Information Technology and the Changing Workplace in Canada: Firm Level Evidence 1 Saeed Moshiri Department of Economics ST. Thomas More College University of Saskatchewan moshiri.s@usask.ca November 4, 2009 Comments Welcome! 1 I would like to thank Wayne Simpson for his comments on the earlier version of the paper and Yunfa Zhu who helped me as a RA in this research. We acknowledge the support of a RDC grant by SSHRC, Canada. We thank participants in the Canadian Economic Association conference held in University of British Colombia in June 2008 for their comments. 1
Abstract Recent advances in information and communication technology (ICT) have had vivid effects on both individuals and workplace performance. Use of computer and the internet as general-purpose technologies has spread rapidly across all sectors of the economy, transforming business organization, increasing competition, and fostering innovation. Understanding the influence of ICT on the dynamics of the workplace requires information on both demand and supply sides of the labour market. However, it is only recently that the study of both sides of the market has become feasible, using linked employer-employee data. In this paper, we apply an empirical framework to investigate the effects of new technology on firm s productivity using the Canadian Workplace Employee Survey data set for the period 1999-2003. In our mixed regression model, we include both firm and employee characteristics as well their interactions with computer use and control for unobserved heterogeneity at the higher levels such as industry, size, and time. Our finding indicates that computer use by employees has a positive and significant effect on the productivity of firms. Moreover, human capital enhances the effect of computer use on productivity, but organizational changes do not interact with computer use in our sample period. Keywords: ICT, Information technology, productivity, spillover, Workplace Employee Survey (WES), mixed model JEL Classification: J0, J24, J8, O33 2
1. Introduction Recent advances in information and communication technology (ICT) have had vivid effects on individuals and workplace performance. The computer and the internet as general-purpose technologies have spread rapidly across all sectors of the economy transforming business organization and increasing competition. The use of ICT by a firm reduces its production costs, leads to better utilization of inputs (Milgrom and Roberts, 1990), increases flexibility in inputs and products, improves product quality (Bartel et al., 2007), and increases productivity and market share (Baldwin and Sabourian, 2002). One of the maor concerns with regard to the effects of new technology is the extent to and channels through which the implementation of new technology affects both individual and business productivity. This concern is particularly important since the introduction of new technology usually is associated with the costly destruction of old technology through replacing and reorganizing inputs and outputs, and even changing location. A new technology affects output not only directly, by lowering production costs, but also indirectly by interacting with human capital and organizational capital. 2 Highly skilled workers can increase the productivity of firms by contributing to problem solving and also by being able to quickly learn the new technology and work with the high-tech machines. Furthermore, the networking and communication characteristics of computer use require the level of workers education in a firm to increase, in order to 2 Organizational capital, refers to factors affecting the structure of the workplace such as ob rotation, number of managerial positions, downsizing, and employee-employee or employee-employer relationships (decentralization, teamwork, ob training, and labour flexibility.) See Black and Lynch (2004), Lev (2003), and Brynolfsson et al. (2002) for details. 3
make the most efficient use of the new technology. Computerization of the workplace also affects the structure of the organization, leading to higher accumulation of organizational capital. Organizational capital is an intangible asset that allows firms to discern new technological and market opportunities and to implement changes to production process and organization as needed. Information technology increases workhour flexibility and the degree of integration among different functional areas, and decreases the degree of centralization as well as the number of managerial levels (Kling and Lamb, 2000, Carley, 2000). Computer use also makes the quality management process shorter and more efficient (Bartel et al., 2007). Further, it provides management with up-to-date information about the production process, logistics, and market conditions, which in turn enables quick decision-making and necessary and timely changes. There exists a large body of empirical literature on the effects of ICT on productivity at the national, industry, and firm levels. However, many of those studies suffer from measurement errors and/or misspecification. Some studies have used manufacturing industry data, excluding service sector; others have failed to include the network and external effects of ICT; and many micro level studies have used one-sided, demand- or supply-side, 3 data. The ICT-productivity studies also suffer from ignoring the effects of intangible assets such as new managerial practices, new skills, and new workplace organization, information not available in official statistics (Basu et al., 2003; Brynolfsson and Hitt, 2002; Dufour et al., 2007). Understanding the dynamics of the workplace and its changes due to the use of new technology requires information on both the demand and supply sides of the labour 3 Demand-side data are usually found in plant or firm level surveys and supply-side data in household or individual level surveys. 4
