Denmark: Selected Issues
Author: International Monetary Fund. European Dept.
Publication Date:
20 Jun 2017
eISBN:
9781484304617
Language:
English
Keywords:
ISCR; CR; migrant; firm; enterprise efficiency; firm leverage; statistics Denmark; knowledge intensity firm; investment response; Corporate sector; Financial statements; Gross fixed investment; Small and medium enterprises
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Selected Issues

Abstract

Selected Issues

Dynamics of firm investment in Denmark: Role of Leverage, demand, and Knowledge Intensity1

This paper provides an investigation into the dynamics of firm investment in Denmark using an augmented version of the traditional accelerator model of investment. It finds evidence on the traditional leverage and demand channels of investment. The response is not significantly differentiated for SMEs and large firms. The paper also documents the presence of a new channel that boosts the investment response to demand, the knowledge intensity channel. Small firms in knowledge-intensive industries benefit most from investing in intangibles.

A. Introduction

1. Investment matters for short-term and long-term economic prospects. Despite its relatively small share in output, investment is a volatile component of GDP and it can have a profound impact on short-term economic fluctuations. Investment also increases the capital stock and, by boosting factor productivity, it can lift potential growth.

2. Investment in Denmark has remained low since the crisis. In the run-up period to the global financial crisis (GFC), investment as percent of GDP in Denmark rose markedly, reaching a very high level, mostly due to an exceptional housing boom. After the crisis, investment dropped sharply and its level in recent years remains low in comparison with the period prior to the housing boom.2 Abstracting from residential housing investment, the corporate investment rate (calculated as investment relative to operating surplus) also dropped from a high level since the GFC and it remains below the average for the Euro area (Figure 1). More generally, while investment growth has lagged the economic recovery across several countries (Banerjee and others, 2015), its level remains low in Denmark.

A03ufig1

Gross Fixed Capital Formation (GFCF)

(Percent of GDP, current prices)

Citation: IMF Staff Country Reports 2017, 159; 10.5089/9781484304617.002.A003

Source: Statistics Denmark.
Figure 1.
Figure 1.

Investment Trends, Rates, and Composition

Citation: IMF Staff Country Reports 2017, 159; 10.5089/9781484304617.002.A003

Sources: Eurostat, OECD, and Fund staff calculations.

3. The policy uncertainty channel is one possible explanation for the slump in physical capital spending (Bloom and others, 2007; International Monetary Fund, 2015). If returns on capital are uncertain or expected to be low, firms become reluctant to make irreversible investments (Guiso and Parigi, 1999; Banerjee and others, 2015). In the context of Denmark, Business Tendency Surveys indicate that low demand continues to play a major role in the low economic activity, suggesting that low investment reflects increased uncertainty about economic conditions and reduced demand.

4. The financing channel may also play an important role as corporate leverage is high in Denmark. High corporate debt pre-crisis in part explains the sharp fall in investment during the GFC, as firms wanted to deleverage to strengthen their balance sheets and retain flexibility in future financing choices (Kuchler, 2015a). Post GFC, credit constraints do not seem to be curbing investment growth, as Danish firms appear to have good access to finance (Andersen and Kuchler, 2016; Kramp and Pedersen, 2015). Yet, although the need for consolidation among firms is subsiding (Danmarks Nationalbank, 2015), firm leverage is still very high in Denmark in comparison with other European countries. In turn, strained balance sheets may reduce firms’ ability and willingness to invest.

A03ufig2

Debt-to-Assets of NFCs, 2010–2015

(Percent)

Citation: IMF Staff Country Reports 2017, 159; 10.5089/9781484304617.002.A003

Sources: Orbis and Fund staff calculations.

5. The strength of the uncertainty and financing channels could differ by firm size. The

literature (Banerjee and others, 2015; Bluedorn and Ebeke, 2016) documents different impact from these two channels for large firms and Small and Medium Enterprises (SMEs). In Denmark, SMEs contribute significantly to the economy, employing about 38 percent of the labor force and accounting for over 42 percent of total investment since 2000 (Figure 2).

Figure 2.
Figure 2.

Firms Contribution to the Economy

Citation: IMF Staff Country Reports 2017, 159; 10.5089/9781484304617.002.A003

Sources: Statistics Denmark and Fund staff calculations.

6. The role of investment in intangibles has received not received much attention. Also known as investment in Knowledge-Based Capital (KBC), intangibles capture investment in computerized information (software and databases) and research and development (R&D). But intangibles are broader than what is recorded in the system of national accounts. Studies by the OECD and the European Investment Bank suggest that the measurement of the knowledge-content of products and services produced would have to consider not just technology and R&D, but also what is known as economic competencies. The latter include, among others, brand equity, firm-specific human capital, networks of people and institutions, and organizational know-how that increases enterprise efficiency (Organization for Economic Cooperation and Development (OECD), 2013a and b; Corrado and others, 2016).3 In the case of Denmark, accounting for such “new intangibles” would broaden KBC by more than 100 percent relative to the current national accounts definition of intangibles.

