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DRAFT – PLEASE DO NOT QUOTE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
ptambe@stern.nyu.edu
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 2000. 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.
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 ecommerce 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.
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 patterns2, 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 ecommerce 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 1987-2006.
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 1995-2000. 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).
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.
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 2000. 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 e-mail.”
(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
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 ecommerce 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 2000. 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 2007. 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 survey5, 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:
5
the “IT-using” firms in this analysis correspond to both the “IT-using” and “Other” categories in Figure 1.
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
http://www.census.gov/cps/.
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 2006. 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 %ITPRODj 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 cities7, so the ability to create this
6
7
The complete list of skills is chosen using Foote Consulting’s list of “hot” IT e-commerce skills.
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.
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 2001. 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
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
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 sets8, 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)
!!"! =
!"!!"#$%
!!"# !"!"
!!!
!
Sit is the IT pool of firm i in year t, the p and u superscripts signify investments in either IT producing or
IT using sectors, IT is the IT intensity of firm j in year t, the weighting term (Φijt) is the fraction of firm i’s
incoming IT workers (summed across both IT producing and IT using firms) that come from firm j in
year t and the superscript signifies that the summation is restricted to IT producing or using firms. The
employee weights across both measures add to 1. A shift in the acquisition of IT labor towards IT 8 Orlando
of each. (2005) is another example of an analysis that separates external investment pools into sets to separate the contribution
producing firms corresponds to a shift in the fractional weighting terms that increases the value of the
measure in (3) relative to that in (4).
The final productivity model that incorporates both IT investment pools and accommodates
differences in how these pools contributed to productivity before and after the bust is:
(5)
!" =∝! ! +∝! ! +∝!" !" +∝!!! ! ! +∝!!! ! ! +∝! ! ! ∗ ρ +∝! ! ! ∗ ρ +∝! ρ + !"#$%"&' + !!"#
Lowercase variables are in logs, k is capital stock, e is employment, it is a firm’s own IT investment
levels, su is the IT know-how of IT-using firms transmitted through IT labor flows and sp 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 2000.
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
(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
filings (Saunders and Tambe 2011). On the right-hand side of the equation, su and sp 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 ecommerce 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 ecommerce 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 40,00050,000 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 2003. 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.
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.
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
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 (1st 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.
short-run productivity differences may have been stronger in regions where dot-com layoffs were
concentrated. Table 4 shows productivity regressions separated by IT-producing/IT-using industry and by
whether the firm is in the top or bottom half according to the labor market index (ITLM). The results in
(1) and (2) suggest that the transfer of know-how from IT-producing firms was only important for firms
in cities in the top half scored by ITLM index, and that these effects appeared only after the dot-com crash
(t=2.33). This is consistent with the geographic concentration in IT layoffs restricting the transfer of
know-how to specific regions. For IT-using firms that were not located in these cities, the dot-com bust
did not have a discernable productivity impact through the transfer of technical know-how. Comparable
results from firms in IT-producing industries are in (3) and (4). The results indicate that IT-producing
firms in high ITLM locations appear to benefit from the flow of skills from other IT-producing firms
(t=1.72), but as expected, these effects are not significantly different after the dot-com bust. IT-producing
firms that are not located in IT-intensive cities do not derive comparable productivity benefits from the
flow of skills.
Columns (5) and (6) estimate the impact of these skills on firms that are technology leaders and
technology laggards, respectively, measured by whether or not the firms’ total IT intensity levels are
above or below mean levels relative to other firms. For technology laggards, the productivity effects are
weakly significant for the acquisition of technical know-how from IT-using firms (t=1.67) but there is no
effect from IT-producing firms, and the dot-com bust had no impact on productivity growth for these
firms—the effects of acquiring IT labor from IT-using or IT-producing firms are not significantly
different before and after the bust. On the other hand, for technology leaders, there is a positive and
significant association between changes in the acquisition of technical know-how from both sectors and
productivity growth after the bust. These results suggest that the flow of IT skills incubated in ITproducing sectors affected productivity for IT leaders, for whom access to the skills needed to install
frontier technologies would have been in demand. For IT laggards, who may have been installing older
generations of technologies, the necessary skills were already freely available from a broader set of ITusing firms. Therefore, the changes in the supply of cutting-edge skills caused by the dot-com bust would
have had little impact on productivity growth.
