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Effects of de-industrialization on unemployment, re-employment, and work conditions in a manufacturing… Ostry, Aleck S; Hershler, Ruth; Kelly, Shona; Demers, Paul; Teschke, Kay; Hertzman, Clyde Dec 3, 2001

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ralBioMed CentBMC Public HealthBMC Public Health 2001, 1:15Research articleEffects of de-industrialization on unemployment, re-employment, and work conditions in a manufacturing workforceAleck S Ostry*, Ruth Hershler, Shona Kelly, Paul Demers, Kay Teschke and Clyde HertzmanAddress: Department of Health Care and Epidemiology, University of British Columbia, Vancouver, CanadaE-mail: Aleck S Ostry* - ostry@initerchange.ubc.ca; Ruth Hershler - hershler@interchange.ubc.ca; Shona Kelly - shona.kelly@ubc.ca; Paul Demers - paul.demers@ubc.ca; Kay Teschke - kay.teschke@ubc.ca; Clyde Hertzman - hertzman@interchange.ubc.ca*Corresponding authorAbstractBackground: The purpose of this study was to investigate the impact of a 20-year process of de-industrialization in the British Columbia (BC) sawmill industry on labour force trajectories,unemployment history, and physical and psychosocial work conditions as these are importantdeterminants of health in workforces.Methods: The study is based on a sample of 1,885 respondents all of whom were sawmill workersin 1979, a year prior to commencement of de-industrialization and who were followed up andinterviewed approximately 20 years later.Results: Forty percent of workers, 64 years and under, were employed outside the sawmill sectorat time of interview. Approximately one third of workers, aged 64 and under, experienced 25months of more of unemployment during the study period. Only, 1.5% of workers were identifiedas a "hard core" group of long-term unemployed. Workers re-employed outside the sawmill sectorexperienced improved physical and psychosocial work conditions relative to those employed insawmills during the study period. This benefit was greatest for workers originally in unskilled andsemi-skilled jobs in sawmills.Conclusions: This study shows that future health studies should pay particular attention to long-term employees in manufacturing who may have gone through de-industrialization resulting inexposures to a combination of sustained job insecurity, cyclical unemployment, and adversephysical and psychosocial work conditions.BackgroundThe primary purpose of this study is to further our un-derstanding of the dynamics of de-industrialization onunemployment and on physical and psychosocial workdustry, sawmilling, in the province of British Columbia(BC) in Western Canada over a 20-year period between1979 and 1999.Published: 3 December 2001BMC Public Health 2001, 1:15Received: 9 October 2001Accepted: 3 December 2001This article is available from: http://www.biomedcentral.com/1471-2458/1/15© 2001 Ostry et al; licensee BioMed Central Ltd. Verbatim copying and redistribution of this article are permitted in any medium for any non-com-mercial purpose, provided this notice is preserved along with the article's original URL. For commercial use, contact info@biomedcentral.comPage 1 of 11(page number not for citation purposes)conditions as these are key determinants of health inworkplaces. We present here a case study of a single in-This investigation is based on a sample of workers em-ployed in 14 BC sawmills in 1979 just prior to a major re-BMC Public Health 2001, 1:15 http://www.biomedcentral.com/1471-2458/1/15cession. These workers were followed up andinterviewed approximately 20 years later in order to de-termine their unemployment history, current employ-ment (sector and occupation), and current physical andpsychosocial work conditions.Canada's resource manufacturing sector was particularlyhard hit by a recession which began in 1980 and lasteduntil 1985 [1–3]. In BC, unemployment in the forestproducts industry rose from 6.4% in 1979 to 19.2% in1982 [4]. In the sawmill sub-sector unemployment was44% in 1982 and, 39% of those downsized during the re-cession were without work 7 years later [5]. The reces-sion was followed by a sustained period of restructuringin many of these sawmills.Canada's resource manufacturing sector was not uniquein experiencing major employment losses in the 1980sand 1990s. Average unemployment rates in G-7 nationsfor the decade 1964–1973 were 3.1% compared to 7.8 %for the decade from 1983 to 1992 [6]. Reductions in em-ployment were uneven across sectors. From 1973 to1990, the annual growth of manufacturing employmentper capita for the United States and for OECD nationswas -0.7 and -1.6, respectively, compared to per capitagrowth of service sector employment of +1.5 in the Unit-ed States and +1.3 the OECD [7]. Canada was particular-ly hard hit as it experienced the largest decrease inmanufacturing employment (32%) compared to a G-7average of 24 percent between 1971 and 1991 [8].Given that de-industrialization in developed nations iswidespread and, because it has affected and continues toaffect millions of workers, it is important to investigatethe long term consequences of this process. Workers af-fected by de-industrialization will fall into three verybroad, and not necessarily