International Trade and Job Polarization: Evidence at the Worker Level Wolfgang Keller1 and Hˆale Utar∗2 1
University of Colorado and NBER 2
Bielefeld University December 4, 2015
Preliminary and Incomplete Draft Job polarization is the shift of employment and earnings from mid-level wage jobs to both high- and low-wage jobs. Employing longitudinal employee-employer matched data on all workers and firms in Denmark we investigate the role of international trade for job polarization as Danish workers faced intense import competition from China over the period 1999 to 2009. Using an instrumental variables approach we show that import competition from China is an important cause of job polarization in Denmark, about four times the size of the effect of offshoring. We confirm a strong role for technical change and computerization for polarization, although these factors cannot explain the rise in low-wage employment by the early 2000s. The removal of restrictions on textile exports with Chinas entry in the World Trade Organization provides a quasi-experimental setting that shows our instrumental variables approach captures the substance of trades causal effect on job polarization. Import competition leads to job polarization by shifting workers from initially abundant manufacturing jobs to both high- and low-paying services jobs. Low-educated workers lose mid-level jobs, and move into low-wage jobs to a greater extent that more educated workers. Finally, when exposed to import competition women transition less well than men into high-wage jobs. ∗
The study is sponsored by the Labor Market Dynamics and Growth Center (LMDG) at the University of Aarhus. Support of the Dept. of Economics and Business, Aarhus University and Statistics Denmark are acknowledged with appreciation. We thank Henning Bunzel for facilitating the access to the confidential database of Statistics Denmark and for his support, David Autor for his insightful discussion of the paper and Nick Bloom, Susanto Basu, Dave Donaldson for helpful comments and suggestions.
1
Introduction
The recent transformation of China from a rather poor and largely closed economy to one of the largest manufacturing producers over little more than two decades has been among the most significant changes in the world economy. China’s share of world manufactures exports rose from 2 % to 4 % over the years 1990 to 1999, only to accelerate pace and account for 16 % of world exports in manufacturing goods by the year 2013.1 For manufacturing producers in rich countries such as Denmark, the emergence of China is felt as a major competitive shock. This is particularly true in the labor-intensive textiles and clothing sector, where the traditional comparative advantage of low-wage countries has been compounded by trade liberalization in form of import quota removals through China’s entry into the World Trade Organization (WTO) in the year 2001. In this paper we study the employment trajectories of Danish workers to determine whether import competition from China has caused job polarization. Job polarization is the shift of employment shares from mid-level wage jobs to both high- and low-wage jobs. This phenomenon is among the most significant labor market developments in recent decades because it has been observed in many rich countries.2 Figure 1 shows that while job polarization has been felt strongly in the United States, developments in Denmark have been even more dramatic. Mid-level employment share losses affect 60 % of all occupations (40 % in the U.S.), and it occurred at a faster pace (seven more years are shown for the U.S.).3 This means that Denmark may provide important new lessons on job polarization. Our focus will be on the textiles and clothing industry, which faces 1
Authors’ calculation using World Development Indicators dataset of the World Bank. For the Unites States, see Autor, Katz, and Kearney (2006, 2008), Autor and Dorn (2013); United Kingdom: Goos and Manning (2007); Germany: Spitz-Oener (2006), Dustmann, Ludsteck, and Schonberg (2009); France: Harrigan, Reshef, and Toubal (2015) and across 16 European countries, see Goos, Manning, and Salomons (2014). 3 The figure for Denmark is based on all employees in Denmark aggregated by 3-digit occupation level (International Standard Classification of Occupations) excluding agriculture. The figure for the United States is taken from Autor and Dorn (2013) and it covers all nonfarm employment in the US. Initial occupational mean wage is based on hourly wage data from 1991 for Denmark and for 1980 for the U.S. 2
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major import competition from China. In our analysis the effect of import competition turns on whether a specific worker is employed by a firm manufacturing a product for which China’s entry into the WTO removes binding import quotas. We study workers’ employment (and to a lesser extent earnings) trajectories over the years 2002 to 2009. Covering the relatively high rates of export growth during China’s first eight years of WTO membership, this period captures the short and medium term implications of increased import competition. The end of our period of observation is determined by data considerations.4
-.2
100 x Change in Employment Share .6 0 .2 .4
Smoothed Changes in Employment by Wage Percentile
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20 40 60 80 Wage Percentile (Ranked by Initial Occupational Mean Wage)
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1980-2005 U.S. 1991-2009 Denmark
Figure 1: Smoothed Changes in Employment in Denmark and in the United States
We employ administrative longitudinal data on the universe of persons aged 15 to 70 years old in Denmark to study the impact of import competition on worker’s movements between jobs.5 The data set is unusually rich in providing information for each individ4
Occupational categories were changed in the year 2009. The Great Recession starting in 2008 took place too late to be driving our results. 5 Our data are not the administrative records themselves but an integrated database created by Statistics Denmark from administrative registers. They are owned by public authorities and supple-
3
ual worker on his or her employer, hours worked, unemployment history, and level of education, among other variables. Information on the tasks workers perform is available at a relatively detailed four-digit level (International Standard Classification of Occupations, ISCO). We can therefore distinguish workers not only by industry and education but also by the tasks they perform. We begin by exploiting a specific trade policy shock which affected workers in Denmark’s textiles and clothing sector before broadening the analysis to the entire Danish economy. China’s accession to the WTO late 2001 meant that it ceased to be bound by the textile quotes of the Multi-fiber Arrangement (MFA), and Chinese textile exports to Denmark surged.6 The typical advantage of studying a policy change is that it is well identified. Here, the explicit purpose of the 1974 MFA was to provide protection against exports of textiles from low-wage countries.7 Neither China nor Denmark had a major role in the negotiations leading to the removal of the MFA quotas starting in 1995. The dramatic surge of Chinese textiles and clothing exports after 2001 is a plausibly exogenous source of import competition, and we employ product-level trade as well as matched workerfirm data to establish a causal link between import competition and the employment trajectories of Danish workers. With the estimate of trade’s effect on textile workers in hand we investigate the role of import competition for job polarization in Denmark as a whole. In the absence of a specific policy change affecting the entire Danish economy, we employ an instrumental variables strategy where cross-industry changes in Danish imports from China are predicted by Chinese exports to high-income countries other than Denmark. The mented with additional information drawn from various surveys (see Bunzel 2008, Timmermans 2010). Earlier work using this data set includes Groes, Kircher, and Manovskii (2015), Hummels, Jorgenson, Munch, and Xiang (2014). 6 The increase in trade with China associated with the MFA quota removals has been employed recently to study technology upgrading of European firms (Bloom, Draca, and van Reenen 2014), as well as in Utar (2014, 2015) to study firms’ and workers’ adjustment. These studies do not focus on job polarization. 7 Utar (2014) shows that the quotas were binding, and that their removal led to increased import competition. Brambilla, Khandelwal, and Schott (2008) examine the effects of the quota lifting in the U.S.
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key identifying assumption is that the increase in imports is primarily due to China’s improved supply capacity, as opposed to demand shocks that are correlated across highincome countries, for example. New light is shed on the plausibility of this assumption by applying the instrumental-variables approach to the textiles sector, where we can estimate the polarization effect of trade from the MFA policy change. While our quantitative results differ, we find broad similarities in the way Chinese import competition has affected Denmark’s textiles sector on the one, and the entire economy on the other hand. We contribute to a growing literature on the importance of international trade in explaining job polarization, much of which examines changes at the industry or region level. Recent work on local labor markets has shown that international trade and offshoring have not played a major role for job polarization in the U.S. or other rich countries (Autor and Dorn 2013, Goos, Manning, and Salomons 2014, Michaels, Natraj, and van Reenen 2014). Job polarization is mainly explained by information and communications technology investments whereby computers, robots and automatization replace workers that perform easily programmable tasks (so-called routine-biased technical change; Autor, Katz, Kearney 2006, Goos and Manning 2007).8 Our work advances the literature by shifting the focus from aggregate labor market consequences to job changes at the worker level. By examining the difference in responses between workers who are ex ante observationally similar except that one works for a firm that competes with Chinese imports while the other worker does not, we provide causal evidence on the impact of trade on employment polarization. By shedding new light both on the nature of frictions that workers face when they move between jobs (such as loss of occupation-specific human capital), this paper contributes to a better understanding of worker welfare and the overall welfare effect of international trade. 8
An exception is Firpo, Fontin, and Lemieux (2011) who find offshoring to be important. Other studies of job polarization that do not specifically address the role of international trade include SpitzOener (2006) and Dustmann, Ludsteck, and Schonberg (2009).
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We provide one of the first accounts of job polarization using micro level data for any country. The case of Denmark illustrates well the widely-shared experience in rich countries from a world characterized by skill-biased technical change to one characterized by job polarization. The paper shows that both worker’s education and gender were important in shaping the emergence of job polarization in Denmark. This paper is in a long tradition of work examining the role of international trade for labor market outcomes, including the increase in relative wage of educated workers in many countries during the 1970s and 1980s (see Feenstra 2000). The focus in recent open economy analysis has shifted to tasks instead of industries or education levels (Grossman and Rossi-Hansberg 2008), which is line with evidence showing that worker outcomes have become strongly related to occupation characteristics (Acemoglu and Autor 2010). By affecting the relative labor demand for mid-level versus high- and low-wage jobs, international trade could in principle be a cause of job polarization. This would be the case if the factor content of imports from low-wage countries was intensive in tasks for which mid-level wages are paid in high-income countries. To date, the existing evidence on this says this is not the case. Our main finding is that international trade in the sense of import competition can explain job polarization. In Denmark’s textiles sector, imports from China reduced the employment of typical mid-wage earning workers such as machine operators by about eight months over the period 2002 to 2009. It also increased employment of machine operators in both high- and low-wage jobs by one month and four months respectively. Import competition from China also matters for explaining economy-wide job polarization patterns, accounting for about 12% in Denmark during the sample period. While this contrasts with earlier work on the causes of job polarization, it is in line with recent work documenting a powerful effect of the China trade for labor market outcomes in rich countries.9 In particular, our finding reconciles worker-level evidence 9
See Autor, Dorn, and Hanson (2013), Bernard, Jensen, and Schott (2006), Pierce and Schott (2013), Utar (2014); also Utar and Torres-Ruiz (2013), and Hummels, Jorgenson, Munch, and Xiang (2014).
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on the sizable effect of import competition from China (Autor, Dorn, Hanson, and Song 2014, Utar 2015) with the scant evidence for trade causing job polarization in studies using aggregate data.10 Trade leads to job polarization mainly by pushing workers from initially abundant midlevel jobs in manufacturing towards both high- and low-wage jobs in services. We evaluate the effect of trade alongside technology and offshoring explanations of job polarization, finding that the trade effect is robust and hardly affected quantitatively by other explanations. Trade affects most strongly manual labor, workers performing both routine and non-routine tasks. In textiles import competition had a similarly large effect on job polarization as routine-biased technical change, while in the economy as a whole RBTC’s effect was larger, perhaps by a factor of two. We also find that the effect of import competition on job polarization is considerably larger than that of offshoring. By presenting results on the effect of import competition from a policy change with those based on an influential instrumental-variables approach side by side, we provide critical information to assess the impact of trade in the absence of a quasi-experimental setting. On the whole, our results are encouraging. Specifically, the instrumental variables approach yields similar estimates for employment losses for mid-level wage occupations, and employment gains for high-wage occupations, as we obtain using the quasi-experimental identification approach. The main shortcoming of the instrumentalvariables approach is that it underestimates the role of trade in pushing workers into low-wage occupations. Overall, if anything the instrumental-variables approach underestimates the role of trade. This is an important finding because to the extent that our findings on trade’s role for job polarization carry over to other questions there should be less concern that instrumental-variables results might exaggerate the true causal effect. A caveat is that our analysis is based on variation across industries within a specific sector, textiles, leaving open the possiblity that results between instrumental-variables 10
Autor, Dorn, and Hanson (2014) present evidence that technology and trade both affect outcomes at the level of US local labor markets; their study does not focus on job polarization.
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and quasi-experimental approach differ more for broader parts of the economy. The paper also provides a number of interesting results on gender differences in the experience of job polarization. We find that while women experience overall job polarization more than men, job polarization caused by trade affects women less than men. Key to understanding this, we believe, is that trade has affected the labor market in a more concentrated way than technical change did. First, trade led to a reduction of labor demand mostly in manufacturing, whereas technical change has changed labor demand both in manufacturing and non-manufacturing. Second, while technical change has gradually changed the workplace in industrialized countries since the 1980s, the impact of trade was felt more suddenly around China’s entry into the WTO. The higher costs of transitioning for women in the presence of a sudden drop in labor demand may be due to differences in the availability of re-training possibilities, or because women have on average a lower labor market attachment than men. The next section discusses the facts on job polarization in Denmark during the 1990s and 2000s. Section 3 introduces a number of possible causes of job polarization by presenting a number of micro facts on individual worker transitions between occupations in Denmark’s textiles and clothing sectors. Section 4 provides background on Denmark’s textiles trade as China entered the WTO, and it describes the data that will be employed. Regression results on the role of trade for job polarization are presented in section 5. In this section we also consider technical change and offshoring as alternative explanations of job polarization, and examine the extent to which trade has affected men and women differently. Section 6 provides a concluding discussion. An number of additional results are shown in the Supplementary Appendix.
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2
Job Polarization in Denmark
This section describes the overall patterns of labor demand in Denmark that can be distinguished over the years 1991 and 2009 to set the stage for our analysis. We then examine demand shifts between broad sectors before focusing on Denmark’s textiles and clothing workers in the years 2001 and 2009.
2.1
Major patterns of Denmark’s labor demand since the early 1990s
Over the last decades the nature of labor demand has changed in industrialized countries, and Denmark is no exception to this. This is made strikingly clear by considering the decade before and after the year 2000 separately. Figure 2 presents the smoothed changes in employment shares ranked by mean initial occupational wage for all of Danish workers outside agriculture, as in Figure 1, except that we distinguish the years 1991 to 2000 from the period 2001 to 2009. During the 1990s, we see that labor demand was increasing with wages. To the extent that we can think of wage as a proxy for worker skills, this pattern implies that skilled workers have tended to benefit while unskilled workers have fallen behind during the 1990s. It has been shown that skill-biased technical change was the most important driver of this pattern (Acemoˇglu 2002). While a lower relative demand for less skilled workers could be important in raising inequality, the policy implication from the 1990s is clear: if skills can shield a worker from declining labor demand, investing in skills, particularly through education, is a plausible welfareenhancing policy. The pattern for the years 2000-2009 in Figure 2 shows that Denmark’s workers now live in a different world. Growth of jobs is increasingly concentrated at the tails of the occupational wage distribution, while the middle of the distribution is hollowed out. The employment growth pattern in Denmark is U-shaped in the 2000s, as in 9
many industrialized countries. We are interested in better understanding the reasons for this, and in particular, the role played by international trade. That would also provide relevant information for policy makers, at least with trade as an additional dimension that should be considered in the formulation of labor market policies.11
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Smoothed Changes in Employment by Wage Percentile
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Figure 2: Smoothed Changes in Employment in Denmark by Occupational Wage Percentile
2.2
Shifts between manufacturing and services, 2000-2009
Given this U-shaped pattern that is the hallmark of job polarization, we follow the literature and distill the pattern into changes for three separate groups, called low-, mid-level, and high-wage workers (Autor 2010, Goos, Manning, and Salomons 2014). The groups are formed based on the median wage paid in an occupation in an initial 11
While this paper does not focus on policy, we touch on some of the issues in the conclusions. Autor (2010) discusses economic policy issues in the light of job polarization in the U.S.
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year.12 The high wage occupations comprise of managerial, professional, and technical occupations. Mid-level wage occupations are clerks, craft and related trade workers, and plant and machine operators and assemblers. Finally, low-wage occupations include service workers, shop and market sales workers, as well as elementary occupations.13 These wage categories capture also broad differences in terms of skills across workers, although each wage group encompasses workers in quite different occupations performing rather diverse sets of tasks.
