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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 Treasury

Geoff Mason 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.

i

Abstract

This paper examines the determinants of firms’ skill shortages, using a specially-

designed 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-to-

fill for non-skill-related reasons.

JEL Classification: J24, J31, L60

Keywords: skill shortages, hard-to-fill vacancies, Business Operations Survey,

Heckman Selection model

ii

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-to-

fill 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

iii

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

iv

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.

v

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

vi

Appendix A2. Comparison of hard-to-fill vacancies in Module C with recruitment difficulties in Module A ...................................................................... 62

vii

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

1

A Good Worker is Hard to Find: Skills

Shortages in New Zealand Firms

1. Introduction


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

2

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

3

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 Perez-

Sebastian, 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

4

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.

5

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).

http://www.stats.govt.nz/tools_and_services/tools/TableBuilder/leed-quarterly-tables.aspx

6

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).

7

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)

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.

Skill shortages Hard-to-fill vacancies

ISG SSV NSR

8

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).

9

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 high-

skilled 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.

10

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. Fast-growing and large firms will be more likely to be seeking

additional staff. 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).

11

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

12

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 policy-

relevant data to be collected on an ad hoc basis14. The BOS is conducted using two-

way 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 policy-makers were considering a particular issue. Thus there was a Business Practices Survey in 2001, an

13

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).

14

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

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.

Vacancies (77%)

HHaarrdd--ttoo--ffiillll

vvaaccaanncciieess ((4499%%))

Skill shortage vacancies (36%)

15

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


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 occupations18. 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)

16

Table 2 Businesses reporting vacancies, %


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 8.7 19.3 25.7 38.5 12.4

Tradespersons and rel. workers 20.4 25.5 27.5 36.3 22.1

Clerical sales and service workers 28.6 45.2 58.7 72.8 34.5

Labourers, production, transport or other workers

26.2 41.1 47.3 53.3 30.7

All occupations 68.5 86.1 89.4 88.0 73.1

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?’


Figure for ‘all occupations’ does not match that for Table 1 because some firms do not report the occupations of their vacancies



* 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.


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.

17

Table 3 Businesses reporting hard-to-fill vacancies, %


E<20* 20≤E<50 50≤E<100 E≥100

Managers 4.9 10.1 16.7 29.4 7.2

Professionals 7.1 11.6 16.1 24.8 9.0

Technicians and associate profs 4.6 8.4 11.3 19.4 6.1

Tradespersons and related workers 13.7 16.3 15.1 19.8 14.4

Clerical sales and service workers 10.2 14.2 18.1 24.6 11.8

Labourers, production, transport or other workers 12.3 17.6 19.9 22.9 13.9

All occupations 44.2 58.8 65.4 73.6 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-to-fill?’


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





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

18

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).

19

Table 4 Year-to-year rank correlation between reported recruitment difficulties

2005 2006 2007 2008 2005 2006 2007 2008

Managers and professionals Tradesmen and related occupations

2006 0.5836*** 1 0.4827*** 1

(0.0000) (0.0000)

2007 0.4522*** 0.5402*** 1 0.4506*** 0.4592*** 1

(0.0000) (0.0000) (0.0000) (0.0000)

2008 0.5151*** 0.5225*** 0.5450*** 1 0.3668*** 0.4462*** 0.6254*** 1 (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000)

Technicians and assistant professionals Other occupations

2006 0.4534*** 1 0.3357*** 1

(0.0000) (0.0000)

2007 0.4225*** 0.5687*** 1 0.3105*** 0.3874*** 1

(0.0000) (0.0000) (0.0000) (0.0000)

2008 0.4161*** 0.4183*** 0.6455*** 1 0.3011*** 0.3362*** 0.4067*** 1 (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (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).

20

Table 5 Skill-related reasons for hard-to-fill vacancies, by size

Business Size

RME <20

20 ≤ RME <50

50 ≤ RME <100

100+ RME

Total

Applicants lack work experience the business demands

25.4 34.7 37.9 48.1 28.4

Applicants lack qualifications or skills the business demands

25.1 35.2 38.3 47.5 28.2

Lack either qualifications or work experience

13.7 16.2 20.9 21.8 14.7

Lack both qualifications and work experience

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





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 skill-

shortage 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

21

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 Less than half Half or more All staff

Managers 9.8 11.7 78.5

Professionals 4.4 7.3 37.7

Technicians and associate professionals 3.3 9.0 33.0

Tradespersons and related workers 4.4 16.1 32.4

Clerical, sales and service workers 4.4 20.3 60.6

Labourers, production, transport and others 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.

