THE DOT-COM CRASH, THE MIGRATION OF TECHNICAL SKILLS, AND PRODUCTIVITY GROWTH AFTER THE BUST

Transcription

1 THE DOT-COM CRASH, THE MIGRATION OF TECHNICAL SKILLS, AND PRODUCTIVITY GROWTH AFTER THE BUST DRAFT PLEASE DO NOT QUOTE Prasanna Tambe New York University, Stern School of Business November 2011 Abstract This paper uses data on IT labor and skills to test the hypothesis that IT-sector investment during the dot-com bubble produced a supply of new, e-commerce related technical skills that diffused to firms in IT-using industries after the bust, contributing to a) e-commerce adoption in IT-using firms and b) shifting patterns of productivity growth towards a new set of industries (the second surge ) after The effects were strongest in cities where dot-com layoffs were concentrated especially cities in California and among technology leaders in IT-using sectors. By contrast, the older generations of technical skills demanded by technology laggards were readily available from other IT-using firms so the dot-com bust had little effect on these firms. These results are robust to estimators that use the geographic distribution of IT employment as a quasi-experimental source of variation in firms exposure to IT labor market changes after the bust. The findings have implications for research and policy related to IT productivity, IT labor markets, and high-tech clusters. Implications for the more recent wave of social media investment and data analysis skills are also discussed. Keywords: IT productivity, IT labor, IT human capital, general-purpose technologies, technology diffusion, financial bubbles, productivity growth, IT spillovers Comments are welcome: ptambe@stern.nyu.edu. I am grateful for valuable feedback from Ashwini Agrawal, Roy Radner, and seminar participants at New York University, the Georgia Institute of Technology, and the INFORMS Conference on Information Systems and Technology.

2 1.0 Introduction Although significant academic attention has been paid to the contributions of investments in IT capital and software to productivity growth, comparatively little emphasis has been placed on how differences in the supply and quality of technical skills and know-how contribute to productivity growth differences, despite IT labor accounting for 30-50% of IT spending in modern firms (Gartner, 2010). This is important because economists have argued that much of the know-how required for installing generalpurpose technologies, such as the electric dynamo or computers, is embodied in skilled technical workers, and this know-how is likely to be an important factor of production for IT-enabled growth. Furthermore, due to labor market frictions, differences in technical human capital have the potential to persist across regions or industry. Therefore, factors affecting the supply of technical skills are of central interest to understanding variation in IT adoption and productivity. This paper examines the role of dot-com investment in producing a supply of e-commerce skills that contributed to productivity growth in other sectors after the dot-com crash. Due to the tacit nature of the skills required to debug a web site or implement online payment processing, development of new technical skills requires hands-on experimentation and on-the-job learning. Therefore, while some IT investment made during the dot-com boom produced intangible assets such as new business processes and information practices, some of this investment produced technical human capital and new e- commerce related skills that, unlike investments in many other forms of intangible capital, may have been transferred to firms in other industries. Therefore, the large waves of investment into IT industries in the late 1990 s may have been important for developing e-commerce skills that were not lost, but instead were transferred to firms in other sectors after the dot-com bust. This transfer, in turn, may partially explain productivity growth patterns after 2000 productivity researchers have observed that productivity growth shifted from IT-producing sectors towards IT-using sectors after the bust, but the sources of this shift remain puzzling (Gordon, 2004; Stiroh 2006; Oliner, Sichel, and Stiroh 2007). This paper tests the hypothesis that the flow of e-commerce skills from IT-producing to IT-using firms after the dot-com crash contributed to these shifts in productivity growth. 1 To address this hypothesis, the paper combines several data sources. A key contribution of the paper is the analysis of archival data describing the flow of IT skills among firms, data that is normally not collected by administrative sources or employers. These data are generated from self-reported 1 This paper classifies IT-producing industries as those defined in Jorgenson, Ho, and Stiroh (2005). The same classification has been used in some prior work (Forman, Goldfarb, and Greenstein 2010). IT-using industries are industries that are not ITproducing. The latter classification is slightly different than some prior work that defines IT-using industries to be an ITintensive subset of non IT-producing industries.

