" Paper to be presented at the DRUID Summer Conference 2003 on "Creating, Sharing and Transferring Knowledge: The Role of Geographical Configurations, Institutional Settings and Organizational Contexts” Copenhagen/Elsinore June 12-14, 2003
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HUMAN CAPITAL SOCIAL CAPITAL AND INDUSTRIAL PERFORMANCE: COMPARISONS AMONG NEW U.S. MANUFACTURING PLANTS Peter Doeringer (Corresponding Author) Department of Economics Boston University 270 Bay State Rd. Boston, Massachusetts, 02215 USA Phone:617-353-4438 Fax: 617-353-4449
[email protected] David Terkla Department of Economics University of Massachusetts Boston 100 Morrissey Blvd., Boston, MA 02125 USA Phone: 617-287-6952; Fax: 617-287-6976
[email protected]; May 12, 2003 Abstract This study examines the efficiency of high performance management practices under two distinct types of corporate cultures: the hierarchical “command and control” culture that has been traditional in American manufacturing and a more participatory Japanese-style “social capital” culture. Firms with social capital cultures invest in social norms of employee cooperation and commitment to the goals of the firm and they develop a collective responsibility among employees for achieving efficient production of high quality. In contrast, under command and control cultures, firms treat efficiency as a function of cost control, managerial expertise, and supervisory authority. Drawing upon 48 case studies of new manufacturing plants, we conclude that there are substantial differences in performance between startups that are characterized by hierarchical cultures and those that systematically integrate new management practices into a larger corporate culture based on social capital investment. We develop a “ramp-up” production function, based on our field interviews with managers of new factories, and use it to test the effects of management practices and cultures on growth in employment and productivity. Consistent with other studies, we find that high performance management practices have a positive upon business performance. However, we break new ground by demonstrating that there are additional large benefits from the presence at the workplace of a social capital culture.
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HUMAN CAPITAL SOCIAL CAPITAL AND INDUSTRIAL PERFORMANCE: COMPARISONS AMONG NEW U.S. MANUFACTURING PLANTS1 There is a substantial literature on how the efficiency of American manufacturing can be improved through reforms in management practices (Dertouzos, Lester, and Solow 1989; Appelbaum and Batt, 1994; Appelbaum, et al., 2000; Osterman, 1994, 2000; Freeman and Kleiner, 2000; Black and Lynch, 1997, 1999; Capelli, 1999; Mohrman, Galbraith and Lawler, 1998). While different studies define “best” management reforms in somewhat different ways, they share a common hypothesis that traditional bureaucratic management practices should be replaced with various new “high performance” management practices including intensive training, flexible work organization, and problem-solving by employees. A growing body of evidence confirms that such practices raise productivity and improve quality (Osterman, 2000; Black and Lynch, 1999; Ichniowski, Shaw, and Prenushi, 1997; Capelli and Neumark, 1999; Ichniowski et al., 2000; Ichniowski et al, 2003). By focusing exclusively on management practices, however, these studies neglect the possibility that the benefits from adopting such management practices may depend upon the management cultures within which they are embedded. Complementarities between efficiency and culture are implicit in the literature on societal differences in the organization of capitalism (Sabel and Zeitlin, 1997; Crouch, 1997; Coates, 2000) and in studies showing that societal differences in management can affect macroeconomic performance (Caroli and Van Reenen, 2001). However, counterpart analyses of relationships among management practices, management culture, and the economic performance of the firm have been largely neglected (Maurice, Sorge, and Warner, 1980; Maurice, Sellier, and Silvestre, 1984; Aoki, 1990). Our study addresses this gap by examining the performance effects of management practices and the management “cultures” in new manufacturing plants in the United States. We find that growth differentials among new manufacturing plants can be partly explained by the extent to
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Special acknowledgement and thanks to Christine Evans-Klock for her field research and for providing the data on employment practices in new factories. Helpful comments were provided by Harry Katz, Kevin Lang, and Eli Berman and by participants in seminars at the W.E. Upjohn Institute, MIT, the Federal Reserve Bank of Boston, the University of Paris, XIII, and Northeastern University. We also thank Jean Poitras for research assistance. We gratefully acknowledge the research support provided by the W.E. Upjohn Institute For Employment Research, the Alfred P. Sloan Foundation, the Georgia Power Company, and the John W. McCormack Institute of Public Affairs at the University of Massachusetts Boston.
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which new firms invest in human capital and adopt other types of high performance management practices, but the major contribution of our study is that growth is further enhanced by social capital investments in workplace cultures that foster social norms of cooperation and commitment to the firm. The performance effects of these social capital investments dwarf those of high performance management practices. Management Practices and Cultures in New Manufacturing Plants High performance management practices -- the use of teams and rotating job assignments, intensive on-the-job training, opportunities for employee voice, problem-solving by “quality circles”, and performance-based pay incentives -- are often featured in economic theories of organizations. The standard interpretation is that these practices represent efficient managerial responses to various market failures, such as information imperfections, transaction costs, firmspecific assets, and difficulties in constructing complete employment contracts (Milgrom and Roberts, 1990; Aoki, 1990; Williamson, 1975, 1985; Lazear, 1998). One of the few examples that place these practices in a larger “cultural” context is provided by Aoki (1990) who posits two stylized management regimes, the “H-mode” and the “J-mode”. H-mode management resembles the efficient hierarchies and internal labor markets described by Williamson (1975) and Doeringer and Piore (1971) for large U.S. manufacturing companies. H-mode firms derive much of their efficiency from simplifying jobs, creating highly specialized labor, training narrowly in firmspecific skills, and maintaining productivity through hierarchical control and discipline. It is the efficiency of H-mode management that is being challenged by the recent emphasis on high performance management. J-mode management, as exemplified by large Japanese enterprises, embodies a less structured and more flexible set of management practices for improving efficiency. Intensive investments are made in general, as well as firm-specific, human capital through rotating job assignments and formal training in technical skills. Flexibility is enhanced both through training in multiple skills and the use of teams that define their own ways of organizing production. Efficiency and coordination are improved by routinely involving employees in problem solving and quality control, and by fostering workplace cultures of cooperation and commitment to the firm. J-mode management is similar in many ways to the high performance management practices being adopted by many U.S. firms and modified versions of J-mode management are prevalent among U.S.