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Figure 3: Changes in employment shares of low-, mid-, and high wage occupations by workers’ initial industry, 2000-2009 Figure 3 analyzes employment growth for these three wage groups in Denmark’s manufacturing and services sector during the years 2000 to 2009 (at the worker-level).14 12
We rank major ISCO occupations using the median log hourly wage in 1991 across full-time workers. Table A-3 in the appendix present this ranking. Goos, Manning, Salomons (2014) use the 1993 wages across European countries (inclusive of Denmark) and arrive at a similar ranking as ours at the 2-digit ISCO occupations. 13 We follow the literature and focus on jobs outside of agriculture, however, the results are not affected by this. 14 Figure 3 is constructed using all employees of the manufacturing and service sectors respectively in 1999, who were born between 1945 and 1984. The figure shows the change in employment shares across high-, mid-level, and low wage occupations of these workers between 2000 and 2009.
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The manufacturing sector employed 440,000 workers in Denmark in 1999, while in the same year 1,800,000 workers were employed in the service sector. The figure shows that manufacturing is much more strongly characterized by job polarization than services. In fact, employment growth in services is monotonically increasing with the wage, which is the pattern of the 1990s. As we will show below, trade does not only help to explain the U-shaped pattern in manufacturing but also the fact that employment growth in services is monotonically increasing across wage groups. This result confirms other findings that the growth of service jobs is to some extent the flip side of the hollowing out of mid-level wage jobs in manufacturing (Autor and Dorn 2013, Utar 2015). It is useful to keep in mind that our analysis tracks workers and their occupation, not their industry. When Figure 3 shows that manufacturing workers experience reductions in mid-level wage employment and increases in low-wage employment, in principle this could be because workers shift within manufacturing from mid-level to low-level jobs (within-industry shift), or because workers shift from mid-level occupations in manufacturing to low-wage occupations in other industries (between-industry shift). If employment shares were constant over time across industries, observing employment polarization in a particular industry such as textiles and clothing would imply that mid-level workers such as assemblers move in roughly equal parts into high wage and low wage jobs. While this is in principle possible, without between-industry shifts the potential for job polarization would necessarily seem to be limited: it is difficult to imagine that many machine operators and assemblers become professionals or managers while others become laborers, all in the same industry. Far from constant employment, however, the Danish textiles and clothing industry has been undergoing massive changes in terms of size: in the year 1999, it employed 13,000 workers, down from 25,000 in 1991, while by the end of 2010 industry employment was down to 4,500 workers.15 Part of this reduced demand for workers producing textiles in Denmark after 1999 is due to the removal of import quotas for China (Utar 2014), 15
In the year 1999, textiles and clothing accounted for 3 % of total manufacturing employment.
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Changes in Occupational Employment Share, 2000-2009
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T&C Workers in 1999 Manufacturing Workers in 1999
Figure 4: Changes in employment shares of low-, mid-, and high wage occupations by workers’ initial industry, 2000-2009 although other factors, including a secular declining trend of the labor-intensive industries and technological factors, are surely present as well. Figure 4 shows the patterns of job polarization among textiles and clothing and manufacturing workers as a whole. We see that the job polarization pattern for textiles and clothing is stronger than for manufacturing as a whole. Thus, job polarization in textiles and clothing accounts over-proportionally for job polarization in manufacturing.16 In the following section we move from the aggregate to the micro level by providing some initial evidence on job polarization in Denmark based on movements between jobs by individual workers. 16
It is important to distinguish the impact of trade on job polarization at the firm- versus workerlevel. Figure 1 in the Supplementary Appendix show occupational employment share changes within the textile and clothing sector. In that figure, rather than following workers and their occupations, we follow employment within the T&C sector. Along the lines of Utar (2014), occupational employment share changes exhibit skill upgrading within the sector, more so among the firms that were exposed to imports coming from China.
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3
Facts from individual worker job transitions
In this section we present several facts about worker transitions between occupations in Denmark during the sample period by looking at two specific two-digit occupations, drivers and mobile plant operators (ISCO 83) versus machine operators and assemblers (ISCO 82). Fact 1: There is substantial variation in worker outcomes at broadly similar wage levels but different occupations.
100 Drivers (ISCO 83) Machine Operators and Assemblers (ISCO 82) 90
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Figure 5: RBTC: Probability of Staying for Machine Operators versus Drivers
Because both drivers and machine operators are among mid-level wage categories (see Table A-3), reduced employment in either of these occupations may help to explain the overall finding of job polarization. Nevertheless workers in these two occupations experienced differences in their job market experience over the sample period. Figure 5 shows the cumulative probability for a worker to stay in her initial occupation over 14
10 Drivers (ISCO 83) Machine Operators and Assemblers (ISCO 82)
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Figure 6: The Rise of Service Worker Employment: Machine Operators versus Drivers the period 2001 to 2009. First, note that there is a considerable amount of movement of these workers. By the year 2009, around two thirds of the workers do not work anymore in the occupation that they worked in the year 2001. This is to some extent the consequence of the relative ease of switching jobs (low hiring costs, low firing costs) in the Danish labor market. Second, over these eight years there are marked differences in the probability of staying in one’s initial occupation: in particular, drivers stay in their initial occupation with a 15 % higher probability than do machine operators and assemblers. In the textile and clothing sector, operators would attend fibre-preparing-, spinning-, and winding machines. They would also operate weaving, knitting, sewing, bleaching, and dying machines. Given the relatively narrow and programmable nature of their tasks it is plausible that the tasks of these operators and assemblers are prone to being automated
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and done by robots. This is reflected in a relatively high ”routine task index” (RTI), which is 0.64 for this occupation.17 ,18 In contrast, drivers in the textile and clothing sector, who would drive cars, vans, trucks, as well as forklifts, perform generally less routine tasks. A driver of a truck takes his load to different destinations, diggers enter new terrain, or the personal driver of an executive makes new runs. The occupation of drivers has a relatively low routine task intensity (RTI of -0.99). Our finding that machine operators and assemblers are less likely to stay in their initial occupation than drivers points to the importance of routine-biased technical change (RBTC). Fact 2: Shrinking mid-level wage occupations are a source of growth of low-wage occupations, especially in services. Figure 6 shows the cumulative probabilities of machine operators and drivers to transition into personal and protective service occupations (ISCO 51). These service occupations include protective service workers as well as housekeepers, cooks, hairdressers, and travel guides. Service occupations require generally relatively few skills and wages are typically low. At the same time, service tasks are characterized by a relative low level of routinization (RTI of -0.23). Figure 6 shows that the cumulative probability that a machine operator transitions into these service jobs is increasing over time, and by the year 2009 7.5 % of all workers that were machine operators in 2001 have moved into person and protective service jobs. In contrast, drivers rarely move into these service worker jobs, and the cumulative probability is hardly increasing over time. This is consistent with recent evidence that the flip side of lower employment in high routine tasks due to RBTC is increasing service employment (Autor and Dorn 2013). Figure 6 confirms this result at the level of individual workers. Fact 3: Import competition leads to worker transitions consistent with job polarization. 17
See Autor, Levy, and Murnane (2003), Autor, and Dorn (2013). Autor (2013) discusses this tasks approach more broadly. 18 We thank Anna Salomons for sharing the RTI measure used in Goos, Manning, and Salomons (2014) with us.
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100 Machine Operators with Trade Shock Machine Operators without Trade Shock Drivers with Trade Shock Drivers without Trade Shock
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Figure 7: Import Competition and Job Polarization In Figure 7 we show cumulative transitions of drivers (ISCO 83) and machine operators and assemblers (ISCO 82) out of their occupation depending on whether they were exposed to increased competition from Chinese producers, or not. We see that both drivers and machine operators are less likely to stay in their initial occupation in the presence of Chinese import competition.19 Over the entire period of 2001-2009, machine operators affected by Chinese import competition have a 29 % lower chance of staying in their initial industry compared to machine operators not affected by import competition. The corresponding figure for drivers is 22 %. In the light of our finding that workers highly exposed to RBTC (such as machine operators) have a 15 % higher chance to switch occupations than workers little exposed 19
The transitions of machine operators over time result in a smoother picture mainly because there are many more of them than drivers. In 2001 there were 2726 machine operators (ISCO 82) as opposed to only 101 drivers.
17
to RBTC (such as drivers), the difference in likelihood of staying associated with trade of 22-29 % appears to be sizable. In section 5 below we will return to this question with an approach that holds constant a range of worker and firm characteristics to estimate the causal effect of trade. Before we turn to this, the next section introduces our data and describes the policy change that is employed.
4
Workers facing import competition: the data
4.1
Employee-employer matched data
The main database used in this study is the Integrated Database for Labor Market Research, IDA, which is comprised of person, establishment, and job files.20 The person files contain annual information on all persons of age 15-70 residing in Denmark with a social security number. The establishment files contain annual information on all establishments with at least one employee in the last week of November of each year. The job files provide information on all jobs that are active in the last week of November in each year. IDA data-sets are complemented with the domestic production data-set (VARES) that covers all manufacturing firms with at least 10 employees, and the annual longitudinal data-set that matches firms with their employees (FIDA). The data-sets are from Statistics Denmark. For each worker we have information on their annual salary, hours worked, industry code of their primary employment, education level, demographic characteristics including age, gender and immigration status, and occupation of primary employment. Of particular interest to us here is the information on workers’ occupations. Occupational codes matter in Denmark because they influence a worker’s wage due to a collective bargaining system. 20
See Bunzel (2008) and Timmermans (2010) for more information.
18
Employers and labor unions pay close attention to occupational codes. This explains the administrative nature of the data. As a consequence, the quality of occupational data in Denmark is high compared to other countries.21 Information on workers’ occupation closely follows the international ISCO-88 classification. For most workers, occupational information is given at a detailed 4-digit level. In the following we describe the sample of textile workers before providing information on the all-economy sample that will be employed in the analysis.
4.1.1
Workers in Denmark’s textiles and clothing industry
There were close to 13,000 workers in Denmark’s textile and clothing industry in the year 1999. In our analysis we focus on workers who are in working age (17 to 67 years) throughout our sample period, leaving around 11,000 workers in our sample (see Table 1, Panel A for summary statistics). The textile and clothing industry is a typical manufacturing industry in which plant and machinery operator tasks (ISCO occupation code 8) play a large role; according to Figure A-1, they account for more than 40 % of all workers. Nevertheless the textile and clothing industry employs workers performing a diverse set of tasks. Technicians and associate professionals (ISCO 3), craft workers (ISCO 7), but also clerks (ISCO 4) account each for around 10 % of the textile and clothing workforce, whereas managers (ISCO 1) make up about 5 % (Figure A-1). Table A-1 presents worker characteristics for several occupation groups separately. We see that a majority of professionals (ISCO 2) in our sample is college educated (61 %) whereas, for example, college educated workers only constitute 6 % among craft workers (ISCO 7). Workers are on average 39 years old, indicating roughly the middle of the career span. The share of female workers is 57 %, and 6 % are immigrants (Table 1). The share of women is highest among clerks (78 %) and lowest among managers (21 %). Eleven 21
See, for example, Groes, Kircher, and Manovskii (2015) on this point.
19
percent of workers are college educated, and 35 % of workers have formal vocational training. In Denmark vocational education is provided by the technical high schools (after 9 years of mandatory schooling) and involves several years of formalized training including both schooling and apprenticeship. Roughly half of the workers have at most high school education. These figures show that our sample includes workers from a range of skill levels, important for our analysis of the effects of globalization on manufacturing workers in advanced countries. Compared to the economy as a whole these workers belong typically to the mid-level wage group. In Table 1 we report the breakdown of workers into a three-way wage classification of occupations. As reported in Table 1, 62 % of our workers held mid-level wage jobs in the year 1999. In the empirical analysis we also employ a number of additional variables to control for both worker and firm characteristics. They include worker experience, measured by the number of years each worker was in the labor force between 1980 and 1999, and the worker’s unemployment history between 1980 and 1999 (see Table 1). Also, note that 80 % of workers are labor union members, and close to 90 % are members in Denmark’s unemployment insurance program. The high degree of unionization is one indication that Denmark’s labor market institutions are quite different from those in the United States, for example, although it is worth noting that this does not make them necessarily more rigid. In particular, Denmark ranked 6th among 148 countries in terms of its hiring and firing practices, more de-regulated than the United States which came in 9th in the same ranking (Global Competitiveness Report 2014). Relatively unrestricted hiring and firing in Denmark is combined with active labor market policies and a generous social safety net to yield a system that has been termed “flexicurity”. While the comparatively limited wage flexibility in Denmark leads us to focus mostly on job polarization in terms of employment, we will show some results on job polarization in terms of earnings as well. The Danish production database is used to identify firms with domestic production in one or more of the goods that were subject to the MFA quotas for China. We identify 20
firms that in 1999 produce 8-digit Combined Nomenclature (CN) level goods that are subject to the MFA quota removal for China, and using a firm identifier we map this information to worker-level information. In the year 1999, about half of the workers are exposed to increased import competition in the sense that they are employed at a firm that will subsequently be affected by quota removals as a consequence of China’s accession to the WTO. Panel (b) of Figure A-1 compares the distribution of occupations in the exposed firms with that in the non-exposed firms as a sample balance check. We see that plant and machine operators is the largest occupation in both sets of firms, accounting for more than 40 % of the workforce in both exposed and non-exposed firms. The two sets of firms have also a similar share of managers (ISCO 1). There are some differences, for example exposed firms have a higher share of clerks (ISCO 4) than nonexposed firms, while non-exposed firms have higher share of craft workers (ISCO 7). Overall, however, we do not see major differences between the two sets of firms in panel (b) of Figure A-1. Given their importance in the textile and clothing industry, we examine the group of “plant and machine operators” occupations in more detail. Table A-2 reports summary statistics on workers initially employed at firms that were exposed to import competition, versus workers that were not exposed to import competition separately. For example, sewing machine operators in exposed firms in 1999 were 95.7 % women, while this fraction was 94 % at non-exposed firms. We also see that 4.3 % of weaving and knitting machine operators were college educated in non-exposed firms, while 4.9 % of these workers were college educated in exposed firms. The weaving and knitting machine operators at exposed firms had a labor market experience of about 16.5 years, compared to about 15.7 years in non-exposed firms. To the extent that longer labor market experience gives valuable skills, and firms do not want to let go relatively skilled workers, this would bias the analysis against finding an effect of trade on job polarization. The main finding from comparing the initial workforce in exposed and non-exposed firms though is that the differences are small and unlikely to explain our findings.
21
Table 1: Descriptive Statistics : Textile Workers Panel A. Characteristics of Workers with Primary Employment in T&C in 1999 Mean SD Age 39.174 10.473 Female 0.571 0.495 Immigrant 0.061 0.240 College Educated 0.112 0.316 Vocational School Educated 0.350 0.477 At most High School 0.514 0.500 Years of Experience in the Labor Market 14.500 5.864 Unemployment History Index 127.241 182.741 Log Hourly Wage 4.968 0.348 High Wage Occupation 0.188 0.390 Mid Wage Occupation 0.622 0.485 Low Wage Occupation 0.112 0.315 Union Membership 0.800 0.400 Unemployment Insurance Membership 0.897 0.304
N 10753 10753 10753 10753 10753 10753 10753 10753 10753 10753 10753 10753 10753 10753
Panel B. Main Outcome Variables Cumulative Years of Employment in High- and Low Wage Jobs
-0.143
4.938
10753
net of Years of Employment in Mid Wage Jobs (2002-2009) Cumulative Hours Worked in High- and Low Wage Jobs net of
-0.062
6.573
10753
Hours Worked in Mid Wage Jobs (2002-2009) Cumulative Salary in High- and Low Wage Jobs net of Salary in
0.175
8.735
10753
Mid Wage Jobs (2002-2009) Cumulative Years of Employment in High Wage Jobs (2002-2009) Cumulative Years of Employment in Mid Wage Jobs (2002-2009) Cumulative Years of Employment in Low Wage Jobs (2002-2009)
1.379 2.548 1.025
2.505 2.847 1.991
10753 10753 10753
Notes: Variables Female, Immigrant, Union Membership, UI Membership, High Wage, Mid Wage and Low Wage Occupations, College Educated, Vocational School Educated and At most High School are worker-level indicator variables. The Unemployment History Index is the cumulative sum of the percentage of working time spent as unemployed within each year since 1980. Data Source: Statistics Denmark.