22

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) iii XSSV 1

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:

23

(2) 01

00

ii

ii

SSVifSSV

SSVifSSV

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) 02 iii ZVac

where

(4)

21

2

1

,

1,0~

1,0~

corr

N

N

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.

24

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).

25

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 one24 (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 Eigenvalue Difference Proportion Cumulative

Factor1 1.03421 0.79462 1.3842 1.3842

Factor2 0.23959 0.23876 0.3207 1.7049

Factor3 0.00083 0.06736 0.0011 1.7060

Factor4 -0.06653 0.01844 -0.0890 1.6169

Factor5 -0.08497 0.07256 -0.1137 1.5032

Factor6 -0.15753 0.06092 -0.2108 1.2924

Factor7 -0.21845 -0.2924 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 Factor1 (BS1) Factor2 Factor3 Uniqueness

Mark_Inter 0.4563 -0.2085 0.0097 0.7482

Customise 0.1627 0.2250 0.0119 0.9228

Price_Leader 0.2047 0.2074 0.0150 0.9148

Innovate 0.2598 0.2691 -0.0100 0.8600

R&D 0.4721 0.0954 -0.0127 0.7678

Export 0.5731 -0.1229 0.0047 0.6565

ODI 0.3725 -0.0765 -0.0093 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). However, some of the

variables (i.e., those in Module C) are not available. 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).

26

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


Factor1 1.49186 1.03350 1.1324 1.1324

Factor2 0.45836 0.47050 0.3479 1.4803

Factor3 -0.01214 0.08671 -0.0092 1.4711

Factor4 -0.09885 0.03521 -0.0750 1.3961

Factor5 -0.13406 0.01513 -0.1018 1.2943

Factor6 -0.14918 0.08937 -0.1132 1.1811

Factor7 -0.23855 . -0.1811 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 0.4245 0.3145 0.7208

Export07 0.3133 0.3712 0.7641

ODI07 0.2351 0.2875 0.8621

prod_innov07 0.5866 0.0259 0.6552

proc_innov07 0.5428 -0.1862 0.6707

org_innov07 0.5153 -0.2451 0.6744

mark_innov07 0.5039 -0.2088 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.

27

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 13th 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.

28

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

(5)

L

l

M

m

jrm

ikm

ioi W

E

EW

1 1

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.

29

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 1 1.50331 0.27826 0.5011 0.5011

Factor 2 1.22505 0.95341 0.4083 0.9095

Factor 3 0.27164 . 0.0905 1.0000

LR test: independent vs. saturated: chi2(3) = 3782.54 Prob>chi2 = 0.0000

Table 12 Rotated factor analysis


Census1 1.41023 0.09211 0.4701 0.4701

Census2 1.31812 . 0.4394 0.9095

Rotation method: orthogonal varimax

Table 13 Rotated factor loadings (pattern matrix) and unique variances

Variable Census1 Census2 Uniqueness

% no qualifications 0.9318 -0.2150 0.0854

% degree or higher -0.1096 0.9553 0.0754

% unemployment 0.7279 0.5995 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

30

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.

31

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.

32

Table 14 Results - SSVs - Contemporaneous variables - Selection equation

(1) (2) (3) (4) (5)

ln(sales) 0.281***

0.283***

0.237***

0.246***

0.239***

(0.071) (0.074) (0.075) (0.072) (0.068)

[ln(Wi)–ln(Wijr)] 0.242 0.343 0.463* 0.385

* 0.417

**

(0.273) (0.239) (0.268) (0.215) (0.203)

SepRate 0.542 0.470 0.449 0.313 (0.447) (0.420) (0.401) (0.384)

ln(E) 0.419***

0.407***

0.397***

0.419***

0.435***

(0.049) (0.050) (0.052) (0.048) (0.044)

ln(LP) 0.041 0.071 0.074 0.052 (0.049) (0.046) (0.052) (0.054)

Train 0.821***

0.831***

0.821***

0.860***

0.852***

(0.074) (0.097) (0.085) (0.080) (0.074)

Union -0.002 -0.031 -0.038 (0.058) (0.049) (0.045)