A03ufig3

Intangible and Tangible Investment (Average 2000–2013)

(Percent of GDP)

Citation: IMF Staff Country Reports 2017, 159; 10.5089/9781484304617.002.A003

Source: Corrado et al. (2016), European Investment Bank.

7. The composition of investment in Denmark has recently shifted away from physical capital to intangibles. The recent period is marked by a shift in composition away from tangible investments and into intellectual property products (computer software and databases, research and development (R&D), mineral exploration, and artistic originals). Across many OECD economies, building on new human knowledge is driving the value of most of the largest firms (OECD, 2013a and b). This is also likely the case in Denmark, where knowledge-intensive sectors—defined as high- and medium-technology manufacturing sectors and knowledge intensive services (Appendix C)—have increased their contribution to gross value added (GVA) over the past two decades from below 40 to over 50 percent.

A03ufig4

Share of HTKIS Sectors in GVA 1/

(Percent)

Citation: IMF Staff Country Reports 2017, 159; 10.5089/9781484304617.002.A003

1/ HTKIS refers to high-and medium-technology manufacturing sectors and knowledge intensive services sectors.Source: Statistics Denmark and Fund staff calculations.

8. There is growing evidence that intangibles represent an important source of growth.

The production, distribution, and use of knowledge are key for innovation and for sustaining a firm’s competitive advantage. Not only do intangible investments produce new ideas and knowledge, but they also generate positive spillovers (Griliches and others, 1991; Jaffe and others, 1993; Corrado and others, 2013). A growing body of literature shows that intangible assets are both a source of value creation for individual firms and a driver of growth at the macroeconomic level (OECD, 2013a and b; Corrado and others, 2013 and 2016). Using a broad classification of intangibles, Corrado and others (2016) find that intangible capital deepening accounted for as much as 30 percent of labor productivity growth on average for Europe and the U.S. between 2000 and 2013.4

9. This paper examines possible drivers of Denmark’s investment and it also explores the role of intangibles. It starts by presenting an overview of corporates in Denmark and peer countries in Section B, focusing on investment, saving, and the status of deleveraging.5 Section C describes the research design and data, investigating the importance of leverage and demand on firm investment and testing for the presence of a new channel—the knowledge intensity channel— using a difference-in-difference approach. Section D discusses the empirical findings. Section E concludes.

B. Overview of the Corporate Sector: Investment, Saving, and the Status of Deleveraging

10. Corporate investment remains sluggish in Denmark. Following a prolonged period of uncertainty—both from the GFC and the sovereign debt crisis in Europe—firms across most countries seem to be reluctant to make irreversible long-term investment commitments (Figure 3).

Figure 3.
Figure 3.

Overview of the Non-Financial Corporate Sector Denmark vs. Peers (1) Gross Saving, Gross Fixed Capital Formation, and Net Lending

(Percent of GDP)

Citation: IMF Staff Country Reports 2017, 159; 10.5089/9781484304617.002.A003

Source: Eurostat and Fund staff calculations.

11. In parallel, corporate savings are high in Denmark due to a number of factors. In the

period leading up to the crisis, saving by corporations in Denmark declined sharply, unlike for other countries, but this trend has since been reversed (Figure 3). The rise in corporate savings can be explained by rising income from investments abroad, along with falling interest expenses and tax payments (Brandt and others, 2012). Improvement in firm profitability since 2009 (despite remaining slightly below peers) coupled with lower dividend payments may have also contributed to greater corporate savings (Figure 4).

12. The resulting increase in corporate surplus in Denmark is not accompanied by a notable increase in cash accumulation on firms’ balance sheets. Together, low investment and high saving have increased the corporate surplus or net lending in Denmark to above 6 percent of GDP since 2009, a level that was previously recorded during the pre-boom years. In parallel, the share of cash and liquid assets in financial assets has not significantly increased. It could be that the corporate savings surplus has been used in recent years for loan repayment, investment in liquid portfolios, and also foreign direct investment (Kuchler, 2015b).

Figure 4.
Figure 4.

Overview of the Non-Financial Corporate Sector: Denmark vs. Peers (2) Net Income and Profit Distribution

(Percent)

Citation: IMF Staff Country Reports 2017, 159; 10.5089/9781484304617.002.A003

1/Operating surplus minus interest, taxes, contributions, and transfers. Source: Eurostat and Fund staff calculations.

13. In addition, corporates do not seem to have significantly deleveraged post-crisis.

The ratio of corporate debt in relation to GDP did not noticeably decline in Denmark in the post-crisis, showing rather a debt-stabilization pattern that is similar to that in other countries (Figure 5). Debt overhang, defined as the ratio of total debt to gross operating surplus, also remains highest in Denmark compared with peers, despite having moderated from the peak that was reached during the GFC. There is thus no clear evidence that firms have used their high surplus in recent years to substantially pay down debt.

A03ufig5

Co-movement between Net Lending to GDP and Cash & Liquid Assets to Financial Assets of NFC (percent)

Citation: IMF Staff Country Reports 2017, 159; 10.5089/9781484304617.002.A003

Source: Eurostat and Fund staff calculations.