We now turn to the relevant characteristics of the larger worker sample drawn from the 22
entire Danish economy.
4.1.2
Workers in the Overall Economy
To carry the analysis to overall economy we constructed two samples. The first sample constitute of all workers who were between 18 and 50 in 1999 and have a primary employment in any of the industry that we have a firm-level information and we have complete information for all our worker and workplace characteristics. These industries are mining, manufacturing, wholesale and retail, hotels and restaurants, transport, storage and communication; financial intermediation; real estate, renting and business activities. There are a little more than 700,000 workers in this sample. As the sample also includes non-manufacturing sectors, the share workers with union membership decreases from 80 to 74 % (Table 2). The share of college educated as well as the average wage are also higher in the bigger sample while the share of female workers are now substantially less.
23
Table 2: Descriptive Statistics : Workers in the Non-Agricultural, Private Sectors Panel A. Characteristics of Workers in the Bigger Sample in 1999 Age Female Immigrant College Educated Vocational School Educated At most High School Years of Experience in the Labor Market Unemployment History Index Log Hourly Wage High Wage Occupation Mid Wage Occupation Low Wage Occupation Union Membership Unemployment Insurance Membership
Mean 33.653 0.327 0.051 0.170 0.422 0.396 12.504 102.641 5.020 0.227 0.495 0.154 0.738 0.783
SD 8.936 0.469 0.221 0.376 0.494 0.489 6.353 172.479 0.467 0.419 0.500 0.361 0.439 0.412
N 713,568 713,568 713,568 713,568 713,568 713,568 713,568 713,568 713,568 713,568 713,568 713,568 713,568 713,568
Panel B. Main Outcome Variables Cumulative Years of Employment in High Wage Jobs (2000-2009) Cumulative Years of Employment in Mid Wage Jobs (2000-2009) Cumulative Years of Employment in Low Wage Jobs (2000-2009)
2.437 3.664 1.212
3.583 3.795 2.358
713,568 713,568 713,568
Notes: Variables Female, Immigrant, Union Membership, UI Membership, High Wage, Mid Wage and Low Wage Occupations, College Educated, Vocational School Educated and At most High School are worker-level indicator variables. The Unemployment History Index is the cumulative sum of the percentage of working time spent as unemployed within each year since 1980. Data Source: Statistics Denmark.
We also constructed an alternative sample that is not restricted with industries that are surveyed at the firm-level, but otherwise includes all workers who are between 18 and 50 years old with primary employment in any sector in 1999.
24
Table 3: Descriptive Statistics: All Economy Workers Panel A. Characteristics of Workers in the Biggest Sample in 1999 Age Female Immigrant College Educated Vocational School Educated At most High School Years of Experience in the Labor Market Unemployment History Index Log Hourly Wage High Wage Occupation Mid Wage Occupation Low Wage Occupation Union Membership Unemployment Insurance Membership
Mean 34.579 0.472 0.046 0.258 0.372 0.360 12.755 103.765 4.984 0.327 0.335 0.215 0.759 0.767
SD 9.051 0.499 0.209 0.437 0.483 0.480 6.354 177.053 0.459 0.469 0.472 0.411 0.428 0.423
N 1,711,166 1,711,166 1,711,166 1,711,166 1,711,166 1,711,166 1,711,166 1,711,166 1,711,166 1,711,166 1,711,166 1,711,166 1,711,166 1,711,166
Panel B. Main Outcome Variables Cumulative Years of Employment in High Wage Jobs (2000-2009) Cumulative Years of Employment in Mid Wage Jobs (2000-2009) Cumulative Years of Employment in Low Wage Jobs (2000-2009)
3.426 2.498 1.550
4.048 3.486 2.866
1,711,166 1,711,166 1,711,166
Notes: Variables Female, Immigrant, Union Membership, UI Membership, High Wage, Mid Wage and Low Wage Occupations, College Educated, Vocational School Educated and At most High School are worker-level indicator variables. The Unemployment History Index is the cumulative sum of the percentage of working time spent as unemployed within each year since 1980. Data Source: Statistics Denmark.
5
Identification
In this section we describe our approach to estimating the causal effect of import competition. We begin with the quasi-experimental approach based on the removal of quotas on Chinese exports, followed by the instrumental-variables approach. 25
5.1
Import competition through quota removals
The Multi-fibre Arrangement was introduced in 1974 to govern world trade in the textiles and clothing. Under this agreement a large portion of textile and clothing exports from low-wage countries to more developed countries was subject to quantitative restrictions called quotas. The arrangement provided extensive protection for developed country textile and clothing industry against competition from low-wage country products. The liberalization of trade in the textile and clothing industry was not agreed until the conclusion of the Uruguay Round in 1995, when the Agreement on Textiles and Clothing (ATC) replaced the MFA, and provisions were made for phasing it out in four steps over a period of 10 years. Quotas were to be eliminated equivalent to 16 percent of 1990 imports at the beginning of 1995 (Phase I), 17 percent at the beginning of 1998 (Phase II), 18 percent at the beginning of 2002 (Phase III), and the remaining 49 percent at the beginning of 2005 (Phase IV). Quotas covered a wide range of both textile and clothing products ranging from bed linens to synthetic filament yarns to shirts but at the same time 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” 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 materials” was under quota but not “corselettes of all types of textile materials”. As one of the smaller members of the EU, the extensive coverage of quotas was largely exogenous to the Danish industrial structure. Coverage of these quotas was determined throughout 1960s and 70s and the negotiations of the MFA were held at the EU level (Spinanger 1999). Since 1993 the quotas were also managed at the EU level. China did not become eligible for quota removal until it joined the WTO at the end of 2001. While there was a considerable amount of uncertainty as to whether China’s negotiations for WTO membership would succeed, we choose 1999 as the year to de26
termine whether a firm had a product in its portfolio that would be subject to a quota removal to limit any anticipation effects. In January 2002, its quotas on Phase I, II and III goods were removed immediately. By being a WTO member, China was also allowed to benefit from the scheduled last phase in January 2005.22 We utilize as our measure of import competition the abolishment of MFA quotas for China in conjunction with her accession to the WTO.23 Most of the quotas for China had more than 90 % filling rates. Figure 8 shows the evolution in textile and clothing import shares of China compared to other developing countries subject to MFA quotas. Clearly China is the country that stands out in this figure. Using transaction-level import data, Utar (2014) confirms that the MFA quotas were binding for China and both the 2002 and the 2005 quota lifting cause a substantial surge of MFA goods from China in Denmark with associated decline in unit prices of these goods.24 As a consequence, virtually all workers employed at firms subject to the quota removals faced increased import competition from China. Employing a twostage least squares strategy in which exposure to Chinese quota removal instruments for import competition generally leads to similar results (see Utar 2014). For this reason, here we employ exposure to Chinese quota removal as our treatment variable. 22
Due to a surge of Chinese imports in the first few months of 2005 at European Union (EU) ports in response to the final phase of the quota removal, the EU retained a few of the quota categories until 2008. Since the sample period extends over 2008, those few quotas are also included in the current analysis. 23 Here we focus on 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. 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 quota Phase IV 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. 24 Khandelwal, Schott, and Wei (2013) shows that the quota removal for China led to an extra efficiency gain in China due to prior mismanagement of quotas by the Chinese government and the decline in prices were a result of entry of more efficient Chinese producers into the export market.
27
Figure 8: Import shares of China and other developing countries subject to MFA quotas in Danish Textile and Clothing Imports 1999-2009
China Argentina Brazil Hong Kong India Indonesia Korea Macao Malaysia Pakistan Peru Phillipines Singapore Sri Lanka Thailand
0.3
0.25
0.2
0.15
0.1
0.05
0 1999
5.2
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
Import competition’s effect on job polarization: an instrumentalvariables approach
Instrumental-variables approaches are widely employed to estimate the impact of globalization on a number of outcomes. For example, authors have instrumented cross-industry variation in FDI inflows into the United Kingdom with FDI inflows into the United States (Haskel, Pereira, and Slaughter 2006). Our approach is similar in spirit to Autor, Dorn, and Hanson (2013, 2014) who instrument for China’s imports in the United States with China’s imports in other high-income countries.
C EM P xi = α0 + α1 ∆IM PiCH + α2 Ini IM PiCH + ZiW + ZiF + ZiI i ,
28
(1)
In equation ∆IM PiCH denotes the change in the share of imports from China over the total imports in worker i’s initial 4-digit industry over the period of 1999 to 2009. We also control for the initial level of the share of Chinese imports in the industry. Ini IM PiCH denotes the initial level of the share of the imports from China over the total imports in worker i’s 4-digit industry of employment in 1999.25 Increased competition from China is proxied by ∆IM PiCH . But of course part of the growth in the Chinese import share could be driven by domestic demand or supply factors. If, for example, relative demand for those goods in which China has a strong comparative advantage increases this would likely to increase imports from China in those goods. In order to extract the exogenous part of the growth in the share of Chinese imports we use the the change in the share of the Chines exports to other advanced countries in the corresponding industries.26 [TBA]
6 6.1
Empirical results Job polarization and Chinese textile imports
To examine the role of trade for job polarization, we consider the following regression:
C EM P xi = β0 + β1 T radei + ZiW + ZiF + i , 25
(2)
To calculate Ini IM PiCH we take the initial year as 1996. We use nine high-income European countries. These countries are Austria, Belgium, Finland, France, Germany, Netherland, Spain, Sweden and Switzerland. We use both the average change in the Chinese imports share across these countries between 1999 and 2009 as well as the change in the total Chinese imports within the nine countries over the initial total imports in these countries. 26
29
where x = h, m, l stands for high, mid-level, and low-wage occupations. The dependent variable CE M P xi is defined as C EM P xi =
T =2009 X
Empxit , ∀x,
(3)
t=2002
where Empxit is a dummy variable that takes the value of one if worker i in year t (t = 2002, ..., 2009) has held a job in high-, mid-level, or low-wage occupations, denoted by x = h, m, l; our classification into these three brackets follows Table A-3.27 Our dependent variable C EM P x,emp gives the number of years that worker i has held prii mary employment in a high-, mid-level, or low-wage occupation between 2002 and 2009. On the right hand side we have the measure of trade, T radei , which 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 sample consists of all workers who are at the working age throughout 2002-2009 and had their primary employment in textiles and clothing in the year 1999. Equation (2) is a cross-sectional, worker-level regression relating cumulative employment in high-, mid-level, or low-level occupations during the sample period to worker-level characteristics in the pre-WTO year of 1999. Recall that employment polarization is the hollowing out of mid-level wage occupations that is accompanied by employment gains in high- and low-wage occupations. If import competition causes employment polarization, T radei should have a negative effect for mid-level wage occupations while T radei should have a positive effect for high- and low-wage occupations. The coefficient on T radei in the mid-level wage regression (i.e., x = m), for example, says that on average workers exposed to import competition from China differed by β1 years of employment from 27
We also use the European ranking of occupations as provided by Goos, Manning and Salomons (2004). It employs the two-digit International Standard Classification of Occupations 1988 (ISCO-88), and the ranking of occupations as high/mid-level/low is based on the 1993 mean European wage. Our results are robust alternatively using European-wide ranking.
30
workers that were not exposed to import competition from China. A value of β1 < 0 means that exposure to trade reduces mid-level wage employment, or, it contributes to hollowing out mid-level wage occupations. Just as for the drivers and machine operators in Figure 7 above, the coefficient on T radei summarizes transitions across many individual workers. For example, workers initially employed in mid-level occupations will often stay there, while some move down into lowwage occupations and others move up into high-wage occupations. Workers starting out in high-wage occupations may move down into mid-level wage occupations while initial low-wage workers might move up into mid-levels. A negative coefficient β1 for x = m would mean that on average workers exposed to import competition spend less time being a mid-level wage worker than workers not exposed to import competition. We can follow workers to see whether they move up or move down to assess the individual welfare consequences of trade, however for the hypothesis that trade causes job polarization all what matters is that β1 < 0 for mid-level occupations while β1 > 0 for high- and lowwage occupations. The regression also includes measures of worker (ZiW ) and firm (ZiF ) characteristics. The vector ZiW includes the following characteristics of worker i in the year 1999: age, gender, immigration status, education level, and the logarithm of i’s average hourly wage.28 Further, ZiW includes initial occupation and education controls: indicators for worker i’s occupation in a high-, mid-level, or low-wage occupation in year 1999, and indicator variables for at least some college education, vocational education, and at most a (non-technical) high school degree. We also include information on worker i’s labor market experience before 1999. First, this is the cumulative sum of the percentage of working time a worker spent as unemployed within each year since 1980, and the number of years of labor market experience before 1999. Finally, ZiW includes indicator variables for worker i’s union and unemployment insurance membership status in year 1999. 28
We take the average wage for years 1999 and 2000 to smooth out temporary effects.
31
The vector of firm controls ZiF holds constant differences across workers’ workplaces in the year 1999 that might induce a spurious correlation. It includes the size of the firm, measured by the number of full-time equivalent employees, firm quality, proxied by the logarithm of the average hourly wage paid in this firm, as well as a measure of the strength of the firm-worker bond, which is the percentage of workers that are not employed in the same firm from year 1998 to year 1999. The focus on textiles in this part of our analysis rules out that the secular decline of labor intensive industries in high-income countries, or the corresponding increase in non-manufacturing affects our results as these forces are in effect in both the control and the treatment group.29 Furthermore, the worker (and firm) controls ensure that our comparison is between ex-ante virtually identical workers; ex-post about half of the workers is exposed to import competition from China while the other half is not. We now turn to the empirical results.
6.1.1
Can trade explain the U-shaped job polarization pattern?
The estimate of β1 in equation (2) captures the impact of lifting the import quotas for China on Danish workers’ cumulative employment. Table 4 presents results from regressions with the three dependent variables, cumulative employment in high-, mid-level, and low-wage occupations (C EM Pih , C EM Pim ,and C EM Pil ). Each regression has n = 10, 753 observations, and included are the full set of worker and firm characteristics (coefficients not shown except for F emale).30 We see that workers exposed to import competition from China spend about 0.2 years 29
Figure A-2 shows that 52 % of all workers in the sample who were working in 2009 moved to the Service Sector by the end of 2009. Figure A-3 shows this figure separately for exposed and non-exposed workers. The figure is 60 % and 44 % respectively for exposed and non-exposed workers. 30 Worker controls are gender, immigration status, age, initial occupation (high, mid-level, or low), education (three levels), hourly wage, experience, unemployment history, union membership, and unemployment insurance membership; firm controls are size, average wage paid, and separation rate. Full results are available upon request.
32
more on average in high-wage jobs, compared to otherwise similar workers that are not exposed to import competition from China. For mid-level employment, the coefficient on T radei is about −0.4, while for low-level employment the coefficient is again about 0.2 (columns 2 and 3, respectively). Import competition has hollowed out the middle of the wage distribution, and it has added jobs both at the top and the bottom of the distribution. This means trade in form of import competition can explain job polarization in our setting. Furthermore, we note that the sum of the T radei coefficients across the occupation categories is approximately equal to zero, making it likely that the fatter tails and the hollowed out middle are the flip sides of the same phenomenon. The coefficients on F emale in these regressions indicate that in general, women experienced job polarization more than men; below we will return to this issue by asking whether women were also more affected than men by import competition causing job polarization.