exclusive categories: the longterm unemployed, those unemployed following downsiz-ing but re-employed in the long term, and, "survivors"who remain employed in industries that will experiencediffering intensities of re-structuring.According to a large body of research, the first class ofpotential "losers" in the de-industrialization process, willlikely be those workers with long term exposure to un-employment since it has adverse effects on general mor-tality and morbidity [9,10]. If de-industrializationproduces a group of workers who are not re-employed orre-employable over the long term, this research indicatesthat such workers will be at high risk for ill health.In terms of the second class of workers – those who aredownsized from an industry but find re-employment into the early 1970s focused on workers downsized becauseof fluctuations in the business cycle. However, accordingto Bartley the population of unemployed workers pro-duced by de-industrialization should be called "redun-dants" [11]. These differ from the population ofunemployed in the immediate post-war era because theirstatus is due to permanent rather than cyclical shifts inthe labour markets of developed economies.The implicit assumptions are first, that structural chang-es in the labour market, associated with de-industrializa-tion, will make it more difficult than in the past forunemployed workers to find re-employment and, sec-ond, that re-employment of these redundants will amel-iorate or reverse the ill effects of unemployment. Mostlongitudinal studies of the impact of unemployment fol-lowed by re-employment have focused on emotional andpsychological outcomes. Some of these studies showedthat adverse psychological impacts of unemploymentcontinued unabated after re-employment [12,13]. Othersshowed psychological recovery following re-employ-ment, but with the extent of recovery depending onwhether the new job was better than the old [14,15].Interestingly, studies which demonstrated the ameliora-tive effects of re-employment following unemploymentwere conducted in situations where workers found jobsthat were superior to their old jobs [16–19]. As far as isknown, besides the general observation that many of there-employment jobs were "better" than the workers' oldjobs, these investigators did not compare occupationalcategory or psychosocial and physical work conditions ofold with re-employment jobs in any detail.The research on survivors is even less complete. Moststudies of survivors focus on short term (3 months orless) psychological or behavioural outcomes [20–25]and have been conceptualized within a "survivor guilt"model [26] in which adverse effects observed among sur-vivors are ascribed mainly to the loss of co-workers andorganizational stability because of downsizing.Several studies have shown that survivors experiencedlowered job satisfaction, organizational commitment,and greater stress [21–23,27,28]. Two of these studiesdetermined that a downsizing process which was per-ceived as "fair" had a positive impact on survivors atti-tude to their job and commitment to their employer[23,27]. Two other studies showed that blue-collar work-ers and technicians were more likely to perceive thedownsizing process as unfair compared to supervisorsand managers [20,29].Page 2 of 11(page number not for citation purposes)the long term – the research is equivocal. Most of the re-search on unemployment conducted in the era from 1945As in the case of re-employment research, most survivorstudies assessed outcomes within a few weeks or monthsBMC Public Health 2001, 1:15 http://www.biomedcentral.com/1471-2458/1/15of downsizing so that long-term conclusions about theimpacts on survivors are difficult to determine. The sur-vivor guilt framework of these studies does not allow forthe possibility that adverse health impacts among survi-vors could also have been due to the new job conditionsthey encountered as their industries restructured.As far as is known, only one long term study has been un-dertaken with survivors of downsizing [30]. This studyinvestigated the effects of a well planned 'strategic'downsizing – conducted in conjunction with an "empow-erment" program among 139 employees in a Britishchemical processing plant over four years. This downsiz-ing was planned and implemented mainly through earlyretirement and "natural wastage" so that less than 5% ofdownsized workers were laid-off. The study observedstatistically significant increases in task-level demand,control, worker participation, as well as in job satisfac-tion over 4 years. The authors concluded that detrimen-tal effects on employee well-being due to increaseddemand may have been moderated by increased task-level control and participation in the downsizing andthat the increased demands were largely due to survivingworkers having to cope with the same amount work butwith fewer co-workers.However, usually the downsizing process involves activerestructuring [31], with complex alterations to existingtechnology, jobs, and work conditions without imple-mentation of "empowerment" programs [32]. Such re-structuring has been shown to adversely impact task-level control, social support and demand [33–35]. Alsoseveral studies have shown that