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Policy reportSeptember 2015

Investing in

productivity: Unlocking ambition

The CIPD is the professional body for HR and people development. The not-for-profit organisation champions better work and working lives and has been setting the benchmark for excellence in people and organisation development for more than 100 years. It has 140,000 members across the world, provides thought leadership through independent research on the world of work, and offers professional training and accreditation for those working in HR and learning and development.

1 Investing in productivity: Unlocking ambition


Investing in productivity: Unlocking ambitionPolicy report

Foreword 2

Summary of key findings 3

Introduction 9

1 Recent investment and growth 11

2 Explaining variation in productivity between organisations 25

3 Expectations for the year ahead 33

Conclusions 45

Appendix 1: Details of multivariate modelling 46

Appendix 2: Relative productivity in public and voluntary sector organisations 49

References 51

Endnotes 52

Contents

AcknowledgementsThis report was written by Mark Beatson of the CIPD.

I am grateful to Laura Gardiner from the Resolution Foundation for supplying the data on median job tenure (reproduced here as Figure 3).

The survey data used in this report were collected by YouGov and I am grateful to Ian Neale and Laura Piggott for comments on the use of the survey data in this report. Any errors that remain are entirely the author’s responsibility. The CIPD is also grateful to all the survey respondents who gave their time to contribute to this survey; without their contribution this report would not have been possible.

2 Investing in productivity: Unlocking ambition 3 Investing in productivity: Unlocking ambition

Since the general election, we have seen the Government pay greater attention to productivity and how the UK can improve its performance. There are encouraging signs in the very latest set of figures, which suggest that output per hour worked is now back above its pre-recession level. Looking ahead, the findings from our most recent survey of almost 1,000 UK employers – conducted in June 2015 – provide grounds for cautious optimism, at least for the private and voluntary sectors. A majority of organisations are looking to maintain or improve their productivity and performance. However, a minority of organisations appear to be stuck in a self-reinforcing cycle where a lack of finance or skills leads to underinvestment and underperformance.

It is vital, therefore, that our attention doesn’t drift away from this fundamental economic priority.

This new analysis builds on the report we published earlier this year, Productivity: Getting the best out of people. It goes beyond our previous work in highlighting the role that the corporate ‘mindset’ can play in shaping the investment behaviour and productivity of organisations. The recession has cast a long shadow, with many organisations saying they have focused on survival and managing costs and others saying it has constrained their ability to invest and improve – even though we have now had more than two years of solid economic growth. However, when asked to think ahead, most organisations seem

to be adopting a positive mindset, seeing the next couple of years as an opportunity to make up ground and improve or maintain their performance.

We are publishing this report alongside the submissions we are making to the Government’s spending review and to the Business, Innovation and Skills Select Committee’s inquiry into the Government’s productivity plan. This report concentrates on the investments in capital equipment and people that organisations make to improve their own performance. It does not cover public investment in education and training, research and infrastructure, but it is vital that their contribution to productivity is recognised in the spending review.

Government can also help businesses to raise their game. Healthy demand and a tightening labour market give firms an incentive to change their ways but may not be enough on their own. If firms do not understand how to change, or lack the capability to innovate and improve, they risk remaining stuck in their past. It often makes sense for government to get out of the way and give businesses the freedom to create and grow (and, quite often, fail). But sometimes government needs to do more than provide a financial incentive or a helpful nudge.

We need vision and leadership from government as well as business – a vision that extends beyond the amount of investment in R&D, technology and equipment or people. We need a stronger

focus on the effectiveness and targeting of what are always limited resources. We also need more active involvement from government in developing better support for improved productivity at the workplace – this requires closer co-ordination and partnership at national, sector and local levels.

We also need a high-productivity public sector. Our research shows that many public sector organisations have adopted a survival or cost-cutting mindset to get them through challenging times. The danger is this has led to a lack of investment in people and technology, which could make it more difficult to deliver the transformational changes needed as the returns from ‘more of the same’ dry up.

Peter Cheese CIPD Chief Executive

Foreword


This report presents an up-to-date analysis of survey data collected from organisations focusing on their investment, growth and productivity. The data used were collected in early June 2015 as part of the CIPD’s regular Labour Market Outlook (LMO) survey, an online survey of almost 1,000 organisations in the private, public and voluntary sectors.

The report updates and extends the analysis underpinning our recent report Productivity: Getting the best out of people, which used data from the summer 2014 LMO survey and showed clear links between a firm’s relative productivity and its product/service strategy, internal culture, training activity and people management practices.

InvestmentSustained productivity growth requires investment in both tangible assets (capital equipment, plant and machinery) and intangible assets (brand, design, organisational capabilities, knowledge and skills). The previous report showed that business investment in tangible assets had just about recovered to its pre-recession level by early 2015. Investment in intangible assets fell less during the recession but the latest comprehensive data are for 2011 and it is unclear just how strong growth has been since then.

The summer 2015 LMO asked organisations about two types of investment:

• 35% of organisations have increased their expenditure

on capital equipment in the previous two years, 32% kept expenditure the same and 10% spent less (with 23% saying ‘don’t know’).

• 26% of organisations have increased their expenditure on learning and development (L&D) activities in the previous two years, 40% kept expenditure the same and 16% spent less (with 18% saying ‘don’t know’).

• Public sector organisations are least likely to say they increased expenditure on either type of investment and are most likely to say investment has decreased.

We asked a new question designed to capture the organisation’s mindset – the approach it had taken to managing through uncertain and often difficult times.

There is a clear link between organisation mindset and investment:

• Balanced investors are the most likely to have increased investment in the previous two years (53% increased expenditure on capital equipment and 43% increased expenditure on L&D).

• Survivors are most likely to have reduced investment in the previous two years (22% reduced expenditure on capital equipment and 30% reduced expenditure on L&D).

• The other three categories are between these extremes. Cost-cutters are the group most likely to have kept investment stable – neither up nor down. Capital- and people-focused investors are more likely to have increased investment in one of capital or people and reduced investment in the other.

Summary of key findings

Organisation mindsetAll organisations where the respondent had been employed for over two years were asked: ‘Which ONE of the following statements best describes the experience of your organisation over the past two years?’ Five choices were presented:

• ‘We were in survival mode for a long time and have not been able to invest in major improvements to the business’ – we label organisations that chose this response as ‘survivors’ (21%).

• ‘We are a leaner business now because we took cost out during the recession and the productivity of our workers has improved as a result’ – ‘cost-cutters’ (19%).

• ‘We have continued to invest in equipment, technology and people and have increased our productivity significantly’ – ‘balanced investors’ (25%).

• ‘We have continued to invest in equipment and technology but we haven’t invested enough in staff to maximise the value of this investment’ – ‘capital-focused investors’ (13%).

• ‘We have continued to invest in our people, but we need to invest more in equipment and technology to see real productivity improvements’ – ‘people-focused investors’ (16%).


Organisations that describe their product/service strategy as based on premium quality are more likely to have increased their investment in both capital equipment and L&D than organisations with a strategy based on basic/standard quality. Organisations that describe their internal culture as dynamic are also more likely to have increased investment.

Output growthThe LMO asked organisations whether their output – their production of goods and services – had increased in the previous 12 months:

• Output increased for 50% of organisations, stayed the same for 32% and fell for 14%.

• 40% of public sector organisations produced more, with the proportions in education (31%) and central and local government (26%) especially low.

• Whereas 59% of premium-quality organisations produced more, this applies to just 41% of basic/standard-quality organisations.

There is a positive link between output growth and investment trends:

• 69% of organisations where expenditure on capital investment had increased in the previous two years also saw output increase in the previous 12 months.

• In contrast, just 24% of organisations where expenditure on capital investment decreased in the previous two years saw their output increase – and 35% saw output fall.

• 67% of organisations where expenditure on L&D had increased in the previous two years also saw output increase in the previous 12 months.

• In contrast, 29% of organisations where expenditure on L&D decreased in the previous two years saw their output increase – the same percentage saw output fall.

There is also a clear relationship to organisation mindset:

• 72% of balanced investors saw output increase and just 7% saw output fall.

• Just 29% of survivors saw output increase – the only group where a higher proportion saw output fall (31%).

Learning and development activity• Organisations have been

trying to get better value for money from their L&D budgets. Whereas 26% of organisations increased expenditure on L&D, 35% of organisations say the amount of L&D activity increased during the previous two years.

• In 32% of organisations, half the workforce or less had participated in L&D activities in the previous two years, compared with 35% of organisations where more than half the workforce had participated in L&D activities – 33% of organisations don’t know the proportion that participated in L&D activities.

• 71% of organisations conduct regular (corporate) evaluations of training and 90% of organisations that have a staff appraisal or review process include regular (individual-level) evaluations of training within it.

• Organisations that carry out regular corporate evaluations of training are more likely to report increased expenditure on L&D activities (31%, compared with just 13% of organisations with no such evaluation). Both types

‘Firms where over 75% of the workforce have participated in L&D activities are more likely to rate their productivity highly.’


of evaluation are associated with higher percentages of the workforce taking part in L&D activities.

Relative productivityOrganisations were asked the same productivity question used in the summer 2014 LMO: ‘To the best of your knowledge, comparing your organisation with your peers or competitors within the UK, how would you rate your productivity? (Productivity being the average value of the goods and services produced in each working hour).’

As we found with the summer 2014 LMO, the distribution of responses is highly skewed:

• 7% of organisations rate their productivity as well above average and 43% rate their productivity above average.

• 37% rate their productivity as average.

• Just 8% of organisations rate their productivity as below or well below average.

The distribution of responses is similar across private, public and voluntary sectors. However, the statistical relationships between productivity and the other variables collected in the survey are quite different in each of the three sectors (with the questions asked in the LMO accounting for much less of the variation in productivity across organisations in the public and voluntary sectors). The analysis of relative productivity therefore focuses on private sector organisations.

In order to control for the inter-relationships within the data – such as between output, investment and mindset, or between size of firm and organisation culture – and to determine which factors are most significant in explaining variation in

relative productivity, we conducted multivariate analysis. This was the approach taken in our previous report. We cannot prove cause and effect with these data but there is a robust and statistically significant relationship between the relative productivity of a firm and the following factors:

• Organisation mindset: Other things being equal, balanced investors are most likely to rate their productivity highly and survivors are least likely to rate their productivity highly – with cost-cutters, capital-focused investors, people-focused investors and ‘don’t know’ organisations all somewhere in between the two.

• Product/service strategy: Firms adopting a basic/standard-quality approach are less likely to rate their productivity highly.

• Organisation culture: Firms that describe their culture as structured, dynamic or results-oriented are all more likely to rate their productivity highly than firms that describe their culture as having a family feel. Cultural fit also matters: whatever their current culture, if firms think they need to change their culture within the next five years – or aren’t sure about it – this has a negative effect on relative productivity.

• Output growth: Where output has not increased in the last 12 months, this has a negative effect on relative productivity.

• Investment in capital equipment: Firms that have cut their expenditure on capital equipment in the previous two years are less likely to rate their productivity highly (note this is additional to the impact of organisation mindset and output growth – both of these are also strongly correlated with past investment).

• L&D activity: Firms where over 75% of the workforce have participated in L&D activities are more likely to rate their productivity highly – as are firms that say the amount of L&D activity in the previous two years has stayed the same or say they don’t know whether it has increased or decreased. Firms with a sophisticated approach to the evaluation of training – measured by the number of criteria they use to evaluate training – are also more likely to have a positive view of their productivity.

• Whether productivity is a business priority: 38% of firms say that ‘improving productivity’ is a current priority for their business (the fourth most common priority chosen – top is ‘cost management’). Other things being equal, these firms have lower productivity ratings. This is probably because the priority they attach to it looking ahead is a reflection of current productivity weakness (rather than strength).

Allowing for the fact that there are differences in the questions asked, most of these results are consistent with those from our previous report analysing the 2014 data. The main differences are:

• The additional question on organisation mindset – which, in numerical terms, is the variable having the largest impact on a firm’s productivity rating.

• Firm size has no significant impact on relative productivity in these results – that is probably because of the inclusion of organisation mindset and culture in the same model (small firms are disproportionately survivors and firms with a family culture).


• The (positive) impact that L&D activity has on productivity is not as strong or as clearly defined as the results obtained using the 2014 LMO. This may be because of the inclusion of organisation mindset, which captures a firm’s approach to investment in people as well as capital. It may also be because the 2015 survey asked organisations about L&D activity whereas the 2014 survey asked about on- and off-the-job training – this change has led to more ‘don’t know’ answers, especially in large organisations.

Investment plansThe summer 2015 LMO also included some forward-looking questions about the 12 months ahead (to June 2016):

• 31% of organisations expect to increase expenditure on capital equipment, 37% expect to spend the same and 8% expect to spend less (with 25% saying ‘don’t know’).

• 27% of organisations expect to increase expenditure on

L&D activities, 45% expect to spend the same and 11% expect to spend less (with 17% saying ‘don’t know’).

• Public sector organisations are less likely to say they will increase their investment in capital equipment (20%) and people (14%). They are also more likely to say investment is expected to fall or that they don’t know what will happen to investment.

• Investment intentions for the coming year are closely correlated with the change in investment in the preceding two years. For example, 68% of organisations that increased investment in capital equipment in the previous two years say they intend to spend even more in the next 12 months, whereas 23% propose to invest at the same level and just 5% expect to invest less.

In a similar vein to the backward-looking question on organisation mindset, we included a question designed to summarise an organisation’s future mindset.

Future mindsetAll respondents were asked: ‘Looking ahead to the next two years, which ONE of the following statements best describes your expectations about your organisation’s future plans?’ Five choices were presented:

• ‘We are performing well and we don’t see the need for major change and lots of investment’ – we label organisations that chose this response as ‘high-performers’ (34%).

• ‘We are not performing well, but we don’t have the financial resources or confidence to invest in equipment, technology and people’ – ‘finance-constrained’ (15%).

• ‘We are not performing well, but don’t have the skills or ambition to invest in equipment, technology and people’ – ‘ambition-constrained’ (4%).

• ‘We will now be able to make the investments in people we haven’t been able to do in the last few years’ – ‘making up ground – people’ (16%).

• ‘We will now be able to make the investments in equipment and technology we haven’t been able to do in the last few years’ – ‘making up ground – capital’ (17%).

‘Sixty-nine per cent of organisations expecting to spend more on L&D in the coming year also expect output to grow.’


Public sector organisations are least likely to identify as high-performers (18%) and just 24% expect to make up ground. Almost a third (31%) say they are finance-constrained.

As with the backward-looking questions, there is a clear link between future mindset and investment plans:

• Organisations that identify with the two ‘making up ground’ categories are the most likely to say they expect to increase investment in the coming year in both capital equipment and L&D.

• Organisations that identify with the two constrained categories are the least likely to say they expect to increase investment in either capital equipment or L&D and are the most likely to say they expect to see investment fall.

• Very few high-performers expect to reduce investment but they are less likely to say they expect to increase investment than organisations seeking to make up ground – in many cases, these are balanced investors who have increased their investment and productivity in previous years and are seeking to maintain it.

• There is a strong element of continuity between the backward-looking and forward-looking mindset categories. Nearly half (49%) of (backward-looking) survivors are (forward-looking) finance-constrained or ambition-constrained. Whereas 16% of survivors rate themselves (forward-looking) high-performers, the proportions are 44% for cost-cutters and 54% for balanced investors.

