A Good Worker is Hard to
Find: Skills Shortages in
New Zealand Firms
Penny Mok, Geoff Mason, Philip Stevens
and Jason Timmins
Ministry of Economic Development
Occasional Paper 12/05
April 2012
ISBN: 978-0-478-38237-2 (PDF)
ISBN: 978-0-478-38236-5 (online)
Ministry of Economic Development Occasional Paper 12/05
A Good Worker is Hard to Find: Skills Shortages in New Zealand Firms
Date: April 2012
Author: Penny Mok
Geoff Mason
Treasury
National Institute of Economic and Social Research, London
Philip Stevens Ministry of Economic Development
Jason Timmins Department of Labour
Acknowledgements
The authors would like to thank Belinda Buxton, Rosie Byford, Elizabeth Chisholm,
Hilary Devine, Sid Durbin, Richard Fabling, Bette Flagler, Julia Gretton, Hamish Hill,
Bill Kaye-Blake, Peter Nunns, Meighan Ragg, Lynda Sanderson, Steven Stillman,
Richard White and participants in the Industry Training Federation, the Labour,
Employment and Work, the New Zealand Association of Economists, and the New
Zealand Centre for SME Research conferences (and others whom we have
neglected to mention).
We acknowledge two lots of funding from the Cross-
Departmental Research Funding for the production of IBULDD (and thence the LBD)
and for the Impact of Skills on New Zealand Firms project.
Contact: [email protected]
Disclaimer
The views, opinions, findings, and conclusions or recommendations expressed in this
Occasional Paper are strictly those of the author(s). They do not necessarily reflect
the views of the Ministry of Economic Development or the Department of Labour,
Treasury or the National Institute of Economics and Social Research. The Ministry
takes no responsibility for any errors or omissions in, or for the correctness of, the
information contained in these occasional papers. The paper is presented not as
policy, but with a view to inform and stimulate wider debate.
Access to the data used in this paper was provided by Statistics NZ in accordance
with security and confidentiality provisions of the Statistics Act 1975. Only people
authorised by the Statistics Act 1975 are allowed to see data about a particular,
business or organisation. The results in this paper have been confidentialised to
protect individual businesses from identification.
The results are based in part on tax data supplied by Inland Revenue to Statistics NZ
under the Tax Administration Act 1994. This tax data must be used only for statistical
purposes, and no individual information is published or disclosed in any other form,
or provided back to Inland Revenue for administrative or regulatory purposes. Any
person who had access to the unit-record data has certified that they have been
shown, have read and have understood section 81 of the Tax Administration Act
1994, which relates to privacy and confidentiality. Any discussion of data limitations
or weaknesses is not related to the data's ability to support Inland Revenue's core
operational requirements.
Abstract
This paper examines the determinants of firms’ skill shortages, using a speciallydesigned survey, the Business Strategy and Skills (BSS) module of the Business
Operations Survey. We combine the BSS module with additional data on firms in the
Statistics New Zealand’s prototype Longitudinal Business Database (LBD).
We
focus on vacancies that were hard-to-fill because the applicants lacked the
necessary skills, qualification or experience - which we define as skill-related reasons
(skill shortage vacancies). We also contrast these with vacancies that were hard-tofill for non-skill-related reasons.
JEL Classification:
Keywords:
J24, J31, L60
skill shortages, hard-to-fill vacancies, Business Operations Survey,
Heckman Selection model
i
Executive Summary
Skills are an important determinant of the economic performance of people, firms,
industries and economies. Shortages of skilled labour directly constrain production
and prevent firms from meeting demand and using available inputs efficiently. They
inhibit innovation and the use of new technologies.
This may have longer-term
impacts on the way firms do business, in terms of their location, size, structure,
production methods and product strategy.
In this paper we have examined factors relating to firms’ skill shortages. Our focus
has been on vacancies that were hard-to-fill because the applicants lacked the
necessary skills, qualification or experience – what we have defined as ‘skill shortage
vacancies’ (SSVs). We have also contrasted these with vacancies that were hard-tofill for reasons other than the skills of applicants.
Worker turnover is a basic fact of economic life. In any given year one in eight
workers leaves their employer. Searching for new staff is, therefore, a position most
businesses find themselves in, regardless of their situation.
Almost half of
businesses find some of these vacancies hard-to-fill and more than a third
experience difficulties in finding workers with the required skills, qualifications or
experience they need.
We have examined patterns of skill shortage vacancies using an econometric
technique that allows us to account, in part, for the interrelationship between the
likelihood of a firm posting a vacancy and for those vacancies being hard-to-fill for
skill-related reasons.
Large firms are more likely to have vacancies – they have more workers and so the
chance of at least one of them leaving is consequently higher. However, larger firms
do not appear to find it more difficult to source workers with the required skills than
smaller firms, once we account for other characteristics. As we might expect, when
firms’ output grows, the need to seek additional staff also grows. However, this is
only in the years the firm is expanding. Expanding sales is not a good predictor of
future additional staff requirements.
Businesses can experience skill shortages internally or externally. A shortage in the
skills it requires can manifest itself: (a) in terms of the ability of its existing staff to do
ii
their job; or (b) in terms of its ability to find appropriately skilled workers through
recruitment.
We have found evidence that these two phenomena co-exist.
Businesses experiencing internal skill gaps are also more-likely to have skill shortage
vacancies. We also find evidence of persistence in this relationship; businesses
experiencing internal skill gaps are also more likely to have SSVs in the following
year.
Businesses have a ‘make or buy’ option when it comes to skills. If they cannot
source the skills internally or externally, they can up-skill existing or new workers
through training. Firms’ training choices are the subject of a companion paper to this
(Mason et al., 2012). In this paper, we find that firms who undertake training are
more likely to have vacancies, suggesting that their response to skill shortages is
likely to be a mixture of recruitment and up-skilling strategies.
Our results confirm earlier work that highlights the importance of two distinctions:
First, it is important to distinguish between firms’ general recruitment activity and it
having skill shortage vacancies.
Second, we must distinguish between firms
experiencing recruitment difficulties related to skills and those for other reasons (such
as the firm not offering good enough pay and conditions). Failing to account for this
may cause us to misdiagnose the problem and will therefore undermine the
appropriateness of any policy prescription.
In any market there will be a number of potential customers that cannot purchase all
they would wish because they are unwilling or unable to pay the market price. This
does not mean that the market is malfunctioning. There is an important question for
researchers and policymakers to ask before interpreting reports of unfilled vacancies
of any kind, and of SSVs in particular, as a ‘skills problem’. This question is: Why do
businesses that cannot fill a vacancy not simply raise the wages they are offering?
There a number of reason why firms may not be able to do so. First, it may be
because the firm cannot afford to pay any more. If this is the case, the problem is not
the labour market or the education system, but the firm’s own productivity. Firms that
that undertake activities associated with high productivity are likely to both have a
higher demand for skills and be more able to afford to pay skill premia.
Second, the supply of labour takes a long time to adjust. Because of the length of
time required to educate and train skilled workers, it takes a long time for changes in
iii
wages to influence changes in skill acquisition (particularly if we are relying on the
rather indirect mechanism of changes in relative wages influencing the choices of
students at school) and immigration is restrained by, among other things, legal
barriers and the costs of relocation.
We have found that the businesses that feel the applicants they are attracting do not
have the required skills, qualifications or experience are those that are paying higher
wages. The fact that businesses that pay higher wages than others in the same
industry and region are actually more likely to experience SSVs suggests that raising
wages is not sufficient to fill these vacancies.
In contrast, when we focus on
vacancies that are hard-to-fill for reasons other than skill, paying higher wages does
appear to be a successful policy to fill vacancies. Firms that pay higher relative
wages are less likely to have non-skill-related hard-to-fill vacancies. The implication
of this is that SSVs and NSRs are different phenomena.
One important issue researchers have to confront when undertaking analysis of this
type is that of causality.
It is one thing to establish a statistical regularity – a
correlation between firm characteristics, activities or environment and an outcome
such as external skill shortages. It is another thing to interpret this as a causal
relationship. In this paper, we have uncovered some clear statistical regularities.
These are consistent we with certain predictions we have discussed in this paper and
provide us with useful information to aid our understanding of how the landscape of
skills in New Zealand and, in particular, we done this from the perspective what
businesses are looking for, rather than what the education and training system has
provided.
iv
Table of Contents
Abstract ...................................................................................................................... i
Executive Summary ................................................................................................. ii
Table of Contents ..................................................................................................... v
List of Figures ......................................................................................................... vii
List of Tables .......................................................................................................... vii
1.
Introduction ....................................................................................................... 1
2.
Background – Skills and Economic Performance .......................................... 2
2.1. Skills and Firm Performance ........................................................................ 2
2.2. Labour Turnover and Vacancies .................................................................. 5
2.3. Skill Shortages ............................................................................................. 6
2.4. Skills and the Labour Market ........................................................................ 7
2.5. Firm’s Responses to Skill Shortages ............................................................ 8
2.6. Summary .................................................................................................... 10
3.
Data and Preliminary Analysis ....................................................................... 12
3.1. Business Operations Survey (BOS) ........................................................... 12
3.2. Vacancies ................................................................................................... 14
3.3. Hard-to-fill vacancies .................................................................................. 16
3.4. Skill and non-skill-related shortage vacancies ............................................ 19
3.5. Internal skill gaps........................................................................................ 20
4.
Method.............................................................................................................. 21
4.1. Basic econometric model ........................................................................... 22
4.2. Selection equation – The likelihood of vacancies ....................................... 23
4.3. Probability of SSVs..................................................................................... 26
5.
Results ............................................................................................................. 30
5.1. Using contemporaneous variables ............................................................. 30
5.2. Using lagged variables ............................................................................... 35
5.3. Skill and non-skill related hard-to-fill vacancies .......................................... 39
6.
Summary and Conclusions ............................................................................ 41
References .............................................................................................................. 45
Appendix A1. Data Appendix .............................................................................. 53
A1.1 BOS Variables ............................................................................................ 53
6.1.1. Module A (2005 – 2008) ....................................................................... 53
6.1.2. Module B – Innovation (2007) ............................................................... 54
6.1.3. Module C – Employment Practices (2006) ............................................ 55
6.1.4. Module C – Business Strategy and Skills ............................................. 55
A1.2 LEED/PAYE Data ....................................................................................... 58
A1.3 Other LBD Data (AES, BAI, and IR10) ....................................................... 60
v
Appendix A2. Comparison of hard-to-fill vacancies in Module C with
recruitment difficulties in Module A ...................................................................... 62
vi
List of Figures
Figure 1 Skill shortages and Hard-to-fill vacancies in Green et al. (1998) .................. 7
Figure 2 Vacancies, hard-to-fill and skill shortage vacancies ................................... 14
List of Tables
Table 1 Vacancies, hard-to-fill, skill and non-skill-related shortage vacancies, % .... 15
Table 2 Businesses reporting vacancies, % ............................................................. 16
Table 3 Businesses reporting hard-to-fill vacancies, % ............................................ 17
Table 4 Year-to-year rank correlation between reported recruitment difficulties ....... 19
Table 5 Skill-related reasons for hard-to-fill vacancies, by size ................................ 20
Table 6 Existing staff with skills required to do their job ........................................... 21
Table 7 Principal component factor analysis of business strategy variables ............ 25
Table 8 Factor loadings (pattern matrix) and unique variances ................................ 25
Table 9 Principal component factor analysis of 2007 business strategy ................... 26
Table 10 Factor loadings (pattern matrix) and unique variances .............................. 26
Table 11 Principal component factor analysis of census variables ........................... 29
Table 12 Rotated factor analysis .............................................................................. 29
Table 13 Rotated factor loadings (pattern matrix) and unique variances .................. 29
Table 14 Results - SSVs - Contemporaneous variables - Selection equation .......... 32
Table 15 Results – SSVs – Contemporaneous variables – Outcome equation ........ 34
Table 16 Results - SSVs - Lagged variables - Selection equation............................ 36
Table 17 Results - SSVs - Lagged variables - Outcome equation ............................ 38
Table 18 Results – Different definitions of dependent variable ................................. 40
Table 19 Staff occupation/role variables ................................................................... 58
Table 20 Weighted counts of firms reporting levels of difficulty recruiting new staff in
Module A by Module C hard-to-fill vacancy response .................................. 63
vii
A Good Worker is Hard to Find: Skills
Shortages in New Zealand Firms
1. Introduction
Skills are an important determinant of the economic performance of people, firms,
industries and economies1. Shortages of skilled labour directly constrain production
and prevent firms from meeting demand and using available inputs efficiently (Haskel
and Martin, 1993a; Stevens, 2007). Indirectly, skill shortages inhibit innovation and
the use of new technologies. This may have longer-term impacts on the way firms
do business, in terms of their location, size, structure, production methods and
product strategy (Mason and Wilson, 2003; Durbin, 2004; Mason, 2005).
The success of policies to enable the emergence and performance of successful
businesses depends upon having a workforce with the appropriate skills. There are,
however, concerns that New Zealand is experiencing a shortage of workers with
particular skills2. If New Zealand is to raise productivity and improve its international
competitiveness, it is important to understand whether skill shortages do indeed exist,
how they manifest themselves and develop policies to address them.
In this paper we investigate skill shortages deriving from recruitment difficulties at
firm level. That is, vacancies that are hard-to-fill because applicants do not have the
1
See Card (1999) or Dickson and Harmon (2011) for an overview of the results on the individual
returns to education and skills, Abowd, Kramarz, Margolis (1999) or Haskel, Hawkes and Periera
(2005) for evidence on the relationship between firm performance and skills, Kneller and Stevens
(2005) present international evidence for skills at the industry level in OECD economies and Stevens
and Weale (2004) or Madsen (2010) discuss evidence on the relationship between education levels
and growth.
2
‘Skills shortage hits agricultural science ‘, Dominion Post, 13/01/12, ‘Half of Kiwi companies facing
skill shortages’, NZ Herald, 30/11/11, ‘Academic warns of major skills shortage’, Waikato Times,
4/3/11
1
skill, qualifications or experience the business requires. In order to carry out this
analysis, we make use of a specially-designed survey, the Business Strategy and
Skills (BSS) module of the Business Operations Survey (BOS) 2008. We combine
this with information from other sections of the current and previous years’ BOS and
data from the prototype Longitudinal Business Database (LBD). The LBD includes
information from tax and survey-based financial data, merchandise and services
trade data and a variety of sample surveys on business practices and outcomes.
This allows us to link the responses of the BSS module to a wealth of information on
firms to inform our analysis.
The rest of this paper is as follows. In section 2 we discuss some of the issues that
influence the interrelationship between the skills of the workforce and the
performance of businesses. In section 3 we describe our data and present some
descriptive information on New Zealand firms’ experiences of skills. We set out our
empirical model in section 4 and our results in section 5. Section 6 concludes.
2. Background – Skills and Economic Performance
Human capital (and education in particular) has been found to be an important
determinant of economic development and explanator of international differences in
aggregate economic growth or productivity (Barro and Sala-i-Martín, 1995; Stevens
and Weale, 2004; Kneller and Stevens, 2005; Madsen, 2010).
There is a large
literature examining the links between the availability of skills and the performance of
firms. These range from detailed case studies (see Keep, Mayhew and Corney,
2002, for a summary of those undertaken by the UK National Institute for Economic
and Social Research) to econometric analysis using firm-level data and industry level
measures of skill shortages (e.g. Haskell and Martin, 1993b, 2001; Haskel, Martin
and Periera, 2005; Stevens, 2007). In this section, we consider the importance of
skills for firms, how labour markets match the skills of workers to their uses in firms,
and how firms respond to skills shortages. We also consider what we mean by a
‘skill shortage’. Can such a thing exist in a well-functioning economy?
2.1. Skills and Firm Performance
There are essentially two elements to the link between skills and firm performance.
First, skills represent a basic input into the firm’s production technology. Individuals
2
with higher skills have more human capital and so produce greater output. There is a
large literature that consistently finds positive private and social returns to skill and
education in particular (Card, 1999; Psacharopoulos and Patrinos, 2004; McMahon,
2004).
Higher skill levels do not only increase firm productivity through the direct impact on
the worker’s own productivity.
They also create synergies with other productive
inputs, such as other workers, physical and knowledge capital, R&D or new
technologies.
An obvious example of how skills enhance the performance of other workers is the
skills of managers, who organise and shape production. The quality of management
in businesses is an important predictor of business performance across a number of
dimensions in many countries (Bloom and Van Reenen, 2007, 2010; 2011), including
New Zealand (UTS, 2009).
Another way skilled labour increases the productivity of other workers is through what
are known as ‘knowledge spillovers’ (Arrow, 1962; Battu, Belfield and Sloane, 2004;
Audretsch and Feldman, 2004). Spillovers occur when other workers pick up these
skills through observation, interaction or tuition. These spillovers can occur within the
firm and across the boundaries of the firms through direct relationships (i.e. with
suppliers and customers) or through geographic proximity (Glaeser, Kallal,
Scheinkman and Shleifer, 1992; Audretsch and Feldman, 2004).
For a long time it has been suggested that skilled labour is more complementary with
capital than unskilled labour (Griliches, 1969; Duffy, Papageorgiou, and PerezSebastian, 2004).
Many capital goods (e.g. computers) require skills to operate
(Autor, Katz and Krueger, 1998; Morrison-Paul and Siegel, 2001; Falk, 2004). Autor,
Levy and Murname (2003) found that over three decades, computer capital has
replaced workers undertaking routine or repetitive manual and cognitive tasks and
complemented those doing non-routine problem-solving and communications tasks.
As well as physical capital, higher skill levels enable a workforce to deal with the
current technology and, moreover, they enable the firm to better adapt to new
technology (Machin and Van Reenen, 1998). For economists, technology is taken
generally to include the means whereby inputs are brought together to produce
outputs, including organisational and product strategy. Mason (2005) found that the
3
ability of firms in a number of industries to execute ‘high value’ business strategies
was contingent on the skills of their workforce. Work such as Abowd, Haltiwanger,
Lane McKinney and Sandusky (2007) finds a strong positive empirical relationship
between advanced technology and skill. Skills are required not only for the creation
of new technology, but also both for its dispersion and implementation (Rosenberg,
1972; Lane and Lubatkin, 1998; Hall and Khan, 2003; Kneller and Stevens, 2006).
Investing in skills
Both workers and firms have an incentive to increase productivity through investing in
skills, but their incentives are different. Individuals have an incentive to invest in skill
formation because this enables them to function better in the economy and society.
Crucially, they provide the basis for them to earn an income. Firms also have an
incentive to increase the skills of their workforce because this enables them to
perform more tasks or increase the productiveness with which they perform existing
tasks. Because the skills embedded in workers can be increased by investing in
them, economists use the term ‘human capital’.
Firms do not have the incentive to invest in all types of human capital equally.
General human capital is of value to all employers, whereas specific human capital is
valuable only to specific firms or groups of firms (Becker, 1962, 1994; Stevens,
1994) 3 .
Firms will tend to under-provide training of all skills that have some
generality to them (i.e. they are of use to other firms) because of the risks of staff
leaving or being poached; there is a risk that they will pay the costs and other firms
will get the benefits. Because they may not reap all of the rewards – staff may move
elsewhere or be poached by other firms – firms may invest less in such training that
would be socially optimal4. The likelihood of staff using recently acquired human
capital by moving elsewhere will depend of the outside options they have, in
particular the wages on offer.
3
The specificity of skills may in also be determined by the structure of the market. Acemoglu and
Pischke (1999) argue that certain skills might be potentially useful to other firms (i.e. general) may
become specific because of market imperfections. For example, if there were only one producer in a
sector a particular skill might only be of use to them, whereas if a number of firms were operating in
the same area they could be competing for the same labour.
4
Both firms and individuals may under invest in skills and education if there are externalities. This
might include agglomeration benefits (such as knowledge dispersion), matching externalities, benefits
in terms of lower crime, higher levels of political engagement and health.
4
This is where labour differs from other factors of production. It is not tied to the firm,
beyond the terms of its contractual relationship. It can decide to take its skill and use
them elsewhere.
2.2. Labour Turnover and Vacancies
Labour turnover is a basic fact of economic life (Davis, Halitwanger and Schuh, 1996;
Davis, Faberman and Haltiwanger, 2006, 2010). For example, from the September
to the December quarter of 2010, total employment in New Zealand expanded from
1,777,110 to 1,810,580, an increase of 33,4705. This change was dwarfed by the
almost half a million workers either leaving or starting jobs from which it resulted.
Because of the dynamic nature of the labour market, we must be careful not to
equate vacancies with job creation. Firms can have positive values for both hires
and separations (Davis, Faberman and Haltiwanger, 2006). In the December 2010
quarter, 12.7% of workers in New Zealand became separated from their jobs6. This
is not merely due to firms shrinking (what is called ‘job destruction’). The total job
destruction in the December 2010 quarter was 90,890 – barely two-fifths of the total
worker separations.
Firms will post vacancies for two reasons: First, to replace staff who have quit, retired
or been fired; Second, to fill new roles that have been created by the expansion of
the firm.
These two sources of vacancies are likely to have different causes.
Voluntary separations may be higher in good times (as the likelihood of alternative
employment increases), but involuntary separations may be lower (as firms seek to
reduce employment) (Pissarides, 2000). New roles are more likely to be created
when a firm is expanding7.
Given this discussion, we might reasonably expect the number of vacancies
generally increase with the number of employees. However, some work has found
vacancy rates to decline with firms size (e.g. Holtz, 1994).
Note that our discussion relates to vacancy rates. Our data is a binary variable
stating whether or not a firm posts a vacancy.
We would generally expect
5
Source: Statistics New Zealand Linked-Employer Employee Database. Data downloaded from:
http://www.stats.govt.nz/tools_and_services/tools/TableBuilder/leed-quarterly-tables.aspx
6
Source: Authors’ calculations based on LEED data. This separation rate is calculated as ‘Worker
separations’ in the December 2010 quarter divided by the average of ‘Total filled jobs’ in the
December and September quarters.
7
For a textbook description of the standard dynamic model of labour demand, see Nickell (1996).
5
phenomena that increase the numbers or rate of vacancies to also increase the
probability of the firm reporting at least vacancy.
2.3. Skill Shortages
The primary focus of this paper is on difficulties firms face in filling roles for reasons
related to the qualifications or experience of applicants – what we call skill shortage
vacancies. However, this is not the only way in which skill shortages can manifest.
Much early work in this area did not distinguish between the types of skill shortage. It
focussed on ‘skill shortages’ as a whole and their impacts. This was driven in part by
the data that were available to researchers.
For example, in the UK, the
Confederation of British Industry has collected information on whether firms output
was limited by shortages of skilled labour since the early 1970s8.
However, it has become clear that it is important to distinguish between shortages of
skills in existing workers and shortages of staff in the labour market with appropriate
skills (Green, Machin and Wilkinson, 1998; Mason and Wilson, 2003). These have
been called ‘internal skill gaps’ and ‘external skill gaps’, respectively (e.g. Forth and
Mason, 2004)9. The primary focus of this paper is on external skill gaps.
When examining external skill gaps, it is important to be clear what we are measuring.
As Green et al., (1998) point out, we must be careful not to simply equate them with
vacancies that are hard-to-fill.
As we shall see in section 3.3, there are many
reasons why vacancies may be hard-to-fill. These reasons range from the conditions
of work to the fact that firms are simply not paying the market wage; not all of them
are skill-related. Because of this, authors such as Mason and Stevens (2003) have
focussed on the subset of vacancies that are hard-to-fill for skill-related reasons;
specifically, by examining the reasons for vacancies being hard-to-fill and only
considering those that relate to a lack of qualifications and/or experience in
applicants. This is the approach that informed the design of the relevant sections of
the Business Strategy and Skills module of the BOS 2008, and one that underlies our
analysis in this paper.
8
This data has been used as an industry-level measure of shortages of skilled workers by Haskel and
Martin (1993a), (1993b) and Stevens (2007).
9
Although, others have called skills deficiencies relating to the external labour market ‘skills shortages’
and those applicable to a firm’s existing workforce ‘skills gaps’ (Schwalje, 2011).
6
We summarise the thinking on the various skill shortage concepts in Figure 1. Skill
shortages can be internal to the firm, what we call internal skill gaps (ISG), or
external, what we call skill shortage vacancies (SSV)10. As well as SSVs, vacancies
can be hard-to-fill for other reasons, what we call non-skill related vacancies (NSR).
Figure 1 Skill shortages and Hard-to-fill vacancies in Green et al. (1998)
Skill shortages
ISG