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branch plants of Japanese multinationals (Liker, Fruin, and Adler, 1999; Kenney and Florida, 1993; Doeringer, Evans-Klock, and Terkla 1998, 2002; Black and Lynch, 1999, Osterman, 1994, 2000). The Case Study Sample In order to develop, test, and interpret specific hypotheses about how management practices and management cultures interact to affect business performance, we compiled detailed information on management practices and cultures from a sample of forty-eight new manufacturing plants that began operation in the United States between 1975 and 1990 (see Table 1). The establishments in the sample are branch plants of multinational corporations. The sample was drawn from three 2digit industry groups -- plastic and rubber products (SIC 30), non-electrical machinery (SIC 35) and electrical equipment (SIC 36). These industries were selected to provide a mix of product lines and technologies ranging from relatively low-skilled, labor-intensive mass production technologies (parts of rubber and plastics), to intermediate-skilled assembly line technologies (electronics), to high-skilled batch production technologies (non-electrical machinery). The plants are located in Georgia, Kentucky, and three northeastern states (Massachusetts, New York, and New Jersey) to provide some diversity in regional labor markets and the specific plants were selected randomly from within each industry/region cell. A unique feature of the sampling methodology is that we matched branch plants of domestic and Japanese companies by broad product line. This distinction in the nationality of ownership is important because Japanese-owned startups adopt high performance organizational practices far more frequently, and reinforce them far more often with social capital investments in high performance management cultures, than do their domestic counterparts (Doeringer, Evans-Klock, and Terkla, 1998). By matching plants by type of product, we reduce the possibility of unobserved variables affecting our results. (A detailed description of the sample is provided in the Appendix.) Each case study involved both unstructured interviews with plant managers, personnel managers, industrial engineers, and union officials, extensive plant tours, and the systematic collection of data on specific management practices and cultures. Site visits took place between 1990 and 1993 and usually lasted a day, with follow-up information obtained by telephone when needed. This methodology allowed us to supplement quantitative data on the adoption rates and performance consequences of different management practices and cultures with qualitative insights from interviews and the on-site observation of production processes.
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Similarities Among Plants This sample of firms provides an unusual opportunity to observe how new technologies and advanced management practices are combined in new plants to raise productivity and lower unit costs. By focusing on startup establishments, we are also able to control for heterogeneity in the vintages of capital, because are sample is also free of distortions from the residual influence of prior management practices (Ichniowski et al., 1997; Black and Lynch, 1999). Regardless of the nationality of ownership, the new plants in our sample were considered to be flagship production facilities by their parent corporations. They universally adopted production functions embodying the next generation technologies for their industries and they staffed these technologies with high quality workers who were very similar in terms of education levels and prior work experience. In order to recruit and retain workers of high quality, all of the plants had relatively high wages, with almost half the plants paying starting wages in the top 20% of the area wage distribution. Two out of five new plants also supplemented high wages with profit sharing or bonuses. New plants routinely adopt a variety of high performance management practices to increase production efficiency (Table 2).2 Almost 70% of the plants in our sample provide two or more types of intensive training including substantial job entry training (79%), technical training (65%), and team training (44%). Half the startups use some form of flexible work organization with teamwork being the most common (42%), followed by job rotation (27%). Opportunities for employee voice are common, with three-fourths of the startups holding daily and weekly discussion meetings between workers and managers and a little over half the startups using quality circles to obtain employee input in solving production and quality control problems. Key Differences Between Japanese and Domestic Plants When the case study data are stratified by nationality of ownership, however, a number of important distinctions emerge. One is that Japanese transplants adopt both individual high performance practices and clusters of practices more frequently than domestic startups (Table 2). New Japanese plants invest heavily in various kinds of human capital and are about 50% more likely than new U.S. plants to provide intensive training to new hires and three times more likely to train their production workers in multiple skills (Table 2). We also find statistically significant 2
High performance management practices are defined to include types of flexible work organization, various forms of intensive training, methods for providing employees with voice in business decisions, and the use of quality circles for solving production problems.