Finally, columns (7) and (8) focus the analysis on California, the region most closely associated
with dot-com investment and dot-com layoffs. Although the productivity effects of IT sector labor
acquired after the dot-com bust are significant in firms both inside and outside of California, the effect
sizes are much larger for California firms (t=2.79). Overall, the results in this table are consistent with the
interpretation that technology leaders would have found it beneficial to locate in high-rent locations (such
as Silicon Valley) for access to cutting-edge technical skills from IT-producers.
When technology
bubbles burst, firms in these regions will derive the strongest productivity benefits. IT laggards, on the
other hand, may be served equally well by locating away from these locations.
In Table 5, estimates are reported using the same fixed-effects specification as in Table 4, but the
sample of firms in each column is limited to a single one digit SIC industry. The results indicate that
productivity spillovers from IT-producing firms after the dot-com bust primarily benefitted firms in
specific industries: heavy and light manufacturing and retail industries. The productivity effects are not
significant for firms in other industries. It is interesting to compare this pattern of results with the
productivity growth results after 2000 from prior work.
The estimates from these regressions are
consistent with the relatively fast pace of productivity growth observed in retail and durable and nondurable manufacturing industries between 2000 and 2004. Although it is likely that this shift had a
number of causes beyond computerization, these results indicate that productivity spillovers of
managerial and technological innovations related to e-commerce were at least one important reason.
Instrumental Variables and Matching Estimator
The results from IV regressions using different sets of instrumental variables are reported in
Table 6. The model is a four-year differences regression, restricted to the years after the dot-com bust and
instruments are used to explain variation in the measure of the IT pool from IT-producing sectors.
Column (1) uses both the IT labor market variable and the IT layoffs variable as instruments. The
estimates from this model are significant (t=1.82), but the first-stage regression has fairly little
explanatory power, resulting in inflated estimates of the effect of IT-producing investment on IT-using
productivity in the second stage. The over-identification statistic (Hansen’s J) is not very close to zero, so
we cannot reject the hypothesis that the instruments are uncorrelated with the disturbance term. Columns
(2) and (3) add interactions between IT labor market and industry to the instrument list. Adding industry
at the 2-digit level significantly increases the explanatory power of the first-stage and lowers the IT pool
estimate closer to the values from our main productivity regressions, but the over-identification statistic is
close to being significant. In aggregate, these various IV regressions suggest that using location as a
quasi-experimental source of variation in IT labor market changes supports the hypothesis that ITproducing investment contributed to IT-using productivity growth through the development of a body of
technical skills. The IV statistics reported at the bottom of the table are consistent with the idea that ITLM
classification was exogenous with respective to post-bust productivity growth for IT-using firms. In (4),
the instruments in (2) are applied to a sample of IT-producing firms and the estimates for this regression
are insignificant.
The results from a matching estimator are reported in Table 7. The sample used for the matching
estimator includes only firms in the highest and lowest IT labor market categories and matches firms that
are similar in employment size, industry, and the location of corporate headquarters, but differ in their IT
labor market classification. The results in the top row are from 4 year productivity growth differences
from 2001 to 2005 matched on different sets of variables where “exact” matches are enforced where
possible.
The first column matches firms on size and the state of corporate headquarters with no
restriction placed on industry, and where exact matches are enforced on state. The percentage of exact
matches is high—over 90%--and the results indicate faster productivity growth levels for firms in the top
IT labor market quintile. Columns (2) through (4) use different levels of industry classification for
matching, from 1 digit industry through 4 digit industry, where increasing the precision of the industry
classification reduces the percentage of exact matches.
The estimates across all combinations of
matching variables indicate that firms in the highest ITLM category experienced significantly faster
productivity growth than firms in the lowest ITLM category, conditional on size, industry, and the
location of corporate headquarters. Column (5) further narrows the location match for firms’ corporate
headquarters to state and county. This significantly reduces the percentage of exact matches but the
results are not substantially different.
