do ict skill shortages hamper firms' performance

Do ICT Skill Shortages Hamper Firms’ Performance?

Evidence from UK Benchmarking Surveys1

John Forth Geoff Mason

* National Institute of Economic and Social Research, London

September 2006

Abstract

Abstract – In light of the increased relative demand for skilled labour associated with

Information and Communication Technologies (ICTs), we combine survey data for UK

enterprises in 1999 with post-survey financial data for the same enterprises to assess the

impact of ICT skill shortages on firms’ financial performance. There is clear evidence

that ICT skill shortages have an indirect negative impact on performance through the

restrictions that such deficiencies place on ICT adoption and on the intensity of ICT use

post-adoption. However, there is only weak evidence of skill shortages impinging

directly on performance at given levels of ICT adoption and utilisation.

I. Introduction

There is now a great deal of evidence that the diffusion of Information and

Communication Technologies (ICTs) over recent decades has helped to enhance the

relative demand for skilled labour (Berman, Bound and Machin, 1998; Acemoglu,

2002). Indeed, the skill-biased nature of the ICT ‘technological revolution’ is one

reason why pay-offs to ICT investments at firm and industry level have taken time to

develop, in contrast to some previous new technologies which were complementary

with low-skilled labour and thus capable of being implemented relatively quickly

(Caselli, 1999).

In this context, it was hardly surprising at times of peak demand for ICT-skilled labour

over the last decade, to hear reports of ICT skill shortages which, it was argued, could

have serious negative effects on firms’ commercial performance (IDC, 1999; European

Commission, 2002; ITAA, 2000, 2002). Since 2002 the emphasis on ICT skill

shortages has diminished on both sides of the Atlantic as many ICT-skilled workers

have experienced lay-offs and difficulties in finding ICT-related employment. But even

when forecasts of shortages were made, they attracted criticism on the grounds of

under-estimating potential sources of ICT skills and, especially, taking little account of

the many ways in which employers might respond to periodic skill shortages, for

example, raising salaries, increasing training and introducing labour-saving changes in

work organisation (Lerman, 1998, UEF, 1999; Mason, 2000; Wharton, 2002).

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mailto:[email protected]

Some years later it remains an open empirical question whether, and to what extent,

ICT skill shortages have negative effects on firm performance. In the present paper we

examine this question by making use of an unusual dataset which combines information

from an ICT benchmarking survey of British enterprises with post-survey financial data

for the same enterprises. The properties of this dataset enable us to test a number of

hypotheses relating to the impact of ICT skill shortages on firm-level performance.

The paper is ordered as follows: Section 2 briefly reviews the separate literatures on

employer responses to skill shortages and the relationship between ICTs and skills

demand in order to develop the theoretical framework from which our central

hypotheses are derived. Section 3 describes the dataset, including the process by which

post-survey financial data were matched to benchmarking survey respondents. Sections

4 and 5 present new summary measures of ICT adoption and utilisation and indicators

of ICT skill shortages at firm level. Section 6 examines the main determinants of ICT

adoption and utilisation. Section 7 presents estimates of the impact of ICT skill

shortages on financial performance at firm level. Section 8 concludes.

II. Discussion and Hypotheses

In assessing the nature and impact of skill shortages, it is important to distinguish

between, on the one hand, employers’ reported difficulties in recruiting skilled workers

on the external labour market and, on the other hand, firms’ ‘internal’ skill deficiencies,

that is, gaps between firms’ current skill levels and their desired or optimum level of

skills (Green and Ashton, 1992). Early analyses of skill shortages tended to focus on

those associated with external recruitment difficulties and were particularly concerned

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with how and why such phenomena should exist. A common argument was that, in

competitive labour markets, excess demand for skills should put upward pressure on

salaries and thus eventually (all else being equal) help to stimulate an increase in supply

which would eliminate the shortage. In response to this line of thinking Arrow and

Capron (1959) suggested that, even in a competitive labour market, a steady increase in

demand over time for skilled workers (such as engineers and scientists) could produce a

‘dynamic shortage’ due to factors impeding rapid salary increases by employers such as

delays in accepting the needs for such increases, the further time needed to implement

them and a reluctance to incur increased salary costs for existing high-level technical

employees as well as new ones.2

At the same time supply responses to any salary improvements could be slowed down

by the length of time required to educate and train skilled workers. In the case of

engineers and scientists, Freeman (1971, 1976) argued that a slow supply response

could potentially lead to a sequence of imbalances in labour markets whereby, for

example, a delayed increase in supply in response to improved salary prospects could

come on stream just as there was a downward shift in demand; the resulting decline in

relative rates of return to qualification as an engineer or scientist could in turn prompt a

reduction in new entrants to engineering and science courses and thus sow the seeds of

future shortages if demand for engineers and scientists happened to increase at much

the same time as a reduction in new supply entering the market.

Subsequent discussion of the reasons why skill shortages might come to exist and even

to persist over some length of time drew heavily on the concept of ‘internal labour

markets’ (ILMs) which effectively detach firms from external market pressures by

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confining new recruits to a narrow set of ‘entry jobs’ and relying on internal promotion

to more senior jobs.3 In the standard literature on segmented labour markets, ILMs are

typically associated with relatively high wages and salaries (often linked to seniority) in

‘primary’ sectors of employment as compared to unstable, low-wage employment in

‘secondary’ sectors (Doeringer and Piore, 1971). However, detachment from external

labour market pressures can also limit the extent to which salaries rise in response to

shortages of particular categories of employee. For example, case studies of British

employers of engineers in the 1970’s found that many employers preferred to respond

to recruitment difficulties with non-salary measures such as reorganisation of

workloads and recruitment of less well-qualified personnel rather than disturb the

internal parities on which salaries were based (Mace and Wilkinson, 1977). The

strength of ILM structures in these firms was shown by persistent salary differentials

between the firms for similar categories of engineer (Mace, 1979).

But even in the absence of ILM-type rules and procedures, firms have a range of

potential non-salary responses and ‘coping mechanisms’ available to them when

confronted by actual shortages – such as asking existing employees to work longer

hours, making increased use of subcontractors or retraining existing staff to develop the

skills in shortage. In a study based on data from the 1984 Workplace Industrial

Relations Survey (WIRS), Haskel and Martin (1993a) found no evidence of firms

setting higher wages in response to difficulties in recruiting skilled workers. Indeed,

they cited other UK survey evidence to suggest that salary responses were much less

important than other means of addressing skilled recruitment difficulties.

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Hence skill shortages deriving from external recruitment difficulties may persist for

some length of time if responses to them take the form of temporary alleviation of their

effects rather than seeking to increase the supply of skills (for example, through

increased training or raising wages for skilled labour). The same is true of ‘internal’

skill shortages, that is, gaps between current skills and desired skills among the existing

workforce. Employers Skill Survey (ESS) data for England show that the proportion of

establishments reporting internal skill shortages is some 2.7 times greater than the

proportion reporting skill-related external recruitment difficulties.4 At sector level the

persistence of internal skill shortages over time is generally greater than for external

recruitment difficulties (Forth and Mason, 2003). In some cases firms may elect to ‘live

with’ internal skill shortages for periods of time rather than incur the costs of training

and updating workers that would be necessary to bridge the gaps in skills. Such

behaviour could reflect the relatively high costs of bringing lower-skilled workers up to

the required skill standards as compared to employers who are starting from a superior

position in terms of existing skills. It could also reflect imperfect information about the

costs and benefits of training versus other potential responses to internal skill shortages.