Monopoly -0.029 -0.057 0.009 (0.103) (0.096) (0.118)

Propman 0.257 0.210 0.078 (0.439) (0.506) (0.440)

Propprof -0.030 0.043 -0.075 (0.318) (0.323) (0.320)

Proptech -0.215 -0.148 -0.256 (0.208) (0.242) (0.263)

Proptrade 0.009 0.089 0.100 (0.126) (0.167) (0.130)

ISG 0.002 0.017 (0.056) (0.047)

Price_Leader 0.303***

(0.060)

Innovate 0.294***

(0.074)

R&D -0.068 (0.123)

Mark_Inter 0.042 (0.161)

Customise 0.164*

(0.088)

Export 0.080 (0.121)

ODI 0.376*

(0.196)

BS1 0.211** 0.208

**

(0.085) (0.086)

Constant -1.513***

-1.816***

-2.030***

-1.652***

-1.081***

(0.489) (0.512) (0.595) (0.587) (0.108)

Industry dummies

Robust standard errors in parentheses

* significant at 10%;

** significant at 5%;

*** significant at 1%

33

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.

34

Table 15 Results – SSVs – Contemporaneous variables – Outcome equation

(1) (2) (3) (4) (5)

ln(Wi) – ln(Wijr) 0.138** 0.146** 0.143*** 0.194*** 0.201*** (0.054) (0.063) (0.053) (0.061) (0.073)

ln(E) 0.037 0.016 0.055 0.046 0.052 (0.049) (0.053) (0.047) (0.043) (0.040)

Census106 -0.002 0.004 -0.001 -0.004 -0.001 (0.038) (0.035) (0.035) (0.038) (0.037)

Census206 -0.073** -0.046 -0.080*** -0.076*** -0.070** (0.031) (0.030) (0.029) (0.027) (0.028)

Union 0.115 0.121 0.108 (0.095) (0.093) (0.098)

Monopoly -0.056 -0.019 -0.073 (0.075) (0.082) (0.079)

Vman 0.091 0.120 0.145 (0.171) (0.187) (0.189)

Vprof 0.518*** 0.459*** 0.534***

(0.164) (0.142) (0.166)

Vtech 0.618*** 0.556*** 0.645*** 0.325*** 0.327***

(0.171) (0.158) (0.188) (0.068) (0.065)

Vtrade 0.652*** 0.657*** 0.643*** (0.115) (0.157) (0.110)

ISG 0.117* 0.129** 0.139** (0.063) (0.059) (0.063)

R&D08 -0.261** (0.117)

Innovate -0.006 (0.119)

Mark_Inter -0.079 (0.109)

Customise 0.070 (0.086)

Price_Leader -0.081 (0.107)

Export08 -0.039 (0.103)

ODI08 0.061 (0.172)

BS1 -0.082 (0.065)

Constant -0.213 -0.100 -0.251 -0.175 -0.203 (0.239) (0.247) (0.311) (0.231) (0.180)

athrho -0.610* -0.881* -0.535 -0.503 -0.424* (0.348) (0.469) (0.405) (0.315) (0.236)

Prob 2 0.0795 0.0601 0.1862 0.1103 0.0723

Observations 4485 4482 4335 4683 4821

See notes to Table 14

35

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.

36

Table 16 Results - SSVs - Lagged variables - Selection equation

(6) (7) (8) (9) (10)

ln(sales)07 -0.027 -0.038 -0.038 -0.112 -0.102 (0.059) (0.062) (0.062) (0.070) (0.142)

[ln(Wi)–ln(Wijr)]07 -0.105 -0.092 -0.150 -0.161 -0.405 (0.276) (0.266) (0.268) (0.280) (0.291)

SepRate07 0.110 0.101 0.118 0.152 -0.326 (0.245) (0.269) (0.265) (0.309) (0.540)

ln(E) 07 0.360***

0.357***

0.365***

0.341***

0.445***

(0.049) (0.051) (0.053) (0.047) (0.059)

ln(LP) 07 0.008 0.003 0.009 (0.059) (0.062) (0.062)

MFP07 0.098 0.076 (0.100) (0.105)

Train08 0.571***

0.584***

0.569***

0.536***

(0.104) (0.096) (0.106) (0.136)

Train06 0.437***

(0.126)

Union07 0.252 0.212 0.292 0.291 (0.238) (0.251) (0.235) (0.283)

