6.2 Distortionary Effects on Healthy Firms
6.3.1 Changing the Zombie Firm Definition
To investigate the sensitivity of the results above, we are doing the same analyses as the last chapter, but changing the zombie definition; the requirement of age is changed from ten to fifteen years and period of consecutive years with interest coverage ratio less than one from three to four years. The regressions can be found in the appendix, table A0.5 and A0.6. According to Gouveia and Osterhold (2018), a more stringent definition of zombies can contribute in addressing cyclical effects. When doing this, the interaction variable loses its significance for employment growth. This is also the case when increasing the minimum firm age to 15 years. The spillovers on employment growth is thus somewhat sensitive to zombie definition.
6.3.2 Including Fishing and Aquaculture
Fishing is one of Norway’s most important export industries, and thus a crucial part of the Norwegian economy. The 03-industries (fishing and aquaculture in NACE Rev.2) are removed during the main analysis, as suggested in McGowan et al. (2017b). We therefore run a regression analysis including 03-industries to test if our results are sensitive to this change. The results are shown in the appendix, table A0.7. Including fishing and aquaculture industries do not change our results.
7. Discussion 43
7 Discussion
We find that the share of zombie firms have increased since 1997, but the growth is not very prevalent. Our analysis suggest that industry capital sunk in zombies lowers capital and employment growth amongst non-zombie firms. We also see indications that this disproportionately affects young firms, in particular when including a regional component in the analysis. In the first section of this chapter we will discuss some limitations of the analysis. Then we move on to a brief section about insolvency regimes, as they could possibly contribute to reduce the zombie firm share and improve market conditions for healthy firms. Lastly, we present possible areas to explore in further research on zombie firms in Norway.
7.1 Limitations of the Analysis
Gouveia and Osterhold (2018) point out that with the current definition, zombies will exit the zombie classification if they experience a single year with interest coverage ratios equal to or more than one. Hence, there is a chance that the prevalence of zombies is underestimated, in particular if many firms have short gaps with interest coverage ratios more than one. A way to address this concern could be to impose a new restriction, where a zombie must have an interest coverage ratio equal to or more than 1 for at least three consecutive years to be "un-classified" as zombies. Another point related to the definition is that our definition does not focus on forbearance lending, which historically has been important in the research field of zombie firms. However, McGowan et al. (2017b) investigate the sensitivity of their results using the modified version of the definition used in Caballero et al. (2008), and only find limited changes in their results. Gouveia and Osterhold (2018) argue that although the share of zombie firms differ using different criteria, the dynamics of the zombie prevalence across time and industries are highlighted with either definition.
In the previous analysis, we investigated the effects of zombie distortions on the average firm. However, as mentioned the consequences could be even larger depending on which firms that are affected by the zombies (McGowan et al., 2017b). In the case where the
44 7. Discussion
most productive firms are disproportionately affected by the zombie prevalence, we could probably expect even bigger consequences from zombie congestion.
A possible sensitivity is the fact that firms with foreign owners could be a part of a cost-or revenue centre. As we saw in the section about determinants, the firms that are part of foreign-owned groups are more likely to be zombies than those which are not. This could indicate that some of the zombies are cost centres, where the (appearing) low profitability is a planned strategy.
Throughout the analysis we have used NACE Rev.2 classifications (McGowan et al., 2017b). However, in the database we use, this is only available for firms that exist in the database after 2007. Thus, firms that only existed before 2007, do not have this industry classification, but rather NACE Rev.1.1. Whilst the number of firms per year has increased over time, we have also removed a larger share of firms in the early periods of the data set due to this restriction. A consequence could thus be that we remove "young"
firms in the early periods of the data set, making young firms underrepresented when we investigate the effects of zombie distortions.
Another drawback with our analysis is related to comparison to the OECD, which could be sensitive to differences in the data set and data cleaning. One of the important differences is related to the calculation of the change in capital stock. As we explained in the Data section, we have not been able to mechanically follow McGowan et al. (2017b) and Gal (2013), as Norwegian accounting standards does not distinguish between depreciation and amortisation. Neither is "change in capital stock" equivalent to investment ratio, which is the suggested measure in McGowan et al. (2017b). Our measure does not account for depreciation and amortisation, making it a simpler, but perhaps less precise measure.
This could question the comparability against the OECD.
