Republic of Slovenia: Selected Issues Paper
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International Monetary Fund. European Dept.
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Publication Date:
16 May 2016
eISBN:
9781475556070
Language:
English
Keywords:
ISCR; CR; Slovenia; export; quantitative easing; GDP; investment flowing; firm investment model; investment rate; investment slump; mezzanine financing; Bank credit; Global financial crisis of 2008-2009; Corporate investment; Global
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Republic of Slovenia: Selected Issues

Abstract

Republic of Slovenia: Selected Issues

Corporate Financial Health and Investment1

A. Introduction

1. When the global asset bubble burst in 2008, credit and investment collapsed in central Europe (Figure 1). Preceding the crisis, bank credit in Slovenia and four central European countries (Czech Republic, Hungary, Poland, and Slovakia, collectively the CE-4) fueled corporate investment. When global credit markets froze in late 2008, bank financing dried up, precipitating credit- and investment-starved recessions in Slovenia and the CE-4, except Poland. Slovenia and Hungary, where non-financial corporates were the most indebted, were hit the hardest, with domestic banks in Slovenia requiring a public-sector bailout in 2013. Today, private investment remains well below pre-bubble levels in Slovenia, and investment in other countries have only returned to 2004 levels, despite a resumption of growth and historically low policy rates.

Figure 1.
Figure 1.

Economic Trends and Corporate Financing

Citation: IMF Staff Country Reports 2016, 122; 10.5089/9781475556070.002.A002

Sources: Eurostat, FSI, Haver Analytics, and IFS.1/ 2015Q3 value is used instead.

2. Today’s low interest rates are supportive of corporate investment. In theory, a firm’s decision to investment depends on the risk-adjusted expected return of the investment. If the return exceeds a pre-determined hurdle rate (the discount rate applied to projected cash flows associated with the investment), it is assumed financing will be available for the project and it will be undertaken. The hurdle rate typically embodies a firm’s weighted average cost of capital, managers’ and owners’ degree of risk aversion, and risks specific to the investment, including uncertainty surrounding cash flow projections. Changes in policy interest rates affect the cost of firm debt, and indirectly the cost of firm equity, thereby influencing a firm’s hurdle rate. Specifically, lower policy interest rates can induce more investment spending as a greater number of potential investments become financially attractive to undertake.

3. However, poor corporate financial health can impair monetary policy transmission. In the presence of financial frictions, a firm’s financial health could play a larger role in determining its investment decisions and the availability of related financing (see Martinez-Carrascal and Ferrando, 2008 for a review of the literature). Indeed, studies have found that a firm’s balance sheet strength, profitability, and the availability of liquid assets are significant determinants of investment (e.g., see Fazzari, Hubbard, and Perterson, 1988). In essence, high corporate debt burdens weigh on the capacity and desire of firms to finance an investment.2 As a result, investment decisions and available financing for investment become less responsive to reductions in interest rates.

4. Against this background, this paper assesses corporate financial health in Slovenia and the CE-4, using firm-level data, and the potential effect on investment. In particular, the paper addresses the following questions: i) How has the financial health of non-financial corporates faired over the last nine years, in terms of profitability, liquidity, and indebtedness, in Slovenia and the CE-4?; ii) Has a structural change occurred following the financial crisis in either a firm’s willingness or ability to undertake investments given its financial health?; and iii) Is there a threshold of indebtedness that leads to lower corporate investment?

5. The rest of the paper proceeds as follows: Section B describes the firm level data and methodology used in the analysis; Section C reviews trends in the financial health of firms in the countries under study; Section D presents an econometric model linking a firm’s investment to its financial health and broader economic and financial conditions; and Section E offers policy considerations.

B. Data and Methodology

6. The analysis relies on a large dataset of harmonized financial data for firms in Slovenia and the CE-4. The initial sample is the set of all non-financial firms (NFCs) for which financial and operating data are available on the Orbis database. The firm-year sample sizes average between 30,000–55,000 for Slovenia, the Czech Republic, Poland and Slovakia and around 130,000 for Hungary. The database includes a large portion of small- and micro-sized firms not readily available elsewhere, allowing for greater coverage of the non-financial sector. Only firm-year observations that contain positive values for tangible fixed assets are included in the analysis.