3 employment histories from hundreds of thousands of IT workers and illuminate previously invisible IT labor mobility patterns. These data on IT labor flow patterns have been used in prior work connecting IT labor flows to aggregate productivity growth patterns 2, and in this paper are used to trace the flow of technical skills from IT-producing to IT-using firms after the bust, as well as to analyze how these movements were affected by the dot-com crash. The paper also uses new data on e-commerce adoption and fine-grained measures of e-commerce skills to provide detailed evidence that the observed productivity effects are due to the spread of e-commerce and not other technologies. The analysis primarily uses fixed-effects and differences estimators to test how changes in IT labor markets after the crash affected productivity growth in IT-using firms. Omitted variable concerns are addressed by treating the geographic concentration of dot-com layoffs as a source of quasiexperimental variation in the magnitude of the changes in IT labor prices across different labor markets immediately before and after the bust. Due to labor market frictions, Firms with IT employees in cities where dot-com layoffs were concentrated such as Starbucks or Boeing in Seattle disproportionately benefited from the rapid growth in supply and sudden fall in demand from IT-producing firms associated with the dot-com boom and bust. These differences are consistent with anecdotal evidence relating dotcom layoffs to changes in the availability of IT skills that were limited to specific cities for instance, although firms in Seattle were flooded with job applications after the dot-com crash, Philadelphia-based firms continued to face an IT skills shortage (Vaas 2001; Cook 2002). This variation is the basis of a) an instrumental variables analysis of the main productivity regressions as well as b) a difference-indifferences matching estimator that compares the productivity growth of otherwise similar firms that differ in the geographic distribution of their IT employment. To establish that observed productivity effects are principally related to the diffusion of e- commerce technologies, supplementary analyses are presented that use fine-grained data to improve the precision of both the dependent and key independent variables. First, the effects of these IT labor market changes on e-commerce adoption in IT-using firms are tested using a new panel data source on the adoption of individual technological innovations, created by text-mining firms financial reports (see Saunders and Tambe 2011). The direct use of e-commerce adoption as a dependent variable has the added benefit that it addresses a number of omitted variable concerns associated with the use of productivity growth as a dependent variable. Second, cross-sectional data on the e-commerce skills of individual IT workers in the sample are used to support the hypothesis that the observed productivity effects are a result of the diffusion of new e-commerce skills, rather than a broader class of technical skills. 2 Tambe and Hitt (2011b) use these IT labor flow data to measure the contribution of IT-related productivity spillovers to productivity growth from

4 The analysis produces a number of key findings. First, the dot-com crash resulted in a flow of IT labor from IT-producing to IT-using firms, primarily for IT-using firms with establishments in cities with a high density of IT-producing firms. Workers acquired by IT-using firms from IT-producing firms after the dot-com crash tended to be those with new and rare Internet and e-commerce skills, and a probit analysis suggests that the acquisition of these skills by IT-using firms after the bust was associated with the adoption of e-commerce technologies. Furthermore, the transfer of these skills across industries was associated with faster productivity growth in acquiring firms these effects appear in fixed-effects and differences and are robust to the use of location-based instrumental variables and the application of the matching estimator. The effects are strongest in cities where dot-com layoffs were concentrated (and especially in California), and for technology leaders in IT-using sectors for whom frontier technical skills were in high demand. By comparison, the older generations of technology skills required by technology laggards were available from other IT-using firms. Finally, the effects are strongest for retail, financial, and some manufacturing firms, which is consistent with the role that these industries played in driving post-2000 productivity growth (Stiroh, 2006), suggesting that some of the growth in these industries after 2000 can be explained by spillovers of e-commerce-related managerial and technological innovations into firms in these sectors. This paper contributes to two established academic literatures. First, it addresses an open question in the productivity literature on the causes of the change in sources of productivity growth after 2000 (Gordon 2004). Notably, the second surge in productivity growth that occurred between 2000 and 2005 appears to have been driven by firms in a broader set of industries than the relatively narrower set of ITproducing industries that drove productivity growth from Some leading explanations for the causes of this shift include spillovers of technical and managerial innovations to other sectors or lag effects associated with the installation of organizational complements in large firms (Stiroh, 2008; Tambe and Hitt, forthcoming), but the lack of firm-level data on IT investments after 2000 has made it difficult to resolve this puzzle. This paper tests the spillovers hypothesis. This paper is also therefore closely connected to an emerging literature on the contribution of various types of IT spillovers to productivity (Cheng and Nault 2007; Chang and Gurbaxani, forthcoming; Cheng and Nault, forthcoming; Tambe and Hitt, 2011b). Second, this is one of the first studies to connect the literature on IT labor markets (Ang and Slaughter 2001; Ang, Slaughter, and Ng 2002; Mithas and Krishnan, 2008; Levina and Xin 2008; Tambe and Hitt, forthcoming) to the larger literature on IT productivity (e.g. see Dewan and Min 1997 or Brynjolfsson & Hitt 2003). Prior work has examined the productivity of IT employment using headcount or labor expenses (Brynjolfsson & Hitt 1993; Lichtenberg 1995; Tambe & Hitt, 2011a), but how IT human capital affects productivity has received almost no attention (Bapna et al 2010 is an exception).