Occupation While the set of regressors includes indicator variables for each worker’s initial occupation in the broad wage distribution (high, mid-level, or low-wage occupation), there are differences across occupations within broader occupation groups in how susceptible workers are to job polarization; we have seen evidence of that in the case of machine operators versus drivers in section 2 above. If this variation would be correlated with exposure to import competition our results might be spurious, even though Figure A-1 shows that the distribution of broad occupational categories for workers at trade exposed and non-exposed firms was quite similar. We address these concerns by adding indicator variables for worker i’s employment in each of the 22 two-digit occupational codes. The results for high-, mid-level, and low-wage employment job polarization regressions turn out to be not strongly affected by the inclusion of the two-digit occupational dummies. The trade coefficients for high-, mid-level, and low-wage employment remain roughly at 0.2, −0.4, and 0.2, respectively (columns 4, 5, and 6 of Table 4). From this it does not appear to be the case that the trade effect on job polarization is driven by differences in the susceptibility of occupations for polarization. 33
34
0.229*** (0.040) 0.199*** (0.045) no
-0.394*** (0.052) -0.335*** (0.057) no
C EM Pim (2) 0.193*** (0.038) 0.171*** (0.041) no
C EM Pil (3) 0.216*** (0.040) 0.032 (0.046) yes
C EM Pih (4)
-0.382*** (0.052) -0.371*** (0.060) yes
C EM Pim (5)
0.178*** (0.038) 0.292*** (0.043) yes
C EM Pil (6)
Notes: In all columns the number of observations is 10753. Although only coefficient estimates of ‘Female’ control is reported, all regressions include a constant and the full-set of controls, ZiW and ZiF . In columns (4)-(6) initial occupations are controlled for using 2-digit ISCO occupation dummies. Robust standard errors are reported in parentheses. ∗ , ∗∗ and ∗∗∗ indicate significance at the 10 %, 5% and 1% levels respectively. Data Source: Statistics Denmark.
2-Digit ISCO Dummies
Female
Trade
C EM Pih (1)
Table 4: Job Polarization and Import Competition - Years of Employment in High, Mid, and Low Wage Jobs
Education We are also interested in how workers with different levels of education fare in the face of import competition from China. We employ another set of interaction variables of the form T radei × Educi , where Educi takes on a value of one if the highest education level of worker i in 1999 is college, vocational school, and at most high school, respectively, and zero otherwise. Including the trade-education interaction together with the T radei variable, we find that college-educated workers do not contribute to job polarization caused by trade (Table 5, Panel A), in fact college-educated workers at firms exposed to import competition tend to gain, not lose, in terms of mid-level employment. In contrast, workers with at most high school education bear the brunt of the job polarization caused by trade (Panel C). This is in line with the general pattern of job polarization observed in Denmark between 2000-2009 (see Figure 4 in the
35
Supplementary Appendix). Table 5: Polarization and Trade by Education Dep. Var. Panel A. Trade Trade*College
JPiemp C EM Pih (1) (2) College Educated
C EM Pim (3)
C EM Pil (4)
0.931*** (0.095) -0.998*** (0.270)
-0.464*** (0.056) 0.609*** (0.140)
0.214*** (0.042) -0.178* (0.078)
-0.388*** (0.063) -0.016 (0.106)
0.250*** (0.048) -0.159* (0.075)
-0.279*** (0.071) -0.226* (0.100)
0.091 (0.049) 0.201** (0.073)
0.253*** (0.040) -0.212 (0.163)
Panel B.
Vocational School
Trade
0.852*** (0.108) -0.103 (0.180)
Trade*Vocational
0.214*** (0.048) 0.040 (0.082)
Panel C.
at most High School Diploma
Trade
0.572*** (0.126) 0.480** (0.170)
Trade*HS
0.201** (0.064) 0.054 (0.077)
Notes: In all regressions the number of observations is 10753. All regressions include a constant and the full-set of controls, ZiW and ZiF . Robust standard errors are reported in parentheses. ∗ , ∗∗ and ∗∗∗ indicate significance at the 10 %, 5% and 1% levels respectively.
6.1.2
Job polarization through shifts within versus between sectors
In this section we discuss the impact of trade for high-, mid-level, and low-wage employment across sectors. The results are shown in Table 6. In the upper left corner, recall that the coefficient on T radei of 0.229 means that workers employed in 1999 in textiles and clothing firms exposed to import competition from China have had between 2002 and 2009 0.229 years of employment more in high-wage occupations than workers that were not employed by exposed textiles and clothing firms. The question addressed in this section is whether the job polarization impact of trade is associated with worker movements between broad sectors. 36
As columns 2 and 3 show, the gain in high-wage employment that trade has caused is entirely due to workers moving into sectors outside of manufacturing (coefficient of 0.283). There is no significant high-wage employment gain through trade within manufacturing (coefficient of -0.054).31 On the other hand, the employment loss in mid-level jobs is entirely due to reductions in mid-level manufacturing jobs (columns 5 and 6). Indeed trade causes increase in mid-wage jobs in the service sector (as it pushes workers toward the service sector), yet the net effect is negative. Columns 8 and 9 show that the increase in low-wage employment is split between manufacturing jobs and non-manufacturing. Overall, the pattern can be summarized by noting that the mid-wage employment losses are due to decline in manufacturing jobs while employment gains at the tails require to leave manufacturing, especially if a worker succeeds in moving to high-wage employment. This finding shows that frictions to the movement of workers across sectors implies that international trade can increase welfare for some workers (those with sufficiently low cost of moving) while at the same time it decreases welfare of other workers (those with high cost of moving), irrespective of any other effects of trade on welfare. Because non-manufacturing includes a broad set of diverse activities, in the following we isolate the services sector, see Panel B of Table 6.32 The results indicate that the employment changes caused by trade into non-manufacturing are almost entirely accounted for by shifts into the services sector.33 Specifically, columns 1-3 of Panel B show that 100 % of all high-wage employment gain is due to service sector jobs and columns 8 through 9 shows that 60 % of all low-wage employment gain is due to ’low-paid’ service jobs. Earlier work has emphasized the service sector in the context of job polarization (Autor and Dorn 2013). We extend this evidence by showing on the basis of micro evidence that this pattern is particularly true for job polarization caused by trade. To summarize, exposure to import competition from China has significantly affected 31
By construction, the coefficient in column 1 is the sum of the coefficients in columns 2 and 3, and analogously for the other columns of Table 6. 32 The other non-manufacturing sectors are agriculture, fishing, energy and construction. 33 See also Utar (2015) on this.
37
employment trajectories of workers initially employed in Denmark’s textile industry. By triggering employment losses in mid-level occupations at the same time that this trade caused gains in high- and low-wage employment, import competition has played a role in generating job polarization in Denmark. Quantitatively, import competition led to 0.4 years of employment less in mid-level and 0.2 years each in high- and low-wage employment over a period of eight years. We have also seen that job polarization is driven by textile workers moving into service sector jobs. To economize on space we introduce the following summary measure of job polarization: JPiemp
=
T =2009 X
{Emphit + Emplit − Empm it }.
(4)
t=2002
JPiemp is simply the sum of cumulative employment of worker i in high- and low-wage occupations, minus the worker’s cumulative employment in mid-level wage occupations. Because job polarization is characterized by employment gains in the tails and losses in the middle, by construction any variable that causes job polarization will increase the measure (4). In addition to this measure of job polarization based on years of employment, we have employed an alternative measure that captures changes in the hours worked of each worker. It is defined as
JPihrs =
PT =2009 t=2002
{Hourshit + Hourslit − Hoursm it } Hoursit0
(5)
where Hoursxit is defined as the total number of hours worked by worker i in year t in high/mid-level/low-wage wage primary occupations. JPihrs captures the cumulative hours worked in high-wage jobs plus the hours worked in low- wage jobs, minus hours worked in mid-level wage jobs throughout 2002-2009 for worker i; the measure is normalized by Hoursit0 , which is worker i’s hours worked in his or her primary employment in initial
38
year t0 .34 We also present results for an earnings polarization measure, defined as
JPiwage
PT =2009 =
t=2002
{Earningshit + Earningslit − Earningsm it } Earningsit0
(6)
where Earningsxit is total labor earnings of worker i from his/her high-, mid-level, or low-wage primary occupation, x = h, m, l. In the denominator of equation (6) are worker i’s initial earnings in his or her primary job. Table 7 shows results for the summary measures of job polarization in terms of years of employment, hours, and earnings side by side (columns 4, 5, and 6), with columns 1 to 3 giving again the separate regressions on cumulative employment in high-, mid-level, and low-wage occupations from Table 2 for convenience.35 First, note that given the definition in (4), the sum of the absolute values of the T radei coefficients in columns 1 to 3 equal 0.816, the coefficient on T radei in column 4. Second, the results for job polarization based on hours worked and earnings are generally similar to those based on years of employment, see columns 5 and 6 compared to column 4. In the following we will largely focus on the years of employment measure.36 Having shown that import competition from China has played a significant role for workers initially employed in Denmark’s textiles industry, we now ask whether this result generalizes to the entire economy.
To reduce measurement errors, both JPihrs and the variable JPiwage , defined below, employ the average of the respective values for years 1999 and 2000. 35 Results for all variables included in the case of the column 4 specification are given in Table X of the Appendix [formerly, Table 2, last column]. 36 In Table A-4 we provide the results from transforming the dependent variables into logarithmic scale (after adding an arbitrary amount to make the minimum value one) and the results are robust. We also employ the manufacturing revenue share of goods that were subject to the removal of quotas for China in 1999 in worker i’s initial employer as an alternative trade exposure variable. The revenue share trade variable takes into account the degree in which the initial firms were exposed to the competition. We find that our results are robust (see Table A-5). 34
39
40
0.229*** (0.040) 0.199*** (0.045)
C EM Pih
0.229*** (0.040) 0.199*** (0.045)
C EM Pih
(1)
C EM Pih Within Manuf. -0.054 (0.034) 0.049 (0.035)
C EM Pih Within Manuf. -0.054 (0.034) 0.049 (0.035)
(2)
C EM Pih Within Service 0.276*** (0.030) 0.158*** (0.034)
C EM Pih Outside Manuf. 0.283*** (0.030) 0.150*** (0.034)
(3)
-0.394*** (0.052) -0.335*** (0.057)
C EM Pim
-0.394*** (0.052) -0.335*** (0.057)
C EM Pim
(4)
C EM Pim Within Manuf. -0.551*** (0.048) -0.339*** (0.053)
C EM Pim Within Manuf. -0.551*** (0.048) -0.339*** (0.053)
(5)
C EM Pim Within Service 0.156*** (0.027) 0.128*** (0.031)
C EM Pim Outside Manuf. 0.157*** (0.029) 0.004 (0.034)
(6)
0.193*** (0.038) 0.171*** (0.041)
C EM Pil
0.193*** (0.038) 0.171*** (0.041)
C EM Pil
(7)
C EM Pil Within Manuf. 0.074** (0.023) -0.120*** (0.025)
C EM Pil Within Manuf. 0.074** (0.023) -0.120*** (0.025)
(8)
C EM Pil Within Service 0.116*** (0.031) 0.332*** (0.032)
C EM Pil Outside Manuf. 0.119*** (0.032) 0.291*** (0.033)
(9)
Notes: In all columns the number of observations is 10753. Although only coefficient estimates of ‘Female’ control is reported, all regressions include a constant and the full-set of controls, ZiW and ZiF . Robust standard errors are reported in parentheses. ∗ , ∗∗ and ∗∗∗ indicate significance at the 10 %, 5% and 1 % levels respectively. Data Source: Statistics Denmark.
Female
Trade
Panel B. Dep. Var
Female
Trade
Panel A. Dep. Var
Table 6: Job Polarization and Job Shifts Within versus Outside Manufacturing
Table 7: Job Polarization and Trade - Employment, Hours, Earnings
Trade
C EM Pih (1) 0.229*** (0.040)
C EM Pim (2) -0.394*** (0.052)
C EM Pil (3) 0.193*** (0.038)
JPiemp (4) 0.816*** (0.089)
JPihrs (5) 0.867*** (0.124)
JPiwage (6) 0.955*** (0.171)
Notes: In all columns the number of observations is 10753. all regressions include a constant and the full-set of controls, ZiW and ZiF . Robust standard errors are reported in parentheses. ∗ , ∗∗ and ∗∗∗ indicate significance at the 10 %, 5% and 1% levels respectively. Data Source: Statistics Denmark.
6.2
Job polarization and Chinese imports
In this section we examine the role of import competition for job polarization in Denmark’s aggregate economy. Because for the entire Danish economy there is no policy change comparable to the MFA quota removal we estimate the causal effect of trade with the instrumental-variables approach outlined in section 5.2 above. The following begins by employing the instrumental-variables approach for the sample of employees that initially worked in Denmark’s textiles industry, which are compared to estimates based on the quasi-experimental MFA setting from above. We begin with the instrumental-variables estimation for the sample of initial textile workers. The change in Denmark’s import share from China between 1999 and 2009 at the four-digit industry-level is instrumented by the average of the corresponding changes in nine high-income countries. The specification also includes the share of China in Denmark’s total imports in 1996, which is instrumented by the corresponding shares in other high-income countries. The regression also industry controls for skill differences, outsourcing, and investment computed at the six-digit industry-level. Results are shown in columns 4, 5, and 6 of Table 8 for employment in high-wage, mid-level, and low-wage occupations. The first-stage regressions appear to be strong as 41
indicated by the F-statistics, see Table 6 at the bottom. The second-stage point estimates are about 1.6, -2.5, and 0.4 for high-, mid-level, and low-wage employment, respectively. The sign of the coefficients is in line with our results based on the MFA policy change, which we provide again in columns 1 to 3.37 Instrumental variables estimation also yields the result that import competition has contributed to job polarization. One difference is that according to the instrumental variables estimates import competition has no significant effect on low-wage employment.
Beyond the sign of the coefficients, how do the results based on MFA quota removal and instrumental variables estimation compare in terms of economic magnitudes? This can be assessed by comparing standardized beta coefficients. Figure ?? shows the beta coefficients for the instrumental variables estimation (columns 4 to 6 of Table 8) side by side the beta coefficients based on the MFA quota removal (using firm’s revenue shares of exposed products). In general, the magnitudes implied by the two sets of estimates are not far apart. In particular, instrumental variables and MFA quota removal approach predict a hollowing out of mid-level wage employment of nearly the same magnitude (beta coefficients of -0.077 versus -0.080). The main difference between the magnitudes of the two sets of coefficients concerns the effect of import competition on low-wage employment; here the MFA approach predicts a magnitude that is three times that implied by the instrumental variables approach. Overall, these results are encouraging. First, the two sets of results are broadly similar. Second, if one believes that the MFA quota results are close to the true causal effect, as we do, then by predicting a relatively low effect on low-wage employment, the instrumental variables approach, if anything, understates the effect of import competition on job polarization. Thus while the instrumental variables approach may be too optimistic in the sense that it predicts import competition to shift employment more strongly into 37
The results here are based on specifications that also include 6-digit industry controls for labor force skills, outsourcing, and investment.
42
43
0.270*** (0.048) [0.044]
-0.455*** (0.059) [-0.067]
C EM Pim (2) 0.274*** (0.043) [0.060]
C EM Pil (3)
1.575*** (0.284) [0.055] 1.329*** (0.339) [0.036]
C EM Pih (4) IV
-2.463*** (0.340) [-0.077] -2.624*** (0.427) [-0.064]
C EM Pim (5) IV
0.442 (0.255) [0.021] 0.421 (0.312) [0.016]
C EM Pil (6) IV
0.992*** (0.256) [0.035] 0.599 (0.314) [0.016]
C EM Pih (7) OLS
-1.750*** (0.311) [-0.055] -2.338*** (0.392) [-0.057]
C EM Pim (8) OLS
0.336 (0.225) [0.016] 0.437 (0.289) [0.016]
C EM Pil (9) OLS
Notes: In all columns the number of observations is 10768. All regressions include a constant and the full-set of controls, ZiW and ZiF . Additionally all regressions include controls for skill and investment characteristics for each 6-digit industries. Robust standard errors are reported in parentheses. ∗ , ∗∗ and ∗∗∗ indicate significance at the 10 %, 5% and 1% levels respectively. Beta coefficients are reported in square brackets. Data Source: Statistics Denmark.
