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Part II Case Studies of amen Workers and In~rm~n bchno~gy

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The Technological Transformation of White-ColIar Work: A Case Study of the Insurance Industry BARBARA BARAN Although white-colIar automation has received considerably less press than robots on the assembly-line, the introduction of computer-based technologies into the office has generated growing concern that industrialized working conditions and technological redundancy may be spreading to white-colIar settings. Because of the pervasiveness of occupational sex segregation within the office work force, it is also feared that women will bear the brunt of the restructuring process. Feldberg and Glenn (1983), for ex- ample, argue that whereas women's jobs are disproportionately disappearing and their opportunities for upward mobility declin- ing, men may actually benefit from the new technologies both because they will dominate the more highly skilled technical and professional jobs being created and because automation may cen- traTize control in the hands of (male) senior managers and systems analysts. The limited numbers of case studies that have been published on the impacts of office automation report conflicting findings. 25

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26 THE INSURANCE INDUSTRY With regard to changes in the occupational structure, some re- searchers have found that job loss is concentrated among low- skilled clericals (Faunce et al., 1962; Roessner et al., 1985; Shep- herd, 1971), implying a general upgrading of labor. Other studies indicate, on the contrary, the elimination of skilled clerical ac- tivities, resulting in a polarization of the occupational structure (Feldberg and Glenn, 1977; Hoos, 1961; U.S. Bureau of Labor Statistics, 1965~. Similarly, whereas some analysts have reported less task fragmentation as the technology becomes more sophisti- cated (Shepherd, 1971; Matteis, 1979; Sirbu, 1982; Adler, 1983; Appelbaum, 1984), others suggest that job content is narrowed and worker autonomy reduced (Murphree, 1982; Greenbaum, 1979; Cummings, 1977; Feldberg and Glenn, 1983, 1977~. Finally, al- though in all cases women experienced the greatest job loss, in some reports it appeared that after automation women were rele- gated to lower-skilled activities (Feldberg and Glenn, 1977; Mur- phree, 1982), whereas in other accounts female clericals seemed to benefit from the new labor process (Matteis, 1979; Cummings, 1977~. The intent of my research on the insurance industry was to contribute to this nascent literature. The findings reported here are based on a two-year study of the impacts of automation on that industry. The first phase of this research was an in-depth case study of a major national property/casualty carrier, which included 26 interviews with employees in various parts of the company's operations (home office, branch office, data-processing center, commercial group, and personal lines centers) and dif- ferent levels of the occupational hierarchy, as well as analysis of extensive quantitative personnel data which the company made available. The second phase involved lengthy interviews with ex- ecutives, personnel managers, and systems analysts in 18 other companies, loosely stratified by size, product type, growth rate, distribution system, and so on; members of the industry's trade associations, agents' associations, and vendor companies were also interviewed. Third, a structures] telephone survey was conducted of 37 companies21 life firms and 16 property/casualty firms- again loosely stratified. All of the firms in both samples were among the top 100 companies in their industry segment. Together these 55 insurers account for approximately 55 percent of industry employment; they range from firms of over 50,000 employees to firms of less than 1,000. Finally, ~ have supplemented this field

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BARBARA BARAN 27 work with secondary source material from government agencies, trade publications, and documents and survey data kindly pro- vided by the trade associations and consultants to the industry. Two kinds of conclusions emerged from this effort. The first, of course, are numbers of concrete observations which wiD be presented in summary form in this paper. In addition to these sectoraIly specific findings, however, analysis of the insurance in- dustry generated a set of more general hypotheses concerning the kinds of factors it is necessary to consider when attempting to as- sess the impacts of office automation technologies on a work force. Since the following discussion is not sufficiently comprehensive to cover all these issues, ~ want to discuss them briefly here before turning to the more detailed findings of the study. First, as the case of the insurance industry made clear, it is virtually impossible to separate technological innovation from other factors affecting the competitive dynamics of an industry. The competitive environment Is both a major determinant of the speed of diffusion of innovation and quite apart from technolog- ical changesignificantly affects the demand for labor. Second, and closely related, is that the impacts of the new technologies on the labor force are not limited to their effects on the organization of the work process. In insurance, changes in product offerings and in the structure of both the industry itself and the firms within it promise to be equally important influences on the kind and amount of labor employed. Third, it was clear from this study that "office automation" cannot be analyzed as a single phenomenon. Impacts vary on the basis of the specific kinds of technology being introduced (includ- ing, importantly, the generation of that technology) and the na- ture of the work process being automated (originating often in the unique characteristics of the product, market, and organizational structure of the industry). In terms of the first, ~ would underline the importance of periodizing the process of office automation; some of the disagreement in the literature can be attributed to did ferences in the generation of the technologies observed. In terms of the second, it ~ obvious that even in this one industry, the new labor processes will vary widely by product line. It is also important to point out that the insurance industry is very differ- ent from office-type settings in which word processing is the core application since systems development in the insurance industry