7. The evolution of corporate financial health since 2006 is assessed based on indicators of profitability, debt, and debt service capability. The following financial ratios are constructed from detailed financial statements: return on total assets, profit margin, cash-to-assets, debt-to-assets, debt-to-equity, debt-to-cash flow, and interest coverage. These indicators capture a firm’s financial prospects and balance sheet strength, which, in turn, influence a firm’s ability and willingness to use internal funds and external financing for investment. Specifically, return on assets and profit margins are proxies for the profitability of a firm and its growth prospects. Debt-to-assets reflects a firm’s degree of leverage, while debt-to-cash flow and the interest coverage ratio are indicators of a firm’s ability to service its debt. Firms are also classified based on size, sector of operation, and initial level of indebtedness (See Annex I for definitions of ratios and firm size). For the latter, firms with financial debt-to-asset ratios that exceed the median for firms in the same country are classified as high-leverage firms.

8. A standard investment model is estimated to examine the relationship between a firm’s financial health, its access to financing, and its investment outlays. The variation over time and across firms in profitability, liquidity, and indebtedness is exploited to help explain the variation in firm investment. Of interest is whether the 2008 financial crisis induced a change in the sensitivity of a firm’s investment spending to indicators of its financial health. Estimations are also run to determine if firm size influences the capacity or desire to invest. Moreover, the impact of changes in bank lending conditions is modeled directly for Slovenia,3 where standards tightened considerably following the global and domestic bank crises, and only began to loosen in late 2015. Threshold levels (the ratio above which debt begins to influence investment negatively) for debt-to-assets and debt-to-cash flow were estimated for Slovenia as well.

C. Developments in firms’ financial condition in Slovenia and the CE-4

9. Corporates are slowly repairing their financial health (Figure 2). The 2008 global financial crisis exposed the underlying vulnerabilities of corporate balance sheets in Slovenia and the CE-4 that grew fat on borrowing in the pre-crisis period. The liquidity shock and concomitant recession induced a deterioration in firm financial indicators across countries and firm sizes. Profitability fell and firms’ debt burdens spiked. By the end of 2014, many financial ratios had returned close to pre-crisis levels, as highly indebted firms deleveraged and growth picked up in 2014. 4 However, corporate investment remained subdued.

Figure 2.
Figure 2.

Summary: Firm-level data, 2006–14

Citation: IMF Staff Country Reports 2016, 122; 10.5089/9781475556070.002.A002

Sources: Orbis; IMF staff calculations.CZ= Czech Republic; HU=Hungary; SK=Slovakia;SI=Slovenia

Profitability and liquidity

10. Firm profitability and liquidity ratios are pointing upwards (Figures 3–7). After falling significantly in 2008 and 2009, firm profitability stabilized before picking up in 2014. This was the case in all countries except Poland, where return on assets continued to fall gradually until 2013 though the overall profitability of Polish firms generally remained higher than in the other countries. Micro-sized firms were the least profitable throughout the post-crisis period, except in Slovenia, where the profitability of micro enterprises was higher and rebounded earlier than other firms. Turning to liquidity, Czech, Hungarian, and Slovakian firms are the most liquid with median aggregate cash-to-asset ratios greater than 10 percent. Slovenia firms are the least liquid, despite a general improvement in liquidity after the 2008 financial crisis, particularly in micro-sized firms.

Figure 3.
Figure 3.

Slovenia, 2006–14

Citation: IMF Staff Country Reports 2016, 122; 10.5089/9781475556070.002.A002

Sources: Orbis; IMF staff calculations.
Figure 4.
Figure 4.

Czech Republic, 2006–14

Citation: IMF Staff Country Reports 2016, 122; 10.5089/9781475556070.002.A002

Sources: Orbis; IMF staff calculations.
Figure 5.
Figure 5.

Hungary, 2006–14

Citation: IMF Staff Country Reports 2016, 122; 10.5089/9781475556070.002.A002

Sources: Orbis; IMF staff calculations.
Figure 6.
Figure 6.

Poland, 2006–14

Citation: IMF Staff Country Reports 2016, 122; 10.5089/9781475556070.002.A002

Sources: Orbis; IMF staff calculations.
Figure 7.
Figure 7.