Ini IM PiCH
∆IM PiCH
Trade
C EM Pih (1)
Table 8
0.08 0.06 0.04 0.02
0 -0.02 -0.04 -0.06 -0.08 -0.1 High-wage
Mid-level wage MFA quasi-experiment
Low-wage
Instrumental variables
Figure 9: MFA Quasi-Experiment and Instrumental Variables Estimation high- than into low-wage employment–while in fact the effect on low- and high-wage employment is symmetric–, overall the instrumental variables approach is conservative in that it does not overstate the polarization effect of import competition. Applying the instrumental variables approach to Denmark as a whole, the results shown in Table 9 emerge. Import competition from China leads to less mid-level wage employment at the same time as it increases both high- and low-wage employment. This provides evidence that import competition plays a role in explaining job polarization in Denmark as a whole.38 In the following we investigate the economic magnitudes that are involved. The first step is to compare the importance of import competition for job polarization for textile versus all Danish workers. In the next section we will quantify the effect of import competition relative to that of other causes of job polarization that are present. 38
In an even broader sample which includes government and public sector jobs as well (n = 1, 710432) we find evidence that import competition hollows out mid-level employment and leads to gains in the tails as well, though not significant for low-wage employment. [regs all 1]. One disadvantage of this sample is that we cannot construct all worker and firm controls that are included in our analysis.
44
Table 9
∆IM PiCH
Ini IM PiCH
C EM Pih (1) IV 0.605*** (0.096) [0.009] -0.048 (0.157) [-0.000]
C EM Pim (2) IV -1.515*** (0.131) [-0.021] -2.381*** (0.208) [-0.016]
C EM Pil (3) IV 0.463*** (0.084) [0.010] 0.118 (0.137) [0.001]
Notes: In all columns the number of observations is 712834. All regressions include a constant and the full-set of controls, ZiW and ZiF . Additionally all regressions include controls for skill and investment characteristics for each 6-digit industries and 2-digit industry dummies. Robust standard errors are reported in parentheses. ∗ , ∗∗ and ∗∗∗ indicate significance at the 10 %, 5% and 1% levels respectively. Beta coefficients are reported in square brackets. Data Source: Statistics Denmark.
Comparing the beta coefficients on trade for the textile sector and the economy as a whole shows that the trade effect in the former appears to be higher. The beta coefficients for the effect of trade in textiles are about four to five times their level for the economy as a whole. This is not unexpected given that trade with China has affected few sectors in Denmark as strongly as the textiles sector. In contrast, the role that worker age or gender, for example, play for job polarization are roughly similar for initial textile workers and workers in the Danish economy as whole. We can also examine the magnitude of import competition as a cause of job polarization compared to all reasons for employment changes. In particular, among the textile workers the share that held mid-level wage jobs falls from 60 % in 1999 to 28 % in 2009. The regression coefficient of -0.455 [MFA 0/1, mid] implies that import competition explains about 14 % of the total decline in mid-level employment over these years. For Denmark as a whole, a similar calculation implies that import competition explains about 7 % of the total decline in mid-level employment over the period 1999 to 2009; 45
based on the results in column 5, Table 6 using the 90th/10th percentile range of the import share change. These calculations indicate that the effect of import competition in the overall economy is sizable. We now consider the role of import competition in the light of alternative explanations for job polarization.
6.3
Technical change, offshoring and other explanations
In existing analyses of the causes of job polarization technology explanations play a central role. We therefore expect that routine-biased technical change (RBTC) helps to explain job polarization in our sample. The question is only to what extent, and whether trade mimics RBTC, in which case after controlling for RBTC the T radei variable will lose its significance. We are also interested in the extent to which the import competition effect is correlated with offshoring. China is a major offshoring destination, especially after it entered the WTO, and an increase of textiles and clothing goods from China could be related to offshoring decisions of Danish firms. Alternatively, if trade turns out to be unrelated to offshoring, it might be more plausible to think of autonomous Chinese production as the result of technology diffusion to Chinese-owned firms. We employ measures of RBTC and offshoring that are well established in the literature. One, the potential for occupational changes due to RBTC is captured by the routine task intensity (RTI; the variable is denoted as RT Ii ) introduced in section 2. Two, the likelihood of polarization due to offshoring, denoted Of f shoringi , is captured by a measure of Goos, Manning, and Salomons (2014).39 Both RBTC and offshoring variables are defined at the two-digit level of occupations.40 Table 10 presents the results for our textile worker sample. All regressions include the 39
We have also considered Blinder and Krueger’s (2013) offshoring measure, finding broadly similar results. 40 Note that RBTC and offshoring measures are taken from Goos, Manning, and Salomons (2014) and they are at the 2-digit ISCO level.
46
full set of worker and firm controls of Table ?? as well as control variables for skill composition of the work force, outsourcing, and investment at the 6-digit industry level. 41
We employ the job polarization summary measure 4 in order to economize on space.
Furthermore, above it has been shown that the instrumental variables and MFA quasiexperimental approach yield similar results for our textile worker sample; consequently we will present in the following only the latter.42 In the first specification we add Of f shoring to the regression, finding that workers that are highly offshorable contribute to the finding of job polarization as measured by JP emp (column 1). The table gives standardized beta coefficients in square brackets, which indicate that import competition has a larger effect on job polarization than offshoring. Given the central role of computers for technical change in manufacturing recently, we add next a measure of the intensity of the worker’s interaction with computers (ICTi , column 2). Note that this does not simply measure whether the worker is prone to be replaced by a computer. Rather, the measure picks up that there are certain aspects of the job that can be codified, explaining the presence of the computer, but the worker’s task might involve to monitor and evaluate output from the computer, which is likely a non-routine task. We find no significant effect for this ICT variable. However, when we add the routine task index (RTI), not only is there more job polarization for routine tasks, as expected, but interacting with computers now enters with a negative sign (column 3). This suggests that the routine task index helps to separate routine from non-routine tasks in our ICT variable. The positive and significant coefficient on RTI in column 3 confirms earlier results that RBTC is a factor explaining job polarization in Denmark. The T radei coefficient is hardly affected by the inclusion of the offshoring and task variables. In terms of beta coefficients the impact of trade appears to be substantial, comparable to the technology factors captured by the RTI variable. 41
We now utilize the 4-digit ISCO codes for each workers, which explains the lower number of observations compared to above. 42 Instrumental variables results are similar and available upon request.
47
Next, we investigate the relationship between import competition from China and offshoring. While offshoring affects primarily intermediate inputs trade and the China quota removal leads to an inflow of final goods into Denmark (Utar 2014), it is nevertheless possible that the two are related. In particular, it could be that certain tasks that workers used to perform in Denmark are offshored to China, where they are combined with other tasks before the final textiles and clothing good is exported from China to Denmark. We employ an interaction variable between import competition from China and offshoring, T radei × Of f shoringi , to see whether there is evidence for a complementary relationship between offshoring and imports from China. The inclusion of the interaction variable turns the linear offshoring effect to zero, while the size of the linear T radei effect shrinks to around 0.5 (column 4). Offshoring and imports from China appear to be complements, most likely because task offshoring leads to new final goods that are being shipped from China to compete for customers in Denmark. We also consider an analogous interaction variable between T radei and ICTi . Trade hardly leads to job polarization through workers whose tasks involve interacting with computers, see column 5. When instead we include an interaction between T radei and the RT Ii variable, it does not enter significantly (column 6). It appears that the ICTi variable captures quite well the task characteristics that shield a worker from job polarization.43 Finally, column 7 introduces four-digit occupational indicator variables. By construction, the occupational dummies fully account for the variation in RTI–our RTI variable varies at the two-digit ISCO level–, and the trade effect is with 0.92 similar in size to before. Analogous results for the all-economy sample are shown in Table 9. [based on ”regs t7 all.scml”] All specifications are estimated using two-stage least squares as described above. Standardized beta coefficients are given in square brackets. We begin by adding Of f shoring to the regression, which comes in with a positive coefficient, consistent with the idea that 43
As we will see below, workers interacting with computers are less likely to lose mid-level wage jobs, and also less likely shift to low-wage jobs than the average worker (Table 12).
48
offshorability of workers contributes to employment polarization. Interacting with computers also enters significantly (column 2), confirming our result for textile workers. The introduction of RT Ii as a measure of routine-biased technical change substantially reduces the importance of Of f shoring, in line with the idea that the communication improvements that make offshoring possible are related to technical change that affect how routine tasks are performed. According to the beta coefficients, import competition is about four times as important for polarization as offshoring, while RBTC as measured by the routine task index is about two times more powerful than import competition. Our all-economy results confirm that offshoring and import competition interact with each other, see column 4. Offshoring has only a significant effect through import competition. The interaction between import competition and computer usage enters positively and roughly doubles the coefficient on the change in the import share from China (column 5). Different from the textiles sample, here we also find that import competition interacts with our measure of RBTC; the interaction of RT Ii with the import share change enters negatively, implying that import competition causes less employment polarization for workers whose tasks are likely performed by computers and robots in the future (column 6). The coefficient on the import share change is now 3.4, up from 2.5 in column 3; this increase however results from the interaction of import competition with computer usage more than with the RT Ii variable (columns 5 and 6, respectively). Finally, we include four-digit occupation fixed effects in the regression. These eliminate any contribution from our variables measuring offshorability and RBTC–recall that these measures vary at the two-digit occupation level. The size of the import share change coefficient is now 1.1, about half the size it had in the regression with all main effects of column 3. Overall, these results suggest that import competition has played a role in generating job polarization in Denmark. Quantitatively, the results above indicate that import competition has been considerably more important than offshoring. In contrast, the effect of import competition has been less important than RBTC, perhaps by a factor 49
of 3. We seek to gain additional insight by looking at the three individual components of job polarization–employment gains in the tails and losses in the middle–separately. Results are shown in Table 10 [based on ”regs comp rti2.scml”] Note that the beta coefficient for the Of f shoring variable in the high-wage employment regression is -5.3 %, which is the opposite of what is needed to explain the high-wage employment gains that are a key characteristic of job polarization. In contrast, routinebiased technical change as measured by RT Ii leads to losses of mid-level employment at the same time as there are gains in the tails. However, during the 2000s in Denmark RBTC does not seem to be very important in explaining the shift to low-wage employment, which is in line with recent findings for the U.S. that the role of RBTC in affecting labor market outcomes has faded recently (Autor, Dorn, Hanson 2014 EJ). In fact RT Ii explains high-wage employment gains far better than low-wage employment gains in our sample, with beta coefficients of 6.4 % for high-wage and 1.4% for low-wage employment. To the extent that RBTC explains job polarization in the 2000s in Denmark, technical change must have operated in conjunction with other processes, in particular those can lead to the increase in low-wage employment that earlier work has emphasized (Autor and Dorn 2013). More research needs to be done to identify the drivers of technical change in the first decades of the 21st century. In contrast, import competition appears to be able to explain job polarization rather well in the sense that the employment losses it triggers in the middle largely mirror the employment gains in the tails. The import share change coefficients on cumulative employment in high-, mid-level, and low-wage occupations are about 0.65, -1.5, and 0.4, respectively, with beta coefficients in square brackets, which are 0.9%, -2.0%, and 0.9%, respectively. We now turn to a more detailed analysis of the relationship between the impact of import competition and task characteristics.
50
51 no
1.045*** (0.114) [0.085] 0.131* (0.051) [0.034]
no
1.038*** (0.114) [0.085] 0.153** (0.054) [0.039] 0.093 (0.077) [0.015]
JPiemp (2)
no
1.025*** (0.114) [0.083] 0.156** (0.054) [0.040] -0.222* (0.098) [ -0.036] 0.570*** (0.124) [0.081]
JPiemp (3)
no
0.559*** (0.153) [0.046] 0.015 (0.061) [0.004] -0.206* (0.098) [ -0.033] 0.589*** (0.124) [0.084] 0.327*** (0.068) [0.071]
JPiemp (4)
ZiW
no ZiF ,
-0.713*** (0.114) [-0.082]
0.902*** (0.117) [0.073] 0.130* (0.054) [0.033] 0.112 (0.112) [0.018] 0.608*** (0.124) [0.087]
JPiemp (5)
-0.094 (0.133) [-0.011] no
1.079*** (0.133) [0.088] 0.155** (0.054) [0.040] -0.217* (0.099) [-0.035] 0.613*** (0.135) [0.088]
JPiemp (6)
yes
0.917*** (0.119) [0.075]
JPiemp (7)
Notes: In all columns the number of observations is 8598. A constant and the full set of controls, and are included in all regressions but not reported. All regressions also include 6-digit industry characteristics. “Offshoring” is the offshorability index of the corresponding two digit initial ISCO-88 occupation code of a worker. It is constructed by Goos, Manning and Salomons (2014). “ICT” is the index variable indicating the degree in which the initial (4-digit) occupation of a worker interacts with computers. It is an O*NET (version 14) variable and varies across 4-digit occupations. “RTI” is the routine intensity index of the corresponding two digit initial ISCO-88 occupation code of a worker. The RTI index follows Autor, Levy and Murnane (2003) and Autor and Dorn (2012). Offshoring index is not available for certain ISCO 2-digit categories (see the Supplementary Appendix) and ICT index is not available across certain 4-digit ISCOs as they are provided by O*NET. Finally 7 % of workers have undetermined initial occupations. Hence running the estimations in a common sample decreases the number of observations to 8702. Control variables are described in the notes of Table ??. Standardized coefficients are reported in square brackets. Robust standard errors are reported in parentheses. ∗ , ∗∗ and ∗∗∗ indicate significance at the 10 %, 5 % and 1 % levels respectively. Data Source: Statistics Denmark.
4-Digit ISCO Dummies
Trade*RTI
Trade*ICT
Trade*Offshoring
Routine Task Intensity (RTI)
Interacting with Computers (ICT)
Offshoring
Trade
JPiemp (1)
Table 10: Job Polarization, Offshoring, Technology, and Trade
52 no
2.638*** (0.246) [0.020] 2.338*** (0.394) [0.008] 0.137*** (0.007) [0.024]
no
2.583*** (0.245) [0.019] 2.426*** (0.393) [0.009] 0.108*** (0.007) [0.019] 0.491*** (0.010) [0.072]
JPiemp (2)
no
2.530*** (0.245) [0.019] 2.411*** (0.393) [0.009] 0.028*** (0.008) [0.005] 0.228*** (0.012) [0.034] 0.470*** (0.014) [0.062]
JPiemp (3)
no
1.806*** (0.268) [0.014] 2.642*** (0.394) [0.010] -0.019 (0.011) [-0.003] 0.222*** (0.012) [0.033] 0.494*** (0.014) [0.065] 1.193*** (0.183) [0.015]
JPiemp (4)
no
3.061*** (0.256) [0.025]
3.558*** (0.264) [0.027] 2.628*** (0.396) [0.009] 0.035*** (0.008) [0.006] 0.155*** (0.014) [0.023] 0.478*** (0.014) [0.063]
JPiemp (5)
-1.642*** (0.269) [-0.012] no
3.419*** (0.282) [0.026] 2.491*** (0.394) [0.009] 0.027*** (0.008) [0.005] 0.226*** (0.012) [0.033] 0.495*** (0.014) [0.065]
JPiemp (6)
yes
1.140*** (0.251) [0.009] 2.243*** (0.406) [0.008]
JPiemp (7)
Notes: In all columns the number of observations is 575168. A constant and the full set of controls, ZiW and ZiF , are included in all regressions but not reported. All regressions also include 6-digit industry characteristics. “Offshoring” is the offshorability index of the corresponding two digit initial ISCO-88 occupation code of a worker. It is constructed by Goos, Manning and Salomons (2014). “ICT” is the index variable indicating the degree in which the initial (4-digit) occupation of a worker interacts with computers. It is an O*NET (version 14) variable and varies across 4-digit occupations. “RTI” is the routine intensity index of the corresponding two digit initial ISCO-88 occupation code of a worker. The RTI index follows Autor, Levy and Murnane (2003) and Autor and Dorn (2012). Offshoring index is not available for certain ISCO 2-digit categories (see the Supplementary Appendix) and ICT index is not available across certain 4-digit ISCOs as they are provided by O*NET. Finally 7 % of workers have undetermined initial occupations. Hence running the estimations in a common sample decreases the number of observations to 8702. Control variables are described in the notes of Table ??. Standardized coefficients are reported in square brackets. Robust standard errors are reported in parentheses. ∗ , ∗∗ and ∗∗∗ indicate significance at the 10 %, 5 % and 1 % levels respectively. Data Source: Statistics Denmark.