14. More broadly, corporate financial liabilities continue to accumulate in Denmark (Figure 6). The steady build-up of financial assets at Danish firms is accompanied by a sharp increase in financial liabilities, which is highest in Denmark at close to 350 percent of GDP. In view of stagnating corporate debt, the rising importance of other financing sources including overdraft facilities and leasing in part account for greater financial liabilities (Andersen and Kuchler, 2016).

Figure 5.
Figure 5.

Overview of the Non-Financial Corporate Sector: Denmark vs. Peers (3) Indicators of Leverage

Citation: IMF Staff Country Reports 2017, 159; 10.5089/9781484304617.002.A003

Sources: OECD and Fund staff calculations.
Figure 6.
Figure 6.

Overview of the Non-Financial Corporate Sector Denmark vs. Peers (4) Financial Assets and Financial Liabilities

(Percent of GDP)

Citation: IMF Staff Country Reports 2017, 159; 10.5089/9781484304617.002.A003

Source: Eurostat.

C. Research Design and Data

15. The baseline investment regression is a variation of the standard specification generally used in the literature. It rests on an augmented version of the traditional accelerator model of investment with demand changes and other controls as the main drivers (Lang and others, 1996; Kalemli-Ozcan and others, 2015; Magud and Sosa, 2015; Bluedorn and Ebeke, 2016). The key additional variables are firm leverage, size, and intensity of intangibles. The baseline specification is:

Yit=η1Levi,t−1+η2Salesi,t+ΘControlsi,t−1+αi+ωjt+εi,t(1)

Where Yit denotes firm i's real net tangible investment ratio at time t, calculated as the ratio of the change in real tangible fixed assets to lagged real tangible assets; Lev is firm leverage—measured as the lag in the ratio of total debt to total assets—reflecting the burden of firm debt.6 Sales is contemporaneous growth in real sales capturing the growth opportunities of the firm or more generally firm-specific demand conditions. Controls include debt maturity proxied by the lagged ratio of long-term debt to total debt and the natural logarithm of total assets. Y, Lev, Sales, and Controls are expressed in percent. α are firm i-specific fixed effects and ω are sector j-year t fixed effects that absorb sector-wide yearly common shocks to firms.7

a) Effect of leverage and demand

The η1 and η2 parameters from equation (1) estimate the direct effect of firm leverage and real sales growth, respectively, on real tangible investment.

Firms with high debt have less external borrowing flexibility relative to less indebted peers when faced with the need to fund a positive net present value project (Lang and others, 1996; Aivazian and others, 2005). By increasing the risk of bankruptcy, a greater debt burden may also incentivize shareholders to forgo value-enhancing investments because expected benefits would mostly accrue to debtholders. Or firms may simply prefer to reduce their debt burden to strengthen their balance sheet to retain financing flexibility and better meet future investment needs (Kuchler, 2015a). Thus, higher firm leverage is expected to correlate negatively with firm investment.

For firms with high real sales growth, capital accumulation rises when demand conditions improve (Guiso and Parigi, 1999). Indeed, a strengthening of domestic conditions improves the responsiveness of investment to a given demand shock (Bloom and others, 2007).8 To assess the broader investment response to firm leverage and demand in the presence of non-linearities, equation (1) is augmented with interaction terms as follows:

Yit=β1Levi,t−1+β2Salesi,t+β3SMEi,t+θ1Levi,t−1×Salesit+θ2Levi,t×SMEi,t+θ3Salesi,t×SMEi,t+θ4Levi,t−1×Salesi,t×SMEi,t+ΘControlsi,t−1+αi+ωjt+εi,t⁢               (2)

Where SME is a dummy variable for firms with less than 250 employees.9 The total marginal effects of firm leverage and real sales on real investment for SMEs are calculated as (β1 + θ1 × Salesit + θ2 × SMEit + θ4 × Salesit × SMEit) and (β2 +θ1 × Levit + θ3 × SMEit + θ4 × Levit × SMEit), respectively. The interaction terms are also used to assess differences in the investment reaction to leverage and demand by SMEs and large firms.

b) Role of knowledge intensity

The idea that knowledge capital is an important driver of modern economic growth is gaining prominence (Corrado and others, 2009). In mature economies like Denmark, investing in ideas and skills (sometimes even more than in physical capital) is driving most of the value of large firms. KBC allows for the design, development, and upgrading of complex and sophisticated products, all of which lie at the heart of competitiveness (OECD, 2013a and b). In the presence of more innovative, higher quality products, a firm is able to differentiate itself and move away from cost-based competition thereby sustaining its position in the market. Indeed, knowledge accumulation from, say computerized information, R&D, product design and branding, employee training, good marketing skills, or efficient organizational management, could improve the quality and desirability of firm products. Thus, if intangible capital strengthens the competitive advantage of a firm, the prospects of higher sales growth could increase, which may in turn boost the investment response. If this is the case, the effect from intangible capital on the firm’s investment response to demand should be larger for sectors that are more knowledge-intensive relative to others.