market. However, only recently has the study of both sides of the market become feasible using linked employer-employee data. Linked or matched employer-employee data allow researchers to take into account in their analyses of labour market outcomes the interactions between the two sides of the market. For instance, wage differentials can be explained by not only the employees characteristics, but also the workplace characteristics. In fact, if employee characteristics are correlated with employer characteristics, then ignoring information from the other side of the market will result in misspecification and therefore biased estimates. Furthermore, biases will occur if firm heterogeneity or employee heterogeneity is correlated with the observable variables. Firms productivity may also be different across industries due to differences in technology, market opportunities, and/or employee characteristics such as levels of skill and experience. Industry-specific technological improvement is expected to affect the productivity of certain firms, but general-purpose technologies such as the computer impact the productivity of firms in different industries. However, the extent to which firms within an industry are influenced by the new technology may depend on the type of the industry. For instance, in the case of ICT, the service industry has adapted to the new technology much faster than has the manufacturing industry, partly due to service industry firms flexibility and lower cost of adaptation. Since labour market outcomes such as productivity are influenced by characteristics on at least three levels (employees, employer, and industry), an empirical analysis for explaining the changes in outcomes should include information about all those levels. Failure to include information on any level will lead to misspecification and, therefore, to biased estimation results. There is a growing literature on analyzing and reexamining many labour market issues using linked employer-employee data, such as that 5
by Abowd et al. (1999, 2004), Haltiwanger et al. (2005), Entrof et al. (1999), and Andrews et al. (2004). Moreover, there is an empirical literature suggesting that employee outcomes such as wages are associated both with employee characteristics such as gender and skill and also with employer characteristics such as firm size and human resources management. For instance, Entorf, Gollac, and Kramarz (1999), Freeman (2002), and Zoghi and Pabilonia (2007) show that the computer use contributes to higher wages and working hours, even after they control for all individual and firm characteristics. However, very little is known about the inverse; that is, the process by which firms outcomes are associated with their employees characteristics. In our research context, this translates to how the use of computers by workers affect outcomes such as firms survival or productivity. In this paper, we use an empirical framework to investigate the effects of new technology on firms productivity using longitudinal linked employee-employer micro data produced by Statistics Canada for the period 1999-2003. Although some studies have used the longitudinal linked employer-employee data in other countries including the US (Abowd et al., 2004; Black and Lynch, 2001), France (Entorf et al., 1999), Germany (Andrews et al., 2004), and Switzerland (Arvantis, 2005), the present study is one of the few that explores the Canadian micro level linked data set to investigate the worker-firm interactions in the context of new technology. The rest of the paper is organized as follows. Section 2 reviews the literature. Section 3 addresses some econometrics issues related to estimation of linked data. Section 4 discusses the data and section 5 presents and analyzes the regression results. Section 6 concludes the paper. 6
2. A Review of the I C T- Productivity Studies Since the early 1990s, there has been an extensive theoretical and empirical research work on ICT and its effects on the economy. The literature on ICT has covered a wide range of topics, but it can be categorized as addressing three main questions: (1) What is ICT? (2) How can it be measured? and (3) What are its effects on the economy? The first question deals with the characteristics of ICT as a new technology and examines how and why it is distinct from other technologies. 4 The theoretical research suggests that ICT is a general purpose technology with spillover effects on both producers and consumers of ICT (Lipsey et al., 2005). ICT, particularly software, is also an intangible, non-rival, and non-appropriable capital that is subect to increasing returns to scale. It is a network and knowledge capital that disrespects distance (Quah, 2001.) Questions regarding the measurement of ICT and its impacts mostly have been motivated by the Solow Productivity Paradox, in reference to Solow s quip that computers can be seen everywhere but in productivity statistics (Solow, 1987). Although ICT spending has been growing rapidly and gaining higher shares of public and private expenditures since the early 1990s, its effect mostly has been absent from official measures of economic activities. Recent literature (Marrano et al., 2009; Corrado et al., 2009; Fukao, et al., 2009) suggests that if ICT spending is treated as investment expenditure rather than intermediate expenditure, GDP and its growth would be considerably higher than what to date has been reported. The third branch of research concerns the effects of ICT on various economic outcomes such as labour productivity, total factor productivity (TFP), wage, and welfare. The empirical research on ICT effects varies in methodology (production function, neoclassical or endogenous growth model, growth accounting 4 ICT in a broad term refers to computers, the internet, software, services, and communication devices such as telephone, wireless. 7