Empirical evidence for the UK suggests that many firms choose to tolerate – or fail to

cope with – skill shortages for long enough for negative effects on performance to be

identified. For example, in a study based on CBI survey data, Haskel and Martin

(1993b) found that skilled labour constraints on output had significant negative effects

on labour productivity at industry level.5 They suggested that the main ways in which

skill shortages had these effects were by shifting the employment mix in favour of less

productive low-skilled workers and by reducing the bargaining power of employers in

relation to worker effort. By contrast Nickell and Nicolitsas (2000) focused on the

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cumulative effects of reduced investments in assets which are complementary to skills,

finding that skilled labour shortages at industry level were significantly and negatively

associated with subsequent reductions in investments in physical capital and R&D at

firm level in the industries concerned. Negative impacts on performance may also occur

through delays in innovation: the Technical Graduates Employers Survey in the UK

found that two thirds of employers which had experienced difficulties in recruiting

high-level skilled personnel reported suffering commercial problems as a result. The

most common problem mentioned was delays in product development and process

improvement projects, impacts which may have no immediate effect on performance

but may contribute to weaker performance in later time periods (Mason, 1999).

The literature on skill shortages thus identifies a number of mechanisms by which

different kinds of shortage may have negative effects on firm performance. The same is

true by implication of the literature on the positive associations between the diffusion of

ICTs and employer demand for skills. Recent studies have focused on the role of skills

in facilitating the effective utilisation of ICTs (for example, Brynjolfsson, Hitt and

Yang, 2002). However, there is also an older literature which highlights the role of

highly-educated or skilled workers in facilitating early adoption of new technologies

(Nelson and Phelps, 1966; Welch, 1970; Schultz, 1975; Bartel and Lichtenberg, 1987).6

In this vein Doms, Dunne and Troske (1997) found that the adoption of

microelectronics-based technologies in US manufacturing plants was positively related

to pre-existing workforce skill levels. Therefore, in the present paper we distinguish

carefully between the adoption of ICTs and the intensity of their use in order to assess

the impact of skill shortages on firm-level performance.

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Our theoretical framework is centred on profit-maximising firms which, all else being

equal, respond to changes in the relative prices of production inputs by increasing

(reducing) their purchases of those factors which have fallen (risen) in price. In recent

decades ICTs have been characterised by rapid declines in price: performance ratios

which have increased their attractiveness relative to the use of non-ICT capital inputs

and other inputs (such as unskilled labour) which are not complementary to the use of

ICTs.

In principle, ICT investments should help early-adopting firms to achieve higher levels

of performance, for example, by improving the efficiency with which various tasks are

carried out by different sections of the workforce; and /or by facilitating more rapid

monitoring of trends in customer demand and improvements in communications with

suppliers of key components and services. A number of studies have now produced

evidence of ICT investments enhancing firm-level productivity performance (for

example: Lichtenberg and Lehr, 1999; Black and Lynch, 2001; Brynjolfsson and Hitt,

2003). However, in common with some previous new ‘general purpose’ technologies

such as electrification, the short-term impact of ICT investments on firm-level

performance may be small or even negative due to the time and resources needed to

develop complementary production inputs (Helpman and Trajtenberg, 1998). Some

indication of the time needed for these complementary inputs to be developed is given

by Basu et al. (2004) who find that TFP growth at industry level in the US is

significantly related to ICT capital growth with long lags ranging between 5-15 years.

In order to make effective use of ICTs, many firms need to pass through periods of

experimentation and learning, investing in the adaptation or development of software,

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the implementation of appropriate new modes of work organisation and the

development of new products and services. The development of ICT-related skills is

central to this process of organisational change. Indeed, Bresnahan et al. (2002) suggest

that organisational investments in assets which are complementary to ICTs may

contribute more to raising the relative demand for skilled labour than the diffusion of

ICTs themselves.

In this context we expect that – all else being equal – shortages of ICT-skilled workers

may have negative effects on firm-level performance via several different but related

channels of influence, for example:

restricting their ability to make early use of cost-saving new ICTs;

limiting the improvements in efficiency which might otherwise have been achieved

by utilising ICTs once they have been adopted;

slowing the rate of adaptation to new forms of work organisation that are

complementary with ICTs;

slowing down the rate of development of new products or services which ICTs

make possible.

On the other hand, we may also expect that such effects of skill shortages might be

reduced if the firms affected by shortages respond in ways (such as increasing

workforce training) that help to alleviate the shortages over time.

These expectations regarding the different impacts of ICT skill shortages and ICT-

related training on firm-level performance can be restated as hypotheses to be tested.

We first consider the possibility that ICT skill shortages affect performance indirectly

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by slowing down ICT adoption and utilisation which would be beneficial for the firm.

This is done by examining the evidence in relation to the following two hypotheses.

Hypothesis 1

All else being equal, reported ICT skill shortages at firm level are negatively

related to measures of ICT adoption and to the intensity of use of ICTs .

Hypothesis 2

All else being equal, these measures of ICT adoption and the intensity of use of

ICTs are positively related to firm-level financial performance.

We then assess the extent to which ICT skill shortages have a direct negative effect on

performance, for example, by limiting firms’ ability to make effective use of ICTs that

have already been installed.

Hypothesis 3

At given levels of ICT adoption and utilisation, ICT skill shortages are

negatively related to firm-level performance.

Finally, we consider the possibility that firms’ efforts to develop skills that are

complementary to the use of ICTs have positive effects on firm performance (thus

helping to offset any negative impact of skill shortages).

Hypothesis 4

At given levels of ICT adoption and utilisation, investments in ICT skills

training contribute positively to firm performance.

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III. The Matched IBS-Dun & Bradstreet Dataset

Our initial company-level data derive from the UK section of the 1999 International

Benchmarking Survey (IBS), commissioned by the UK Department of Trade and

Industry (DTI), which compared the use of ICTs by British enterprises against that in

other industrialised countries such as France, Germany, the US and Canada (Spectrum,

1999). The target population was UK businesses as a whole, including partnerships and

sole proprietorships as well as private and public limited companies. The sampling

frame was derived from Dun & Bradstreet’s (D&B’s) company financial database

supplemented by companies listed on the Yellow Pages database. A total of 2,410

company respondents were interviewed; our analysis excludes 488 organisations

located in Public Administration, Education and Health in order to focus on the 1,922

private sector organisations.7 Data from the International Benchmarking Surveys of

1997 and 1998 are also called upon in Section IV to provide information on changes in

ICT adoption and utilisation in the late 1990s.

In order to explore the determinants of company performance, we sought to match

survey responses from the 1999 IBS against post-survey financial data up to 2001

provided by Dun and Bradstreet (D&B). Usable data on employment, sales and capital

assets were obtained for some 459 companies, representing 24 per cent of all private

sector organisations which participated in the 1999 IBS. The attrition which occurred

was primarily due to gaps in D&B records along with the exclusion of some companies

which either failed our matching checks or for which insufficient information was

available to enable such checks to be carried out. In addition, holding companies were

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excluded from the analysis because of uncertainty over the correspondence between the

interviewed unit and that which provided financial data to D&B.

As a result of this process, smaller companies (with fewer than 50 employees) are

under-represented in the matched dataset as compared to the original IBS sample,

largely due to limited coverage of those size-groups by D&B (Table 1). And in terms of

sectors, manufacturing companies turn out to be somewhat over-represented at the

expense of service companies. However, these selection biases are smaller than might

have been feared and we can expect any potential impact on our regression parameters

to be largely mitigated by the inclusion of size-group and industry dummies. In

addition, the matched dataset still benefits from a fairly wide spread of companies

across size-groups and sectors.

* Table 1 about here *

IV. ICT Adoption and Utilisation

The findings from successive IBS surveys between 1997 and 1999 highlight the rapid

growth in take-up of ICTs over a relatively short period. By 1999 some 88 per cent of

IBS respondents made use of at least one computer - this proportion had not changed

much in the previous two years (Table 2). However, this was a period of rapid growth

in the use of email (65 per cent of businesses in 1999, up from 37 per cent in 1997), the

Internet (53 per cent in 1999, up from 26 per cent) and the development of companies’

own websites (reported by 42 per cent of respondents in 1999 compared to only 17 per

cent in 1997). In addition, during this period there were marked increases in the

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proportions of businesses using Intranets or Extranets and those facilitating remote

access to company computer systems by employees.