• There is also evidence that organisations that described their past behaviour as unbalanced are aiming to

achieve a more balanced future growth path. Capital-focused (past) investors are much more likely to be in the (forward-looking) ‘making up ground – people’ category than in the capital-focused category. Similarly, organisations where past investment focused on people are looking to make up ground by investing more in capital equipment.

Expected output growthThe LMO asked organisations whether they expect output to increase in the next 12 months:

• 55% of organisations expect to produce more goods and services, 32% expect to produce the same and 8% expect to produce less.

• 39% of public sector organisations expect to produce more and 15% expect to produce less. The proportions expecting output growth are particularly low in central and local government (25%) and education (33%).

• Organisations with a dynamic culture (77%) and with a premium-quality strategy (62%) are especially likely to expect output growth.

There is a positive link between output growth and investment plans:

• 74% of organisations expecting to spend more on capital equipment in the coming year also expect output to grow.

• In contrast, just 27% of organisations expecting to spend less on capital equipment expect output to grow – whereas 40% expect output to fall.

• 69% of organisations expecting to spend more on L&D in the coming year also expect output to grow.

• In contrast, just 36% of organisations expecting to spend less on L&D expect output to grow – whereas 30% expect output to fall.

There are also clear links between expected growth and current productivity and future mindset:

• 69% of organisations describing their productivity as well above average expect output to grow, compared with 44% of organisations describing their productivity as below average.

• 70% of organisations in the ‘making up ground – people’ category and 64% of organisations in the ‘making up ground – capital’ category expect output to grow – slightly higher proportions than for high-performers (58%) and well in excess of the finance-constrained (32%) and ambition-constrained (27%).

Pay growth and productivityThe LMO asks organisations expecting to make a pay decision in the next 12 months what they expect the outcome to be (pay cut, pay freeze or pay rise) and by how much they expect to increase/decrease pay. Although the number of firms involved is relatively small – because a high proportion are unable to provide an advance estimate of what they expect to pay – the average expected pay increase for private sector firms rating their productivity as above average or better is slightly higher than for firms rating their productivity as average or worse. Firms with above-average productivity are also more likely to say that improved productivity is one of the factors underpinning their expected pay decision. This provides tentative evidence of a correlation between productivity and pay at the firm level – and


possibly a sign that higher pay and higher productivity go hand in hand.

Productivity outlookThe data presented in this report provide grounds for cautious optimism about future productivity growth – at least in the private sector.

A majority of organisations are looking to maintain or improve their productivity and performance. Over a third of organisations in both the private and the voluntary sectors say their investment efforts are designed to make up ground over the next two years. In some cases, these efforts are focused on capital and technology; in others, the focus is on investing in people. However, a minority of organisations appear to be stuck in a self-reinforcing cycle where a lack of finance or skills leads to underinvestment and underperformance.

The strategic choices that organisations make – deliberately or through force of events – are important. These include positioning in the market, internal culture and the approach taken to managing the present and preparing for the future. The recession is still having an impact on some organisations through its impact on these choices.

There are signs that more firms are looking to take advantage of the benign economic conditions to invest and improve. But do they have the vision and management and leadership skills to make the necessary changes? Will corporate memory and past experience hold firms back more than they would want it to?

Policy implicationsThe analysis in this report suggests that more attention needs to be given to particular aspects of productivity policy.

The importance of strategic choices in general, and mindset in particular, must have implications for policies aiming to improve business performance. It suggests that policies aiming to improve access to capital, for example, or encourage employers to train their workforce may have limited effect if a firm’s current mindset is focused on survival or cost-cutting – or if they lack the confidence and ambition to embark on new investments. This might need new or reinforced mechanisms for the delivery of business support. Helping firms change their mindset may not be easy.

This report looks only at organisations’ investments in capital, technology and people. Public investment also has a vital role to play and this needs to be maintained and strengthened as economic conditions improve.

‘The importance of strategic choices in general, and mindset in particular, must have implications for policies aiming to improve business performance.’


‘Labour productivity in the UK – defined here as the value that we produce for each hour worked – has barely changed over the last seven years.’

Labour productivity in the UK – defined here as the value that we produce for each hour worked – has barely changed over the last seven years (see Figure 1). The very latest data, covering the second quarter of 2015, imply stronger productivity growth than we have become accustomed to but they still show productivity only slightly higher than it was in the first quarter of 2008.

The new government has made improving productivity one of its top economic priorities and has published a productivity plan (HM Treasury 2015b). The UK is not the only country where productivity growth

has been weak in recent years. The Organisation for Economic Co-operation and Development (OECD) recently published a report on future productivity growth that highlighted the importance of improving the diffusion of innovation and tackling skills mismatch (OECD 2015).

Earlier this year, the CIPD published a report on productivity in private sector organisations that showed clear links between relative productivity and workplace factors, such as investment in training and people management practices (CIPD 2015a). The report was based on analysis of two CIPD surveys of HR leaders. One of the

Introduction

Figure 1: Labour productivity, 2008–15(Output per hour worked, 2008 Q1 = 100)

GDP is chained volume measure, seasonally adjustedSource: Office for National Statistics

2008 Q

1Q2 Q3 Q4

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2013

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2014

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Q2 Q3 Q4

2015

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105

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surveys used was the CIPD Labour Market Outlook (LMO), a quarterly survey of about 1,000 HR leaders in private, public and voluntary sectors in the UK. The summer 2014 survey had included a section with additional questions focusing on productivity.

As low productivity remains a topic of widespread concern, some of the questions used in the summer 2014 LMO were repeated in the summer 2015 LMO (for headline results from this survey and a breakdown of participants, see CIPD 2015b). Organisations were again asked about recent trends in investment and production and they were again asked to rate their productivity relative to peers and competitors. We included additional questions probing a little deeper on some aspects of the people management practices as well as asking organisations about their perceptions of how well they had weathered the last few years and about their ambitions for the future. This report presents the results.

Multivariate statistical analyses have been used to help explain the variation in self-reported productivity among the private sector organisations surveyed – recognising that we cannot establish causal relationships – and the detailed model results are reported in Appendix 1. The same questions were put to all organisations taking part in the survey, which means that, in principle, we can compare private, public and voluntary sector organisations. In practice, the drivers of productivity in public and voluntary sector organisations appear to be different – or operate differently – from those in the private sector. We therefore restrict the analysis of productivity in Section 2 of this report to private sector organisations. However, a brief descriptive analysis of relative productivity in the public and voluntary sectors is presented in Appendix 2.

‘The new government has made improving productivity one of its top economic priorities.’


Although productivity is not just a matter of investment in tangible and intangible assets, they are important enablers.

The quantity of investment being made by organisations matters – is enough being invested to maintain or grow the stock of assets? These might be especially difficult judgements when the assets are intangible, such as brand, knowledge or organisational routine and culture. These do not feature explicitly in financial accounts and meaningful and realistic methods of data collection and valuation are often lacking.

The quality of investment is also important. Is the new investment simply ‘more of the same’ in terms of its functional impact or is it transformative, enabling new or improved products, services and ways of doing things? How well balanced is the organisation’s investment portfolio? Does it address long-term as well as short-term value-creation? Does the investment strategy recognise the need for investments in people, structures and systems, brand and reputation management alongside – and in support of – major strategic investments (such as in new technology, new markets or new business ventures)?

And, perhaps most important of all for productivity, how well is investment used in combination with existing assets to generate value for the organisation (the economic concept known as total factor productivity)? Management and leadership occupy centre stage. The quality

of leadership and the management practices within an organisation have significant effects on productivity and other measures of performance.1 These include the broader people management agenda – getting the best out of people – as well as the development of systems, processes and ways of working that optimise value-creation across all the organisation’s assets. Investments in automation, machine learning or robotics, for example, may well reduce the number of people required but technology can rarely operate effectively without complementary investments in training so employees understand the new technology and their (changed) role in ensuring it operates effectively. Organisations should also be using major investments as an opportunity to question existing methods and assumptions and to redesign work in ways that play to the strengths of both machines and people.

Some of the factors that influence the effectiveness of investment and its impact on productivity are partially or wholly outside the organisation’s direct control, such as market conditions, the actions of competitors and regulators or what is said about the organisation in the newspapers or on social media. This is a reminder that productivity isn’t entirely a business issue. The quantity and quality of investments made by individuals in the productive capability of themselves and their families and by government (acting on behalf of citizens, including those yet to be born) matter just as much in building and sustaining UK

productivity. The role of citizens, acting individually and collectively in civil society and through the political process, is especially important in the accumulation of social capital – the rules, customs, values, social norms, networks and institutions that underpin functioning economies.2 However, the nature of the data we have collected means this report concentrates on the productivity of organisations – rather than the economy as a whole – and on the investments in capital equipment and people made by organisations in the private, public and voluntary sectors.

The economic factors shaping investment decisionsInvestment is the act of forgoing well-being today by committing resources to an activity (an investment project) in the expectation that the project will deliver a stream of well-being in the future. The resource sacrificed today is often money but it could be physical goods or the right to enjoy a service, it could be time, attention, even affection. All investments involve an expectation that the stream of future well-being (the benefits) will exceed the value of what is being given up (its cost), otherwise it is in part a gift. All investments therefore involve an element of risk – usually the main risks are that the investment project costs more than expected or that it fails to deliver the expected stream of benefits. But there is no such thing as a risk-free investment.3 Issues of legality and ethics are involved – after all, a bribe could be described as a form of investment.

1 Recent investment and growth


This report concentrates on the investments made by organisations. The cost of an investment project can usually be valued and monetised in some form (such as payments for equipment and machinery or the value of staff time). The benefits can usually be related directly or indirectly to the organisation’s objectives or success criteria. In the private sector, economic theory conventionally summarises the return on an investment as the expected value of future profits (which equates, other things being equal, to the value of the business), although this can be a simplification both in theory and in practice.4 In the public and voluntary/not-for-profit sectors, organisations typically have multiple objectives. Sometimes these will be defined explicitly, for example, in legislation or statements of charitable purpose, but often they are implicit. While financial outcomes may often be a consideration and other objectives may be capable of quantification (such as service delivery standards and client outcomes), there will be others that cannot easily be quantified. Concepts such as public value may help organisations assess the merits of different investment options and then explain the reasoning behind investment decisions to stakeholders, but they do not remove entirely the scope for ambiguity and different points of view about which projects deliver the greatest benefit for the organisation.

Both microeconomic and macroeconomic models of investment behaviour typically highlight the importance of two factors in shaping the investment decisions made by individual organisations: the cost and availability of capital to finance the investment; and the future stream of benefits.

Private sector firms wishing to undertake an investment project may be able to finance it internally (from reserves and retained profits) but they also have the option of raising the finance from external sources as either debt or equity (or a mixture). For very small firms, typical sources might be family and friends, banks, other commercial lenders (such as credit cards), business angels, customers or suppliers, public agencies and crowdsourcing. Companies listed on a stock market, in contrast, have access to much larger capital markets.

What matters for the investment decision is the cost of capital and its availability. The cost of capital is an issue for all firms (including voluntary sector organisations and some public sector organisations). Reserves and retained profits are not ‘free money’ as they could be invested elsewhere in the economy and the firm could expect a return on them, or they could be paid to the owners of the organisation as dividends or salary (in the case of partnerships and owner-managers) and the owners could in turn reinvest them. Sources of external finance will expect a return either through the interest rate charged (for debt finance) or in terms of their stake in the business.5 The cost of capital to any organisation will therefore depend in part on overall market conditions and, in particular, the interest rates set by the monetary authorities (for the UK, this is the Monetary Policy Committee of the Bank of England). But it will also depend on the perceived riskiness of the investment project and/or the perceived creditworthiness of the organisation seeking external finance. At one extreme, central governments with their own currency and the power to raise funds from taxation can often borrow at very low rates, as the UK and the USA have found

throughout the financial crisis.6 Whereas a small start-up proposing a venture in a new (or already crowded) market can expect to face lots of questions about its credentials, its business model and plans, about how it will cope with uncertainty and manage its costs and so on – and the cost of capital required could still be high and may be available only on certain terms (for example, venture capital firms require a large equity share and will be expecting to sell on their stake within a set time horizon).

It is often claimed that external finance is sometimes unavailable: no one is prepared to fund certain types of firm (usually small firms) or certain types of investment project. These claims have to be examined critically. Some projects placed may attract no interest simply because potential investors think they are fundamentally flawed. Or the risk may be seen as too high unless loans are backed by collateral (such as housing equity). Often lack of available finance really means a lack of investors offering acceptable terms. However, this doesn’t rule out the possibility that credit effectively is rationed in some parts of the market.

The cost of capital reflects project risk and one of the greatest sources of project risk is uncertainty over the future stream of benefits. Even if the project runs to cost, specification and timing, it may turn out that potential customers actually want something different or that a competitor has the edge on price, quality or convenience. Or the economy might be in a downturn and potential customers have less money to spend. Macroeconomic models find that aggregate private sector investment tends to move up and down in line with aggregate demand.


The availability of capital and uncertainty over future demand help to explain why business investment in both tangible and intangible assets fell in the UK during the 2008–09 recession and why they seem to have taken a long time to get back to their pre-recession values (see CIPD 2015a).

Do psychological factors help explain the productivity standstill?Economic decisions are made by human beings – either individually or collectively – and this means these decisions may not always be as simple or as rational as standard economic theory might predict (or wish). Non-rational thought processes or decision rules are involved, especially when outcomes are highly uncertain. How individuals and organisations perceive situations, frame options and thereby reach decisions are affected by prior experience and values as well as by the social setting.7

The question is whether such factors have been unusually significant in influencing the course of events in what was an exceptionally deep recession followed by a recovery that was, at first, both slow and faltering. One recent study suggests this might be the case (Tuckett et al 2014). The authors use textual analysis of data from a business news website to construct a measure of economic sentiment and show that the UK measure helps explain the path of output over time. It also suggests that economic sentiment remained subdued during the post-recession period. At the end of 2013, the measure was still well short of its (possibly over-exuberant) pre-recession level.

Andy Haldane, Chief Economist at the Bank of England, gave a speech recently on the impact

that perceptions of risk have on economic behaviour (Haldane 2015). He identified two types of risk that might explain the slow speed of recovery in the UK and other Western economies. One is ‘dread risk’ – the long-lasting psychological damage wrought by events that involve high losses to high numbers of people at the same time, such as a financial crisis. The other is ‘recession risk’ – perceptions of the probability of the economy going into a recession at some future point. Haldane produces evidence to show that the psychological scars of the 1930s Great Depression affected equity markets for decades after. And while events of the magnitude of the 1930s depression or the recent financial crisis may be rare, history suggests that it is more likely than not that the UK economy will go into recession at some point during any given ten-year period. Individuals and companies factor that information into their plans.

The implications of ‘dread risk’ are summarised as follows:

‘In principle, dread risk affects demand in two ways. First, it generates an attitude of caution when making major decisions – moving house or job or making an investment. People will tend to look before they leap, to think twice before diving in. It creates the mindset of the glass being half empty.