The second set of results at the bottom of the table differ only in that the four year period
considered is 1995 through 1999, which predates the dot-com bust. Notably, the productivity effects of
being in the highest IT labor market quintile disappear entirely, and the estimates weakly suggest that
firms in the highest IT labor market category during this time period were actually less productive than
their competitors in other locations.
Therefore, it does not appear that the faster productivity growth
reported in the top row were due to persistent differences in firms that locate establishments in these
regions.
Evidence for an E-commerce Based Explanation
A remaining question is whether the observed productivity effects are due to the diffusion of ecommerce technologies, or can be explained by the spread of other types of technical human capital out of
the IT sector. Table 8 presents results from probit adoption regressions that span the years 1995 to 2006
and use e-commerce adoption as a dependent variable. The binary dependent variable in (1) takes the
value 1 if the firm indicates making e-commerce related investments in its 10-K financial reports in any
prior year and 0 otherwise. The results from (1) suggest that the acquisition of IT labor after the bust is
positively associated with the adoption of e-commerce technologies, but these effects are only weakly
significant (t=1.93). In (2), the sample is limited only to firms in the two highest IT labor market
quintiles.
After restricting the sample to these regions, The effects are larger and more precisely
estimated (t=2.81), indicating that the e-commerce adoption benefits that IT-using firms derived from IT
firms after the bust were primarily within high-tech cities.
As a falsification test, the adoption of two other technologies, data mining and ERP, are used
instead of e-commerce as the dependent variable. If dot-com investment primarily produced e-commerce
related skills, know-how from the IT sector made available after the bust should not have statistical
associations with the adoption of other technologies. The regression in (3) uses the adoption of data
analysis technologies—including storage, analytics, machine learning—rather than e-commerce
technologies, as the dependent variable, and the sample is limited to firms in the top labor market
quintiles for direct comparability with (2). The estimates from this regression indicate that the adoption of
data technologies is associated with the flow of skills from other IT-using firms (t=3.73) with no
significant differences before or after the bust—in other words, the skills required to install these
technologies were not produced in the IT sector during this time period, but instead, were broadly
circulated among firms in all sectors. Using ERP technologies in (4), rather than e-commerce
technologies, produces no statistical associations between ERP adoption and any of the measures of IT
investment during this time period.
These analyses indicate a specific relationship between the acquisition of IT labor from the IT
sector and e-commerce adoption. To provide further support for the causal direction of this relationship,
the probit analysis in (5) is restricted to years after the dot-com bust, and uses the same location-based
instrumental variables that were used for the productivity regressions above as instruments for the change
in the structure of the IT labor flow network. The association between access to skills produced in the IT
sector and subsequent e-commerce adoption persists in the IV regression (t=3.05) suggesting that the
change in demand for e-commerce skills benefitted IT-using firms in dot-com cities, facilitating ecommerce investments in these firms.
Table 9 reports estimates using measures of the external IT investment pool created using the ecommerce skills data. OLS estimates are reported first due to cross-sectional limitations with the skills
data—they are most accurate in recent years, so the distribution of these skills across firms is more
representative than within-firm changes in this distribution over time—furthermore, the number of
samples of movements for workers with these skills is relatively small compared to the number of
samples of workers in firms, so the signal to noise ratio will be higher across firms. The OLS estimates in
columns (1) and (2) suggest that the IT investment of firms from which IT skills were acquired had a
positive association with productivity after the bust (t=1.87) and that the productivity effects of the
broader measure of labor-weighted IT investment disappears when jointly estimated with a measure based
on e-commerce weights. There are also significant association with the IT-using pool (t=1.75), although
these do not appear to be significantly different before or after the dot-com bust. These effects disappear
in the fixed-effects estimates in (3) and (4) which is likely due to the poor signal to noise ratio in the
longitudinal dimension of the skills data. The fixed-effects estimates when including the e-commerce pool
measure are similar to those reported earlier with no e-commerce measures included in the model.
Nevertheless, the fact that the productivity effects of the technical know-how measure after the bust
disappear when including direct e-commerce measures provides some support that the earlier results
specifically reflect the transfer of e-commerce know-how from IT-producing to IT-using firms.