These data permit a summary measure of the extent of ICT adoption to be derived from

a simple count of the different types of ICT equipment and facilities in use in each

company at the time of the annual surveys. As shown in Table 3, Part A, by 1999 only

10 per cent of IBS respondents did not use at least one of the eight listed types of ICT

while 35 per cent used six or more. The rapid increase in ICT take-up can be seen by

the position of the median enterprise in each year which changed progressively from

using two different types of ICT equipment or facilities in 1997 through to using four

different types in 1999.


In addition we define two different summary measures of the intensity of ICT

utilisation:

(i) the proportion of employees making use of computers, available for 1998-99

(Table 3, Part B)

(ii) an index ranging from 0 to 6 which captures both the extent of usage of three

key ‘connectivity technologies’ (networks, Internet, company websites) and the

extent to which these technologies are used to communicate with and engage in

on-line transactions with customers and suppliers (Table 3, Part C)

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As indicated in the notes to Table 3, Part C, a score of three out of six on the measure

of intensity of use of connectivity technologies corresponds to, for example, using

networks for local communications only, and using the Internet for email, information

and marketing purposes but not for on-line purchasing or sales, and using a company

website for advertising, marketing, etc., but not for on-line sales. In 1997 some 91 per

cent of IBS respondents were rated at point 3 or lower on this scale but by 1999 the

equivalent proportion had dropped to 67 per cent with the remaining 33 per cent all

rated at points 4-6, that is, possessing wide area networks and/or using the Internet or

their own websites for on-line commercial transactions.8

V. ICT Skill Shortage Measures

The main question on skill shortages in the 1999 IBS captures information about

internal skill gaps among existing employees. It was phrased as follows: ‘Do your

employees have sufficient understanding of the ICTs available in your company to

enable them to maximise the competitive advantage that these technologies bring?’

Only 20 per cent of enterprises replied that all employees possessed sufficient

understanding. A further 40 per cent replied that ‘Yes, some’ employees fitted this

description while the remaining 41 per cent simply answered ‘No’. 9

The 1999 survey also asked, firstly, about the extent to which ‘Information and

communication technology skills in the workplace’ were ‘influential in dictating your

business’s uptake of [ICTs]’ and, secondly, how much of a ‘barrier’ these skills had

been to ICT adoption. Since the responses to both these questions were allocated to 1-5

point scales, we defined a new ‘Net skills influence’ variable defined as ‘Influence

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score’ less ‘Barrier score’ which ran from a minimum of –4 (=1-5) through to +4 (=5-

1). High positive scores (+3 or +4) on this scale suggest that the companies concerned

were well-endowed with ICT skills while low negative scores (–3 or –4) point to a

severe ICT skills constraint. About 21 per cent of respondents had negative scores

implying that ICT skills had been more of a barrier than a positive influence on the

uptake of ICTs. Just under half (48 per cent) had positive scores, implying the reverse.

And 31 per cent had zero scores, implying that the effect of ICT skills had been largely

neutral. This ‘net skills influence’ variable turned out to be significantly negatively

correlated with the ICT skills shortage measure (r = -0.23, p<0.01), which is a form of

validation of managers’ assessments of the presence or absence of ICT skill

deficiencies.

VI. The Determinants of ICT Adoption and Utilisation

In order to examine the relative importance of different factors influencing the extent of

ICT adoption and utilisation, we estimated ordered logistic regression equations, taking

the three measures presented in Table 3 as dependent variables. The analysis was

confined to private sector organisations responding to the 1999 survey, which gathered

more comprehensive information on participating firms than did the 1997-98 surveys.

Independent variables in these equations comprised a vector of company-level

characteristics including information on employee size-group, sector and regional

location, as well as a range of other factors which might conceivably operate as either

positive or negative influences on the level of ICT utilisation:

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Skill shortages: Ordered three-point measure (on a 0-2 scale) of whether or not

enterprises reported deficiencies in employees’ ICT skills (Section V refers).

Training: based on responses to a question ‘Does your company provide

training for its employees in the use of ICTs, such as those described in this

survey? If YES, how often?’, with a 4-point scale ranging from ‘rarely’ to

‘frequently’. Given the well-established positive association between training

provision and formal qualifications (Dickerson and Wilson, 2003), this training

variable serves in part as a proxy indicator of human capital endowments in the

model.

Single-site operations: whether enterprises were based on one site only or on

more than one site

Multinational operations: whether enterprises had ‘more than one site

internationally’

Competitiveness: a measure of the perceived importance of ICTs to each

company’s present and future competitiveness (comprising the mean value of

responses on a 1-5 scale to two separate questions about the links between ICTs

and competitiveness).

Use of external information sources: a measure of companies’ ‘openness’ to

external sources of information and advice on ICTs (eg, from technology

suppliers, external consultants or government business support organisations).

A number of measures indicating the sensitivity of the enterprise to a variety of

environmental factors, calculated in the same way as the ‘net skills’ indicator discussed

in Section V:

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Firm culture: A measure of the extent to which the ‘cultural willingness of …

senior management to accept and use ICTs’ and the ‘cultural willingness to

accept and use ICTs within [each] business as a whole’ had served as a positive

influence on ICT uptake rather than as a barrier.

Costs of ICT adoption: A measure of the extent to which ‘pricing of ICTs’ had

served as a positive influence on ICT uptake rather than as a barrier.

Infrastructure: A measure of the extent to which ‘access to ICT infrastructure’

had served as a positive influence on ICT uptake rather than as a barrier.

Regulation: A measure of the extent to which the ‘regulatory framework’ had

served as a positive influence on ICT uptake rather than as a barrier.

The main results of our ordered logistic regression analyses are shown in Table 4. The

analyses of ICT adoption are based on all private sector survey respondents, whereas

those for the intensity of ICT usage relate to ICT users only.

As expected, both the extent of ICT adoption and the intensity of use of connectivity

technologies are positively and significantly related to enterprise size-group (Table 4,

columns 1-2 and 5-6). Equally unsurprisingly, the intensity of computer use – measured

as the proportion of the workforce using computers – tends to be higher in small and

medium-sized enterprises than in larger organisations (column 3-4). Other control

variables to perform much as expected are those for single-site enterprises –

significantly less likely to make intensive use of connectivity technologies – and the

multinational indicator which is significantly positively related to both ICT adoption

and intensity of use.

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With these controls in place, the measure of internal ICT skill shortages is found to

have a strong negative and significant association with all three ICT measures (p<0.01

for intensity of computer use and of connectivity; p=0.05 for the extent of ICT

adoption; see columns 1, 3 and 5 of Table 4). When variables representing a number of

other prospective influences on ICT usage are introduced, the skills deficiency measure

is no longer a significant determinant of the extent of ICT adoption (Table 4, column 2)

but remains negative and statistically significant in the connectivity and computer use

equations (columns 4 and 6).

ICT training is significantly positively related to all three ICT measures and the

coefficients remain positive and very well-defined when the additional control variables

are added to the model. Of course, in this case there is very likely to be two-way

causation, with some firms increasing their ICT training provision in response to needs

which become apparent after ICT equipment has been purchased.10 The regression

results also highlight other influences on the level and intensity of ICT usage. For

example, the measures of the perceived importance of ICTs to firm competitiveness are

strongly positive and significant throughout. Other positive influences include ‘access

to ICT infrastructure’ (in all three models) and openness to external sources of

information on ICTs in the models for ICT adoption and connectivity.