Hard-to-fill vacancies
SSV
NSR
ISG = Internal Skill Gap; SSV = Skill Shortage Vacancy; NSR = Non-Skill-Related vacancy
In order to understand why firms might find it difficult to source skilled labour, we
need to consider the wider labour market. We turn our attention to this next.
2.4. Skills and the Labour Market
Modern theories of labour markets are based around the ability of the market to
match individuals to jobs (Pissarides, 2000; Petrongolo, and Pissarides, 2001;
Mortensen, 2005). Trading in the labour market is not without cost. It takes time and
money for businesses to advertise for and assess potential employees. It takes time
and money for workers to search and apply for potential employment. We call these
‘search costs’. Furthermore, costs are incurred when incoming workers start in a
new role; workers need to learn the specific tasks involved in the role and the
10
Of course some external skill gaps may not manifest themselves as SSVs because the firm may not
bother to post vacancies they know they cannot fill.
7
employer has to learn about the specific abilities of their new employee11. Another
cost is the time a worker or a firm stays in a state awaiting a ‘good’ match. Workers
stay in less-preferable jobs or quit to unemployment while they wait for a better offer
to come along. Firms either allocate a worker who is less aptly skilled to a role, or
hold it open until the ‘right’ worker comes along. We call these ‘mismatch costs’.
Average search costs and mismatch costs are likely to be lower in larger labour
markets; the more jobs and workers there are, the more likely it is that a good match
will exist. Thus, businesses and workers operating in big cities, for example, will
benefit from lower search costs, ceteris paribus. In part because of this, they are less
likely to be stuck in ‘bad’ worker-job matches (as the cost of exiting a low productivity
match is much lower). These ‘thick labour market agglomeration benefits’ can also
come from co-location of economic activity with similar demand for skills; whilst there
is more competition between firms for skilled labour, there will also be a greater
supply (as workers with the appropriate skills are attracted to the area).
Larger
markets will be driven less by market frictions and more by the fundamental
economic determinants of the productivity of the match (i.e. those that determine the
productivity of the firm, the productivity of the worker and their complementarity).
Firms’ ability to recruit and retain skilled staff will depend on local labour market
conditions. These are typically summarised using the local unemployment rates or
proportions of the population with particular qualifications (e.g. Mason and Stevens,
2003). The firm’s ability to recruit new staff will also be affected by the ‘outside wage’.
The opportunity cost of taking up a job at the firm for the worker is the value of the
next best potential offer foregone.
This will depend on the distribution of job offers
and associated wages. These can be measured by average wage in similar jobs –
for example other jobs in that industry and/or region.
2.5. Firm’s Responses to Skill Shortages
How firms respond to labour market conditions will affect their performance. This is
part of how firms compete. The obvious question to ask if a business cannot fill a
vacancy is: ‘why don’t they just pay more?’ Here we discuss two reasons why this
may not be possible. First, it may be because the firm cannot afford to pay any more.
11
For more on the training of incoming staff by New Zealand firms, see the companion paper to this
(Timmins, et al., 2012).
8
In this case, the problem is not the labour market, but the firm’s own productivity.
Firms that that undertake activities associated with high productivity may well both
have a higher demand for skills but also be more able to afford to pay skill premia.
Firms with some degree of market power may not be more productive, but will be
able to compete more aggressively for scarce inputs such as skilled staff.
Second, the supply of labour takes a long time to adjust; it takes a long time for
changes in wages to influence changes in skill acquisition (particularly if we are
relying on the rather indirect mechanism of changes in relative wages influencing the
choices of students at school) and immigration is restrained by, among other things,
legal barriers and the costs of relocation.
In an early study of apparent shortages of engineers and scientists,
Arrow and
Capron (1959) defined a skill 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: 301). Although they recognised that
excess demand for skills in competitive labour markets should put upward pressure
on salaries, Arrow and Capron suggested that, even in a competitive labour market,
a steady increase in demand over time for skilled workers could produce a ‘dynamic
shortage’ if there were 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 highskilled employees as well as new ones. 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, as shown by Freeman (1971, 1976) in the case of
engineers and scientists.
Later studies have recognised that firms have a range of potential non-salary
responses and ‘coping mechanisms’ available to them when confronted by shortfalls
in skills – 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 in the UK,
Haskel and Martin (1993b) 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.
9
Increased training provision is a potentially important non-salary response to external
skill shortages which, like salary increases, should help alleviate the shortages in
question rather than just help firms to cope with them12. However, just as some firms
may elect to ‘live with’ external skill shortages for periods of time rather than incur the
costs of raising salaries for new recruits with knock-on effects on existing salary
differentials, some may also be reluctant to respond immediately to skill shortages by
increasing training provision for existing workers. Such reluctance could reflect
imperfect information about the costs and benefits of training versus other potential
responses to skill shortages.
But economic theory also points to other possible explanations for variation between
firms in the extent and speed of training responses to different kinds of skill shortage.
For example, Stevens (2007) suggests that the speed of firms’ adjustment behaviour
is likely to vary inversely with the degree of competition in their particular labour
markets for skills specific to their industries.
More generally, resource- and
knowledge-based theories of the firm suggest that heterogeneity of firms’ training
responses to external skill shortages is to be expected. This is because the ability of
any firm to provide training will be strongly conditioned by the specific resources and
capabilities (such as management skills and training capacity) which it has
accumulated over time (Teece, Pisano and Shuen, 1997; Eisenhardt and Martin,
2001; Teece, 2007).
2.6. Summary
This brief overview of the literature suggests a number of avenues for us to pursue in
our analysis.
First, we need to differentiate between firms’ general recruitment
activity and it having skill shortage vacancies. Labour turnover is a basic fact of
economic life.
additional staff.
Fast-growing and large firms will be more likely to be seeking
Second, we need to distinguish between firms experiencing
recruitment difficulties because of the availability of labour with the appropriate skills
and those for other reasons, such as the firm merely not offering good enough pay
and conditions. Failing to account for this may cause us to misdiagnose the problem
and will therefore undermine the appropriateness of any policy prescription. In any
12
We examine the relationship between training and skill shortages in more detail in Mason, Mok,
Stevens and Timmins, (2012).
10
market there will be a number of potential customers that cannot purchase all they
would wish because they are unwilling or unable to pay the market price.
Our quick survey of the literature suggests a number of patterns we might expect to
see, or hypotheses we might test:
Firms are more likely to suffer SSVs the greater the proportion of higher skilled staff
they are looking for. Firms operating in tighter local labour markets will find it more
difficult to fill roles. Firms with high-productivity strategies will have a higher demand
for skills, but will be more able to pay skill premia. Firms with a degree of market
power are likely to be more able to pay skill premia relative to their competitors.
From a policy perspective, we wish to know whether these skill shortages are ‘real’ or
not. In any market, there will always be potential buyers who would wish to buy more,
but are unwilling to do so at the current price. We know that labour markets do
adjust supply to meet demand instantaneously; in the short run, the labour supply
curve is likely to be almost vertical13. Thus, a positive shock to demand (caused, for
example, by a firm getting a large export order) is not likely to create an
instantaneous increase in the supply of labour. In the short run, the effect may only
be to increase wage inflation, as the firm outbids its competitors. In some cases the
firm may source skilled workers internationally, but this takes time and resources.
This suggests two alternative hypotheses, one for a labour market with some slack
and one where the supply of labour is tight. If the market is working satisfactorily,
firms that offer higher wages will be less likely to experience SSVs. If there are
shortages of skilled labour and the firms with the highest demand for skills are those
that are the most productive, it may be the firms that pay the highest wages that have
SSVs.
As we shall see below, because we only have a cross-section of data relating to
SSVs, it is likely to be difficult to distinguish some of these phenomena.
Nevertheless, we shall examine both the contemporaneous relationship and that
between current SSVs and previous years’ firm characteristics.
13
there may of course be some economically inactive workers that are on the margins of entering the
labour market, such as single parents for whom the costs of childcare may raise the reservation wage
higher than their contemporaries
11
3. Data and Preliminary Analysis
The data come from Statistics New Zealand’s prototype Longitudinal Business
Database (LBD). The LBD is built around the Longitudinal Business Frame (LBF), to
which are attached, among other things, Goods and Services Tax (GST) returns,
financial accounts (IR10) and aggregated Pay-As-You-Earn (PAYE) returns, all
provided by the Inland Revenue Department (IRD). The full LBD is described in
more detail in Fabling, Grimes, Sanderson and Stevens (2008) and Fabling (2009).
The survey data considered in this chapter relate to the Business Operations Survey
(BOS).
The administrative data we use have four sources: the Linked Employer Employee
Database (LEED), the Business Activity Indicator (BAI) dataset of GST returns, the
Annual Enterprise Survey (AES) and IR10 forms. These are described in more detail
in the Data Appendix.
3.1. Business Operations Survey (BOS)
The Business Operation Survey (BOS) is an annual three part modular survey, which
began in 2005. The first module is focussed on firm characteristics and performance.
The second module alternates between biennial innovation and business use of ICT
collections. The third module is a contestable module that enables specific policyrelevant data to be collected on an ad hoc basis14. The BOS is conducted using twoway stratified sampling, with stratification on rolling-mean-employment (RME) and
two-digit industry according to the ANZSIC system15. The survey excludes firms with
fewer than six RME and firms in the following industries: M81 Government
Administration, M82 Defence, P92 Libraries, Museums and the Arts, Q95 Personal
Services, Q96 Other Services, and Q97 Private Households Employing Staff. The
2008 survey achieved an 81.1% response rate (after adjusting for ceases), a total of
5,543 usable responses, representing a population of 36,075 firms.
The BOS is something approaching best practice in such surveys internationally. It
has removed replication of surveys16 – and thus reduces respondent load and makes
14
In 2005 and 2009 this was a ‘Business Practices Module’ and in 2006 an ‘Employment Practices
Survey’. The 2007 module was on ‘International Engagement’.
15
Note that there was some minor additional stratification conducted at the three-digit level.
16
Prior to the BOS, surveys tended to occur on a fairly ad hoc basis – one assumes when policymakers were considering a particular issue. Thus there was a Business Practices Survey in 2001, an
12
sampling simpler.
It is explicitly designed with a panel element; enabling more
sophisticated analysis to be undertaken allowing us to better understand issues of
causality and – as the panel element increases – dynamic issues17.
We do not use SNZ-imputed values in cases of item non-response where it is
impossible to obtain them by simple edit rules (e.g. more than one expenditure
categories are missing).
When we discuss questions from the BOS, we shall denote the question by their
module followed by their question number. So, for example, question 15 of Module C
is denoted C15.
When there are multiple categories of response in the same
question (e.g. the different occupational groups in question C18, we use the code
included on the form (e.g. C1801). Question numbers will relate to the 2008 BOS,
unless otherwise specified.
The ‘Business Strategy and Skills’ (BSS) Module
The Business Strategy and Skills (BSS) module of the 2008 Business Operations
Survey was produced as part of the ‘Impact of Skills on New Zealand Firms’ project.
This project involved the Ministry of Economic Development, the Department of
Labour, New Zealand Treasury and the Ministry of Research, Science and
Technology and was partly funded by the Cross Departmental Research Pool. The
module was designed by the project team in conjunction with Statistics New Zealand
and Geoff Mason, from the National Institute of Economic and Social Research in
London.
In this section we consider all firms that report vacancies of any kind and focus in on
hard-to-fill vacancies and what we call skill shortage vacancies (SSV). The skill
shortage vacancies are defined as vacancies that were hard-to-fill because the
applicants lacked the necessary skills, qualification or experience, which is a subset
of hard-to-fill vacancies. As there are other reasons for having hard-to-fill vacancies,
this allows us to distinguish the non-skill-related vacancies from the skill shortage
vacancies.
Innovation Survey in 2003 and a Business Finance Survey in (2004). Elements of each of these are
considered either every year as part of the Business Performance Module (Module A) or every two or
more years (i.e. the Innovation Module is run every other year and the Business Practices Module was
run in 2005 and is scheduled to repeat in 2009).
17
The panel element is in fact larger than it first seems as there is considerable overlap with previous
surveys, such as the 2001 Business Practices Survey (Fabling, 2007).
13
The overall percentages of firms reporting each type of vacancy are depicted as
concentric circles in Figure 2. More detail on the construction and patterns of our
measures of vacancies, skill and non-skill-related shortage vacancies are set out in
the following sections.
Figure 2 Vacancies, hard-to-fill and skill shortage vacancies
Skill shortage
vacancies (36%)
Hard-to-fill
vacancies (49%)
Vacancies (77%)