Slovakia, 2006–14

Citation: IMF Staff Country Reports 2016, 122; 10.5089/9781475556070.002.A002

Sources: Orbis; IMF staff calculations.

Leverage

Financial debt-to-assets

11. Deleveraging has been gradual. The median level of debt-to-assets in Slovenia, the Czech Republic, and Slovakia hovered around 60-70 percent throughout 2007–14, while the median ratio was closer to 50 percent and 35 percent in Poland and Hungary, respectively. The ratio for all firms decreased somewhat toward the end of the period in each country except Slovakia where it jumped in 2009 and stayed at the more elevated level through 2014. However, the aggregate figures mask significant differences among firms of different sizes. In all countries except Slovakia, micro-sized firms have considerably higher debt burdens than larger firms throughout most of the post-crisis period. The evolution of firm indebtedness also varies depending on the initial level of firm leverage. Highly leveraged firms shed more debt, while those with less debt maintained or increased their leverage.

Debt overhang (excessive debt)
A02ufig1

Excessive debt (all firms)

(Percent of annual debt > 5x EBITDA)

Citation: IMF Staff Country Reports 2016, 122; 10.5089/9781475556070.002.A002

12. Micro-enterprises are still burdened with excessive debt. In aggregate, firms in the Czech Republic, Poland, and Slovenia have reduced their debt overhang to pre-crisis levels, while Hungarian and Slovakian firms generally have the most excessive debt. It is worth noting, that developments in the measure of excessive debt (defined in this paper as financial debt greater than 5 times earnings before interest, taxes, depreciation, and amortization (EBITDA)) are sensitive to annual fluctuations in cash flow from operations.5 Thus, looking solely at micro enterprises Slovenia firms suffer the least from excessive debt and Polish ones join their Hungarian counterparts with the largest amounts of excessive debt since 2011. Nonetheless, 45 percent of all sampled Slovenian firms faced excessive debt levels in 2014.

Debt service capacity

13. Corporate capacity to pay interest generally improved in 2013–14. However, a significant number of firms (10 to 30 percent of the sampled firms, depending on the country) have interest coverage ratios (EBITDA over financial expense) below one.6 In Slovenia and Slovakia, 2/5 of micro enterprises do not have the cash flow to cover annual interest payments. These firms have to rely on other sources of financing such as cash balances, asset sales, or credit lines to cover annual interest payments, suggesting that many firms still face significant financing constraints. However, with the exception of micro-sized firms, Slovenian firms in general can more easily cover interest payments out of cash flow from operations than their peers in comparator countries.

A02ufig2

Interest Coverage Ratio (ICR)

(Percent of micro-sized firms with ICR<1)

Citation: IMF Staff Country Reports 2016, 122; 10.5089/9781475556070.002.A002

D. Firms’ financial condition, investment and the effect of the crisis

14. With the onset of the financial crisis, investment by companies in Slovenian and the CE-4 fell significantly and continued to contract through 2014. 7 The sharp decline across the board can be largely explained by the widespread fall in aggregate demand after the 2008 financial crisis as documented in Chapter 4 of the April 2015 World Economic Outlook (IMF, 2015c). However, the contraction in economic activity does not explain the entire fall in corporate investment nor the duration of the investment slump, suggesting that other factors also contributed to the decline.

15. The financial crisis may have changed the sensitivity of firms’ investment to its financial health. Studies of Slovenian firms have found that weak balance sheets, as indicated by high debt burdens e.g., a debt-to-EBITDA ratio greater than 5, an interest coverage ratio less than 1.5 (Damijan, 2014), or a firm financing structure heavily reliant on debt are more susceptible to financing constraints. With the exception of micro-enterprises, indicators of financial health have broadly returned to their pre-crisis levels, yet investment has not recovered. This suggests the possibility of a more cautious approach by corporates in assessing their ability to undertake investments. For micro-enterprises, this dynamic would be compounded by their still weak financial positions.