method, stochastic frontier approach), in estimation method (static, dynamic, fixed or random effects), in type of data (time series, cross-section, panel data), and in the level (firm, national, and international) being applied. For instance, Jorgenson and Stiroh (1999) use production function with information technology as an input to estimate TFP growth in the US over the period 1948-1996. They show that computer inputs contributed 0.16 percentage points of annual output growth of 2.4 percent for 1990-1996. Black and Lynch (2001, 2004) examine the effect of workplace practices and information technology on productivity using a large sample of US manufacturing businesses. Brynolfsson et al. (2002) explore the computerization effect on productivity using firm level data for the US. Using a growth accounting approach and lags of different lengths, they find that the contribution of computerization to productivity growth is larger in the long run than in the short run. They also see the excessive return of computerization in the long run as the effect of time-consuming investment in complementary inputs such as organizational capital. Arvanitis (2005) studies the effects of ICT, human capital, and organizational changes on labour productivity using cross-sectional firm-level data from a Switzerland business sector. He finds positive effects of all three factors on productivity, and a positive complementarity between ICT and human capital. Gera et al. (1999) use Canada and US industry panel data to examine the impact of ICT on productivity. They find evidence for a positive and significant effect of ICT on labour productivity. Baldwin and Sabourin (2002) investigate the impact of advanced technologies on labour productivity and market share using a linked Census of Manufactures and the Survey of Advanced Technology in Canadian Manufacturing data. They show that advanced technology-using plants in the 1990s increased both market shares and productivity relative to plants that were not using advanced technologies. 8
They further argue that the greatest effects of ICT on productivity are realized when firms effectively combine the advantages of machines with human cognitive capabilities. There is also a growing empirical literature on the effects of new technology on labour market outcomes using employee-employer of matched/linked data in different countries. For instance, Haltiwanger et al. (2005) use longitudinal matched employeremployee data for the US to investigate the underlying sources of different outcomes in both workforce composition and productivity of firms. Although they conclude that technology is among the factors that explain the differences in firms outcomes, they do not specify ICT as a technology. Entorf et al. (1999) use a French household-based labour force survey along with three other labour force surveys to examine the effects of new technologies on wages and employment. They show that computer users have more secure obs and are better paid than computer non-users. In Canada, Zoghi and Pabilonia (2006) is one of the few studies that have used the Workplace and Employed Survey (WES) data to examine the effects of computer on labour market outcomes. They also focus on the relation between employees adoption of computers and their wage rate growth. Our work builds upon the ICT-productivity research by using, for what we believe to be the first time, the longitudinal WES data in Canada, and applying a multilevel regression method to capture the possible effects of different worker and employer characteristics on the productivity of firms. WES is a rich data set that allows us to control for a variety of observed firm and worker characteristics as well as unobserved factors at different levels of the data in the regression model. The firm-level characteristics include foreign ownership, market location, labour unionization, training practices, and organizational changes. Worker characteristics include factors such as 9
gender, birth place, education, and experience. Although the effect of any of those factors on labour productivity has been a subect of a separate theoretical and empirical study 5, our broad coverage of those factors in the regression model will generate a better understanding of the relationship between technological changes and productivity. 3. Econometrics of Linked Employment-Employee Data Linked data contains information about at least two levels of observations, where one level has unobserved effect on the other level. This data structure is useful in different applications dealing with individuals or firms that are part of a cluster. For instance, in education, a student s performance depends not only on personal characteristics such as the amount of time spent on studying, but also on observed and unobserved family and school characteristics. Other examples are health care, where health-seeking behaviour depends on patient characteristics as well as on family and hospital characteristics, and sports, where the performance of an athlete depends on both personal factors along with team and club factors. In the labour market, variations in employee or employer outcomes can also be related to both personal and firm characteristics, which are represented in the supply and demand sides of the market. For instance, observed wage differentials among employees can be explained both by employee characteristics such as skill and experience and by firm characteristics such as size, location, and human resource management. In these examples, individual characteristics can be classified as one level, while group-level characteristics such as 5 For instance, many studies in the 1990s have examined the effect of new human resource management practices on productivity (Ichniowski et al.,1997, and Huselid, 1995). Other studies have focused on unionization and labor-management relations on firm performance (Clark, 1984, and Cooke, 1994). There is also a rich literature on the effect of education on wages and the returns to schooling, but not much research has been done on the impacts of education on productivity at firm level. In most of the studies, the positive effect of education on wages indirectly implies the similar effect on labor productivity (Black and Lynch, 2001). 10
school, family, hospital, and employer characteristics comprise another level. Ignoring information in any level when the levels are correlated with each other will result in biased estimates (see appendix A for details.) A multilevel data structure, comprising two levels, can be modeled as follows: 6 yi c xi i, i 1,..., n; 1,..., m where and i refer to levels (clusters, and units within the clusters) and i is an idiosyncratic error term with the standard features E. In this 2 ( i ) 0, Var( i ) model, it is assumed that both intercept and slope vary according to level. For instance, if y i and x i covariates represent employee i s wage and education in firm, then the wage-education relation is subect to unobserved firm effects and, therefore, expected to be different across firms. In most applications, it is customary to assume that the unobserved -level effects are not correlated with the covariates. Therefore, the model can be specified as follows. c c u v E( u ) E( v ) 0 2 2 Var( u ), Var( v ), Cov( u, v ) E( u x ) E( v x ) 0 i u i uv 6 The model is also referred to as a hierarchical model, a cluster model, or a mixed model. 11