Figure shows the percentage of firms that report each type of vacancy
Figures based in sample strata and weights
Note that figures for the percentage of businesses with vacancies and hard-to-fill vacancies will not match the
tables in the Statistics New Zealand Hot of the Press release because: (a) we use a slightly different sample
and (b) we do not use imputed values
3.2. Vacancies
Respondents were asked: ‘During the last financial year, has this business had any
vacancies?’ (C14). The first row of Table 1 summarises these data. We shall return
to this table when we discuss hard-to-fill and skill shortage vacancies below.
Overall, 76.6% of firms reported that they had posted a vacancy. As one might
expect (given the greater number of employees), the likelihood of posting a vacancy
increases with firm size.
14
Table 1 Vacancies, hard-to-fill, skill and non-skill-related shortage vacancies, %
Business size
Overall
E <20*
20≤E<50
50≤E<100
E≥100
Vacancies
71.8
89.5
93.6
95.2
76.6
Hard-to-fill Vacancies
44.2
58.8
65.2
73.4
48.7
Skill Shortage Vacancies
32.1
43.1
48.6
58.6
35.7





Table shows percentage of firms reporting each type of vacancy
Figures based in sample strata and weights
Business size (E) is measured by rolling mean employment, or RME.
Note that the figure for business size being fewer than 20 RME is not all firms in the total business population
with fewer than 20 RME, but rather firms in the BOS sample. For more on these see the Data Appendix.
Note that figures for number of businesses with vacancies and hard-to-fill vacancies will not match the tables
in the Statistics New Zealand Hot of the Press release because: (a) we use a slightly different sample; (b) we
do not use imputed values; and (c) we use rolling mean employment (RME) from the 2008 financial year,
rather than 2007.
We can break the reporting of vacancies down by occupation. Respondents that
reported they had posted vacancies in the last year were asked a follow-up question:
‘During the last financial year, how many vacancies has this business had for the
following roles?’ (C15). The responses to this question are set out in Table 2.
It is for ‘clerical, sales and services workers’ that the greatest proportion of firms had
vacancies, followed by ‘labourers, production, transport or other workers’.
This
reflects the greater number of staff in these occupations 18. This is not quite true
across all firm sizes. ‘Managers’ is the second most popular category for firms with
more than one hundred employees (and also, marginally, for those with between 50
and 99 employees).
18
See Figure 6 of Stevens (2012)
15
Table 2 Businesses reporting vacancies, %
Business size
Overall
E<20*
20≤E<50
50≤E<100
E≥100
Managers
10.2
23.2
40.1
62.4
15.8
Professionals
11.5
19.1
29.1
42.6
14.8
Technicians and associate profs
Tradespersons and rel. workers
8.7
19.3
25.7
38.5
12.4
20.4
25.5
27.5
36.3
22.1
28.6
45.2
58.7
72.8
34.5
26.2
41.1
47.3
53.3
30.7
68.5
86.1
89.4
88.0
73.1
Clerical sales and service workers
Labourers, production, transport or
other workers
All occupations







Table presents data from questions C14: ‘During the last financial year, has this business had any vacancies?’
and C15: ‘During the last financial year, how many vacancies has this business had for the following roles?’
Table shows percentage of firms reporting each type of vacancy
Figure for ‘all occupations’ does not match that for Table 1 because some firms do not report the occupations
of their vacancies
Figures based in sample strata and weights
Business size (E) is measured by rolling mean employment, or RME.
* Note that the figure for business size being fewer than 20 RME is not all firms in the total business
population with fewer than 20 RME, but rather firms in the BOS sample. For more on these see the Data
Appendix.
Note that figures for number of businesses with vacancies and hard-to-fill vacancies will not match the tables
in the Statistics New Zealand Hot of the Press release because: (a) we use a slightly different sample; (b) we
do not use imputed values; and (c) we use rolling mean employment (RME) from the 2008 financial year,
rather than 2007.
3.3. Hard-to-fill vacancies
Respondents were asked: ‘During the last financial year, was this business easily
able to fill all vacancies with suitable applicants?’ (C16). Those who answered ‘no’ to
this question were classified as having a hard-to-fill vacancy. The second row of
Table 1 (repeated at the bottom of Table 3) summarises these data. Well over half of
the firms that have vacancies find them hard-to-fill (47.9% compared to 76.6%).
Again, the probability of having a hard-to-fill vacancy increases with firm size, with
almost three-quarters of firms with rolling mean employment of one hundred or more
having hard-to-fill vacancies.
16
Table 3 Businesses reporting hard-to-fill vacancies, %
Business size
Managers
Professionals
Technicians and associate profs
Tradespersons and related workers
Clerical sales and service workers
Labourers, production, transport or
other workers
All occupations