16. Empirical analysis supports the hypothesis that the financial crisis altered the relationship between firms’ financial strength and investment spending. A log-linear form of a standard firm investment model, as in (Budina et al., 2015; Kalemli-Özcan et al., 2015, Damijan, 2014), is applied to annual observations on a sample of firms from each country. In addition, potential differences in investment rates related to firm size are also modeled, by running separate regressions with sub-samples based on firm size (large, medium, and small and micro). The econometric approach has the following specification:

d l n ( I i , t ) = α + β 1 C A S i , t − 1 + β 2 R O A i , t − 1 + β 3 D A i . t − 1 + β 4 D A ( p o s t ) i , t − 1 + β 5 O V E R i , t − 1 + β 6 O V E R ( p o s t ) i , t − 1 + β 7 A S S E T S i , t − 1 + α i + α t + ε i , t p o s t i , t − 1 = { 1   i f   y e a r i , t − 1 ≥ 2009 0   i f   d a i , t − 1 < 2009
article image

17. The gross investment rate is modeled as a function of lagged variables that describe a firm’s liquidity, profitability, and indebtedness. Firm fixed effects absorb all time-invariant heterogeneity across firms and the year fixed effects control for factors that may affect investment equally across firms in a country, such as fluctuations in aggregate demand and interest rates. The debt-to-assets and EBITDA-to-debt variables are interacted with a post-2008 indicator to examine if the relationship between annual investment spending and firm indebtedness changed following the 2008 global financial crisis. Total assets are included in sample with all firms to control for differences arising from firm size. This variable is dropped in regressions on the sub-samples based on firm size. The samples include all firms that have been active throughout 2006–14.10

18. For Slovenia, bank credit conditions are directly modeled as well. A lagged explanatory variable (BLS) is added to the model for Slovenia to capture the potential role changes in bank credit conditions may have played in Slovenian firms’ access to credit for investment.11 The variable is the diffusion index calculated by the European Central Bank for Slovenia based on quarterly survey data that is designed to assess bank credit conditions.

19. The repercussions of the global crisis altered the debt/investment relationship. (See Text Table and Annex II). In periods of high growth and low corporate debt burdens, we would expect a positive relationship between the ratio of debt-to-assets and the investment rate, as firms are more likely and able to borrow and invest. We would also expect a negative relationship between the EBITDA-to-debt ratio and investment, as investment is mainly funded by borrowing rather than retained earnings. In contrast, with higher debt burdens and low growth, we would expect the relationship between the debt ratios and investment to reverse, i.e., higher corporate debt ratios in periods of depressed economic growth constrain investment. The regression results are in line with these expectations. Post crisis, firms’ investment rates are more sensitive to their indebtedness and capacity to pay interest relative to the pre-crisis period. In addition, empirical results indicate that for Slovenian firms, thresholds exist for debt ratios, i.e., a turning-point level in the ratios’ values above which debt begins to influence investment negatively.

  • More debt on top of already high debt levels leads to less investment. A higher level of indebtedness reduces investment by Slovenian and Polish firms post crisis.12 In the pre-crisis period, the coefficient on debt-to-assets (DA) is negative though insignificant. Post-crisis the coefficient becomes more negative and significant. In other words, as the indebtedness of firms in these countries increased, their investment rates fell. For firms in the other countries, some of which are less indebted, the coefficient on debt-to-assets turns positive post crisis and in some case is significant. A threshold analysis, described in Annex III, finds that in Slovenia large firms face a threshold debt-to-asset ratio of close to 76 percent, while the threshold for SME’s is much lower at about 10 percent.

  • Earnings (and operational cash flow) matter more for investment. The coefficient on EBITDA-to-debt becomes less negative or turns positive across all countries for the sample of all firms, and for the sub-sample containing only small and micro firms. This implies that a firm’s ability or willingness to invest becomes more sensitive to operational cash flow relative to debt financing. The coefficient on EBITDA-to-debt for the pre-crisis period is more negative as firms took on more debt to finance investment. This possibly reflected overly optimistic assumptions by firms and their financiers about the ability of firms to service their debt burdens. The threshold analysis for Slovenian firms indicates the level for debt-to-EBITDA at which investment begins to decline is 8 for large firms and 1 for SMEs (See table).

  • Profits and cash matter. As expected, more profitable and liquid firms invest more. The coefficients on cash-to-assets and return on assets are positive and statistically significant.