In a special case, also called the variance component or random intercept model, where only the intercept varies across level, that is v = 0, the variance covariance matrix of the model is m 2 2 u J I. 0... 0. 2 2 J I u where m is the dimension of the matrix, and I and J are (n x n) identity matrix and a matrix of ones, respectively. The variance-covariance matrix of the level residuals ( ) can be generalized to allow for random slope and for more than two levels, which would render it more complicated. 7 If it is assumed that the unobserved -level effects are uncorrelated with the covariates (x), it would mean that the level effect in the hierarchical structure of the data is random. Therefore, in this model, individual behaviour can be explained both by observed individual characteristics and observed and unobserved higher-level factors. In other words, the multilevel model implies that individuals whose behaviours are highly correlated are grouped in clusters (higher level), and the individual outcomes can be explained by observed and unobserved factors within a cluster (intracluster heterogeneity) as well as by observed and unobserved differences between clusters (inter-cluster heterogeneity.) The intra-cluster correlation that is shown by 7 See Goldstein (2003) for different cases of the multilevel models. 12
2 u 2 u 2 measures the proportion of the total variance, which is due to variation between clusters. The multilevel or mixed model above can be estimated by using the Iterated General Least Square or Restricted Maximum Likelihood Estimation methods, both of which produce unbiased estimation results (Wooldridge, 2002, and Goldstein, 2003). In this study, we use a mixed or multilevel model to estimate the impact of ICT on the productivity of firms controlling for a series of observed and unobserved factors at the firm and industry levels. 4. Data 4.1 Workplace and Employee Survey Data The Workplace and Employee Survey (WES) data provide information about both employee and employer characteristics and outcomes in Canada. The first survey was conducted in 1999. It is a longitudinal data since the firms are followed each year, an the employees are re-sampled every two years. The employer population is defined as all business locations operating in Canada that have paid employees in March, with the exception of employers in Yukon, Nunavut, and Northwest Territories; employers operating in crop and animal production or fishing, hunting, and trapping; private households; religious organizations; and public administration. The employee population consists of all employees working or on paid leave in March in the selected workplaces who receive a Customs Canada and Revenue Agency T-4 Supplementary form. A person with two T-4 slips is counted as two employees. A maximum of 24 employees are 13
sampled using a probability mechanism, with the exception of workplaces with fewer than four employees, where all employees are selected. In 1999, in total 6,322 workplaces (about half of the population) and 23,540 employees (about one quarter of the population) are surveyed. The numbers of surveyed workplaces and employees in 2000-2003 are approximately the same. The response rates are comparatively high (85-95 percent for the workplaces and 80-90 percent for the employees). The survey contains detailed questions about workplace and employee outcomes, as well as about workplace and employee characteristics. The data on workplace outcomes include productivity and growth in employment and revenues, and the data on employee outcomes include wage, earning, and hours by worker type. Workplace characteristics include data on expenditures, business strategies, unionization, compensation schemes, organizational change, implementation of technologies, and changing human resource practices and the data on employees characteristics include information on education, age, gender, work history, training, occupation, etc. 4.2 Estimation Data Our main variable of interest is labour productivity. We measure the labour productivity by dividing the firm total revenue by total number of employees. We also use industry price indexes to deflate firm total revenue (see appendix C for details of data construction). A better measure of productivity would use total hours rather than total number of people employed, and would deflate total revenue by a firm-specific price deflator. The use of number of employed as a productivity measure may generate biased results if certain employees, such as females, are more likely to work part-time. 14
The covariates are divided into three maor groups: Firm characteristics, employee characteristics, and a set of dummies. The firms are clustered into fourteen industries, four sizes, and five years. The industry categories are listed in appendix B and the firm sizes are defined as large with 500 employees or more, up mid with 100-499 employees, low mid with 20-99 employees, and small with 1-19 employees. Firm characteristics include the ratio of employees who use a computer, use of other technologies, materials use, foreign ownership, market place (local, Canada, US, world), unionization, and employee training programs. We also consider five types of organizational changes: Downsizing, decrease in the degree of centralization, adoption of flexible working hours, greater reliance on ob rotation, and greater inter-firm collaboration in R&D. Employee characteristics, which are aggregated at the firm level, include gender, origin of birth, education (four categories: No high school, high school, some college, and university), experience, and time spent on a computer. The dummies include industry, size, and year dummies as well as interaction dummies between the computer use and firm and employee characteristics. The overall mean of the log of productivity in the sample period is 11.50 with a standard deviation of 0.91. In addition, while 11 percent of the firms reported lower unit costs, about half of the firms reported improvement in performance, production process, or products. The average productivity varies across industries and firm sizes. In general, during the period of study, trade, financial services, and oil and gas industries enoyed the highest productivity, and the education and health services industry had the lowest average productivity. The former is due to the booms in financial services and the energy sectors in the sample period, and the latter is due to the public offering of health care in 15