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28 THE INSURANCE INDUSTRY has been driven by its data-processing needs. In fact, in some re- gards, more accurate parallels can be drawn between automation in the insurance industry and automation in manufacturing indus- tries. In both cases, production work is increasingly performed by machines while administrative support activity remains relatively labor-~ntensive (although, in insurance, services activities are also becoming extremely automated). Fourth, in assessing the impacts of automation on skill re- quirements and on the occupational structure of an industry, would stress the importance of analyzing the changes occurring in the entire labor process. In failing to do so, analysts often miss the forest for the trees. When the organization of work is being fundamentally restructured, it is not so useful to talk about how specific jobs are changing; instead, we need to begin to assess how particular job functions or activities are being reconstituted and recombined to produce new kinds of job categories. Although this observation may seem banal, most studies of the effects of office automation have focused almost exclusively on the clerical work force. As such, they miss one of the most important features of the current wave of applications, that is, the automation of professional functions and their transfer to less- skilled labor. As a result, these studies may be overly pessimistic both about the decline of clerical-type occupations and their likely skit levels; at the same time, they may be overly optimistic about the expansion of higher-level, challenging, well-paid work. Fifth, we have to be careful not to assume any necessary identity between skill levels and other job attributes, such as pay, satisfaction, or occupational mobility. Indeed, many of the new jobs emerging in the insurance industry may require- fairly high levels of skill and yet offer few rewards, material or otherwise. Finally, this last point is true in part because different cat- egories of workers will not only be differentially affected by the process of transformation, but the nature of the available labor force also shapes job design. In this case, the nature of the female labor market may be an import ant determinant of the emerging occupational structure. Some, although not all, of these themes will be explored in greater depth in the remainder of this paper. To give context to the discussion of the ways in which new technologies are having an impact on the labor force, the first section begins by describing the significant changes that have occurred in the last decade in terms

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BARBARA BARAN 29 of the rate of diffusion and the kinds of computerized systems implemented in the insurance industry. THE PACE OF DIFFUSION AND THE NEW~IMPLEMENTATIONS Insurance companies, along with financial institutions and the government, were among the earliest users of electronic data- processing (EDP) computers. As early as 1959, the finance, in- surance, and real estate sector boasted the greatest number of computer installations per million employees (Phister, 19793; by 1970, the insurance industry employed a higher ratio of computer specialists than any except the high-technology manufacturing in- dustries (Bureau of Labor Statistics, 19Blb). Size of the organization was initially the primary factor influ- encing the implementation decision; companies attempted little, if any, formal cost-benefit justification. Applications were generally limited to structured accounting tasks, billing, and claims dis- bursements. With the exception of the move to direct billing- a transaction which had traditionally been handled at the agency level, the automation of these functions had little impact on the rest of the organization. For the first two decades, the pace of diffusion was extremely civilized and the industry proceeded much as it had for the last hundred years. The barriers to rapid adoption of more sophisti- cated office systems were both technological and organizational.t Beginning in the early 1970s, however, systems development took two unport ant new turns. First, the focus of automation shifted ~ The technological barriers included (a) equipment incompatibility, both among the various vendors and even within the product offerings of a partic- ular vendor, that prevented network extension and integrated system devel- opment; (b) substantial work station connection costs and communications costs; and (c) the difficulty of representing more complex white-collar activi- ties in computer algorithms. The attempt to impose a standardized logic on many procedures often generated too many exceptions to be cost-eEective and in some cases even diminished the efficiency of the organization. The organizational problems associated with the introduction of the new systems were perhaps even more substantial than these technical problems: these integrated systems required a fundamental reorganization of production, ser- vice, and distribution, since their effect was not limited to clerical labor in word-processing and data-processing departments; higher-level workers had their jobs redefined and in some cases even eliminated. Their resistance erected a powerful barrier to diffusion.