‘Second, insecurity generates an asymmetric response to news flow. Good news will be banked or used to strengthen balance sheets. Roofs get repaired during the sunshine. But when bad news strikes, the response is immediate and defensive. There is a hunkering down, with cuts in spending and risk-taking.’ (Haldane 2015)

‘Economic decisions are made by human beings – either individually or collectively – and this means these decisions may not always be as simple or as rational as standard economic theory might predict (or wish).’


Box 1: Is low employee turnover a sign of a ‘half-empty’ mindset?

Turnover rates appear to have been on a downwards trend in both the UK and the USA since the end of the 1990s (CIPD 2013). Job-to-job turnover then fell sharply during the 2008–09 recession, which is typical in a recession. But turnover appears to have been slow to recover despite the unexpectedly strong growth seen in employment (see Figure 2).

–0.1

Figure 2: Job-to-job flows, 1998–2015

The job-to-job flow rate is calculated as the number of people who report resigning three months ago, and report being in employment for less than three months as % of 16–64 employment.

Source: Bank of England (2015) plus author’s calculations.

1998 Q1

1998 Q3

1999 Q1

1999 Q3

2000 Q

1

2000 Q

3

2001 Q

1

2001 Q

3

2002 Q

1

2002 Q

3

2003 Q

1

2003 Q

3

2004 Q

1

2004 Q

3

2005 Q

3

2006 Q

1

2006 Q

3

2007 Q

1

2007 Q

3

2008 Q

1

2008 Q

3

2009 Q

1

2009 Q

3

2010

Q1

2010

Q3

2011

Q1

2011

Q3

2012

Q1

2012

Q3

2013

Q1

2013

Q3

2014

Q1

2014

Q3

2015

Q1

2005 Q

1

1.4

1.6

1.8

2.0

1.2

1.0

0.8

0.4

0.2

–0.5

–0.4

–0.3

–0.2

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.0

Moving average of change in employment rate (RHS) (%) Job-to-job flow rate (LHS) (%)

Haldane goes on to note that ‘despite the recovery, evidence continues to suggest a “half-empty” mindset’. Among the evidence cited in support is weak job-to-job moves in the labour market (see Box 1).

Furthermore, ‘this cautious behaviour is, to a degree, also

mirrored among companies. This can be seen in the weakness of global investment since the crisis, despite the cost of company borrowing being at its lowest-ever levels.’ Haldane goes on to note that: ‘This uncertainty can be seen, too, in how companies are reinvesting their earnings. The trend over the past 20 years

has been for companies to invest more in passive assets such as cash, than active ones such as physical capital. But this trend has been accentuated since the crisis.’ Companies investing more in passive assets may be evidence of both dread and recession risk. It reflects memories of the financial crisis, when cash balances


Box 1 continued

The corollary of this is that median job tenure for both men and women increased all the way through the recession (see Figure 3).

Demographic change is a factor putting downwards pressure on the overall rate of turnover (young people, who have the highest rates of job turnover, now account for a smaller share of employment). Even so, turnover has been slow to pick up. This is all the more surprising as, until very recently, average earnings were failing to keep pace with inflation and a job move has, under most market conditions, been one of the most common means for people to secure a significant pay rise.

However, it is consistent with Haldane’s discussion of ‘dread risk’ if one of its consequences is that decisions are looked at from a ‘half-empty’ rather than ‘half-full’ mindset. A potential job change always involves uncertainties that cannot be appraised, let alone mitigated, through negotiations over a contract of employment – such as whether or not you get on with managers and colleagues. This is why the concept of the psychological contract still has practical, moral and empirical meaning. But there is still an element of a leap in the dark and the ‘half-empty’ mindset suggests that many people might look [at a job change] but few would then leap.

Figure 3: Median employment tenure, 1994–2014(Employees and self-employed, age 16+)

Jun 19

94

Jun 20

01

Jun 20

08

Jan 19

95

Jan 20

02

Jan 20

09

Aug 1995

Aug 2002

Aug 2009

Mar 1996

Mar 20

03

Mar 20

10

Oct 1996

Oct 20

03

Oct 20

10

May 19

97

May 20

04

May 20

11

Dec 19

97

Dec 20

04

Dec 20

11

Jul 19

98

Jul 2

005

Jul 2

012

Feb 19

99

Feb 20

06

Feb 20

13

Sep 19

99

Sep 20

06

Sep 20

13

Apr 2000

Apr 2007

Apr 2014

Nov 20

00

Nov 20

070

60

70

80

50

40

30

20

10

Mon

ths

All Male FemaleSource: Gregg and Gardiner (2015).

were critical. But it also reflects pessimism about the returns from future investment projects that are affected by the likelihood of recession – hence the move towards safer asset classes.

Can memories of the financial crisis and perceptions of risk help to explain the UK’s productivity

standstill? If it affects individuals, it affects firms indirectly through its effect on demand. But has something similar been happening in some firms (remembering that managers and employees are both consumers)?

Have managers been tempted to play it safe and stick with the approach that got them through

tough times? Have they been slow to take advantage of economic growth because of concerns about its sustainability? Have organisations (or decision-makers within them) got out of the habit of driving for improvement and expansion? Have necessity, habit and corporate memory combined to place a ceiling on many organisations’ ambitions?


Box 2: Organisation mindset

All organisations where the respondent had been employed for over two years were asked: ‘Which ONE of the following statements best describes the experience of your organisation over the past two years?’ Five choices were presented:

• ‘We were in survival mode for a long time and have not been able to invest in major improvements to the business’ – we label organisations that chose this response as ‘survivors’ (21%).

• ‘We are a leaner business now because we took cost out during the recession and the productivity of our workers has improved as a result’ – ‘cost-cutters’ (19%).

• ‘We have continued to invest in equipment, technology and people and have increased our productivity significantly’ – ‘balanced investors’ (25%).

• ‘We have continued to invest in equipment and technology but we haven’t invested enough in staff to maximise the value of this investment’ – ‘capital-focused investors’ (13%).

• ‘We have continued to invest in our people, but we need to invest more in equipment and technology to see real productivity improvements’ – ‘people-focused investors’ (16%).

Totals do not add to 100% because ‘don’t know’ responses are not reported.

Source: CIPD Labour Market Outlook survey, summer 2015.

Central and local government

Finance and business services

Transport and communications

Wholesale, retail, hospitality etc.

Health and social care

Education

Manufacturing

10,000+ employees

1,000–9,999 employees

250–999 employees

Medium (50–249) employees

Small (10–49) employees

Micro (2–9) employees

Voluntary sector

Public sector

Private sector

Total

Survivors

Cost-cutters

Balanced investors

Capital-focused investors

People-focused investors

37 18 198 2

17 9 1920 32

14 12 1125 30

25 22 1816 11

14 19 3211 16

24 12 1915 22

13 18 1822 23

12 6 1722 35

18 15 1813 34

30 13 1317 22

34 7 1022 16

27 23 1113 18

28 19 2111 10

19 10 1522 30

21 13 1619 25

25 3 1918 33

27 9 224 36

Figure 4: Organisation mindset by sector, employee size band and industry(% of organisations where the respondent had been employed at the organisation for two or more years, n=790)

The significance of organisation mindsetThe summer 2015 LMO included a new question designed to categorise the mindset that organisations had adopted in weathering the uncertainties

of recent years and how it had affected their approach to productivity improvement (see Box 2).

The results show that 40% of organisations identify themselves

as either survivors or cost-cutters, whereas 54% identify themselves as one of the three investor types with the remaining 6% unable to choose (see Figure 4).


There are (statistically) significant differences between sectors.8 While the combined proportion of cost-cutters and survivors is virtually identical across all three sectors, cost-cutters are more prevalent in the private sector (22%) than in the public or voluntary sectors (11% and 13% respectively), where survival mode has been a more common approach. Similarly, whereas 30% of private sector organisations identify as balanced investors, just 10% of public sector organisations and 18% of voluntary sector organisations are in this category.

There are also significant differences by size of organisation and industry.9 Micro and small businesses (those with fewer than 50 employees) are disproportionately survivors. Balanced investors are most prominent among the 50–249 employee and 250–999 employee

bands. In the very largest organisations, those with 10,000 or more employees, 24% are survivors, which may be because they are disproportionately in the public sector. This also explains why 37% of organisations in central and local government are survivors.

If this measure of organisational mindset contains some information on the organisation’s strategy for managing periods of market uncertainty, it makes sense to compare it against two other measures that capture other elements of strategic choice: its product/service strategy (‘basic/standard quality’ versus ‘premium quality’) and its organisational culture (whether it has a ‘family’, ‘structured’, ‘dynamic’ or ‘results-oriented’ culture). The proportion of organisations in one of the three investor categories is only slightly higher for premium-

quality organisations (50%) than it is for basic/standard-quality organisations (47%), but the proportion of survivors and cost-cutters is 10 percentage points higher in basic/standard-quality organisations (see Figure 5).10 The measure of organisational culture was first used by the CIPD in the 2014 HR agility survey and explained in CIPD (2015a).11 It has now been incorporated into the LMO and an equivalent question using the same four cultural types was also included in the spring 2015 CIPD Employee Outlook survey. Dynamic organisations stand out for having the smallest proportions of survivors and cost-cutters. Whereas the combined total of the three investor descriptors is 60% or more in structured, dynamic and results-oriented organisations, it is just 50% in family organisations (which are disproportionately SMEs).



Results-oriented culture

Dynamic culture

Structured culture

Family culture

Premium-quality strategy

Basic/standard-quality strategy

Total

9 9 2220 31

25 7 1819 25

28 19 2111 10

28 15 1321 19

18 11 1916 29

24 17 1317 20

Figure 5: Organisation mindset by product/service strategy and culture(% of organisations where the respondent had been employed at the organisation for two or more years, n=790)

15 15 1720 29

Survivors Cost-cutters Balanced investors Capital-focused investors People-focused investors


Figure 6: Organisation mindset by output growth in the previous 12 months(% of organisations where the respondent had been employed at the organisation for two or more years, n=790)





Balanced investors

Cost-cutters

Survivors

Output increased Output stayed the same Output decreased

39 41 16

51 33 15

29 37 31

72 19 7

56 32 7

There are strong associations between organisational mindset and recent trends in investment in capital equipment and in learning and development (L&D) activities (see Table 1).12

Balanced investors are the category most likely to have increased their investment in capital equipment (53%) and L&D (43%) – indeed, 28% of this group had increased spending

on both types of investment. The descriptors also match behaviour with capital-focused investors more likely to have increased investment in capital than people-focused investors and vice versa. Cost-cutters and survivors are much less likely to have increased investment and significant numbers of survivors have cut back on investment (14% of survivors cut back on both types of investment).

Balanced investors are also the organisations most likely to report an increase in the amount of goods and services their organisation had produced in the previous year (see Figure 6). More than half of people-focused investors (56%) and cost-cutters (51%) also saw output grow, whereas survivors are twice as likely as any other group to be producing less than they did a year previously.13

Table 1: Organisation mindset and recent investment in capital equipment and L&D activities(% of organisations where the respondent had been employed at the organisation for two or more years, n=790)

Change in capital investment in previous two years

Change in expenditure on L&D in previous two years

IncreasedStayed

the same Decreased IncreasedStayed

the same Decreased

Survivors 20 39 22 11 41 30

Cost-cutters 29 46 7 14 49 16

Balanced investors 53 20 4 43 32 7

Capital-focused investors 48 26 12 24 49 16

People-focused investors 32 29 13 31 36 17

Don’t know 22 28 7 15 30 16

Total 36 32 11 25 40 17

Row totals in each of the two panels do not add up to 100% because ‘don’t know’ responses to the questions on changes in capital investment and expenditure on L&D are not reported. In both cases, approximately one-fifth of the sample are in this category.





Agriculture, energy, construction

Manufacturing

Education









250–999 employees


10,000+ employees

Private sector

Public sector

Voluntary sector

All organisations

Output increased Output stayed the same Output decreased

52 30 18

57 24 19

51 26 15

59 25 13

52 34 12

26 47 14

46 35 17

50 37 10

60 29 9

55 24 15

48 31 14

46 33 16

53 29 15

40 40 11

48 38 10

50 32 14

51 32 12

31 46 13

Figure 7: Output growth in the previous 12 months(% of organisations, n=930)

Output growthAcross all organisations surveyed in the LMO, 50% have seen output increase in the previous 12 months, with 32% saying it had stayed the same and just 14% reporting a fall (see Figure 7).

Private sector organisations are most likely to say production has increased (53%), with lower proportions in the voluntary sector (48%) and public sector (40%).14 Organisations in the medium employee size bands (50–249 employees and 250–999 employees) are more likely to have grown than smaller or

larger organisations and, indeed, there is no statistically significant relationship between output growth and organisation size.15 Wholesale, retail and hospitality (59%) and manufacturing (57%) have the highest proportions of organisations where output has grown. Central and local government and education stand out for having far lower proportions of growing organisations than any other industry groups (26% and 31% respectively). However, they did not have higher proportions of organisations where output decreased.

‘Central and local government and education stand out for having far lower proportions of growing organisations than any other industry groups.’


As might be expected, there are significant positive associations between output growth and changes in investment.17 Organisations that have increased their capital equipment or their expenditure on L&D activities in the preceding two years are much more likely to report increased output during the past 12 months than organisations where these investments had stayed the same or fallen (see Table 2).

Whereas 59% of premium-quality organisations increased production in the past year, this applies to just 41% of basic/standard-quality organisations.18 Organisations with a dynamic culture are most likely

to have grown (reported by 69%) with structured organisations the least likely to do so (reported by 37%) and most likely to be producing less (reported by 18%).19

The significance of product strategy, culture and people management practices for investmentThe mindset of the organisation would appear to influence its recent investment decisions. In addition, the choice of product/service strategy is likely to have implications for investment. People-related factors including organisational culture and management practices may also lead to more or less investment in people and capital equipment.

The proportion of premium-quality organisations that increased investment in capital equipment in the previous two years is higher than it is for basic/standard-quality organisations (see Figure 8).20 Organisations with a dynamic culture are more likely to have increased investment than organisations with other cultural types, and structured organisations are most likely to have cut back on capital investment.21

We see a similar pattern for change in expenditure on L&D activities (see Figure 9).22 Organisations with a basic/standard-quality strategy and/or a structured culture are those most

Table 2: Output growth and recent investment in capital equipment and L&D activities(% of organisations, n=928)

Change in capital investment in previous two years


Change in output over previous 12 months: Increased

Stayed the same Decreased Increased

Stayed the same Decreased

Increased 69 39 24 67 49 29

Stayed the same 18 47 38 21 39 34

Decreased 9 13 35 9 12 29

Column totals do not add up to 100% because ‘don’t know’ responses to the question on changes in output are not reported (which accounts for just over 2% of all organisations).




Premium quality

Basic/standard quality

Family culture

Structured culture

Dynamic culture


Total

Increased

Stayed the same

Decreased

32 35 14

32 24 17

47 38 6

40 30 4

35 32 10

32 41 7

Figure 8: Change in expenditure on capital equipment in the previous two years(% of organisations, n=929)

40 32 8


likely to have reduced their L&D expenditure. This relationship in the data is much stronger in the public sector, where over a third of basic/standard-quality and structured organisations report decreased expenditure on L&D.

According to the Employer Skills Surveys, UK employer investment in training peaked in 2009, with the 2011 and 2013 surveys both reporting reductions. However, these surveys also show that employers have been taking steps to increase the cost-effectiveness of their training expenditure by, for example, delivering training in-house rather than through

an external contractor and/or by making greater use of distance learning and e-learning (Winterbotham et al 2014).