Canada. The larger firms are also associated with higher productivity, regardless of the type of productivity measures. [Table 1 here] Table 1 presents a summary statistics of the data for the period 1999-2003. It shows that on average, 56 percent of the employees used a computer in their workplaces. The number of employees using a computer is higher proportionally in the service industry than in the manufacturing industry. The ratio of computer users is highest in the finance, insurance, and business services industries and lowest in the labor-intensive tertiary manufacturing and construction industries. The ratio of computer users across firms varies over time and with size: It has been increasing since 1999, but is greater in small firms than that in mid- and large-size firms. The data also show that 3.4 percent of firm assets are held by foreign interests, and most firms (86 percent) sell their products in local markets. 9.1 percent of firms sell their products in Canada, 3.1 percent in the US, and 1.5 percent to the rest of the world. Moreover, the data on the organizational changes in Table 1 show that 8 percent of the firms downsized, 2 percent decreased the degree of centralization, 10 percent adopted flexible working hours, 10 percent increased reliance on ob rotation, and 6 percent had greater inter-firm collaboration in R&D. The firms with more flexible working hours and increased reliance on ob rotation had 2 percent more computer users than did the firms with other types of organizational changes. The summary of the aggregated employee characteristics shows that 54 percent of the employees were female (30 percent of whom used computers) and 82 percent were born in Canada. The data also shows that 81 percent of the employees had high school degrees, 2 percent some college education and 12 percent a university degree of which 97 percent were bachelor degrees. On average, employees had 16.5 percent years of 16
experience. The survey also provides information about the types of computer applications used by employees. The summary results show that on average about half of the employees used word processors; about one third used specialized office applications spreadsheets, and communications; 10 percent used data analysis, desktop publication, general management applications, and graphics and presentations; and finally 10 percent used programming language and development tools, computer-aided design, and computer-aided engineering applications. The trend of the variables for the period 1999-2003 shows that productivity and the ratio of computer users have been rising since 1999. The share of workplace assets held by foreign interests and the ratio of sales to US and international markets have also been increasing, but the ratio of firms undergoing organizational changes has been relatively stable. Furthermore, employee characteristics show comparatively slight changes. Two interesting changes, however, are the decreasing ratio of employees with no high school education, and a rising ratio of employees aged over 55. Although the ratios of employees using both general and very specialized types of computer applications have not been undergone significant changes, the ratio of employees using specialized types of computer applications (data bases, specialized office applications, etc.) has been increasing. 8 Figure 1 shows a simple correlation between the productivity of firms and the ratio of computer users by industry. The figures indicate that there is a positive correlation between productivity and share of computer users, but the relationship varies across the industries. We examine these observations more rigorously in the next section, 8 General computer applications include word processing, while specialized applications include databases and very specialized applications including computer-aided engineering. 17
where we estimate the relationships by the regression equations controlling for other relevant variables. 5. Estimation Results Our estimation model is based on the following mixed model. yi c xi i zi i, i 1,..., n; 1,..., m where y i is the (log) productivity of firm i in industry, x i and z i are vectors of firms and employees observed characteristics, and i is the error term varying across firm and industry with zero mean and constant variance. c is the unobserved industry-level effect with the following properties. c c u, 2 E( u ) 0, Var( u ), E( u x ) E( u z ) 0 u i i Our model specification and estimation strategy aim at providing insights on four issues. The first issue is the effect of using new technology on productivity of firms. The second issue is to test the hypothesis that computer technology has reached its peak. The third question is whether the computer has been used as a network and knowledge capital and therefore generated spillover effects. And finally, we would like to know how computer interacts with human capital and organizational capital. We use the Canadian 18
Workplace Employee Survey (WES) data for the period 1999-2003 and a mixed or multilevel estimation model to address the questions above. To address the first question, we use two proxies for new technology: The ratio of computer users in a firm and the time spent by employees on computers. The former shows the overall use of computers in a firm, while the latter shows the intensity of computer use by employees. To test whether the computer as a general purpose technology has run its course after about three decades of being used in all sectors of the economy, we include a squared term of computer use proxy in the model. A significant negative coefficient of this quadratic term would indicate that the era of computers as a new technology is over, whereas a non-significant coefficient would show that the peak in computer-productivity relation is yet to be reached. To address the spillover effect question, we define and include two spillover terms in the model: Intra-industry spillover and inter-industry spillover. It is postulated that because computer usage represents network and knowledge capital, a firm within an industry would benefit from other industry firms investments in computers. Moreover, competitive pressure or peer effect would also force firms to rapidly acquire and adapt the new technology used by other firms within the industry. We, therefore, define the intra-industry spillover for firm in industry k as the ratio of computer users in the rest of the firms within the industry, as follows: int raspill n k nk k ( cpuuers ik ) / ( i1 i i1 i emp ) ik 19