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differences in employee voice, as measured by the frequency of meetings between managers and employees. Almost all of the Japanese startups (86%) use two or more high performance management practices, compared to 40% of the domestic startups, and the rate of adoption of all four practices by Japanese-owned plants (50%) is double that of domestic startups. The most important difference between the Japanese and domestic plants, however, is in their propensity to invest in social capital. Social capital formation can take many forms, but in the context of manufacturing plants it involves investing in high productivity workplace cultures. These cultures foster social norms of employee cooperation and commitment to the goals of the firm and they develop a collective responsibility among employees for achieving efficient production of high quality. Social capital investments are harder to quantify than investments in physical and human capital, but they are readily apparent in our interviews. Descriptions of how managers try to motivate cooperation and collective problem solving among employees and to foster commitment in the workforce were an almost universal feature in our interviews with managers in Japaneseowned plants. The managers of the domestic plants in our sample rarely invest in high productivity workplace cultures and, instead, prefer a “managerial control” culture in which efficiency is seen as a function of managerial expertise, supervisory authority, and the engineering of efficiency and quality. Managers of Japanese-owned plants also frequently discussed the complementarities between social capital investments and the high performance management practices that they adopted. For example, allowing employees to manage their own teams, participate in quality circles, and control quality were reported to contribute to the culture of collective responsibility for production efficiency. The efficiency effects of high performance management practices and social capital investments are also frequently reinforced by commitment incentives. The most common type of commitment incentive is to offer employees a high degree of job security in the expectation that they will reciprocate in return with loyalty, cooperation, and identification with the goals of the firm. The use of job guarantees to foster commitment is present in twenty of the Japanese-owned plants, but in only one domestic plant (Table 2). Given differences in human capital investment, in the rate of adoption of high performance management practices, in the degree of investment in social capital, and in the use of commitment incentives, it is obvious that the production in Japanese-owned manufacturing plants is managed
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very differently than in counterpart domestic plants. This comparison is best illustrated by two examples from our case studies. The first is a new Japanese-owned plant in Massachusetts with 270 employees that manufactures floppy disks using a state-of-the-art highly automated technology. It has adopted a wide range of high performance management practices including flexible work, intensive training, employee involvement, and quality circles, and its wages and benefits are the highest in the area. Management emphasized the importance of developing employee commitment to raising productivity. After adopting commitment incentives that include an explicit no layoff policy and extensive reliance on quality circles, the plant manager was moving to the “next stage beyond quality circles” by creating self-managed production teams and using small groups of operators, technicians, and engineers to focus on plant-wide efficiency and quality issues. Front-line employees participated in a variety of production decisions and management was replacing managerial supervision with collective “peer” supervision on the shop floor. At the time of our study, the plant had grown rapidly to its full three-shift capacity, had the highest productivity of any plant in the parent company, and was adding additional production capacity. The second example is a U.S.-owned plant in Georgia with 300 employees that makes internal combustion engines. Like the Japanese diskette manufacturer, it is highly automated, offers the highest wages in the area, and has adopted a wide range of high performance management practices. However, this plant has a distinctly different management culture. Management’s approach to raising productivity in this plant was to “engineer the operator out” and to “methodize” jobs so that they are “foolproof” in terms of human error. There is intensive entry training, but jobs have been designed to be routine and repetitive and there is little subsequent training. The plant has experimented with small production teams, but they are not widely used, and there is no job rotation. The plant manager prefers to use technology, rather than workers, to control quality. Management feels that “workers are not skilled enough to identify problems, let alone solve them” and quality circles exist only to provide one-way communication from management to production workers. There are no guarantees of job security or other commitment incentives to encourage cooperation with high performance management practices and identification with the goals of the company. Nevertheless, the parent company regards this plant as one of its most efficient and the plant has grown steadily since opening.
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Management Differences and Business Performance Given these management differences, it would seem reasonable to expect that new Japanese-owned plants would out-perform the new domestic plants in our sample. They should be better at learning how to solve the inevitable production and quality control problems that occur during the startup of new plants, their greater human capital investment should create a labor productivity advantage, and their larger investment in social capital should facilitate coordination and motivate higher effort. However, there is also the possibility that foreign-owned plants are at a competitive disadvantage because they lack knowledge about U.S. markets that is readily available to new branch plants of domestic companies. In the latter case, Japanese-owned plants could be adopting a larger number of high performance management practices and making more intensive investments in human and social capital in order to compensate for the disadvantages of operating in an unfamiliar economic environment. We probed these alternative hypotheses carefully during our interviews with Japaneseowned plants and encountered no reports of such disadvantages. Instead, managers of both Japanese-owned and domestic plants offered similar efficiency explanations for adopting high performance management practices and for implementing their different management cultures. Nor do we find any obvious evidence of a performance disadvantage among Japanese-owned plants in simple measures of growth. In fact, simple comparisons of the rates of growth in employment between Japanese-owned and domestic plants suggest are consistent with the findings of other studies of a positive correlation between business performance and the frequency of adoption of high performance management practices. The average annual increase in employment among the Japanese-owned plants in our sample is 20% compared to only 6% for the domestic plants. Nevertheless, there could be other explanations of this growth differential. For example, it could be a statistical artifact of Japanese plants starting from a smaller initial employment level than U.S.–owned plants, or it could be the result of cyclical differences in the timing of startups or in the accumulated years of production learning that are correlated with nationality of ownership. A more refined test is, therefore, needed to confirm that Japanese productivity and growth advantages is caused by differences in management practices and cultures. Ramp-up Cycles and Ramp-up Production Functions in New Manufacturing Plants The traditional approach to testing the effects of managerial practices on business performance is to incorporate measures of these practices explicitly as inputs into a standard neo-
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classical production function. We use a somewhat similar approach, but with a different specification of the production function, since our case study research shows that standard production functions do not accurately characterize the learning process that occurs during the “ramping-up” of production in new plants. All of the new plants in our sample, regardless of nationality of ownership, go through the same start-up process in which growth in output and employment occurs in stages. New plants begin their ramp-up cycles by operating a single shift in order to correct initial manufacturing and quality control problems associated with the new facility. Only after these plants have “learned” to meet relatively demanding corporate efficiency standards, based on achieving "output per employee" or "unit costs" benchmarks set by the most efficient plants of the parent corporation, are they allowed to expand output by adding a second and then a third shift. Once full three-shift capacity is reached, further growth in output requires the authorization of additional capital investment that triggers a new ramp-up cycle. The rate at which startups pass through the stages of this ramp-up cycle is a direct function of workforce learning, problem solving, and employee effort. Since there was remarkably little technological change in our sample of plants for periods of up to a decade or more, we are confident that the productivity gains required for moving through the ramp-up cycle come almost exclusively from improvements in labor efficiency. In our case study sample, the plants with the most effective training and problem-solving practices exhibited the fastest ramp-up cycles whereas those that were unable to meet corporate efficiency standards were not allowed to add shifts or otherwise expand capacity. Continued failure to achieve corporate productivity standards eventually led to cutbacks in output in some cases because production was reallocated to more efficient branch plants.3 Changes in employment follow roughly the same pattern as changes in output as new shifts are added and new investments are authorized. While “learning by doing” produces some modest gains in output within each shift without any change in labor inputs, the gains are dwarfed by output growth from adding a second or a third shift with corresponding increases in employment (see Figure 1).4 Several alternative ramp-up cycles reflecting different rates of learning and productivity growth are illustrated in Figure 2. 3