Respondents were therefore asked an additional question: ‘During the past two years, has the total amount of learning and development activity at your organisation increased, decreased or stayed the same? In learning and development activity please include more cost-effective methods of learning and development such as coaching and self-directed learning.’

There is a strong positive correlation between the change in

expenditure on L&D activities and the change in L&D activities (see Table 3).23 For example, almost two-thirds (64%) of organisations with increased L&D expenditure report increased L&D activity and just 4% of organisations that reduced L&D expenditure report increased L&D activity. The net effect is that the proportion of organisations reporting increased L&D activity in the past two years (35%) is higher than the proportion of organisations reporting increased expenditure on L&D (26%).

Table 3: Change in L&D activities, by change in expenditure on L&D(% of organisations, n=929)

Change in L&D expenditure in past two years

Change in L&D activity in past two years: Increased


Increased 64 6 4

Stayed the same 18 73 19

Decreased 7 10 69

Column totals do not add up to 100% because ‘don’t know’ responses are not reported. Almost one-tenth of respondents answered ‘don’t know’ to both of these questions.


Increased

Stayed the same

Decreased



Premium quality


Family culture

Structured culture

Dynamic culture


Total

24 36 21

19 33 27

44 38 8

32 32 12

26 40 16

25 53 9

Figure 9: Change in expenditure on L&D activities in the previous two years(% of organisations, n=929)

28 44 13




Family culture

Structured culture

Dynamic culture



Manufacturing

Education









250–999 employees


10,000+ employees

Private sector

Public sector

Voluntary sector

Total



Zero 1–25% 26–50% 51–75% 76–100%

5 7 3017 13

4 7 2617 13

2 10 2712 9

4 8 2022 20

4 4 2617 12

9 7 1629 15

1 15 327 10

9 494 8

5 3 2318 11

4 2 1629 13

9 8 2715 12

8 274 14

22 4 2315 13

8 8 2924 16

6 5 2519 12

5 7 2816 12

3 14 2629 8

9 3112 10

15 3913 18

8 3013 22

11 336 8

3 259 5

6 8 2724 20

9 6 3218 13

Figure 10: Workforce L&D participation(% of organisations, n=930)

Organisations were also asked: ‘What proportion of your organisation’s workforce has participated in learning and development activity during the past two years?’ (see Figure 10).

The striking feature of Figure 10 is how far short of 100% the heights of the bars are. Almost a third of respondents (32%) don’t know how many employees participated in L&D activity. In the public sector, the proportion is 42% and, in organisations with 10,000 or more employees, it is 58%! In the

summer 2014 LMO, the proportion saying ‘don’t know’ was just 8%, but the question was different: it referred to on- and off-the-job training rather than L&D activity and it used 12 months as the reference period rather than two years.24 It appears to be the case that many larger organisations do not collect – or do not aggregate – information on L&D activities in the way they do for training. L&D activities may not involve specially budgeted costs and are often tailored to the needs and interests of individual learners and

built around (and into) their jobs, all of which keeps them ‘under the radar’. In fact, the proportion of ‘don’t know’ responses is higher for organisations that say they have a budget for L&D (33%) than it is for organisations without one (27%).

The high proportion of ‘don’t know’ responses means we cannot read too much into the distribution of workforce L&D activity. The polar cases are 28% of organisations where more than three-quarters of the workforce took part in L&D and 5% of organisations


where nobody took part in L&D. It seems that organisations where little or no L&D has taken place are disproportionately SMEs, but sizeable proportions of SMEs do have all or nearly all of their workforce engaged in L&D, so the overall correlation between workforce participation and employee size band is significant, positive but relatively small.25

The majority of organisations (71%) conduct regular (corporate) evaluations of training and 90% of organisations that have a staff appraisal or review process include regular (individual-level) evaluations of training within it. Organisations that carry out regular corporate evaluations of training are more likely to report increased expenditure on L&D activities (31%, compared with just 13% of organisations with no such evaluation).26 In contrast, individual-level evaluation has no such effect.27

Both types of evaluation are associated with higher percentages of the workforce taking part in L&D activities. In organisations with corporate evaluation, the mean percentage of the workforce that had taken part in L&D activities in the past two years is 64%, compared with 38% for organisations without corporate evaluation. The gap is narrower but still significant for individual-level evaluation (62% compared with 49%).28

The two forms of evaluation are complementary.29 Incorporating reviews of past L&D activity in the appraisal process makes conversations about the future development needs of each employee systematic rather than relying on ad hoc processes or the initiative of managers and employees. They are also one way in which organisations can disseminate the insights gained from corporate evaluation of

training (and broader development activities) and thus influence future development activity.

Organisations that conduct corporate evaluations of training were also given a set of six generic performance criteria and asked whether they are used in their evaluations (see Figure 11).

Although the so-called ‘happy sheet’ has now made the transition from cliché to mild annoyance for many, employee satisfaction is still the most popular evaluation measure, used by 66% of all organisations that evaluate training.30 Measures of change in corporate and employee knowledge and skills are used by about half of organisations. Less common are attempts to measure the impact of training on productivity or profitability or to judge what impact training has had outside the organisation.

Figure 11: Measures used by organisations to evaluate training(% of organisations who evaluated training, n=622)

Employee satisfaction

Transfer of knowledge

into the workplace

Measures of productivity,

eg sales, profits etc.

Don’t knowMeasures of change in employee

skills

Wider impact on business or society of our L&D initiatives

43

OtherMeasures of change in employee

knowledge


6

29

38

4949

54

66

2

Distribution of organisations by number of training evaluation criteria used

7

16

2124

15

108

0 1 2 3 4 5 6


A small proportion of organisations (8%) say they use all six performance criteria to evaluate training, although nearly as many (7%) use none of them (see inset to Figure 11). The median number of criteria in use is three. Regression analysis shows that the number of criteria used by an organisation is not related to sector or industry but does increase with organisation size. Organisations where L&D expenditure had increased in the past two years and organisations where a quarter or more of the workforce participate in L&D activities use more criteria than others, as do cost-cutters and balanced investors.

These count data cannot tell us how rigorously they have been applied in training evaluations – some might not even be relevant for some organisations. However,

they might be an indicator of the breadth of vision and rigour that an organisation attaches to people management. In addition, even if they are an indicator of sophistication of approach, a high criteria count might be the result of prior decisions to invest in L&D – that now require time and effort to validate them – rather than one of the factors influencing those decisions.

Nevertheless, the material presented here suggests there is a possibility that an organisation’s approach to L&D and performance management may be related to its productivity, which is discussed in the next section.

‘The material presented here suggests there is a possibility that an organisation’s approach to L&D and performance management may be related to its productivity.’


Figure 12: Current priorities by sector(% of organisations selecting the objective as a current priority for their organisation, n=930)

Cost management

Growth of market share in new or existing markets

Customer service improvement

Improving productivity

Improving organisational responsiveness to change

Product innovation and quality improvement

Significant refocus of business direction

Improving corporate responsibility, reputation

and brand

Regulatory compliance

Increasing sustainability

Don’t know

Other

57

46

45

35

31

27

27

25

20

14

4

3

The data labels on the graph are the percentages for all organisations. Respondents could select all priorities that they thought applied to their organisation.


10 20 30 40 50 60 70

Voluntary sector

Public sector

Private sector

All organisations

0

This section presents an analysis of the factors that might help to explain variation across organisations in their (self-assessed) relative productivity. As well as providing an up-to-date perspective on business thinking, the questions included in the summer 2015 LMO mean that the analysis is, in a number of respects, more comprehensive than that presented in CIPD (2015a).

Productivity as a business priorityRespondents in the summer 2015 LMO – typically the senior person in the organisation with responsibility for HR – were given a list of generic business priorities and asked to identify those which are current priorities for their organisation. Similar questions have been asked in a number of previous CIPD surveys targeted at HR leaders and results from

the 2014 HR agility survey were presented in CIPD (2015a).

Cost management is the most common business priority, named by 57% of organisations, followed by growth of market share and customer service improvement (see Figure 12). Improving productivity is fourth in the list and is a current priority for 35% of organisations.

2 Explaining variation in productivity between organisations


Figure 13: Improving productivity as a current organisation priority(% of organisations selecting improving productivity as a current priority)

SuvivorsCost-cutters

Balanced investorsPeople-focused investorsCapital-focused investorsPremium-quality strategy

Basic/standard-quality strategyFamily culture

Structured cultureDynamic culture

Results-oriented cultureAgriculture, energy, construction

ManufacturingEducation

Health and social careWholesale, retail, hospitality etc.

Transport and communicationsFinance and business servicesCentral and local government

Micro (2–9) employeesSmall (10–49) employees

Medium (50–249) employees250–999 employees

1,000–9,999 employees10,000+ employees

All organisations

3835

4058

3232

46

3828

3542

52

34

41

2437

3433

4021

3038

423131

4035


Priorities vary by sector. The public sector stands out in two respects: far fewer organisations want to increase market share (many do not operate in commercial environments) and far more organisations want to improve their responsiveness to change. Voluntary sector organisations are much more likely to see increasing sustainability as a priority than organisations in the public or private sectors.

Improving productivity was a priority for 41% of private sector organisations in September 2014, according to the HR agility survey (CIPD 2015a). This is close to the private sector percentage here (38%). Public and voluntary sector organisations attach less weight on the whole to productivity

improvement (29% and 24% respectively).31

While a higher proportion of small and medium-sized organisations say improving productivity is a priority, there is no clear relationship with size of organisation (see Figure 13).32 There is significant variation by industry which, to a large extent, mirrors the pattern by sector, although manufacturing also stands out as the only industry where productivity is a priority for the majority of businesses.33 There is also a very substantial difference between premium-quality organisations and basic/standard-quality organisations in the importance they attach to productivity – a gap of 24 percentage points – and results-

oriented organisations are most likely to regard productivity as important while structured organisations are least likely to do so.34 While the three categories of investor are more likely to prioritise productivity than survivors and cost-cutters, the overall relationship with organisation mindset is not statistically significant.35

Recent trends in investment and in L&D activity also seem to have relatively little effect on the priority given to improving productivity (see Table 4). There is a modest (but significant) positive relationship between past investment in capital equipment and productivity as a current priority but no significant association with the L&D measures.36


There is a somewhat stronger positive relationship between the importance attached to productivity and output growth in the previous 12 months.37 Organisations producing more are also more likely to say productivity is a current concern. This has an intuitive explanation: increasing productivity is a way of meeting growing demand without the cost (and potential disruption) of taking on more people or asking staff to work more overtime.

Self-reported relative productivityOrganisations were asked the same productivity question used in the summer 2014 LMO: ‘To the best of your knowledge, comparing your organisation with your peers or competitors within the UK, how would you rate your productivity? (Productivity being the average value of the goods and services produced in each working hour).’ As we found with the summer 2014 LMO, the distribution of responses is highly skewed. Half of organisations rate their productivity as above average (43%) or well above average (7%). Just over a third (37%) rate their productivity as average and just 8% of organisations rate their productivity as below or well below average (with the number in the ‘well below average’ category

so small it had to be combined with the ‘below average’ category for reporting purposes).

Our analysis of the summer 2014 LMO was restricted to private sector organisations. The summer 2015 LMO data show that public and voluntary sector organisations are less likely to rate their productivity above average than private sector organisations, and more likely to rate their productivity below average – although the overall pattern of differences is not (quite) statistically significant using standard criteria.38 However, it became clear when analysing the data that the statistical relationships between productivity and the other variables collected in the survey are quite different in each of the three sectors. In particular, the questions asked in the LMO accounted for much less of the variation in productivity across organisations in the public and voluntary sectors. Analysing the combined data risks drawing inferences that might not be especially reliable for any sector. The analysis of relative productivity in the main body of this report therefore focuses on private sector organisations. Appendix 2 presents brief descriptive analyses for public and voluntary sector organisations.

Table 4: Improving productivity as a current priority, by change in investment in capital equipment and L&D and by output growth(% of organisations selecting improving productivity as a current priority for their organisation, n=929)

Measure has:

Increased in past two years

Stayed the same over past two years

Decreased in past two years Don’t know

Expenditure on capital equipment 37 32 32 37

Expenditure on L&D activities 33 38 36 31

L&D activity 32 38 38 29

Output** 40 33 29 *

* Not reported because unweighted n<50. ** Change here is over previous 12 months.


‘Organisations producing more are also more likely to say productivity is a current concern.’


Figure 14: Self-reported productivity in the private sector(% of private sector organisations, n=547 except*, n=464)

Suvivors*

Cost-cutters*

Balanced investors*

People-focused investors*

Capital-focused investors*



Prefer new culture

Content with culture

Family culture

Structured culture

Dynamic culture


Manufacturing







250–999 employees


10,000+ employees

All private sector organisations



Well above average Above average Average Below/well below average

1 1914 57

3 254 40

1 151 41

4 758 27

8 551 31

6 944 37

7 350 35

4 742 41

7 657 29

12 351 28

8 545 38

3 450 42

6 452 36

7 645 37

11 742 36

5 834 49

16 354 26

6 839 42

1 749 37

8 1233 39

3 643 44

5 551 36

9 845 34

8 347 36

Micro and small firms are less likely than larger firms to rate their productivity as above average or better (see Figure 14). However, the differences by employee size band and industry do not pass tests of statistical significance – they could have arisen by chance.39

There is a large gap between dynamic and results-oriented firms – where 64% and 63% respectively rate their productivity above average – and structured and family firms (46% and 45%

respectively). Respondents were asked a follow-up question: ‘In five years’ time, does your organisation have an ambition to have changed your culture to any of the following [the other cultural types] or do you plan to keep the same culture?’ Nearly two-fifths (39%) of organisations think they need to change their culture, whereas almost half (49%) plan to keep the current culture with the remainder not sure. Although a higher proportion of those planning to keep their current culture rate

their productivity above average or better – 57% versus 50% – the differences between the two groups are not statistically significant.40

There is a large and significant gap between premium-quality organisations (59% above or well above average) and basic/standard organisations (39%).41 There are even larger differences between mindset categories, with 70% of balanced investors rating themselves above average or


better, but just 15% of survivors rating themselves this highly.42 How organisations think about themselves and the approach they take to key issues such as market positioning and investment strategy appear to be key differentiators when it comes to their assessment of productivity.43

Whether or not an organisation regards improving productivity as a business priority is unrelated to its current assessment of its productivity! While 50% of those

saying it is a priority describe their productivity as above average or better, this proportion is slightly higher (53%) for firms where it isn’t a current priority.