Nearly all these findings provide support for Hypothesis 1, namely that, all else being

equal, reported ICT skill shortages at company level are negatively related to measures

of ICT adoption and the intensity of use of ICTs (once installed). In one specification

relating to ICT adoption (Table 4, column 2) the coefficient on the skills shortage

indicator is no longer statistically significant although it remains negatively signed.

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However, in similar specifications the two measures of ICT utilisation are significantly

negatively related to skill shortages. We now go on to test the support for Hypotheses

2-4, making use of the matched financial dataset described in Section III.


VII. The Impact of ICT Skill Shortages on Firm-Level Performance

A. Estimation Methodology

We base our analysis on an augmented Cobb-Douglas production function containing

annual sales (Q), assets (K), employment (L) and a variety of additional variables (Z),

including sector and region identifiers, indicators of ICT-related skills or training and

indicators of ICT utilisation. With a single type of labour this production function takes

the form:

(1) Q=AK L Z .

Taking logs we obtain:

(2) Ln(Q) = ln(A) + ln(K) + ln(L) + ln(Z).

Equation (2) represents the basis for our analysis of the level of sales. The coefficients

on those parts of Z which are indicators of ICT-related skills and training will enable us

to examine whether these have an impact on sales for given capital and labour inputs.

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However, we first need to discuss various practical issues that arise in the estimation of

equation (2), in particular, the likely endogeneity of ICT investments with respect to

company performance. On the one hand, it is conceivable that ICT investments may be

prompted by poor performance, with such technologies being seen as a means of

making production or service delivery more efficient and thereby as a means of helping

the company to become more competitive. Alternatively, investments in ICT may be

facilitated by the profits generated by previous above-average performance. Either

scenario raises the possibility that the decision to invest in ICT may be at least partially

determined by company performance. If this is indeed the case in practice, estimation

via Ordinary Least Squares (OLS) will lead us to biased estimates of the impact of ICT

investments on company performance.

A standard means of dealing with endogeneity issues is through the use of instrumental

variables (IV) regression. Brynjolfsson and Hitt (1996) used once-lagged values of ICT

investments as instruments for current investments in an analysis of the returns to

information systems spending among US organizations. They found that the estimated

returns to ICT investments were twice as large under the IV specification, but the

Hausman specification test (Hausman, 1978) did not support the rejection of their more

efficient OLS estimates. In a more recent analysis of the impact of ICT investments on

productivity among German establishments, Zwick (2003) used two external

instruments – a variable identifying establishments that expected an increase in their

demand for qualifications and training, and one identifying establishments that

expected an increase in the importance of formal external training. The coefficient on

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Zwick’s binary indicator of ICT investments increased ten-fold under his IV

specification.

In this paper we address the potential endogeneity of ICT adoption and utilisation

through the use of external instruments. The precise sets of instruments vary according

to the particular indicator of ICT usage that is being used, but they broadly comprise

indicators of: the anticipated impact of ICT on future competitiveness; company

attitudes to the use of ICT; and the importance of particular environmental factors in

facilitating or inhibiting the company’s uptake of ICT. Each of these variables is found

to be highly correlated with the relevant potentially endogenous regressors (measures of

ICT adoption or utilisation) while at the same time being orthogonal to error processes

in the firm-level performance regression equations.

Other practical issues concern the specific nature of our data. Values for total sales in

1999, 2000 and 2001 were adjusted to constant prices using output price deflators for

the United Kingdom that were assembled at a broad sectoral level from the National

Accounts (OECD, 2003). Total sales were also adjusted to remove distortions caused

by changes in a company’s accounting period. 11 Small numbers of cases in the

matched IBS-financial dataset were excluded because of outlying values on the

financial variables.

In what follows, we first present cross-sectional estimates for IBS 1999 and then report

on panel estimates over the period 1999-2001. In principle, estimating equation (2) via

panel regression methods should remove estimation biases caused by unobserved time-

invariant characteristics such as management quality. However, the full application of

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such an approach relies on the availability of repeat observations for all of the variables

of interest. The IBS 1999 survey offers only a single year of data on ICT investments

and many other characteristics of interest. Nonetheless, we have been able to match

longitudinal data on sales (Q), assets (K) and employment (L) to survey respondents.

We are therefore able to employ a two-stage methodology, previously adopted by Black

and Lynch (2001) among others. Under this methodology, a fixed-effects estimator is

first used to provide unbiased estimates of α and β. The average residual (the firm fixed

effect) is then regressed on Z in order to examine whether the components of Z are

related to having above or below-average levels of performance over a specified

period.12

B. Results

Table 5 shows the results of cross-sectional regressions of the determinants of log sales

in IBS 1999, highlighting the key regressors relevant to our assessment of the impact of

ICT skill shortages on company performance. In each equation, the coefficients on

capital and labour are positive, well-defined and broadly within the range of expected

values.

Table 5, Part A shows the results when our measure of ICT adoption is taken as a

determining variable, with three different IV specifications presented alongside their

OLS counterparts. Parts B and C of this table repeat the exercise for, respectively, our

measures of intensity of computer use and intensity of use of connectivity technologies.

In Columns (3) and (4) indicators of ICT skill shortages and ICT training provision are

added to the base specification in each case. In Columns (5) and (6) an interacted model

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is estimated in order to explore what effects, if any, the presence of ICT skill shortages

and ICT training provision have on the relationship between ICT adoption and

utilisation and company sales performance.

Three variables are used as external instruments in the IV regressions: a variable

indicating the likely importance of ICT investments to company competitiveness over

the next two years; a variable indicating whether access to ICT infrastructure was a

positive or negative influence on the uptake of ICT within the company; and a general

indicator of company attitudes to ICT.13 As it turns out, in each IV regression shown in

Table 5, the Sargan test statistics support the validity of the chosen instruments but

Hausman tests for the endogeneity of ICT adoption and utilisation suggest that the more

efficient OLS estimates should be preferred to the more consistent IV estimates.

In the case of the ‘extent of ICT adoption’ and the ‘intensity of computer use’, the

relevant coefficients are positive and significant throughout. The same applies to two of

the three (preferred) OLS equations for the ‘intensity of use of connectivity

technologies’. Taking the first of these three equations in each case (column 2 of Table

5), we can estimate that the marginal effect of increasing the extent of ICT adoption

score by one standard deviation from its mean value (i.e. from 4.13 to 6.58) would be to

raise the value of sales by 27 per cent (95 per cent confidence interval: 15 per cent - 39

per cent), all else being equal. The marginal effect of increasing the intensity of

computer use by one standard deviation from its mean value (i.e. from 2.12 to 3.52)

would be to increase sales by 21 per cent (95 per cent confidence interval: 12 per cent -

31 per cent). And the effect of increasing the intensity of use of connectivity

technologies by one standard deviation from its mean value (i.e. from 2.45 to 4.28)

22

would be to increase sales by 20 per cent (95 per cent confidence interval: 10 per cent -

31 per cent). By contrast, ICT skill shortages and ICT training provision are not found

to have any statistically significant effects on sales performance after controlling for

levels of ICT adoption or utilisation (Columns 3-4). Similarly, in the interacted models

the relevant coefficients lack significance in all three preferred OLS specifications

(Column 6).

Before considering the implications of these findings for our main hypotheses, we first

examine the principal coefficients of interest from our panel analyses for IBS 1999. As

for our cross-sectional analyses, three separate sets of equations are presented, with

each of our measures of ICT adoption and utilisation taken in turn as a determining

variable. Column (1) of Table 6 presents the coefficients on K and L from the fixed-

effects panel regression that forms the first stage of our two-step procedure for all three

sets of equations. Columns (2), (4) and (6) present the coefficients on our indicators of

ICT adoption and utilisation, skills and training from the IV regression that forms the

second stage in each case. The contrasting OLS estimates for similar second-stage

specifications are shown in Columns (3), (5) and (7).