Such cutbacks occurred in about 10% of the plants in our sample. These periodic employment increases as shifts are added involve roughly equal numbers of new workers since each shift uses the same capital equipment and approximately the same complement of production workers, technicians, and 4
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We can model the ramp-up and learning process using a production function that incorporates high performance management practices and social capital investments in high performance management cultures. Output at time t (Qt) in this “ramp-up” production function depends on fixed plant and equipment (K*), the fixed labor input required for each shift (L) multiplied by the number of shifts (St) operating at time t, the number of high performance management practices adopted (P), and the investment in social capital cultures (J). The function et(P, J) represents a productivity driver that captures the improvement in productivity resulting from the learning, problem-solving, and effort effects associated with adoption of high performance management practices (P) and investments in social capital cultures (J), as shown in Equation (1). (1)
Qt = f [K*, StL, et(P, J) ]
L is exogenously determined by the fixed capital and technology embodied in the plant. P and J are parameters chosen by management. St is a function of the productivity driver et(P, J) according to the rule observed in the case studies that the parent corporation will authorize adding a new shift when a target level of productivity is achieved.5 Estimating the Productivity Gains From Management Practices and Social Capital The preferred way of using Equation (1) to test for the productivity effects of high performance management practices and social capital management cultures would be to see if output grew at a faster rate in plants that adopted such practices and cultures than in those that did not. Most of the firms in our sample, however, considered establishment-level data on output to be proprietary, and were also unwilling to release productivity or unit cost data. Because we were able to obtain data on employment, we take advantage of the high correlation between output and employment as corporate efficiency benchmarks are achieved and shifts are added, to estimate a close approximation of the ramp-up production function in Equation (1) in which employment is our instrument for output.
direct supervisors. Productivity improvements within a single shift, however, do not affect employment because staffing levels are fixed on each shift. 5 Output per worker is the average of all shifts. Because the incumbent workforce is more productive than the new employees at the time of the addition of each new shift, the weighted average productivity for the plant declines by a smaller amount with each new shift and continuous learning among incumbent employees can accelerate the achievement of target productivity with each additional shift.
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Our sample is large enough to allow us to estimate a simple ramp-up model (Equation 2) that captures the essence of how management practices and cultures affect the ramp-up process. Since we are primarily interested in measuring the speed of the ramp-up cycle, the dependent variable in this model (RAMPUP) is the average annual compound growth rate of employment in each establishment from the first year of operation to the date of the plant interview. HIPERFORM is the number of high performance management practices adopted by plant and NATJ is a binary variable that serves as an instrument for capturing the effects of the social capital cultures of Japanese-owned plants. IND is a vector of industry dummies to control for industry differences in growth rates; employment in the base year of operation (STARTSIZE) controls for scale economies and for the algebraic effect of differences in the starting size of establishments on the rate of employment growth; STARTYEAR controls for the effects of accumulated experience and cyclical influences on different cohorts of plants (see Table 3). (2) RAMPUPi = a + b1HIPERFORMi + b2 NATJi + b3IND i + b4STARTSIZEi + b5STARTYEARi + u We first test for the effects of high performance management practices without including our NATJ measure of management culture. The results are consistent with those of other studies in that high performance practices contribute to higher rates of establishment growth. The point estimate on the coefficient of HIPERFORM implies that each additional high performance practice raises the annual rate of employment growth by about six percentage points (Table 4, col. 1). We then introduce NATJ to test for any additional effects of Japanese-style social capital cultures (Table 4, col. 2). NATJ has a large and significant influence on the ramp-up rate and including it in the model somewhat lowers the estimated effect of high performance management practices. A comparison of the estimated coefficient on the NATJ variable with that on HIPERFORM indicates that investing in a social capital management culture is roughly equivalent to adopting two high performance management practices.6 We conclude that the management cultures and greater use of high performance management practices found in new Japanese-owned 6
In order to examine possible non-linearities in the effects of high performance management practices, a second specification of this model was tested using binary variables for HIPERFORM in place of a continuous HIPERFORM measure. The effects of adopting one or two high performance practices are not significant, but the adopt of three or four practices has a statistically significant effect on the ramp-up rate.
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manufacturing plants in the United States result in faster growth in jobs, output and productivity when compared to counterpart new U.S.-owned manufacturing plants. Corroborating Evidence From National Data The combination of case study interviews and econometric analysis provides compelling evidence of the advantages of Japanese social capital investments in contributing to job growth, at least for the three manufacturing industries examined. In order to provide a more general test of this effect, we compiled a national panel database consisting of new branch manufacturing plants in the United States using information from the Small Business Administration's USEEM longitudinal establishment data file. We merged this U.S. data with corresponding information on new Japanese plants in the United States obtained from a national directory of Japanese firms issued by the Japan Economic Institute and from our own mail and telephone surveys. Only data on branches and subsidiaries of multi-plant enterprises that opened between 1978 and 1986 and continued in operation through 1988 were included so that the national data would be comparable to the case studies. These national data further confirm the findings from the case studies that new Japaneseowned plants grow faster than their domestic counterparts. Annual rates of employment growth in Japanese-owned plants averaged 29%, compared to 6% for new U.S.-owned plants, which is an even larger difference than we found in our case study sample. The size and scope of this national database allows us to control for a wider range of influences on employment growth, but it lacks the detailed information on high performance management practices provided by the case studies. Nevertheless, because our case studies show that Japanese-ownership is so highly correlated with the intensive use of high performance management practices and the development of social capital management cultures, we can use differences in the nationality of ownership to test for the combined effects of differences in management practices and cultures on employment growth in new plants. The “national” ramp-up model that we estimate (Equation 3) uses the same measures of ramp-up rates (RAMPUP) as in the case study analysis and the same controls for the initial size of establishment (STARTSIZE) and the year the plant opened (STARTYEAR). In the national model, however, NATJ is an instrument for both the higher rates of adoption of high performance management practices and the presence of social capital management cultures that our case studies indicate are characteristic of new Japanese-owned manufacturing plants in the United States.
