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Bulgaria: Selected Issues

Author(s):
International Monetary Fund. European Dept.
Published Date:
May 2015
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Non-Financial Corporate Debt Overhang in Bulgaria1

A. Introduction

1. High debt overhang in the non-financial corporate (NFC) sector can act as a drag on corporate profitability and investment, weighing on banks’ balance sheets through increasing NPLs and heightening risk of corporate bankruptcies. In this context, orderly deleveraging is critical to allow resources to be redirected to productive segments of the economy, while minimizing macro-financial costs, as well as the potential migration of losses from private to sovereign balance sheets.

2. This Selected Issues Paper investigates NFC balance sheets in Bulgaria, testing whether some of these macro-financial channels might be at play given the relatively high indebtedness of its NFC sector. It also discusses available policy tools to promote a smooth deleveraging process.

3. The paper is organized as follows. The first section analyzes the status of firms’ balance sheets in Bulgaria vis-à-vis standard metrics used in the literature and other New Member States (NMS),2 with a view to detecting potential corporate liquidity and solvency risks. Diagnostics are based on aggregated data from Eurostat sector accounts. The second section makes use of firm-level data from the Orbis database to investigate corporate balance sheets at the firm and industry level. This is complemented by an empirical analysis of the drag on investment, and in turn growth, engendered by the increase in corporate leverage in Bulgaria and other NMS. Policy considerations, based on past deleveraging cases, conclude.

B. Bulgaria’s Corporate Balance Sheets in Perspective

4. Bulgaria’s total non-financial sector indebtedness is third highest among NMS—below only Croatia and Slovenia at over 150 percent of GDP on a consolidated basis, as of end-2013.3 This debt is largely concentrated in the non-financial corporate sector, with household and general government debt remaining at relatively low levels, also compared to peers. As a result, NFC debt stood at over 110 percent of GDP in 2013 in Bulgaria, based on Eurostat sector accounts, the highest level among NMS and well-above the Euro area average. Across time, corporate leverage has remained near its pre-crisis peak levels, a feature common to most other NMS. In terms of creditors, NFC debt in Bulgaria is largely in the form of loans, with less of 4 percent of GDP in debt securities, given limited access to capital markets. Among NFC loans, about 45 percent of GDP consists of loans from Bulgarian banks (Section IV), while foreign direct (largely inter-company) loans4 are estimated at roughly 40 percent of GDP, as of end-2013.5

New Member States: Total Non-financial Debt, 2013

(Percent of GDP)

Sources: Eurostat (ESA 2010); and IMF staff calculations.

Note: Data for HU is not available.

New Member States: Non-Financial Corporate Debt

(percent of GDP)

Sources: Eurostat (ESA 2010); and IMF staff calculations.

Historical trends in corporate leverage

5. The origins of Bulgaria’s high corporate indebtedness can be traced back to the pre-crisis period. In the run-up to EU accession (which took place eventually in 2007), Bulgaria experienced large scale foreign capital inflows, mainly in the form of FDI and foreign bank-intermediated loans, attracted by Bulgaria’s relative-price competitiveness and increased prospects for faster income convergence. Abundant liquidity and credit conditions fueled a domestic-demand boom contributing to sustained large current account deficits and the build-up of a significant net external debt. Moreover, private sector credit was increasingly directed to non-tradable sectors, ranging from construction to retail sales, while sizable catch-up increases in wages and prices narrowed Bulgaria’s competitiveness in tradable sectors.6

Bulgaria: Market Conditions and External Imbalances, 2000–13

(Percent of GDP; unless otherwise indicated)

Sources: BNB; Bloomberg; and IMF staff calculation.

New Member States: NFC debt, 2000–13

(percent of GDP)

Sources: Eurostat (ESA 2010); and IMF staff calculations.

6. Against this backdrop, the flow-of-funds identity linking corporate funds’ uses and sources provides a useful reference to understand the main channels of the corporate debt build up (ΔD) in Bulgaria in the pre-accession period:

where I refers to capital investment, ΔFA to the change in net financial assets, IF to the firm’s internal funds arising from its gross savings, and ΔE to the change in equity.

7. In the pre-EU accession period, as presented in the text charts below, firms’ net borrowing in Bulgaria increased significantly. This largely tended to reflect significant increases in investment, against only limited improvements in gross savings. In particular, sizable improvements in gross value added were for a large part offset by increases in operating costs, notably compensation of employees, and net interest payments.

8. Following the onset of the global crisis, firms’ net lending position reversed on a consolidated basis, through a sharp contraction in investment as well as sizable improvements in gross savings. At the same time, companies’ savings performance has been associated with improvements in other factors, including retained dividends and net interest and partly lower wage bills, while gross value added has declined compared to the pre-crisis period.

Bulgaria: Contributions to the change in NFCs Gross Saving

(Billions of euros)

Bulgaria: Contributions to the change in NFCs Net Lending 1/

(Billions of euros)

Sources: Eurostat ESA95; and IMF staff own calculations.