Column (1) shows that we obtain reasonable coefficients on assets (K) and employment

(L) in our first-stage regression. The coefficient on K is lower than one might anticipate

– and lower than that found in the cross-sectional regressions reported in Table 5 – but

this is a common outcome of the application of panel methods (Griliches and Mairesse,

1997). A favoured solution in the literature is to apply general method of moments

(GMM) estimators (see, for example: Arellano and Bond, 1991; Blundell and Bond,

1998). However, such approaches rely on the availability of lagged values of both the

23

levels and changes over time of sales, assets and employment for use as instruments. In

our dataset, we have financial data for only three time periods. This proves insufficient

to yield sensible results and so we do not discuss this option further.

Columns (2) and (3) show the impact of each measure of ICT adoption and utilisation

when they are entered in turn into the second-stage IV and OLS regressions alongside

our control variables. In Columns (4)-(7) ICT skill shortage and training variables are

entered in the same way as described for the cross-section analysis above. Since the

White test statistics indicate the presence of heteroscedasticity throughout the panel

regressions, we follow Baum, Shaffer and Stillman (2003) and rely on (1) Hansen J

statistics to test the validity of our chosen instruments and (2) C statistics which test the

null hypothesis that the potentially endogenous regressors (here measures of ICT

adoption and utilisation) are in fact exogenous.14 While instrument validity is supported

for all equations shown, the C statistics indicate that the OLS specifications should be

preferred to their IV counterparts in all cases except for the first model of intensity of

computer use (Column 2) and each of the interacted models (Column 6).

In all preferred models each of the three measures of ICT utilisation have positive and

statistically significant coefficients. In the models for extent of ICT adoption (Table 6,

Part A) and intensity of use of connectivity technologies (Part C), the coefficients and

associated estimates of marginal effects on sales performance are similar to those

identified in the cross-sectional estimates above. However, in the preferred IV

specification relating to intensity of computer use (Part B), the relevant coefficient is

over twice as high as that estimated in the cross-sectional analysis.15 Taking the panel

and cross-sectional estimates together, therefore, we find clear support for Hypothesis 2

24

that company-level performance is positively related to measures of ICT adoption and

utilisation. Since we have already established support in Section VI for Hypothesis 1 –

that skill shortages restrict both the extent of ICT adoption and the intensity of use of

ICTs – we conclude that ICT skill shortages have a clear indirect negative impact upon

company performance.

However, in order to find out whether our evidence supports Hypothesis 3 – that ICT

skill shortages have a direct negative effect on firm-level performance – we need to

consider whether such shortages have some additional impact on company performance

over and above any indirect impact that occurs through the restriction of ICT

utilisation. In other words, we need to examine whether ICT skill constraints are

significantly negatively related to company performance even after controlling for the

extent of ICT adoption and the intensity of ICT use. Columns (4)-(5) in Table 6 show

the effect of adding an indicator of whether the company has an ICT skills shortage to

the second-stage regression. In both the IV and OLS regressions, the relevant

coefficients are small and poorly defined in respect of the three measures of ICT

adoption and utilisation and thus provide no support for Hypothesis 3.

To what extent are the potential effects of ICT skill shortages on ICT adoption and

utilisation alleviated by employers’ provision of training for their employees?

Regression analysis of the determinants of training provision, with similar controls to

those in Table 4, finds that the ICT skills shortage measure is significantly negatively

related to training provision (p<0.01). The relative absence of training by firms

reporting skill shortages highlights the potential for those shortages to persist over some

length of time in the firms concerned.

25

The equations reported in Table 6, Columns (4)-(5) also contain indicators of ICT

training provision that enable us to test Hypothesis 4 that, at given levels of ICT

adoption and intensity of use, investments in ICT skills training contribute positively to

firm performance. ICT skills training is expected to be an important complementary

investment to spending on ICTs themselves, as it should help employees to make more

productive and efficient use of investments in ICT hardware and software. However,

the coefficients on this indicator in Columns (4)-(5) turn out to be not significantly

different from zero. And replacing this indicator of the frequency of training with a

dichotomous variable which indicates whether any training is provided yields the same

outcome.

In order to subject Hypotheses 3 and 4 to further scrutiny we also consider the results of

specifications where indicators of ICT skill shortages and ICT training provision are

interacted with each of our measures of ICT adoption and utilisation (Table 6, Columns

6-7). In the case of the adoption measure (Table 6, Part A), this enhanced model still

provides no support for either Hypothesis 3 or Hypothesis 4. However, in the case of

the intensity of computer use measure (Part B), the coefficient on the computer use /

skills shortage interacted variable is negatively-signed and statistically significant in the

IV regression, suggesting that the performance of companies making more intensive

use of computers is negatively affected by ICT skill shortages. (The relevant C statistic

in this case suggests that the IV estimates should be preferred to the OLS variants).

And in the case of the intensity of use of connectivity technologies measure (Table 6,

Part C), the results shed some light on the relationship between ICT skills training and

26

firm-level performance. Firstly, the coefficient on the training provision variable is

positive and significant in the IV regression; secondly, the coefficient on the interacted

connectivity / training variable is negatively-signed and significant. Taken together,

these findings do provide support for Hypothesis 4 but the coefficient on the product

term suggests that the advantage derived from training is greatest at low levels of

connectivity and tails off as connectivity rises.

To recapitulate, we have found strong evidence that internal ICT skill shortages – skill

gaps among existing employees – have negative indirect effects on firm-level

performance because of the ways in which such skill deficiencies restrict companies

both in terms of ICT adoption and the intensity of use of ICTs once they have been

installed. However, the evidence for direct effects of skill constraints – and of ICT-

related training – on financial performance at given levels of ICT adoption and

utilisation is more limited. This may reflect the relatively short run of post-survey

financial data available to us. Basu et al. (2003) suggest that UK firms invested heavily

in inputs complementary to ICTs during the second half of the 1990s and that this

diversion of resources helps to explain the slowdown in total factor productivity growth

in the UK during this period. It is possible that these complementary investments will

have positive lagged effects on future performance and that, in this context, future

analysis may reveal more evidence of a systematic relationship between skills and

financial performance at given levels of ICT adoption and utilisation.

27

VIII. Summary and Assessment

It is now widely recognised that the technological revolution associated with ICTs is

skill-intensive, not just because of the skill requirements associated with ICT adoption

but also due to the lengthy periods of experimentation and learning that many firms

need to go through in order to make effective use of ICTs. Indeed, the process of

investing in assets which are complementary to ICTs (such as software, business

reorganisation and new product development) may contribute more to raising the

relative demand for skilled labour than the diffusion of ICTs themselves. In this context

it is widely assumed that shortages of ICT skills will have negative effects on firms’

ability to make early and effective use of ICTs.

In this paper we make use of benchmarking survey data for UK enterprises in 1999,

combined with post-survey financial data for the same enterprises, in order to assess the

impact of ICT skill shortages on financial performance at firm level. Overall, the results

provide very clear evidence for the UK about the positive impacts on firm-level

performance of the rapid adoption and deployment of ICTs during the late 1990s, and

the negative effects on performance experienced by those companies in which ICT skill

shortages inhibited the adoption or intensive utilisation of ICTs.

We define three different ICT measures at firm level, summarising the extent of ICT

adoption, the intensity of computer use and the intensity of usage of three key

connectivity technologies (networks, Internet and company web-sites). After

controlling for employee size-group, sector and regional location and other pertinent

28

enterprise characteristics, a measure of internal ICT skill shortages is found to have a

negative and significant impact on both measures of ICT utilisation and (in most

specifications) on the extent of ICT adoption as well. At the same time, cross-sectional

and panel estimates of the determinants of company sales performance suggest that all

three measures of ICT utilisation are positively and significantly related to sales

performance after controlling for capital assets, labour inputs, sector, region and a

number of other company characteristics. These findings are robust to the use of

instrumental variable techniques to allow for the likely endogeneity of ICT investments

with respect to company performance.