restructuring may in-volve the introduction of new forms of work organizationsuch as total quality management (TQM) and of new pro-duction methods, in particular lean production, all ofwhich may impact physical and psychosocial work condi-tions profoundly [36,37].In a systematic review of 20 studies on the effects of in-dustrial restructuring involving lean production tech-niques, Landsbergis showed that most of theseworkplaces were characterized by increased work paceand limited job autonomy [38]. In other words, the re-structuring – at least in as much as it involved moves tolean production – may produce work conditions whichare detrimental to workers health. Any long term inves-tigation of survivors of de-industrialization must takeinto account the impact of restructuring on health via itsinfluence on persistent threats to employment and byway of changed physical and psychosocial work condi-tions.Did de-industrialization, occurring over a 20-year peri-od, in BC's sawmill sector, produce a core of long-termunemployed workers? The next group of questions con-cerns the second and third categories of workers affectedby de-industrialization; those who were re-employed,over the long term outside the sawmill sector, and thesurvivors who remained employed in sawmills? How dothese two groups of workers differ socio-demographical-ly and in terms of their unemployment histories? And,how do the physical and psychosocial work conditionsdiffer for these two groups approximately 20 years afterthe recession?MethodsThis investigation is based on a sample of 3,000 sawmillworkers drawn randomly from a cohort that was origi-nally gathered to study the impact of chlorophenol anti-sapstain chemicals on BC sawmill workers [39].Selection of sawmills and workers for the original studyFourteen medium to large sized sawmills, located mainlyin Southwest BC, participated in a retrospective cohortstudy which was conducted between 1987 and 1998.Mills were selected on the basis of a long-term history ofchlorophenol use and availability of intact personnelrecords. A total of 28,794 workers were enrolled in thecohort, representing approximately 20 percent of all BCsawmill workers. To be eligible, a worker had to be em-ployed at a study mill for at least one year between Janu-ary 1, 1950 and December 31, 1998. The cohort containsjob history data on all cohort members from 1950 to1998.Because a recession and major restructuring of sawmillsbegan in 1980, the year 1979 was chosen as the pre-re-cession/restructuring "baseline" year. All workers en-rolled in the cohort during 1979 were included in thisbaseline sub-cohort. A sample of 3,000 workers was ran-domly selected from the 9,806 workers working in astudy sawmill in 1979.Locating intervieweesIn order to locate interviewees the 1979 sub-cohort waslinked to the British Columbia Linked Health Database(BCLHDB). Through the BCLHDB we had access to thefirst 3-digits of the 6-digit postal codes allowing us toidentify the community where cohort members lived, sothat we could then locate individuals through local publicinformation sources.The 9,806 workers employed at a study mill in 1979 werelinked probabilistically to the BCLHDB. Linkage effi-ciency was 94.7% such that 3-digit postal codes were ob-Page 3 of 11(page number not for citation purposes)This paper addresses several questions. What was thedemographic impact of the de-industrialization process?tained for 9,282 of the 9,806 workers in the sub-cohortincluding 2,920 (97.3%) of the 3000 sampled workers.BMC Public Health 2001, 1:15 http://www.biomedcentral.com/1471-2458/1/15Searches of union pension plans, electronic telephonedatabases, and telephone books (by hand) were under-taken to obtain full addresses for the 3,000 workers. Forthe 80 unlinked workers in the sample, address searcheswere undertaken using names only.Administering the interviewsFace-to-face interviews were conducted between No-vember 1997 and March 1999. Subjects living in remoteregions of the province were interviewed by telephone. Ashort version of the questionnaire (requiring about 20minutes compared to one hour) was administered by tel-ephone when a respondent was only willing to conduct abrief interview or when proxy interviews were conductedfor deceased and incapacitated interviewees. However,because work-related variables were incompletely deter-mined with the short version of the questionnaire, onlythe long version of the questionnaire was used in theanalysis described here.The instrumentThe instrument was developed after a thorough review ofthe literature on technological change, restructuring, un-employment, and health and work. Two focus groupswere conducted with experienced sawmill workers to fi-nalize the questionnaire; it was then pilot tested on 29retired sawmill workers.Socio-demographics characteristics were measured. Toascertain their labour market experience, the history ofcross-sectoral and occupational mobility and the historyof unemployment, measured by the number of episodesand duration, was determined from 1979 to time of inter-view.Task-level work characteristics were measured using ashortened version [40,41] (See Additional file) of the de-mand/control instrument [42]. The questions