This paper tests this prediction by exploiting industry variation in knowledge intensity using a difference-in-difference approach (Rajan and Zingales, 1998). The difference-in-difference approach consists in identifying an industry-specific factor that affects the way that knowledge intensity could impact the firm’s decision to invest more. One such latent characteristic is the knowledge-intensive feature of an industry. If knowledge intensity matters for firm investment, then we should observe a higher investment response to demand in sectors that are knowledge intensive.

We identify knowledge-intensive sectors using Eurostat’s taxonomy for high- and medium-technology manufacturing sectors and knowledge intensive services (Htkis) (Appendix C) and generate a dummy variable Htkis equal to 1 if the firm belongs to such a sector.10 We then test for the strength of the knowledge intensity channel by extending the baseline equation as follows:

Yit=γ1Levi,t−1+γ2Salesi,t+δ1Salesi,t+Knowledgei,t×Htkis+ΘControlsi,t−1+αi+ωjt+εi,t(3)

Where Knowledge denotes the knowledge intensity measured as the ratio of firm intangible fixed assets to total assets in percent. The coefficient of interest in this specification, δ1 captures the extent to which knowledge intensity leads to a greater investment response to demand in knowledge-intensive sectors relative to other sectors. To assess heterogeneity in the investment responsiveness more broadly across knowledge-intensive and other sectors, a similar analysis is run by abstracting from the Htkist term in equation (3) and running the same specification separately for Htkis and Non-Htkis sectors.

16. Data from the Orbis database is used for Danish firms over the period 2010–2015.11 Unconsolidated data are retained in the sample, allowing the analysis to focus on firms at the plant level.12 Data prior to 2010 is dropped from the sample and, similar to Kuchler (2015a), sole proprietorships are also excluded. Annex I details the cleaning and filtering procedures that were applied to the original data sample.

17. Key variables of interest are constructed. The ratio of real net investment to the capital stock in the previous year—the dependent variable—is calculated as the annual change in real tangible fixed assets net of depreciation in percent of the previous year’s stock of real net tangible fixed assets.13 Leverage is measured as the sum of short- and long-term debt scaled by total assets, also in percent. Short-term debt is generated by excluding creditors (debt to suppliers and contractors) and other current liabilities not payable to financial institutions (pension, personnel costs, taxes, intragroup debts, etc.) from total current liabilities. As for long-term debt, it is retrieved from the non-current liabilities portion of the balance sheet which includes, in addition to long-term loans and credits, provisions and other non-interest bearing long-term liabilities not related to financial institutions but to taxes, group companies, pension loans, etcetera. Debt maturity is the share of the long-term debt in percent of total debt. Firm sales growth is the annual percent change in real operating revenue and knowledge intensity is the ratio of intangible assets to total assets, all in percent.

18. The data exhibit variability across firm size and knowledge-intensity of the industry.

Large firms invest in tangible assets more than small firms and, as would be expected given their size and capabilities, their knowledge intensity is much higher too. Further, firms in Htkis or knowledge-intensive sectors invest in tangible assets more than firms in Non-htkis sectors and their knowledge intensity is naturally also higher. Firm size does not seem to impart significant differences in leverage (real sales growth), with average debt-to-assets (real sales growth) of 43 and 45 percent (5.4 and 5.5 percent), respectively, for SMEs and large firms. Leverage also does not seem to differ for firms in Htkis sectors or otherwise, although firms in Non-htkis sectors are able to borrow with longer maturities. However, real sales growth is also higher in Htkis versus Non-htkis sectors. These statistics suggest that traditional leverage and demand channels of investment may not alone account for differences in investment across different firms.

D. Empirical Findings

19. Lower leverage and better demand conditions are associated with higher investment ratios. The direct effects of leverage and real sales reported in Appendix Table B1—η1 and η2 parameters from equation (1)—are aligned with those documented for the Euro Area using a similar analysis (Bluedorn and Ebeke, 2016). From the baseline specification, a 10 percentage point reduction in leverage raises tangible investment by 2.9 percentage points; the corresponding figure for the Euro Area is 3.6 percentage points. Similarly, for a 10 percentage points increase in real sales growth, the investment ratio rises by 1.3 percentage points, whereas for corresponding estimate for the Euro Area is 2.6 percentage points.

20. The economic significance of these effects varies slightly by firm size. The empirical literature (Lang and others, 1996; Kalemli-Ozcan and others, 2015; Bluedorn and Ebeke, 2016) documents that the adverse effect of leverage is statistically stronger and the positive demand effect weaker for smaller firms, typically attributing this finding to more limited access to finance and/or higher financing costs for SMEs. For Danish firms, the results in Table B1 do not point to a statistically different response to leverage and demand conditions across SMEs and large firms, a finding that is similar to Kuchler (2015a) who also reports an insignificant effect for the more heterogeneous group of large firms. This result can be attributed to the fact that access to finance is not reported as a hurdle to investment growth in Denmark (Kramp and Pedersen, 2015; Andersen and Kuchler, 2016). More detailed results by firm size category (Table 2) indicate that the parameter estimate of leverage is slightly larger for the smallest firms, suggesting a more adverse investment response than for other firms. As for demand conditions, although there is similarly no statistical difference in the response of small and large firms (Table B1), the magnitude of the parameter estimates on real sales growth increases as the firm size gets bigger (Table 2). Larger firms respond to better growth opportunities more strongly than small firms maybe because of higher retained earnings and smaller firms may also prefer a wait-and-see strategy before committing to long-term investments.