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30 THE INSURANCE INDUSTRY from administrative functions such as accounting to automation of the production process itself, meaning primarily the underwriting, rating, and physical production of the insurance policy and the handling of claims disbursements. Second, in the place of single- task, batch-oriented machines, multitask, multimachine systems began to be introduced, often operating on-line. The process of systems integration was an evolutionary one. As the price/performance ratio of the hardware continued to im- prove and the software became more sophisticated, more and more functions in the operational areas were automated and systems became increasingly decentralized. Independent systems began to proliferate, often geared exclusively to a company's in-house data-/word-processing activities. Gradually, however, a counter- tendency developed as companies moved to link these systems together into what at first were fairly rudimentary networks. Ini- tially, in some of the local settings, applications were developed that went beyond the automation of a discrete task toward the performance of a wider set of connected operations. Automated linkages were then slowly constructed among systems, so that information could be electronically transferred and shared. On this basis, entry and processing functions were increas- ingly decentralized and distributed throughout the organization, but data bases were integrated and centralized. In the mid to late 1970s, what are often referred to as Voice automation" (OA) applications also began to be linked to these DP-based systems. A multitude of new office machines were introduced. Optical char- acter recognition devices were applied to premium billing and collection operations; computer output microfilm (COM) which permits the transfer of data directly from computer to microfiche- was used extensively by all insurers, and in conjunction with computer-assisted retrieval technology, increasingly replaced pa- per files; finally, more and more companies began to experiment with electronic mail, teleconferencing, electronic fund transfer, in- teractive data access via television, and other "office of the future" technologies. To an important extent, the introduction of these more so- phisticated systems in the 1970s was driven by a new competitive environment. Unprecedented levels of inflation and correspond- ingly high interest rates and important demographic and socio-

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BARBARA BARAN 31 economic changes2 combined to shake up this once stodgy industry by increasing uncertainty in financial markets and, consequently, dramatically increasing competition in the financial services sec- tor. Deregulation fueled these competitive flames. Giants in the industry found themselves struggling for survival; and companies with a long, proud history of paternalism were suddenly forced to lay oh up to 1,500 employees virtually overnight. In many ways, In fact, the insurance industry was hit by a crisis not un- like the one faced by U.S. auto manufacturers; and in both cases, one important response on the part- of companies wan to turn to automationboth to increase the efficiency of their operations and to enhance their product offerings. Whereas the early applications were basically limited to the automation of discrete tasks (e.g., typing, calculating), the new implementations called for rationalization of an entire procedure (e.g., new business issuance, claims processing), and ultimately the restructuring and integration of all the procedures involved in a particular division, product line, or group of product lines. As such, in the categories of Bright's (1958) original analysis, there has been a dramatic leap in the span, level, and penetration of the automated systems and, therefore, in the impacts on the labor force. The changes that have occurred in the underwriting and claims support systems best illustrate this evolution, although will also touch briefly on three others: administrative/clerical, decision support, and agency systems. 2 On the demand side, inflationary pressures initiated the demand for alternatives to ordinary insurance that would provide adequate protection at lower cost or higher rates of return on savings. Demographic shifts fueled these trends. Dual income households and the more affluent dual career householdsgrew rapidly and consumer assets were increasingly concentrated here. The needs and tastes of this population differed significantly from the traditional insurance consumer with more emphasis on investment over mere savings, less concern with thrift and more with spending, and a demand for a fuller range of sophisticated financial services. On the supply side, competitors outside the insurance industry responded to the new market conditions by offering reasonable substitutes for many traditional insurance products and services. In addition, commercial banks began to lobby for the lifting of regulatory restrictions which bars them from the sale of insurance. Finally, insurance companies themselves moved to diversify their product offerings and successfully lobbied authorities to deregulate rates.

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32 THE INSURANCE INDUSTRY UNDERWRITING AND CLAIMS S UPPORT SYSTEMS The underwriting function i' s a critical component of the in- surance policy production process; it entails an analysis of the risk involved and acceptance or rejection of that risk. As such, the heart of this operation has always been performed by profes- sional labor: insurance underwriters. However, today, in numbers of product lines, clerksaided by decision parameters embedded in computer software are responsible for the risks their compa- nies accept; and in the most standardized lines, computers are even performing this risk calculation task themselves. The un- derwriting systems now being used to produce property/casualty personal lines products (such as auto and homeowners' insurance) are a good example of these new applications. Traditionally, the production process for personal lines prod- ucts involved roughly the following: an agent gathered the client's policy information and forwarded this to an underwriting depart- ment, located either in the home or branch office; the underwriters established a file on the client, evaluated the risk, and determined the risk parameters, then sent the policy to the rating section. A rater (a skilled clerical employee) calculated the premium charges based on guidelines contained in numerous manuals; sometimes that information had to be communicated back to the agent so that the client could make a decision whether or not to use this particular company. In most cases, the policy went back to the underwriter who reviewed it and then sent it to a typing pool where policy typists prepared the various forms and documents. Finally, the policy was mailed back to the agent who forwarded it to the customer. In most large property/casualty carriers the first applications of computerized equipment to the underwriting process were im- plemented in the early 1970s and were aimed at speeding the underwriter's access to client files, shortening the time involved in rate malting, and of course Reproving the efficiency of policy production. A typical system of this vintage might simply have involved a stand-alone computerized rating system with most of the rating guidelines from the manuals built into the machine- and an automated policy issuance system, which performed the typing and assembly function. In many personal lines departments today, this kind of config-