Taken together these two sets of results clearly show that ICT skill shortages have an

indirect negative impact upon company performance. However, there is only limited

evidence in this dataset to support a hypothesis that ICT skill shortages have a direct

negative effect on firm-level performance, that is, some additional impact on company

performance over and above the indirect impact that occurs through the restriction of

ICT adoption and utilisation.

These findings in respect of the direct links between ICT skills and performance at firm

level may reflect the relatively short run of post-survey financial data available to us. If

UK firms have been investing heavily in inputs complementary to ICTs during the

second half of the 1990s, then these complementary investments may have positive

lagged effects on performance. In this context, future analysis of longer periods of post-

sample financial data may reveal more evidence of a systematic relationship between

skills and financial performance at given levels of ICT adoption and utilisation.

29

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NOTES

1. This paper is based on research which was kindly supported by the UK Department of Trade and

Industry, Department for Education and Skills and HM Treasury; none of these departments are responsible for any views expressed in the paper. We would like to thank Ian Brades (Dun & Bradstreet), Graham Craigmile (HI Europe, formerly Romtec) and Chris Kay (NOP) for their help in matching Dun & Bradstreet data to the IBS datasets. We are also grateful to Kate Robinson, Michela Vecchi and other colleagues at NIESR for comments and advice, and to Ana Rincon-Aznar and Lucy Stokes for excellent research assistance. Responsibility for any errors is ours alone.

2. Arrow and Capron define the term ‘shortage’ as ‘a situation in which there are unfilled vacancies in positions where salaries are the same as those currently being paid in others of the same type and quality’ (1959, p.301).

3. The main reasons advanced why it is efficient for some firms, particularly larger ones, to operate ILM’s centre on the benefits to employee motivation, the cost savings from lower labour turnover and firms’ efforts to maximise returns from job-specific and company-specific training (Taubman and Wachter, 1986; Wachter and Wright, 1990; Siebert and Addison, 1991).

4. In 2001 some 16 per cent of English establishments with 5 or more employees reported internal skill gaps, defined as employees lacking ‘full proficiency’ in their current jobs. The equivalent proportion reporting skill-related hard-to-fill vacancies was 6 per cent (Forth and Mason, 2003).

5. The CBI data on skill shortages are based on employers’ responses to a question about whether constraints on ‘skilled labour’ in general are likely to limit their output in the following four-month period. A follow-up study of participants in this survey suggested that about 60 per cent of respondents interpreted the question as referring primarily to external recruitment difficulties while 45 per cent thought it referred instead (or as well) to deficiencies in the skills possessed by their existing workforce (Mann and Junankar, 1998).

6. In a recent study of the relationship between information technology and the demand for educated workers at industry level in the US, Chun (2003) distinguishes carefully between the adoption and use effects of information technology and finds that both have contributed substantially to the increased relative demand for college graduates.

7. For details of sampling procedures and response rates, see Spectrum (1999). 8. We experimented with different versions of this ICT connectivity measure which made use of other

survey responses on the ways in which email, EDI and Extranets were used. However, these other data were not available for all three years and the alternative measures were in any event all highly correlated with the connectivity measure described in Table 3, Part C. Hence we elected to proceed with this as our preferred measure of the intensity of use of connectivity technologies.

9. No further information was gathered about the types or levels of skills and knowledge in shortage or the occupations most affected.

10. Due to a lack of suitable instruments we do not investigate this issue further. Rerunning the equations in Table 4 without the potentially endogenous training variable does not materially alter the other findings presented here, for example, the findings relating to skill shortages.

11 Such changes in accounting periods mean that a small number of companies recorded total sales over a period of less than, or more than, 12 months; sales figures were adjusted pro rata to a 12-month basis as appropriate.

12. This method is also employed by Zwick (2003). The Hausman specification test (Hausman, 1978) can be used to determine whether the fixed effects estimator is more appropriate than a random effects model. Application of the Hausman test justifies use of a fixed effects estimator for the IBS 1999 data. Nonetheless, the use of a random effects estimator in place of the two-stage methodology does not substantively change the results of the analysis.

13. The latter of these three variables was only used as an instrument for ‘the intensity of connectivity technologies’, as it was not found to be significantly associated with ‘the extent of ICT adoption’ or ‘the intensity of computer use’.

14. Note that we account for heteroscedasticity in the estimation itself by using the robust (Huber/ White/ sandwich) variance estimator.

15. The panel results suggest that the marginal effect of increasing the intensity of computer use by one standard deviation from its mean value (i.e. from 2.12 to 3.52) would be to increase sales by as much as 47 per cent. However, the standard error attached to this estimate is relatively large (95 per cent confidence interval: 15 per cent - 78 per cent).

34

TABLE 1

Comparison of Original IBS Sample, 1999, and Dataset Matched to Post-Survey Dun & Bradstreet Data, Analysed by Employee Size-Group and Sector

Original

sample Matched dataset

Original sample

Matched dataset

Column percentages Column percentages

Employee size-group:

Sector:

1-9 21 <1 Primary 3 5 10-49 21 5 Manufacturing 28 24 50-99 6 6 Construction 6 8 100-249 21 34 Wholesale / retail 21 22 250-499 10 21 Communications /

utilities / transportation

4 5

500-999 9 20 Finance 5 4 1000-4999 8 11 Business services /

other market services32 31

5000-plus 4 4

TOTAL 100 100 100 100 1922 345 1922 345 Base: Private sector organisations. Source: International Benchmarking Survey, 1999

TABLE 2

Types of ICT Equipment and Facilities Used by Enterprises, 1997-99

1997 1998 1999 Cell percentages Computers/PCs used by employees

90 85 88

Networked – computers/PCs networked or interconnected for communications purposes

55 66 65

Remote access – employees able to access company’s computer system remotely from non-company sites

22 29 35

Email used for internal or external communications

37 54 65

EDI (Electronic Data Interchange) used to communicate with suppliers and/or customers

21 24 26

Internet – employees have access to Internet for other than email use

26 42 53

Website – company has site on World Wide Web

18 31 42

Intranet and/or Extranet – private secure network running on Internet protocol (a)

6 12 21

n= 449 485 1922

(a) 1997 data refer to Intranet only Base: Private sector organisations. Source: International Benchmarking Surveys, 1997-1999

36

TABLE 3 A: Extent of Adoption of ICTs, 1997-99

1997 1998 1999 1997 1998 1999 Number of different types of ICT equipment and facilities in use(a):

Column percentages

Cumulative percentages

0 10 17 12 10 17 12 1 28 12 12 38 29 25 2 19 15 8 56 44 32 3 11 9 9 68 53 41 4 10 12 13 78 65 54 5 9 13 15 87 78 69 6 7 9 14 94 87 83 7 3 9 11 98 97 94 8 2 3 6 100 100 100

TOTAL 100 100 100

(a) Scored as 1= use and 0=non-use for each of the following: computers/PCs; networks; remote access; email; EDI; Internet; company website; Intranet and/or Extranet. B: Intensity of Use of Computers, 1998-99

1998 1999 Proportions of employees making use of computers/PCs:

Column percentages

No computer use 15 12 Used by up to a quarter of employees 32 39 Used by 26-50 per cent of employees 12 14 Used by 51-75 per cent of employees 13 11 Used by more than 75 per cent of employees 29 25

TOTAL 100 100 Continued on next page…

37

TABLE 3 (continued)

C: Intensity of Use of Connectivity Technologies, 1997-99

1997 1998 1999 1997 1998 1999 Intensity of use of connectivity technologies on 0-6 scale(a):