Table 1.

Denmark: Descriptive Statistics by Firm Size and Sector

article image
Source: Eurostat’s Labor Force Survey and Fund staff calculations.

21. The investment response to leverage and demand conditions is insignificant for startups but pronounced for young firms. Table 3 shows results similar to those in Table 2 but with firm age substituted for firm size. Columns (1) and (2) show insignificant effects from leverage and real sales growth on tangible investment for start-ups (less than 5 years of age). It could be that those companies operate with more equity than debt financing relative to older firms and they are also less sensitive to real sales growth, as their new products need time to make it to the market. In comparison, the leverage and demand effects are most pronounced for young firms (companies that have been operating between 5 and 14 years).

Table 2.

Denmark: Role of Leverage and Demand by Firm Size Category

article image
Micro are firms with 2-9employees; Small employ 10-49 employees; Medium have 50-249 employees; and Large have 250 employees and more. All regressions include firm fixed effects and sector-year fixed effects. Robust standard errors are dustered at the sector level in brackets. *** p<0.01, ** p<0.05, * p<0.1.
Table 3.

Denmark: Role of Leverage and Demand by Firm Age Category

article image
Start-ups are less than 5 years old; Young have ben operating for 5-14 years old; and Well-Established have more than 35 years of operations. All regressions indude firm fixed effects and sector-year fixed effects. Robust standard errors are dustered at the sector level in brackets. *** p<0.01, ** p<0.05, * p<0.1.

22. The results lend support to the presence of a knowledge intensity channel. When considering the role of knowledge intensity (Table 4), the coefficients on leverage and real sales growth are little changed relative to the baseline estimation (Table B1, Column 1). The parameter of interest—δ1 from equation (3)—is positive and statistically significant (Column 3). Its order of magnitude is about 10 percent of the response of investment to real sales growth and the total marginal effect of real sales growth is highly significant. This result provides evidence that a greater knowledge intensity boosts the investment response to demand for firms operating in knowledge-intensive sectors.

23. Additional tests by sector confirm the strength of the knowledge intensity channel, especially for smaller firms. Table B2 shows the results from the baseline specification (equation 1) and the difference-in-difference specification (equation 3) by knowledge-intensive and non-knowledge-intensive sector.14 What stands out is the persistent strength of the knowledge intensity channel for knowledge-intensive sectors. The coefficient on the interaction term is positive and statistically significant for the Htkis sector (Column 4) but not for other industries (Column 3). Another interesting finding is a smaller size effect on firm investment in Htkis sectors, whereby the economic significance of the parameter estimate of log(total assets) drops by half. Further tests by firm size class (Table B3) indicate that the knowledge intensity channel in Htkis sectors is stronger for smaller firms with less than 50 employees.

Table 4.

Denmark: Role of Knowledge Intensity

article image
Htkis refer to high- and medium-technology manufacturing sectors and knowledge intensive services (Appendix Table A3). All regressions include firm fixed effects and sector-year fixed effects. Robust standard errors clustered at the sector level in brackets. *** p<0.01, ** p<0.05, * p<0.1.

24. Greater knowledge intensity is associated with a stronger investment response to demand. Figure 7 illustrates the predicted effect of demand on investment for high and low knowledge intensity firms in htkis sectors, overlaid on the sample distribution of real sales growth which is roughly symmetric. For these sectors, the investment response to demand is significantly boosted for high knowledge-intensive firms. A 10 percent rise in sales growth is associated with a 5 percentage point increase in the real investment ratio for a high knowledge intensity firm (intangibles-to-assets ratio above 95 percent), while the response is less than 2 percentage points for a low knowledge intensity firm (intangibles-to-assets ratio below 5 percent) in htkis sectors. Figure 8 brings in an additional dimension to the picture, comparing the investment response to demand for htkis and non-htkis sectors also at different levels of knowledge intensity. Going from the 5th percentile to the 95th percentile of knowledge intensity (from 0 to 37 percent of the intangibles-to-assets ratio) is associated with a further 5 percentage points higher investment response to a 10 percentage point rise in real sales growth, with the results also positive but insignificant for non-htkis sectors. These findings are consistent with the hypothesis of a knowledge intensity channel. It appears that firms in htkis sectors are better able to respond to a demand shock if their knowledge intensity is greater.

Figure 7.
Figure 7.

Predicted Effect of Real Sale Growth for Firms in HTKIS Sectors

Citation: IMF Staff Country Reports 2017, 159; 10.5089/9781484304617.002.A003

Note: Lines show the predicted change in firm real tangible investment given the indicated value of real sales growth, with the intensity of intangibles at either the 5th or 95th percentile of its distribution. Bars show the histogram of real sales growth, with percent of the sample shown on right axis. Change in Real Tangible Investment is a ratio in percentage points. Parameter estimates are significant at the 1 percent level
Figure 8.
Figure 8.