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BARBARA BARAN 33 Oration remains the current state of the art. Beginning in the late 1970s, however, some of the largest carriers moved to institute what they variously call "underwriting by exception," "pigeon- hole underwriting," or ~computer-assisted underwriting." The aim is very simply to have the computer itself perform the risk assessment function on as large a fraction of policies as possible, on the basis of underwriting decision rules which are built into the machine. In general, the production process associated with these kinds of systems looks something like the following: the agent sends the policy information to a personal lines department where a cleri- cal worker screens it and enters all routine risks directly into the machines; these policies are then often relayed in batch form to the carrier's national (or regional) computer center; the computer evaluates the risk, rates the policy, and produces it. Policies which fail to fit within the pre-established guidelines are kicked out by the machine and returned to an underwriter, who often now calls up the information on a terminal and works with it on the screen. Although these systems are extremely new, a few companies al- ready report that 50 percent to 90 percent of their personal lines policies are completely underwritten by the computer. Most personal lines systems fall between the two just described in their level of sophistication, but, in almost all cases, clerks are assuming greater, and sometimes almost exclusive, responsibility for underwriting. For example, in one major property/casualty carrier, the introduction of computer-assisted underwriting two years ago shifted the bulk of the underwriting function from the underwriting department to the operations department, a clerical operation which had formerly been confined to assembling and producing the physical policies. Although auto policies still go to the underwriters for an initial screening process, most property policies are sent directly to skilled clerks in the operations depart- ment, who themselves order all necessary inspection reports and issue the policies. In some rare instances, the underwriting function has even been integrated into the activity of a sales worker, entirely elim- inating it as a separate department. In these cases, a highly skilled clerical positioncustomer representative has been de- signed; this job includes answering inquiries from potential cus- tomers over the phone; accessing a terminal to produce on-the-spot

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52 THE INSURANCE INDUSTRY Not surprisingly, then, although women are moving up in the occupational hierarchy, female wage rates in the industry re- mam extremely low. In our case study company, for example, female professionals earn only 16 percent more than male clericals (whereas male professionals earn 50 percent more); and the ma- jority of white female managers (57 percent) earn on the average $4 less per week than white male clericals. Overall in the in- surance industry, real average hourly earnings for nonsupervisory personnel fell by 4 percent between 1968 and 1978, in contrast to a slight rise in real wages during this same period across all industries. Disaggregat~ng by industry segment, the drop in wages corresponds directly to the increase in the proportion of female labor (Bureau of Labor Statistics, 1983~. Automation and feminization are thus proceeding as twin and highly interrelated processes and we should expect this trend to continue. Automation of professional functions is creating pre- cisely the kinds of jobs women have traditionally held in offices: the work is fairly routine, semi-skilled, and responsible, but offers Tow monetary reward and little opportunity for occupational mo- bility. As Oppenheimer (1968) has argued, employment of men instead of women in such occupations would mean a rise in the price of labor, a decline in its quality, or both. In today's environment, however, insurance companies are having an increasingly difficult time finding low-cost, high-quality female labor. As a result, such labor may be becoming a significant locational determinant. Nelson's (1982) study of the locational determinants of automated office activities (including insurance) concluded that, holding land costs constant, companies have cho- sen to site such operations in areas with a disproportionately high percentage of suburban married women. Compared with women in the central cities, suburban wives are often less career-oriented and may therefore be more willing to accept jobs with lirn~ted occupational mobility; because they are much more likely to be supplementing rather than solely providing the household income, they may be more content with modest pay scales.~ t An executive of the Fantus Co., a subsidiary of Dun and Bradstreet, which specializes in corporate location, made an argument similar to Nelson's regarding insurance company relocations in particular (Best'& Rechew, 1979~; see also Kroll (1984~.