where cpuuser ik is the number of computer users in firm i of industry k, emp ik is the total number of employees in firm i, and n k is the total number of firms in industry k. The spillover effect may also exist across industries; therefore, we define the inter-industry spillover for a firm in an industry as the ratio of the total number of computer users in all other industries to the total number of employees in those industries. That is, int erspill k K n k k 1 k h i1 ( cpuuser ) / ik K n k k 1 k h i1 ( emp ) ik where K is the total number of industries and interspill k is the spillover for firm in industry k, which would be the same for all firms within the K industries. A positive and significant estimate of the spillover terms would suggest that computer effects on firm productivity go beyond physical capital deepening effect of computers. In other words, the use of computers would have a positive effect on total factor productivity shifting the firm s production function upward. Finally, to address the fourth issue, we include in our model a series of interaction terms between computer use, human capital, and organizational capital. We use two sets of regression equations to address the four questions above. The first set of regression equations will shed light on the first three questions, and the second set of regression equations on the fourth question. Figure 1 shows that the relationship between productivity and computer use varies across industries. This suggests that the variation among the firms productivities may also be explained by differences among industries. We treat industry as a cluster and use a mixed regression model to control for the hierarchical structure in the data. Furthermore, since the effect of computer use on 20
productivity varies across firms of different sizes, we may therefore control for the size effect in the regression by assuming size to be another level in the hierarchical structure of the data. Moreover, since the relationship between computer use and productivity may vary across time, we estimate an alternative mixed model assuming time as an additional level. We include a series of both firm and employee characteristics in our regression model. The firm characteristics are ownership, market area, labour unionization, training, and organizational change, and the employee characteristics are gender, birthplace, education, and experience. The computer use variables, which include both firm and employee characteristics, are categorized as information technology. The results of a two-level (firm and industry) mixed model are reported in the first column of Table 2. Columns 3 and 4 present the results for the regression model with the two additional levels of size and year. The random-effects parameters are significant for all three levels. Moreover, the LR test results reect the null of linear regression against the alternative of non-linear multilevel regression indicating the statistical importance of the levels. The estimated coefficients in all three regression models are very close to each other; slight difference occurs only in the magnitude of the coefficients. Most of the coefficients are significant and have the right sign. Specifically, two main coefficients under the information technology category, namely the ratio of computer users and time spent on computer, are positive and significant. The squared term of the ratio of computer users is positive and marginally significant in the first column, but insignificant in the other cases. This might be seen as evidence that the computer has not yet run its course. Furthermore, our results do not support ICT spillover among firms and industries in Canada. Although the results might be sensitive to the measure of spillover, they are consistent with 21
findings in other studies such as Jaffee et al. (1993) with regard to examining knowledge spillover. As Quah (2001) points out, empirical studies suggest that although technology and knowledge are not localized, they spread only gradually and incompletely. The lack of spillover effects might also be a sign of weak linkage and communication among Canadian firms within and between industries. Among the characteristics of firms, (log) material has a positive and significant effect on productivity as expected. The coefficient of ownership, measured by the percentage of assets held by foreign interests, is positive and significant. This result is consistent with the foreign direct investment literature that suggests that multinational corporations may be more productive than local corporations due to their easy access to international input and output markets. The coefficients of market areas are somehow consistent with the ownership results, as they all indicate that firms selling their products in national and international markets are on average more productive than firms selling their products in local markets. The coefficient of unionization is also positive and significant, indicating that firms in which employer-employee relationships are covered by collective bargaining arrangements are more productive than firms in which the labour is not unionized. The rest of the firm characteristics are grouped under training and organizational changes. Training turns out to have a positive effect on productivity if it takes the form of either on-the-ob training or classroom training. Subsidized training does not have any significant effect on productivity. Our measures of organizational changes, however, do not show any significant effect on productivity, except for downsizing (a positive effect) and adoption of flexible working hours (a negative effect). The insignificant coefficient on inter-firm collaboration on R&D, production, and marketing (d_orgchg14) is consistent with the lack of spillover effects discussed above. 22
