Manufacturing Labourers (N=1812) CompExp*Post02 -6.553 -7.033** (5.349) (2.199)

0.524 (0.953)

-0.954 (0.820)

3.733** 0.355 (1.166) (0.266)

2.321*** (0.599)

-1.622** (0.616)

8.528*** 0.376 (1.322) (0.244)

0.661 (0.535)

1.886* (0.939)

3.298 (1.977)

-0.237 (0.417)

-1.369* (0.619)

-2.094** (0.803)

1.593 (1.088)

-1.036* (0.512)

-0.169 (0.392)

-0.010 (0.427)

1.601** -0.326** (0.593) (0.126)

1.262 (0.781)

0.452 (1.809)

0.306 (4.665)

-1.540 (1.592)

Notes: Each cell present a DID coefficient estimate of Equation 2 where CompExp = CompExpC and the dependent variable is the cumulative earnings. All regressions include a constant, post-shock period indicator and worker fixed effects. Robust standard errors reported in parentheses are clustered at worker-level. ∗ , ∗∗ and ∗∗∗ indicate significance at the 5%, 1% and 0.1% levels respectively.

27

Table 14: Adjustment of Workers by Initial Occupation– Cumulative Employment

Panel A. Managers (N=1066) CompExp*Post02

All Employers Initial Firm other T&C other Manuf Service Other (1) (2) (3) (4) (5) (6) Dep. Var. Cumulative Hours Worked (in pre-shock annual hours )

-1.386 -4.825*** (0.997) (0.975) Professionals and Technicians (N=2936) CompExp*Post02 1.149 -4.534*** (0.860) (0.595) Clerks and Service Workers (N=2704) CompExp*Post02 -0.192 -3.981*** (1.195) (0.730) Craft Workers (N=1780) CompExp*Post02 -2.886** -3.087*** (1.003) (0.829) Operators and Assemblers (N=9090) CompExp*Post02 -3.785*** -4.750*** (0.510) (0.292) Manufacturing Labourers (N=1786 ) -0.809 -5.247*** CompExp*Post02 (1.733) (0.935)

Panel B. Managers (N=1062) CompExp*Post02

-0.132 (0.663)

-0.157 (0.573)

3.400*** (0.899)

0.328 (0.299)

1.958*** (0.447)

-1.388** (0.429)

4.769*** (0.903)

0.343 (0.227)

0.680 (0.417)

1.227 (0.739)

2.179* (1.077)

-0.296 (0.361)

-0.634 (0.479)

-1.236 (0.661)

2.553** (0.821)

-0.482 (0.321)

-0.191 (0.316)

-0.135 (0.322)

1.531*** (0.428)

-0.240* (0.102)

1.550* (0.614)

-0.058 (0.695)

2.553 (1.747)

0.394 (0.381)

Dep. Var. Cumulative Years with Employment

-1.415 -4.700*** (0.728) (0.916) Professionals and Technicians (N=2948) CompExp*Post02 0.785* -4.273*** (0.391) (0.514) Clerks and Service Workers (N=2730) CompExp*Post02 0.879* -3.326*** (0.396) (0.536) Craft Workers (N=1780) -1.065 -3.048*** CompExp*Post02 (0.703) (0.721) Operators and Assemblers (N=9106) CompExp*Post02 -1.295*** -4.380*** (0.284) (0.249) Manufacturing Labourers (N=1812) CompExp*Post02 0.602 -3.876*** (0.598) (0.619)

0.025 (0.594)

-0.297 (0.545)

3.287*** (0.783)

0.270 (0.288)

1.724*** (0.359)

-1.197*** (0.348)

4.261*** (0.537)

0.269* (0.135)

0.646 (0.331)

0.916** (0.352)

2.660*** (0.579)

-0.018 (0.166)

-0.198 (0.451)

-0.671 (0.534)

3.371*** (0.674)

-0.519* (0.261)

0.156 (0.209)

0.340 (0.237)

2.752*** (0.294)

-0.163 (0.093)

1.273** (0.476)

-0.113 (0.492)

3.197*** (0.720)

0.121 (0.244)

Notes: Each cell present a DID coefficient estimate of Equation 2 where CompExp = CompExpC . The dependent variable is the cumulative hours worked in Panel A and the cumulative employment in Panel B. All regressions include a constant, post-shock period indicator and worker fixed effects. Robust standard errors reported in parentheses are clustered at worker-level. ∗ , ∗∗ and ∗∗∗ indicate significance at the 5%, 1% and 0.1% levels respectively.