4-Digit ISCO Dummies
Trade*RTI
Trade*ICT
Trade*Offshoring
Routine Task Intensity (RTI)
Interacting with Computers (ICT)
Offshoring
Ini IM PiCH
∆IM PiCH
JPiemp (1)
Table 11: Job Polarization, Offshoring, Technology, and Trade–All Economy
6.4
Unpacking the trade effect
It is important to go further because as noted above, it has been challenging at times to distinguish trade from technology causes of job polarization. In this section we look at the intersection of exposure to import competition and specific tasks from the O*NET data base at the level of individual workers.44 Our approach will be to interact the 0/1 variable T radei with a measure of the importance of a particular task in that worker i’s four-digit occupation class. The regression of cumulative worker employment in 2002 to 2009 then includes three terms, in addition to all worker and firm characteristics from before. These three variables are the linear trade and task measures, denoted by T radei and ON ETi , together with their interaction T radei × ON ETi . In Table 12 we show results for six specific O*NET measures; additional results can be found in Tables 2 to 8 of the Supplementary Appendix.45 The six O*NET tasks are representative of routine manual tasks (Panel A), non-routine manual tasks (Panel B), routine cognitive tasks (Panel C), abstract tasks (Panel D), information and communication technology-intensive tasks (Panel E), and tasks that are intensive in face-to-face communication (Panel F). Tasks along these lines have been emphasized in influential studies of job polarization, and based on this work one would expect that routine tasks contribute to job polarization (Panes A and C) while nonroutine tasks do not (Panels B and D) (Autor, Levy, Murnane 2003, Autor and Dorn 2013). Tasks that involve intensively interacting with computers would also tend to be non-routine (the computer itself performs the routine part of the tasks), while faceto-face communication intensive tasks tend to prevent offshoring and thus reduce job polarization (Blinder and Krueger 2013, Firpo, Fortin, and Lemieux 2011). We present results both for the measure that sums the absolute value of employment changes in 44
We use O*NET June 2009 version 14. See the data appendix section in the Supplementary Appendix for further details. 45 In order to keep the analysis transparent we utilize individual O*NET measures and check the robustness of the results by employing similar O*NET measures classified under the same broad group, rather than creating new composite indices; on this point, see Autor (2013).
53
high-, mid-level, and low-wage jobs (JPiemp ), and for each of these three wage groups separately. Starting with the former, we see that workers have been affected quite differently by trade depending on the specific tasks they perform. On average trade has caused an employment churn of about 0.8 years in our JPiemp measure. The first column of Table 12 shows that the marginal effect of T radei ranges from essentially zero for routine cognitive tasks (0.524+(-0.587)= -0.063, see Panel C) to a whopping 1.4 years in the case of nonroutine manual tasks such as those requiring gross body coordination (Panel B). At the same time, while there is variation in the size of trade’s impact on job polarization, trade has contributed to job polarization through workers performing most kinds of tasks. We also see that trade’s effect is particularly high for manual tasks. In contrast, as expected there are tasks (and associated technologies) that contribute to job polarization (a positive effect on JPiemp ) while other tasks are associated with lower levels of job polarization (a negative effect on JPiemp ). The marginal effect of ON ETi ranges from 0.31 (-0.30+0.61=0.31, Panel B for non-routine manual tasks) to -0.42 (for ICT intensive tasks Panel E). While routine manual tasks are prone to polarization, we do not find evidence that this is the case for routine cognitive tasks.46 In line with expectations is the result that tasks that involve interacting with computers and faceto-face tend to be less associated with job polarization (Panels E and F). Looking at the three individual wage groups, we see that across different tasks trade has caused between almost 0.8 and virtually 0 years of employment less in the midlevel wage occupations (Panels B and E, column 3). While on average trade leads to a higher employment of 0.2 years for high-wage occupations, workers performing routine cognitive tasks do not experience that (column 2, Panel C). In contrast, trade has a particularly strong effect of shifting workers performing manual tasks into low-wage occupations (column 4). At the same time, workers that perform ICT-intensive tasks 46
This is confirmed by results for additional O*NET categories, see Tables 2 and 4 in the Supplementary Appendix.
54
or require face-to-face communication are less likely shifted through trade into low-wage jobs (column 4, Panels E and F). Overall, there is evidence that technology factors and trade overlap to some extent in causing job polarization. Manual tasks, in particular, whether routine or non-routine, are prone to job polarization both through technology factors and trade. The fact that T radei and ON ETi variables are often both significant in Table 12 provides evidence that technology and trade are sufficiently distinct that we can separately identify their effect on labor demand. It is also useful to contrast our trade and technology measures with each other. The O*NET measures highlight important aspects of the worker’s tasks. At the same time, while the variable Repetitive Motions suggests that these tasks could be replaced by a robot (e.g. an automated machine-driven process controlled by a computer), it is not direct evidence of robotization, and it is hard to delineate the factors that the variable Repetitive Motions picks up. Our comparison of workers who suddenly are or are not exposed to new import competition from China makes for a comparatively easy interpretation of the coefficient on T radei . Strong identification of the impact of trade is also suggested by the fact that the T radei × ON ETi interaction effect is estimated quite precisely in all six panels of Table 8 (column 1).47 We now examine gender differences in the way that trade has caused job polarization in Denmark.
47
Also note that in many cases it is the trade-task interaction that yields what one would generally expect as the task measure’s marginal effect on job polarization based on earlier research.
55
Table 12: Tasks, Trade, and Polarization Dep. Var. Panel A. Trade Repetitive Motions Trade*Repetitive Motions Panel B. Trade GBC Trade*GBC Panel C. Trade Evaluating Trade*Evaluating Panel D. Trade Thinking Trade*Thinking Panel E. Trade ICT Trade*ICT Panel F. Trade Interpersonal Trade*Interpersonal
JPiemp JPih,emp JPim,emp JPil,emp (1) (2) (3) (4) Routine Manual Tasks Tasks with Repetitive Motions 0.570*** 0.270*** -0.229*** 0.071 (0.117) (0.066) (0.062) (0.043) -0.224* -0.312*** 0.050 0.137*** (0.089) (0.041) (0.053) (0.034) 0.397*** -0.022 -0.274*** 0.145*** (0.100) (0.052) (0.055) (0.038) Non-Routine Manual Tasks Tasks with Gross Body Coordination (GBC) 0.792*** 0.196*** -0.399*** 0.197*** (0.095) (0.043) (0.055) (0.040) -0.304*** -0.494*** 0.050 0.240*** (0.091) (0.047) (0.052) (0.030) 0.610*** 0.073 -0.374*** 0.163*** (0.114) (0.058) (0.064) (0.040) Routine Cognitive Tasks Evaluating Information to Determine Compliance with Standards 0.524*** 0.123* -0.281*** 0.120** (0.107) (0.054) (0.060) (0.043) 0.289*** 0.019 -0.130** 0.139*** (0.081) (0.038) (0.048) (0.031) -0.587*** -0.152** 0.263*** -0.171*** (0.113) (0.056) (0.064) (0.047) Abstract Tasks Tasks that involves thinking creatively (Thinking) 0.633*** 0.253*** -0.314*** 0.066 (0.113) (0.063) (0.061) (0.045) 0.200* 0.123** -0.070 0.008 (0.081) (0.038) (0.048) (0.034) -0.280** 0.038 0.137* -0.181*** (0.098) (0.054) (0.053) (0.040) ICT Tasks that involves interacting with computers (ICT) 0.669*** 0.179*** -0.330*** 0.160*** (0.096) (0.048) (0.054) (0.038) 0.200* 0.359*** 0.009 -0.150*** (0.080) (0.038) (0.047) (0.029) -0.641*** -0.014 0.387*** -0.239*** (0.097) (0.048) (0.055) (0.037) Face to Face Communication Intensive Tasks Tasks that involve establishing interpersonal relationships 0.526*** 0.230*** -0.221*** 0.076 (0.113) (0.062) (0.061) (0.041) 0.201** 0.337*** -0.006 -0.141*** (0.072) (0.033) (0.043) (0.026) -0.420*** -0.007 0.264*** -0.150*** (0.085) (0.043) (0.048) (0.033)
Notes: The number of observations is 8819, 9505, 9887, 8876, 9776, and 8816 in Panels A through F respectively. All regressions include a constant and the full-set of controls, ZiW and ZiF . Tasks measures are from O*NET and vary by workers’ initial 4-digit occupations (ISCO). The availability of task measures across occupations determine the number of observations. Robust standard errors are 56 reported in parentheses. ∗ , ∗∗ and ∗∗∗ indicate significance at the 10 %, 5% and 1% levels respectively. Data Source: Statistics Denmark.
6.5
Technology, Trade, Man, Woman
We have shown that women tend to experience job polarization more strongly than men in Denmark. Controlling for worker and firm characteristics, women experience an additional gross churn of 0.7 years of employment away from mid-level and into high- and low-wage jobs (column 8 of Table ??). This finding is in line with the aggregate pattern in Denmark during the sample period (see Figure 2 in the Supplementary Appendix) and it also parallels results for the U.S. (Autor 2010, Figure 4). While job churn can entail substantial job switching costs, the fact that reductions in mid-level employment for women are accompanied by substantial gains in high-wage occupations may be considered evidence that women have more successfully adapted to shifts in demand that have eroded mid-level wage job opportunities than men (Autor 2010). At the same time to date we know very little on gender patterns of job polarization at the level of individual workers. In this section we begin by presenting such evidence for Denmark. First, is there reason to be more encouraged by the pattern for women than for men, as is the case in the U.S.? Second, we examine the role of trade for these genderspecific patterns of polarization. In particular, does import competition allow women to move up from mid-level to high-level jobs, or does import competition increase the chance that women fall down the job ladder to low-wage jobs? If women move both up and down the job ladder, are both shifts of equal importance quantitatively? The following section provides initial answers on these questions.
57
Table 13: Trade, Man, Woman Dep. Var. Panel A. Trade Trade*Female
JPiemp (1)
JPih,emp (2)
JPim,emp (3)
JPil,emp (4)
1.145*** (0.134) -0.575** (0.176)
0.364*** (0.065) -0.236** (0.082)
-0.546*** (0.078) 0.265* (0.104)
0.236*** (0.053) -0.074 (0.074)
1.112*** (0.135) -0.584*** (0.177) yes
0.317*** (0.064) -0.176* (0.081) yes
-0.549*** (0.079) 0.290** (0.104) yes
0.245*** (0.053) -0.117 (0.074) yes
1.110*** (0.140) -0.659*** (0.181) yes
0.283*** (0.065) -0.171* (0.082) yes
-0.562*** (0.082) 0.371*** (0.106) yes
0.266*** (0.055) -0.118 (0.075) yes
Panel B. Trade Trade*Female 2-digit ISCO Dummies Panel C. Trade Trade*Female 4-digit ISCO Dummies
Notes: In all regressions the number of observations is 10753. All regressions include a constant and the full-set of controls, ZiW and ZiF . Robust standard errors are reported in parentheses. ∗ , ∗∗ and ∗∗∗ indicate significance at the 10 %, 5% and 1% levels respectively. Data Source: Statistics Denmark.
To get a better feel of job polarization for women in Denmark, we begin by reporting results for a number of major occupations: managers, professionals, associate professionals and technicians, clerks, craft workers, and machine operators. We find that women that were technicians and clerks in year 1999 experience job polarization much less than men in these occupations, while women who were machine operators in 1999 experience job polarization to a far greater extent than male machine operators (Table A-10). We also see that there is no difference between 1999 female and male managers in the extent to which they are employed in high-wage jobs during the subsequent years 2002 to 2009. In terms of education, we see that women with college education are less prone to job polarization than men, while in contrast women with at most high school education are more prone to job polarization than men (Table A-11, column 1, Panels A and C). College-educated women are less likely than college-educated men to shift into high-wage occupations, while high school educated women are just as likely to move into high-wage 58
jobs as men (column 2, Panels A and C). On the other hand, college educated women are less likely than college-educated men to move into low-wage jobs (column 4, Panel A). The fact that women with at most a high school diploma are substantially more prone to job polarization and they move more into low-wage jobs compared to men is in line with the general pattern observed in Denmark between 2000-2009 (see Supplementary Appendix, Figure 4). Next we examine employment polarization through trade in which women are separated from men (see Table 13). While job polarization overall has affected women more than men, the extent to which it is driven by trade is only about half as large for women as it is for men (column 1, Panel A). One might be concerned that this does not control for differences in the propensity with which men and women work in different occupations. To address this issue, we control in Panel B for two-digit and in Panel C for four-digit occupation fixed effects. The result remains largely unchanged: trade causes substantially less job polarization for women than for men. Before we look into the reasons for this it is important to ask whether trade-driven job polarization has had an impact on gender inequality. Columns 2, 3, and 4 of Table 13 show the impact of trade on cumulative employment in high-, mid-level, and low-wage occupations separately. The key finding here is that trade has shifted women less than men into high-wage jobs while at the same time trade has pushed women to the same extent as men into low-wage jobs.48 Trade is not a woman’s best friend because it raises gender inequality. An important question is why we estimate that trade affects women and men differently in leading to job polarization even when we control for detailed occupation characteristics (at the four-digit ISCO level). In the following we consider movements within and between industries along the lines of our earlier analysis in Table 6. In Table 14 we report employment changes for the three wage groups (high, mid-level, low) separately 48
This is true even if we look only at the point estimates and ignore the standard errors.
59
for manufacturing and non-manufacturing. The question is whether there are differences in how women and men behave in response to a trade shock across wage groups and sectors. We see that trade does not lower mid-level wage employment for women as much as for men within manufacturing (column 4). At the same time, trade does not shift women as much as men to high-wage jobs outside of manufacturing (column 3). This suggests that women stay more in the jobs that are hollowed out (mid-level manufacturing) and move less to high-paying jobs outside of manufacturing than men. Women tend to be more stayers, not movers, compared to men. This finding is limited, however, to job polarization caused by trade, as women in general are found experience job polarization more (they are more prone to leaving mid-wage jobs). While foregoing high-wage jobs outside of manufacturing relative to men (column 3), trade exposed womens’ tendency to stay in mid-level manufacturing jobs might benefit women because they do not shift as much as men into low-wage jobs within manufacturing as men (column 6).
If trade exposed women are less likely to be separated by their competition exposed firms compared to trade exposed men, then this may explain why women are relatively less prone to polarization caused by trade. To better understand this we focus on mid-wage level jobs within the manufacturing sector (see Table 15). In column (1) of Panel A is the cumulative employment in mid-wage occupations within the manufacturing sector. We then decompose the asymmetric effect of trade on women into the effect within and outside the initial firm in columns 2 and 3 respectively. We repeat the analysis with cumulative hours worked since women may be relatively likely to accept reduced hours of work at the initial firm instead of leaving for another job (Utar 2015). First, comparing the T radei coefficients of columns 1 and 2, in Panel A, Table 15 shows that the decrease in mid-level employment caused by trade is entirely due to employment losses at the initial firm, not due to lower employment in other manufacturing firms. Given the importance of employment losses at the initial firm, does this differ between 60
61
1.145*** (0.134) -0.575** (0.176) 0.966*** (0.125)
(1)
JP emp i
-0.003 (0.056) -0.090 (0.068) 0.090 (0.046)
Within Manuf. (2) 0.367*** (0.048) -0.146* (0.063) 0.216*** (0.041)
JP h,emp i Outside Manuf. (3) -0.661*** (0.074) 0.192* (0.096) -0.426*** (0.071)
Within Manuf. (4) 0.115** (0.044) 0.073 (0.059) -0.029 (0.041)
JP m,emp i Outside Manuf. (5)
0.127*** (0.038) -0.092* (0.045) -0.078* (0.031)
Within Manuf. (6)
0.109** (0.039) 0.018 (0.062) 0.283*** (0.043)
JP l,emp i Outside Manuf. (7)
Notes: In all columns the number of observations is 10753. A constant is included but not reported. All regressions include a constant and the full-set of controls, ZiW and ZiF . Robust standard errors are reported in parentheses. ∗ , ∗∗ and ∗∗∗ indicate significance at the 10 %, 5% and 1% levels respectively. Data Source: Statistics Denmark.