5 This is an important omission because a) technical human capital is a poorly understood input into production, b) prior work suggests significant variation in technical human capital across geographic regions (Saxenian 1996; Bresnahan and Gambardella 2000) and c) explaining regional differences in ITenabled productivity growth has become an active research area (Dewan and Kraemer 2000; Bloom, Sadun, and Van Reenen 2008; Forman, Goldbarb, and Greenstein 2010; Tambe and Hitt 2011b). Identifying how the dot-com bust affected local labor markets and productivity growth should also be of considerable interest to managers and policy makers because unlike IT hardware or software, local institutions and policies create significant and persistent differences in IT labor quality, which decisionmakers should consider when organizing production or evaluating factors that affect urban growth. 2.0 Background Computerization has been a key source of economic growth for over two decades (Jorgenson & Stiroh 2000; Brynjolfsson & Hitt 2003). Yet, there is still little understanding of how the skills and technical know-how required for computerizing business processes are developed and how they diffuse through the economy. Unlike R&D know-how, which can typically be codified and transferred through patent documents or other channels, the skills and expertise required for installing IT innovations tend to be embodied in the IT labor force. 3 Experienced and skilled technical workers can help implement new technologies, adapt technologies to an organization s needs, and adapt organizational practices to fit a new technology. Therefore, the stock of technical know-how embodied in the IT workforce is likely to be an important factor of production for IT-related productivity growth (Dedrick et al. 2003; Tambe and Hitt 2011a). From the acquiring firm s perspective, the supply of technical know-how embodied in the IT labor pool is affected by the IT investments of other firms in the same labor market for two reasons: a) learning-by-doing and b) economies of specialization. Learning-by-doing has been theorized as an important mechanism for increasing labor productivity with new tools and technologies (Arrow, 1962), and robust empirical evidence linking labor productivity to learning-by-doing has been provided in contexts as varied as building aircraft (Benkard 2000), constructing naval ships (Thornton & Thompson 2001), and chemical processes (Lieberman 1984). For earlier waves of general-purpose technologies, the development of a cohort of factory engineers with experience redesigning workflow in electrified factories has been identified as a key mechanism for the diffusion of the new production methods (David 1990). For IT implementation, trial-and-error in an organizational context is important for the formation 3 Breschi and Lissoni (2009) provide recent evidence that most R&D knowledge localization can be explained by inventor mobility. Labor mobility, therefore, may play a disproportionate role even in the context of R&D knowledge diffusion.

6 of technical human capital and recent studies have provided evidence of learning-by-doing specific in software development or IT systems implementation (Boh, Slaughter, and Espinosa 2007; Avgar, Tambe, and Hitt 2011). Higher IT investment levels within a labor market also lead to specialization in emerging categories of skills. Economies of specialization have been recognized as an important source of economic growth since Adam Smith. Rising investment levels within a labor market enable employees to specialize in cutting-edge and rare combinations of skills, leading to higher labor productivity (Rosen, 1983; Romer, 1987; Kim 1989; Duranton and Puglia 2003). These increasing levels of specialization are associated with higher productivity levels because workers who concentrate on a narrower range of skills tend to have a higher marginal product (Becker and Murphy, 1992). This paper focuses specifically on the technical human capital formed by investments in ITproducing firms between 1995 and Although two decades of IT-enabled productivity growth have spanned multiple waves of IT innovation beginning in the 1980 s, the period associated with the dotcom bubble is viewed by many as being qualitatively different from prior waves of computerization because of the scale of investment and because of its focus on e-commerce and Internet technologies (see Forman, Goldfarb, and Greenstein 2010 for a similar claim). IT workers employed in dot-com companies during these years were exposed to the development and standardization of a new family of technical skills required for web programming, networking, e-selling and e-buying, and for communicating with customers online, all of which were required to build infrastructure for the Internet communication driven culture that emerged during the mid to late 1990 s. After the collapse of the Internet bubble and the subsequent fall in demand for these skills from the IT-producing sector, some e-commerce skills diffused to old economy companies, such as banks and retailers developing e-commerce infrastructures, as well as to consulting firms that developed web sites and e-commerce infrastructures for other old economy firms. One perspective representing the view of many HR managers towards dot-com workers after the crash was that employees coming from startups bring with them a dot-com know-how that includes a comfort zone using interactive tools. They have learned how to communicate electronically: how to speak to customers online and through . (John Challenger). Yet, anecdotal evidence suggests that HR managers were selective about their hiring, and that the dot-com boom and bust principally raised demand for the IT skills require for building the new e-commerce infrastructures: While the economic slowdown and dot-com demise have increased the overall supply of IT workers somewhat, shortages of key skills- particularly those most sought by enterprises- remain as pronounced as ever. There are several reasons for that: One, experts say, is that many dot-com refugees simply don t have the skills for which enterprise IT hungers; another is that many enterprises have become more picky about the people- and skill sets- they ll bring on board; and a third is that dot-com layoffs have been concentrated in a few geographic areas. More and more, those hiring managers are after specific skills- not just anything that fits underneath the umbrella