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(3) RAMPUP = a + b1NATJi + b2INDi + b3REGi + b4STARTYEARi + b5STARTSIZEi + u In addition, we are able to include an expanded set of controls for industry differences (IND) and for regional variations in cost structures and access to markets (REG), as defined in Table 3. The empirical results from this model correspond closely to those of the case study analysis. The coefficient on the NATJ variable (Table 5, col. 1) implies that the annual growth rates of new Japanese-owned plants average over 20 percentage points above those of domestic plants. We also find significant industry effects on ramp-up rates. We then tested a second ramp-up model that included an interaction term NATJ i * IND j to determine whether the effects of high performance management practices and cultures universal or confined to specific industries.7 This model shows that the effects of Japanese ownership are confined to six industries [furniture (SIC25), rubber and plastics (SIC30), fabricated metals (SIC34), non-electrical equipment (SIC35), electrical equipment (SIC36), and transportation (SIC37)] (Table 5, col. 2). The Japanese advantage in these industries is large, ranging from 25 percentage points per year in transportation to 49 percentage points per year in rubber and plastics and non-electrical equipment. These industries are mainly in the durable goods sector that manufactures relatively high value-added products with variable demand, and are exactly the sectors where Aoki (1990) predicts that J-mode management is most likely to have an influence on productivity. Conversely, high performance management practices and cultures have no significant influence on relatively low value-added industries, such as apparel, where traditional management practices appear to retain their efficiency advantage. This group also includes all three of the industries examined in the case studies, which gives us further confidence in our general conclusion that the sources of the growth advantage of new Japanese-owned manufacturing plants are rooted in a combination of high performance management practices and social capital investments in high performance management cultures.
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We also tested for the possibility that the higher growth rates among Japanese plants are being driven special circumstances associated with the growth of the Japanese automobile industry in the United States during the period covered by our data by interacting Japanese ownership with a vector of dummy variables for new plants in specific 4digit automotive-related industries (3711-automobile assembly, 3714-auto parts fabrication, 3089-automotive plastic
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Potential Biases in Measuring the Employment Effects of Japanese Management Cultures While all of our case study and econometric evidence points to positive employment and productivity effects from high performance management practices and cultures, it is possible that the results are biased in some way. One possibility is that our relatively simple ramp-up models omit some important variables that accelerate ramp-up cycles and that are also correlated with Japanese ownership. However, we designed our case study sample to include only plants with new technologies to avoid unobserved “vintage” effects, we control for other cohort effects in our national ramp-up model, we found no evidence of major differences in workforce "quality" between domestic and Japanese-owned plants in our case studies, at least as measured by age, education, and experience (Doeringer, Evans-Klock, and Terkla 1998), and we uncovered no other hard-to-observe factors during our interviews. What remains are three potential sources of bias -the effects of pay incentives on worker productivity, the role of import substitution, and possible differences between the new plants that “survive” to be included in our study and those that are excluded because they failed. Performance Incentives Many of the plants in the case study sample offer their employees performance incentives, such as profit sharing and productivity bonuses. To test for the possibility that these incentives are driving employment growth, we substituted various measures of performance incentives for high performance management practices in the case study ramp-up model. We also tested a linear measure of the number incentives used and various clusters of incentives. None of these specifications revealed a statistically significant relationship between ramp-up rates and incentives.8 Import Substitution An additional concern is that the high growth rates of Japanese-owned plants could result from substituting production in the United States for imports from Japan. While there is a widespread finding that foreign direct investment in general is a complement, rather than a substitute, for imports (Lipsey, 2002), it is also widely-reported that import-substitution has motivated much of the Japanese manufacturing investment in the United States (Kenney and Florida, 1993; Caves, 1993). components, and 2399-seat covers and seat belts. None of the auto industry interactions was statistically significant and the coefficient on NATJ was largely unchanged. 8 This result is contrary to that of Blank and Lynch (1999) who find that profit sharing matters to productivity in their study of established plants.
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While we cannot conclusively refute the thesis that import substitution is the primary source of the Japanese growth advantage, several pieces of evidence suggest that it is unlikely to be the sole consideration. For example, if import substitution is driving the growth in output of Japanese plants, output and employment should be sensitive to changes in exchange rates.9 We explicitly tested for such exchange rates influences by adding exchange rates as an explanatory variable in the national ramp-up models and found no statistically significant influence of exchange rates on output.10 Our plant interviews also show that the source of growth in many of the Japanese-owned plants was coming from markets other than those involving import substitution. Finally, the growth of Japanese plants is being measured against counterpart U.S.-owned branch plants that also have growth opportunities within their corporations that resemble those of import substitution by Japanese-owned branch plants. When new domestic plants meet the internal efficiency criteria of their parent companies they are allowed to expand their output at the expense of less efficient branch plants in much the same way as production by Japanese plants in the United States can substitute for imports produced by other branch plants of Japanese multinationals.11 There is no a priori reason that opportunities for import substitution by efficient U.S. branch plants within Japanese multinationals should differ from supply substitution by efficient branch plants within U.S. corporations. Survivor Bias A third possibility is that our findings could be biased because both the case study and the national databases include only startup plants that have survived for up to a decade or more. Survivor bias would be a problem if we were trying to estimate levels of productivity or employment because we do not take into account the declines in productivity, output, and 9 This exchange rate hypothesis is somewhat questionable on theoretical grounds because the positive effect of exchange rates on import-substituting output should be at least partially offset by the negative effect on repatriated profits (McCulloch, 1991). 10 We looked for the effect of the Japanese/U.S. exchange rate using two different specifications. In one case, we included the percentage change in the exchange rate over the lifetime of the firm interacted with NATJ as an independent variable. This was insignificant and did not substantially change the coefficients in the original regression. We also tried including the interacted exchange rate change without the NATJ variable and this also proved to be insignificant. Exchange rates were taken from Economic Report of the President, 1996. 11 The allocation of production among branch plants depends on a complicated set of marginal cost calculations and company-specific transfer pricing decisions (Scherer, 1975). Domestic startups have substantial market guarantees by being designed to serve markets already established by less efficient branch plants within the parent company, as well as new markets with high growth potential. Japanese plants that come to the United States for reasons other than export substitution face demand curves that are steeper than export-substituting plants. These plants are analogous to domestic startups that are entering new markets, as opposed to markets previously served by outdated branch plants that they are replacing.