in this in-strument measure decision lattitude (control),psychological and physical demand, and co-worker andsupervisor social support for each job title held by a re-spondent. Psychosocial work conditions were deter-mined in job held at time of interview for those stillemployed.Figure 1Labour force participation of workers in 1979 and at time of interview. The number of labour force participants, aged 64 andunder, was 1,239 (69 unemployed + 570 Non-Sawmill workers + 600 Sawmill workers).Page 4 of 11(page number not for citation purposes)BMC Public Health 2001, 1:15 http://www.biomedcentral.com/1471-2458/1/15Data analysisIn order to measure cross-sectoral mobility industrieswere coded into the following sectors: sawmill, other for-est products manufacturing (including pulp and paper,plywood, shingle and shake etc.); fishing and farming;construction/renovation; non-forest products manufac-turing and mining; the service sector; and transporta-tion.In order to measure cross-occupational mobility, all saw-mill job titles obtained in the interviews were re-coded toone of 86 basic sawmill job titles [40]. All jobs were alsocoded using the Standard Occupational Classification[43] and then translated into the Pineo16 OccupationalStatus Scale [44]. This 16-category scale was collapsedinto 4 basic categories; professional/managerial, trades,semi-skilled, and unskilled. Employment trajectoriesand sociodemographic characteristics were determinedfor workers who remain employed in the sawmill sector,for workers who were re-employed in other sectors, andfor unemployed workers.In the first set of descriptive analyses, the labour forceparticipation status and sector of last or current employ-ment was determined for all respondents (See File 2:fig-ure 1.doc). In the second set of analyses, socio-demographic characteristics for labour force partici-pants 64 years and under at time of interview were com-pared between the unemployed, those employed insawmills and workers employed outside the sawmill sec-tor. Chi square statistics were calculated for all possiblecomparisons among the three groups of workers (Table2).In the third set of analyses, unemployment history wasthose employed in sawmills and workers employed out-side the sawmill sector. Chi square statistics were calcu-lated for all possible comparisons among the threegroups of workers (Table 3). As well, in order to deter-mine the size and characteristics of workers most affect-ed by de-industrialization, length and duration ofunemployment were calculated (for the three groups ofworkers) for workers experiencing 25 months or moreunemployment during the study period (Table 4).Next, one-way analysis of variance was used to comparethe mean scores for psychosocial and physical work con-ditions at time of interview between those still employedin the sawmill sector with those workers employed out-side the sawmill sector (Table 5). A main effects modelwas constructed controlling for age, place of birth, andeducation. Separate models were run within each occu-pational category at time of interview so that the F sta-tistic represents a test of significance for the impact ofsector on physical and psychosocial work conditions.Finally, one-way analysis of variance was used to com-pare the mean scores for psychosocial and physical workconditions at time of interview between those still em-ployed in the sawmill sector with those workers em-ployed outside the sawmill sector (Table 6). A maineffects models was constructed controlling for age, placeof birth, and education. Separate models were run withineach occupational category held at baseline in 1979 sothat the F statistic represents a test of significance for theimpact of sector on physical and psychosocial work con-ditions with occupational categories held a baseline.ResultsSurvey responseTable 1 shows that 62.9 percent of respondents complet-ed the long questionnaire and 9.1 percent completed theshort questionnaire for an overall survey response rate of72 percent. The refusal rate was 4.2 percent, and 19 per-cent of respondents were not located. The proportion ofworkers "not found" was highest among those who hadworked in isolated "mill towns". Although refusal ratesdid not vary by age category, the "not found" rate washighest in younger age groups and workers with the low-est duration of work in a study sawmill. The analysis isbased on the 1,885 respondents (62.9%) who answeredthe long questionnaire.Cross sectoral mobility of the labour forceWhat were the labour force circumstances for workers attime of interview? In 1999, 464 (24.6%) were aged 65and over. Of the remaining 1,421 (75.4%) respondentsaged 64 or under, 600 (42.2%) were still employed in aTable 1: Interview statusInterview status Number PercentLong questionnaire (face-to-face) 1885 62.9Short questionnaire* 270 9.1Questionnaire sub-total 2155 72.0Refusals 126 4.2Deceased 18 0.6Needs translator 8 0.3Not located 582 19.0Unresolved 111 3.8Total 3000 100.0*32 short questionnaire interviews were with the relatives of deceased workers. The total number of deceased workers in the sample was therefore 50.Page 5 of 11(page number not for citation purposes)compared and contrasted between the unemployed,sawmill, 