The positive relationship we found in the 2014 data between productivity and the recent health of the organisation, as measured by recent changes in output and employment, applies here. We observe a similar relationship with measures of investment in capital equipment or L&D activities

(see Table 5). In every case, organisations seeing an increase in production, employment or investment are more likely to rate their productivity as above average or better. Likewise, falling output, employment or investment is associated with higher proportions in the below/well below average category.44

There is a significant and positive association between regular corporate evaluations of training and relative productivity: 56%

Table 5: Self-reported productivity by change in output, employment and investment (% of private sector organisations, n=547)

Productivity well above average

Productivity above average

Productivity average

Productivity below/well below average

Change in output in last 12 months:

Increased 9 58 26 4

Stayed the same 4 35 49 7

Decreased 6 23 56 14

Change in employment in last 12 months:

Increased 9 54 26 4



Change in capital investment in last two years:

Increased 10 49 34 4



Change in expenditure on L&D in last two years:

Increased 12 47 32 5



Change in L&D activity in last two years:

Increased 8 51 34 5



Row totals do not add up to 100% because ‘don’t know’ responses to the relative productivity question are not reported.



of organisations conducting evaluations rate themselves above average or better compared with 40% of organisations that do not evaluate training. The presence of a performance appraisal or review system, however, has no such effect.45

Multivariate analysis of relative productivityIn order to determine which of these variables are most important in explaining the variation in relative productivity, we repeat the approach taken in our previous productivity report and conduct

multivariate analysis of the LMO data. The full model results for private sector organisations are set out in Appendix 1. Table 6 summarises the results in terms of the variables found to have a significant positive (and negative) effect on relative productivity.

Table 6: Multivariate analysis of self-reported relative productivity for private sector organisations

Variables included in the analysis Variables with a statistically significant effect

Organisation size

Industry

Organisation culture Compared with family:Structured (+)Dynamic (+)Results-oriented (+)Organisation wishes to change culture in next five years (–)Don’t know if wishes to change culture in next five years (–)

Product/service strategy Basic/standard quality (–)

Organisation mindset Compared with survivors:Cost-cutters (+)Balanced investors (+)People-focused investors (+)Capital-focused investors (+)Don’t know (+)

Change in output in previous 12 months Stayed the same (–)Decreased (–)Don’t know (–)

Change in employment in previous 12 months

Use of zero-hours contracts

Use of short-hours contracts

Temporary workers as % of total employment

Change in expenditure on capital equipment in previous two years

Decreased (–)


Change in amount of L&D activities in previous two years Stayed the same (+)Don’t know (+)

% of workforce participating in L&D in previous two years 76–100% of workforce participating (+)

Organisation has L&D budget

Regular corporate evaluation of training Number of evaluation criteria (+)

Regular performance appraisal

Improving productivity is a priority for the organisation Yes (–)

+ indicates a positive effect on relative productivity; – indicates a negative effect on relative productivity.


Organisation size and industry do not feature in the final model results. This does not mean that productivity is the same in all industries (ONS labour productivity measures show this is clearly not the case). It suggests that firms are comparing themselves against their industry peers when answering the relative productivity question and that the (skewed) distribution of responses differs little from one industry to the next. The analyses of the 2014 LMO and HR agility survey data did point to a relationship between productivity and organisation size – with larger organisations tending to report higher relative performance and productivity – although in statistical terms it was not especially well defined. Here there is no such effect.

This might be because this analysis includes organisation culture and mindset as explanatory variables. Other things being equal, organisations with a family culture report lower relative productivity than any of the other three culture types and results-oriented organisations have higher relative productivity than structured or dynamic organisations.46 Similarly, organisations with a survivor mindset report lower relative productivity than any other organisations – including those unable to choose between the descriptors – with balanced investors outperforming all other organisations.47 Furthermore, the quantitative significance of both organisation culture and mindset is large when compared with other variables listed in Table 6. And small firms, of course, are over-represented among both family organisations and survivors.

Explanations for the observed difference in productivity between large and small firms usually focus on ease of access to capital

markets and larger firms’ ability to reap economies of scale and scope – such as the ability to make better use of specialised equipment and skills or the ability to apply the knowledge gained in one market or locality to another. This analysis does not contradict these hypotheses but suggests there may be an additional aspect: small firms often have distinct cultures, ways of doing things and of being that can affect their attitudes to investment and expansion and thus their relative productivity. For those running a small firm who are strongly attached to a family culture, a survivor mindset could almost be the default option if both investment-led expansion and cost-cutting are seen as likely, in different ways, to risk losing that family feel. At the same time, culture and mindset could be a reflection as much as a determinant of circumstances. Small firms who lack the information and resources to access finance and specialist advice may well describe themselves as survivors even if that is not their desired direction of travel.

The analysis finds that organisations with a premium-quality approach to product and service strategy have higher relative productivity, which is consistent with previous analyses.

As with the 2014 LMO data, there is a strong positive relationship between recent output growth and relative productivity and, once this is controlled for, there is no additional effect arising from recent employment growth. Nor is there any independent effect arising from the use of zero-hours contracts, short-hours contracts or the deployment of temporary workers.

Firms that have seen their investment in capital equipment fall in the previous two years have lower relative productivity

controlling for other factors, but there is no such effect for expenditure on L&D activities.

The change in the amount of L&D activities in the previous two years did exert an independent effect but in an unusual way: firms where L&D activity has stayed the same and firms where respondents don’t know whether L&D activity has changed tend to have higher relative productivity than firms reporting negative or positive changes in L&D activity! This result could of course be down to chance, or it may be the result of mis-specification in the model not picked up elsewhere. Alternatively, it may reflect particular patterns of response to this question. In particular, 40% of firms answering ‘don’t know’ to the change in L&D activity question identified as balanced investors – a group with very high relative productivity – so the model could be capturing a small part of that effect here.

Firms where over three-quarters of the workforce participate in L&D activities also have higher relative productivity than firms where less of the workforce take part. The presence of an L&D budget or corporate evaluation of training are not associated with higher or lower relative productivity – although the number of evaluation criteria used in any evaluation does exert a positive effect.

The results for measures of L&D activity and expenditure are somewhat different from our analysis of the 2014 LMO, where the percentage of the workforce trained in the previous 12 months and the change in expenditure on training in the previous 12 months both had significant and more systematic associations with relative productivity in the direction expected – wider participation in training and more


investment in training being associated with higher relative productivity. Why do these results look different?

One possible explanation centres on the difference in the questions, in particular the shift away from questions about training in the 2014 LMO to questions about L&D activity in the 2015 LMO. It could be the case that formal training actually is more important than generalised L&D when it comes to generating productivity improvements. But the shift in questions has led to an increase in the proportions of organisations saying they don’t know whether L&D activity has increased or decreased and organisations saying they don’t know the proportion of their workforce participating in L&D activities. Nor are these ‘don’t know’ firms the productivity no-hopers: the proportion of ‘don’t know’ responses to the questions

on change in L&D activity and the proportion of the workforce participating in L&D activities are highest for firms that rate their productivity well above average (see Table 7).

Another explanation is that the summer 2014 LMO did not ask firms about their investment in capital equipment. Nor did it ask the question on organisation mindset. The latter captures investment in human capital and, as we have seen, both balanced investor and people-focused investor categories are associated with large positive effects on relative productivity. Investment in human capital remains important but affects productivity in this model through two channels: primarily through mindset – the choice of investment strategy – and only secondarily through measures of L&D activity.

Finally, the model results suggest a negative relationship between relative productivity and whether or not the organisation sees improving productivity as a priority. Now it is possible to think of situations where a drive to improve productivity could backfire and result in lower productivity (for example, if investment in technology is not accompanied by necessary changes to systems and processes or if people are not trained how to use it effectively). This result is more likely, though, to be due to one (or both) of two other effects. One is an awareness effect: firms that attach priority to improving productivity have probably spent more time studying their own productivity and, as a result, may have a less rose-tinted view of their performance than other firms. The second effect is reverse causality: firms that say improving productivity is a priority do so because they believe their current productivity is below par.

Table 7: Self-reported productivity, by L&D activity (% of private sector organisations, excluding those answering ‘don’t know’ to the relative productivity question, n=519)

Self-reported relative productivity: Change in L&D activity in previous two years

Increased Stayed the same Decreased Don’t know

Well above average 41 31 6 22

Above average 41 43 9 7

Average 34 46 14 6

Below/well below average 28 42 26 3

% of workforce participating in L&D activity in previous two years

0–25% 26–75% 76–100% Don’t know

Well above average 10 11 45 35

Above average 24 16 30 30

Average 29 21 21 29

Below/well below average 28 27 15 29



The LMO asked a number of questions on organisations’ plans and expectations for the year ahead.

Investment growthWe asked organisations whether they expected to spend more or less in the coming year on capital equipment (see Figure 15).

Almost a third (31%) of organisations plan to invest more in the year ahead, with just 8% expecting to spend less. This leaves 37% expecting to spend the same and 24% who don’t know whether they will spend more or less.48 There is, however, significantly greater optimism in the private and voluntary sectors

than there is in the public sector.49 The proportion of public sector organisations expecting to invest less is almost as high (18%) as those expecting to spend more (20%), with over a third unable to say. Note that fieldwork for this survey took place in early June 2015, after the general election. Public sector respondents will

3 Expectations for the year ahead





Family culture

Structured culture

Dynamic culture



Manufacturing

Education









250–999 employees


10,000+ employees

Private sector

Public sector

Voluntary sector

Total

Increase Stay the same Decrease

24 40 14

42 42 9

13 21 27

18 21 14

27 32 13

20 35 12

31 50 4

20 28 18

42 42 2

29 35 5

43 36 7

31 46 7

33 35 5

34 36 9

42 32 6

31 37 8

46 40

35 44 2

27 35 9

30 43 5

31 35 9

31 60 3

34 39 5

Figure 15: Expected change in expenditure on capital equipment in the next 12 months(% of organisations, n=929)

36 37 4

0






Family culture

Structured culture

Dynamic culture



Manufacturing

Education









250–999 employees


10,000+ employees

Private sector

Public sector

Voluntary sector

Total


21 43 18

29 58 8

8 30 39

16 21 21

26 37 18

25 38 17

25 62 3

14 34 28

36 53 4

32 43 4

40 47 6

28 51 10

29 37 8

29 45 14

28 56 8

27 45 11

28 51 4

34 32 17

24 56 7

20 45 18

19 71 2

31 47 6

Figure 16: Expected change in expenditure on L&D activities in the next 12 months (% of organisations, n=929)

31 48 7

38 41 5

have answered these questions expecting further cuts but without knowing the precise implications for their organisation.

Uncertainty on future investment plans is especially common for large organisations with 10,000 or more employees – almost half (47%) say they don’t know next year’s plans. The pattern is identical in public and private sectors, which suggests that size and organisational complexity may be the reason rather than concern about the future business

environment. As a result, firms in the middle size bands are most likely to say they will be investing more in the year ahead.50

Twice as many organisations in central and local government expect to invest less (27%) than invest more (13%), whereas firms in agriculture, energy and construction and in manufacturing are most positive about investment plans. Dynamic organisations have the highest proportion of any cultural type, saying they expect to invest more (42%), with structured organisations

least likely to say this (27%) and most likely to say investment will fall. Premium-quality organisations are more positive about their investment plans than basic/standard-quality organisations.51

We asked a similar question about expenditure on L&D activities over the coming 12 months (see Figure 16). Just over a quarter (27%) of organisations expect to spend more and just over a tenth (11%) expect to spend less, while nearly half (45%) expect to spend the same and about one in six don’t know.


Comparing responses to this question with those for the question on capital equipment, there are fewer ‘don’t know’ responses but the proportion expecting to spend more on L&D activities is also smaller and the proportion expecting to spend less is a little higher. This may reflect the current push within organisations to make their training and L&D budgets go further and deliver better value from what’s already there. Otherwise, the

pattern of response is very similar to that for investment in capital equipment.52

Investment intentions for the coming year are closely correlated with the change in investment in the preceding two years (see Table 8).53 For example, 68% of organisations that increased investment in capital equipment in the previous two years say they intend to spend even more in the next 12 months, whereas 23%

propose to invest at the same level and just 5% expect to invest less. At the other end of the spectrum, almost half (45%) of organisations where capital investment has fallen in the last two years expect a further reduction in the year ahead.

Future mindsetIn addition, we asked a question designed to capture the mindset guiding an organisation’s approach to future investment and growth (see Box 3).

Table 8: Comparison of expected changes in investment with recent changes in investment(% of organisations, n=929)

Expected change in expenditure on capital equipment in next 12 months

Expected change in expenditure on L&D activities in next 12 months

Increase Stay the same Decrease Increase Stay the same Decrease

Change in expenditure on capital equipment in previous two years:

Increased 68 23 5 43 41 8

Stayed the same 13 80 3 18 64 9

Decreased 16 32 45 24 43 29

Change in expenditure on L&D activities in previous two years:

Increased 52 27 3 67 26 3

Stayed the same 29 56 5 16 80 4

Decreased 19 35 26 15 33 49

Row totals do not add up to 100% because ‘don’t know’ responses are not reported.


Box 3: Future mindset

All respondents were asked: ‘Looking ahead to the next two years, which ONE of the following statements best describes your expectations about your organisation’s future plans?’ Five choices were presented:

• ‘We are performing well and we don’t see the need for major change and lots of investment’ – we label organisations that chose this response as ‘high-performers’ (34%).

• ‘We are not performing well, but we don’t have the financial resources or confidence to invest in equipment, technology and people’ – ‘finance-constrained’ (15%).

• ‘We are not performing well, but don’t have the skills or ambition to invest in equipment, technology and people’ – ‘ambition-constrained’ (4%).

• ‘We will now be able to make the investments in people we haven’t been able to do in the last few years’ – ‘making up ground – people’ (16%).

• ‘We will now be able to make the investments in equipment and technology we haven’t been able to do in the last few years’ – ‘making up ground – capital’ (17%).


Just over a third (34%) of organisations identify themselves with the high-performer description (see Figure 17). Almost a fifth are in one of the constrained categories, mainly due to constraints on finance and confidence rather than a lack of skills and ambition. Another third identify with one of the two making up ground categories, divided almost equally between those focusing on people and those focusing on capital. One in seven respondents (14%) could not identify with any category.

As with other measures of investment, there are clear differences across sectors. Whereas 39% of private sector organisations identify as high-performers, just 18% of public sector organisations place themselves in this category. Public sector organisations are much more likely to describe themselves as finance-constrained (31% against 10% in the private sector) and less likely to say they intend to make up ground. Voluntary sector organisations are somewhere between these

two cases, with more finance-constrained organisations than the private sector but with nearly as many aiming to make up ground.54

Small and medium-sized organisations have high proportions (over 40%) looking to make up ground in the coming two years. This is also the case with the very largest organisations – those with 10,000 or more employees – although, in contrast, this group has a very low proportion of high-performers (just 22%).55



Family culture

Structured culture

Dynamic culture



Manufacturing

Education









250–999 employees


10,000+ employees

Private sector

Public sector

Voluntary sector

Total



High-performers Finance-constrained Ambition-constrained

Making up ground – people Making up ground – capital

38 20 1710 3

29 14 1623 6

27 16 1719 5

40 20 1810 1

36 20 179 6

41 13 1916 5

40 19186 1

35 24 208 5

41 17 1118 4

35 25 1616 3

39 19 1810 4

37 21 218 4

54 14 129 8

37 16 1415 4

Figure 17: Future organisation mindset(% of organisations, n=929)

22 15 1920 6

40 14 1913 2

24 18 526 10

11 7 1537 6

45 10 812 7

30 19 1717 4

22 15 2615 4

18 10 1431 7

29 20 1418 3

34 17 1615 4


The public–private sector difference shows through in the low proportions of organisations in central and local government, health and social work and education describing themselves as high-performers, whereas over half (54%) of organisations in agriculture, energy and construction place themselves in this category. Finance and business services has a relatively high proportion of organisations in one of the two making up ground categories (44%) but very few (13%) in one of the constrained categories. This would appear to be an industry with ambitions for the near future and with relatively few organisations now seeing themselves held back by a lack of finance or skills.56

Structured organisations – disproportionately in the public sector – are least likely to describe themselves as high-performers and most likely to be finance-constrained. Otherwise, differences by organisation culture are not significant.57 Premium-quality organisations are more likely to describe themselves as high-performers than basic/standard organisations. They are also more likely to be looking to make up ground and less likely to describe themselves as constrained.58

These descriptions of future mindset correlate well with expectations for investment in capital equipment and L&D activities (see Table 9).59 Those organisations who assign themselves to one of the making

up ground categories are most likely to say they intend to increase investment in the coming year, while those expecting to invest less are predominantly in one of the two constrained categories. The table also suggests that organisations choosing the high-performer category are not, in general, being overly complacent. The proportions of high-performers intending to invest more are not too far behind those in the making up ground categories, especially for capital investment.