RecDiff07 0.688***

0.680***

0.665***

0.607***

0.581***

(0.073) (0.076) (0.077) (0.075) (0.096)

RecDiff06 0.220*

(0.116)

Monopoly07 -0.109 -0.104 -0.028 -0.037 (0.087) (0.092) (0.078) (0.079)

Export07 0.247** 0.231

*

(0.125) (0.128)

R&D 07 0.114 0.102 (0.149) (0.145)

ODI07 0.255 0.255 (0.268) (0.260)

Innovate07 0.131 (0.115)

Prod_Innov07 0.063 (0.099)

Proc_Innov07 0.033 (0.099)

Org_Innov07 0.114 (0.095)

Mark_Innov07 0.069 (0.090)

BS107 0.175***

0.088 (0.060) (0.092)

Constant -1.139* -1.101 -1.018 -0.806

*** -1.189

***

(0.638) (0.675) (0.685) (0.220) (0.245)

Robust standard errors in parentheses *

significant at 10%; ** significant at 5%;


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

37

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.

38

Table 17 Results - SSVs - Lagged variables - Outcome equation

(3) (4) (5) (6) (7)

ln(Wi,07) – ln(Wijr,07) 0.205***

0.212***

0.199***

0.295***

0.361***

(0.075) (0.075) (0.074) (0.059) (0.099)

ln(E07) -0.018 -0.019 -0.028 -0.072 -0.083 (0.030) (0.030) (0.032) (0.050) (0.054)

Census106 -0.011 -0.015 -0.014 -0.042 -0.057

(0.027) (0.029) (0.030) (0.051) (0.061)

Census206 -0.029 -0.027 -0.032 -0.087***

-0.153***

(0.030) (0.031) (0.030) (0.028) (0.037)

ISG07 0.084* 0.086

* 0.086

* 0.089 0.078

(0.049) (0.050) (0.050) (0.065) (0.097)

Vman,07 0.087 0.081 0.083 -0.097 -0.134 (0.139) (0.134) (0.148) (0.297) (0.265)

Vprof,07 0.392***

0.396***

0.393***

0.420** 0.424

**

(0.115) (0.115) (0.118) (0.191) (0.197)

Vtech,07 0.403***

0.392***

0.408***

0.365* 0.220

(0.150) (0.148) (0.150) (0.194) (0.180)

Vtrade,07 0.592***

0.586***

0.583***

0.716***

0.642***

(0.104) (0.101) (0.108) (0.112) (0.179)

Union07 0.210 0.240 (0.181) (0.182)

Monopoly07 0.159** 0.164

**

(0.065) (0.065)

Export07 -0.067 -0.071 (0.139) (0.141)

R&D07 -0.164 -0.173 (0.144) (0.141)

ODI07 -0.413***

-0.406***

(0.158) (0.155)

Innovate07 0.002 (0.092)

Prod_Innov07 0.028 (0.096)

Proc_Innov07 0.080 (0.072)

Org_Innov07 0.017 (0.100)

Mark_Innov07 -0.102 (0.102)

BS107 -0.061 (0.054)

Constant 0.205 0.200 0.194 0.283 0.390*

(0.159) (0.161) (0.149) (0.247) (0.225)

athrho -1.699***

-1.543***

-1.637***

-1.774 -2.451***

(0.509) (0.397) (0.498) (1.249) (0.306)

Prob 0.0008 0.0001 0.0010 0.1556 0.0000

Observations 3,495 3,495 3,492 2,595 1,851

Robust standard errors in parentheses *

significant at 10%; ** significant at 5%;


All models include industry dummies

39

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-fill-

vacancy. 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.

40

Table 18 Results – Different definitions of dependent variable

(11) (12) (13) (14)

NSR NSR Only

NSR NSR Only

Outcome equation

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)

Census106 -0.035 0.033 0.001 0.038 (0.056) (0.032) (0.051) (0.050)

Census206 -0.049 0.063 -0.026 0.040 (0.058) (0.045) (0.042) (0.064)

ISG 0.027 -0.186*** 0.077* -0.064 (0.058) (0.059) (0.044) (0.063)

Vman -0.395 -0.410 -0.397*** -0.508 (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)

Constant 0.304* -0.292 0.243 -0.646** (0.159) (0.235) (0.150) (0.287)