E<20*
4.9
7.1
4.6
13.7
10.2
20≤E<50
10.1
11.6
8.4
16.3
14.2
50≤E<100
16.7
16.1
11.3
15.1
18.1
E≥100
29.4
24.8
19.4
19.8
24.6
12.3
44.2
17.6
58.8
19.9
65.4
22.9
73.6
Overall
7.2
9.0
6.1
14.4
11.8
13.9
48.7
Table presents data from questions C16 ‘During this last financial year, was this business easily able to fill all
vacancies with suitable applicants?’ and C18: ‘Mark all that apply/ for this business, which roles were hard-tofill?’
Table shows percentage of firms reporting each type of vacancy
Figure for ‘all occupations’ does not match that for Table 1 because some firms do not report the occupations
of their hard-to-fill vacancies
Figures based in sample strata and weights
Business size (E) is measured by rolling mean employment, or RME.
Note that the figure for business size being fewer than 20 RME is not all firms in the total business population
with fewer than 20 RME, but rather firms in the BOS sample. For more on these see the Data Appendix.
Note that figures for number of businesses with vacancies and hard-to-fill vacancies will not match the tables
in the Statistics New Zealand Hot of the Press release because: (a) we use a slightly different sample; (b) we
do not use imputed values; and (c) we use rolling mean employment (RME) from the 2008 financial year,
rather than 2007.
Respondents that reported that they found some vacancies hard-to-fill were asked:
‘For this business, which roles were hard-to-fill?’ (C18). ‘Tradespersons and related
workers’ were the occupations with which most businesses had recruitment
difficulties (Table 3). This reflects a more even spread of hard-to-fill vacancies by
firm size and hence the influence of the greater number of small (6-19 employees)
firms.
‘Managers’ were the role for which most large (100+) firms found difficult to fill
vacancies. Given that managers represent a relatively small proportion of total staff,
and one that has an important impact on firm performance (Bloom and Van Reenen,
2007, 2010; UTS, 2010), this is a worrying result.
Comparison with Recruitment Difficulties in Module A
In order to place these figures in context, it is useful to compare these results with the
responses to a similar question that is asked in Module A.
In a section on
employment, firms are asked ‘Over the last financial year, to what extent did this
business experience difficulty in recruiting new staff for any of the following
17
occupational groups?’ (A33). The different language used in module C (vacancy plus
hard-to-fill vacancy) and Module A (‘difficulty in recruitment’), differences in the
response categories and the influence of previous questions mean that these are not
identical19. The results of such a comparison are presented in Appendix A2. In
summary, there is a high degree of correlation between the two measures, but that
this is not total. The majority of respondents that said they had a severe difficulty in
recruiting each type of staff in Module A said that vacancies were hard-to-fill in
Module C.
The Persistence of Recruitment Difficulties
Notwithstanding the differences between the Module A recruitment difficulties
variables and the Module C hard-to-fill vacancies variable, we can use the Module A
question to consider the persistence of recruitment difficulties. In order to prevent us
becoming overwhelmed with tables, we focus on the year-to-year rank correlations
between the responses to the recruitment difficulty question (A33) for the 2005, 2006,
2007 and 2008 surveys.
As we can see from Table 4, the correlations are both high and significant.
Interestingly, whilst the correlation does decline over time, this decline is not very
steep. There may be a number of reasons for this. One might be that firms with
recruitment difficulties are more likely to have had them in the past, but not
necessarily every year. Another may be a function of the attrition in the survey, as
firms enter and leave.
Whatever the reasons for the particular pattern, there is clearly considerable
persistence in the existence of recruitment difficulties.
We shall consider the
predictive power of previous recruitment difficulties for the probability of the firm
posting a vacancy in our econometric analysis in the following section.
19
For more on the subject of how respondents interpret and answer questions in business surveys,
with particular reference to the BOS, see Fabling, Grimes and Stevens (2008, 2012).
18
Table 4 Year-to-year rank correlation between reported recruitment difficulties
2005
2006
2007
2008
Managers and professionals
2006
0.5836***
1
2005
2008
0.4506*** 0.4592***
1
(0.0000)
(0.0000)
0.5151*** 0.5225*** 0.5450***
(0.0000)
(0.0000)
1
2006
0.4534
(0.0000)
0.3357***
1
2008


1
0.3105*** 0.3874***
1
(0.0000)
(0.0000)
0.4161*** 0.4183*** 0.6455***
(0.0000)
1
(0.0000)
(0.0000)
0.4225*** 0.5687***
(0.0000)
(0.0000)
Other occupations
(0.0000)
2007
1
(0.0000)
0.3668*** 0.4462*** 0.6254***
(0.0000)
Technicians and assistant professionals
***
2008
(0.0000)
0.4522*** 0.5402***
(0.0000)
2007
Tradesmen and related occupations
0.4827***
1
(0.0000)
2007
2006
(0.0000)
1
1
(0.0000)
0.3011*** 0.3362*** 0.4067***
(0.0000)
(0.0000)
(0.0000)
1
(0.0000)
p value in parenthesis
* significant at 10%; ** significant at 5%; *** significant at 1%
3.4. Skill and non-skill-related shortage vacancies
Respondents that had hard-to-fill vacancies were asked ‘For which of the following
reasons did this business find it hard-to-fill vacancies?’ (question C17). They were
given twelve categories, from which they could choose as many as they wished.
Those that replied ‘applicants lack the work experience the business demands’ or
‘applicants lack the qualifications or skills the business demands’ were defined as
having skill shortage vacancies (SSVs) 20 .
Around the same numbers of firms
reported each of these reasons across all firm sizes (Table 5). However, only around
half of businesses that reported that applicants lacked appropriate experience also
reported that they lacked the appropriate qualifications or skills (and vice versa).
Thus, we should be careful as considering these two explanations as synonymous.
Table 5 shows that there are attributes workers obtain through their on-the-job
experience that businesses value over and above qualifications and, in particular,
separate from the things that they call ‘skills’. It may be that experience connotes a
depth of skill rather than merely its presence.
20
For the full list of responses, see SNZ (2008).
19
Table 5 Skill-related reasons for hard-to-fill vacancies, by size
Business Size
Applicants lack work experience the
business demands
Applicants lack qualifications or skills
the business demands
Lack either qualifications or work
experience
Lack both qualifications and work
experience






RME
<20
20 ≤ RME
<50
50 ≤ RME
<100
100+
RME
Total
25.4
34.7
37.9
48.1
28.4
25.1
35.2
38.3
47.5
28.2
13.7
16.2
20.9
21.8
14.7
18.4
26.8
27.7
36.8
20.9
Table presents data from questions C17 ‘For which of the following reasons did this business find it hard-to-fill
vacancies?’
Table shows percentage of firms reporting each response
Figures based in sample strata and weights
Business size (E) is measured by rolling mean employment, or RME.
Note that the figure for business size being fewer than 20 RME is not all firms in the total business population
with fewer than 20 RME, but rather firms in the BOS sample. For more on these see the Data Appendix.
Note that figures for number of businesses with vacancies and hard-to-fill vacancies will not match the tables
in the Statistics New Zealand Hot of the Press release because: (a) we use a slightly different sample; (b) we
do not use imputed values; and (c) we use rolling mean employment (RME) from the 2008 financial year,
rather than 2007.
Firms that replied having other reasons besides ‘applicants lack the work experience
the business demands’ or ‘applicants lack the qualifications or skills the business
demands’ were defined as having non-skill-related shortage vacancies (NSR). We
shall use this information allows us to better understand what is unique about skillshortage vacancies than other recruitment difficulties that would have different
implications for policy.
3.5. Internal skill gaps
In this section, we explore the internal skill gaps from the BOS 2008 and skill
improvement needs obtained from the computer-aided telephone interview (CATI).
From the BOS, respondents were asked ‘How many of this business’s existing staff
have the skills required to do their job?’ for 6 given occupation groups of managers;
professionals; technicians and associate professionals; tradespersons and related
workers; clerical, sales and service workers; and labourer, production, transport or
other workers. Respondents were further asked the reasons for their staff not having
the skills required. Table 6 shows that more than half of the firms have all their
managers and clerical, sales and service workers with skills required. Internal skill
20
gaps are more prominent for less skilled workers, such as clerical, sales and service
workers and labourers, production, transport and other workers.
Table 6 Existing staff with skills required to do their job
Proportion of staff with skills (%)
Occupation
Managers
Professionals
Technicians and associate professionals
Tradespersons and related workers
Clerical, sales and service workers
Labourers, production, transport and others



Less than half
Half or more
All staff
9.8
4.4
3.3
4.4
4.4
11.7
7.3
9.0
16.1
20.3
78.5
37.7
33.0
32.4
60.6
4.2
19.0
36.6
Table presents data from questions C20 ‘How many of this business’s existing staff have the skills required to
do their job?’
Source: SNZ (2009)
Note that the percentages in this table are taken from ‘SNZ (2009) and are not exactly comparable with the
Table 1 and Table 2 and Figure 2. For more on this, see the footnotes to Table 1 and Table 2.
4. Method
In this section we outline our empirical strategy.
First we set out our basic
econometric model. This is a two stage, ‘Heckman selection’ model, where we allow
for the interrelationship between the firm’s decision to look for staff externally and
post a vacancy and the probability that these vacancies prove to be hard-to-fill for
skill-related reasons.21
In the following two sections we consider the variables included in each stage of the
model. One of the key issues we face is that we only have one year of information
on our dependent variable, so our model is essentially a cross-sectional model. This
makes interpreting coefficients as causal relationships, rather than merely uncovering
statistically significant correlations, difficult. This is a problem faced by many studies
of this nature (e.g. Mason and Stevens, 2003). We seek to overcome (or at least
mitigate) this by using the fact that the Business Operations Survey is embedded in
the prototype Longitudinal Business Database (LBD). We use additional data from
21
An earlier version of this paper (Mason et al., 2010), shows the importance of accounting for the
relationship between in this way. In unpublished work we also considered using multinomial logit and
bivariate probit models, but concluded that these were not appropriate also.
21
previous years’ BOS and information on the firm we have from other sources,
specifically tax and survey data.
Another important issue to bear in mind is the fact that the unit of observation in our
empirical analysis is the firm (in LBD parlance, the enterprise).
Thus, when we
estimate the probability of a skill shortage vacancy, we are estimating the probability
of a firm with a vacancy having at least one that is hard-to-fill for skill-related reasons.
We are not estimating the probability of a particular vacancy being hard-to-fill for skill
related reasons. For this we would need vacancy-level information. Our focus is on
the impact on firms rather than on individual vacancies.
Finally, in order to aid interpretation, we also supplement our analysis of SSVs by
considering different specifications of our dependent variable (whether the firm has
an SSV): we investigate specifications where the outcome is restricted to firms that
have SSV only (i.e. not for other reasons)
4.1. Basic econometric model
The primary model we estimate is the probability of reporting skill shortage vacancies
accounting for sample selection. We model the first stage (the selection equation) as
being the probability of reporting having posted a vacancy. The second stage is the
probability that a firm with vacancies finds at least one of them hard-to-fill for reasons
of skill. We also investigate different outcomes in the second stage equations (i.e.
non-skill-related hard-to-fill vacancies and various variations on the SSV/NSR theme).
The discussion of method that follows, however, focuses on the model for SSVs, to
keep the exposition clear.
We suppose that the propensity of firm i to post a skill shortage vacancy can be
expressed as
(1)
SSVi   X i    1i
where SSV* is the propensity to have a skill shortage vacancy, Xi is a (1×k) vector of k
explanatory variables,  is a (k×1) vector of parameters to be estimated and  i an
error term. We do not observe the SSV* terms, but the binary realisation of them.
Therefore we assume:
22
(2)
SSVi  0 if
SSVi   0
SSVi  1 if
SSVi   0
This variable SSVi takes one of three values. If the firm reports a skill shortage, SSV
takes the value of 1. It takes the value zero if the firm does not have a skill shortage
vacancy, but does have a vacancy. It takes a missing value when the firm does not
have any vacancies.
The dependent variable in (2) for observation i is observed if:
(3)
Vaci  Z i   2i  0
where
(4)
1 ~ N 0,1
 2 ~ N 0,1
corr 1 ,  2   
When  ≠ 0, standard probit techniques applied to (2) yield biased results. The
selection model provides consistent, asymptotically efficient estimates for all the
parameters in the model. For the model to be identified, we should have at least one
variable in the selection equation (3) that is not in the outcome equation.
4.2. Selection equation – The likelihood of vacancies
In our selection equation (the probability of reporting a vacancy), we include the
following variables in the X vector22. Scale will be an important issue; clearly the
more employees a firm has, the more likely it is to have lost one and need to
advertise for a replacement. We measure firm size by the (natural) logarithm of
rolling mean employment plus a count of working proprietors (from the Linked
Employer-Employee Database or LEED), ln(E).
This highlights an important link
between the workers in a firm and the vacancies it posts. The more workers that quit
or are fired from a firm, the more it must replace. We include a direct measure of this
with the separation rates (the total number of employees that leave a firm in a year
divided by the number of employees, again from the LEED), SepRate.
It may be the case that occupations differ in their likelihood of quitting (because they
have different outside options, for example). Therefore we include variables for the
22
For more information on the variables see the Data Appendix to this paper.
23
proportions of staff that are ‘managers’, ‘professionals’, ‘associate professionals or
technicians’ or ‘tradespersons’, Propman to Proptrade.
Firms that are already experiencing internal skill gaps may be more likely to attempt
to fill these via external recruitment. Therefore, we include a variable that indicates
whether the firm feels that their staff lack the skills to perform their roles, ISG.
We consider the firms productivity levels using both (the natural logarithm of) their
labour productivity, ln(LP), and multifactor productivity, MFP. The latter is generated
as the residual from a series of industry-level production functions estimated by OLS.
Relative wages
Workers choose to accept a job offer contingent on their knowledge of all alternative
offers. However, if their wages slip behind other alternatives (because wages in the
firm grow more slowly or wages in similar firms grow more rapidly), workers may
leave23. We therefore include the change in wages paid by the firm relative to other
firms in the same industry, j, in the same region, r, (we discuss the calculation of this
in more detail in section 4.3 below), [ln(Wi) – ln(Wijr)].
It may be the case that firms with market power in their product market have greater
scope to compete for scarce skilled labour (their supernormal profits are, in effect,
shared between the owners of capital and labour). Because of this, we also include
a dummy variable to indicate whether the firm is effectively a monopoly (Monopoly).
Business Strategy
To investigate the relationship between he firms’ business strategy and the demand
for skills, we include a set of variables to indicate the business strategy of the firm.
The variables we consider are: whether it was able to obtain a higher price than its
competitors for its goods and services (Price_Leader); whether it developed or
introduced any new or significantly improved goods or services, operational
processes, organisational/ managerial processes or marketing methods (Innovate);
whether it undertook research and development (R&D); whether its primary market
was international, rather than local or national (Mark_Inter); whether it provided
customised goods or services (Customise); whether it exported (Export); or whether it
undertook outward foreign direct investment (ODI).
23
For a discussion on the influences on individual’s decisions to quit, see Stevens (2005).
24
There are a number of factors that make it difficult to include the whole suite of
potential business strategy variables from the BOS in our analysis. First, we only
have a single cross-section of information on our dependent variables. Second, the
business strategy variables are highly correlated with each other. Third, business
practices are highly correlated with firm performance and, thus, productivity and the
firm’s ability to pay higher wages than its competitors.
Fourth, they are all binary or
other forms of categorical variables. These combine to make analysis difficult and
potential make our estimated coefficients erratic and even counter-intuitive. Because
of these issues, we also performed factor analysis and took the factor that had an
Eigenvalue of one 24 (which in our econometric analysis will be called BS1). The
results of our factor analysis are set our it Table 7 and Table 8.
Table 7 Principal component factor analysis of business strategy variables
Factor
Factor1
Factor2
Factor3
Factor4
Factor5
Factor6
Factor7