7 term "IT." According to hiring managers, the skills they still hunger for include networking, customer relationship management, ERP (enterprise resource planning), security and e-commerce/internet skills such as database management and server administration. (Vaas, 2001) Therefore, of the technical human capital developed during the dot-com bubble, it was primarily the e- commerce related know-how that was demanded by other firms after the bust. The transfer of productive capital produced during an economic bubble is not new. Prior research has suggested that technology bubbles are important for the development of capital that drives productivity growth in later periods. For instance, although companies that invested in broadband infrastructure disappeared during the dot-com bust, much of this infrastructure was later acquired and productively repurposed by firms such as Google (Gross 2007). Economists have noted that productivity growth from 2000 onwards was driven by a different set of industries than the IT-producing industries that drove much US productivity growth from 1995 to The scatter plot in Figure 1 illustrates this shift (from Stiroh, 2006). 4 This paper argues that the e-commerce related skills incubated during the dotcom bubble and subsequently diffused to IT-using firms facilitated this shift. The next two sections describe the data and methods used to test these hypotheses. 3.0 Data IT Labor Flow Data IT labor flow patterns were computed from fielded employment history information for about ten million users who posted career histories on a leading online jobs board in In addition to employment histories, users provided information about occupations (e.g. IT, sales, management, accounting, etc.), education, and other demographic and human capital variables including IT skills and certifications. This study focuses on IT workers, self-identified by choosing the IT occupation from a drop-down box. Because there are no other sources of IT labor flow data, these data cannot be directly validated against other measures. However, the IT labor data and their sampling characteristics have been benchmarked in other work (see Tambe and Hitt 2011b for a thorough description of these data). When compared to IT workers sampled in the 2006 Current Population Survey (CPS), a nationally representative Census administered survey 5, these data are not particularly skewed towards higher or lower education levels. The average job tenure of IT workers in our sample is approximately two years lower than the average job tenure of IT workers in the CPS survey. A key reason for this difference is that the sample contains a 4 Note: the IT-using firms in this analysis correspond to both the IT-using and Other categories in Figure 1. 5 The survey is administered monthly to 50,000 households and is conducted by the Bureau of Labor Statistics (BLS). The sample is selected to represent the civilian non-institutional population. More information is available at

8 disproportionate number of young workers and job-hoppers. However, this may not be a significant limitation because the key population of interest in this study is the job-hopping IT worker, so the sample may be a better representation of this population than the CPS sample, which includes workers who never or infrequently switch jobs. The 2006 wages of the workers in our sample are similar to the 2006 IT wages computed in the Occupational Employment Survey. Nevertheless, IT workers that post on this jobs board may still be different than other job-switchers along unobservable dimensions. This selection concern is addressed further below. IT Skills and Location Data This study introduces new data on technical skills and certifications, self-reported by IT workers. From these skills and certifications, IT workers are identified who report having e-commerce and Internet related skills such as HTML, Web programming, or SQL programming. 6 To the best of my knowledge, these data represent the first large-scale measures of skills in the IT labor market. However, one significant limitation of these data is that they are reported only in Therefore, assigning these skills to a particular employer-employee pair in years prior to 2006 produces measurement error if workers acquired the skill after the year of assignment. This measurement error will be larger in earlier years than later years, and will be one-sided it will misclassify IT workers in some years as having an e-commerce skill who have not (yet) acquired that skill but will not omit any IT workers who do have the skill. However, as long as these misclassification errors are exogenous to the hiring firms productivity levels, measurement error of this type will only tend to make it more difficult to find a meaningful result when using these skills data. Metropolitan areas are also reported by workers in 2006 and in combination with the employer data are used to create measures of the extent to which IT-using firms participate in the same labor markets as IT-producing firms. For each firm i, a labor market index ITLM is computed as:!!"#$! =!!" (%!"#$%&! )!!! where w is the percentage of a firm s IT workers in metro-area j, and %ITPROD j is the percentage of IT workers in metro-area j employed at IT-producing firms and j indexes all of the J metro areas in which a firm employs IT labor. It is worth noting that there are, to the best of my knowledge, no alternative data sources describing the distribution of a firm s IT workers across US cities 7, so the ability to create this 6 The complete list of skills is chosen using Foote Consulting s list of hot IT e-commerce skills. 7 The closest alternative are the CITBD data published by Harte Hanks which only have information on the number of programmers in establishments with at least 100 people.