28

Table 15: Adjustment of Workers by Initial Occupation– Cumulative Unemployment Dep. Var. Cumulative Unemployment Spells depending on the sector of last employment: All U. Spells Managers (N=1062) CompExp*Post02

10.115*** (2.823) Professionals and Technicians (N=2948) CompExp*Post02 1.177 (1.379) Clerks and Service Workers (N=2730) CompExp*Post02 -1.897 (1.574) Craft Workers (N=1780) CompExp*Post02 4.133 (2.264) Operators and Assemblers (N=9106) CompExp*Post02 6.823*** (1.164) Manufacturing Labourers (N=1812) CompExp*Post02 1.903 (2.362)

Textile

Manuf.

Service

Other

4.891* (1.967)

0.981 (0.927)

3.395* (1.369)

0.848 (0.692)

-0.559 (0.948)

0.039 (0.362)

1.725 (0.907)

-0.028 (0.151)

-1.222 (0.989)

-0.194 (0.571)

-0.270 (1.069)

-0.211 (0.230)

0.765 (1.496)

-0.692 (0.682)

4.475** (1.503)

-0.417 (0.368)

1.206 (0.814)

-0.019 (0.424)

5.674*** (0.795)

-0.038 (0.178)

2.110 (1.466)

-1.727* (0.786)

2.038 (1.582)

-0.518 (0.565)

Notes: Each cell present a DID coefficient estimate of Equation 2 where CompExp = CompExpC and the dependent variable is the cumulative unemployment spells. All regressions include a constant, postshock period indicator and worker fixed effects. Robust standard errors reported in parentheses are clustered at worker-level. ∗ , ∗∗ and ∗∗∗ indicate significance at the 5%, 1% and 0.1% levels respectively.

29

Table 16: Adjustment of Workers by Initial Occupation– Per Year Hours Worked Dep. Var. Hours worked per year of employment All Employers Initial Firm other T&C other Manuf Service (1) (2) (3) (4) (5) Managers CompExp*Post02

0.031 (0.089) Professionals and Technicians CompExp*Post02 -0.071 (0.073) Clerks and Service Workers CompExp*Post02 -0.131 (0.122) Craft Workers CompExp*Post02 -0.177 (0.109) Operators and Assemblers CompExp*Post02 -0.224*** (0.047) Manufacturing Labourers CompExp*Post02 -0.231 (0.213)

-0.053 (0.032)

-0.027 (0.327)

2.006* (0.917)

-0.178 (0.515)

0.075 (0.056)

-0.269 (0.240)

-0.336 (0.325)

-0.231 (0.278)

-0.062 (0.068)

-0.183 (0.284)

0.306 (0.689)

-0.627 (0.421)

-0.095 (0.079)

-0.330 (0.312)

-0.075 (0.385)

-0.095 (0.540)

-0.112** (0.036)

-0.331* (0.153)

0.293 (0.184)

-0.540* (0.222)

-0.137 (0.117)

0.451 (0.393)

0.717 (0.458)

-0.812 (0.458)

Notes: Each cell present a DID coefficient estimate of Equation 2 where CompExp = CompExpC . All regressions include a constant, post-shock period indicator and worker fixed effects. Robust standard errors reported in parentheses are clustered at worker-level. ∗ , ∗∗ and ∗∗∗ indicate significance at the 5%, 1% and 0.1% levels respectively.