Eigenvalue Difference Proportion Cumulative
1.03421
0.23959
0.00083
-0.06653
-0.08497
-0.15753
-0.21845
0.79462
0.23876
0.06736
0.01844
0.07256
0.06092
1.3842
0.3207
0.0011
-0.0890
-0.1137
-0.2108
-0.2924
1.3842
1.7049
1.7060
1.6169
1.5032
1.2924
1.0000
LR test: independent vs. saturated: chi2(21) = 2374.58 Prob>chi2 = 0.0000
Table 8 Factor loadings (pattern matrix) and unique variances
Variable
Mark_Inter
Customise
Price_Leader
Innovate
R&D
Export
ODI
Factor1 (BS1)
Factor2
Factor3
Uniqueness
0.4563
0.1627
0.2047
0.2598
0.4721
0.5731
0.3725
-0.2085
0.2250
0.2074
0.2691
0.0954
-0.1229
-0.0765
0.0097
0.0119
0.0150
-0.0100
-0.0127
0.0047
-0.0093
0.7482
0.9228
0.9148
0.8600
0.7678
0.6565
0.8553
The panel nature of the BOS allows us to consider the firms business strategy in
years prior to the current year’s vacancies (and SSVs).
variables (i.e., those in Module C) are not available.
However, some of the
Therefore, we consider a
different set of variables and also undertake a separate factor analysis of these. The
24
The so called ‘Kaiser-Guttman rule’ after Guttman (1954) and Kaiser (1970).
25
variables we take from the 2007 BOS included the three variables from Module A in
the above analysis: whether they conducted R&D (R&D07), exported (Export07), or
undertook ODI (ODI07). Because we have access to the innovation module B in 2007,
we are able to divide their innovation activity into the four constituent parts, that is,
new or significantly altered: products (prod_inno07), processes (proc_inno07),
organisation (org_innov07) and marketing (mark_inno07).
The results of our factor
analysis are set out in Table 9 and Table 10.
Table 9 Principal component factor analysis of 2007 business strategy
Factor
Factor1
Factor2
Factor3
Factor4
Factor5
Factor6
Factor7

Eigenvalue Difference Proportion Cumulative
1.49186
0.45836
-0.01214
-0.09885
-0.13406
-0.14918
-0.23855
1.03350
0.47050
0.08671
0.03521
0.01513
0.08937
.
1.1324
0.3479
-0.0092
-0.0750
-0.1018
-0.1132
-0.1811
1.1324
1.4803
1.4711
1.3961
1.2943
1.1811
1.0000
LR test: independent vs. saturated: chi2(21) = 3637.14 Prob>chi2 = 0.0000
Table 10 Factor loadings (pattern matrix) and unique variances
Variable
Factor1
(BS07)
Factor2
Uniqueness
R&D07
Export07
ODI07
prod_innov07
proc_innov07
org_innov07
mark_innov07
0.4245
0.3133
0.2351
0.5866
0.5428
0.5153
0.5039
0.3145
0.3712
0.2875
0.0259
-0.1862
-0.2451
-0.2088
0.7208
0.7641
0.8621
0.6552
0.6707
0.6744
0.7025
4.3. Probability of SSVs
In addition to some of the control variables included in the selection equation, we
also investigate the relationship between SSVs and relative wages, the local labour
market and types of staff for whom firms are seeking to hire.
26
Wages
One might expect a major determinant of firms’ ability to fill any vacancy to be the
wages it pays relative to other potential employers. We have discussed above why
this might not be as clear as it first seems and so the sign on the coefficient of
relative wages will tell us something about the function of the New Zealand labour
market.
In considering the appropriate measure of outside wages, there are a number of
factors to take into account. The unit of observation for the data on wages is the
establishment. We do not have information on wages by skill level. Our measure of
wages is the total wage bill divided by the number of employees on the 13 th of the
month. This can be aggregated from a monthly establishment measure to an annual
enterprise measure using employment weights (or, equivalently, by summing wage
costs and rolling mean employment over the year and dividing one by the other)25.
The two dimensions over which we can measure variation in the outside wage are
industry classification and geography.
However, firms often operate in multiple
industries and/or multiple locations. Firm level analysis generally uses the concept of
‘predominant industry’. This is typically measured by taking the industry in which the
firm employs the most workers.
Thus, if a firm has two establishments, one
employing 100 workers constructing boats and one employing 20 to sell marine
safety equipment, we say the firm is a boat-maker. If the firm has establishments in
more than one region, we could proceed by analogy to determine its ‘predominant
region’. This is the course followed by Grimes, Ren and Stevens (2012)26. Because
we have information of the numbers of employees and wages at the establishment
level, along with the region in which that establishment is situated and its ANZSIC
code, we can calculate a more sophisticated measure. We calculate the relative
wage at the establishment level and aggregate it across industries and locations in
which it operates using employment weights.
In the analysis for this paper, we actually considered three outside wage measures:
the average wage in the 4-digit industry; the average wage in the local region and;
25
As with other measures using LEED employment, this measure does not take into account the
hours worked by employees (we cannot calculate hourly wages), but it does take into account monthly
variations within the year (e.g. seasonal working).
26
This is because in the questions on internet connectivity in the 2006 Business Operations Survey
used by Grimes et al., respondents are answer with respect to their largest operation.
27
the average wage of firms in the industry in the region. However, the measures are
highly correlated with each other at the enterprise level and the influence on the
results is marginal.
Therefore, in this paper, we present results from only the
industry-regional measure as it reflects both the national market for skills of a similar
nature and the geographic area.
We chose the Territorial Authority as the appropriate region as we believe that this
most closely approximates a travel-to-work area27. In order to maintain cell sizes, the
definition of industry on which we settled was the 2-digit level according to the
ANZSIC 1996 classification.
In cases where the number of firms in a cell dropped
below 50, we used data at the next level of industry aggregation.
The industry-region outside wage for an enterprise (the unit of observation in the LBD
that broadly corresponds with what economists would call the firm) was calculated as
the average wage in the industry-region cell in which the establishments in the
enterprise operated, aggregated using establishment employment weights. That is,
the outside wage of enterprise i, consisting of L establishments, is given by
L
(5)
M
Ei
W jrm
l 1 m 1 Eikm
Woi  
where Wjrm is the average wage of the establishments in the 2-digit industry j in the
Territorial Authority r in month m (M will typically, but not exclusively, be 12), Ei is the
total employment in the enterprise and Eikm is the employment in each establishment
in a given month.
Local Labour Market Conditions
We consider three measures of the local labour market conditions (beyond the
industry-region wage).
These come from the 2006 Census of Population and
Dwellings. The first two are measures of the supply of labour at the two ends of the
education distribution. First, we consider the percentage of the population with a
degree or higher qualification. Second, we consider the percentage of the population
with no qualifications. The third variable we consider is the unemployment rate in the
Territorial Authority.
27
It is possible to calculate more sophisticated measures of outside wages and local labour market
conditions using more disaggregated (Area Unit or even Meshblock) data and weighting it by some
function of the distance between the two areas. We leave this to later work.
28
Because these variables are highly correlated, we perform a factor analysis. The
factor analysis produces two primary factors with Eigenvalues of greater than one. In
order to facilitate interpretation, we undertake factor rotation and obtain the two
factors (Census1 and Census2) as set out in Table 11 to Table 13. The first factor is
primarily made up of the percentage of the population with no qualifications and
unemployment rate variables. The second is a combination of the percentage with
degree and unemployment rate.
Table 11 Principal component factor analysis of census variables
Factor
Factor 1
Factor 2
Factor 3

Eigenvalue
Difference
Proportion
Cumulative
1.50331
1.22505
0.27164
0.27826
0.95341
.
0.5011
0.4083
0.0905
0.5011
0.9095
1.0000
LR test: independent vs. saturated: chi2(3) = 3782.54 Prob>chi2 = 0.0000
Table 12 Rotated factor analysis
Factor
Eigenvalue
Difference
Proportion
Cumulative
Census1
Census2
1.41023
1.31812
0.09211
.
0.4701
0.4394
0.4701
0.9095

Rotation method: orthogonal varimax
Table 13 Rotated factor loadings (pattern matrix) and unique variances
Variable
% no qualifications
% degree or higher
% unemployment
Census1
Census2
Uniqueness
0.9318
-0.1096
0.7279
-0.2150
0.9553
0.5995
0.0854
0.0754
0.1108
Occupational breakdown of vacancies
Given the specific nature of many skills, an important element of the ease with which
firms will be able to vacancies will be for whom they are seeking. We therefore
include four variables for the proportion of vacancies that are for the following
occupational groups: managers (Vman), professionals (Vprof), technicians and associate
professionals (Vtech) and tradespersons and related workers (Vtrade). The baseline is a
29
combination of clerical sales and services workers and labourers, production,
transport or other workers28.
5. Results
Our results are set out in three sections. In the first section (5.1) we examine SSVs
using contemporaneous variables.
These will show us which types of firms are
currently experiencing SSVs. Next we consider lagged values of the BOS and other
LBD variables (5.2). This allows us to look at the characteristics of firms prior to the
year in which they reported SSVs. Finally, we consider different specifications of the
dependent variable to give some contextual information to help understand our main
results (5.3).
5.1. Using contemporaneous variables
The results from our estimation using contemporaneous variables are set out in
Table 14 and Table 15.
The first two columns show the impact of our industry
dummies, with column (1) excluding industry dummies and the subsequent columns
including them.
In column (3) we introduce internal skill gaps and our business
strategy variables.
Column (4) replaces the strategy variables with the principal
factor and the final column presents a more parsimonious specification.
Table 14 presents the results for the selection equation of our contemporaneous
specifications. Comparison of (1) and (2) suggests that the impact of our industry
dummies on the remaining coefficients is relatively minor.
In general, larger firms and those that are growing are more likely to be posting a
vacancy. The separation rate of the firm has no more predictive power beyond the
total number of employees in explaining the variation in the probability of posting a
vacancy.
Firms that are increasing their wages faster than their competitors tend to be more
likely to have a vacancy, although this is not statistically significant in all
specifications. Note, however, that in this contemporaneous specification this may
reflect firms raising wages in response to staff turnover or firm expansion. This is
something we return to when we consider our lagged specifications in section 5.2
28
We also included the first of these in preliminary analysis, but its coefficient was never statistically
significant.
30
below. The productivity of the firm does not appear to help explain the probability of a
firm posting a vacancy, ceteris paribus29.
Firms that undertake training in the current year are more likely to have vacancies.
Firms enjoying a monopoly in their product market and those with collective
agreements are no more likely to have vacancies.
None of the occupations appear to be any more likely to create a need for vacancies.
When we account for the occupational make-up of the firms employment (in columns
(1) to (3)), we find no relationship between this and the likelihood of posting a
vacancy. Because of this, we exclude the occupational variables from specifications
(4) and (5).
We introduce our measure of internal skill gaps (ISG) and business strategy
variables in column (3). Firms with internal skill gaps are no more likely to have
vacancies, ceteris paribus. Firms that consider themselves to be price leaders and
those that have innovated are more likely to have vacancies, a result that is
statistically significant at the 1% level. Firms that undertake a high degree of
customisation and ODI are also more likely to have a vacancy, although these results
are significant only at the 10% level.
When we replace these binary variables with the factor (BS1), this is positively
correlated with the probability of having vacancies (columns (4) and (5)). This result
is significant at the 5% level.
29
Note that we estimated a number of different specifications of the productivity variable and obtain
similar results. We also estimated the models in Table 14 and Table 15 with both labour productivity
and MFP, but the models with MFP either failed or experienced difficulty converging. We take this as
a sign of a less well-specified model and so do not present the results.
31
Table 14 Results - SSVs - Contemporaneous variables - Selection equation
(1)
ln(sales)
0.281
(2)
***
(0.071)
[ln(Wi)–ln(Wijr)]
SepRate
ln(E)
ln(LP)
(3)
***
0.283
(4)
***
0.237
(0.072)
0.242
0.343
*
0.463
*
0.385
0.417
(0.273)
(0.239)
(0.268)
(0.215)
(0.203)
0.542
0.470
0.449
0.313
(0.447)
(0.420)
(0.401)
(0.384)
0.419
***
***
0.407
***
0.397
(0.050)
(0.052)
(0.048)
0.041
0.071
0.074
0.052
(0.049)
(0.046)
(0.052)
(0.054)
***
***
(0.074)
(0.097)
(0.085)
-0.031
-0.038
(0.058)
(0.049)
(0.045)
-0.029
-0.057
0.009
(0.103)
(0.096)
(0.118)
Proptrade
0.831
***
-0.002
Proptech
***
0.419
(0.049)
Union
Propprof
***
0.239
(0.075)
0.821
Propman
0.246
(0.074)
Train
Monopoly
(5)
***
0.821
0.257
0.210
0.078
(0.439)
(0.506)
(0.440)
-0.030
0.043
-0.075
(0.318)
(0.323)
(0.320)
-0.215
-0.148
-0.256
(0.208)
(0.242)
(0.263)
0.009
0.089
0.100
(0.126)
(0.167)
(0.130)
ISG
***
0.860
(0.080)
0.002
0.017
(0.056)
(0.047)
(0.068)
**
***
0.435
(0.044)
***
0.852
(0.074)
***
0.303
Price_Leader
(0.060)
***
Innovate
0.294
R&D
-0.068
(0.074)
(0.123)
0.042
Mark_Inter
(0.161)
*
0.164
Customise
(0.088)
0.080
Export
(0.121)
*
0.376
ODI
(0.196)
**
0.211
BS1
(0.085)
Constant
Industry dummies