7.0 Conclusion
The main finding of this study is that the technical human capital developed in IT sectors during
the dot-com bubble contributed to productivity growth in IT-using sectors after the bust. These results are
robust to multiple specifications and dependent variables. They are also robust to IV regressions that use
the geographic concentration of IT layoffs after the dot-com bust as a source of quasi-experimental
variation in changes in the local IT labor pool. Productivity benefits were largest for IT-using firms
located near IT-producing firms, suggesting that firms in IT-intensive cities had the best access to ecommerce know-how immediately after the dot-com crash. The effects are particularly strong in
California, and they are stronger for technology-intensive IT-using firms, for which access to cutting-edge
e-commerce skills would have been a bottleneck to technological progress. For IT-using firms investing
in older generations of information technologies, the technical skills required for productivity growth
were readily available from other IT-using firms.
For managers, these results provide insight into how a supply of technical skills is created and
diffused across region, time, and industry. After the dot-com bust, hiring managers commented on the
skills and broad e-commerce exposure that dot-com employees brought to new employers. These findings
provide empirical support for these statements, especially regarding the importance of location for access
to new and rare skills developed through on-the-job learning at other firms. However, the estimates
suggest that access to these skills is important only for IT leaders. For IT laggards, regional location may
not be particularly important. These findings also have implications for understanding how technology
bubbles facilitate the development of new skills which can diffuse to other industries. For instance, a
concern with the current “social media” bubble was voiced by an early Facebook employee, who was
quoted as saying “The best minds of my generation are thinking about how to make people click ads. That
sucks.” (Vance, 2011) This study suggests that the “big data” skills being developed in social media
companies may be productively transferred to companies in the financial, retail, and healthcare sectors as
firms in these industries begin to exploit valuable databases using techniques that are currently being
pioneered by social media companies. Consequently, new skills related to big data (e.g. Hadoop and
MapReduce) that are currently concentrated in a few cities like Sunnyvale, CA may diffuse to firms in
other industries if and when there is a shakeout in the social media sector—with firms in close geographic
proximity to social media companies being among the first to benefit.
For policy makers, these results provide some insight into the importance of technical skills for
productivity growth, and the important role that dot-com bubble investment played in incubating a supply
of technical human capital. Although evaluating the efficiency of technology bubble investment through
this lens is well beyond the scope of this paper, these results provide evidence that at least some of the
“intangible assets” produced through dot-com investment were transferrable to other firms. The
importance of on-the-job learning for the development of technical skills suggests that regulatory
environments that encourage experimentation with new technologies may be an effective way to develop
a supply of skills around new technologies. The regional results in this paper also provide support for
policies that encourage investment to facilitate the development of a skilled workforce within a region.
They also suggest the importance of encouraging labor mobility for economic growth, which is relevant
to the assessment of laws such as non-compete agreements or health insurance portability that affect labor
mobility rates.
Finally, these findings suggest directions for future research. This paper suggests the importance
of technical skills for productivity but does not discuss how to attract and retain high-quality IT workers.
Research on the role of compensation and other firm-level factors for locating, attracting, retaining, and
motivating skilled IT workers to complement a firm’s technical initiatives would provide important
guidance about how firms can build a productive IT workforce. Furthermore, this study discusses the
productivity benefits of the dot-com bubble captured by firms through the transfer of skills and human
capital, but importantly, but it does not discuss how the dot-com crash affected labor outcomes for IT
workers. An interesting question for future research is how the collapse of this bubble affected the value
of skills for IT workers, and how this affected long-term labor outcomes for IT workers, especially in
conjunction with offshoring and other changes to IT labor supply that became increasingly important after
the bust.
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Table 1: Top 10 large metropolitan areas for IT-Sector employment
Rank
City
State
1.
San Jose
CA
2.
San Diego
CA
3.
Austin
TX
4.
Phoenix
AZ
5.
Los Angeles
CA
6.
Raleigh
NC
7.
Dallas
TX
8.
Denver
CO
9.
San Francisco
CA
10.
Seattle
WA
Cities are ranked by percentage of IT workforce employed in IT
industries. Only includes large cities where large cities are those with at
least 1000 IT workers in the data sample.