30

Table 17: Adjustment of Workers by Initial Occupation– Hourly Wage Dep. Var. Log Hourly Wage per year of employment All Employers Initial Firm other T&C other Manuf Service (1) (2) (3) (4) (5) Managers CompExp*Post02

-0.230* (0.097) Professionals and Technicians CompExp*Post02 0.079 (0.046) Clerks and Service Workers CompExp*Post02 0.064 (0.046) Craft Workers -0.059 CompExp*Post02 (0.052) Operators and Assemblers 0.110*** CompExp*Post02 (0.021) Manufacturing Labourers CompExp*Post02 -0.944 (0.620)

-0.031 (0.090)

-0.513* (0.222)

-0.159 (0.232)

-0.526 (0.574)

0.046 (0.052)

0.240 (0.177)

0.045 (0.152)

-0.052 (0.105)

-0.014 (0.041)

-0.012 (0.111)

0.046 (0.182)

0.041 (0.119)

0.094 (0.050)

-0.025 (0.185)

-0.095 (0.226)

-0.221 (0.221)

0.037 (0.019)

-0.039 (0.060)

0.025 (0.066)

-0.005 (0.095)

-0.432 (0.436)

0.330 (0.675)

1.991 (1.825)

-2.549 (1.845)

Notes: Each cell present a DID coefficient estimate of Equation 2 where CompExp = CompExpC . All regressions include a constant, post-shock period indicator and worker fixed effects. Robust standard errors reported in parentheses are clustered at worker-level. ∗ , ∗∗ and ∗∗∗ indicate significance at the 5%, 1% and 0.1% levels respectively.

31

5.4

The Triple Difference Analysis

Workers’ Adjustment and Industry Specificity of Occupation Table 18 and Table 19 present estimation of equation 3 where Y is the specificity of worker i’s occupation to the manufacturing sector, ManuSpec, and its specificity to the textile and clothing industry, TexSpec, respectively. The analysis shows that specificity of occupation to manufacturing is the main determinant of adjustment costs.

˜ is = β0 + β1 CompExpi ∗ P ost02s + β2 P ost02s + β3 P ost02s ∗ Yi + X β4 CompExpi ∗ P ost02s ∗ Yi + δi + is , s = 0, 1

(3)

Table 20 presents estimation of equation 3 where Y is the routine task intensity (RTI) of worker i’s occupation (two-digit ISCO). The analysis shows that exposure to technical change did not interplay with workers’ trade-induced adjustment which is in line with Keller and Utar (2016) who show at worker-level that trade and technology are independent factors. Table 21 presents estimation of equation 3 where Y is the occupational, manufacturing and textile and clothing experience of worker i respectively in panels A from C. The analysis shows that experience per se whether in occupation or in industry is not a main determinant of adjustment costs.

32

Table 18: Manufacturing Specificity of Occupation and Adjustment Costs (1) (2) (3) (4) (5) All Employers Initial Firm other T&C other Manu Service A. Cumulative Labor Earnings (expressed in pre-shock annual wage) CompExp*PostWTO 1.420 -5.386*** 1.595*** -0.419 (1.788) (0.821) (0.435) (0.660) PostWTO 8.659*** 1.507*** 0.730*** 1.215*** (0.416) (0.200) (0.085) (0.139) PostWTO*ManuSpec -3.693*** -1.225*** 0.351 0.366 (0.570) (0.273) (0.184) (0.205) CompExp*PostWTO*ManuSpec -7.330** 0.753 -2.288** 0.479 (2.540) (1.158) (0.830) (1.072) N 19550 19550 19550 19550 B. Cumulative Employment CompExp*PostWTO PostWTO PostWTO*ManuSpec CompExp*PostWTO*ManuSpec N C. Cumulative Hours Worked CompExp*PostWTO PostWTO PostWTO*ManuSpec CompExp*PostWTO*ManuSpec N