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employment in plants that fail during the period covered by our data. However, we at least partly avoid this problem by looking at relative differences in ramp-up rates between Japanese and U.S.owned plants. In this case, survivor bias could only explain our results if Japanese plants had a lower survival rate than similar domestic plants. Otherwise survival biases would cancel out or favor an observed employment growth advantage among domestic plants. Furthermore, we know from our Japanese panel data that almost all new Japanese-owned plants “survived” during the period of our study.12 Summary Both our case studies and a representative national sample of new manufacturing plants show that new branch plants of Japanese multinationals ramp up faster and generate jobs at a far higher rate than counterpart new branch plants of domestic corporations. Qualitative evidence from case study interviews points to productivity gains from more intensive on-the-job investment in human capital, relatively greater use of high performance management practices, social capital investments in high performance management cultures, and commitment incentives that reinforce the complementarities among these factors as the source of this ramp-up advantage. New plants that incorporate all four of these components into their efficiency strategies have a substantial productivity and growth and advantage over similar plants that rely on more traditional style management practices and cultures. This conclusion is supported in a number of ways – by in-depth case studies and by econometric analyses of ramp-up rates using both detailed plant-level data from the case studies and a national database on new manufacturing plants. The performance advantages from adopting coherent management regimes incorporating high performance management practices and social capital investments are robust across alternative specifications of the ramp-up model and across different sources of data. We find little evidence to support alternative hypotheses about why new Japanese might have faster ramp-up rates than similar domestic plants and cannot identify any obvious sources of bias in the results. The effects of social capital formation and management cultures have been neglected in previous analyses of high performance management practices. By omitting these factors the 12
There is also a related question of selection bias. It may be that the Japanese multinationals that invest in new plants in United States are among the higher performing plants in Japan and are transferring their successful management practices to their U.S. branches. For our purposes, however, it is sufficient to demonstrate that there is a Japanese
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independent contribution of high performance management practices to efficiency is likely to be overstated. Even more important, ignoring the contribution of social capital investment to improving productivity and efficiency means that recommendations for adopting high performance management practices are overlooking the potential of even greater gains in productivity growth that might be achieved by radical reforms in workplace cultures.
advantage that is derived from management practices and cultures that are different from those of counterpart U.S.owned plants.
18
Table 1 Description of Startup Plants in the Sample
SIC State Product
Number of Workers
Annual Avg. Start Employment Year Growth (%)
30* 30* 30* 30* 30* 30* 30* 30* 30* 30 30 30 30 30 30 30 35* 35* 35* 35* 35* 35* 35* 35* 35* 35 35 35 35 35 35
129 300 375 120 100 340 110 39 600 105 175 216 77 131 50 200 411 52 15 275 21 25 788 27 60 250 350 64 70 65 160
1980 1989 1980 1988 1988 1988 1985 1986 1979 1983 1985 1978 1987 1978 1978 1978 1985 1988 1987 1982 1989 1987 1983 1988 1990 1978 1983 1984 1984 1990 1978
GA GA GA KY KY KY NJ NJ NY GA GA GA KY KY MA NJ GA GA GA KY KY KY MA NJ NJ GA GA KY KY MA MA
misc plastic products tv frames gaskets industrial belts rubber components auto dashboards plastic labels plastic labels/seal auto parts plastic parts auto molding plastic food packaging plastic parts plastic parts misc. plastic products plastic bottles PC monitors construction equip. construction equip. machine tools cutting tools metal prod centers personal computers textile machinery computer peripherals industrial saws motors motor brushes precision mach. parts industrial equip. industrial equip.
9.6 26.0 11.6 18.9 42.5 8.0 27.6 14.7 31.4 9.1 -1.9 8.2 17.8 4.6 1.5 -5.0 22.4 24.0 -27.9 23.1 51.4 3.8 16.5 22.0 7.1 2.6 12.6 4.2 24.1 -16.2 9.7
19
Table 1 (cont.)