Figure 1: Job separations in IT-producing industries
100000
0
50000
Separations
150000
200000
(Source: BLS Mass Layoffs Data)
1995
2000
2005
2010
Year
Computer Hardware
Software and Computer Services
Communications Equipment
Communications Services
All Industries
Table 2: Key variable definitions
Variable
Value Added
Capital
Non-IT Employment
IT Employment
IT Produce Pool
IT Using Pool
Post-Crash
E-commerce Skills
Industry
ITLM
E-commerce (binary)
ERP (binary)
Data mining (binary)
Data Source
Compustat
Compustat
Compustat
IT Labor Data
IT Labor Data
IT Labor Data
IT Labor Data
Compustat
IT Labor Data
Adoption Data
Adoption Data
Adoption Data
Description
Sales minus materials
Physical capital stock
Number of non-IT employees
Number of IT employees
IT Intensity of firms in IT-producing sectors, weighted by IT labor flows
IT Intensity of firms in IT-using sectors, weighted by IT labor flows
Dummy variable indicating whether the year is before or after the dot-com crash.
Self-reported IT skills and certifications
2 digit SIC classification
% of IT labor in city employed in IT-producing firms
Extracted from 10-K text, from Saunders and Tambe (2011)
Extracted from 10-K text, from Saunders and Tambe (2011)
Extracted from 10-K text, from Saunders and Tambe (2011)
0
% IT Workers Entering Each Industry
.2
.4
.6
Figure 2: Destinations for IT Labor Displaced from IT Producing Industries
1998
2000
2002
Year
Retail
2004
2006
IT
Manufacturing
FIRE
.1
% Source Industries for IT Workers
.15
.2
.25
.3
.35
Figure 3: Source Industries for IT Labor Hired into FIRE Industries
1998
2000
2002
Year
Retail
Manufacturing
2004
IT
FIRE
2006
0
% IT Workers from IT Sector
.02
.04
.06
.08
.1
Figure 4: Lowest, Middle, and Highest ITLM Quintiles
1996
1998
2000
2002
2004
2006
Year
Lowest 20% LMO
Middle 20% LMO
Highest 20% LMO
0
% IT Workers from IT Industries
.2
.4
.6
.8
Figure 5: Narrowed to e-commerce skills
1998
2000
2002
Year
2004
Other IT (High 20%)
E-commerce (High 20%)
Other IT (Low 20%)
E-commerce (Low 20%)
2006
-.2
0
Mean Removed IT Pool
.2
.4
.6
.8
Figure 6: External IT Investment Across Industries
1985
1990
1995
Year
2000
retail
2005
finance
IT
.005
.01
.015
.02
.025
Figure 7: Growth in Factors by Sector (Financial Firms, All IT)
1996
1998
2000
2002
2004
year
IT-Using
IT-Producing
2006
0
.001
.002
.003
.004
Figure 8: Growth in Factors (Top 20% ITLM, E-commerce)
1996
1998
2000
2002
2004
2006
year
IT-Using
IT-Producing
0
.001
.002
.003
Figure 9: Growth in Factors (Bottom 20% ITLM, E-commerce)
1996
1998
2000
2002
2004
2006
year
IT-Using
IT-Producing
-.2
-.15
-.1
-.05
4 year growth in logged productivity
0
Figure 10: Productivity Growth of IT Producing Firms from 1999-2003 by ITLM Quintile
1
2
3
4
5
.04
.02
0
-.02
4 year growth in logged productivity
.06
Figure 11: Productivity Growth of IT Using Firms from 1999-2003 by ITLM Quintile
1
2
3
4
5
0
.02
.04
.06
.08
4 year growth in logged productivity
.1
Figure 12: Productivity Growth of IT Using Firms 1994-1998 by ITLM Quintile
1
2
3
4
5
Table 3: Main productivity growth estimates
DV: Log(VA)
Log(IT-Use)
Log(IT-Prod)
Log(IT-Use)⋅Post
Log(IT-Prod)⋅Post
Log(IT Emp)
Log(Capital)
Log(Non IT Emp)
Observations
R2
(1)
FE
IT Using
Firms
0.00194
(0.00282)
-0.00184