(6) Other

6.227*** (1.609) 4.735*** (0.355) -2.980*** (0.476) -6.613** (2.234) 19550

-0.598 (0.453) 0.472*** (0.127) -0.206 (0.160) 0.338 (0.597) 19550

1.057*** (0.310) 4.567*** (0.061) -0.520*** (0.098) -2.912*** (0.559) 19550

-3.851*** (0.370) 0.869*** (0.080) -0.585*** (0.121) -0.517 (0.582) 19550

1.235*** (0.251) 0.510*** (0.044) 0.301*** (0.073) -1.328** (0.431) 19550

-0.406 (0.265) 0.700*** (0.051) 0.458*** (0.084) 0.870 (0.494) 19550

4.082*** (0.398) 2.310*** (0.075) -0.762*** (0.114) -1.784** (0.660) 19550

-0.003 (0.132) 0.178*** (0.025) 0.069 (0.041) -0.154 (0.231) 19550

0.468 (0.819) 5.828*** (0.170) -1.315*** (0.265) -4.997*** (1.292) 19466

-4.424*** (0.486) 1.058*** (0.106) -0.730*** (0.158) -0.289 (0.744) 19466

1.452*** (0.332) 0.556*** (0.062) 0.417** (0.138) -2.025** (0.646) 19466

-0.388 (0.427) 0.863*** (0.074) 0.477*** (0.128) 0.313 (0.725) 19466

3.882*** (0.779) 3.113*** (0.159) -1.481*** (0.227) -2.833* (1.171) 19466

-0.054 (0.227) 0.238*** (0.046) 0.002 (0.066) -0.163 (0.341) 19466

Notes: Estimation of equation 3. All regressions include worker fixed effects, and a constant. Robust standard errors reported in parentheses are clustered at worker-level. ∗ , ∗∗ and ∗∗∗ indicate significance at the 5 %, 1% and 0.1% levels respectively.

33

Table 19: Textile Specificity of Occupation and Adjustment Costs (1) (2) (3) (4) (5) All Employers Initial Firm other T&C other Manu Service

(6) Other

A. Cumulative Labor Earnings (expressed in pre-shock annual wage) CompExp*Post02 -0.519 -4.746*** 0.700* -0.439 (1.030) (0.459) (0.343) (0.429) Post02 7.598*** 1.082*** 0.889*** 1.421*** (0.231) (0.106) (0.075) (0.087) Post02*TexSpec -4.265*** -1.080*** 0.142 -0.031 (0.417) (0.219) (0.148) (0.199) CompExp*Post02*TexSpec -5.278* -0.450 -1.315 0.815 (2.099) (0.999) (0.797) (1.145) N 19550 19550 19550 19550

4.467*** (0.918) 3.788*** (0.193) -3.031*** (0.351) -4.778** (1.848) 19550

-0.501 (0.257) 0.419*** (0.070) -0.264* (0.123) 0.449 (0.494) 19550

B. Cumulative Employment CompExp*Post02 0.316 (0.212) Post02 4.444*** (0.040) Post02*TexSpec -0.723*** (0.115) CompExp*Post02*TexSpec -2.927*** (0.686) N 19550

-3.618*** (0.243) 0.641*** (0.051) -0.406** (0.136) -1.891** (0.663) 19550

0.761*** (0.171) 0.594*** (0.030) 0.359*** (0.090) -0.989 (0.546) 19550

-0.314 (0.188) 0.894*** (0.035) 0.248* (0.102) 1.193 (0.613) 19550

3.608*** (0.264) 2.098*** (0.047) -0.913*** (0.120) -1.379 (0.749) 19550

-0.121 (0.087) 0.218*** (0.017) -0.011 (0.046) 0.138 (0.268) 19550

C. Cumulative Hours Worked CompExp*Post02 -0.924+ (0.513) Post02 5.507*** (0.106) Post02*TexSpec -1.776*** (0.221) CompExp*Post02*TexSpec -4.150*** (1.222) N 19466

-4.121*** (0.305) 0.782*** (0.065) -0.545*** (0.162) -1.582* (0.779) 19466

0.660* (0.260) 0.716*** (0.056) 0.298* (0.118) -1.221 (0.660) 19466

-0.490+ (0.289) 1.090*** (0.054) 0.148 (0.140) 0.772 (0.796) 19466

3.171*** (0.474) 2.666*** (0.092) -1.615*** (0.192) -2.149 (1.130) 19466

-0.144 (0.133) 0.253*** (0.026) -0.063 (0.059) 0.030 (0.323) 19466

Notes: Estimation of equation 3. All regressions include worker fixed effects, and a constant. Robust standard errors reported in parentheses are clustered at worker-level. ∗ , ∗∗ and ∗∗∗ indicate significance at the 5 %, 1% and 0.1% levels respectively.