  • Tighter bank lending standards lower investment. In all Slovenia-specific estimations, the coefficient on credit standards was negative, i.e., the investment rate was lower at higher bank credit standards (see chart), and highly significant. The magnitude of the impact was somewhat less for smaller firms. This result is consistent with the finding in Vodopivec and Čede (BoS, 2013) that the majority of firms with 1–15 employees do not rely on banks for financing.

Table 1.

Point Estimates of Coefficients on Debt-Related Variables 1/

article image
Source: Orbis, IMF staff calculations

Significance level: * p<0.10 **p<0.05 ***p<0.01

“Pre” refers to observations in the period prior to 2009. “Post” is a linear combination of the coefficients on pre- and postcrisis observations.

Red = Change in coefficient value between pre- to post-crisis periods is consistent with economic theory.
Table 2.

Optimal Debt Thresholds for Slovenia

article image
Sources: Orbis; IMF staff calculations
Table 3.

Point Estimates of Coefficients 1/

article image
Source: Orbis, IMF staff calculations

Significance level: * p<0.10 **p<0.05 ***p<0.01

20. A “back of the envelope” calculation suggests that the aggregate debt of small- and micro-sized firms should be reduced by up to 30 percent of GDP from 2014 levels. The amount of further debt reduction depends on the amount of new equity financing obtained. With an average debt-to-asset ratio of 56 percent in 2014, medium-, small- and micro-size firms would need to reduce debt by about €12 billion, absent new equity, to lower the ratio to the threshold level of about 10 percent. New equity would lower the needed amount of debt reduction. This matters for investment. Based on the firm-level data from Orbis, SMEs accounted for 60 percent of non-financial corporate investment over the period 2006–14. With a few exceptions, large firms’ debt-to-asset ratios are under the relevant threshold.

A02ufig3

Debt Exceeding Debt/Assets Threshold, 2014

(Percent of total by firm size)

Citation: IMF Staff Country Reports 2016, 122; 10.5089/9781475556070.002.A002

E. Policy options and conclusions

21. This papers findings indicate that corporate leverage, particularly for SMEs, needs to be reduced further to accelerate investment. In addition, financial frictions are likely to remain elevated for some time in Slovenia and central European countries. International experience suggests possible further measures to restore corporate financial health and get investment flowing. Below are examples of potential policy interventions, with a focus on Slovenia, that would be supportive of reducing corporate debt burdens and stimulating financing for corporate investment:

  • Closely monitor bank implementation of the NPL guidelines provided by the Bank Association and the Bank of Slovenia, and adjust these guidelines if needed, based on the implementation experience.

  • Consider again the benefits of a centralized privately funded entity (SPV) for SME NPL resolution. This would quickly reduce the lingering burden NPLs place on bank lending and stimulate greater economic activity by freeing up productive resources (e.g., blocked collateral).

  • Further transfer assets to the Bank Asset Management Company (BAMC), especially claims on companies that are already part of its portfolio. This would facilitate speedier resolution of bad debts by mitigating creditor coordination issues.

  • Explore ways to facilitate equity financing, including mezzanine financing, particularly for SMEs. Additional equity would provide resources for investment, and improve corporate leverage ratios enhancing the creditworthiness of corporate borrowers. Mezzanine financing, which may give the lender the right to convert to an ownership or equity interest in the company if the loan is not paid back, could help firms gain quicker access to financing for investment.

  • Consider sponsoring or supporting regional efforts to develop a market in distressed debt (IMF, 2015d).

References

  • Banka Slovenije, 2015, “Policy Strategy Paper for Slovenia, 2015,” Ljubljana, Slovenia.

  • Barkbu, Bergljot, S. Pelin Berkmen, Pavel Lukyantsau, Sergejs Saksonovs, and Hanni Schoelermann, 2015, “Investment in the Euro Area: Why Has it Been Weak,” IMF Working Paper No. 15/32 (Washington: International Monetary Fund).

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  • Bending, Tim, Markus Berndt, Frank Betz, Philippe Brutscher, Oskar Nelvin, Debora Revoltella, Tomas Slacik and Marcin Wolski, 2014, “Unlocking Lending in Europe,” Working paper, Economics Department (Luxembourg: European Investment Bank).