570 (40.1%) were employed outside the sawmillsector, 131 (9.2%) had taken early retirement, 69 (4.9%)BMC Public Health 2001, 1:15 http://www.biomedcentral.com/1471-2458/1/15Table 2: Sociodemographic characteristics by sector for labour force participants 64 years of age or under (percent).SOCIO-dEMGRAPHICS Sawmill (1) N = 600Other sector(2) N = 570Unemploy (3) N = 69Chi square 1*2*3Chi square 1*2Chi square 1*3Chi square 2*3Age category 157.1*** 153.5*** 5.5 21.3**35–39 48 (8.0) 151 (26.5) 8 (11.9)40–44 128 (21.3) 192 (33.6) 19 (26.9)45–49 131 (21.9) 106 (18.6) 19 (26.9)50–54 143 (23.8) 71 (12.5) 10 (14.9)55–59 108 (17.9) 20 (3.5) 9 (13.4)60–64 42 (7.0) 30 (5.3) 4 (6.0)Marital status 0.47 0.46 0.06 0.01% Not married 91 (15.2) 95 (16.7) 11 (16.4)Place of birth 39.3*** 39.3*** 0.80 4.4*Non-Canadian born 212 (35.3) 108 (19.0) 21 (29.9)Highest education 78.9*** 68.8*** 3.9 25.5***University 48 (8.0) 94 (16.5) 2 (3.0)Community college 68 (11.4) 126 (22.1) 10 (14.9)Apprentice 113 (18.8) 93 (16.3) 11 (16.4)Secondary 170 (28.3) 160 (28.1) 18 (25.4)Elementary or less 201 (33.5) 97 (17.0) 28 (40.3)Income in 1998 66.0*** 42.4*** 43.3*** 18.9**< $39,999 49 (8.2) 111 (19.5) 10 (15.0)$40,000–$79,999 421 (70.1) 312 (54.8) 42 (60.0)>$80,000 130 (21.6) 147 (25.8) 17 (25.0)Home ownership 15.3** 15.3*** 1.3 0.5% own home 547 (91.1) 475 (83.3) 60 (86.6)***p > 0.00; **p = 0.001–0.01; *p = 0.05–0.01.Table 3: Unemployment history by sctor for labour force participants 64 years of age or under (percent).SOCIO-dEMGRAPHICS Sawmill (1) N = 600Other sector(2) N = 570Unemploy (3) N = 69Chi square 1*2*3Chi square 1*2Chi square 1*3Chi square 2*3Ever/never 26.5***Ever unemployed 220 (36.7) 295 (51.7) 69 (100.0)# of episodes 42.9** 1.2 37.2*** 32.0***1 159 (72.1) 200 (67.7) 24 (34.3)2 45 (20.5) 69 (23.5) 25 (35.8)3 or more 16 (7.3) 26 (8.8) 20 (29.9)Duration 24.8** 4.5 10.9** 24.3***1–12 months 76 (35.2) 78 (26.4) 38 (56.1)13 to 24 months 80 (36.5) 130 (44.1) 13 (18.2)>25 months 62 (28.3) 87 (29.5) 18 (25.8)***p > 0.00; **p = 0.001–0.01; *p = 0.05–0.01.Page 6 of 11(page number not for citation purposes)BMC Public Health 2001, 1:15 http://www.biomedcentral.com/1471-2458/1/15were unemployed, 40 (2.8%) were disabled, and 11(0.8%) were either working as volunteers, looking afterchildren at home, or attending educational institutions.Among the 570 workers age 64 and under who were em-ployed outside the sawmill sector 212 (37.2%) were in theservice sector, 167 (29.3%) were employed in non-saw-mill forest products manufacturing – such as pulp mills,paper mills or logging operations, 73 (12.8%) were inconstruction or renovation, 56 (9.8%) were in transpor-tation, 49 (8.6%) were in non-forest products manufac-turing and 13 (2.3%) were employed in fishing orfarming.Socio-demographic characteristics of labour force partici-pants 64 years old and underThere were no significant differences in marital statusamong the 3 groups (i.e. the unemployed, employed inthe sawmill sector, and employed outside the sawmillsector) (Table 2). In comparing sawmill sector workerswith the unemployed, no statistically significant differ-ences were observed except for current income as 23 un-employed workers (33.4%) earned less than $49,000 inthe year preceding interview compared to 49 (8.2%) saw-mill workers (Chi square 43.3; p < 0.00)).The greatest differences in socio-demographic charac-teristics were found between groups of the currently em-ployed. Approximately 50% of workers employedoutside the sawmill sector were under age 45 comparedto 29.3% of sawmill workers. As well, non-sawmill work-ers were approximately twice as likely to have a college oruniversity education and to be Canadian born than saw-mill workers.For both employed groups, approximately 25% earnedmore than $80,000 in the year before interview. Howev-income category (less than $39,000). Home ownershipwas significantly greater for sawmill workers (91.1%)compared with non-sawmill workers (83.3%). Interest-ingly, more unemployed workers (86.6%) owned homesthan workers employed outside the sawmill sector.Unemployment history of labour force participants 64 years old and underStatistically significant differences for the "ever" unem-ployed were observed as 51.7% of non-sawmill sectorworkers had experienced unemployment compared to36.7% of sawmill workers (Table 3). As well, workers un-employed at time of interview were approximately 4times as likely to have experienced 3 or more episodes ofunemployment compared to workers employed at timeof interview although long durations of unemployment(>25 months) were similar across the 3 groups.A total of 167 (13.2%) of labour force participants 64years of age or under experienced an average cumulativeduration of unemployment of 25 months or more duringthe study period (Table 4). The survivor group experi-enced an average of 38.3 months and 1.9 episodes of un-employment, the group re-employed outside the sawmillsector experienced an average of 36.4 months and 2.0episodes of unemployment, and the group unemployedat time of interview experienced 50.4 months and 4.1 ep-isodes of unemployment.Work conditions in re-structured sawmills compared to work conditions for those re-employed outside the sawmill sectorTable 