34

Table 20: Routine Task Intensity of Occupation and Workers’ Adjustment (1) (2) (3) (4) (5) All Employers Initial Firm other T&C other Manu Service

(6) Other

A. Cumulative Labor Earnings (expressed in CompExp*Post02 -1.758 -5.197*** (0.906) (0.427) Post02 6.622*** 0.905*** (0.200) (0.101) Post02*RTI 0.116 -0.033 (0.147) (0.089) CompExp*Post02*RTI 0.867 0.502 (0.806) (0.423) N 17602 17602

pre-shock annual wage) 0.911*** -0.709 3.596*** (0.272) (0.376) (0.828) 0.841*** 1.424*** 3.093*** (0.048) (0.081) (0.173) 0.020 -0.022 0.127 (0.043) (0.063) (0.133) -0.351 0.752* 0.060 (0.235) (0.373) (0.746) 17602 17602 17602

-0.359 (0.217) 0.359*** (0.058) 0.025 (0.044) -0.097 (0.186) 17602

B. Cumulative Employment CompExp*Post02 -0.363 (0.223) Post02 4.262*** (0.041) Post02*RTI 0.042 (0.039) CompExp*Post02*RTI 0.191 (0.197) N 17602

-4.262*** (0.244) 0.617*** (0.052) -0.049 (0.054) 0.312 (0.245) 17602

0.776*** (0.172) 0.642*** (0.030) 0.019 (0.028) -0.237 (0.153) 17602

-0.312+ (0.185) 0.962*** (0.036) -0.029 (0.032) 0.342* (0.167) 17602

3.486*** (0.260) 1.827*** (0.047) 0.103* (0.049) -0.163 (0.256) 17602

-0.051 (0.085) 0.215*** (0.016) -0.001 (0.015) -0.063 (0.079) 17602

C. Cumulative Hours Worked CompExp*Post02 -1.811*** (0.472) Post02 5.063*** (0.102) Post02*RTI 0.038 (0.094) CompExp*Post02*RTI 0.450 (0.476) N 17524

-4.687*** (0.291) 0.706*** (0.063) -0.015 (0.067) 0.289 (0.314) 17524

0.747*** (0.209) 0.723*** (0.037) 0.005 (0.033) -0.206 (0.183) 17524

-0.639** (0.247) 1.122*** (0.052) -0.009 (0.043) 0.481 (0.303) 17524

2.820*** (0.463) 2.285*** (0.096) 0.038 (0.090) 0.010 (0.432) 17524

-0.052 (0.118) 0.227*** (0.021) 0.019 (0.034) -0.124 (0.143) 17524

Notes: Estimation of equation 3. All regressions include worker fixed effects, and a constant. Robust standard errors reported in parentheses are clustered at worker-level. ∗ , ∗∗ and ∗∗∗ indicate significance at the 5 %, 1% and 0.1% levels respectively.

35

Table 21: Occupation and Industry Experience and Adjustment Costs (1)

(2)

(3)

(4)

(5)

Dependent Var. Cumulative Primary Labor Earnings obtained across All Employers Initial Firm other T&C other Manu Service

(6) Other

Occupational Experience CompExp*Post02 Post02 Post02*OccExp CompExp*Post02*OccExp N

-4.034* (1.895) 11.587*** (0.437) -0.968*** (0.065) 0.505 (0.276) 21022

-2.936*** (0.717) 0.179 (0.167) 0.147*** (0.026) -0.387*** (0.111) 21022

-0.013 (0.570) 1.212*** (0.128) -0.059** (0.019) 0.076 (0.087) 21022

-1.400 1.778 -1.462** (0.826) (1.686) (0.510) 2.242*** 7.096*** 0.857*** (0.180) (0.373) (0.142) -0.169*** -0.790*** -0.097*** (0.028) (0.055) (0.021) 0.224 0.383 0.209** (0.126) (0.243) (0.077) 21022 21022 21022