Female
Trade*Female
Trade
Dep. Var
Table 14: Trade, Man, Woman-Within and Outside Manufacturing
men and women? Table 15 shows that import shock does not have any significant asymmetric effect on women compared to men at the initial firm. That is, both women and men are affected by the shock to the same extent. This is reassuring because we have controlled for a substantial number of worker and firm characteristics (see Table ??). Panel A shows that 64 % of the asymmetric effect of trade on women (0.123/0.192) is due to mid-wage jobs within manufacturing but outside the initial firm. In terms of hours worked, 71 % of the effect is due mid-wage manufacturing jobs outside the initial firm. Thus, the difference in the extent that trade causes job polarization for men and women is largely due to gender differences in the propensity of moving to different sectors. What might explain these different responses in the face of a trade shock? It is important to keep in mind that women do not in general switch jobs and sectors less than men, because we have seen above that women are more likely than men to gain both highand low-wage employment in the services sector (Table 6). The question is why women are more attached to mid-level manufacturing jobs than men in the presence of a trade shock? We believe that this could be related to both the suddenness of the trade shock, as well as its massive size. The shock to labor demand through trade in the Danish textiles and clothing industry lead to a drastic resizing of the industry. When the industry is undergoing massive changes, the labor market environment might be similar to that in times of mass layoffs during recessions (Jacobson, Lalonde, and Sullivan 1993). In the more recent 2008 Great Recession, aggregate data for the U.S. suggests that women’s employment rates have recovered less well than those of men (Pew Research Center 2011). It is important to better understand similarities and differences between the labor market impact of trade on the one, and of recessions on the other hand. What we have documented is that a sudden and large-scale reduction in labor demand through trade is relatively more challenging for women than for men, compared to a gradual shift of labor demand across sectors as, for example, the introduction of information
62
technology and computers since the early 1980s.49
Table 15: Women and Mid-Wage Occupations Panel A. Dep. Var Trade Trade*Female Female Panel B. Dep. Var Trade Trade*Female Female
Cumulative employment Within Manuf. (1) -0.661*** (0.074) 0.192* (0.096) -0.426*** (0.071)
in mid-wage occupations within manufacturing 2002-2009 Within the Initial Firm Outside the Initial Firm (2) (3) -0.668*** 0.007 (0.058) (0.059) 0.069 0.123 (0.076) (0.074) -0.052 -0.375*** (0.061) (0.052)
Cumulative hours worked in mid-wage occupations within manufacturing 2002-2009 Overall Within Manuf. Within the Initial Firm Outside the Initial Firm (1) (2) (3) -0.768*** -0.688*** -0.080 (0.085) (0.061) (0.071) 0.205 0.059 0.146 (0.116) (0.082) (0.096) -0.390*** -0.038 -0.352*** (0.091) (0.066) (0.075)
Notes: In all columns the number of observations is 10753. A constant is included but not reported. All regressions include a constant and the full-set of controls, ZiW and ZiF . Robust standard errors are reported in parentheses. ∗ ∗∗ , and ∗∗∗ indicate significance at the 10 %, 5% and 1% levels respectively. Data Source: Statistics Denmark.
7
Conclusions
TBA
References [1] Acemoˇ glu, Daron 2002. “Technical Change Inequality, and the Labor Market”, Journal of Economic Literature, 40(1) : 7-72. 49
We have also examined how gender affected the impact of technology factors on job polarization by employing the interaction of F emalei and O N ETi , see Table A-12. Overall, these results show that the gender difference plays an important role in our finding that trade and technology factors are distinct causes of job polarization in Denmark.
63
[2] Acemoˇ glu, Daron, and David Autor 2010. “Skills, Tasks and Technologies: Implications for Employment and Earnings”, Handbook of Labor Economics Volume 4, Orley Ashenfelter and David E. Card (eds.), Amsterdam: Elsevier, forthcoming. [3] Autor, David 2010. “The Polarization of Job Opportunities in the U.S. Labor Market: Implications for Employment and Earnings”, Center for American Progress and The Hamilton Project. [4] Autor, David 2013. “The “task approach” to labor markets: an overview”, Journal for Labour Market Research, 46(3): 185-199. [5] Autor, David, and David Dorn 2013. “The Growth of Low-Skill Service Jobs and the Polarization of the US Labor Market”, American Economic Review, 103(5): 1553-1597. [6] Autor, David, David Dorn, and Gordon Hanson. 2013. “The China Syndrome: Local Labor Market Effects of Import Competition in the United States”, American Economic Review, 103(6): 2121-2168. [7] Autor, David, David Dorn, and Gordon Hanson. 2014. “Untangling Trade and Technology: Evidence from local Labor Markets”, Economic Journal, forthcoming. [8] Autor, David, David Dorn, Gordon Hanson and Jae Song. 2014. “Trade Adjustment: Worker Level Evidence”, The Quarterly Journal of Economics, 129: 1799-1860. [9] Autor, David, Lawrance F. Katz, and Melissa S. Kearney 2006. “The Polarization of the U.S. Labor Market”, American Economic Review: Papers and Proceedings, 96(2), 189-193. [10] Autor, David, Lawrance F. Katz, and Melissa S. Kearney 2008. “Trends in U.S. Wage Inequality: Revising the Revisionists”, The Review of Economics and Statistics, 90(2), 300-323. [11] Autor, David, Levy, Frank, and Richard Murnane 2003. “The Skill-Content of Recent Technological Change: An Empirical Investigation”, Quarterly Journal
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[21] Goos, Maarten, Alan Manning, and Anna Salomons 2009. “Job Polarization in Europe”, American Economic Review: Papers and Proceedings, 99(2), 58-63. [22] Goos, Maarten, Alan Manning, and Anna Salomons 2014. “Explaining Job Polarization: Routine-Biased Technological Change and Offshoring”, American Economic Review, 104(8), 2509-2526. [23] Groes, Fane, Philipp Kircher, and Iorii Manovskii 2015. “The U-Shapes of Occupational Mobility”, Review of Economic Studies, 82(2). [24] Grossman, Gene and Esteban Rossi-Hansberg 2008. “Trading Tasks: A Simple Theory of Offshoring”, American Economic Review, 98:5, 1978ˆae“1997. [25] Harrigan, James, Ariell Reshef, and Farid Toubal 2015. “The March of the Techies: Technology, Trade, and Job Polarization in France, 1994-2007”, presented at Barcelona GSE Summer Forum 2015. [26] Hummels, David, Rasmus Jorgensen, Jacob Munch, and Chong Xiang 2014. “The Wage Effects of Offshoring: Evidence From Danish Matched WorkerFirm Data”, American Economic Review, 104(6): 1597-1629. [27] Jacobson, Louis, Robert LaLonde and Daniel Sullivan 1993. “Earnings Losses of Displaced Workers”, American Economic Review, 83(4): 685-709. [28] Keller, Wolfgang, Stephen Yeaple, and Nick Zolas 2015. “Innovation in the Age of Offshoring”, in progress. [29] Khandelwal, Amit, Peter Schott and Shang-Jin Wei 2013. “Trade Liberalization and Embedded Institutional Reform: Evidence from Chinese Exporters”, American Economic Review, Vol. 103(6): 2169-2195. [30] Michaels, Guy, Ashwini Natraj and John Van Reenen 2014. “Has ICT Polarized Skill Demand? Evidence From Eleven Countries Over Twenty-Five Years”, The Review of Economics and Statistics, 96(1), 60-77. [31] Moretti, Enrico 2012. The New Geography of Jobs. New York: Houghton Mifflin Harcourt
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[32] Pew Research Center 2011. “Two Years of Economic Recovery: Women Lose Jobs, Men Find Them”, by Rakesh Kochhar, July 6, 2011. [33] Pierce, Justin R. and Peter K. Schott. 2014. “The Surprisingly Swift Decline of U.S. Manufacturing Employment”, NBER Working Paper No. 18655. [34] Spitz-Oener, Alexandra 2006. “Technical Change, Job Tasks and Rising Educational Demand: Looking Outside the Wage Structure”, Journal of Labor Economics, 24, 235-270. [35] Timmermans, Bram 2010. “The Danish Integrated Database for Labor Market Research: Towards Demystification for the English Speaking Audience”, DRUID Working Papers 10-16. [36] 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. [37] Utar, Hale 2015. “Workers beneath the Floodgates: Impact of Low-Wage Import Competition and Workers’ Adjustment”, Bielefeld Working Papers in Economics and Management, No.12. [38] Utar, Hale, and Luis Torres Ruiz. 2013. “International Competition and Industrial Evolution: Evidence from the Impact of Chinese Competition on Mexican Maquiladoras”, Journal of Development Economics, 105: 267-287.
Appendix A
67
(b) By Import Competition
s
an
ni ci
ch
Te
an
IS
IS C
0
Density
O -1 C d As O-2 M an so P a IS r ci at ofe ger C O e s s -6 Pr sio of n Sk I es als ille SC I d O-5 SC sion Ag O al IS s r a Ser -4 C O nd vic Cle -8 e rk F W Pl s is IS h o a IS nt CO ery rke an -7 C W rs O -9 d M Cra or ke El f a em ch t W rs en ine ork er O ta p s ry O era cc to IS up rs C at O i -3 U ons Te nk ch no ni w ci n an IS C s O an IS -1 C d As O-2 M a so Pr na IS ci at ofe ger C O e s s -6 Pr sio of n Sk I es als ille SC I d O-5 SC sion Ag O al IS s r a Ser -4 C O nd vic Cle -8 e rk F W Pl s is I h o a S IS nt CO ery rke an -7 C W rs O d C or -9 r M ke El a af em ch t W rs en ine ork er O ta p s ry O era cc to up rs at i U ons nk no w n
-3
O
C
IS
.1
.2
.3 .4 -3
C O
IS
(a) All Workers
Workers employed in exposed firms
OneDigOc
68 Workers employed in non-exposed firms
Graphs by AffW
OneDigOc
Figure A-1: Histogram of Workers across Major Occupations in 1999 IS
IS
-9
-5
O
ar y
O
U nk
no w
n
ns
up at io
rs
rs
or ke
rs
or ke
pe ra to
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cc
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W
W or ke rs
rk s
io na ls C le
tW
ra f
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ry
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ac hi n
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-7
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Fi s
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en t
em
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an d
IS C
Ag ra nd
an t
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C O
-8
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Pr of
es
er s
ag
M an
Pr of
-1
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-2
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at e
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IS
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an
ille
Sk
O
C
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-6
C O
s
an
ni ci
ch
Te
IS
0
.1
Density .3 .2
.4
Table A-1: Workers’ Characteristics in 1999 by Selective Occupation Groups ISCO 1: Managers Age Female Immigrant College Educated Vocational School Educated At most High School ISCO 2: Professionals Age Female Immigrant College Educated Vocational School Educated At most High School ISCO 3: Technicians and Associate Professionals Age Female Immigrant College Educated Vocational School Educated At most High School ISCO 4: Clerks Age Female Immigrant College Educated Vocational School Educated At most High School ISCO 7: Craft Workers Age Female Immigrant College Educated Vocational School Educated At most High School ISCO 8: Plant and Machine Operators Age Female Immigrant College Educated Vocational School Educated At most High School ISCO 9: Elementary Occupations Age Female Immigrant College Educated Vocational School Educated At most High School Notes: Data Source: Statistics Denmark.
69
Mean
SD
N
44.912 0.211 0.020 0.256 0.436 0.297
8.302 0.408 0.140 0.437 0.496 0.457
555 555 555 555 555 555
39.461 0.362 0.013 0.612 0.178 0.184
9.634 0.482 0.114 0.489 0.383 0.389
152 152 152 152 152 152
37.756 0.652 0.028 0.371 0.365 0.226
9.097 0.477 0.164 0.483 0.482 0.418
1338 1338 1338 1338 1338 1338
36.423 0.780 0.019 0.151 0.531 0.317
9.569 0.414 0.137 0.358 0.499 0.466
1093 1093 1093 1093 1093 1093
40.702 0.377 0.040 0.061 0.487 0.425
10.381 0.485 0.197 0.240 0.500 0.495
916 916 916 916 916 916
40.617 0.594 0.090 0.032 0.276 0.665
10.181 0.491 0.287 0.175 0.447 0.472
4679 4679 4679 4679 4679 4679
38.046 0.510 0.052 0.027 0.325 0.627
11.247 0.500 0.222 0.162 0.469 0.484
923 923 923 923 923 923
Table A-2: Workers’ Characteristics in 1999 by Selective Occupations and Import Exposure Mean SD N Weaving and Knitting Machine Operators employed in non-exposed firms Age 40.566 10.479 304 Female 0.247 0.432 304 Immigrant 0.089 0.285 304 College Educated 0.043 0.203 304 Years of Experience in the Labor Market 15.740 5.439 304 Unemployment History Index 1019.102 1509.095 304 Weaving and Knitting Machine Operators employed in exposed firms Age 41.966 9.459 205 Female 0.288 0.454 205 Immigrant 0.112 0.316 205 College Educated 0.049 0.216 205 Years of Experience in the Labor Market 16.493 4.813 205 Unemployment History Index 1122.215 1557.671 205 Sewing Machine Operators employed in non-exposed firms Age 42.849 9.368 537 Female 0.946 0.226 537 Immigrant 0.143 0.351 537 College Educated 0.020 0.142 537 Years of Experience in the Labor Market 15.294 5.579 537 Unemployment History Index 2573.940 2511.583 537 Sewing Machine Operators employed in exposed firms Age 43.257 10.026 915 Female 0.957 0.202 915 Immigrant 0.072 0.259 915 College Educated 0.026 0.160 915 Years of Experience in the Labor Market 16.216 5.255 915 Unemployment History Index 1429.028 1695.268 915 Post-Processing Textile Machine Operators employed in non-exposed firms Age 40.478 9.894 502 Female 0.329 0.470 502 Immigrant 0.084 0.277 502 College Educated 0.064 0.245 502 Years of Experience in the Labor Market 14.964 5.535 502 Unemployment History Index 1374.861 1736.771 502 Post-Processing Textile Machine Operators employed in exposed firms Age 39.030 9.667 133 Female 0.323 0.470 133 Immigrant 0.045 0.208 133 College Educated 0.030 0.171 133 Years of Experience in the Labor Market 15.609 5.145 133 Unemployment History Index 1464.586 2002.921 133 Cutting, Rope Making, Netting, Leather Textile Machine Operators in non-exposed firms Age 39.888 9.311 251 Female 0.199 0.400 251 Immigrant 0.008 0.089 251 College Educated 0.020 0.140 251 Years of Experience in the Labor Market 16.275 4.791 251 Unemployment History Index 1713.175 2117.432 251 Cutting, Rope Making, Netting, Leather Text. Machine Operators in exposed firms Age 40.466 10.201 221 Female 0.674 0.470 221 Immigrant 0.081 0.274 221 College Educated 0.032 0.176 221 Years of Experience in the Labor Market 15.330 5.396 221 Unemployment History Index 1618.543 2068.907 221 Notes: Data Source: Statistics Denmark.
70
71
5.460 5.320 5.101 4.945 5.013 4.898 4.873 4.799
5.032 5.000 4.907 4.842 4.842
Mean Log Hourly Wage in 1991
5.430 5.269 5.078
Median Log Hourly Wage in 1991
0.154
0.106
0.149 0.065 0.170
0.042 0.128 0.186
Employment Share in 1991
5
9 (except 92)
7 8 4
1 2 3
Corresponding Major ISCO
Data are from 1991. Values are expressed in 2000 Danish Kroner. All hourly wages are calculated among workers with full-time jobs employed continuously with at least one year tenure. Employment shares are calculated using the number of employees and excluding army and agriculture/fishery occupations. Data Source: Statistics Denmark.