5 shows mean scores for control, social support,psychological demand, physical demand and noise forworkers in the sawmill and non-sawmill sectors, withinoccupational categories at time of interview, after con-trolling confounders. Control and social support scoresdecreased moving down the occupational hierarchy inboth sawmill and non-sawmill sectors. In contrast, de-mand and noise scores increased moving down the occu-pational hierarchy except for physical demand and noiseamong tradesmen and the semi-skilled.Control and social support were greater among non-saw-mill workers, except in the case of control for managerswhich was greater for workers employed in a sawmill.Scores for demand variables, with the exception of psy-chological demand for managers, were greater for saw-mill compared to non-sawmill workers.Statistically significant differences between the sawmilland non-sawmill sectors were observed for noise withinall occupational categories. Noise scores were alwaysTable 4: Length and duration of unemployment for those workers experiencing 25 or more months of unemployment by sector for labour force participants 64 years of age or under.Labour force participation statusN Duration in months*Mean # of episodesSawmill 62 38.3 (25–87)** 1.9 (1–5)***Non-sawmill 87 36.4 (25–90) 2.0 (1–8)Unemployed 18 50.4 (25–89) 4.1 (2–11)*Average cumulative duration. **Numbers in brackets =Range in months. ***Numbers in brackets=Range in the number of episodes of unemployment.Page 7 of 11(page number not for citation purposes)er, 19.5% of workers employed outside the sawmill sec-tor, and 8.2% of sawmill workers were in the lowesthigher in the sawmill sector with differences rangingfrom a low of 12.5% within the semi-skilled category to aBMC Public Health 2001, 1:15 http://www.biomedcentral.com/1471-2458/1/15high of 27.2% within the unskilled category. For physicaldemand, differences between the sectors were not statis-tically significant except for trades, where it was slightlygreater for sawmill workers. Tradesmen and unskilledworkers employed in sawmills experienced 4.5% and6.8%, respectively, greater psychological demand (statis-tically significant) than their colleagues employed out-side the sawmill sector.Semi-skilled and unskilled workers employed in saw-mills experienced 9.2% and 15.4% less social support(statistically significant) than their colleagues employedoutside the sawmill sector. And, semi-skilled and un-skilled workers employed in sawmills experienced 5.0%and 7.6%, respectively less control, (statistically signifi-cant) than their colleagues employed outside the sawmillsector.Table 6 compares work conditions at time of interviewaccording to occupational category in 1979 for sawmillworkers and those who left the industry. This analysis as-sesses the impact of moving away from employment insawmills for workers starting in the same occupationalcategory at baseline. No statistically significant differ-ences in work conditions were observed for workers whowere managers at baseline and who had obtained re-em-ployment outside the sawmill sector 20 years later.Workers who left the sawmill sector for re-employmentelsewhere had reduced noise scores relative the survivorswho stayed employed in sawmills for all occupationalcategories except managers. Reductions in noise scoreswere greatest for those who were originally unskilledworkers in sawmills (27.3%). For workers who weretradesmen in a sawmill at baseline and moved to re-em-ployment outside the sawmill sector at time of interview,Table 5: Analysis of variance* for physical and psychosocial work scores at time of interview for workers employed outside the sawmill sector and in a sawmill by occupational category at time of interviewControl Social support Psychological demand Physical demand NoiseJob category Sm* Non** Sm Non Sm Non Sm Non Sm NonManagers 27.5 26.9(0.60) 6.0 6.7(0.71) 12.8 13.7(0.25) 2.4 2.1(0.72) 2.6 2.1(0.00)Trades 24.2 24.8(0.72) 5.8 5.8(0.58) 13.2 12.6(0.01) 3.0 2.9(0.01) 3.4 2.9(0.00)Semi-skilled 22.2 23.3(0.05) 5.4 5.9(0.005) 13.2 12.9(0.35) 2.7 2.7(0.60) 3.2 2.8(0.00)Unskilled 20.7 22.4(0.01) 5.2 6.0(0.00) 13.3 12.4(0.01) 3.0 2.7(0.22) 3.3 2.4(0.00)Numbers in parentheses indicate p values, for the F statistic, after controlling for occupational category at time of interview, income, education, age, and place of birth. *Sm = Sawmill sector **Non = Non-sawmill sector Range in adjusted noise, social support, and physical demand scores was from 1 to 4. Range in adjusted psychological demand scores was from 8 to 20. Range in adjusted control scores was from 18 to 32.Table 6: Analysis of variance* for physical and psychosocial work scores at time of interview for workers employed outside the sawmill sector and in a sawmill by occupational category in 1979Control Social support Psychological demand Physical demand NoiseJob category SM* Non ** Sm Non Sm Non Sm Non Sm NonManagers 23.9 24.3(0.08) 5.9 5.9(0.42) 13.3 13.7(0.90) 2.6 2.3(0.72) 2.9 2.3(0.17)Trades 23.8 24.7(0.17) 6.0 5.9(0.96) 13.0 12.4(0.01) 2.9 2.6(0.004) 3.3 2.8(0.000)Semi-skilled 22.8 24.2(0.09) 5.6 6.1(0.009) 13.1 12.8(0.18) 2.6 2.7(0.57) 3.1 2.5(0.000)Unskilled 22.4 25.0(0.000) 5.4 6.2(0.000) 13.4 13.2(0.16) 2.9 