-1.513
***
***
-1.816
***
-2.030
***
-1.652
**
0.208
(0.086)
***
-1.081
(0.489)
(0.512)
(0.595)
(0.587)
(0.108)





Robust standard errors in parentheses
*
**
***
significant at 10%; significant at 5%; significant at 1%
32
Table 15 presents the coefficients in the outcome equation of our contemporaneous
specifications, i.e. the probability of an SSV.
One clear result across the
specifications is that firms that pay higher wages than others in their industry and
region are more likely to be experiencing SSVs. This result is significant at the 5%
level in the first two columns and at the 1% level in the remaining specifications.
Once we have accounted for the probability of a firm reporting a vacancy, we find no
statistically significant relationship between the size of a firm in terms of employment
and the probability it reports an SSV.
In contrast to our results in the selection equation for the probability of reporting a
vacancy, the occupational breakdown of vacancies is related to the probability that
they are hard-to-fill for reasons of skill.
Firms with a higher proportion of their
vacancies for professional, technicians and associate professional, and trade
occupations are more likely to report SSVs. The proportion of vacancies that are for
managers is not significant. Because the coefficients on each variable are so similar
(we can accept the restriction that they are identical) we replace them with a common
variable for the proportion of vacancies which are for either professional, technicians
and associate professional or trades occupations (and drop the management
variable) in columns (4) and (5).
Of the two regional labour market variables, only Census2 is statistically significant (in
all but column (2)). Firms operating in Territorial Authorities with a higher proportion
of the population with degrees and higher unemployment are less likely to find
vacancies hard-to-fill for reasons of skill. Regions in which there is both a high
proportion of the population with no qualifications and unemployment are no easier to
fill such vacancies.
Firms with internal skill gaps also appear to also suffer external skill gaps in the form
of SSVs. Of the business strategy variables, only the R&D variable is statistically
significant. When we replace the suite of business strategy variables with the BS1
factor, it is statistically insignificant.
33
Table 15 Results – SSVs – Contemporaneous variables – Outcome equation
(1)
ln(Wi) – ln(Wijr)
ln(E)
Census106
Census206
Union
Monopoly
Vman
(2)
**
(0.063)
(0.053)
(0.061)
(0.073)
0.037
0.016
0.055
0.046
0.052
(0.049)
(0.053)
(0.047)
(0.043)
(0.040)
-0.002
0.004
-0.001
-0.004
-0.001
(0.038)
(0.035)
(0.035)
(0.038)
(0.037)
-0.073
-0.076
(0.027)
(0.030)
(0.029)
0.115
0.121
0.108
(0.095)
(0.093)
(0.098)
-0.056
-0.019
-0.073
(0.075)
(0.082)
(0.079)
0.091
0.120
0.145
(0.171)
(0.187)
(0.189)
***
0.459
***
-0.080
(0.031)
-0.070**
(0.028)
0.534***
(0.164)
(0.142)
(0.166)
0.618***
0.556***
0.645***
0.325***
0.327***
(0.171)
(0.158)
(0.188)
(0.068)
(0.065)
***
0.652
Vtrade
-0.046
***
0.194
0.201***
(0.054)
**
0.143
(5)
***
0.146
0.518
Vtech
(4)
***
0.138
***
Vprof
(3)
**
(0.115)
***
0.657
(0.157)
ISG
***
0.643
(0.110)
0.117*
0.129**
0.139**
(0.063)
(0.059)
(0.063)
**
-0.261
R&D08
(0.117)
-0.006
Innovate
(0.119)
-0.079
Mark_Inter
(0.109)
0.070
Customise
(0.086)
-0.081
Price_Leader
(0.107)
Export08
-0.039
ODI08
0.061
(0.103)
(0.172)
-0.082
BS1
(0.065)
Constant
athrho
Prob 
Observations
2

-0.213
-0.100
-0.251
-0.175
-0.203
(0.239)
(0.247)
(0.311)
(0.231)
(0.180)
-0.610*
-0.881*
(0.348)
(0.469)
0.0795
4485
0.0601
4482
-0.535
(0.405)
0.1862
4335
-0.503
(0.315)
0.1103
4683
-0.424*
(0.236)
0.0723
4821
See notes to Table 14
34
Our estimate of  (or, rather, athro) is significant in specifications (1), (2) and (5) and
borderline in (4). This suggests that in those specifications at least, it is important to
account for the probability of posting a vacancy in the first place when one considers
which types of firms are likely to report a SSV.
5.2. Using lagged variables
The results of our estimation using lagged variables are set out in Table 16 and
Table 17. Once more, firms with more employees are more likely to have vacancies.
Once we lag sales, we no longer see the positive relationship between sales growth
and the probability of the firm posting a vacancy. Clearly, the relationship between
sales growth and vacancies are two sides of the same phenomena; as the firm grows
it needs more labour input to produce the output and thus needs to post vacancies to
fill the additional roles created. Because of the erratic nature of firm growth, as
opposed to the level of sales, (Hull and Arnold, 2008) , current sales growth is not a
very good predictor of future vacancies.
Our training variable retains a similar sign if we use its current value or even if we
use a training variable taken from the 2006 Employment Practices Module C (column
(10)).
More productive firms are no more or less likely to post a vacancy in the following
year. This result does not change whether we use labour productivity or MFP.
In this analysis we consider the persistence of recruitment difficulties. Firms that
reported recruitment difficulties in 2007 are more likely to report a vacancy in 2008.
This result is significant at the 1% level across all specifications. There is further
evidence of persistence in recruitment difficulties when we include the second lag of
our RecDiff variable.
Although the coefficient on the 2006 recruitment difficulty
variable is only significant at the 10% level, this affect is over and above that of the
2007 variable.
35
Table 16 Results - SSVs - Lagged variables - Selection equation
ln(sales)07
[ln(Wi)–ln(Wijr)]07
SepRate07
(6)
(7)
(8)
(9)
(10)
-0.027
-0.038
-0.038
-0.112
-0.102
(0.059)
(0.062)
(0.062)
(0.070)
(0.142)
-0.105
-0.092
-0.150
-0.161
-0.405
(0.276)
(0.266)
(0.268)
(0.280)
(0.291)
0.110
0.101
0.118
0.152
-0.326
(0.245)
ln(E) 07
ln(LP) 07
(0.269)
***
0.360
***
0.357
(0.265)
***
0.365
(0.049)
(0.051)
(0.053)
0.008
0.003
0.009
(0.059)
(0.062)
(0.062)
MFP07
Train08
***
0.571
(0.104)
***
0.584
(0.096)
***
0.569
(0.106)
(0.309)
***
0.341
(0.047)
(0.540)
***
0.445
(0.059)
0.098
0.076
(0.100)
(0.105)
***
0.536
(0.136)
***
0.437
Train06
(0.126)
Union07
0.252
0.212
(0.238)
RecDiff07
(0.251)
***
0.688
(0.073)
***
0.680
(0.076)
0.292
(0.235)
***
0.665
(0.077)
0.291
(0.283)
***
0.607
(0.075)
***
0.581
(0.096)
*
0.220
RecDiff06
(0.116)
Monopoly07
Export07
-0.109
-0.104
-0.028
-0.037
(0.087)
(0.092)
(0.078)
(0.079)
0.247
**
(0.125)
R&D 07
ODI07
Innovate07
*
0.231
(0.128)
0.114
0.102
(0.149)
(0.145)
0.255
0.255
(0.268)
(0.260)
0.131
(0.115)
0.063
Prod_Innov07
(0.099)
0.033
Proc_Innov07
(0.099)
0.114
Org_Innov07
(0.095)
0.069
Mark_Innov07
(0.090)
***
0.175
BS107
(0.060)
Constant
-1.139
(0.638)



*
-1.101
-1.018
(0.675)
(0.685)
0.088
(0.092)
***
-0.806
(0.220)
***
-1.189
(0.245)
*
Robust standard errors in parentheses
**
***
significant at 10%; significant at 5%; significant at 1%
All models include industry dummies
Turning to the outcome equation (Table 17), the results with the lagged variables are
similar to those with the contemporaneous variables, although once we include the
past recruitment difficulties and training variables from the 2006 survey (RecDif06 and
36
Train06), the number of observations does fall somewhat and the significance of some
of the contemporaneous variables drops (e.g. ISG and Vtech08). Once more, we find
that the positive impact of the relative wage variable remains when we use its lag.
Firms that pay more than their peers are more likely to report SSVs.
Dividing
innovation into the four different types does not uncover any differences in their
implications for the likelihood of SSVs.
Our measure of the correlation between the two equations, altrho, is statistically
significant in all but one of the specifications described in Table 16 and Table 17.
37
Table 17 Results - SSVs - Lagged variables - Outcome equation
(3)
ln(Wi,07) – ln(Wijr,07)
ln(E07)
Census106
Census206
ISG07
Vman,07
Vprof,07
(4)
***
0.205
Monopoly07
Export07
R&D07
(0.099)
-0.018
-0.019
-0.028
-0.072
-0.083
(0.030)
(0.030)
(0.032)
(0.050)
(0.054)
-0.011
-0.015
-0.014
-0.042
-0.057
(0.027)
(0.029)
(0.030)
(0.051)
(0.061)
***
-0.087
***
-0.029
-0.027
-0.032
(0.030)
(0.031)
(0.030)
*
0.084
*
0.086
*
0.086
0.089
0.078
(0.049)
(0.050)
(0.050)
(0.065)
(0.097)
(0.028)
-0.153
(0.037)
0.087
0.081
0.083
-0.097
-0.134
(0.139)
(0.134)
(0.148)
(0.297)
(0.265)
***
0.392
***
0.403
***
0.592
***
0.396
(0.115)
***
0.392
(0.148)
***
0.586
(0.101)
0.210
0.240
(0.181)
(0.182)
**
0.164
(0.065)
(0.065)
-0.067
-0.071
(0.139)
(0.141)
-0.164
-0.173
***
-0.413
***
0.393
(0.118)
***
0.408
(0.150)
***
0.583
(0.108)
**
0.420
(0.191)
**
0.424
(0.197)
*
0.365
0.220
(0.194)
(0.180)
***
0.716
***
0.642
(0.112)
(0.179)
**
0.159
(0.158)
Innovate07
***
0.361
(0.059)
(0.144)
ODI07
0.295
(0.074)
(0.104)
Union07
0.199
(7)
***
(0.075)
(0.150)
Vtrade,07
0.212
(6)
***
(0.075)
(0.115)
Vtech,07
(5)
***
(0.141)
***
-0.406
(0.155)
0.002
(0.092)
0.028
Prod_Innov07
(0.096)
0.080
Proc_Innov07
(0.072)
0.017
Org_Innov07
(0.100)
-0.102
Mark_Innov07
(0.102)
-0.061
BS107
(0.054)
Constant
athrho
Prob 
Observations



*
0.205
0.200
0.194
0.283
0.390
(0.159)
(0.161)
(0.149)
(0.247)
(0.225)
***
-1.699
(0.509)
0.0008
3,495
***
-1.543
(0.397)
0.0001
3,495
***
-1.637
(0.498)
0.0010
3,492
-1.774
(1.249)
0.1556
2,595
-2.451***
(0.306)
0.0000
1,851
*
Robust standard errors in parentheses
**
***
significant at 10%; significant at 5%; significant at 1%
All models include industry dummies
38
5.3. Skill and non-skill related hard-to-fill vacancies
In Table 18 we present results for estimating a two-stage model with different
definitions of the dependent variable in the outcome equation. In columns (11) and
(12) we model the probability of the firm posting a non-skill-related hard-to-fillvacancy. In columns (13) and (14) we restrict the dependent variable to take a
positive value when the firm has NSR only (i.e. no SSVs). The specifications are
similar to (8) and (9) for SSVs in the previous section to allow comparability. The
differences between the them are that the first two columns measure productivity
using MFP and the final two columns use ln(LP).
Perhaps the most obvious difference between the SSV specifications in section 5.1
and (more importantly) 5.2 and the NSR specifications set out in Table 18 is the
result for the relative wage term (ln(Wi,07) – ln(Wijr,07)). In the results for our model of
SSVs, we find a statistically significant positive relationship between the wages the
firm pays relative to others in its industry and region, and the likelihood of it reporting
a SSV. This is not the case when we consider NSRs.
If we consider at all firms that have vacancies that are hard-to-fill for non-skill-related
reasons (columns (11) and (12)), we find no evidence of a relationship between
relative wages and the probability of reporting an NSR. However, this may conflate
the influence of SSVs and NSRs, as many of the firms with NSRs will also have
SSVs. Therefore, if we focus on those firms that only have NSRs ((13) and (14)) a
different picture emerges. Firms that pay higher relative wages are less likely to
have NSRs. When there are no constraints caused by a lack of skilled labour,
markets appear to clear in the way predicted by standard economic theory. If a firm
cannot find staff, it raises its wages relative to its competitors. Those that do not
compete in this way are more likely to suffer from vacancies that are hard-to fill.
39
Table 18 Results – Different definitions of dependent variable
(11)
(12)
(13)
(14)
NSR
NSR
Only
NSR
NSR
Only
ln(Wi,07) – ln(Wijr,07)
0.019
-0.264***
-0.039
-0.306***
(0.063)
(0.079)
(0.059)
(0.085)
ln(E)07
-0.025
-0.073
0.011
-0.009
(0.040)
(0.051)
(0.040)
(0.059)
-0.035
0.033
0.001
0.038
(0.056)
(0.032)
(0.051)
(0.050)
-0.049
0.063
-0.026
0.040
(0.058)
(0.045)
(0.042)
(0.064)
Outcome equation
Census106
Census206
ISG
***
*
0.027
-0.186
0.077
-0.064
(0.058)
(0.059)
(0.044)
(0.063)
***
-0.395
-0.410
(0.301)
(0.407)
(0.151)
(0.351)
Vprof
0.442**
0.102
0.353**
0.168
(0.195)
(0.250)
(0.164)
(0.226)
Vtech
0.307
-0.523***
0.226
-0.574***
(0.222)
(0.202)
(0.167)
(0.215)
Vtrade
0.352***
-0.282**
0.238**
-0.418***
(0.096)
(0.127)
(0.114)
(0.124)
-0.292
0.243
-0.646**
(0.159)
(0.235)
(0.150)
(0.287)
-0.077
0.025
-0.006
0.066
(0.076)
(0.102)
(0.063)
(0.088)
-0.136
0.208
-0.099
0.095
(0.285)
(0.257)
(0.258)
(0.316)
0.056
0.205
0.223
0.361
Vman
Constant
Selection equation
ln(sales)07
[ln(Wi)–ln(Wijr)]07
seprate07
ln(E) 07
MFP07
0.304
*
(0.212)
(0.287)
(0.241)
(0.295)
0.414***
0.370***
0.419***
(0.040)
(0.050)
(0.048)
(0.054)
0.069
0.075
(0.085)
(0.085)
0.027
0.062
(0.047)
(0.047)
0.563
***
(0.125)
RecDiff07
bss107
Constant
0.621
***
0.583
(0.111)
***
***
0.495
***
0.623
(0.097)
0.636***
(0.105)
***
0.652
0.514***
(0.065)
(0.077)
(0.068)
(0.088)
0.138
0.151*
0.171**
0.191***
(0.095)
(0.081)
(0.071)
(0.068)
-0.821
***
(0.145)
athrho
-1.619
Observations
2,592
(0.963)