9 measure using the firm-city distribution is somewhat unique and improves the accuracy of the analysis over one in which IT employee locations would be approximated using the location of firms corporate headquarters. Table 2 lists the cities with the highest values for %ITPROD. The top ten regions ranked according to this measure are consistent with prior expectations, and include San Jose, Seattle, and San Francisco. The firm-level ITLM index value, therefore, will be highest for firms that employ a large percentage of their IT workforce in cities such as these where dot-com layoffs were concentrated. ITusing firms with a high ITLM index would have been the first firms to acquire the skills and human capital displaced from IT-producing firms. Like the skills data, these IT worker location data are also reported only in 2006 cross-section, but the magnitude of the measurement error produced by this data limitation is bounded by the rate of entry and exit for establishments in different cities. The size of the measurement error should be the fraction of establishments that in 2006 were in different locations than they were in For much of this analysis, firms are grouped according to ITLM quintiles for ease of interpretation. Selection Concerns and the Direction of the Bias Term A concern regarding sample selection is that although the measure of interest is the network of firms defined by all IT labor movements, IT workers who post on job boards may be systematically different than other IT worker who switch employers. If the historical movements of jobs board candidates are an accurate representation of the other employers in a firm s IT labor pool (weights between firms), then there is no measurement error. However, systematic differences between these workers and the larger population of job-hoppers introduces error into the measurement of this network of firms. However, this error term should only impose an upward bias if for a focal firm, the network of firms defined by the movements of IT jobs boards candidates is more likely than other types of candidates to transfer skills and human capital, which is somewhat unlikely if the concern about jobs board candidates is one of adverse selection. In other words, selection problems will tend to lower the estimate on our parameter of interest by overweighting the investments of the ex-employers of workers who post on jobs boards. Furthermore, because most of the analysis is in differences, this bias is most severe when the measure of the IT-producing pool disproportionately rises as a result of the dot-com bust, which will only be the case if the jobs board over samples IT labor in IT-producing sectors, which is also inconsistent with adverse selection. A similar analysis can be applied to the use of the most recently reported IT employment location to create measures of the geographic distribution of a firm s IT workers. The measure of interest is the geographic distribution of a firm s IT hires. If the workers in a firm s IT labor force that are posting on

10 the jobs board are geographically distributed in a different way than the true distribution of firm s new IT hires, there will be error in the geographic measure. This will tend to overweight the geographic presence of establishments in which employees are leaving at disproportionately high rates (e.g. layoffs, offshoring). This imposes an upward bias if employees entering these establishments are disproportionately likely to transfer productivity spillovers but this is also unlikely to be the case because events that would raise the rate of jobs board postings in specific establishments within a firm will tend to reduce hiring in a specific location from the underlying base rate. IT Investment Data, E-commerce Adoption Data, and Compustat Economic Data The IT investment data are from the IT labor series constructed and described in prior published work (Tambe and Hitt, forthcoming). These data are based on IT labor intensity within the firm and have been shown to perform comparably with other IT datasets such as the CITDB capital stock data in productivity regressions. For the purposes of this analysis, they have the advantage that they are collected in a consistent manner in the years leading up to and after the dot-com bust. The e-commerce and datamining adoption data used in this study are created by text-mining firm s financial reports between 1995 and 2010 and these data and their error characteristics are also described in other work (Saunders and Tambe 2011). Other economic data used in productivity regressions, such as value added, capital, non-it employment, industry, and sales, were collected from the Compustat database. Variables were created using methods common in the micro-productivity literature. 4.0 Methods and Measures Main Productivity Regressions To test the main hypothesis, the productivity regressions treat the IT investments of IT-producing and IT-using firms as separate factors of production and estimate how productivity growth is associated with the growth of each of these factors. The Cobb-Douglas functional form is used because it has been the most commonly used specification in the literature on IT productivity and forms the basis for US productivity growth measurement (Brynjolfsson and Hitt 1996; Dewan and Min 1997). The starting point for the analysis is the large literature that estimates the social returns to R&D investments (see Griliches 1992 for a review of this literature). In logs, the most basic productivity model that includes a measure of external IT investment is: (2)!" =!! +!! +!"!" +!! +!"#$%"&' +!!"# where all lowercase variables are in logs, k is capital stock, e is employment, it is a firm s own IT investment levels, and s is the firm s external pool of IT investment. Similar specifications have been

11 used in much prior work on R&D productivity and have recently been adapted for the study of returns to external IT investments (e.g. see Chang and Gurbaxani 2010). IT investment and IT intensity have been used in prior research as a measure of the firm s technical capabilities (Bharadwaj and Bharadwaj 1999; Brynjolfsson, Hitt, and Yang 2002; Saunders 2010) these studies argue that firms with greater IT intensity tend to have superior IT capabilities and superior performance. Furthermore, higher demand for frontier technologies in IT-intensive firms suggests that these firms are more likely to employ technical employees with newer and specialized technical skills therefore, firms investing in frontier technologies will tend to obtain these skills from other IT-intensive firms that have made similar investments. The use of IT intensity as a measure of the skills embodied in individual workers is also supported by a literature demonstrating that firm-level investment in R&D is a superior measure of human capital accumulation than calendar time or other measures (Lieberman 1984). Variation in the measure of the external IT pool across firms requires measuring differences in firms abilities to capture returns from this pool of investment (Griliches 1992). In keeping with the skills-based mechanism of productivity transfer proposed in this paper, measures of each firm s pool of external IT investment are computed by weighting the IT-intensity of other firms by the share of IT employment flows from that firm in each year. Because the hypothesized channel for this productivity transfer is specialization and the accumulation of technical human capital, this tests the hypothesis that firms derive productivity benefits from the IT investments of firms from which they acquire IT labor. To test the hypothesis that IT-sector investment generated a new stock of technical skills, the analysis further separates firms into two sets 8, treating the IT investments made by IT-using and IT-producing firms as separate factors of production. The aggregate IT investment measures from the two different sectors are computed as in (3) and (4). (3)!!!"!!"#$!" =!!!!!"#!"!" (4)!!!"!!"#$%!" =!!!!!"#!"!" S it is the IT pool of firm i in year t, the p and u superscripts signify investments in either IT producing or IT using sectors, IT is the IT intensity of firm j in year t, the weighting term (Φ ijt ) is the fraction of firm i s incoming IT workers (summed across both IT producing and IT using firms) that come from firm j in year t and the superscript signifies that the summation is restricted to IT producing or using firms. The employee weights across both measures add to 1. A shift in the acquisition of IT labor towards IT- 8 Orlando (2005) is another example of an analysis that separates external investment pools into sets to separate the contribution of each.