-1.147 (2.472) 13.773*** (0.536) -0.714*** (0.043) 0.056 (0.195) 21022

-3.633*** (0.872) -0.213 (0.210) 0.115*** (0.018) -0.134 (0.073) 21022

0.286 (0.672) 1.108*** (0.141) -0.017 (0.011) 0.003 (0.054) 21022

-0.822 4.779* -1.756** (1.100) (2.246) (0.677) 2.656*** 9.154*** 1.069*** (0.224) (0.459) (0.182) -0.128*** -0.613*** -0.071*** (0.018) (0.036) (0.015) 0.069 -0.022 0.140* (0.088) (0.177) (0.055) 21022 21022 21022

0.336 (2.008) 12.133*** (0.422) -0.625*** (0.035) 0.009 (0.164) 21022

-4.338*** (0.729) 0.105 (0.173) 0.094*** (0.016) -0.081 (0.065) 21022

0.572 (0.546) 0.945*** (0.108) 0.002 (0.009) -0.035 (0.046) 21022

-0.684 5.997** -1.211* (0.923) (1.844) (0.523) 2.640*** 7.535*** 0.908*** (0.182) (0.362) (0.137) -0.149*** -0.509*** -0.062*** (0.016) (0.030) (0.011) 0.102 -0.079 0.103* (0.077) (0.150) (0.043) 21022 21022 21022

Manufacturing Experience CompExp*Post02 Post02 Post02*ManExp CompExp*Post02*ManExp N Textile Experience CompExp*Post02 Post02 Post02*TexExp CompExp*Post02*TexExp N

Notes: Robust standard errors reported in parentheses are clustered at worker-level. ∗ , ∗∗ and ∗∗∗ indicate significance at the 5 %, 1% and 0.1% levels respectively. OccExp is worker i’s experience in his/her occupation ((two-digit ISCO)) in years since 1991 until 1999. ManExp and TexExp are worker i’s experience in years within the manufacturing and the textile and clothing sector respectively since 1985 until 1999.

36

5.5

Trade-induced Skill Upgrading at the Worker Level

Table 22: Trade-induced Skill Upgrading: Worker Level Evidence Dep. Var. Number of Years with School Enrollment across (1) (2) (3) (4) (5) (6) All Initial other Service SelfUnemployed Positions Firm Manuf. Sector Employed Jobs All Workers ( N=21,022) CompExp*Post02

0.243*** 0.029* 0.015 0.124*** 0.004 (0.056) (0.013) (0.014) (0.029) (0.006) College Educated Workers (N=2,398) CompExp*Post02 0.186 0.008 0.019 0.049 0.019 (0.148) (0.038) (0.034) (0.065) (0.021) Workers with Vocational Education (N=7,352) CompExp*Post02 0.216* 0.039* 0.031 0.103* -0.001 (0.085) (0.020) (0.022) (0.044) (0.009) Workers with Manufacturing Specific Vocational Education, N=2,590 CompExp*Post02 0.433** 0.006 0.091* 0.141 0.018 (0.158) (0.037) (0.042) (0.079) (0.018) Workers with Service Related Vocational Education, N=5,366 CompExp*Post02 0.081 0.042 -0.014 0.055 0.000 (0.093) (0.023) (0.022) (0.047) (0.012) Workers with at most a High School Diploma (N=10,774) CompExp*Post02 0.292*** 0.028 0.011 0.161*** 0.002 (0.083) (0.020) (0.021) (0.046) (0.007)

(7) Outside Labor Market

0.030 (0.023)

0.042 (0.026)

0.066 (0.060)

0.033 (0.076)

-0.011 (0.035)

0.061 (0.042)

0.103 (0.057)

0.087 (0.085)

-0.036 (0.039)

0.034 (0.044)

0.054 (0.035)

0.029 (0.038)

Notes: Estimation of Equation 2. Each cell presents coefficient estimate of CompExpC ∗ P ost02. The numbers of observations across all rows are provided in the table. All regressions include a constant, worker fixed effects, and the post WTO accession period indicator, P ost02. Robust standard errors are clustered at worker level and reported in parentheses. ∗ , ∗∗ and ∗∗∗ indicate significance at the 5%, 1% and 0.1% levels respectively.

37