2.6(0.05) 3.3 2.4(0.000)Numbers in parentheses indicate p values, for the F statistic, after controlling for occupational category in 1979 (baseline), income, education, age, and place of birth. *Sm = Sawmill sector **Non = Non-sawmill sector Range in adjusted noise, social support, and physical demand scores was from 1 to 4. Range in adjusted psychological demand scores was from 8 to 20. Range in adjusted control scores was from 18 to 32Page 8 of 11(page number not for citation purposes)statistically significant reductions were observed for psy-BMC Public Health 2001, 1:15 http://www.biomedcentral.com/1471-2458/1/15chological demand (4.6% decrease) and physical de-mand (10.3%).For workers who were in semi-skilled occupations in asawmill at baseline and moved to re-employment out-side the sawmill sector at time of interview, a statisticallysignificant increase of 8.9% was observed for social sup-port. For workers who were in unskilled occupations in asawmill at baseline and moved to re-employment out-side the sawmill statistically significant increases in con-trol (2.7%), social support (14.8%), and statisticallysignificant decreases in physical demand (10.3%) wereobserved.DiscussionDe-industrialization has been widespread in manufac-turing workforces in developed nations over the pastquarter of a century. This trend is likely to continue withtechnological innovation in manufacturing and, further-more, is likely to continue in conjunction with sustainedrestructuring of manufacturing industries. The long-term impacts on threat of unemployment, unemploy-ment, and working conditions have been under investi-gated in spite of the fact that this process is widespreadin the industrialized world, is likely to have major im-pacts on health, and has affected and continues to affectmany workers.The purpose this study was to better understand the dy-namics of de-industrialization on intermediate work-place determinants of health in a sample of BC sawmillworkers. The first question, addressed in the study was,what was the demographic impact of the de-industriali-zation process? Of those workers 64 years and under,and employed at time of interview, approximately halfwere employed outside the sawmill sector indicating thatthe non-sawmill sector was vibrant enough during thestudy period to provide employment opportunities forworkers who exited the sawmill sector.Workers who exited the sawmill sector were slightlyyounger than those who remained employed in mills andalso had significantly lower incomes, were better educat-ed, and more likely to be Canadian born. This demo-graphic profile may be partially explained because lay-offs in the industry proceeded strictly on a seniority ba-sis. With the onset of recession and restructuring in1980, younger workers were laid off first many of whomtook further education leading to subsequent employ-ment within the expanding non-sawmill sector. Availa-bility of education for these young adults in conjunctionwith economic expansion in BC's non-sawmill segmentsof the economy partially explains these observations.Did de-industrialization, occurring over a 20-year peri-od, in BC's sawmill sector, produce a core of long-termunemployed workers? The impact of de-industrializationcan be gauged by the scope and depth of unemploymentamong workers in this sample. For example, approxi-mately 40% of workers aged 64 and under at time of in-terview had experienced unemployment at least onceduring the study period. Within this group of workers,approximately one third experienced unemployment foran average cumulative duration of 36 or more months.And, within the group of workers who were unemployedat time of interview, one quarter experienced over 4years of unemployment.In other words, among the large number of workers inthis sample who experienced unemployment, thoseworkers who were employed at time of interview wereout of work 15 % of the study period and those workersunemployed at time of interview were out of work forover 20% of the study period. Although a hard core oflong-term unemployed workers was not evident in thisstudy, it is clear that in both groups of workers manywere exposed to unemployment for long periods of time.This, means that the length of time, during the study pe-riod, that workers were exposed to a combination of thethreat and experience of unemployment was also likelyvery high.How did the physical and psychosocial work conditionsdiffer for sawmill survivors and exiters approximately 20years after the recession? In general, physical and psy-chosocial work conditions experienced by workers em-ployed, in similar occupations, outside the sawmillsector were better than for workers employed at saw-mills. And, the work conditions benefits of re-employ-ment in a similar job category outside the sawmill sectorrelative to continued work in the sawmills were greaterfor those employed in unskilled or semi-skilled occupa-tions and trades at time of interview.In particular, statistically significant improvements insocial support and control were observed for unskilledand semi-skilled workers and statistically significant im-provements were observed in psychological demand forunskilled workers. Statistically significant improve-ments