Marginal Effect of Real Sales Growth

Citation: IMF Staff Country Reports 2017, 159; 10.5089/9781484304617.002.A003

Note: Lines show the marginal effect of one percentage point change in real sales growth on firm real tangible investment, given the indicated value of intensity of intangibles and whether or not the firm is in Htkis sector. Bars show the histogram of the intensity of intangibles, with percent of the sample shown on the right axis. Change in Real Investment is a ratio in percentage points. Parameter estimates are significant for htkis firms (1 percent level).

E. Conclusion

25. There is a negative effect from leverage on firm investment.15 Abstracting from the effects of the crisis and focusing on the more recent period of 2010–2015, this study examined the dynamics of firm investment in Denmark. The results indicate that high corporate leverage dampens investment in tangible fixed assets. Debt overhang in Denmark constrains firms of all sizes, unlike other European countries where SME financing constraints are more important.

26. Strong demand conditions raise investment. The positive relationship between real sales growth and firm investment in Denmark is similar to what is documented for other European countries (Banerjee and others, 2015; Bluedorn and Ebeke, 2016), although firm size also does not seem to play a significant role in Denmark.

27. The responsiveness of investment to demand is boosted by greater knowledge intensity for firms operating in knowledge-intensive industries. By identifying intangible assets or KBC on corporate balance sheets and using a difference-in-difference approach, this chapter shows that greater knowledge intensity leads to a stronger investment response to a positive demand shock in sectors that are more knowledge-intensive. This knowledge intensity channel is strong not only in sectors that are highly dependent on KBC but also for small firms. Such evidence on the mechanisms through which the demand channel works could benefit policy assessment on how to minimize macroeconomic volatility from variations in investment.

28. Policies to reduce firm leverage, including via the tax system, could stimulate corporate investment in Denmark. Despite the reduction of corporate tax rates and limits to interest rate deductions, the Danish tax system considerably favors debt over equity financing (Kuchler, 2015b). Considering the very high corporate debt in Denmark, policies should seek to reduce firm leverage thereby also helping contain macroeconomic stability risks. In some countries, introducing an incremental Allowance for Corporate Equity (ACE) has proved to be effective in mitigating the debt bias, helping harmonize the tax treatment of various types of financing (IMF, 2016a).16 One of the attractive properties of the incremental ACE is that it neutralizes the debt bias, which renders corporate income tax neutral with respect to marginal investment decisions. By shifting the capital structure from debt to equity, the ACE would reduce leverage ratios, which this study shows can help boost investment in Denmark.

29. Encouraging investment in intangibles will further broaden KBC and help boost investment as the economy strengthens further. Policies aimed at encouraging the accumulation of intangibles—notably in knowledge-intensive industries—are helpful, especially if directed toward smaller firms. Fiscal incentives for one form of intangible investment, R&D, are already in place in Denmark, but their scope could be broadened. Whereas it is difficult to establish which type of instrument fosters innovation more effectively, subsidies and tax incentives each have their own strengths and can usefully complement each other (IMF, 2016b).

A03ufig6

Direct Government Funding of Business Expenditures on R&D (BERD) and Tax Incentives for R&D, 2014 1/

(Percent of GDP)

Citation: IMF Staff Country Reports 2017, 159; 10.5089/9781484304617.002.A003

1/ In 2014, DEU and CHE did not provide R&D tax incentives and no data are currently available for Sweden. Source: OECD.

30. Deeper understanding of the role of intangibles requires improved measurement. By

using a firm-reported measure of intangibles, the analysis likely accounts for wide-ranging intangible investments that are beyond what is recorded in the national accounts. To better capture the benefits from knowledge capital, it would be helpful to extend the capitalization of intangibles in the national accounts to expenditures on economic competencies that refer to firm investment in reputation and human and organizational capital.

References

Appendix I. Data Sample

A number of basic filtering procedures are applied to the sample. The Orbis sample excludes sectors of agriculture, forestry and fishing; financial industry; mining and quarrying; public administration and defense; utilities; and real estate activities. Observations for which key financial variables are nonsensical (e.g., negative values for total assets, fixed assets, current assets and liabilities, and sales) were removed. The original sample included 26,101 firms or a total of 69,922 firm-year observations. After applying the filtering rules detailed above, the final sample was reduced to 19,699 firms or 46,653 firm-year observations. Roughly 96 percent of companies in the dataset are privately held and the majority of firms are SMEs (fewer than 250 employees) distributed across the services, manufacturing, and construction sectors (Table A).

Table AI.

Descriptive Statistics by Board Sector

article image
Source: Eurostat’s Labor Force Survey and Fund staff calculations.

Key variables are winsorized following the practice in the literature. All Orbis nominal data are first converted to local currency and transformed into real values using sector-specific national accounts deflators. Key variables are also winsorized in line with the literature (Cleary, 1999; Aivazian and others, 2005); Bluedorn and Ebeke, 2016). Real sales growth is set to 100 (-100) percent if greater (less) than 100 (-100); the ratios of debt to assets and long-term debt to assets are set to 100 (0) if greater (less) than 100 (0); the ratios of tangible and intangible investment to capital are set to 200 (-200) if greater (less) than 200 (-200).