Column percentages

Cumulative percentages

0 42 30 27 42 30 27 1 27 15 11 68 45 38 2 13 17 14 81 62 52 3 10 16 18 91 77 70 4 5 14 16 96 91 86 5 3 8 10 99 99 97 6 1 1 3 100 100 100

TOTAL 100 100 100 Notes: (a) This index is defined as the sum of network, Internet and website use scores which are allocated as follows: Network: 0=No network; 1=Local network only; 2=Networked between national sites or wider area [1997: 1= Networked within site or between local sites (within 20 miles); 2= Networked between national sites or international sites;1998-99: 1= Networked within a site; 2= Networked between sites or with suppliers or with business customers] Internet/WWW use: 0=No use of Internet/WWW; 1= Internet/WWW used mainly for email, advertising, marketing and/or information purposes; 2=Internet/WWW used for purchasing and/or making sales on-line Website: 0=No website; 1= Website used mainly for advertising, marketing and/or information distribution; 2=Website used for making sales on-line Base: Private sector organisations. Source: International Benchmarking Surveys, 1997-1999

38

TABLE 4

Determinants of ICT Adoption and Utilisation (Ordered Logistic Regressions) – Private Sector Organisations

Extent of ICT

adoption Intensity of

computer use Intensity of use of

connectivity technologies

(1) (2) (3) (4) (5) (6) Number of employees: (Ref. 1-9)

10-49 0.728*** 0.752*** -1.674*** -1.678*** 0.454*** 0.470*** (0.149) (0.149) (0.193) (0.193) (0.174) (0.175) 50-99 1.199*** 1.213*** -1.803*** -1.813*** 1.065*** 1.063*** (0.220) (0.222) (0.265) (0.267) (0.249) (0.253) 100-249 1.912*** 1.941*** -1.842*** -1.829*** 1.407*** 1.438*** (0.176) (0.178) (0.208) (0.207) (0.185) (0.187) 250-499 2.234*** 2.295*** -1.878*** -1.871*** 1.571*** 1.606*** (0.220) (0.219) (0.244) (0.243) (0.219) (0.221) 500-999 2.390*** 2.424*** -1.999*** -1.995*** 1.755*** 1.792*** (0.236) (0.241) (0.280) (0.284) (0.243) (0.247) 1000-4999 2.371*** 2.373*** -2.006*** -2.026*** 1.779*** 1.789*** (0.252) (0.259) (0.263) (0.264) (0.242) (0.247) 5000 or more 3.029*** 3.050*** -2.445*** -2.484*** 2.001*** 2.015***

(0.314) (0.309) (0.331) (0.336) (0.292) (0.294) Single-site enterprise -0.204* -0.185 -0.205 -0.222 -0.257** -0.264** (0.122) (0.122) (0.133) (0.135) (0.124) (0.125) International 0.757*** 0.783*** 0.590*** 0.598*** 0.670*** 0.658*** (0.136) (0.136) (0.141) (0.144) (0.127) (0.128) ICT skill shortage -0.109* -0.048 -0.377*** -0.332*** -0.179*** -0.131* (0.062) (0.064) (0.071) (0.073) (0.067) (0.069) Competitiveness 0.443*** 0.400*** 0.319*** 0.292*** 0.376*** 0.338*** (0.027) (0.028) (0.033) (0.035) (0.030) (0.032) ICT training provision 0.300*** 0.277*** 0.255*** 0.245*** 0.234*** 0.212*** (0.041) (0.041) (0.045) (0.045) (0.042) (0.043) External information sources 0.480*** 0.416** 0.022 0.019 0.363* 0.324* (0.168) (0.167) (0.212) (0.214) (0.191) (0.190) Net influence of firm culture 0.039 0.063* 0.032 (0.033) (0.036) (0.035) Net influence of pricing -0.004 0.007 0.038 (0.034) (0.042) (0.039) Net influence of infrastructure 0.185*** 0.085** 0.140*** (0.036) (0.039) (0.038) Net influence of regulation -0.059* -0.054 -0.054 (0.036) (0.041) (0.041) Sector dummies included Yes Yes Yes Yes Yes Yes Regional dummies included Yes Yes Yes Yes Yes Yes Observations 1717 1717 1639 1639 1598 1598 Log-likelihood -2863.11 -2832.90 -1784.19 -1776.67 -2493.26 -2479.32 McFadden R2 0.23 0.24 0.14 0.15 0.16 0.17 Robust standard errors in parentheses Significance: * significant at 10 per cent; ** significant at 5 per cent; *** significant at 1 per cent Bases: Columns 1 and 2 – ICT users and non-users; columns 3 to 6 – ICT users only.

39

TABLE 5

Determinants of (Log) Firm Sales, 1999 – Cross-Section Regressions A: The impact of ICT adoption, ICT skill shortages and ICT training provision

(1) (2) (3) (4) (5) (6) Ln Q

(IV) Ln Q (OLS)

Ln Q (IV)

Ln Q (OLS)

Ln Q (IV)

Ln Q (OLS)

Ln K 0.126*** 0.133*** 0.125*** 0.133*** 0.134*** 0.135*** (0.038) (0.039) (0.039) (0.039) (0.042) (0.039) Ln L 0.812*** 0.817*** 0.827*** 0.823*** 0.822*** 0.821*** (0.061) (0.063) (0.062) (0.064) (0.065) (0.064) Extent of ICT adoption 0.180** 0.112*** 0.201** 0.115*** 0.397** 0.128* (0.073) (0.025) (0.095) (0.027) (0.166) (0.066) ICT skills shortage 0.004 0.012 0.466 0.088 (0.058) (0.059) (0.634) (0.203) ICT training provision -0.040 -0.007 0.304 -0.021 (0.049) (0.035) (0.274) (0.110) Extent of ICT adoption * ICT skills shortage -0.085 -0.014

(0.115) (0.036) Extent of ICT adoption * ICT training -0.062 0.003

(0.047) (0.020) Adjusted R2 0.72 0.72 0.72 Sargan test of overidentification 0.12 0.38 1.27

P-value 0.73 0.54 0.74 White test of heteroscedasticity 171.67 212.06 287.00

P-value 0.80 0.85 0.68 Hausman Test: endogeneity of ICT adoption 1.01 0.92 5.15

P-value 0.31 0.34 0.16 No. of companies 289 289 287 287 287 287

Continued on next page…

40

Table 5 (continued) B: The impact of computer use-intensity, ICT skill shortages and ICT training provision

(1) (2) (3) (4) (5) (6) Ln Q

(IV) Ln Q (OLS)

Ln Q (IV)

Ln Q (OLS)

Ln Q (IV)

Ln Q (OLS)

Ln K 0.169*** 0.164*** 0.172*** 0.166*** 0.176*** 0.167*** (0.038) (0.038) (0.038) (0.038) (0.040) (0.038) Ln L 0.822*** 0.825*** 0.824*** 0.826*** 0.818*** 0.823*** (0.058) (0.059) (0.058) (0.060) (0.062) (0.060) Intensity of computer use 0.208* 0.151*** 0.224* 0.157*** 0.673** 0.197** (0.108) (0.034) (0.136) (0.035) (0.298) (0.089) ICT skills shortage 0.073 0.055 0.583 0.064 (0.066) (0.059) (0.364) (0.123) ICT training provision -0.012 0.000 0.114 0.035 (0.040) (0.034) (0.168) (0.066) Intensity of computer use * ICT skills shortage

-0.227 -0.005

(0.161) (0.049) Intensity of computer use * ICT training provision

-0.063 -0.016



P-value 0.48 0.64 0.49 Hausman Test: endogeneity of computer use-intensity

0.30 0.27 3.33



41

Table 5 (continued) C: The impact of use of connectivity technologies, ICT skill shortages and ICT training provision

(1) (2) (3) (4) (5) (6) Ln Q

(IV) Ln Q (OLS)

Ln Q (IV)

Ln Q (OLS)

Ln Q (IV)

Ln Q (OLS)

Ln K 0.145*** 0.144*** 0.146*** 0.145*** 0.151*** 0.144*** (0.036) (0.038) (0.036) (0.038) (0.039) (0.038) Ln L 0.835*** 0.840*** 0.837*** 0.842*** 0.842*** 0.843*** (0.059) (0.060) (0.060) (0.061) (0.063) (0.062) Intensity of use of connectivity technologies

0.156 0.111*** 0.177 0.110*** 0.529** 0.095

(0.096) (0.029) (0.121) (0.029) (0.211) (0.075) ICT skills shortage 0.042 0.033 0.554 -0.029 (0.059) (0.059) (0.381) (0.143) ICT training provision -0.012 0.001 0.312* 0.014 (0.041) (0.034) (0.169) (0.076) Intensity of connectivity tech. * ICT skills

shortage -0.163 0.019

(0.116) (0.039) Intensity of connectivity tech. * ICT training provision

-0.101** -0.004



P-value 0.64 0.63 0.57 Hausman Test: endogeneity of intensity of connectivity tech.