(0.00289)
0.00888**
(0.00347)
0.0115***
(0.00349)
0.0296***
(0.00408)
0.173***
(0.00626)
0.727***
(0.00780)
29,324
0.688
(2)
FE
IT Prod
Firms
-0.00717
(0.00854)
0.00649
(0.00690)
0.0157
(0.0101)
-0.000823
(0.00813)
0.0538***
(0.0142)
-0.00576
(0.0179)
1.069***
(0.0243)
6,972
0.571
(3)
1 Yr Diffs
IT Using
Firms
-0.000924
(0.00164)
-0.000813
(0.00173)
0.00140
(0.00266)
0.00122
(0.00246)
0.0350***
(0.00581)
0.0996***
(0.0229)
0.693***
(0.0338)
24,951
0.304
(4)
2 Yr Diff
IT Using
Firms
0.000973
(0.00204)
-0.00150
(0.00222)
0.00414
(0.00311)
0.00475
(0.00304)
0.0354***
(0.00632)
0.114***
(0.0199)
0.742***
(0.0294)
21,653
0.453
(5)
3 Yr Diff
IT Using
Firms
0.00138
(0.00237)
-0.00137
(0.00274)
0.00557
(0.00340)
0.00585
(0.00376)
0.0313***
(0.00667)
0.112***
(0.0211)
0.769***
(0.0303)
18,945
0.536
(6)
4 Yr Diff
IT Using
Firms
0.00407
(0.00268)
-0.000689
(0.00273)
0.00382
(0.00376)
0.00861**
(0.00387)
0.0305***
(0.00685)
0.121***
(0.0217)
0.765***
(0.0309)
16,649
0.595
(7)
4 Yr Diff
IT Prod
Firms
-0.00410
(0.00985)
0.0134*
(0.00744)
0.0140
(0.0131)
-0.0130
(0.00982)
0.0549**
(0.0234)
-0.00164
(0.0318)
1.033***
(0.0439)
3,081
0.615
(8)
4 Yr Diff
IT Using
Firms
0.00371
(0.00268)
-0.000611
(0.00273)
0.00409
(0.00373)
0.00820**
(0.00384)
0.0322***
(0.00690)
0.118***
(0.0210)
0.765***
(0.0301)
16,649
0.605
Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1; Estimates are from a Cobb-Douglas production function using value added as the
dependent variable. Regressions (1)-(7) include controls for year and one-digit industry. Column (8) uses 2 digit industry, year, and state controls.
Table 4: Fixed-Effects Estimates by IT Labor Market and IT Investment
DV: Log(VA)
Log(IT-Use)
Log(IT-Prod)
Log(IT-Use)⋅Post
Log(IT-Prod)⋅Post
Log(IT Emp)
Log(Capital)
Log(Non IT Emp)
Observations
R-squared
(1)
IT-Using
High ITLM
0.00592
(0.00422)
-0.000285
(0.00429)
0.00474
(0.00578)
0.0128**
(0.00555)
0.0444***
(0.00916)
0.0753**
(0.0299)
0.827***
(0.0406)
8,891
0.603
(2)
IT-Using
Low ITLM
0.00287
(0.00340)
-0.000487
(0.00353)
0.00274
(0.00493)
0.00324
(0.00541)
0.00955
(0.0102)
0.173***
(0.0289)
0.699***
(0.0429)
7,758
0.591
(3)
IT-Prod
High ITLM
-0.00258
(0.0123)
0.0155*
(0.00911)
0.0148
(0.0156)
-0.0178
(0.0122)
0.0468*
(0.0280)
-0.0158
(0.0355)
1.054***
(0.0533)
2,081
0.603
(4)
IT-Prod
Low ITLM
-0.00781
(0.0161)
0.0111
(0.0129)
0.0112
(0.0235)
-0.00373
(0.0167)
0.0726*
(0.0420)
0.0282
(0.0637)
0.989***
(0.0771)
1,000
0.648
(5)
Below
IT mean
.00465*
(.00279)
.00193
(.00312)
.00295
(.00406)
.00542
(.00470)
.0370***
(.00765)
.277***
(.0236)
.648***
(.0313)
11,840
2,128
(6)
Above
IT Mean
.00699
(.00461)
.00259
(.00372)
.0131**
(.00582)
.0122**
(.00505)
.0208***
(.00796)
.235***
(.0136)
.715***
(.0178)
17,484
3,570
(7)
Drop
CA
.00469*
(.00283)
.00131
(.00245)
.00708*
(.00376)
.00747**
(.00362)
.0247***
(.00622)
.215***
(.0142)
.721***
(.0186)
26,173
3,129
(8)
Only
CA
-.00227
(.0125)
-.0152
(.0118)
.0220
(.0168)
.0419***
(.0150)
.0287
(.0232)
.301***
(.0658)
.656***
(.0846)
3,151
441
Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1; Estimates are from a Cobb-Douglas production function using value added as
the dependent variable. Each regression includes controls for year and one-digit industry.