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BARBARA BARAN 53 Ross's (1985) study of insurance company relocations and our own case work and survey tended to corroborate this hypothesis. Of the 75 percent of our sample who indicated that major spatial changes had occurred in their company within the last 5 years, 71 percent had moved a greater percentage of their operations to suburban locations. OverwheIrningly, they cited labor quality, labor costs, or both as the primary locational criteria. EFFECTS OF AUToMAT~oN ON MINoRITY WORKERS Although the relocations and, more generally, the changes occurring in the labor process may favor some categories of female labor, they threaten to have a negative effect on other categories. The situation of minority workers is particularly problematical. In general over the last 15 years, paralleling the progress of women, the position of minority labor in the insurance indus- try has improved significantly. Whereas in 1970, the percentage of blacks in insurance was seriously below the economy-wide av- erage in all occupational categories, by 1980 the EEOC reports indicated higher than average minority employment in many oc- cupational categories including higher percentages of black em- ployment. Disaggregating these gains by industry segment, it is clear that minority workers also fared best In highly automated sectors. Given the occupational structure of the industry, the majority of these minority employees are women. Approximately one-fifth of the female work force is composed of minorities as opposed to one-tenth of the mate work force. Overwhelmingly these women are concentrated in clerical positions (74 percent). Minority men also hold a disproportionate share of clerical jobs; 32 percent of all minority men in the industry are clericals compared to 7 percent of all white men. ii In the medical/health segment, in 1980, minority workers held 24 percent of all jobs; although their greatest share of employment was in clerical work (almost 30 percent), minority workers held unusually high percentages of jobs in all occupational categories. Minority employment was lowest in property/casualty firms (14.1 percent) and insurance agencies (12.4 percent), although even here in both cases minority workers held a disproportionate share of clerical jobs (U.S. Equal Employment Opportunity Commission, 1980).

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54 THE INSURANCE INDUSTRY The explanation for the influx of minority workers is identical in most respects to the argument ~ made with regard to women: affirmative action gains and the automation of the labor process. At the same time, there was an unportant difference in the dynamic of entry that may be extremely important in the longer run. Long excluded from office-type employment, at least in the private sector, minority women especially black womenbegan entering the clerical work force in significant numbers for the first tune in the late 1960s. To some extent these women sought and were granted new job opportunities as a result of the Civil Rights movement and its demands for greater equality in the job market; to some extent, they were pushed to enter the clerical labor force as opportunities for employment as domestic service workers declined. Nevertheless, they entered the office after the division of labor in administrative support activity had become excessively detailedand they entered at the bottom, in what are often referred to as ~back-office" jobs. This is still where minority clericals are overwhelmingly con- centrated in the data-processing centers, typing pools, and filing departments. As of 1980, almost 90 percent of all secretaries and 84 percent of all receptionists were white, whereas minority workers represented 25 percent of ah typists; 27 percent of all file clerks, office machine operators (other than computer operators), and messengers; and 28 percent of ad mad! clerks. Undoubtedly, then, the high proportion of minority clericals in the insurance industry is a direct indication that much of its office activity is production-oriented, rather than more traditional administrative support. In addition, the kinds of industrialized work settings created by the first waves of mechanization in the industry favored minority employment. Today the concern is that these jobs are disappearing, through mechanization, relocation, and perhaps also the new labor process configurations. First, as Table 4 suggests, of the eleven clerical occupations with the greatest minority representation, all but two are declining; in contrast, virtually all of the growing occupations are dominated by whites. Second, the movement of automated insurance activities out of the central cities to suburbs and white t2 These data are for the economy as a whole (U.S. Equal Employment Opportunity Commission, 1980~.

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BARBARA BARAN TABLE 4 Occupations in the Insurance Industry, by Race 55 Occupation Percent White Percent Black All occupations 82.0 10.0 Professional/technical/sales Underwriter 88.95 6.27 Computer systems analyst 89.05 4.64 Operations and systems research 88.73 6.02 Actuary 94.16 1.79 Statistician 84.86 7.88 Computer programmer 86.75 . 5.58 Insurance sales occupations 90.81 5.16 Clerical: above average blacka G eneral office supervisor 83.12 10.17 Computer operator 80.13 11.64 Peripheral equipment operatorb 79.44 12.67 Typistb'C 75.30 15.78 Correspondence clerks 81.08 13.43 Order clerk 81.08 11.69 File clerkb'C 73.13 17.52 Billing, posting, calculating machine operatorb 77.67 12.50 Duplicating machine operatorb'C 73.25 17.48 Office machine operator, not elsewhere classifiedb'C 73.17 Telephone operatorb 79.05 Mail clerkb 72.28 Messengerb'C 73.03 Clerical: below average blacka 17.12 14.38 18.72 17.59 Computer equipment supervisor 86.78 7.30 Financial record-processing supervisor 90.16 4.67 Secretary 89.00 5.73