SIC State Product
Number of Production Workers
Start Year
Annual Avg. Employment Growth (%)
36* 36* 36* 36* 36* 36* 36* 36* 36* 36* 36 36 36 36 36 36 36
931 300 300 676 534 1800 125 215 350 44 350 440 350 134 300 104 54
1987 1980 1987 1984 1985 1988 1989 1989 1989 1982 1989 1978 1978 1985 1978 1987 1982
20.3 8.8 37.7 17.6 19.9 34.5 46.2 6.1 36.8 7.2 -11.2 9.7 7.6 4.3 11.0 26.8 0.0
GA GA GA GA KY KY KY MA MA NJ GA GA GA KY KY MA MA
auto & consumer elec. videocassettes compact disks tvs, phones electronic auto parts wire assembly electronic auto parts micro diskettes micro diskettes elect. optical instru. circuit boards elect. auto part wire harnesses truck wire harnesses air condition circuit boards computer equipment
__________________ * indicates Japanese ownership
20
Table 2 Adoption Rates of Selected Management Practices: Japanese-owned and Domestic Manufacturing Plants Practice
All Plants
U.S. Plants
Japanese Plants
79.2% 31.3 64.6
60.0% 15.0 55.0
92.9%** 42.9* 71.4
41.7% 27.1
35.0% 15.0
46.4% 35.7
52.1
35.0
64.3*
75.0%
40.0%
100.0%**
58.3
20.0
50.0**
27.1%
5.0%
71.4%**
Human Capital Investment Intensive Entry Training Multi-skill Training Technical Training High Performance Management Production Teams Job Rotation Problem- solving (Quality Circles) Employee Voice Frequent meetings with Employees Quality Control by Production Workers Commitment Incentives Strong job guarantees N=
48
20
28
Source: Authors’ Establishment Survey Significance Test: Significance of Difference Test using Pearson Chi Square with Yates continuity correction. When expected frequency is too small, Fisher's Exact Test is used. Significance of differences is measured between U.S.-owned and Japanese-owned plants. * = 0.05 ** = 0.01
21
Figure 1 Illustrative Ramp-up of a High Performance Startup Output, Employment, Output/worker
Output
Target Output/worker Actual Output/worker Employment
First shift
Second shift
Third shift
Plant Expansion
22
Figure 2 Alternative Ramp-up Paths Output and Employment High Performing Plant
Plant Expansion
Average Performing Plant Low Performing Plant
Time
23
Table 3 Definitions of Variables Used in the Ramp-up Models RAMPUPi = Average annual compound growth rate of employment in establishment i from year of startup to final year (1991-92 for case study sample, 1988 for national sample) HIPERFORM = Number of high performance management practices adopted by establishment i [daily and weekly meetings with employees, employee involvement in managerial decisions (quality circles, peer supervision, quality control by production workers), flexible work practices (teams, job rotation) NATJi = Binary variable; 1 if establishment is Japanese-owned. STARTSIZEi = Log of employment in the startup year of establishment i. INDi = Vector of binary variables for the two-digit SIC of establishment i. REGi = Vector of eight binary variables for regional location of establishment i. STARTYEARi = Vector of binary variables for two-year cohort in which establishment i began production [1988-89 is omitted cohort from case study analysis, 1986-87 is omitted cohort from national panel data] . NATJ i * INDj = Vector of binary variables for Japanese-owned plant i being in the twodigit SIC industry j. u = Random error term
24
Table 4 Estimated Effects of High Performance Practices on Ramp-up Rates: Case Study Sample (absolute value of t-statistics in parentheses) Dependent Variable: RAMPUP Independent Variable Constant HIPERFORM
(1) (2) Without NATJ With NATJ 44.80** 36.88** (3.66) (2.95) 6.07** (2.91)
NATJ STARTSIZE PLAS MACH
4.77* (2.25) 10.33* (1.95)
-6.92** (3.40)
-6.43** (3.25)
-4.82 (0.91) -8.94 (1.58)
-5.04 (0.98) -8.68 (1.59)
-9.84 (1.55) -15.83 (1.71) -11.13 (1.58) -6.22 (0.97) -16.81** (2.53)
-3.44 (0.50) -17.17 (1.92) -8.07 (1.16) -3.34 (0.53) -14.81* (2.28)
Starting year cohorts 1978 1980 1982 1984 1986 # of plants
48
Adjusted R-square
0.68
0.72
3.70**
3.95**
F value ________________________________ * significant at the .05 level ** significant at the .01 level Source: Authors' establishment survey.
48
25
Table 5 Estimated Effects of Organizational Regimes On Ramp-up Rates: National Panel Data 1978-88 (absolute value of t statistics in parentheses) Dependent Variable: RAMPUP Basic Model (1)
Basic model Plus sectoral interactions (2)
Variable Constant NATJ STARTSIZE
0.20** (25.32) 0.24** (7.54) -0.04** (38.96)
0.20** (25.34) 0.21** (5.54) -0.04** (38.96)
-0.01 (0.81)
0.00 (0.75) 0.08 (0.85) 0.04** (3.57) 0.52 (1.69) 0.03** (2.97) -0.06 (0.18) 0.03** (2.62) 0.05 (0.17) 0.02 (1.31) 0.35* (1.97) 0.02 (1.49) 0.06 (0.20)
Industries SIC20 NATJ*SIC20 SIC22
0.04** (3.59)
NATJ*SIC22 SIC23
0.03** (2.94)
NATJ*SIC23 SIC24
0.03** (2.60)
NATJ*SIC24 SIC25
0.02 (1.34)
NATJ*SIC25 SIC26 NATJ*SIC26
0.02 (1.47)
26
Industries SIC28
Table 5 (cont) Basic model Plus sectoral Basic Model interactions (1) (2) -0.02* (2.36)
NATJ*SIC28 SIC30
0.05** (5.37)
NATJ*SIC30 SIC32
-0.01 (1.19)
NATJ*SIC32 SIC33
0.02* (2.02)
NATJ*SIC33 SIC34
0.02* (2.42)
NATJ*SIC34 SIC35
0.00 (0.15)
NATJ*SIC35 SIC36
0.04 (5.11)
NATJ*SIC36 SIC37
0.06** (5.54)
NATJ*SIC37 SIC38
0.02 (1.56)
NATJ*SIC38 SIC39 NATJ*SIC39
-0.01 (0.87)
-0.02* (2.34) 0.16 (1.00) 0.05** (5.26) 0.49** (4.11) -0.01 (1.18) 0.16 (0.74) 0.02* (2.09) 0.11 (0.88) 0.02* (2.39) 0.44** (3.13) 0.00 (0.07) 0.49** (5.00) 0.04** (5.15) 0.19** (2.79) 0.06** (5.52) 0.25** (2.84) 0.02 (1.62) 0.07 (0.52) -0.01 (0.86) 0.18 (1.00)
27
Industries SIC38
Table 5 (cont) Basic model Plus sectoral Basic Model interactions (1) (2) 0.02 (1.56)
NATJ*SIC38 SIC39
-0.01 (0.87)
NATJ*SIC39
0.02 (1.62) 0.07 (0.52) -0.01 (0.86) 0.18 (1.00)