12 producing firms corresponds to a shift in the fractional weighting terms that increases the value of the measure in (3) relative to that in (4). The final productivity model that incorporates both IT investment pools and accommodates differences in how these pools contributed to productivity before and after the bust is: (5)!" =!! +!! +!"!" +!!!!! +!!!!! +!!! ρ +!!! ρ +! ρ +!"#$%"&' +!!"# Lowercase variables are in logs, k is capital stock, e is employment, it is a firm s own IT investment levels, s u is the IT know-how of IT-using firms transmitted through IT labor flows and s p is the IT knowhow of IT-producing firms transmitted through IT labor flows, ρ is a dummy variable that assumes the value 1 in the years after the dot-com crash and 0 for all other years. The model in (5) is tested in fixedeffects and differences. Fixed-effects models remove most sources of firm-specific unobserved heterogeneity. However, differences estimates provide more suitable controls for idiosyncratic firm characteristics that evolve over time. The hypothesis that the flow of skills from IT-producing industries contributed to productivity growth in other industries after the bust is supported if the coefficient estimate!!!! is significantly greater than zero. This specification is used to estimate effects across the entire sample of IT-using firms, as well as within specific regions and industries. Where appropriate, standard errors are clustered on firm. The next sections describe approaches used to address endogeneity concerns associated with estimating (5). Endogeneity The regression approach described above filters out many omitted variable concerns. It uses both fixed effects and differences, and any remaining omitted variables that bias the value of!!!! must share the idiosyncratic timing of the dot-com bust they must be correlated with the pool of IT investment captured through IT labor from the IT-producing sector, but only after However, a serious remaining concern is that high-growth IT-using firms were most successful in acquiring IT skills from the IT sector after the bust. To address these, the analysis takes two different approaches both of which are described below. Instrumental Variables To test the causal direction of the relationship in (5), a quasi-experimental source of variation in IT labor price changes is differences in dot-com layoffs across IT labor markets. Because layoffs were concentrated in a relatively small number of cities, changes in IT labor prices after the crash also varied significantly across cities. If IT-using firms are otherwise evenly distributed across labor markets, these differences identify the effects of these changes on productivity growth for IT-using firms. This analysis uses two instrumental variables based on geographic location: a) the labor market index described above

13 (ITLM) and b) the total layoffs from IT-producing firms in the county of the firm s corporate headquarters. These variables are meant to capture the extent to which the dot-com bust affected a firm s local IT labor market. The descriptive evidence in the next section presents evidence that skills from ITproducing firms flowed into IT-using firms within the same metropolitan areas. However, the validity of this research design requires productivity growth to be randomly distributed over the different labor market categories in the absence of the dot-com bust. Matching Estimator A second experimental comparison uses a difference-in-differences matching estimator. Matching estimators have the advantage of being non-parametric, and have been widely used in the empirical economics literature on program evaluation (e.g. see Heckman, Ichimura, and Todd, 1997; Abadie et al 1994). Firms are placed in the treatment group when they are in the top ITLM quintile, and the treatment effect is estimated by comparing differences in productivity growth immediately after the bust for a treated firm with that of another, otherwise similar firm that differs only in its exposure to thick IT labor markets. The analysis matches firms on total employment, the location of its corporate headquarters, and industry. Therefore, the productivity growth effect in this analysis is estimated, for example, by comparing two similar-size pharmaceutical companies located in New Jersey, where one firm operates an IT development center located in a high-tech city. For this analysis, using matching estimators to complement the prior analyses has the advantage that the results do not rely on the specification of the external IT pool, and therefore estimate the average productivity growth effect of being located in these regions, irrespective of their success in hiring IT labor. Evidence for E-commerce The approaches described above link IT investment in the IT sector to subsequent productivity growth in IT-using firms. The analysis takes two approaches to further establish that these effects reflect the diffusion of new e-commerce technologies. First, results are reported from an analysis that associates the flow of IT labor to e-commerce adoption, rather than productivity growth. The use of e-commerce adoption as a dependent variable is somewhat less subject to the endogeneity concerns that accompany productivity growth. To test the hypothesis that IT labor from IT-producing industries specifically facilitates e-commerce adoption, a probit regression of the following form is tested: (1)!"#$$ =!!!!! +!!!!! + (!!!)!!! ρ + (!!!)!!! ρ +! ρ +!"#$%"&' +!!"# The dependent variable ecomm is a binary variable indicating whether a firm has invested in e-commerce technologies in any prior year. This dependent variable is measured using a text-mining dataset described in other work that captures whether a firm mentions making e-commerce related investments in their SEC