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56 TABLE 4 (continued) THE INSURANCE INDUSTRY Occupation Percent White Percent Black Stenographe b,c 84.74 9.10 Receptionistb 84.24 8.15 Bookkeeper/accountant/audit clerk 90.01 4.33 Payroll/timekeeping clerk 85.53 8.15 Billing clerk 85.34 7.95 Cost and rate clerks (including raters)C 85.29 7.88 NOTE: Employment share percentages are for the entire economy, not the insurance industry, for 1980. aClerical occupations are grouped according to whether the proportion of blacks is higher or lower than the proportion of blacks for "All occupations" 610.0 percent). Absolute decline 1970-1978 in insurance industry employment (Occupational Employment Matrix, Bureau of Labor Statistics, 1981b). CAbsolute decline 1978-1981 in insurance industry employment (Occupational Employment Surrey, Bureau of Labor Statistics, 1978, 1981a). SOURCE: U.S. Equal Employment Opportunity Commission (1980~. towns also threatens minority employment. Again using the ex- ample of our case study company in the absence of reliable ag- gregate data, the three new, highly automated "personal-lines" centers described earlier were located in towns where minorities represented 3.1 percent, 3.3 percent, and 14.3 percent of the popu- lation (whereas their representation in the population as a whole is closer to 20 percent). This company's centralized data-processing center (soon to be closed) and home office also relocated from a central city with a minority population of over 40 percent to sub- urban locations which are 90 percent white. Third, and somewhat more speculatively, the move to team-type work configurations, which involve close working relations among high-level and lower- leve! employees, may well favor the hiring of "socially compatible" white women (Nelson, 1982; Storper, 1981~. The historically low level of rn~nority representation in secretarial and receptionist jobs

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BARBARA BARAN 57 lends circurr~stantial evidence to this hypothesis. As the bulk- processing centers are closed and key entry functions are moved to decentralized settings, minority clericals are in real danger of being displaced. And the centers are closing. A vice president in one company we interviewed predicted that within 2 years their six processing centers located across the country, which now em- ploy approximately 1,500 to 1,800 women, will have been closed. Although word processing is still somewhat centralized in many companies, over 70 percent of our sample in the survey responded that the trend in their company is toward decentralization of this function. Finally, and perhaps most important, are the prior disadvan- tages minorities face in the educational arena. If it is true, as believe, that the lesser-skilled jobs in the industry will continue to disappear, workers without adequate mathematics and literacy skills will be disadvantaged. To the extent that these occupa- tions increasingly involve interpersonal communication, workers for whom English is their second language may also be passed over. CONCLUSION To conclude, it seems appropriate to return briefly to the primary concerns raised in the office automation literature and to summarize the perspectives presented here on the most contentious questions: Will automation deskill white-collar work? Will office automation result in widespread technological redundancy? Will women workers bear the burden of the restructuring process? In terms of the deskilling debate, ~ have argued that three kinds of processes seem to be raising skill requirements in the in- surance industry. First, the rapid automation and elimination of much low-skilled work as computers assume responsibility for the most-structured functions is making the occupational structure increasingly top heavy. Conservatively, we can expect that during this coming decade roughly two-thirds of all new jobs in the indus- try will be nonclerical; over the next decade, net job gain should all occurinnonclerica~jobcategories(Baran,1985:234-238~. Second, the transfer of higher functions to less-skilled laboras decision parameters are embedded within computer softwareis creating new categories of skilled clerks, biasing the clerical hierarchy up- ward as well. For just this reason, in fact, there is some evidence

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58 THE INSURANCE INDUSTRY that clerical skills are higher in mas~produced product lines where clerks are able to assume greater responsibility for the entire pro- duction process than in specialty lines where they remain adjuncts to professional labor. Finally, especially within the context of inte- grated data bases, computer-mediated work seems to demand new skills of the clerical work force. In all these ways, therefore, what we are witnessing ~ a rolling process of deskilling and reskilling which, in aggregate, should increase the industry's demand for skilled labor. At the same time, however, as ~ suggested at length earlier, opportunities for occupational mobility may decline, the quality of work life may deteriorate, and salary scales may remain low. Turning to the question of job redundancy, an important corol- lary to this argument about skills, ~ have argued that clerical job loss may indeed reach serious proportions over the next two decades. In the last 10 years women have moved rapidly into profes- sional, technical, and managerial occupations. However, since ap- proximately 70 percent of the almost ~ million female employees in the insurance industry are clericals, the predicted drop in clerical jobs would have important implications for employment opportu- nities for women. Assuming that women gain even 50 percent of ad new nonclerical jobs and continue to constitute 94 percent of all clerical employment gained or lost, under conditions of moder- ate declines in clerical employment women will gain slightly less than half of all new jobs created in the industry over the next two decades; in contrast, between 1970 and 1980, women gained closer to 88 percent of all new jobs. If the decline in clerical employment is more substantial as two of the studies cited predict (Leontief and Duchin, 1986; Roessner et al., 1985) female employment in the industry would actually fall by 10 percent or more, unless, of course, women claim a much larger share of nonclerical employ- ment (Baran, 1985:238-239~. While women are moving up the occupational hierarchy in significant numbers, at the same time women at the bottom may be losing their jobs. In this sense, the effects of automation on the female work force will vary importantly by class and race. For minority clericals and less-educated white women, especially in the central cities, the threat of redundancy is serious. For skilled clerks, particularly in suburbs and small towns, there will prob- ably be jobs but not opportunities, unless new kinds of training