The neutral effect of organizational changes may also be due to the fact that only 2 to 10 percent of the firms underwent any form of organizational change during the sample period. One might argue that organizational changes occur with the introduction of new technology, and therefore that the interaction of those two might be of more interest than a study of individual effects. We will examine the possible interaction of computerization and organizational changes in the next set of estimations. The characteristics of employees included in the model are gender, birthplace, time spent on other technologies, education, and experience. The results show that firms are on average more productive when they have more male employees, more non- Canadian born employees, more highly experienced employees, and more employees with high school, college, and university educations (the reference is employees with no high school diplomas). The gender estimation result can be misleading since our measure of productivity is based on the number of employees rather than hours worked, which would lead to biased results if more female employees work part-time. In the next section, we examine the possible interaction between computer use, human capital, and organizational capital. 5.1. Computer Use, Human Capital, and O rganizational Change We tease out the direct and indirect effects of human capital and organizational changes on firm productivity by re-estimating the model that includes interaction terms between the ratio of computer users and various variables. The results of the mixed regression model with two, three, and four levels are reported in Table 3. The levels are firm, industry, size, and year. The overall results are similar to the previous estimation results with minor changes in the magnitudes of some variables. The exception is the 23
ratio of computer users variable, whose interactions with other variables are included in the model. Although the computer coefficient becomes very small and insignificant, its marginal effect, calculated at the mean of the variables whose interactions are included in the model, is 0.06, which is almost the same as the estimated coefficient in the first model presented in Table 2. The results bear out that human capital enhances the productivity of computer technology. Among the education levels, high school and university level educations have positive and significant effects on productivity. The average productivity of firms whose employees hold high school diplomas or university degrees are 4 and 7 percent more, respectively, than that of those whose employees do not have high school diplomas. Moreover, having employees with high school, some college, and university educations enhances the effect of computer use on productivity by 4, 14, and 7 percent, respectively. Years of experience does not have any significant effect on the computerproductivity relation, perhaps because of the fact that the more highly experienced workers are less likely to adopt the new technology. Subsidized training does not have any significant effect on productivity, but on-the-ob and classroom training are positive and highly significant. Specifically, a 10 percentage point change in the proportion of onthe-ob or classroom training will increase average productivity by about 0.2 and 0.5 percent, respectively. The classroom training, however, does not impact the computer effect on productivity. Our results are not consistent with the complementarity hypothesis between computerization and organizational capital. This may be explained by the fact that relatively few firms (between 2 to 10 percent) implemented different types of organizational changes during the sample period, since most of the firms would have 24
likely gone through those processes in the early- and mid-1990s when computers started to be widely used. Most of the studies for the US and for European countries found positive direct effects of human capital and organizational change on productivity, but weak evidence for complementarity between human capital, organizational change, and ICT. For instance, Breshnahan et al. (2002) find that organizational changes are more effective on productivity in the long run. Gretton et al. (2002) and Hempell (2003) find evidence for the complementaritiy between ICT and organizational changes in Australia and Germany, respectively. However, relationship is not significant in Black and Lynch (2000) and Capelli and Neumark (2001) for the US, Bertschek and Kaiser (2001) for Germany, Caroli and Van Reenen (2001) for France and UK, and Arvanitis (2005) for Switzerland. Although the studies above use different model specifications, methods, and data sets, we may conclude that the use of a longer data period, which would allow for a more dynamic analysis, would help uncover the complementary relationship between the computer use and the organizational changes. The coefficient of birthplace is negative and significant; however, its interaction with computer is positive and significant and almost of the same magnitude. This suggests that even though on average non-canadian born employees contribute more to the productivity of firms, Canadian born employees enhance productivity by using computers. Overall, the marginal effect of birthplace on productivity in this regression model is still negative, but reduces to about half of that in the previous regression model. Females interaction term with computer use is not significant, indicating that gender does not influence the computer-productivity relationship. 25