Future mindset can be compared with perceptions of relative productivity (see Table 10). Respondents selecting the high-performer descriptor – which includes the phrase ‘we are

Table 9: Future mindset and investment intentions(% of organisations, n=929)

Expected change in capital investment in next 12 months

Expected change in expenditure on L&D in next 12 months

Increase Stay the same Decrease Increase Stay the same Decrease

High-performers 35 44 3 25 58 4

Finance-constrained 10 43 27 8 49 32

Ambition-constrained 19 46 17 19 46 17

Making up ground – people 43 35 3 56 29 3

Making up ground – capital 44 24 3 32 34 9

Don’t know 14 30 7 14 39 15

Total 31 37 8 27 45 11

Row totals in each of the two panels do not add up to 100% because ‘don’t know’ responses to the questions on changes in capital investment and expenditure on L&D are not reported.



performing well…’ – are more likely than others to rate their productivity above average or better. However, over half of organisations in the two making up ground categories also rate their productivity as above average or better – the difference is only a few percentage points. While organisations in these categories recognise a need to step up investment, this doesn’t necessarily mean they see themselves as underperforming at present. Many presumably see more investment

as a means to perform even better (or, at least, to secure their competitive position). In contrast, the majority of those selecting the finance- and ambitioned-constrained descriptors – which include the phrase ‘we are not performing well…’ – rate their productivity as average or worse.60

For the majority of organisations – those where the respondent had been employed for at least two years – choice of future mindset can be compared with choice of past

mindset (see Figure 18). These two choices clearly are not independent of each other.61 Unsurprisingly, organisations that summarise their recent past as being in survival mode are least likely to say they have been doing well and, as high-performers, see no need for major change – although 16% of them do still choose this combination! Almost half (49%) of survivors see the near future as one where they continue to underperform because of shortages of finance or ambition.

Table 10: Future mindset and relative productivity(% of organisations, n=927)

Self-defined relative productivity

Well above average Above average Average

Below/well below average

High-performers 7 54 34 4

Finance-constrained 4 24 50 17

Ambition-constrained 3 26 36 25

Making up ground – people 10 47 35 4

Making up ground – capital 10 41 37 5

Don’t know 3 36 35 9

Total 7 43 37 8

Row totals do not add up to 100% because ‘don’t know’ responses to the relative productivity question are not reported.


Survivors

Cost-cutters

Balanced investors



Total

Total % are not exactly the same as in Figure 14 because the questions on [past] organisation mindset were only asked if the respondent had been with their employer for two years or more.



High-performers Finance-constrained Ambition-constrained

Making up ground – people Making up ground – capital

16 14 1037 12

44 14 218 3

28 34 913 6

26 16 3012 3

35 16 1714 5

54 173 13

Figure 18: Future organisation mindset by past mindset(% of organisations, n=790)


A relatively high proportion of backward-looking cost-cutters (44%) see themselves as forward-looking high-performers. This may indicate a ‘more of the same’ approach based on an assumption that they can perform well without needing to invest a lot if they continue to pay attention to efficiency and cost-minimisation. However, 35% of this group think they will now be able to raise their investment in capital equipment or people and build on their current position.

Balanced investors are the most successful group as measured by recent growth, investment and relative productivity. Over half (54%) see themselves in the high-performer

category going forward. Almost a third (30%) see the next couple of years as an opportunity to further strengthen their performance by investing more in people or capital.62

Organisations aware they have not been able to pursue a balanced investment approach in the recent past (people- and capital-focused investors) are the groups most likely to select one of the making up ground categories when thinking ahead. Strategies focused on making up ground tend to be directed towards remedying past weakness in investment. Hence 34% of (past) capital-focused investors say their future approach is to make up ground by investing

in people (compared with 9% who say they wish to make up ground through more capital investment). Similarly, 30% of (past) people-focused investors say their future approach is to make up ground through investment in capital (compared with 16% who say they wish to invest more in people).

Output growthIn line with current forecasts, the majority of organisations are optimistic about the coming year.63 Over half of respondents (55%) expect their production of goods and services to increase, with 32% expecting to produce roughly the same and just 8% expecting to produce less (see Figure 19).

Totals do not add to 100% because ‘don’t know’ responses are not reported.Source: CIPD Labour Market Outlook survey, summer 2015.

Premium quality


Family culture

Structured culture

Dynamic culture



Manufacturing

Education









250–999 employees


10,000+ employees

Private sector

Public sector

Voluntary sector

All organisations


48 37 13

56 30 12

25 44 20

52 29 11

47 34 11

60 20 12

55 38 5

39 34 15

77 20 1

61 33 1

58 29 11

57 31 7

56 30 7

67 22 9

62 26 4

55 32 8

62 29 5

55 30 8

56 34 7

33 47 7

49 40 7

59 31 6

Figure 19: Expected output growth in the next 12 months (% of organisations, n=929)

62 29 4

43 36 12


Figure 20: Expected output growth in the next 12 months by past output growth(% of organisations, n=929)

Bar heights do not add up to 100% because ‘don’t know’ responses are not reported.


Increased

Output in the previous 12 months

85

122


25

64

7

25

37

32

IncreaseStay the sameDecrease

There is a significant difference, though, between the expectations of public sector organisations and those of organisations in the private and voluntary sectors.64 Only 39% of public sector organisations expect to be producing more and 15% expect to be producing less. Concerns about future public expenditure cuts and the likely knock-on effects explain why central and local government and education also contain relatively low proportions of organisations expecting to produce more in the coming year. Interestingly, this pessimism does not extend to the health and social care sector, where 60% of organisations expect output to increase, on a par with private service industries.65 This may be because demographic and other pressures mean demand for services is likely to increase regardless of the precise spending decisions taken by government – recognising that public expenditure on the NHS has been granted a degree of protection from cuts in the forthcoming spending review.

Although organisations in the middle size bands are a little more likely to expect growth than larger or smaller organisations, differences by employee size band are not statistically significant.66 More than three-quarters (77%) of dynamic organisations expect to increase output, which is 30 percentage points higher than the equivalent proportion for structured organisations. Premium-quality organisations are also more optimistic about growth prospects than their basic/standard-quality counterparts.67

Expectations for growth in the year ahead are strongly influenced by growth performance during the previous year (see Figure 20).68 Whereas 85% of organisations reporting growth in the previous year (to June 2015) expect to grow again in the 12 months to summer 2016, the proportion is just 25% among organisations where output had stayed the same or decreased.

‘Concerns about future public expenditure cuts and the likely knock-on effects explain why central and local government and education also contain relatively low proportions of organisations expecting to produce more in the coming year.’




Productivity well above average

Productivity above average

Productivity average

Productivity well below/below average

High-performers

Finance-constrained

Ambition-constrained

Making up ground – people

Making up ground – capital

Total


63 27 5

70 24 1

44 32 20

55 32 8

58 34 3

32 37 25

49 39 9

64 30 2

Figure 21: Expected output growth by relative productivity and future mindset(% of organisations, n=929)

69 19 6

27 31 39

As expected, there is also a clear link between forward-looking expectations of investment growth and output growth (see Table 11). Almost three-quarters (74%) of organisations expecting to spend more on capital equipment in the coming year also expect to produce more products and services, whereas just 27% of those planning to invest less in capital

equipment say they are doing this while at the same time expecting to produce more. The same pattern is evident when it comes to expectations for investment in L&D activities.69

In the previous section, we found a clear relationship between (self-reported) relative productivity and output growth in the preceding

12 months. Given the positive correlation between past output growth and expected output growth, organisations with relatively high productivity are therefore more likely to expect growth than organisations describing their productivity as average or below average (see Figure 21).70

Table 11: Expected output growth and expected investment in capital equipment and L&D activities(% of organisations, n=927)

Expected output growth in next 12 months


Expected change in expenditure on capital equipment in next 12 months:

Increased 74 20 3

Stay the same 46 46 6

Decreased 27 29 40

Expected change in expenditure on L&D activities in next 12 months:

Increased 69 24 2

Stay the same 51 41 5

Decreased 36 29 30

Row totals do not add up to 100% because ‘don’t know’ responses are not reported.



Growth expectations are also related to an organisation’s forward-looking mindset.71 Organisations in the making up ground categories are most likely to expect growth – among those making up ground by investing more in people, 70% expect to produce more. In these organisations, growing demand presumably provides the means as well as the impetus for higher levels of investment. Similarly, organisations in the constrained categories – especially the small proportion where the constraints are a lack of skills and ambition – are much more likely to expect output to stay steady or even fall. For these organisations, economic growth appears unlikely to offer a means of rectifying underperformance. Interestingly, organisations in the high-performer category are somewhere between the two. Few expect to be cutting back output but a third (34%) do not expect to be producing more in the coming year.

It seems the high-performers might well consist of two groups. One – the larger of the two – consists of organisations that have done well in recent years

by adopting a balanced investor mindset and who expect to continue this approach with increased output and investment helping to maintain their superior productivity performance. The second group – the smaller of the two – consists of organisations that have improved their past performance by adopting a cost-cutter mindset and who expect to continue this approach without major changes in investment or growth plans. The risk facing this second group is that ‘more of the same’ becomes complacency and they find themselves outperformed by, and losing market share to, investment-minded or more quality-focused competitors.

Productivity and pay growthThe LMO always includes backward-looking and forward-looking questions on changes in pay, which provides an opportunity to explore the link between productivity and pay growth.

For the forward-looking questions, respondents are first asked when they next expect to make a pay decision.72 If this is expected to be within 12 months of the survey taking place, respondents

are asked whether they expect to increase, decrease or freeze pay and – if it isn’t a freeze – the average pay increase/decrease they expect to make.

The LMO main report discussed the pay outlook, which remains subdued (CIPD 2015b). The overall median pay increase is 2%, which is also the median pay increase for the private sector – a figure that has not changed once in the last three years.

The distributions of expected pay changes for the private, public and voluntary sectors are listed in Table 12. These data are restricted to organisations that say they plan to make a pay decision within the next 12 months and are prepared to supply an estimate of the expected outcome. Many respondents either do not know what the outcome will be or say that it is hard to tell at this stage (since it will depend on factors such as the performance of the business). In the summer 2015 LMO, 50% of organisations expecting to take a pay decision within the next 12 months did not supply an estimate of the expected pay change.

Table 12: Distribution of expected pay changes, by sector

Average change in basic pay expected at next pay decision (%)

Average change in total pay expected at next pay decision (%)

Private sector (n=192)

Public sector (n=116)

Voluntary sector (n=75)

Private sector (n=153)

Public sector (n=87)

Voluntary sector (n=65)

10th percentile 0 0 0 0 0 0

25th percentile 1.5 0 1 1 0 0

Median 2 1 1.5 2 1 1.5

75th percentile 3 1 2 3 1 2

90th percentile 5 1.5 3 5 2 3

Mean 2.63 0.83 1.54 2.89 1.45 1.18

Base: Organisations planning to make a pay decision within the next 12 months that were prepared to quantify the expected change in pay (including wage freezes and cuts).



Economic theory suggests there should be a relationship between earnings and labour productivity. The UK’s poor productivity record since 2008 is often cited as one of the most important – if not the most important – reason why average earnings in the UK are still lower in real terms than they were in 2009 (CIPD 2014). If wages are set competitively, we might expect to see a relationship between pay and productivity at the firm level. Some firms might pay more than their competitors in order to attract and retain the most productive workers. Payment schemes might be used to reward and incentivise effort and initiative. It is likely that such effects, if they exist, would be most visible when comparing the level of pay across firms. However, an effect might show up in comparisons of pay growth.

We restrict the analysis to the private sector. It is clear from Table 12 that the distribution of pay increases in the public sector is highly compressed around the Government’s 1% pay rise ceiling. There is more variation in the distribution of pay increases in the voluntary sector but the number of organisations supplying data

is too small for any more detailed analysis.

Even for private sector organisations, the relatively small number of observations means we are able to only differentiate between organisations with relatively high productivity (above average/well above average) and those with relatively low productivity (average/below average/well below average) (see Table 13).

Looking at expected changes in basic pay, the median for both high- and low-productivity firms is 2%. There is a difference of approximately 0.9 percentage points in the mean increase. This is because the high-productivity group contains a small number of firms that expect to award very high pay increases. Since these are atypical and could well be erroneous, Table 13 also reports trimmed means that exclude the top 5% and the bottom 5% of the distribution. For average basic pay, this reduces the difference in mean values to approximately 0.3 percentage points. The differences in trimmed means are statistically significant.73

Table 13: Distribution of expected pay changes in private sector organisations, by relative productivity

Average change in basic pay expected at next pay decision (%)

Average change in total pay expected at next pay decision (%)

Productivity well above/above average

(n=108)

Productivity average/below average/well

below average(n=80)

Productivity well above/above average

(n=83)

Productivity average/below average/well

below average(n=65)

Median 2.0 2.0 2.5 2.0

Mean 3.02 2.10 3.62 1.99

Trimmed mean* 2.21 1.90 3.20 1.98

Base: Private sector organisations planning to make a pay decision within the next 12 months that were prepared to quantify the expected change in pay (including wage freezes and cuts) excluding organisations answering ‘don’t know’ to the relative productivity question.

* Recalculated excluding the observations with the 5% highest and 5% lowest expected pay changes. For changes in basic pay, this meant excluding changes in pay above 5% or below zero. For changes in total pay, this meant excluding changes in pay above 10% or below zero.


‘The UK’s poor productivity record since 2008 is often cited as one of the most important – if not the most important – reason why average earnings in the UK are still lower in real terms than they were in 2009.’


Looking at expected changes in average total pay – which should include bonuses and similar incentive payments – there is a larger gap between high- and low-productivity firms. The differences at both the median and the mean (untrimmed and trimmed) are statistically significant.74

Respondents who said they expected to increase basic pay by 2% or more were asked to identify the main cause or causes from a given list of factors, which provides a complementary perspective (see Figure 22).75

Improved employee productivity or performance is one of the more commonly referenced factors behind these pay expectations.