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BARBARA BARAN 59 programs are developed. For college-educated women, there may be new opportunities but there is also the danger, discussed ear- lier, that numbers of professional, technical, and lower managerial positions may resegregate females and be implicitly or explicitly reclassified downward as a result. The truth is that in a real sense the future is both uncer- tain and open-ended. Ultimately the question of whether the new technologies are used to create more good jobs than bad ones will depend on decisions made by the leadership of the firm; these decisions will be nonetheless shaped in significant ways by public policy. There is, for example, historical evidence that skill avail- abilities and shortages impact directly on job design (Levitan et al., 1981~. Public programs that absorb the costs of training- including both general education and the kind of ongoing retrain- ing that a period of rapid technological change requiresmake "working smarter" strategies both more attractive to companies and, in fact, possible. Policy wiD also play an important role in determining which workers bear the burden of the transition. Without a greater pub- lic emphasis on education and training, for example, it is likely that many of the workers presently employed in great numbers in the insurance industry will not only be expelled, but may also find themselves unable to secure comparable work. Similarly, aggres- sive affirmative action policies Knight act as a countertendency to the gender bias of job loss. One of the greatest dangers of studies such as this, which at- tempt to analyze the impacts of technological change on the work force, is their underlying assumption of technological determinism. In fact, the findings of these studies are ambiguous and indeter- minate not only because the processes we are analyzing are in a state of flux but, more important, because collectively we have considerable control over the eventual outcome of these processes. Nevertheless, with these caveats firmly in mind, ~ think that it is safe to predict that we need to begin to prepare our work force particularly our female work forcefor a labor market in which there will be many fewer routine clerical jobs.

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60 THE INSURANCE INDUSTRY REFERENCES Adler, Paul 1983 Rethinking the Skill Requirements of the New Technologies. Work- ing Paper. Boston: Harvard Business School. Appelbaum, Eileen 1984 The Impact of Technology on Skill Requirements and Occupational Structure in the Insurance Industry, 1960-1990. Unpublished pa- per. Temple University, Philadelphia. Baran, Barbara 1985 Technological Innovation and Deregulation: The Transformation of the Labor Process in the Insurance Industry. Final Report for the U.S. Congress Office of Technology Assessment under Contract No. 433-3610.0. University of California, Berkeley. Bcst's Review 1979 Insurance office locations in the 1980s. But' Review (August):62- 63. Braverman, Harry 1974 Labor and Monopoly Capital. New York: Monthly Review Press. Bright, James R. 1958 Does automation raise skill requirements? Harvard Brained Review (July-August) :85-98. Bureau of Labor Statistics, U.S. Department of Labor 1965 Impact of Office Automation on the Insuranec Industry. Bulletin 1468. 1969 Tomorrow's Manpower Needs. Vol. II. National Rend and Outlook: Industry Employment and Occupational Structure. Bulletin 1606. 1978 Occupational Employment Survey: Insurance. Unpublished data. 1981a Occupational Employment Survey: Insurance. Unpublished data. 1981b The National Indwtry-Ocetlpation Employment Matriz, 1970, 1978, and Projected 1990. Bulletin 2086, Vols. I and II. 1983 Employment and Earnings 30(January). Cummings, Laird 1977 The Rationalization and Automation of Clerical Work. Unpub- lished Master's thesis. Brooklyn College, New York. DeKadt, Maarten 1979 Insurance: a clerical work factory. Pp. 242-256 in Andrew Zim- balist, ea., Case Studies in the Labor Process. New York: Monthly Review Press. Drennan, Matthew P. 1983 Implications of Computer and Communications Necrology for Less Skilled Service Employmcut Opportunities. Final Report to the U.S. Depart- ment of Labor under Grant No. USDL 21-36-80-31. New York: Columbia University. Driscoll, James W. 1980 Office Automation: The Dynamics of a Technological Boondoggle. Presented at the International Office Automation Symposium, Stanford University. 1979 People and the automated office. Datamation (November):106-112. Faunce, William, Einar Hardin, and Eugene H. Jacobson 1962 Automation and the employee. Annals of the American Academy of Political and Social Science (340~:60-68.