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  • Budina, Nina, Sergi Lanau and Petia Topalova, 2015, “The Italian and Spanish Corporate Sectors in the Aftermath of the Crisis,” IMF Selected Issues Paper, IMF Country Report No. 15/167, pp. 22–48.

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  • Caprirolo, Gonzalo, and Miha Trošt, 2015, “Deleveraging of Non-financial corporations: Taking stock,” (Forthcoming in Bancni Vestnik)

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  • Christiansen, Lone, and Annette Kyobe, 2014, “Corporate Sector Vulnerabilities,” Selected Issues Paper, IMF Country Report No. 14/174 (Washington, D.C.: International Monetary Fund).

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  • Damijan, Jože, 2014, “Corporate financial soundness and its impact on firm performance: Implications for corporate debt restructuring in Slovenia,” EBRD, Working Paper No. 168.

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  • Fazzari, Steven, Glenn Hubbard and Bruce Peterson, 1988, “Financing Constraints and Corporate Investment,” Brookings Papers on Economic Activity, Vol. 1. pp. 141–95.

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  • EBRD, 2014, Strategy for Slovenia (London: European Bank for Reconstruction and Development).

  • EBRD, Transition Report 2015-16, 2015, “Rebalancing Finance,” (London: European Bank for Reconstruction and Development) (Draft).

  • European Central Bank, 2014, “Deleveraging Patterns in the Euro Area Corporate Sector,” ECB Monthly Bulletin (February).

  • Gabrijelčič, Mateja, Uroš Herman, and Andreja Lenarčič, 2015, “Debt Financing and Firm Performance before and during the Crisis: Micro-Financial Evidence from Slovenia,” Ljubljana, Slovenia.

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Annex I. Definitions of Financial Ratios, Variables, and Firm sizes

article image

Firm Size Classification

(in millions of euros, unless otherwise specified)

article image
Source: Orbis by Bureau van Dijk

Must match at least one of the conditions to be included in the size designation.

Annex II. Regression Results by Country and by Firm Size

Dependent variable: Log-difference of tangible fixed assets

article image
Note: All regressions include firm and year fixed effects. Standard errors are clustered at the firm level. p-values in parentheses: * p<0.10; ** p<0.05; *** p<0.01

First country column is for sample with all firms in that country. Other country-columns: 1=large firms; 2=medium firms; 3=small and micro firms.

Annex III. Threshold Effects

We tested for the existence of a “turning point” threshold effect between the rate of investment and the debt–to–assets and debt-to-EBITDA ratios in Slovenia. The turning-point level is the ratio value above which debt begins to influence investment negatively. The base for the model is the same as equation (1) in the main text. The primary difference is that the pre- and post-designations for the variables that reflect a firm’s debt burden have been replaced with a variable that measures the degree to which a given debt burden varies relative to potential threshold value. The methodology follows that in Hansen (1999) and Khan and Senhadji (2000). Immediately below is the specification for the test for a debt-to-assets threshold: 1

d l n ( I i , t ) = γ 1 d a i , t − 1 + γ 2 d u m i , t − 1 D A * ( d a i , t − 1 − D A * ) + X i , t − 1 β + α i + α t + ε i , t d u m i , t − 1 D A * = { 1   i f   d a i , t − 1 ≥ D A * 0   i f   d a i , t − 1 < D A *
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The threshold level DA* is chosen so as to minimize the residual sum of squares S(da) with the threshold level fixed at da.

D A * = a r g m i n d a { S ( d a ) , d a _ , … , d a ¯ } ,  where  d a _ = 1  percent and  d a ¯ = 100  percent .

Regressions were run for a sub-sample of large firms and another one for a sub-sample of SMEs, i.e., all firms not classified as large. In the test for a debt-to-asset threshold, this amounted to one hundred regressions per sub-sample. The procedure was repeated to test for a threshold level of EBITDA-to-debt, substituting over-1 for da, with a threshold range of 1 to 10 and an increment of one. The γs are the coefficients of interest:

  • γ1 is an estimator of the effect of the debt burden on the change in investment for firms whose debt burden is less than the potential threshold;

  • γ2 is the coefficient on the difference between the observed debt burden and the threshold if the observed debt burden is higher than the potential threshold; and

  • γ1+ γ2 is the impact of the debt burden variable on the change in investment when the variables value is higher than the potential threshold.