0.24 0.33 6.34


Notes: 1. Robust standard errors in parentheses; * significant at 10 per cent; ** significant at 5 per cent; ***

significant at 1 per cent 2. Equations 1, 3 and 5: Estimated via instrumental variables, where the endogenous variables are the

respective measures of ICT adoption and utilisation. Summary statistics from the initial equations are specified, along with the additional instruments used, below:

A Extent of ICT adoption: Instruments: Anticipated impact of ICT on competitiveness; Net influence of

infrastructure Summary statistics: Adjusted R2 – 0.17; Partial R2 of excluded instruments – 0.11; F

test of excluded instruments – 17.09***. B Intensity of computer use:

Instruments: Anticipated impact of ICT on competitiveness; Net influence of infrastructure

Summary statistics: Adjusted R2 – 0.14; Partial R2 of excluded instruments – 0.09; F test of excluded instruments – 14.09***.

C Intensity of connectivity technologies: Instruments: Anticipated impact of ICT on competitiveness; Net influence of

infrastructure; Company attitude to ICT Summary statistics: Adjusted R2 – 0.11; Partial R2 of excluded instruments – 0.08; F

test of excluded instruments – 7.96***. 4. Equations 2, 4 and 6: OLS estimates. Regressions also include six industry dummies, ten region dummies and dummies for: single-site enterprise-site companies, multi-national companies; and use of external information sources on new technologies. Full results are available on request from the authors.

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TABLE 6

Determinants of (Log) Firm Sales, 1999-2001 – Panel Regressions

First step Second step

(1) (2) (3) (4) (5) (6) (7) Dependent variable: Ln Q Avg.

resid. (IV)

Avg. resid. (OLS)

Avg. resid. (IV)

Avg. resid. (OLS)

Avg. resid. (IV)

Avg. resid. (OLS)

Ln K 0.053*** (0.020) Ln L 0.787*** (0.035) Observations 970 No. of companies 335 Adjusted R2 0.36 A: The impact of ICT adoption, ICT skill shortages and ICT training provision Extent of ICT adoption 0.228*** 0.131*** 0.241** 0.126*** 0.516** 0.133* (0.069) (0.026) (0.096) (0.027) (0.211) (0.070) ICT skills shortage -0.001 0.010 0.733 0.093 (0.057) (0.057) (0.702) (0.201) ICT training provision -0.022 0.024 0.464 -0.011 (0.052) (0.035) (0.329) (0.104) Extent of ICT adoption * ICT skills shortage

-0.134 -0.015

(0.126) (0.035) Extent of ICT adoption * ICT training provision

-0.086 0.006

(0.056) (0.019) Adjusted R2 0.13 0.13 0.13 Hansen J test of over-identification 0.03 0.00 2.46


P-value 0.00 0.00 0.00 Test of endogeneity of ICT adoption (C-statistic)

2.12 1.54 8.16

P-value 0.14 0.21 0.04 Observations 871 871 865 865 865 865 No. of companies 301 301 299 299 299 299


43

Table 6 (continued)

First step Second step (1) (2) (3) (4) (5) (6) (7)

Dependent variable: Ln Q Avg. resid. (IV)

Avg. resid. (OLS)

Avg. resid. (IV)

Avg. resid. (OLS)

Avg. resid. (IV)

Avg. resid. (OLS)

B: The impact of computer use, ICT skill shortages and ICT training provision See

panel A

Intensity of computer use 0.333*** 0.144*** 0.320*** 0.139*** 0.879** 0.174** (0.115) (0.033) (0.139) (0.034) (0.382) (0.086) ICT skills shortage 0.097 0.049 0.894* 0.063 (0.073) (0.060) (0.477) (0.123) ICT training provision 0.019 0.049 0.069 0.076 (0.041) (0.033) (0.177) (0.063) Computer use * ICT skills shortage -0.349* -0.006

(0.203) (0.048) Computer use * ICT training provision -0.030 -0.013



P-value 0.00 0.00 0.00 Test of endogeneity of computer use (C-statistic)

3.09 1.84 8.78

P-value 0.08 0.18 0.03 Observations 902 902 896 896 896 896 No. of companies 312 312 310 310 310 310 C: The impact of use of connectivity technologies, ICT skill shortages and ICT training provision See

panel A

Intensity of use of connectivity technologies

0.230*** 0.126*** 0.209* 0.117*** 0.614*** 0.091

(0.091) (0.028) (0.118) (0.028) (0.233) (0.073) ICT skills shortage 0.047 0.036 0.620 -0.014 (0.061) (0.058) (0.396) (0.142) ICT training provision 0.027 0.047 0.395** 0.036 (0.043) (0.033) (0.186) (0.072) Connectivity * ICT skills shortage -0.180 0.015 (0.115) (0.038) Connectivity * ICT training provision -0.113** 0.004



P-value 0.00 0.00 0.00 Test of endogeneity of use of connectivity technologies (C-statistic)

1.46 0.46 8.85

P-value 0.23 0.50 0.03 Observations 892 892 886 886 886 886 No. of companies 308 308 306 306 306 306


44

Table 6 (continued) Notes: 1. Robust standard errors in parentheses; * significant at 10 per cent; ** significant at 5 per cent; ***

significant at 1 per cent 2. Equation 1 (first stage): Estimated using panel (fixed effects) regression. Regression also includes

two year dummies plus six industry dummies interacted with these year dummies. 3. Equations 2, 4 and 6: Second stage regressions estimated via instrumental variables, where the

endogenous variables are the respective measures of ICT adoption and utilisation. Summary statistics from the initial equations are specified, along with the additional instruments used, below:

A Extent of ICT adoption: Instruments: Anticipated impact of ICT on competitiveness; Net influence of

infrastructure Summary statistics: Adjusted R2 – 0.21; Partial R2 of excluded instruments – 0.12; F

test of excluded instruments – 56.30**. B Intensity of computer use:

Instruments: Anticipated impact of ICT on competitiveness; Net influence of infrastructure

Summary statistics: Adjusted R2 – 0.18; Partial R2 of excluded instruments – 0.08; F test of excluded instruments – 40.26***.

C Intensity of connectivity technologies: Instruments: Anticipated impact of ICT on competitiveness; Net influence of

infrastructure; Company attitude to ICT Summary statistics: Adjusted R2 – 0.11; Partial R2 of excluded instruments – 0.08; F

test of excluded instruments – 27.66***. 4. Equations 3, 5 and 7: Second stage OLS estimates. 5. Regressions also include six industry dummies, ten region dummies and dummies for: single-site

enterprise-site companies, multi-national companies; and use of external information sources on new technologies. Full results and tables of descriptive statistics are available on request from the authors.

45

do ict skill shortages hamper firms' performance

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