14 filings (Saunders and Tambe 2011). On the right-hand side of the equation, s u and s p are the IT investments of the relevant group of IT-using firms and IT-producing firms respectively, ρ is a dummy variable that assumes a value of 1 in the years after the dot-com crash (2000 and onwards) and 0 for prior years (these variables are identical to the aggregate investment variables used in the productivity regression). An estimate of (!!!)! that is significantly greater than zero indicates that IT-producing investment prior to the dot-com bust had a significant association with IT-using firms adoption of e- commerce technologies through the flow of technical skills. Standard errors for all probit regressions are clustered on firm. The robustness of this result is further tested by using the adoption of other types of technologies as the dependent variable. For example, if skills developed during the dot-com bubble are primarily e-commerce related, the pool of investment associated with the (!!!)! coefficient should not have a significant economic impact on the adoption of non e-commerce related technologies. The second approach to establishing that any observed productivity growth is an e-commerce effect addresses the limitations of using the flow of undifferentiated IT labor in creating the weighting matrix. The use of aggregate IT labor flow data does not distinguish between the flow of old and new technical skills although a key argument of the paper is that IT-sector investment produced a new category of e-commerce skills. To test the hypothesis that the productivity growth results reflect the returns to e-commerce skills, alternative measures of the pools of external IT investment are computed using only the flow of e-commerce labor (i.e. any IT worker reporting having at least one e-commerce skills) to create the weighting terms for the IT-intensity of prior employers. When embedded in a production function, this measure connects productivity growth with IT-producing sector investment specifically through e-commerce skills, which suggests that productivity benefits were primarily transferred through the flow of new e-commerce skills rather than aggregate IT skills. The statistical power of this measure is limited relative to the first measure due to the cross-sectional nature of the e- commerce skills data. However, it can provide corroborating evidence that the observed productivity effects are in fact returns to a shift in prices for e-commerce skills rather than technical skills overall. 5.0 Descriptive Statistics IT Labor Flows Figure 2 uses Bureau of Labor Statistics (BLS) data to illustrate the employment collapse that occurred in IT-producing industries after the dot-com crash. The job separation rate rose from 40,000-50,000 annual separations in IT industries during the late 1990 s to a rate of about 200,000 in 2000, and remained at higher than normal rates through These changes indicate a qualitative shift in the levels of IT labor flowing out of IT sectors during the dot-com bust and for a few years afterwards.

15 The micro-data used in this study enable detailed analysis of the migration paths of IT labor displaced from the IT sector. Figure 3 shows industry destinations for IT employees leaving IT sector firms during the years surrounding the dot-com crash. A drop in the percentage of displaced IT workers hired back into IT-sector firms was offset by a rise in the percentage of IT workers entering firms in the retail, finance, and manufacturing sectors. Figure 4 shows source industries for IT labor hired into the finance, real estate, and insurance (FIRE) industries in the years surrounding the dot-com crash; the trends indicate a sharp rise in the percentage of IT labor acquired from IT-sector firms during and after the dotcom crash, offset by smaller percentages of IT labor being hired from within the FIRE sector. These figures reflect aggregate trends across regions and skills, but anecdotal evidence suggests significant differences in the geographic concentration of IT layoffs during the dot-com bust. For ITusing firms, Figure 4 shows changes in the percentage of IT labor acquired from the IT sector, separated by labor market quintile (bottom, middle, and top 20% by ITLM). The flow of IT labor from ITproducing to IT-using firms was principally concentrated in firms with establishments in the highest ITLM quintile. There is comparatively little effect for firms in the middle and lower labor market quintiles. These differences suggest that IT-using firms located close to IT-producers may have disproportionately benefited from the flow of IT skills out of the IT sector after the dot-com bubble bust. It is also somewhat interesting to note that prior to the dot-com bust, IT-using firms in the top labor market quintile absorbed a smaller percentage of workers from IT-producing firms than IT-using firms in the middle quintile. This is consistent with claims of skills shortages in these cities due to high levels of IT labor demand in these regions during this period, IT-using firms may have had a particularly difficult time hiring workers when competing with IT-producing firms. This effect becomes significantly more pronounced after narrowing IT labor to those employees with e-commerce skills in Figure 5. For IT-using firms in the top labor market quintile, the fraction of IT workers with e-commerce skills from IT-producing firms jumps significantly, from about 20% before the crash to 80% after the crash. By contrast, there is little change in the composition of acquired IT labor for non e-commerce related skills, indicating that the patterns observed in the prior figures were primarily driven by the migration of e-commerce skills from IT sectors to other industries. Collectively, these figures suggest that the dot-com bust was associated with a flow of e-commerce and Internet skills from the IT sector to IT-using firms, but that IT-using firms that were located in the same geographic regions as IT-producing firms disproportionately benefited from this shift. External IT Investment Figures 7 through 10 consider growth in external IT investment as a factor of production and examines how this input changed during the dot-com bubble and bust in different sectors and regions.