The “turning point” thresholds are identified on the ground of best fit (minimizing the RSS). For example, the thresholds reported in the table in the main text for debt-to-assets, are the threshold levels that correspond to the lowest residual sum of squares across the 100 regressions run for the specified sub-sample of firms. In practical terms, the turning-point threshold implies: the marginal debt accumulated after debt hits the threshold diminishes investment growth.
1

Prepared by John Ralyea with assistance from Luisa Calixto and Tingyun Chen.

2

References to a firm’s capacity or ability to investment include the availability of internal financing such as retained earnings or fresh equity and external financing provided by bank and non-bank entities.

3

ECB bank lending survey data that covers the crisis period and afterwards is only available for Slovenia.

4

The reported summary statistics are based on firm-level observations excluding the top and bottom 5th percentile of values for each indicator to avoid distortions from extreme outliers. Eurostat data presented in Figure 1, suggests that these trends continued in 2015.

5

EBITDA is an approximate measure of operating cash flow. The measure of excessive debt is consistent with empirical results from a threshold analysis of debt-to-EBITDA on investment. The threshold level for debt-to-EBITDA at which the marginal return in terms of more investment begins to diminish is 4 for all firms in Slovenia. See Annex III.

6

With the potential for “sudden stops” during and after a financial crisis, it would be preferable to analyze cash flow to debt service, given the high probability that principal would not be rolled over. However, current data limitations regarding debt repayment profiles for firms prevent calculation of this statistic across countries.

7

Corporate investment is measured as the change in total tangible assets between t and t-1 plus depreciation and amortization over total tangible assets at t-1.

8

Sum of the annual difference of tangible fixed assets and depreciation and amortization. Tangible fixed assets equals fixed assets less intangible fixed assets, e.g., goodwill.

9

Earnings before interest, depreciation, amortization, and taxes. As in Budina and others (2015) and Kalemi-Ozcan and others (2015), the model uses the inverse of the indicator for a debt overhang, i.e., EBITDA over financial debt, as cash flow from operations may be zero or negative.

10

This potentially introduces “survivor” bias in some results. In Slovenia’s case, this may induce an underestimation of the magnitude of the effects of leverage on investment, based on the findings of Vodopivec and Čede (2013). They found that the median leverage increased slightly between 2008 and 2012 for firms with 250+ employees that were still in existence in 2012, while if not accounting for changes in composition, median leverage decreased for these firms.

11

The European Central Bank diffusion index is only available for Slovenia for the entire sample period.

12

Replacing the debt-to-assets ratio with an alternative measure of leverage, i.e., debt-to-equity, in the regression yields qualitatively similar results, though the coefficients are generally smaller. Also the direction of change on the coefficients for debt-to-equity from the pre- to post-crisis periods for the Czech and Slovak Republics is consistent with economic theory, whereas using debt-to-assets it is not.

1

Financial debt equals long-term debt plus current liabilities. Alternatively, it equals total liabilities less other non-current liabilities.

1

The specification for the test for a debt-to-EBITDA threshold replaces the regressor dai,t-1 with overi,t−1−1 (i.e., debt-to-EBITDA) and threshold variable DA* with OVER-1*.

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Cover IMF Staff Country Reports
Republic of Slovenia: Selected Issues
Author:
International Monetary Fund. European Dept.
Volume/Issue:
Volume 2016: Issue 122
Publisher:
International Monetary Fund
ISBN:
9781475556070
ISSN:
1934-7685
Pages:
70
DOI:
https://doi.org/10.5089/9781475556070.002
Figure 1.

Economic Trends and Corporate Financing
Figure 2.

Summary: Firm-level data, 2006–14
Figure 3.

Slovenia, 2006–14
Figure 4.

Czech Republic, 2006–14
Figure 5.

Hungary, 2006–14
Figure 6.

Poland, 2006–14
Figure 7.

Slovakia, 2006–14

Excessive debt (all firms)

(Percent of annual debt > 5x EBITDA)

Interest Coverage Ratio (ICR)

(Percent of micro-sized firms with ICR<1)

Debt Exceeding Debt/Assets Threshold, 2014

(Percent of total by firm size)