In addition, high-productivity firms are more likely than low-productivity firms to name it as a reason why they expect to increase basic pay by 2% or more (the difference of 14 percentage points is statistically significant).76

Hence the data suggest that firms with above average productivity expect to award a higher pay increase than firms with average or below average productivity – especially once incentives, bonuses and so on are included. High-productivity firms are also more likely to say employee productivity and performance is one of the important factors behind their pay decision. We do need to remember that the difference in expected pay increases between high- and low-

productivity firms could be due to another variable that happens to be correlated with both pay and productivity. In addition, we only have pay rise data for half the firms intending to make a decision on pay in the next 12 months. It is possible that firms prepared to provide a pay estimate are qualitatively different from those that didn’t provide an estimate.

Nevertheless, the data suggest this might be a topic worth exploration in a more focused way in a future LMO survey. A more robust test of the relationship between pay and productivity might be to include questions on the relative level of pay alongside the question on relative productivity.

Figure 22: Main factors behind pay rise decisions(% of private sector organisations where average basic pay is expected to increase by 2% or more, n=139)

Falling labour costs

Ability to pay more

Improved productivity/performance

Going rate

Current inflation rate

Pay catch-up

Anticipated inflation

Union/staff pressures

National Minimum Wage/Living Wage

Recruitment and retention

‘Ripple’ of higher starting pay

3958

4636

1933

4027

1325

3121

1921

3220

811

810

56


Low productivity

High productivity


The data presented in this report provide grounds for cautious optimism about future productivity growth – at least in the private sector. A majority of businesses are looking to maintain or improve their productivity and performance and, in most cases, they expect to continue making the investments in capital and people needed to achieve their objectives. Over a third of organisations in both the private and the voluntary sectors say their investment efforts are designed to make up ground over the next two years. In some cases, these efforts are focused on capital and technology; in others, the focus is on investing in people. There are signs that many firms understand the importance of a balanced approach to investment. Thankfully, only a minority of organisations appear to be stuck in a self-reinforcing cycle where a lack of finance or skills leads to underinvestment and underperformance, which then means demand is falling or stable at best (meaning there is little or no room for fresh investment).

Our previous report considered in some detail the implications of its analysis for businesses aiming to improve their productivity – for example, which of many different management practices might be associated with higher performance and how firms could work out which might suit the particular circumstances of their business. Nothing in this analysis contradicts the recommendations in that report.

This report draws out more clearly the importance of the strategic choices that organisations make

– deliberately or through force of events. These include positioning in the market, internal culture and the approach taken to managing the present and preparing for the future. The recession is still having an effect on some organisations through its impact on these choices.

There are signs that more firms are looking to take advantage of the benign economic conditions to invest and improve. But do they have the vision and management and leadership skills to make the necessary changes? Will corporate memory and past experience hold firms back more than they would want it to?

The report demonstrates the links between investment and productivity. The findings on L&D expenditure and activity here are not as clear-cut as those for training that were presented in our previous report (possible reasons why this is the case are discussed in Section 2). Whether training and L&D activity are qualitatively and quantitatively similar in terms of their impact on business productivity is something that could be explored in more depth in future productivity research.

Our previous report also included an in-depth discussion of the policy implications. Since that report was published, the summer Budget has taken place and the Government has published its productivity plan (HM Treasury 2015b). An analysis of the productivity plan and recommendations about how its shortcomings could be put right are set out in the CIPD’s

submission to the Business, Innovation and Skills Select Committee (CIPD 2015d).

The analysis in this report suggests that more attention needs to be given to particular aspects of productivity policy.

The importance of strategic choices in general, and mindset in particular, must have implications for policies aiming to improve business performance. It suggests that policies aiming to improve access to capital, for example, or encourage employers to train their workforce may have limited effect if a firm’s current mindset is focused on survival or cost-cutting – or if they lack the confidence and ambition to embark on new investments. This might need new or reinforced mechanisms for the delivery of business support. How to change mindset may not be easy. Encouraging contact and networking may be one way to lift sights and raise ambitions – though, in a challenging context, it could backfire and become a counsel of despair!

This report looked only at organisations’ investments in capital, technology and people. The CIPD’s submission to the government spending review stresses the importance of maintaining policies that support business investment (CIPD 2015c). Public investment also has a vital role to play and this needs to be maintained and strengthened as economic conditions improve. These are investments in future prosperity and need not be subject to the same constraints as current expenditure.

Conclusions


Initial model Streamlined model(a)

CoefficientStandard

error CoefficientStandard

error

Organisation size [base = 2–9 employees]

10–49 employees 0.156 0.394

50–249 employees 0.138 0.436

250–999 employees 0.222 0.483

1,000–9,999 employees 0.451 0.501

10,000+ employees 0.302 0.545

Industry [base = Agriculture, energy, construction]

Manufacturing 0.850 0.516 *

Education 0.145 0.996

Health and social care 1.211 0.638 *

Wholesale, retail, hospitality etc. 1.007 0.461 **

Transport, communication, IT 0.553 0.549

Finance and business services 0.632 0.462

Organisation culture [base = family]

Structured –0.792 0.247 ** –0.643 0.264 **

Dynamic –0.825 0.401 ** –0.776 0.362 **

Results-oriented –1.341 0.335 *** –1.306 0.287 ***

Whether organisation wishes to change culture in next five years [base = no]

Yes 0.608 0.247 ** 0.470 0.215 ***

Don’t know 0.490 0.378

Product/service strategy [base = premium quality]

Basic/standard quality 0.494 0.253 * 0.565 0.219 ***

Don’t know 0.655 0.653

Organisation mindset [base = survivors]

Cost-cutters –1.704 0.374 *** –1.565 0.339 ***

Balanced investors –2.238 0.388 *** –2.306 0.350 ***

Capital-focused investors –1.357 0.454 *** –1.448 0.405 ***

People-focused investors –1.371 0.420 *** –1.561 0.373 ***

Don’t know –1.707 0.630 *** –1.751 0.569 ***

Ordered logit model explaining variation in self-reported relative productivity

Dependent variable: relative productivity [1=‘well above average’ to 4=‘below/well below average’] – a positive model coefficient implies a negative effect on [relative] productivity

Appendix 1: Details of multivariate modelling



CoefficientStandard


error

Change in output in previous 12 months [base = increased]

Stayed the same 0.580 0.287 ** 0.715 0.249 ***

Decreased 0.892 0.362 ** 1.116 0.307 ***

Don’t know 4.737 1.172 ***

Overall change in employment in previous 12 months [base = increased]

Stayed the same 0.159 0.294

Decreased 0.542 0.360

Whether organisation uses zero-hours contracts [base = yes]

No –0.202 0.283

Don’t know 2.209 1.179

Whether organisation uses short-hours contracts [base = yes]

No 0.328 0.324

Don’t know –1.341 0.757 *

Temporary workers as a share of total employment [base = zero]

1–10% –0.545 0.297 *

11–25% –0.384 0.418

26%+ –0.789 0.375 **

Change in expenditure on capital equipment in past two years [base = increased]

Stayed the same 0.534 0.293 *

Decreased 1.208 0.432 *** 0.825 0.361 **

Don’t know 0.415 0.375

Change in expenditure on L&D activities in past two years [base = increased]

Stayed the same 0.231 0.371

Decreased 0.509 0.479

Don’t know 0.609 0.488

Change in amount of L&D activities in past two years [base = increased]

Stayed the same –1.164 0.348 *** –0.562 0.222 **

Decreased –0.505 0.456

Don’t know –1.894 0.657 *** –1.116 0.440 **

Proportion of workforce that have participated in L&D activities in past two years [base = zero]

1–25% –0.177 0.527

26–50% 0.088 0.562

51–75% 0.776 0.668

76–100% –0.971 0.538 * –0.757 0.245 ***

Don’t know –0.478 0.537



CoefficientStandard


error

Whether organisation has an L&D budget [base = yes]

No 0.226 0.321

Don’t know –0.355 1.056

Whether organisation regularly conducts corporate evaluation of training [base = yes]

No –0.366 0.378

Don’t know –1.618 0.702 **

Number of evaluation criteria used in corporate evaluation of training [range from 0 to 6] –0.243 0.086 *** –0.143 0.054 ***

Whether organisation has regular performance appraisal or review [base = yes]

No –0.161 0.354

Don’t know 0.448 1.096

Whether improving productivity is a current business priority [base = no]

Yes 0.723 0.229 *** 0.587 0.209 ***

N 441 437

Likelihood ratio X² test statistic X²(57) =414.25 *** X²(18) =371.22 ***

McFadden Pseudo R² 0.365 0.329

(a) Variables were dropped that failed a likelihood ratio test of joint significance. Combinations of coefficients were also dropped on the basis of similar tests.

* = significant at 10% level** = significant at 5% level*** = significant at 1% level

Base: Private sector respondents, excluding ‘don’t know’ responses to the relative productivity question and respondents who had been with their current employer for less than two years (as these were not asked the mindset question). ‘Don’t know’ responses to the employment change question were also excluded from the initial model and ‘don’t know’ responses to the change in output question were excluded from the streamlined model.


Appendix 2: Relative productivity in public and voluntary sector organisations

The proportion of public sector organisations that consider their relative productivity to be above average or better is 44%, compared with 50% for organisations as a whole (see Figure A2.1).

There are no significant differences between small and large organisations or by industry, although premium-

quality organisations rate their productivity much more highly than their basic/standard-quality peers. The link between productivity and output growth is much weaker than it is for private sector organisations. And the correlations between productivity and changes in investment (capital equipment and L&D activities) as well as with the change in L&D activity are not significant.

Expenditure on capital increased

Expenditure on capital stayed the same

Expenditure on L&D stayed the same

Expenditure on L&D decreased

L&D activity increased

L&D activity stayed the same

L&D activity decreased

Output increased

Output stayed the same



Education



2–999 employees

1,000+ employees

All public sector organsations

All organisations

10 843 35

1 1439 43

8 840 39

11 938 38

4 737 43

5 1045 34

7 1234 37

7 1729 45

3 643 37

7 1331 42

4 836 45

7 1138 34

7 1037 37

7 1439 36

8 439 45

Figure A2.1: Self-reported productivity in the public sector(% of public sector organisations, n=230)

7 550 30

8 1141 29

7 843 37

Well above average

Above average

Average





The distribution of relative productivity for voluntary sector organisations is very similar to that of public sector organisations (see Figure A2.2).

Again, there are no significant differences according to employee size band, but the choice of product/service strategy again makes a very noticeable difference. Although organisations where expenditure on capital equipment increased or where output increased are more likely than others to rate their productivity above average (or better), neither of these associations are robust in statistical terms. However, there are robust positive relationships between productivity and the change in expenditure on L&D activities as well as with the change in the amount of L&D activity.

The data presented here illustrate why it did not make sense to try and model productivity across all three sectors. Although the (positive) impact of a premium-quality strategy applies across all three sectors, other variables important in explaining variation in productivity among private sector organisations – such as output growth and investment – have less explanatory power in the public and voluntary sectors. Measures of change in L&D activity and expenditure have a positive association with relative productivity in the voluntary sector but not in the public sector.

Figure A2.2: Self-reported productivity in the voluntary sector(% of voluntary sector organisations, n=151)

Well above average

Above average

Average

Expenditure on capital increased

Expenditure on capital stayed the same

Expenditure on L&D increased

Expenditure on L&D stayed the same

L&D activity increased

L&D activity stayed the same

Output increased

Output stayed the same



2–249 employees

250+ employees

All voluntary sector organsations

All organisations

14 1137 34

10 1040 31

7 639 40

14 641 29

6 835 43

4 35 40

12 644 26

14 1034 32

1 1233 40

10 1133 35

15 640 35

16 841 27

6 2018 50

7 843 37




13


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1 See, for example, Bloom and Van Reenen (2010).

2 For further information on the definition and measurement of social capital, see the Office for National Statistics Social Capital Guide, available at: http://www.ons.gov.uk/ons/guide-method/user-guidance/social-capital-guide/index.html [accessed 2 August 2015].

3 If nothing else, there is always the possibility that the investor may not be alive to enjoy the benefits, which is why the discount rates used in cost–benefit analyses usually include an element for so-called pure time preference of 1–2% a year.

4 Even if we assume that all the competing investment opportunities available to a business can be quantified, the choice of investment option may not be straightforward. When an owner-manager takes the decisions, there is inevitably some balancing of the financial benefits to the business with the personal interests and beliefs of the owner-manager. The time horizon over which returns are calculated (the effective discount rate) may be determined by investors, market sentiment or other stakeholders such as government or insurers. In large firms, the extent to which decisions are being taken in the interests of shareholders rather than managers may be unclear and contested (this is known as the ‘principal-agent problem’).

5 The negotiations in Dragons’ Den about the dragons’ ‘cut’ illustrate the process.

6 Both fact and perception can matter when it comes to the returns the market expects from any organisation that is seen as being backed by the taxpayer. The most vivid example in recent times was the way that the bond yields on long-term government debt issued by national governments within the euro zone converged dramatically once the euro became the common currency. Investors didn’t differentiate between German debt and Greek debt because they believed the euro zone would stand behind any of its members, which meant the default risk of Greek debt was assumed to be much the same as on German debt. Following the closure of Lehmann Brothers, investors reassessed their assumptions and yield differentials began to emerge very quickly and very sharply in countries thought to be at risk of default. For this reason, governments often restrict external financing by organisations within their control. In the UK, many public sector organisations are not allowed to raise external finance.

7 Two particular examples may be relevant. One is the discussion of the psychological underpinnings of long-term investment behaviour in Keynes’s General Theory, famously summarised in the phrase ‘animal spirits’ (see Dequech 1999 and Dow and Dow 2011). The other is the concept of ‘habitus’ as developed by Pierre Bourdieu with its implication that the ways in which economic actors perceive events, opportunities and risks will depend on values, beliefs and rules of thumb accumulated over time as much as on the ‘observable facts’ of the current situation. It has been suggested, for example, that habitus may help explain why some sole traders decide not to take on employees even when they are made aware that their perceptions of the barriers to doing so – such as red tape and legal risk – have been overstated (see Allinson et al 2013, which explicitly refers to habitus on page 74). Holland and Oliveira (2013) suggest that both of these examples form part of a broader discourse which can be traced back to David Hume and Adam Smith.

8 Since much of the data in this survey are ordinal or categorical in nature, unless stated otherwise, Pearson X² tests are used to test the null hypothesis of statistical independence between variables. For organisation mindset and sector, X²(8) = 50.81, Pr = 0.000. This means the probability of the variables not being independent is at least 99.9%. Any value of Pr that is less than 0.05 means the null hypothesis has been rejected using a standard 5% significance level.

9 For organisation mindset and size of organisation, X²(20) = 53.46, Pr = 0.000 and for industry X²(32) = 95.92, Pr = 0.000. The industry categories used in this report have been aggregated from a more detailed breakdown and have been chosen so that unweighted n>50 for analyses of the full sample.

10 For organisation mindset and product/service strategy, X²(4) = 23.84, Pr = 0.000.

11 For organisation mindset and organisation culture, X²(12) = 33.63, Pr = 0.001.

12 For organisation mindset and change in capital equipment, X²(4) = 53.84, Pr = 0.000 and for change in L&D expenditure X²(4) = 30.79, Pr = 0.000.