High-Wage Legislators, Senior Officials, and Managers Professionals Technicians and Associate Professionals Mid-Wage Craft and Related Trade Workers Plant and Machine Operators and Assemblers Clerks Low-Wage Elementary Occupations in Sales, Services, Mining, Construction, Manufacturing, and Transport Service Workers and Shop Sales Workers
Occupation
Table A-3: Ranking of Major Occupations in Denmark
The T&C Workers' Industry Affiliation in 2009
43%
52%
Agr, Fishing, Mining Manufacturing Service
Construction Others
Figure A-2: Industry Affiliation of the 1999 T&C Workers in 2009 (conditional on working)
The T&C Workers' Industry Affiliation in 2009 Exposed
Non-Exposed
34%
44% 50%
60%
Agr, Fishing, Mining Manufacturing Service
Construction Others
Graphs by ImportCompetition
Figure A-3: Industry Affiliation of the 1999 T&C Workers in 2009 by exposure to import competition (conditional on working)
72
Appendix B
Table A-4: Job Polarization and Import Shock Dep. Var. Trade
ln(9 + JPiemp ) (1) 0.132*** (0.015) [0.079]
ln(1 + JPih,emp ) (2) 0.084*** (0.012) [0.053]
ln(1 + JPim,emp ) (3) -0.109*** (0.015) [-0.064]
ln(1 + JPil,emp ) (4) 0.073*** (0.013) [0.054]
Notes: In all regressions the number of observations is 10753. All regressions include a constant and the full-set of controls, ZiW and ZiF . Robust standard errors are reported in parentheses. ∗ , ∗∗ and ∗∗∗ indicate significance at the 10 %, 5% and 1% levels respectively. Standardized coefficients are reported in square brackets. Data Source: Statistics Denmark.
Table A-5: Job Polarization and Import Shock–Alternative Trade Exposure Dep. Var. T^ rade
JPiemp (1) 3.220*** (0.316) [0.092]
JPih,emp (2) 0.904*** (0.146) [0.051]
JPim,emp (3) -1.582*** (0.181) [-0.079]
JPil,emp (4) 0.734*** (0.138) [0.052]
Notes: T^ rade is the revenue share of the quotas at worker i’s initial employer in 1999. In all regressions the number of observations is 10753. All regressions include a constant and the full-set of controls, ZiW and ZiF . Robust standard errors are reported in parentheses. ∗ , ∗∗ and ∗∗∗ indicate significance at the 10 %, 5% and 1% levels respectively. Standardized coefficients are reported in square brackets. Data Source: Statistics Denmark.
73
Table A-6: Polarization and Trade by Initial Occupation Dep. Var. Panel A. Trade Trade*Managers
JPiemp JPih,emp (1) (2) Managers (ISCO: 1)
JPim,emp (3)
JPil,emp (4)
0.830*** (0.093) -0.972** (0.321)
-0.404*** (0.055) 0.393** (0.140)
0.186*** (0.040) -0.149 (0.081)
-0.398*** (0.053) 1.088*** (0.322)
0.184*** (0.038) -0.431 (0.233)
0.240*** (0.040) -0.429 (0.241)
Panel B.
Professionals (ISCO: 2)
Trade
0.810*** (0.090) -2.349** (0.724)
Trade*Profs
0.228*** (0.040) -0.830 (0.496)
Panel C.
Associate Professionals and Technicians (ISCO: 3)
Trade
0.864*** (0.096) -0.706** (0.259)
Trade*Technicians
0.191*** (0.039) 0.203 (0.169)
Panel D.
Clerks (ISCO: 4)
Trade
0.879*** (0.093) -0.977** (0.313)
Trade*Clerks
0.258*** (0.041) -0.396** (0.150)
Panel E.
Craft Workers (ISCO: 7)
Trade
0.803*** (0.093) -0.328 (0.321)
Trade*Craftsmen
0.214*** (0.042) 0.029 (0.130)
-0.438*** (0.057) 0.449*** (0.126)
0.235*** (0.042) -0.460*** (0.077)
-0.442*** (0.054) 0.570** (0.184)
0.179*** (0.041) -0.010 (0.087)
-0.402*** (0.054) 0.241 (0.204)
0.187*** (0.040) -0.116 (0.114)
Panel F.
Plant and Machine Operators and Assemblers (ISCO: 8)
Trade
0.360** (0.120) 0.937*** (0.175)
Trade*Operators
0.252*** (0.064) -0.081 (0.073)
-0.080 (0.065) -0.681*** (0.104)
0.028 (0.046) 0.337*** (0.077)
Notes: In all regressions the number of observations is 10753. All regressions include a constant and the full-set of controls, ZiW and ZiF as well as the 2-digit ISCO (initial occupation) dummies. Robust standard errors are reported in parentheses. ∗ , ∗∗ and ∗∗∗ indicate significance at the 10 %, 5% and 1% levels respectively.
74
Table A-7: Trade induced Job Polarization among Machine Operators Dep. Var.
JPiemp JPih,emp JPim,emp (1) (2) (3) Among Machine Operators (ISCO: 82)
JPil,emp (4)
Trade
1.079*** (0.148)
0.323*** (0.072)
0.097* (0.039)
-0.659*** (0.095)
Notes: Estimation sample consist of workers who were machine operators (ISCO=82) in the textile and clothing sector in 1999. In all regressions the number of observations is 3773. All regressions include a constant and the full-set of controls, ZiW and ZiF . Robust standard errors are reported in parentheses. ∗ ∗∗ , and ∗∗∗ indicate significance at the 10 %, 5% and 1% levels respectively.
75
76 no
0.912*** (0.132) 0.168** (0.058)
no
0.922*** (0.132) 0.142* (0.064) -0.104 (0.097)
JPihrs (2)
no
0.910*** (0.132) 0.139* (0.064) -0.339** (0.128) 0.417** (0.156)
JPihrs (3)
no
0.514** (0.177) 0.032 (0.072) -0.329* (0.128) 0.434** (0.157) 0.270*** (0.076)
JPihrs (4)
no
-0.737*** (0.134)
0.774*** (0.135) 0.118 (0.064) -0.003 (0.139) 0.454** (0.156)
JPihrs (5)
-0.088 (0.161) no
0.962*** (0.154) 0.138* (0.063) -0.334** (0.129) 0.455** (0.166)
JPihrs (6)
yes
0.864*** (0.138)
JPihrs (7)
Notes: In all columns the number of observations is 8702. A constant and the full set of controls, ZiW and ZiF , are included in all regressions but not reported. “Offshoring” is the offshorability index of the corresponding two digit initial ISCO-88 occupation code of a worker. It is constructed by Goos, Manning and Salomons (2014). “ICT” is the index variable indicating the degree in which the initial (4-digit) occupation of a worker interacts with computers. It is an O*NET (version 14) variable and varies across 4-digit occupations. “RTI” is the routine intensity index of the corresponding two digit initial ISCO-88 occupation code of a worker. The RTI index follows Autor, Levy and Murnane (2003) and Autor and Dorn (2012). Control variables are described in the notes of Table ??. Robust standard errors are reported in parentheses. ∗ , ∗∗ and ∗∗∗ indicate significance at the 10 %, 5% and 1% levels respectively. Data Source: Statistics Denmark.
4-Digit ISCO Dummies
Trade*RTI
Trade*ICT
Trade*Offshoring
RTI
ICT
Offshoring
Trade
JPihrs (1)
Table A-8: Job Polarization, Offshoring, Technology, and Trade - Hours Worked
77 no
0.992*** (0.177) 0.186* (0.077)
no
1.000*** (0.175) 0.164 (0.087) -0.088 (0.132)
JPiinc (2)
no
0.988*** (0.175) 0.161 (0.087) -0.336 (0.174) 0.438* (0.217)
JPiinc (3)
no
0.764** (0.243) 0.101 (0.097) -0.330 (0.174) 0.447* (0.217) 0.153 (0.095)
JPiinc (4)
ZiW
no
-0.738*** (0.176)
0.851*** (0.177) 0.140 (0.086) 0.002 (0.187) 0.475* (0.217)
JPiinc (5)
ZiF ,
-0.114 (0.211) no
1.055*** (0.207) 0.160 (0.086) -0.329 (0.175) 0.487* (0.229)
JPiinc (6)
yes
0.951*** (0.180)
JPiinc (7)
Notes: In all columns the number of observations is 8702. A constant and the full set of controls, and are included in all regressions but not reported. “Offshoring” is the offshorability index of the corresponding two digit initial ISCO-88 occupation code of a worker. It is constructed by Goos, Manning and Salomons (2014). “ICT” is the index variable indicating the degree in which the initial (4-digit) occupation of a worker interacts with computers. It is an O*NET (version 14) variable and varies across 4-digit occupations. “RTI” is the routine intensity index of the corresponding two digit initial ISCO-88 occupation code of a worker. The RTI index follows Autor, Levy and Murnane (2003) and Autor and Dorn (2012). Control variables are described in the notes of Table ??. Robust standard errors are reported in parentheses. ∗ , ∗∗ and ∗∗∗ indicate significance at the 10 %, 5% and 1% levels respectively. Data Source: Statistics Denmark.
4-Digit ISCO Dummies
Trade*RTI
Trade*ICT
Trade*Offshoring
RTI
ICT
Offshoring
Trade
JPiinc (1)
Table A-9: Job Polarization, Offshoring, Technology, and Trade - Earnings
Table A-10: Polarization and Women by Initial Occupation Dep. Var. Panel A. Trade Managers*Female
JPiemp (1) Managers
JPih,emp (2)
JPim,emp (3)
JPil,emp (4)
0.778*** (0.090) -0.575 (0.385)
0.216*** (0.040) -0.082 (0.292)
-0.383*** (0.052) 0.304 (0.164)
0.178*** (0.038) -0.189 (0.126)
0.216*** (0.040) 0.456 (0.458)
-0.383*** (0.052) 0.805* (0.360)
0.179*** (0.038) -1.043*** (0.207)
Panel B.
Professionals
Trade
0.778*** (0.090) -1.392 (0.742)
Profs*Female Panel C.
Associate Professionals and Technicians
Trade
0.775*** (0.089) -2.174*** (0.261)
Technicians*Female Panel D.
Clerks
Trade
0.734*** (0.090) -2.769*** (0.360)
Clerks*Female Panel E.
Craft Workers
Trade
0.775*** (0.090) 0.635 (0.368)
Craft*Female
0.216*** (0.040) -0.640*** (0.175)
-0.382*** (0.052) 1.104*** (0.125)
0.178*** (0.038) -0.430*** (0.075)
0.209*** (0.040) -0.472* (0.184)
-0.357*** (0.052) 1.659*** (0.208)
0.168*** (0.038) -0.638*** (0.109)
0.216*** (0.040) 0.202 (0.150)
-0.381*** (0.052) -0.490* (0.234)
0.178*** (0.038) -0.057 (0.138)
-0.334*** (0.052) -1.089*** (0.111)
0.156*** (0.038) 0.490*** (0.078)
Panel F.
Machine Operators
Trade
0.684*** (0.090) 2.073*** (0.184)
Operators*Female
0.194*** (0.040) 0.494*** (0.078)
Notes: In all regressions the number of observations is 10753. All regressions include a constant and the full-set of controls, ZiW and ZiF as well as the 2-digit ISCO (initial occupation) dummies. Robust standard errors are reported in parentheses. ∗ , ∗∗ and ∗∗∗ indicate significance at the 10 %, 5% and 1% levels respectively.
78
Table A-11: Polarization and Women by Education Dep. Var. Panel A. Trade College*Female
JPiemp JPih,emp (1) (2) College Educated
JPim,emp (3)
JPil,emp (4)
0.816*** (0.089) -1.613*** (0.269)
-0.394*** (0.052) 0.786*** (0.140)
0.194*** (0.038) -0.452*** (0.078)
-0.391*** (0.052) 0.190 (0.106)
0.190*** (0.038) -0.204** (0.073)
-0.387*** (0.052) -0.392*** (0.102)
0.187*** (0.038) 0.397*** (0.071)
0.229*** (0.040) -0.375* (0.165)
Panel B.
Vocational School
Trade
0.810*** (0.089) -0.371* (0.180)
Vocational*Female
0.229*** (0.040) 0.023 (0.082)
Panel C.
at most High School Diploma
Trade
0.802*** (0.089) 0.844*** (0.172)
at most High School*Female
0.228*** (0.040) 0.056 (0.078)
Notes: In all regressions the number of observations is 10753. All regressions include a constant and the full-set of controls, ZiW and ZiF . Robust standard errors are reported in parentheses. ∗ , ∗∗ and ∗∗∗ indicate significance at the 10 %, 5% and 1% levels respectively.
79
Table A-12: Tasks, Trade, Man, Woman Dep. Var. Panel A. Trade Repetitive Motions Female*Repetitive Motions Panel B. Trade GBC Female*GBC Panel C. Trade Evaluating Female*Evaluating Panel D. Trade Thinking Female*Thinking Panel E. Trade ICT Female*ICT Panel F. Trade Interpersonal Female*Interpersonal
JPiemp JPih,emp JPim,emp JPil,emp (1) (2) (3) (4) Routine Manual Tasks Tasks with Repetitive Motions 0.752*** 0.246*** -0.368*** 0.138** (0.099) (0.044) (0.057) (0.043) -0.550*** -0.405*** 0.169** 0.024 (0.095) (0.049) (0.055) (0.032) 0.884*** 0.139* -0.431*** 0.313*** (0.104) (0.055) (0.059) (0.038) Non-Routine Manual Tasks Tasks with Gross Body Coordination (GBC) 0.800*** 0.195*** -0.405*** 0.200*** (0.094) (0.041) (0.055) (0.041) -0.854*** -0.611*** 0.355*** 0.112*** (0.103) (0.059) (0.055) (0.034) 1.343*** 0.241*** -0.773*** 0.329*** (0.119) (0.065) (0.064) (0.043) Routine Cognitive Tasks Evaluating Information to Determine Compliance with Standards 0.807*** 0.199*** -0.409*** 0.199*** (0.093) (0.042) (0.054) (0.040) 0.009 -0.105* 0.013 0.127*** (0.083) (0.041) (0.049) (0.030) -0.004 0.104 -0.033 -0.141** (0.116) (0.058) (0.066) (0.046) Abstract Tasks Tasks that involves thinking creatively (Thinking) 0.796*** 0.226*** -0.396*** 0.174*** (0.098) (0.044) (0.057) (0.043) 0.377*** 0.265*** -0.089 0.023 (0.096) (0.047) (0.056) (0.034) -0.488*** -0.187*** 0.135* -0.165*** (0.104) (0.056) (0.058) (0.040) ICT Tasks that involves interacting with computers (ICT) 0.754*** 0.171*** -0.385*** 0.198*** (0.093) (0.041) (0.054) (0.040) 0.744*** 0.512*** -0.270*** -0.038 (0.092) (0.048) (0.052) (0.032) -1.308*** -0.242*** 0.715*** -0.351*** (0.102) (0.052) (0.057) (0.038) Face to Face Communication Intensive Tasks Tasks that involve establishing interpersonal relationships 0.678*** 0.216*** -0.329*** 0.132** (0.099) (0.044) (0.058) (0.042) 0.534*** 0.420*** -0.147*** -0.033 (0.075) (0.039) (0.043) (0.025) -0.961*** -0.151*** 0.486*** -0.324*** (0.087) (0.045) (0.049) (0.033)
Notes: The number of observations is 8819, 9505, 9887, 8876, 9776, and 8816 in Panels A through F respectively. All regressions include a constant and the full-set of controls, ZiW and ZiF . Tasks measures are from O*NET and vary by workers’ initial 4-digit occupations (ISCO). The availability of task measures across occupations determine the number of observations. Robust standard errors are reported in parentheses. ∗ , ∗∗ and ∗∗∗ indicate 80significance at the 10 %, 5% and 1% levels respectively. Data Source: Statistics Denmark.