were also noted for psychological and physical de-mand among tradesmen who were re-employed outsidethe sawmill sector.By conducting the same analysis within occupationalcategory at baseline, we were able to compare physicaland psychosocial work conditions in 1999, for workerswho started from the "the same place" in 1979. As in thePage 9 of 11(page number not for citation purposes)previous analysis, workers who moved to re-employ-ment outside the sawmill sector, in general, showed im-BMC Public Health 2001, 1:15 http://www.biomedcentral.com/1471-2458/1/15provement in work conditions relative to workers whoremained in sawmills. And again, as in the previous anal-ysis the benefits of improved work conditions were mostpronounced for the unskilled, semi-skilled, and trades-men.In evaluating the balance of change in control and de-mand conditions, unskilled workers appear to have ben-efited most from modest improvements in physical andpsychosocial working conditions in restructured saw-mills. In contrast, managers may have gained the leastbenefit from restructuring (they were the only group toshow a decline in control scores in combination with anincrease in psychological demand, indicating that jobstrain for managers may be higher outside than insidethe sawmill sector).There are several limitations to this study. First, the saw-mill cohort, by selecting workers who worked for a mini-mum of one year excluded workers with the leastseniority. This investigation, therefore likely underesti-mated unemployment relative to the entire BC sawmillworkforce. Second, this bias will be reinforced becausethe workers "not found" in the sample of 3000 workerstended to be younger with less seniority than interviewrespondents. The "not found" likely consisted of youngworkers with low seniority who left the province duringthe initial recession between 1980 and 1985.Because downsizing in the early 1980s proceeded strictlyon the basis of seniority, those most likely to be laid off inthe early 1980s were also those with the lowest durationof employment. This group is over-represented amongnon-respondents. By 1999, members of this group wouldhave likely been located if they were employed at a studysawmill so they also represent those workers in 1999 whowere either living outside BC, or if employed, were work-ing outside the sawmill sector in the province.Another limitation of this investigation pertains to itsgeneralizability. As noted in the introduction, the way inwhich workers experience de-industrialization will de-pend on the extent of the process, the occupational mo-bility of downsized workers and their ability to obtaineducation, and the availability of alternative labour mar-kets. The results of this study are based on a particularsituation in the resource sector in BC in the 1980s and1990s.However, the general trend to de-industrialization ofblue-collar manufacturing was widespread in the indus-trial world during this time and is continuing. The partic-ular finding in this study that "survivors" of this processhighlights the possibility that de-industrialization in oth-er industries may pose risks for survivors.ConclusionsThis potential for adverse exposures among survivors ofde-industrialization has been noted, as far as is known,in one other study [45]. Studies related to de-industrial-ization usually focus on those who are downsized as theworkers who retain jobs within these industries are usu-ally considered the "winners" in the situation. This studypoints out that in the context of de-industrialization, in-volving both downsizing and re-structuring and techno-logical change, the histories of unemployment as well aswork conditions for those surviving who remain attachedto the industry may also be worthy of study for their po-tential impacts on health.Competing InterestsNone declared.AcknowledgementsThe Institute of Work and Health, Center for Health Services and Policy Research (UBC), Forest Renewal British Columbia, Canadian Institute for Health Research, and the Canadian Population Health Initiative for their contributions to this project.References1. Barnes T, Hayter R: British Columbia's private sector in reces-sion, 1981–86: employment flexibility without trade diversi-fication. BC Studies 1993, 98(1):20-422. Hayter R, Barnes T: Innis's staple theory, exports, and reces-sion: British Columbia, 1981–1986. Economic Geography 1990,12:156-1733. 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British Medical Journal 1989, 299:1073-1076Publish with BioMed Central  and  every scientist can read your work free of charge"BioMedcentral will be the most significant development for disseminating the results of biomedical research in our lifetime."Paul Nurse, Director-General, Imperial Cancer Research FundPublish with BMC and your research papers will be:available free of charge to the entire biomedical communitypeer reviewed and published immediately upon acceptancecited in PubMed and archived on PubMed Central yours - you keep the copyrightPage 11 of 11(page number not for citation purposes)editorial@biomedcentral.comSubmit your manuscript here:http://www.biomedcentral.com/manuscript/BioMedcentral.com

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