Table 5: Fixed Effects Estimates by Industry
DV: Log(Value Added)
Industry
Industry SIC range
Log(IT-Use)
Log(IT-Prod)
Log(IT-Use)⋅Post
Log(IT-Prod)⋅Post
Log(IT Employment)
Log(Capital)
Log(Non IT Employment)
Observations
R-squared
Number of panelid
(1)
Heavy
Manuf
24,25
32-39
.00866
(.00530)
-.00628
(.00527)
.00448
(.00657)
.0164***
(.00633)
.0484***
(.00708)
.0411***
(.0133)
.921***
(.0156)
8,312
0.692
982
(2)
Light
Manuf
20-23
26-31
.00163
(.00588)
-.00398
(.00635)
.0177**
(.00738)
.0141*
(.00780)
.0499***
(.00914)
.291***
(.0183)
.662***
(.0200)
6,382
0.635
703
(3)
(4)
(5)
(6)
(7)
Business
Services
Transport
Wholesale
Retail
Finance
41-49
50,51
52-59
60-67
70-87
.00260
(.00581)
-.00816
(.00607)
-.00774
(.00716)
.00658
(.00740)
.0118
(.00766)
.389***
(.0137)
.548***
(.0157)
3,458
0.808
358
.0167
(.0132)
-.00102
(.0125)
-.0464***
(.0161)
.00530
(.0157)
.0122
(.0203)
.00277
(.0253)
.977***
(.0382)
1,506
0.685
155
.0130**
(.00636)
.00972
(.00684)
.00182
(.00804)
.0177**
(.00827)
.0151
(.00931)
.0112
(.0165)
.958***
(.0212)
3,669
0.817
446
-.00164
(.0124)
.00651
(.0120)
.0253*
(.0148)
-.00325
(.0145)
-.00839
(.0197)
.123***
(.0218)
.563***
(.0280)
2,345
0.640
357
-.0124
(.00851)
.0107
(.00841)
.0303***
(.0103)
-.000722
(.00992)
.0227*
(.0130)
.202***
(.0147)
.698***
(.0206)
3,615
0.732
539
Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Estimates are from a Cobb-Douglas production function using value added
as the dependent variable. Each regression includes controls.
Table 6: Instrumental Variable Tests
DV: Log(VA)
Instruments
Log(IT-Use)
Log(IT-Prod)
Log(IT Emp)
Log(Capital)
Log(Non IT Emp)
R2
First-stage R2
Anderson LR
Hansen’s J
N
(1)
ITLM
IT Layoffs
IT-Using
2001-2006
0.114*
(0.0628)
0.0116
(0.00786)
-0.0348
(0.0305)
0.0986*
(0.0504)
0.821***
(0.0618)
0.247
.044
.085
.66
2,355
(2)
ITLM
IT Layoffs
1 dig Ind*ITLM
IT-Using
2001-2006
0.120***
(0.0395)
0.0138**
(0.00690)
-0.0306
(0.0254)
0.110***
(0.0425)
0.813***
(0.0524)
0.450
.053
.294
.81
2,355
(3)
ITLM
IT Layoffs
2 dig Ind*ITLM
IT-Using
2001-2006
0.0264
(0.0165)
0.0106*
(0.00629)
0.00139
(0.0190)
0.119***
(0.0404)
0.787***
(0.0501)
0.0264
.136
.002
.12
2,355
(4)
ITLM
IT Layoffs
1 dig Ind*ITLM
IT-Producing
1995-2000
-0.0144
(0.0463)
0.00180
(0.00498)
0.0385
(0.0238)
0.157***
(0.0507)
0.752***
(0.0728)
.718
.101
.113
.28
1,991
All regressions are 4 year differences regressions and include controls for 1-digit industry and year. Standard errors are
clustered on firm.