7.2 Policy Implications
There are different reasons why zombies are kept alive. During the financial crisis of 2007–2008, we saw large financial institutions being "too big to fail". This phenomenon describes the situation where the authorities fear the consequences of bankruptcy and therefore rather choose to save companies by providing grants or guarantees. Banerjee
7. Discussion 45
and Hofmann (2018) argue that the overall shares of zombie firms in fourteen advanced economies have been increasing since the late 1980s and can be linked to reduced financial pressure, possibly affected by the low-interest rate environment of recent years. Reduced financial pressure might also have contributed to the presence of increased possibilities of financing for non-viable firms through roll over of debt, as the alternative use of capital for creditors yields low returns. The question regarding insolvency regimes is related to which parts of the responsibility of the zombie prevalence that can be reasoned to the design of the regimes. Loose regimes can lead to creditors increasing their risk in the hope of short-term profits and give low incentives to terminate bad debt.
Having an economy where, over a six-year period, ten to fifteen percent of the zombies endure as zombies, could lead us to believe that the insolvency regimes have weaknesses, as these zombies are kept alive. Since our results also indicate that increased capital sunk in zombies reduces employment and capital growth at the industry level amongst non-zombies, and that zombies probably create entry barriers for young, innovative firms, there should be incentives for policy makers to address the issue. Given that a reduced amount of resources sunk in zombies could increase growth opportunities for healthy firms, it could possibly be an important step in ensuring productivity growth.
Even though the indicators describing the Norwegian insolvency regime is characterised as medium to high compared to other OECD countries, there is probably room for improvement. The results presented in McGowan and Andrews (2018) show that Norwegian insolvency regimes have limited initiatives concerning prevention and streamlining measures, which includes early warning systems, pre-insolvency regimes, and special insolvency procedures for small and medium-sized enterprises (SMEs).
Kapitaltilgangsutvalget (2018) recommends earlier possible initiation of restructuring negotiations as a preventive measure. In addition, they suggest to increase the reporting requirements in the aftermath of bankruptcies since this is important information about why firms fail.
Successfully implementing appropriate additional policy measures could further improve the economic environment, influence the prevalence and resources sunk in zombies, and possible distortions.
46 7. Discussion
7.3 Suggestions for Further Research
The main effort of this masters thesis has been to identify zombies, their distortionary effects on healthy firms, as well as their characteristics. It has also included a brief policy discussion. We have the following suggestions on further research on the field of zombie firms in Norway.
As noted earlier, research on zombie firms has historically placed much of its focus on the channel of bank forbearance. This has not been the focus of this master thesis. However, a contribution to the research would be to investigate the zombie distortions using the definition in Caballero et al. (2008). It could be particularly interesting to examine the link between zombie firms and creditors; the existence of numerous local Norwegian savings banks could lead us to believe that this could be an interesting addition to existing research on the channel of bank forbearance.
An analysis of the features and consequences of insolvency regimes on the share and consequences on zombie firms could also be an interesting research topic. Changes in the insolvency regime throughout the data coverage period could be analysed against the zombie shares, spillovers and similar, to investigate whether they were effective and achieved the preferred outcome.
8. Conclusion 47
8 Conclusion
In this masters thesis, we have identified the presence of zombie firms in Norway, in addition to presenting an analysis of characteristics associated with being a zombie firm and possible distortionary effects on healthy firms. We have applied the definition suggested in McGowan et al. (2017b), where a firm is classified as a zombie if firm age is ten years or more, and it has an interest coverage ratio less than one for three consecutive years. The zombie prevalence has, similar to other countries in the OECD area, increased during the past years. While the share of zombies was about 0.97 percent in 1997, it was 2.12 percent in 2016, peaking at 3.44 percent in 2011. However, the trend is not obvious, and the zombie share has declined steadily since 2011. We find that a stable share of between roughly 15–20 percent of the zombies remain zombies after three years.
Only looking at size plots, we observe that there are more zombies amongst the companies with the highest share of employees. We also observe that there are big differences between industries concerning zombie shares and that the prevalence of zombies seems to increase with age. Our results suggest, using measures of size, age, financial structure, and ownership, that increased total assets reduce the likelihood of being a zombie amongst the smaller firms, but for the relatively big firms more total assets increases the likelihood of being a zombie. The results also suggest that increasing number of employees reduces the probability of being a zombie firm. In line with our hypothesis, we find that foreign owned firms have an increased probability of being a zombie firm.