13 For organisation mindset and output change in the previous 12 months, X²(8) = 100.52, Pr = 0.000.

14 For output growth and sector, X²(4) = 16.11, Pr = 0.003.

15 For output growth and employee size band, X²(10) = 6.29, Pr = 0.790.

16 For output growth and industry, X²(16) = 39.97, Pr = 0.001.

17 If ‘don’t know’ responses are excluded, both output growth and the change in investment (for either capital equipment or L&D) are ordinal variables. This means the Kruskal-Wallis test can be used, which has a null hypothesis that the distributions of the two variables being tested are equal. The test statistic is again distributed X². We can also calculate the Spearman’s rank correlation coefficient Þ. For output growth and the change in capital equipment, X²(2) = 65.31, Pr = 0.000, Þ = 0.328, Pr = 0.000. For output growth and the change in expenditure on L&D activities, X²(2) = 35.09, Pr = 0.000, Þ = 0.236, Pr = 0.000.

18 For output growth and product/service strategy, X²(1) = 19.49, Pr = 0.000.

19 For output growth and organisation culture, X²(9) = 40.31, Pr = 0.000.

Endnotes


20 For change in expenditure on capital equipment and product/service strategy, X²(2) = 18.81, Pr = 0.000. This question asks about the change in expenditure. There could also be a difference in levels – in other words, premium-quality organisations invest more per employee than basic/standard-quality organisations in the same industry – but levels data were not collected.

21 For change in capital investment and organisation culture, X²(6) = 32.53, Pr = 0.000.

22 For change in expenditure on L&D and product/service strategy, X²(2) = 46.81, Pr = 0.000. For change in expenditure on L&D and organisation culture, X²(6) = 70.07, Pr = 0.000.

23 Excluding ‘don’t know’ observations, for change in expenditure on L&D and change in L&D activities, the Kruskal-Wallis test statistic is X²(2) = 277.2, Pr = 0.000, Þ = 0.66, Pr = 0.000.

24 About 15% of respondents have been with their current organisation for less than two years. Although a high proportion (46%) of those in post for less than six months answered ‘don’t know’ to the workforce coverage question, there was no systematic relationship between answers to this question and the amount of time the respondent had been with their organisation. This implies it is probably the extension in the question to broader L&D activity which has created so many ‘don’t know’ responses among larger organisations rather than the lengthening of the reference period.

25 Tests of the statistical independence of sector and industry with workforce participation in L&D are rejected with (Pearson) X² test statistics of X²(8) = 70.73, Pr = 0.000 and X²(32) = 124.32, Pr = 0.000. The Kruskal-Wallis test statistic for employee size band and workforce participation in L&D is X²(5) = 32.17, Pr = 0.000, Þ = 0.22, Pr = 0.000.

26 For expenditure on L&D activities and corporate evaluation of training, X²(6) = 162.95, Pr = 0.000.

27 For expenditure on L&D activities and individual-level evaluation of training, X²(6) = 6.31, Pr = 0.390.

28 T-test statistics for the differences in means are 9.196 (Pr = 0.000) for corporate evaluation and 3.725 (Pr = 0.001) for individual-level evaluation.

29 Looking at organisations with corporate evaluations of training, 72% state they have individual-level evaluation and just 5% state they do not have individual-level evaluation. Organisations with corporate-level evaluation but no appraisal system were not asked this question but it is likely that some of these organisations have individual-level evaluation in place but conducted outside an appraisal process (such as development reviews, Continuous Professional Development assessment and so on).

30 Employee feedback has the advantages of being readily available (at the time of delivery via the dreaded ‘happy sheets’) and easy to interpret (using simple satisfaction scales). It can also pinpoint strengths and weaknesses of the experience and capture suggestions for improvement in a way that might be difficult using other techniques. As a measure of the impact on corporate performance, employee satisfaction seems less relevant. However, this might again understate its significance. If the purpose of L&D activity is to acquire or increase knowledge and skills and then to deploy these in the workplace in order to raise individual (and organisation) performance, employee satisfaction has limitations. For example, the individual may not turn out to use the knowledge and skills in their job (or not in the way originally anticipated), which the employee may not even realise. Ideally, there would also be some mechanism for verifying the employee’s assessment of their (changed) knowledge and skills. However, formal and informal L&D opportunities also form part of the psychological contract with employees and thus have indirect effects on organisation performance through engagement levels, release of discretionary effort, organisational citizenship and damage to employer brand (via exit, damage to reputation and so on). Employee satisfaction with the development opportunities available to them is measured in the CIPD Employee Outlook survey by asking employees whether or not they agree with the statement ‘my organisation gives me opportunities to learn and grow’. When last asked, in the spring 2015 survey, 49% of employees agreed or strongly agreed with the statement, 25% neither agreed nor disagreed and 26% disagreed or strongly disagreed with it. And whereas 80% of employees who agreed/strongly agreed with this statement also said they would be likely or very likely to recommend their organisation as an employer, the proportion was just 25% among employees who disagreed/strongly disagreed with the statement.

31 For improving productivity and sector, X²(8) = 28.26, Pr = 0.000.

32 For improving productivity and employee size band, X²(5) = 9.03, Pr = 0.108.

33 For improving productivity and industry, X²(8) = 28.26, Pr = 0.000.

34 For improving productivity and product/service strategy, X²(1) = 4.07, Pr = 0.044. For improving productivity and organisation culture, X²(3) = 9.07, Pr = 0.021.

35 For improving productivity and organisation mindset, X²(4) = 2.21, Pr = 0.698.

36 For improving productivity and change in capital investment, X²(2) = 7.13, Pr = 0.028. For improving productivity and change in L&D expenditure, X²(2) = 0.88, Pr = 0.643. For improving productivity and change in L&D activity, X²(2) = 0.56, Pr = 0.972.

37 For improving productivity and change in output over previous 12 months, X²(2) = 18.51, Pr = 0.000.

38 For relative productivity and sector, X²(6) = 12.24, Pr = 0.057.

39 For relative productivity and employee size band, Kruskal-Wallis test statistic X²(5) = 5.09, Pr = 0.405. For relative productivity and industry, X²(21) = 9.99, Pr = 0.979.

40 For relative productivity and organisation culture, X²(9) = 13.76, Pr = 0.131. For relative productivity and whether content with current culture, X²(6) = 10.42, Pr = 0.108.

41 For relative productivity and product/service strategy, X²(6) = 15.35, Pr = 0.018.

42 For relative productivity and organisation mindset, X²(15) = 92.10, Pr = 0.000.

43 For relative productivity and productivity as a current priority, X²(3) = 2.17, Pr = 0.538.


44 The results of the significance tests and Spearman’s rank correlation coefficients are summarised in the table below:

Association of relative productivity with: Kruskal-Wallis test statistic Pr=Spearman’s rank correlation coefficient Pr=

Output change X²(2) = 51.48 0.000 0.345 0.000

Employment change X²(2) = 23.03 0.000 0.230 0.000

Change in expenditure on capital equipment

X²(2) = 15.68 0.001 0.197 0.000

Change in expenditure on L&D X²(2) = 14.84 0.001 0.196 0.000

Change in L&D activities X²(2) = 10.06 0.007 0.148 0.001

45 For relative productivity and corporate evaluation of training, X²(6) = 15.25, Pr = 0.018. For relative productivity and performance appraisal, X²(6) = 8.15, Pr = 0.228.

46 The null hypothesis that the model coefficients are equal for structured, dynamic and results-oriented firms is rejected at the 10% significance level but is not quite rejected using a 5% significance level (the test statistic is X²(2) = 5.69, Pr = 0.058).

47 The null hypothesis that the model coefficients are equal for cost-cutters, balanced investors, people-focused investors, capital-focused investors and ‘don’t know’ organisations was clearly rejected (the test statistic is X²(3) = 10.07, Pr = 0.018). However, the same null hypothesis but excluding balanced investors was not rejected (X²(2) = 0.11, Pr = 0.947). From this, we can conclude that the coefficient for balanced investors exceeds that for cost-cutters, people- and capital-focused investors and ‘don’t know’ organisations.

48 The proportion of ‘don’t know’ responses, although high, is only slightly higher than for the backward-looking question on capital investment and the vast majority (87%) of those answering ‘don’t know’ to the forward-looking question also answered ‘don’t know’ to the backward-looking question.

49 For expected growth in capital expenditure and sector, X²(4) = 43.11, Pr = 0.000.

50 For expected growth in capital expenditure and employee size band, Kruskal-Wallis test statistic X²(5) = 14.00, Pr = 0.016. Note the relationship is inverse U-shaped, hence Þ = 0.017, Pr = 0.653.

51 For expected growth in capital expenditure and industry, X²(16) = 77.36, Pr = 0.000. For expected growth in capital expenditure and organisation culture, X²(6) = 31.34, Pr = 0.000. For expected growth in capital expenditure and product/service strategy, X²(2) = 26.13, Pr = 0.000.

52 For expected growth in L&D expenditure and the covariates displayed in Figure 16, the relevant test statistics are as follows: sector, X²(2) = 91.71, Pr = 0.000; employee size band, Kruskal-Wallis test statistic X²(5)=25.28, Pr=0.000; industry, X²(16) = 117.41, Pr = 0.000; organisation culture, X²(6) = 52.22, Pr = 0.000; and product/service strategy, X²(2) = 38.91, Pr = 0.000.

53 The results of the significance tests and Spearman’s rank correlation coefficients for the variables analysed in Table 8 are summarised in the table below:

Association: Kruskal-Wallis test statistic Pr =Spearman’s rank correlation coefficient Pr = n

Past change capital – future change capital

X²(2) = 156.74 0.000 0.509 0.000 730

Past change capital – future change L&D

X²(2) = 46.91 0.000 0.278 0.000 708

Past change L&D – future change capital

X²(2) = 58.78 0.000 0.300 0.000 692

Past change L&D – future change L&D

X²(2) = 165.60 0.000 0.506 0.000 774

54 For future mindset and sector, X²(8) = 59.54, Pr = 0.000.

55 For future mindset and employee size band, X²(20) = 45.42, Pr = 0.001.

56 For future mindset and industry, X²(32) = 88.04, Pr = 0.000.

57 For future mindset and organisation culture, X²(12) = 14.54, Pr = 0.268.

58 For future mindset and product/service strategy, X²(4) = 41.46, Pr = 0.000.

59 For future mindset and expected change in expenditure on capital equipment, X²(15) = 191.14, Pr = 0.000. For future mindset and expected change in expenditure on L&D activities, X²(15) = 170.91, Pr = 0.000.

60 For future mindset and relative productivity, X²(20) = 117.26, Pr = 0.000.

61 For future mindset and past mindset, X²(16) = 208.40, Pr = 0.000.

62 The proportions of balanced investors/high-performers expecting to increase their investment in capital equipment and L&D activities over the coming 12 months are not very different from the proportions of balanced investors/making up ground (people or capital) organisations expecting to do so. This suggests that the choice of future mindset by balanced investors may be more a signal of relative priorities and ambition than a guide to short-term behaviour. If we exclude ‘don’t know’ responses, over 95% of organisations that have been balanced investors in the recent past intend to pursue an investment-focused approach over the next couple of years.


63 According to the May issue of the Treasury’s comparison of independent economic forecasts, the average of the GDP forecasts for 2015 was 2.5% and the average of the 2016 forecasts was 2.3% (HM Treasury 2015a). Equally important, even the lowest forecast for GDP growth in 2016 was 1.2%, so a downturn in the short term appears highly unlikely.

64 For expected output growth and sector, X²(6) = 45.41, Pr = 0.000.

65 For expected output growth and industry, X²(24) = 97.78, Pr = 0.000.

66 For expected output growth and employee size band, Kruskal-Wallis test statistic is X²(5) = 2.91, Pr = 0.714, Þ = 0.0003, Pr = 0.9934.

67 For expected output growth and organisation culture, X²(9) = 41.06, Pr = 0.000. For expected output growth and product/service strategy, X²(6) = 144.35, Pr = 0.000.

68 For expected output growth and past output growth, Kruskal-Wallis test statistic is X²(2) = 206.29, Pr = 0.000, Þ = 0.544, Pr = 0.000.

69 For expected output growth and expected change in expenditure on capital equipment, Kruskal-Wallis test statistic is X²(2) = 59.14, Pr = 0.000, Þ = 0.317, Pr = 0.000. For expected growth and expected change in expenditure on L&D activities, Kruskal-Wallis test statistic is X²(2) = 47.54, Pr = 0.000, Þ = 0.268, Pr = 0.000.

70 For expected output growth and relative productivity, Kruskal-Wallis test statistic is X²(3) = 24.44, Pr = 0.000, Þ = 0.192, Pr = 0.000.

71 For expected output growth and future mindset, X²(15) = 172.04, Pr = 0.000.

72 In the majority of private sector organisations, the ‘pay decision’ will be taken by management, possibly following a review or consultation. In some cases, it may be the result of collective bargaining. There will also be situations where the impetus for change arises from outside the organisation. For example, an increase in the National Minimum Wage or (for accredited employers) the Living Wage.

73 The variance in pay change is not equal for the high- and low-productivity groups, which makes standard analysis of variance and t-tests problematical. For the untrimmed means, a t-test correcting for unequal variance using unweighted data rejects the null hypothesis of equality of means (Pr = 0.0332), whereas an alternative approach using weighted data (regressing pay increase on high/low productivity using robust standard errors to correct for unequal variance) fails to reject the same null hypothesis (Pr = 0.055). For the trimmed means, both approaches rejected a null hypothesis of equality of means.

74 The test statistic for equality of medians is X²(1) = 4.86, Pr = 0.044. Both t-tests and regression analysis show that the differences in the untrimmed and trimmed means are statistically significant.

75 Respondents who said they expected average basic pay to increase by less than 2% were given a different list of factors. Only 52 private sector organisations answered this question, which is insufficient for further analysis by relative productivity. However, just 3% of these firms cited poor employee productivity and performance as a reason for not paying more.

76 A t-test of proportions produces a z-statistic of 2.13, Pr = 0.0355.

77 Statistics for tests of significance for all the variables analysed in Appendix 2 and Spearman’s rank correlation coefficients (where relevant) are summarised in the table below:

Association between relative productivity and:

Public sector Voluntary sector

Test statistic

Spearman’s rank correlation coefficient Test statistic

Spearman’s rank correlation coefficient

Employee size band X²(20) = 13.26Pr = 0.866

X²(20) = 22.65Pr = 0.306

Industry X²(28) = 21.33Pr = 0.811

Product/service strategy X²(8) = 51.55Pr = 0.000

X²(8) = 18.14Pr = 0.020

Output growth in previous 12 months KW: X²(2) = 3.24Pr = 0.198

0.138Pr = 0.056

KW: X²(2) = 3.50Pr = 0.174

0.170Pr = 0.048

Change in expenditure on capital equipment in previous two years

KW: X²(2) = 3.85Pr = 0.146

0.051Pr = 0.533

KW: X²(2) = 1.62Pr =0.446

0.084Pr = 0.357

Change in expenditure on L&D activities in previous two years

KW: X²(2) = 0.057Pr = 0.972

–0.019Pr = 0.805

KW: X²(2) = 6.98Pr = 0.031

0.239Pr = 0.007

Change in L&D activity in previous two years KW: X²(2) = 1.83Pr = 0.401

0.065Pr = 0.365

KW: X²(2) = 10.19Pr = 0.006

0.285Pr = 0.001

KW = Kruskal-Wallis test statistic.

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Issued: September 2015 Reference: 7097 © CIPD 2015

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