Appendix II. Additional Tables

Table AII.1.

Role of Leverage and Demand

article image
All regressions include firm fixed effects and sector-year fixed effects.Robust standard errors clustered at the sector level in brackets. *** p<0.01, ** p<0.05, * p<0.1.
Table AII.2.

Role of Knowledge Intensity by Sector

article image
Htkis refer to high- and medium-technology manufacturing sectors and knowledge intensive services(Appendix C). All regressions include firm fixed effects and sector-year fixed effects.Robust standard errors clustered at the sector level in brackets. *** p<0.01, ** p<0.05, * p<0.1.
Table AII.3.

Role of Knowledge Intensity by Firm Size Category

article image
Htkis refer to high- and medium-technology manufacturing sectors and knowledge intensive services (Appendix C). Micro are firms with 2-9 employees; Small employ 10-49 employees; Medium have 50-249 employees; and Large have 250 employees and more. All regressions include firm fixed effects and sector-year fixed effects. Robust standard errors are clustered at the sector level in brackets. *** p<0.01, ** p<0.05, * p<0.1.

Appendix III. High-Tech and Knowledge-Intensive Sectors (NACE Rev. 2)

Manufacturing

High-technology:

  • Manufacture of basic pharmaceutical products and pharmaceutical preparations (21)

  • Manufacture of computer, electronic and optical products (26)

  • Manufacture of air and spacecraft and related machinery (30.3)

Medium-high-technology:

  • Manufacture of chemicals and chemical products (20)

  • Manufacture of weapons and ammunition (25.4)

  • Manufacture of electrical equipment (27)

  • Manufacture of machineryand equipment n.e.c. (28)

  • Manufacture of motor vehicles, trailers and semi-trailers (29)

  • Manufacture of other transport equipment (30) excluding Building of ships and boats (30.1) and excluding Manufacture of air and spacecraft and related machinery (30.3)

  • Manufacture of medical and dental instruments and supplies (32.5)

Services

High-tech knowledge-intensive services:

  • Motion picture, video and television programme production, sound recording and music publishing activities (59)

  • Programming and broadcasting activities (60)

  • Telecommunications (61)

  • Computer programming, consultancy and related activities (62)

  • Information service activities (63)

  • Scientific research and development (72)

Knowledge-intensive market services (excluding financial intermediation and high-tech services):

  • Water transport (50) Air transport (51)

  • Legal and accounting activities (69)

  • Activities of head offices; management consultancy activities (70)

  • Architectural and engineering activities; technical testing and analysis (71)

  • Advertising and market research (73)

  • Other professional, scientific and technical activities (74)

  • Employment activities (78)

  • Security and investigation activities (80)

Knowledge-intensive financial services:

  • Financial service activities, except insurance and pension funding (64)

  • Insurance, reinsurance and pension funding, except compulsory social security (65)

  • Activities auxiliary to financial services and insurance activities (66)

Other knowledge-intensive services:

  • Publishing activities (58)

  • Veterinary activities (75)

  • Public administration and defence; compulsory social security (84)

  • Education (85)

  • Human health activities (86)

  • Residential care activities (87)

  • Social work activities without accommodation (88)

  • Creative, arts and entertainment activities (90)

  • Libraries, archives, museums and other cultural activities (91)

  • Gambling and betting activities (92)

  • Sports activities and amusement and recreation activities (93)

Source: Eurostat, European Commission websites:http://ec.europa.eu/eurostat/statistics-explained/index.php/Glossary:High-tech_classification_of_manufacturing_industrieshttp://ec.europa.eu/eurostat/statistics-explained/index.php/Glossary:Knowledge-intensiveservices(KIS).
1

Prepared by Rima A. Turk. I would like to thank John Bluedorn and Christian Ebeke for kindly sharing the code they used for a related project the Euro Area, and David Hofman, Andreas Kuchler, and the Danish authorities for helpful comments and feedback. All remaining errors are my own.

2

The Danish Economic Council argues that, when measured on a real PPP-basis, there is no evidence of weak capital formation in Denmark relative to GDP (Danish Economic Council, 2016).

3

Economic competencies are categorized as: brand-building advertisement, marketing research, worker training, management consulting, and own-organizational investment.

4

In comparison, the contribution of tangible capital deepening to growth in labor productivity was 40 percent on average over the same period.

5

The corporate sector refers to non-financial corporations (NFC).

6

Since leverage may be persistent (such as from building-up debt over time to prepare for a future investment opportunity, or from past borrowing associated with previous investments), consideration was given to lagging leverage by two periods instead of one. The main results are generally unchanged under those robustness checks.

7

The results are robust to removing firm fixed effects and controlling for firm characteristics such as firm age and profitability. Adding region fixed effects also does not affect the main findings.

8

Strong domestic conditions weaken the real option channel, according to which firms hold back irreversible investment as means to increase resilience to future shocks.