An important question to answer is how resources sunk in zombies affect non-zombies. In particular, it is an important question for policy makers as it provides valuable information about the consequences of the zombie prevalence. We have therefore investigated whether the presence of zombie firms in Norway lowers employment or capital growth amongst non-zombies. We see indications that the zombies distort capital (real tangible assets) and employment growth within industries.
In addition, our results suggest that concerning capital growth, young firms are disproportionately affected of capital sunk in zombies in their industry. Assuming that the relevant labour market is the regional, this is also true for employment growth.
48 8. Conclusion
Overall, many of our results are in line with results from other countries in the OECD.
We believe the distortions of zombies on healthy firms in Norway could be an important finding for policy makers, whilst ensuring continued economic growth in Norway in the years to come.
References 49
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51
Appendix
Figure A0.1: The share of zombie firms in Norway (1997–2016) - All firms 10 years
Source: Our own calculations based on SNF’s and NHH’s database of accounting and company information for Norwegian companies.
Figure A0.2: Firm classification six years after zombie classification
Note: The status att5for firms which att0 were aged 10 years and had an interest coverage ratio<1 over three consecutive years. I.e. the firm status six years after being classified as a zombie firm.
Source: Our own calculations based on SNF’s and NHH’s database of accounting and company information for Norwegian companies.
52
Table A0.1: Linear Probability Model Determinants of Zombie Firms With Regions -Years 1999–2016
(1) (2) (3) (4) (5)
log Employment -0.00549⇤⇤⇤ -0.00663⇤⇤⇤ -0.00657⇤⇤⇤
(0.0006) (0.0014) (0.0016)
(log(Employment))2 0.00030 -0.00118⇤⇤⇤
(0.0003) (0.0004)
log Total Assets 0.00133⇤⇤⇤ -0.01336⇤⇤⇤ -0.01331⇤⇤⇤
(0.0005) (0.0023) (0.0027)
(log(Total Assets))2 0.00086⇤⇤⇤ 0.00114⇤⇤⇤
(0.0001) (0.0002) Total Debt / Total Assets 0.00004 0.00004 0.00004⇤ 0.00002 0.00002 (0.0000) (0.0000) (0.0000) (0.0000) (0.0000)
Firm Age 0.00023⇤⇤⇤ 0.00023⇤⇤⇤ 0.00016⇤⇤ 0.00013⇤⇤ 0.00014⇤⇤
(0.0001) (0.0001) (0.0001) (0.0001) (0.0001)
Public Sector Ownership 0.00952 0.00917 0.00037 -0.00395 -0.00009
(0.0100) (0.0100) (0.0100) (0.0101) (0.0101) Foreign Ownership 0.01794⇤⇤⇤ 0.01743⇤⇤⇤ 0.00686⇤⇤ 0.00390 0.01131⇤⇤⇤
(0.0034) (0.0034) (0.0035) (0.0035) (0.0035)
Year, region and industry fixed effects YES YES YES YES YES
Observations 398,342 398,342 398,342 398,342 398,342
AdjustedR2 0.012 0.012 0.011 0.012 0.013
(6) (7) (8)
log Employment -0.00655⇤⇤⇤ -0.00654⇤⇤⇤ -0.00660⇤⇤⇤
(0.0016) (0.0016) (0.0016) (log(Employment))2 -0.00118⇤⇤⇤ -0.00118⇤⇤⇤ -0.00119⇤⇤⇤
(0.0004) (0.0004) (0.0004) log Total Assets -0.01333⇤⇤⇤ -0.01346⇤⇤⇤ -0.01275⇤⇤⇤
(0.0027) (0.0027) (0.0028) (log(Total Assets))2 0.00114⇤⇤⇤ 0.00114⇤⇤⇤ 0.00109⇤⇤⇤
(0.0002) (0.0002) (0.0002) Total Debt / Total Assets 0.00002 0.00002 0.00002 (0.0000) (0.0000) (0.0000)
Firm Age 0.00014⇤⇤ 0.00014⇤⇤ 0.00013⇤⇤
(0.0001) (0.0001) (0.0001)
Public Sector Ownership -0.00008 0.00017 0.00082
(0.0101) (0.0101) (0.0102) Foreign Ownership 0.01131⇤⇤⇤ 0.01126⇤⇤⇤ 0.01186⇤⇤⇤
(0.0035) (0.0035) (0.0035) Year, region and industry fixed effects YES YES YES
Observations 398,342 398,342 391,580
AdjustedR2 0.013 0.013 0.013
*p <0.1, **p <0.05, ***p <0.01