Table 7: Matching Estimator Output
(1)
DV: Productivity Growth
Top quintile ITLM
Exact matches
Observations
Top quintile ITLM
Exact matches
Observations
State
2001-2005
293.4**
(117.4)
92.6%
479
1994-1999
-34.0
(90.3)
93.2%
368
(2)
State
1 digit
2001-2005
270.5**
(118.1)
65.6%
479
1994-1999
-168.1*
(96.9)
62.6%
368
(3)
State
2 digit
2001-2005
209.7**
(105.4)
30.8%
479
1994-1999
-145.0
(97.2)
22.0%
368
(4)
State
4 digit
2001-2005
228.2**
(107.0)
9.4%
479
1994-1999
-143.7
(97.2)
5.4%
368
(5)
State/County
1 digit
2001-2005
246.9**
(105.7)
15.9%
479
1994-1999
-44.0
(86.9)
18.5%
368
Robust standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1. Estimator matches firms in top ITLM quintile to closest match in bottom ITLM
quintile. Matching variables include total employment, location, and industry (level of location and industry variables described in column headers).
Table 8: E-commerce adoption estimates
DV: Technology Adoptiona
(1)
(2)
(3)
(4)
e-commerce
e-commerce
data mining
erp
All
0.0536
(0.0332)
-0.0109
(0.0283)
-0.00808
(0.0328)
0.0586*
(0.0303)
8,346
Top ITLM
0.0592
(0.0603)
-0.0928*
(0.0560)
0.00922
(0.0469)
0.164***
(0.0584)
2,597
Top ITLM
0.196***
(0.0526)
0.0349
(0.0497)
-0.0418
(0.0573)
0.0683
(0.0488)
2,597
Top ITLM
0.0306
(0.0529)
0.00131
(0.0382)
-0.00173
(0.0533)
0.0565
(0.0421)
2,597
Location
Log(IT-Use)
Log(IT-Prod)
Log(IT-Use)⋅Post
Log(IT-Prod)⋅Post
Observations
a
(5)
e-commerce
IV
All
-.088
(.054)
.452***
(.148)
4,170
Technology adoption dependent variables are based on text-mining 10-K reports and described in Saunders and Tambe (2011).
Robust standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.
IV Regression in (5) only uses observations after 2000.
Table 9: Regressions Using E-Commerce Skill Measures
DV: Log(VA)
(1)
OLS
Log(IT-Prod)
Log(IT-Prod)⋅Post
Log(IT-Use)
Log(IT-Use)⋅Post
Log(IT-Prod E-Commerce)
Log(IT-Prod E-Commerce) ⋅Post
Log(IT-Use E-Commerce)
Log(IT-Use E-Commerce) ⋅Post
Observations
R-squared
Number of firms
-0.00934
(0.0260)
0.0519*
(0.0278)
0.0412*
(0.0235)
-0.00611
(0.0262)
5,577
0.928
(2)
OLS
0.0359***
(0.0113)
-0.0241*
(0.0134)
0.0357***
(0.0119)
0.000786
(0.0134)
-0.0308
(0.0274)
0.0664**
(0.0297)
0.0150
(0.0243)
0.00610
(0.0270)
5,577
0.929
(3)
FE
-0.0297
(0.0292)
0.0367
(0.0303)
0.0257
(0.0295)
-0.0234
(0.0304)
5,577
0.680
1,726
(4)
FE
-0.00941
(0.00678)
0.0139*
(0.00773)
0.00965
(0.00694)
-0.00858
(0.00782)
-0.0274
(0.0294)
0.0330
(0.0305)
0.0232
(0.0297)
-0.0217
(0.0308)
5,577
0.680
1,726
Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Estimates are from a
Cobb-Douglas production function using value added as the dependent variable. Each regression
includes controls for year and one-digit industry. All regressions also include IT employment,
capital, and non IT-employment.