Regions MT WNC ENC NNE WSC ESC SAT
0.01 (0.57) -0.01 (0.80) 0.00 (0.19) 0.00 (0.65) 0.00 (0.24) 0.03** (3.98) 0.02** (3.83)
0.00 (0.54) -0.01 (0.84) 0.00 (0.25) 0.00 (0.68) 0.00 (0.28) 0.03** (3.88) 0.02** (3.76)
-0.04** (6.08) -0.02** (3.91) -0.03** (5.16) -0.02** (4.62)
-0.03** (6.06) -0.02** (3.84) -0.03** (5.13) -0.02** (4.63)
33647 0.05 61.79**
33647 0.05 41.23**
Starting year cohorts 1978 1980 1982 1984 # plants Adjusted R-square F value * significant at the .05 level ** significant at the .01 level
28
Appendix Description of Data Sources The Interview Sample The field research is based primarily on face-to-face interviews with managers of 48 new manufacturing establishments -- 20 U.S.-owned and 28 Japanese-owned. This sample was restricted to branch plants of large, and typically multinational, corporations in three 2-digit industry groups -- plastic and rubber products (SIC 30), non-electrical machinery (SIC 35) and electrical equipment (SIC 36). Average starting employment among this sample of startups is 236, with 19% having fewer than 50 employees and 12.5% having more than 500 employees. The plants are located in three regions (Georgia, Kentucky, and a northeast region consisting of New York, New Jersey, and Massachusetts). Georgia is a southern state with a very low rate of unionization and the lowest wages in manufacturing among the three regions. Kentucky is a border region with some tradition of unionization and relatively high wages in manufacturing. New York, New Jersey, and Massachusetts represent the northeast region, which has a long record of militant unionism, high wages in manufacturing, and a well-educated technical workforce. The universe for the sample of new U.S.-owned plants was the list of plants in state business directories in each of the regions. The sample of Japanese startups was drawn from the universe of Japanese startups listed in the Japan Economic Institute's Directory of Japanese Manufacturing Plants in the United States (MacKnight, 1989). The sample was restricted to plants with startup dates between 1978 and 1989 in order to ensure that they would have been through a substantial ramp-up period by the time of the case studies in the early 1990s. Both universes of startups were stratified by region and industry and the samples were randomly selected from within each region/industry cell. The few plants that declined to participate in the study were replaced by the same random procedure. The National Sample Employment data come from the U.S. Establishment and Enterprise Micro-data Files (USEEM), a panel database compiled by the Small Business Administration (SBA) from Dun and Bradstreet records. An abstract of the USEEM database containing biannual employment, ownership, industry, and location information for individual manufacturing plants established between 1978 and 1988 was obtained from the SBA.
29
By matching an exhaustive list of Japanese-owned plants in the United States in directories compiled by the Japan Economic Institute (MacKnight, 1989) with the USEEM data, we identified sufficiently complete data for seventy-nine Japanese startups. We supplemented these data with questionnaires mailed to other Japanese plants in the JEI directories and follow-up telephone surveys. Through these direct contacts, responses were obtained for additional twenty-seven plants, bringing the total Japanese sample to one hundred and six. From the nearly 150,000 startup establishment records in the USEEM data file, 33,541 domestic plants that met our inclusion criteria were selected (Table A-1). This sub-sample of domestic startups is restricted to establishments in the continental United States that report employment continuously from startup through 1988 (in order to avoid problems of differential survival rates between Japanese and domestic firms) and it consists only of plants that are part of multi-establishment enterprises. Establishments in 2-digit SIC industries with fewer than two Japanese startups, and those with missing data, are excluded from the sample. Growth rates reported for domestic establishments are, therefore, not representative of the manufacturing sector as a whole.
30
Table A-1 Average Annual Compound Growth Rates by Industry: Japanese and Domestic Startups, 1978-88 (National Panel Data 1978-88) SIC
Industry
Domestic # Growth Plants Rate
Japanese # Growth Plants Rate
Distribution by Industry Domestic Japanese
20 Food Products 22 Textiles 23 Apparel 24 Lumber 25 Furniture 26 Paper 27 Printing 28 Chemicals 30 Rubber and Plastic 32 Stone, Clay, Glass 33 Primary Metals 34 Fabricated Metal 35 Non-Electrical equip. 36 Electrical equip. 37 Transportation 38 Instruments 39 Miscellaneous
3551 824 1537 1340 830 1202 2989 2843 1729 1761 1171 2946 4321 2911 1326 1406 854
4% 7% 6% 8% 7% 5% 7% 4% 10% 5% 6% 7% 6% 8% 9% 7% 5%
11 1 1 1 3 1 0 5 8 2 6 5 11 26 16 6 3
11% 60% 0% 6% 35% 0% 19% 54% 18% 20% 50% 53% 28% 28% 13% 23%
10.59% 2.46% 4.58% 4.00% 2.47% 3.58% 8.91% 8.48% 5.15% 5.25% 3.49% 8.78% 12.88% 8.68% 3.95% 4.19% 2.55%
10.38% 0.94% 0.94% 0.94% 2.83% 0.94% 0.00% 4.72% 7.55% 1.89% 5.66% 4.72% 10.38% 24.53% 15.09% 5.66% 2.83%
Overall 2-Digit Average
33541
6%
106
29%
100.00%
100.00%
209 Canned Fruit/Veg 380 7% 307 Plastic Products n.e.c. 1384 10% 331 Steel 371 7% 357 Computers, Office Equip 941 8% 354 Machine Tools 636 5% 365 Radio/TV Receivers 109 7% 367 Electrical Components 945 11% 371 Auto Assembly/Parts 713 8%
6 5 5 3 3 7 12 13
17% 22% 13% 66% 25% 37% 19% 19%
Sources: USEEM database; authors' survey
31
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