9

Using an alternative the definition of SMEs such as the classification by Statistics Denmark (as in Figure 2) does not change the results. It should be noted, however, that SMEs as defined by the number of employees may also include subsidiaries of foreign firms operating in Denmark, which are part of a larger firm structure and may therefore behave differently from Danish SMEs.

10

Eurostat classifies manufacturing industries according to their technology intensity (based on the ratio of R&D expenditures to value added) and services according to their degree of knowledge intensity (based on the share of people with tertiary education in the activity).

11

Danish firm representation in Orbis prior to 2010 is scant in the Orbis database.

12

Consolidated data report financial statements at the parent level for all firm subsidiaries, whether the subsidiaries are located in Denmark or abroad. In contrast, unconsolidated statements focus on the operations of firms in Denmark at the plant level, all of which contribute to GDP.

13

In Orbis, tangible fixed assets refer to buildings, machinery, etcetera. Intangible fixed assets include expenses for formation, research, development, goodwill, and all other expenses with a long-term effect. Fixed assets additionally comprise other fixed assets (long-term investments, shares and participations, pension funds) which are not used.

14

These sectoral tests suggest potentially greater heterogeneity in the investment response than what is usually captured by sector fixed effects only, but a fuller investigation is beyond the current scope of the paper.

15

The analysis has documented sectoral heterogeneity in the results, suggesting that payoffs to investment projects may vary across industries, an issue that was not further investigated in this paper.

16

The ACE seems well-established in Italy; Switzerland is planning to introduce it; and Belgium is considering removing it for reasons unrelated to implementation or effectiveness. Other countries have a similar tax scheme for sectors where economic rents are important; e.g., Norway has a special petroleum tax scheme under which the tax base equals taxable income minus an allowance of 7.5 percent of the investment cost for the first four years.

Same Series

Other IMF Content

Other Publishers

Cover IMF Staff Country Reports
Denmark: Selected Issues
Author: International Monetary Fund. European Dept.
Volume/Issue:
Volume 2017: Issue 159
Publisher:
International Monetary Fund
ISBN:
9781484304617
ISSN:
1934-7685
Pages:
57
DOI:
https://doi.org/10.5089/9781484304617.002
  • View in gallery

    Gross Fixed Capital Formation (GFCF)

    (Percent of GDP, current prices)

  • View in gallery

    Investment Trends, Rates, and Composition

  • View in gallery

    Debt-to-Assets of NFCs, 2010–2015

    (Percent)

  • View in gallery

    Firms Contribution to the Economy

  • View in gallery

    Intangible and Tangible Investment (Average 2000–2013)

    (Percent of GDP)

  • View in gallery

    Share of HTKIS Sectors in GVA 1/

    (Percent)

  • View in gallery

    Overview of the Non-Financial Corporate Sector Denmark vs. Peers (1) Gross Saving, Gross Fixed Capital Formation, and Net Lending

    (Percent of GDP)

  • View in gallery

    Overview of the Non-Financial Corporate Sector: Denmark vs. Peers (2) Net Income and Profit Distribution

    (Percent)

  • View in gallery

    Co-movement between Net Lending to GDP and Cash & Liquid Assets to Financial Assets of NFC (percent)

  • View in gallery

    Overview of the Non-Financial Corporate Sector: Denmark vs. Peers (3) Indicators of Leverage

  • View in gallery

    Overview of the Non-Financial Corporate Sector Denmark vs. Peers (4) Financial Assets and Financial Liabilities

    (Percent of GDP)

  • View in gallery

    Predicted Effect of Real Sale Growth for Firms in HTKIS Sectors

  • View in gallery

    Marginal Effect of Real Sales Growth

  • View in gallery

    Direct Government Funding of Business Expenditures on R&D (BERD) and Tax Incentives for R&D, 2014 1/

    (Percent of GDP)

Gross Fixed Capital Formation (GFCF)

(Percent of GDP, current prices)

Investment Trends, Rates, and Composition

Debt-to-Assets of NFCs, 2010–2015

(Percent)

Firms Contribution to the Economy

Intangible and Tangible Investment (Average 2000–2013)

(Percent of GDP)

Share of HTKIS Sectors in GVA 1/

(Percent)

Overview of the Non-Financial Corporate Sector Denmark vs. Peers (1) Gross Saving, Gross Fixed Capital Formation, and Net Lending

(Percent of GDP)

Overview of the Non-Financial Corporate Sector: Denmark vs. Peers (2) Net Income and Profit Distribution

(Percent)

Co-movement between Net Lending to GDP and Cash & Liquid Assets to Financial Assets of NFC (percent)

Overview of the Non-Financial Corporate Sector: Denmark vs. Peers (3) Indicators of Leverage

Overview of the Non-Financial Corporate Sector Denmark vs. Peers (4) Financial Assets and Financial Liabilities

(Percent of GDP)

Predicted Effect of Real Sale Growth for Firms in HTKIS Sectors

Marginal Effect of Real Sales Growth

Direct Government Funding of Business Expenditures on R&D (BERD) and Tax Incentives for R&D, 2014 1/

(Percent of GDP)