Selection equation

ln(sales)07 -0.077 0.025 -0.006 0.066 (0.076) (0.102) (0.063) (0.088)

[ln(Wi)–ln(Wijr)]07 -0.136 0.208 -0.099 0.095 (0.285) (0.257) (0.258) (0.316)

seprate07 0.056 0.205 0.223 0.361 (0.212) (0.287) (0.241) (0.295)

ln(E) 07 0.358*** 0.414*** 0.370*** 0.419*** (0.040) (0.050) (0.048) (0.054)

MFP07 0.069 0.075 (0.085) (0.085)

ln(LP)07 0.027 0.062 (0.047) (0.047)

Train08 0.563*** 0.583*** 0.623*** 0.636*** (0.125) (0.111) (0.097) (0.105)

RecDiff07 0.621*** 0.495*** 0.652*** 0.514*** (0.065) (0.077) (0.068) (0.088)

bss107 0.138 0.151* 0.171** 0.191*** (0.095) (0.081) (0.071) (0.068)

Constant -0.821*** -0.911*** -1.181** -1.691*** (0.145) (0.196) (0.482) (0.491)

athrho -1.619* -1.310*** -1.453*** -0.718** (0.963) (0.409) (0.352) (0.334)

Observations 2,592 3,495 Robust standard errors in parentheses

* significant at 10%;

** significant at 5%;


Weighted and Stratified

41

6. Summary and Conclusions


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-to-

fill 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

42

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

43

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

44

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.

45

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53

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

54

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 processes;

organisational/managerial processes; marketing methods?’ 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.

55

Operational process innovation (Proc_Innov07)


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 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 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). Respondents could answer one of either ‘local’,

‘national’ or ‘international’. The variable mark_int takes the value of 1 if the

respondent indicates that their market focus is ‘international’ and zero otherwise.

56

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.

57

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.

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Table 19 Staff occupation/role variables

Occupation/role Data item

Variable

Managers

(i.e. those who supervise staff or determine policy and future direction) 30

C1002 Propman

Professionals

(i.e. those who have specific expertise, but no managerial responsibility)31

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).

C1201 Proptrade

Clerical, sales and service workers

(i.e. those who perform administrative, sales or customer service tasks) C1302 Baseline


(i.e. those who operate vehicles or equipment or perform manual tasks) C1203 Baseline

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)’.

59

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 self-

employment 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

60

(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.

61

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.

62

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-to-

fill 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

63

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 Professionals Managers & professionals

Module A No H2F H2F No H2F H2F No H2F H2F

No Difficulty 4,767 279 4,782 270 4,578 471

Moderate difficulty 3,900 1,140 3,846 1,191 3,030 2,010

Severe difficulty 1,908 1,242 1,488 1,665 873 2,280

Don't know 1,206 57 1,158 105 1,119 144 Not applicable 19,215 351 19,077 489 18,792 774

Total 30,999 3,072 30,348 3,720 28,389 5,679

Pearson test

2 294.48 502.97 702.50

F 31.77 54.28 75.15

p 0.0000 0.0000 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

The test of independence that is displayed by default is based on the usual Pearson 2 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)

64

Table 20 (continued)

Module C

Technicians and

associate professionals

Tradespersons

and related workers

Module A No H2F H2F No H2F H2F

No Difficulty 3,645 84 4,278 219

Moderate difficulty 3,267 963 4,146 2,262

Severe difficulty 1,509 1,164 1,329 2,970

Don't know 1,116 24 1,086 30 Not applicable 20,991 378 16,533 366

Total 30,528 2,619 27,372 5,844

Pearson test

2 340.62 631.65

F 58.18 92.67

p 0.0000 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


Table 20 (continued)

Module C

All other occupations

Clerical, sales and

service workers


Both

Module A No H2F H2F No H2F H2F No H2F H2F

No Difficulty 4,575 528 4,512 588 4,071 1,032

Moderate difficulty 7,575 2,400 7,449 2,526 5,481 4,491

Severe difficulty 1,392 753 1,080 1,071 477 1,671

Don't know 1,212 72 1,080 204 1,011 273 Not applicable 9,501 426 9,381 546 8,991 936

Total 24,255 4,179 23,502 4,935 20,037 8,400

Pearson test

2 116.07 216.88 384.94

F 12.53 27.96 51.68

p 0.0000 0.0000 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


65

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)’.

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