Note: Zombie firm classification (IRCR < 1 for three consecutive years and firm age 10) is the dependent variable where "1" equals zombie firm. Each line reports variable coefficients with standard error in parenthesis and significance level symbolised by stars (*). Accounting Figures included in NOK
’000 and CPI adjusted. Employment is the number of registered employees (ansatte). Total Assets is all assets of a firm (sumeiend). Total Debt / Total Assets is all interest bearing debt (average of rgjeld_minandrgjeld_max) divided by total assets (sumeiend). Firm Age is the age of firm defined as the accounting year minus year of incorporation (stif taar). Public Sector Ownership is "1" if a firm has > 50 % public sector ownership (eierstruktur = 5). Foreign Ownership is "1" if a firm is owned by foreigners (eierstruktur= 9). Female General Manager and Chairperson is "1" if the general manager/chairperson of a firm is a female (daglsex= ”K”andstledsex= ”K”). Female Board Member Share equals the number of females in the board of a firm (st_kvimdl) divided by the total number of board members (st_medl) in the same firm. Public Limited Company is "1" if the firm is this entity type (selskf = ”ASA”). Standard errors are clustered at the firm level.
Source: Our own calculations based on SNF’s and NHH’s database of accounting and company information for Norwegian companies.
53
Table A0.2: Linear Probability Model - Determinants of Zombie Firms Without Regions - Years 1999–2016
(5-1) (5-2) (5-3) (5-4)
log Employment -0.00603⇤⇤⇤ -0.00603⇤⇤ -0.00629⇤⇤⇤ -0.00629⇤⇤
(0.0011) (0.0026) (0.0012) (0.0025) (log(Employment))2 -0.00125⇤⇤⇤ -0.00125 -0.00122⇤⇤⇤ -0.00122 (0.0003) (0.0008) (0.0003) (0.0008) log Total Assets -0.01305⇤⇤⇤ -0.01305⇤⇤⇤ -0.01324⇤⇤⇤ -0.01324⇤⇤⇤
(0.0017) (0.0037) (0.0017) (0.0036) (log(Total Assets))2 0.00112⇤⇤⇤ 0.00112⇤⇤⇤ 0.00113⇤⇤⇤ 0.00113⇤⇤⇤
(0.0001) (0.0003) (0.0001) (0.0003) Total Debt / Total Assets 0.00002 0.00002 0.00002 0.00002 (0.0000) (0.0000) (0.0000) (0.0000)
Firm Age 0.00014⇤⇤⇤ 0.00014 0.00013⇤⇤⇤ 0.00013
(0.0000) (0.0001) (0.0000) (0.0001) Public Sector Ownership 0.00005 0.00005 -0.00045 -0.00045 (0.0073) (0.0067) (0.0072) (0.0067) Foreign Ownership 0.01216⇤⇤⇤ 0.01216⇤ 0.01083⇤⇤⇤ 0.01083⇤ (0.0028) (0.0062) (0.0028) (0.0061)
AdjustedR2 0.015 0.015 0.013 0.013
*p <0.1, **p <0.05, ***p <0.01
Note: Zombie firm classification (IRCR < 1 for three consecutive years and firm age 10) is the dependent variable where "1" equals zombie firm. Each line reports variable coefficients with standard error in parenthesis and significance level symbolised by stars (*). Accounting Figures included in NOK ’000 and CPI adjusted. Employment is the number of registered employees (ansatte). Total Assets is all assets of a firm (sumeiend). Total Debt / Total Assets is all interest bearing debt (average ofrgjeld_minand rgjeld_max) divided by total assets (sumeiend). Firm Age is the age of firm defined as the accounting year minus year of incorporation (stif taar). Public Sector Ownership is "1" if a firm has > 50 % public sector ownership (eierstruktur= 5). Foreign Ownership is "1" if a firm is owned by foreigners (eierstruktur= 9). Female General Manager and Chairperson is "1" if the general manager/chairperson of a firm is a female (daglsex= ”K”andstledsex= ”K”). Female Board Member Share equals the number of females in the board of a firm (st_kvimdl) divided by the total number of board members (st_medl) in the same firm.
Source: Our own calculations based on SNF’s and NHH’s database of accounting and company information for Norwegian companies.