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BARBARA BARAN 61 Feldberg, Roslyn, and Evelyn Nakano Glenn 1983 Technology and work degradation: effects of automation on women clerical workers. Chap. 4 in J. Rothschild, ea., Machina En Dea: Feminist Per~pectiocs on Technology. Elmsford, N.Y.: Pergamon. 1977 Degraded and deskilled: the proletarianization of clerical work. Social Problems 25:52-64. Greenbaum, Joan M. 1979 In the Name of Efficiency. Philadelphia: Temple University Press. Hoos, Ida 1961 Automation in the Office. Washington, D.C.: Public Affairs Press. Hunt, H. Allan, and Timothy L. Hunt 1985 An assessment of data sources to study the employment effects of technological change. Pp. 1-116 in Technology and Employment Effete. Interim Report of the Panel on Technology and Women's Employment, Committee on Women's Employment and Related Social Issues, National Research Council. Washington, D.C.: Na- tional Academy Press. Kroll, Cynthia 1984 Employment Growth and Office Space Along the 680 Corridor: Booming Supply and Potential Demand in a Suburban Area. Working Paper 84-75, Center for Real Estate and Urban Eco- nomics. Berkeley: University of California. Leontief, Wassily, and Faye Duchin 1986 The F~urc Intact of Automation on Workers. New York: Oxford University Press. Levitan, Sar, Garth L. Mangum, and Ray Marshall 1981 Human Repoured and Labor Maraca: Employment and Training in the American Economy. New York: Harper and Row. Life Office Management Association 1979 Word Processing Survey. Atlanta, Gal: Life Office Management Association. Matteis, Richard J. 1979 The new back office focuses on customer service. Harvard Bwinc~` Review 5 7(March-April) :146-159. Murphree, Mary 1982 Impact of Office Automation on Secretaries and Word Processing Operators. Presented at the International Conference on Office Work and New Technology, Boston. Nelson, Kristin 1982 Labor Supply Characteristics and Trends in the Location of Rou- tine Offices in the San Francisco Bay Area. Paper presented at the 78th annual meeting of the Association of American Geographers, San Antonio, Tex. 9 to 5, National Association of Working Women 1984 The To-5 National Survey on Women and Stress. Cleveland, Ohio: National Association of Working Women. Nussbaum, Karen, and Judith Gregory 1980 Race Against Time: Automation of the Office. Cleveland, Ohio: Working Women's Education P`und. Oppenheimer, Valerie 1968 The sex labeling of jobs. Indwirial Relations 7,3:219-234.

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62 THE INSURANCE INDUSTRY Phister, Montgomery, Jr. 1979 Data Processing Technology and Economics. Santa Monica, Calif.: Santa Monica Publishing Company. Roessner, J. David, Robert M. Mason, Alan L. Porter, Frederick A. Rossini, A. Perry Schwartz, and Keith R. Nelms. 1985 The Impact of Ofi.cc Automation on Clerical Employment, 1985-2000: Forecasting Tcchr~iq~ce and Plausible Futures in Barging arid In~uranec. Westport, Conn.: Quorum Books. Ross, Jean 1985 Technology and the Relocation of Employment in the Insurance Industry. Unpublished master's thesis. Department of City and Regional Planning, University of (California, Berkeley. Shepherd, J. 1971 Automation and Alienation: A Study of Office and Factory Workers. Cambridge, Mass.: MIT Press. Sirbu, Marvin A. 1982 Understanding the Social and Economic Impacts of Office Au- tomation. Unpublished paper. Cambridge, Mass.: Massachusetts Institute of Technology. Storper, Michael 1981 Toward a structural theory of industrial location. In J. Rees, G. Hewing, and H.A. Stafford, eds., Industrial Location and Regional Syatenu. New York: J.F. Bergen Publishers, Inc. U.S. Department of Labor 1977 Dictionary of Occupational Fitly, 4th edition. 1981 Selected Characteristics of Occupations Defined in the Dictionary of Oc- cupational Titlce. U.S. Equal Employment Opportunity Commission 1980 Minoritice and Women in Pupate Industry. ZuboR, Shoshanah 1982 Problems of symbolic toil. Dissent 29(Winter):51~1.