-0.508
0.358***
ln(LP)07
Train08
-0.397
*
***
**
-0.911
-1.181
(0.196)
(0.482)
***
-1.310
(0.409)
***
-1.453
(0.352)
-1.691***
(0.491)
-0.718**
(0.334)
3,495
Robust standard errors in parentheses
*
**
***
significant at 10%; significant at 5%; significant at 1%
Weighted and Stratified
40
6. Summary and Conclusions
Skills are an important determinant of the economic performance of people, firms,
industries and economies. Shortages of skilled labour directly constrain production
and prevent firms from meeting demand and using available inputs efficiently. They
inhibit innovation and the use of new technologies.
This may have longer-term
impacts on the way firms do business, in terms of their location, size, structure,
production methods and product strategy.
The success of policies to enable the emergence and performance of successful
businesses depends upon having a workforce with the appropriate skills. If New
Zealand is to raise productivity and improve its international competitiveness, it is
important to understand whether skill shortages do indeed exist, how they manifest
themselves and develop policies to address them.
In this paper we have examined factors relating to firms’ skill shortages. Our focus
has been on vacancies that were hard-to-fill because the applicants lacked the
necessary skills, qualification or experience – what we have defined as ‘skill shortage
vacancies’ (SSVs). We have also contrasted these with vacancies that were hard-tofill for reasons other than the skills of applicants.
Worker turnover is a basic fact of economic life. In any given year one in eight
workers leaves their employer. Searching for new staff is, therefore, a position most
businesses find themselves in, regardless of their situation.
Almost half of
businesses find some of these vacancies hard-to-fill and more than a third
experience difficulties in finding workers with the required skills, qualifications or
experience they need.
We have examined patterns of skill shortage vacancies using an econometric
technique that allows us to account, in part, for the interrelationship between the
likelihood of a firm posting a vacancy and for those vacancies being hard-to-fill for
skill-related reasons.
Large firms are more likely to have vacancies – they have more workers and so the
chance of at least one of them leaving is consequently higher. However, larger firms
do not appear to find it more difficult to source workers with the required skills than
smaller firms, once we account for other characteristics. As we might expect, when
firms’ output grows, the need to seek additional staff also grows. However, this is
41
only in the years the firm is expanding. Expanding sales is not a good predictor of
future additional staff requirements.
Businesses can experience skill shortages internally or externally. A shortage in the
skills it requires can manifest itself: (a) in terms of the ability of its existing staff to do
their job; or (b) in terms of its ability to find appropriately skilled workers through
recruitment.
We have found evidence that these two phenomena co-exist.
Businesses experiencing internal skill gaps are also more-likely to have skill shortage
vacancies. We also find evidence of persistence in this relationship; businesses
experiencing internal skill gaps are also more likely to have SSVs in the following
year.
Businesses have a ‘make or buy’ option when it comes to skills. If they cannot
source the skills internally or externally, they can up-skill existing or new workers
through training. Firms’ training choices are the subject of a companion paper to this
(Mason et al., 2012). In this paper, we find that firms who undertake training are
more likely to have vacancies, suggesting that their response to skill shortages is
likely to be a mixture of recruitment and up-skilling strategies.
Our results confirm earlier work that highlights the importance of two distinctions:
First, it is important to distinguish between firms’ general recruitment activity and it
having skill shortage vacancies.
Second, we must distinguish between firms
experiencing recruitment difficulties related to skills and those for other reasons (such
as the firm not offering good enough pay and conditions). Failing to account for this
may cause us to misdiagnose the problem and will therefore undermine the
appropriateness of any policy prescription.
In any market there will be a number of potential customers that cannot purchase all
they would wish because they are unwilling or unable to pay the market price. This
does not mean that the market is malfunctioning. There is an important question for
researchers and policymakers to ask before interpreting reports of unfilled vacancies
of any kind, and of SSVs in particular, as a ‘skills problem’. This question is: Why do
businesses that cannot fill a vacancy not simply raise the wages they are offering?
There a number of reason why firms may not be able to do so. First, it may be
because the firm cannot afford to pay any more. If this is the case, the problem is not
the labour market or the education system, but the firm’s own productivity. Firms that
42
that undertake activities associated with high productivity are likely to both have a
higher demand for skills and be more able to afford to pay skill premia.
Second, the supply of labour takes a long time to adjust. Because of the length of
time required to educate and train skilled workers, it takes a long time for changes in
wages to influence changes in skill acquisition (particularly if we are relying on the
rather indirect mechanism of changes in relative wages influencing the choices of
students at school) and immigration is restrained by, among other things, legal
barriers and the costs of relocation.
We have found that the businesses that feel the applicants they are attracting do not
have the required skills, qualifications or experience are those that are paying higher
wages. The fact that businesses that pay higher wages than others in the same
industry and region are actually more likely to experience SSVs suggests that raising
wages is not sufficient to fill these vacancies.
In contrast, when we focus on
vacancies that are hard-to-fill for reasons other than skill, paying higher wages does
appear to be a successful policy to fill vacancies. Firms that pay higher relative
wages are less likely to have non-skill-related hard-to-fill vacancies. The implication
of this is that SSVs and NSRs are different phenomena.
One important issue researchers have to confront when undertaking analysis of this
type is that of causality.
It is one thing to establish a statistical regularity – a
correlation between firm characteristics, activities or environment and an outcome
such as external skill shortages. It is another thing to interpret this as a causal
relationship. In this paper, we have uncovered some clear statistical regularities.
These are consistent we with certain predictions we have discussed in this paper and
provide us with useful information to aid our understanding of how the landscape of
skills in New Zealand and, in particular, we done this from the perspective what
businesses are looking for, rather than what the education and training system has
provided.
The issues this research has raised and other questions unanswered suggest a
number of potentially useful avenues for future research. Perhaps the most obvious
is the question of the impact of shortages of skilled labour.
The Longitudinal
Business Database has ample data to consider this question. We can use the panel
element of the Business Operations Survey to investigate the impact on future
43
business practices.
The following year’s BOS contains a ‘Business Practices’
Module, which would be particularly useful for this. There is now enough information
to examine the impact of skills shortages on measures of firm performance, like
productivity, profitability, employment or skill-intensive activities such exporting, R&D
and other innovation activity and outputs.
It is possible, for example, to use
techniques previously used with the LBD to tackle issues of causality when
evaluating government business assistance programmes (Morris and Stevens, 200),
the impact of broadband (Grimes, Ren and Stevens, 2012) or foreign acquisition
(Fabling and Sanderson, 2012) on firm outcomes.
This paper has focussed on external skill gaps – difficulties sourcing workers with the
required skills from the labour market. A fuller picture will only emerge when we
have also investigated the firms that suffer internal skill gaps – the fact that the firms;
current workforce may not have all the skills it requires – and their impact of firm
performance.
We have only lightly touched the issue of the persistence of skill shortages and found
prima facie evidence of persistence in recruitment difficulties. The increasing length
of the BOS panel will enable researchers to use the Module A questions on
recruitment difficulties to investigate this. This is not, of as we have noted, the same
as a skill shortage, however. One means to overcome this and some of the issues of
causality is to repeat the Business Skills and Strategy or something like it.
An
alternative option to a whole new survey module would be to append some of the
questions to another module, such as a repeat of the Business Practices Module.
44
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52
Appendix A1. Data Appendix
A1.1 BOS Variables
6.1.1. Module A (2005 – 2008)
Data items for questions for Module A relate to the 2008 survey.
Because of
changes to questions, these may differ slightly in earlier years.
Exporting (Export)
This variable relates to question 11 of Module A: ‘Estimate from question 8 the
percentage of sales that came from exports’. Data Item A1101. The variable Export
takes the value of 1 if the firm reports a non-zero percentage of sales as coming from
exports, zero otherwise.
Research and Development (R&D)
This variable relates to question 23 of Module A. Data item (A2300). The question is:
‘For the last financial year, did this business undertake or fund any research and
development (R&D) activities?’ The respondents are asked to include: ‘any activity
characterised by originality: it should have investigation as its primary objective, and
an outcome of gaining new knowledge, new or improved materials, products,
services or process’; or ‘the buying abroad of technical knowledge or information’.
They were asked to not include: ‘market research’; ‘efficiency studies’; or ‘style
changes to existing products’. The variable R&D takes the value of 1 if the response
is yes, zero otherwise.
Ownership of overseas businesses (ODI)
This variable relates to question 25 of Module A. Data item (A2500). The question
asked is ‘As at the end of the last financial year, did this business hold any ownership
interest or shareholding in an overseas located business (including its own branch,
subsidiary or sales office)? The variable ODI takes the value of 1 if the response is
yes, zero otherwise.
Recruitment difficulties (RecDif)
This variable relates to question 33 of Module A: ‘Over the last financial year, to what
extent did this business experience difficulty in recruiting new staff for any of the
53
following occupational groups?: managers and professionals; technicians and
associate
professionals;
tradespersons
and
related
workers
(including
apprenticeships); all other occupations’ (Data Items A3301 to A3304). The variable
RecDif takes the value of 1 if the respondent reports a severe difficulty in any of the
three occupations, zero otherwise.
For more on this variable, including an
examination of its persistence and a comparison with the hard-to-fill vacancy variable
from Module C of BOS 2008, see the discussion in section 3.3 and Appendix A2.
Collective agreements (union)
This variable relates to question 36 of Module A: ‘As at the end of the last financial
year, what percentage of this business’s employees were covered by a collective
employment agreement?’ (Data item A3600.) The variable union takes the value of 1
if the respondent reports any value above zero and zero otherwise.
Innovation (innovate)
This variable relates to question 42 of Module A. Data item (A4200). The question is:
‘In the last financial year, did this business develop or introduce any new or
significantly
improved:
goods
or
services;
operational
organisational/managerial processes; marketing methods?’
processes;
The variable innovate
takes the value of 1 if the response is yes, zero otherwise.
Competition (monopoly)
Competition is measured through binary variable monopoly. These variables relate to
question 47 of Module A. Data item (A4700). The respondents were asked ‘How
would you describe this business’s competition?’ The variable monopoly takes the
value of 1 if the response is yes, zero otherwise.
6.1.2. Module B – Innovation (2007)
Product innovation (Prod_Innov07)
This variable relates to question 3 of Module B: ‘During the last 2 financial years, did
this business introduce onto the market any new or significantly improved goods or
services?’ Data Item B0300. Respondents are asked to exclude the selling of new
goods or services wholly produced and developed by other businesses. The variable
Prod_Innov07 takes the value of 1 if the firm has innovated, zero otherwise.
54
Operational process innovation (Proc_Innov07)
This variable relates to question 7 of Module B: ‘During the last 2 financial years, did
this business implement any new or significantly improved operational processes (i.e.
methods of producing or distributing goods or services)?’ Data Item B0700. The
variable Proc_Innov07 takes the value of 1 if the firm has innovated, zero otherwise.
Organisational/managerial process innovation (Org_Innov07)
This variable relates to question 10 of Module B: ‘During the last 2 financial years, did
this business implement any new or significantly improved organisational/managerial
processes (i.e. significant changes in this business’s strategies, structures or
routines)?’ Data Item B1000. The variable Org_Innov07 takes the value of 1 if the firm
has innovated, zero otherwise.
Marketing innovation (Mark_Innov07)
This variable relates to question 12 of Module B: ‘During the last 2 financial years, did
this business implement any new or significantly improved sales or marketing
methods which were intended: to increase the appeal of goods or services for
specific market segments; to gain entry to new markets’ Data Item B1200. The
variable Mark_Innov07 takes the value of 1 if the firm has innovated, zero otherwise.
6.1.3. Module C – Employment Practices (2006)
Training (train06)
This variable relates to question 13 of Module C. Data item (C1300). Respondents
were asked: ‘Over the last financial year, have employees in this business received
training of any type?’ The variable train06 takes the value of 1 if the response is yes,
zero otherwise.
6.1.4. Module C – Business Strategy and Skills
Market focus (mark_int)
This variable relates to question 2 of Module C: ‘In the last 2 financial years, what
market accounted for the largest proportion of this business’s total sales of goods or
services?’
Data item (C0200).
‘national’ or ‘international’.
Respondents could answer one of either ‘local’,
The variable mark_int takes the value of 1 if the
respondent indicates that their market focus is ‘international’ and zero otherwise.
55
Customisation (Customise)
This variable relates to question 6 of Module C: ‘Which of the following best
describes this business’s goods or services?’
Data item C0600.
Respondents
answer from one of the following four options: ‘standard range of goods or services’,
‘minor differences in goods or services according to customer requirements’,
‘substantial differences in goods or services according to customer requirements’, or
‘don’t know’. The variable Customise takes the value 1 if the respondent answers
‘substantial differences in goods or services according to customer requirements’,
zero otherwise.
Price leadership (Price_Leader)
This variable relates to question 8 of Module C: ‘How often is this business able to
obtain a higher price than competitors for its main goods or services?’ Data item
C0800.
The variable Price_Leader takes the value 1 if the respondent answers
‘always’, zero otherwise.
Occupational vacancy rates (Vman, Vprof, Vtech, Vtrade, Vproftechtra)
These variables relate to the question 15 from Module C: ‘During the last financial
year, how many vacancies has this business had for the following roles? managers;
professionals; technicians and associate professionals; tradespersons and related
workers; clerical, sales and services workers; labourers, production transport or other
workers’. Data items C1501 to C1506. Each variable relates to the proportion of
vacancies that are made up of the respective occupations as a proportion of all
vacancies.
For example, that for Vman equals the following data items:
C1501/( C1501+ C1502+ C1503+ C1504+ C1505+ C1506).
Internal skill gaps (ISG)
This variable relates to question 20 from Module C: ‘How many of this business’s
existing staff have the skills required to do their job?’ Data items C2000 to C2006.
There are four response categories: ‘less than half’, ‘half or more’, ‘all staff’ or ‘no
staff of this type’. The variable ISG is a binary variable, taking the value of one if the
respondent answers that ‘less than half’ for any of the occupations, zero otherwise.
56
Training (train)
This variable relates to question 22 of Module C. Data item (C2200). Respondents
were asked: ‘During the last financial year, have the staff of this business received
training of any type?’ The variable train takes the value of 1 if the response is yes,
zero otherwise.
Occupational breakdown of staff (Propman, Propprof, Proptech, Proptrade)
This variable relates to questions 10-13 of Module C. Note that every year in Module
A, businesses are asked to provide a breakdown of their staff by four occupations
(A3201-A3204). Respondents are asked to copy the totals from Module A into boxes
in Module C. They are then asked to provide a further breakdown of two of these
(‘Managers and professionals’ and ‘all other occupations’).
We calculate the
proportion of the workforce in each of the occupations, with ‘labourers, production,
transport or other workers’ as the baseline category.
57
Table 19 Staff occupation/role variables
Data
item
Occupation/role
Managers
(i.e. those who supervise staff or determine policy and future direction)
30
Professionals
(i.e. those who have specific expertise, but no managerial responsibility)
31
Variable
C1002 Propman
C1003 Propprof
Technicians and associate professionals
Technicians and associate professionals perform complex technical or
administrative tasks, often in support of professionals or managers (e.g.
technical officer, building inspector, legal executive)
C1101 Proptech
Tradespersons and related workers
Tradespersons and related workers perform tasks requiring trade specific
technical knowledge. Include all apprentices and trade supervisors (e.g.
electrician, mechanic, hairdresser, baker).
Clerical, sales and service workers
(i.e. those who perform administrative, sales or customer service tasks)
Labourers, production, transport or other workers
C1201 Proptrade
C1302 Baseline
C1203 Baseline
(i.e. those who operate vehicles or equipment or perform manual tasks)
A1.2 LEED/PAYE Data
Our data on employment come from the Linked Employer-Employee Database. It
has two components, counts of employees and working proprietors.
Employees
Employment is measured using an average of twelve monthly PAYE employee
counts in the year. These monthly employee counts are taken as at 15th of the
month.
This figure excludes working proprietors and is known as Rolling Mean
Employment (RME).
30
In Module A, where Mangers and professionals are grouped together, there are separate
descriptions of managers and professionals. In addition to the description of managers given in the
question in Module C, respondents are also offered two examples: ‘General Manager’ and ‘Finance
Manager’
31
In Module A, respondents are also offered a different description: ‘Professionals perform analytical,
conceptual or creative tasks with skills equivalent to a bachelor degree or higher (e.g. accountant,
engineer, journalist, computer programmer)’.
58
Working proprietors
The working proprietor count is the number of self-employed persons who were paid
taxable income during the tax year (at any time). In LEED, a working proprietor is
assumed to be a person who (i) operates his or her own economic enterprise or
engages independently in a profession or trade, and (ii) receives income from selfemployment from which tax is deducted.
From tax data, there are five ways that people can earn self-employment income
from a firm:

As a sole trader working for themselves (using the IR3 individual income tax
form [this is used for individuals who earn income that is not taxed at source]);

Paid withholding payments either by a firm they own, or as an independent
contractor (identified through the IR348 employer monthly schedule);

Paid a PAYE tax-deducted salary by a firm they own (IR348);

Paid a partnership income by a partnership they own (IR20 annual partnership
tax form [this reports the distribution of income earned by partnerships to their
partners] or the IR7 partnership income tax return);

Paid a shareholder salary by a company they own (IR4S annual company tax
return [this reports the distribution of income from companies to shareholders
for work performed (known as shareholder-salaries)]).
Note that it is impossible to determine whether the self-employment income involves
labour input. For example, shareholder salaries can be paid to owner-shareholders
who were not actively involved in running the business. Thus there is no way of
telling what labour input was supplied, although the income figures do provide some
relevant information (a very small payment is unlikely to reflect a full-year, full-time
labour input).
Separations
Labour separation rate (SepRate) is measured as the annualised number of
separations to the firm divided by RME. Note that we also considered measures of
net employment growth (accessions less separations) and labour turbulence
59
(accessions plus separations), but neither of these were statistically significant in our
specificaitons.
Wages
Wages are calculated as ‘total employee gross earnings’ from the LEED database,
divided by RME (i.e. excluding working proprietors).
This data comes from the
Employers Monthly Schedule (EMS).
Relative wages
Our measure of relative wages measures the wages of the firm relative to other firms
in the same industry in the same Territorial Authority. Because many of the firms are
multi-plant firms we account for this by calculating employment weighted values.
The entity in the LBD that corresponds to the concept of a ‘firm’ or ‘businesses’ in
economic parlance is called the enterprise. Just as a firm can be made up of many
plants or establishments, we say an enterprise can have many PBNs32. In the LBD
we have information on the location, industry, [wages] and employment at the
establishment (PBN) level.
First we calculate the average wage a described above for each plant in each month.
These can be averaged across the establishments within an enterprise and across
months in the year using employment weights. This is mathematically equivalent to
the average wage calculated at the level of the enterprise. We can then calculate the
average wage of an establishment relative to other firms in the same 2-digit industry
and the same Territorial Authority. Note that if there were fewer than 30 firms in the
same 2-digit industry and the same Territorial Authority, we calculated this at the
one-digit level.
A1.3 Other LBD Data (AES, BAI, and IR10)
Sales (ln(sales))
Our measure of sales from the Business Activity Indicator (BAI) database. The
Business Activity Indicator uses GST data from the Inland Revenue matched to the
Statistics NZ Business Frame. The BAI data come from the Goods and Services Tax
32
In fact, the geographic unit that makes up an enterprise is called the geographic using (or GEO).
However, after work conducted to improve the longitudinal linking of GEOs created a new identifier,
the Permanent Business Number, hence the name.
60
return form, GST 101. For more information on this and other LBD datasets, see
Fabling, Grimes, Sanderson and Stevens (2008) and Fabling (2009).
Labour Productivity (ln(LP))
Labour productivity is calculated from the AES, BAI, IR10 and LEED data.
The
primary data source for gross output, intermediate consumption, changes instocks
and capital services is the AES. These are supplemented with data from the IR10
financial accounts form when missing. Finally, for the purposes of calculating labour
productivity, if the data is still missing, we calculate value added as sales minus
purchases from the BAI. Labour input is total labour input (RME plus the count of
working proprietors).
Multifactor productivity (MFP)
This is generated from a simple 2-digit industry-level OLS regression of the log of
gross output on the logs of intermediate consumption, capital services and labour
input. In earlier work, we also considered a measure based on Levinsohn and Petrin
(2003), but this had little effect on results.
61
Appendix A2. Comparison of hard-to-fill vacancies in Module C
with recruitment difficulties in Module A
In this appendix, we compare responses to the hard-to-fill vacancies question in
Module C with those to a similar question that is asked in Module A. Module A is a
core module, asked every year, containing questions about businesses’ general
activities, environment and performance.
In a section on employment, firms are
asked ‘Over the last financial year, to what extent did this business experience
difficulty in recruiting new staff for any of the following occupational groups?’ (A33).
Respondents are asked to rank these difficulties on a three-point scale (‘no difficulty’,
‘moderate difficulty’ or ‘severe difficulty’) along with a ‘don’t know’ or ‘not applicable’
category. Whilst these two questions (C18 and A33) appear to be measuring the
same concept, we must be wary of expecting responses to be identical. In Module A
this is a generic stand-alone question (although the previous question does ask them
to break down staff numbers by occupation). In Module C, respondents have just
been asked a number of questions about vacancies during the last financial year and
been asked to provide reasons why their business found it hard-to-fill vacancies.
This, combined with the different language used in module C (vacancy plus hard-tofill vacancy) and Module A (‘difficulty in recruitment’), are likely to create a certain
amount of dissonance. For more on the subject of how respondents interpret and
answer questions in business surveys, with particular reference to the BOS, see
Fabling, Grimes and Stevens (2008, 2012).
With these caveats I mind, Table 20 sets out a comparison of responses to questions
C18 and A33 for 2008.
Because Module C has a more disaggregated set of
occupational classifications we present cross-tabulations with both the more
narrowly-defined occupational groups and for both combined.
The first panel of Table 20 compares responses to the first occupational group in
A33, ‘managers and professionals’ (A3301), with those for ‘managers’ (C1801),
‘professionals’ (C1802) and both C1801 and C1802 combined. The final column
counts there being a hard-to-fill vacancy if the respondent ticks the box for either
C1801 or C1802.
Perhaps the first thing that is clear is that there is a high degree of correlation
between the two measures, but that this is not total. Just under three-quarters of
62
respondents that said they had a severe difficulty in recruiting managers and
professionals in Module A said that vacancies for either managers or professionals
were hard-to-fill in Module C. Nevertheless, over a quarter did not.
Table 20 Weighted counts of firms reporting levels of difficulty recruiting new
staff in Module A by Module C hard-to-fill vacancy response
Module C
Managers and Professionals
Managers
Module A
Professionals
Managers &
professionals
No H2F
H2F
No H2F
H2F
No H2F
H2F
No Difficulty
Moderate difficulty
Severe difficulty
4,767
3,900
1,908
279
1,140
1,242
4,782
3,846
1,488
270
1,191
1,665
4,578
3,030
873
471
2,010
2,280
Don't know
Not applicable
1,206
19,215
57
351
1,158
19,077
105
489
1,119
18,792
144
774
Total
30,999
3,072
30,348
3,720
28,389
5,679
Pearson test
2
F
p





294.48
31.77
0.0000
502.97
54.28
0.0000
702.50
75.15
0.0000
The three tables are cross-tabulations of Q18 from Module C (columns) and Q33 from Module A (rows)
In this particular table, the first pair of columns relates whether firms ticked box C0801 (manger roles were
hard-to-fill (H2F)), the second pair to C0802 (professionals). The final pair of columns relate to firms who
ticked neither (No H2F) or one of the two boxes (H2F).
The rows represent responses to A3301.
Counts based on population weights and random-rounded to base 3
2
The test of independence that is displayed by default is based on the usual Pearson  statistic for two-way
tables. To account for the survey design, the statistic is turned into an F statistic with non-integer degrees of
freedom by using a second-order Rao and Scott (1981, 1984) correction. Note that this test is performed on
the first three rows only (i.e. those that report whether they have a difficulty)
63
Table 20 (continued)
Module C
Technicians and
associate
professionals
Module A
No Difficulty
Moderate difficulty
Severe difficulty
No H2F
Tradespersons
and related
workers
H2F
No H2F
H2F
3,645
3,267
1,509
84
963
1,164
4,278
4,146
1,329
219
2,262
2,970
Don't know
Not applicable
1,116
20,991
24
378
1,086
16,533
30
366
Total
30,528
2,619
27,372
5,844
Pearson test
2
340.62
58.18
0.0000
F
p




631.65
92.67
0.0000
See notes for table 1a
The pairs of columns relate to data items C1803 and C1804
The rows relate to the responses to data items A3302 and A3303
Counts based on population weights and random-rounded to base 3
Table 20 (continued)
Module C
All other occupations
Clerical, sales and
service workers
Module A
No H2F
H2F
Labourers, production,
transport or other workers
No H2F
H2F
Both
No H2F
H2F
No Difficulty
Moderate difficulty
Severe difficulty
4,575
7,575
1,392
528
2,400
753
4,512
7,449
1,080
588
2,526
1,071
4,071
5,481
477
1,032
4,491
1,671
Don't know
Not applicable
1,212
9,501
72
426
1,080
9,381
204
546
1,011
8,991
273
936
24,255
4,179
23,502
4,935
20,037
8,400
Total
Pearson test
2
F
p




116.07
12.53
0.0000
216.88
27.96
0.0000
384.94
51.68
0.0000
See notes for table 1a
The pairs of columns relate to data items C1805 and C1806 and the combination of the two, as set out in the
footnote to table 1a
The rows relate to the responses to data items A3304
Counts based on population weights and random-rounded to base 3
64
Of the two occupational groups with direct mappings from A33 to C18, the correlation
is less strong for ‘Technicians and associate professionals’ (A3302 and C1803), but
similar for ‘Tradespersons and related workers’ (A3303 and C1804)33.
The final panel of Table 20 shows the results for another category that is split into
two groups in Module C. The ‘all other occupations’ (A3304) category in Module A
maps to two categories in Module C: ‘clerical, sales and service workers’ (C1805)
and ‘labourers, production, transport or other workers’ (C1806). The results for the
final combined group are similar to the ‘managers and professional’ combined group.
Whilst the correlation is not very strong between the combined A3304 and the
component parts C1805 and C1806 individually, that with the combined group is
much higher. More than three quarters of the respondents that stated they had
severe difficulty recruiting ‘all other occupations’ in Module A answered that they
found vacancies for either ‘clerical, sales and service workers’ or ‘labourers,
production, transport or other workers’.
33
Note that for the question in Module A (A33), the occupational title includes the parenthesis
‘(including apprenticeships)’.
65