5.2 Computer Applications Computers have various applications ranging from simple word processing to much more complicated applications such as programming and design, and their effects on firms productivity are not expected to be similar. In this section, we examine the effects of different computer applications on firms productivity. WES breaks down computer applications to 14 categories and asks employees what types of applications they use. We apply this information in the regression model to tease out the effects of specific applications on productivity. The mixed model estimation results, presented in Table 4, show that applications such as spreadsheets, data analysis, and communications, have positive effects on productivity at the 5 percent significance level. These applications can be categorized as medium or upper-middle application levels, based on their degrees of complexity and their extent of use by employees. They require a high school or college or university level educations, and are used by 40 percent, 8 percent, and 34 percent of the employees, respectively. The use of other applications are not statistically significant, but computer-aided design application, which can be categorized as a high-level application and is used by only 4 percent of the employees, shows a negative effect on productivity. 6. Conclusion The computer as a representation of the new information and communication technology has both direct and indirect effects on firms productivity. The direct effect is the capital deepening effect through which a firm would increase its production by using more computers as their relative prices decrease. The indirect effect is through the interaction of computer with human capital and organizational capital. Computer as a 26
general-purpose technology with network and knowledge characteristics would also have spillover effects on productivity. In this paper, we investigate the effects of computer technology on the productivity of firms using the Canadian linked longitudinal Workplace and Employee Survey (WES) data. We use a mixed regression model to control for the unobserved effects of the industry, size, and year levels. Our estimation results show that computer use by employees has positive and significant effects on firms productivity. Specifically, we find that a 10 percentage point increase in the ratio of computer users in a firm would result in about 0.67 percent higher productivity. Moreover, employee s spending one more hour on a computer application enhances the productivity of firms on average by about 0.3 percent. We find, however, that computer use has not had a spillover effect on productivity during our sample period. This can be interpreted from three different views. First, our measure of spillover might not be a best representative of the network and knowledge effects of the computer. While the number of computer users in other firms may represent spillover through network, it may not well capture the flow of knowledge generated by the use of technology in other firms. The second possibility is that Canadian firms network more with US firms than with local and national firms. Therefore, one should look for the spillover effects occurring across the border rather than within. 9 And finally, one might argue that technological spillover would prevail when the technology is at its growth stage. This is particularly true in the case of networking. When the size of a network passes a critical point, then there should not be any spillover due to the growth of the network. Computer use in Canadian firms has probably reached a point where the growth of the network does not add any extra benefit to an individual firm. 9 Moshiri and Nikpour (forthcoming) find evidence of ICT spillover among OECD countries. 27
The inclusion of interaction terms between computer use and employee and firm characteristics reveals interesting outcomes. For instance, although on average female has a negative effect on productivity (ignoring the possible bias due to measurement error in productivity), its insignificant interaction with computer use indicates that computer effect is gender neutral. Furthermore, although the contribution to productivity by employees born abroad is on average more than that by Canadian born employees, the latter increase productivity through the use of computers. The results also show that experience and unionization on average favour productivity, but reduce the positive effect of computer use on productivity. Human capital, measured by level of education, is the only variable that has a positive effect on productivity both on average and through its interaction with computer. Organizational changes, however, do not have any significant impact on productivity and the relationship between computer use and productivity. This unappealing result may stem from the fact that most firms likely underwent their organizational changes in the early- and mid-1990s when computer technology began to be used widely. In our sample period, most firms have stable organizational structures, and therefore there is no contemporaneous interaction between computer use and organizational changes. Finally, we find that among the computer application types, the mid-level applications, defined as widely used and not very complex applications such as spreadsheets and data analysis, are the applications that enhance firms productivity significantly. Our estimation results also imply that the impact of computer technology on productivity has not lost its momentum and will likely continue in the future. Although our focus in this paper is the impact of information technology on productivity, the rich Workplace Employee Survey (WES) data set allows us to examine the effects of many factors from both demand and supply sides of the labour market on 28
productivity of firms. Our mixed regression model estimated by the restricted maximum likelihood method controls for possible hierarchical structure in the data and produces unbiased estimates. However, some caveats in interpreting the results apply. Mixed models produce better results when the number of higher level clusters is large. Although our number of industries is relatively large, the number of size categories and years are not. Nevertheless, since our mixed estimation results, when size and year levels are added to the model, are not significantly different from those in the two-level model, this might not cause a maor concern. Furthermore, our measure of productivity is subect to a bias, particularly because it uses number of employees rather than number of hours. This possible bias might have been reflected in the female coefficient, assuming that females are more likely to work part-time. Alternatively, one may use other measures of productivity and computer using WES data. For instance, there are some categorical data on different dimensions of productivity such as improving quality, producing new products, and lowering costs. Although our measure of productivity is more obective than those categorical answers, examining the questions using those alternative measures would be a worthwhile exercise. 29