16 Figure 7 examines how external IT investment changed over time in select industries. The vertical axis in Figure 7 is the mean-removed growth in the measure of external IT investment this measure rises when the fraction of IT labor acquired from IT-intensive firms rises faster than at other firms. For IT-sector firms, this growth rate peaks in the late 1990 s, which is consistent with IT investment levels during that period and the flow of IT labor within the IT sector during this time period. The timing of the transfer of the technical skills developed in this industry is suggested by the lines associated with the financial and retail industries. This rises for finance firms as it begins to fall in the IT sector but before the dot-com crash, suggesting that financial firms are not sensitive to changes in the price of new IT skills. However, rising levels of this measure for retail firms correspond with falling demand for this input from the IT sector. Figure 8 illustrates these trends for financial firms, deconstructed by sector to illustrate how much of the shift in this measure is driven by IT-producing firms. The measure is rising in both sectors in Figure 8, but there an apparently sharp upward shift in the measure coincides with the dot-com crash. One of the main arguments of this paper is that this shift was qualitatively different in its skill content and its geographic distribution. Therefore, to sharpen the comparison, Figure 9 narrows IT labor to workers with e-commerce skills and to IT using firms located in the highest IT labor market quintile. These adjustments produces a sharp rise in external IT measure for IT-using firms, driven almost exclusively by IT-producing firms and suggesting the emergence of a new class of skills from the IT sector. By contrast, Figure 10 displays the same e-commerce based measures for firms in the lowest IT labor market quintile for these firms, the dot-com bust did not produce a comparable increase. The next section reports results from regressions that analyze the associations between the variation in this production input and a) e-commerce adoption and b) productivity growth. Aggregate Productivity Growth Finally, figures 9 through 11 provide some preliminary evidence associating these changes with faster productivity growth. Figures 9 and 10 compare the productivity growth of IT-using and ITproducing firms from 1999 to 2003 where the vertical axis corresponds to productivity growth and the categories on the horizontal axis correspond to the five IT labor market quintiles. There is a distinct difference in the relationship between productivity growth and labor markets for IT-producing and ITusing firms, with IT-using firms in thicker IT labor markets achieving higher levels of IT productivity growth during these years, and IT-producing firms in these markets doing relatively poorly, consistent with a potential transfer of assets across organizations for firms in these areas. For comparison, Figure 11 indicates that prior to the dot-com bust, IT-using firms in thicker IT labor markets did not experience particularly high levels of productivity growth from 1994 to 1998 in fact, firms in the upper labor

17 market quintiles appear to have been somewhat less productive in the years leading up to the dot-com bust. The next sections attempt to more rigorously test how the IT labor flow patterns observed above are associated with these productivity growth patterns. 6.0 Regression Results Main Productivity Regressions Table 3 presents productivity estimates from equation (5). The estimates from columns (1) and (2) use fixed-effects estimators which remove the effects of time-invariant omitted variables, including most persistent differences associated with firms positions in the IT labor flow network. The estimates in (1) are from IT-using firms only and indicate that faster productivity growth is associated with growth in investment from both IT-using (t=2.56) and IT-producing industries (t=3.30) after the dot-com bust. The results in (2) are restricted to firms in IT-producing industries and do not reject the hypothesis of no statistical association between productivity growth and growth in the investment of either sector. As discussed earlier, fixed effects estimates may not provide suitable controls for idiosyncratic firm characteristics that evolve over time. Therefore, in columns (3)-(6), differences results for IT-using firms with varying difference lengths are presented. In addition to providing the same control for firmspecific effects as a fixed-effects analysis, the coefficient estimates at varying difference lengths can be interpreted as a comparison of the short run (1 st differences) versus long run (3 year or more differences) (Bartelsman, Caballero and Lyons 1994). Longer differences may also be less subject to biased estimates due to measurement error (Greene 1993). Estimates from the differences regressions also suggest a statistically significant association between the acquisition of IT skills from IT-producing firms after the bust and productivity growth but the point estimates do not become significantly different from zero until the differences window is extended to four years (t=2.22). 9 For comparison, the results from a four-year differences regression on the sample of IT-producing firms are reported in (7). These indicate that the acquisition of skills from other IT-producing firms has a significant association with productivity but it does not change after the dot-com bust. Together, the results from the differences estimates are consistent with the interpretation of IT-producing firms as incubators for skills that drove productivity spillovers within the IT producing sector during the dot-com bubble and in IT-using sectors after the bust. Productivity Regressions by Region, IT investment, and Industry Dot-com layoffs were geographically concentrated, and due to housing and family constraints, displaced IT labor is more likely to seek employment within the same metropolitan area. Therefore, 9 They also remain significant for five and six year differences, but are not shown for compactness.