1/ A negative (positive) value corresponds to a decline (increase) in net lending, i.e, an increase (decline) in net borrowing.

Liquidity and Solvency Risk Indicators

9. Alternative measures and relevant thresholds are frequently used in the literature to assess corporate liquidity and solvency risks stemming from NFC balance sheets. These are calculated in this section for the entire non-financial corporate sector by using Eurostat national annual sector accounts.7 However, given the ongoing transition of the European System of Accounts from the ESA95 to the new ESA2010 standards, data for most NMS, including Bulgaria, are only available on an ESA95 basis up to 2012 for most non-financial transaction series.

10. Liquidity risk reflects the potential inability of firms to service their debt obligations out of their current income (i.e., without recourse to additional borrowing). The liquidity risk metrics for Bulgaria and other NMS are constructed by comparing firms’ debt servicing burden to their debt servicing capacity and include the debt-to-income and interest coverage ratio (ICR), respectively. Following Iossifov and Zumer (2014), for the debt-to-income ratio, firms’ debt service capacity is proxied by gross disposable income before interest payments and shareholders’ distributed earnings to avoid double-counting with the numerator and to acknowledge seniority of bondholders’ claims. For the ICR, firms’ debt service capacity is proxied by EBITDA (earnings before interest, taxes, depreciation, and amortization) over interest payments (incl. Financial Intermediation Services Indirectly Measured, FISIM), as it is standard in the literature.

New Member States: Corporate Net Leverage Ratio

(Percent)

Sources: EUROSTAT ESA95 Annual Sector Accounts, quarterly financial accounts, and IMF staff calculations.

Note: Ratio of stock of debt net of holdings of cash, deposits, loans and debt securities to firms’ capital.

New Member States: NFC Interest Coverage Ratio

(Percent)

Sources: EUROSTAT ESA95 Annual Sector Accounts; and IMF staff calculations.

Note: Ratio of gross operating surplus and mixed income to interest payments (incl. FISIM). Data for Romania is not available.

11. While the debt-to-income ratio declined significantly in Bulgaria in the period since 2008 to 2012, it remained at significantly high levels (over 400 percent of income), well above its peers. The ICR points to a relatively comfortable coverage of interest payments by gross operating income, as defined above, with a consolidated ratio of 5 as of end-2012, supported both by a gradual improvement in operating surpluses, although interest costs have remained relatively high compared to the pre-crisis period. While Bulgaria’s aggregate ICR level is above the standard thresholds of 2 or 1 normally used in the literature to identify firms with “debt at risk” (Glen, 2005), it remains one of the lowest among NMS, after Hungary and Slovenia. Moreover, Bulgaria’s corporate balance sheets appear to be sensitive to macro-financial shocks, as evidenced by the marked reduction in ICR experienced by firms during the global crisis, with ICR levels as low as 3 for the entire system.

Bulgaria: Interes Coverage Rate, 2006–12

(Percent)

Sources: EUROSTAT ESA95 Annual Sector Accounts; and IMF staff calculations.

12. Solvency risk measures the potential inability of firms to keep the value of their assets above that of their liabilities. Leverage ratios like debt-to-equity are normally used in the literature to compare the stock of debt to firms’ capital (as proxied by shares and other equity in the Eurostat sector accounts). Moreover, following Iossifov and Zumer (2014), a net leverage ratio can be constructed by deducting from the debt stock firms’ assets (including their holdings of currency and deposits, securities, and loans).

New Member States: NFC Debt-to-Equity Ratio

(Percent)

Sources: EUROSTAT ESA95 Annual Sector Accounts; and IMF staff calculations.

New Member States: NFC Debt-to-Income Ratio

(Percent)

Sources: EUROSTAT ESA95 Annual Sector Accounts; and IMF staff calculations.

Note: Ratio of stock of debt (loans and debt securities) to augmented gross disposable income (before interest payments and before payments to shareholders). Data for Croatia are not available, while data for Romania are for 2011.

13. Bulgaria’s corporate sector presents high gross leverage ratios, at about 85 percent of equity, with no evidence of adjustment in recent years, based on the latest available data for 2012. Nevertheless, it appears less exposed to solvency risks than other NMS, like Croatia, Latvia, and Slovenia. Moreover, the ratio is significantly lower once firms’ assets are accounted for, with a net debt-to-equity ratio of about 45 percent.

C. Macro-Financial Implications of Corporate Leverage

14. This section explores the macro-financial implications of firms’ debt overhang resulting from the way it affects their investment decisions. According to the literature, while a firm’s investment decisions should be completely unaffected by the type of security used to finance it, since the market value of a firm would be independent of its capital structure (Modigliani and Miller, 1985), in presence of market frictions (e.g., asymmetric information between external investors and company managers), firms’ capital structures would increasingly deviate from a well-defined leverage target at least in the short term, with firms favoring internal to external financing, debt to equity (Meyers, 1984). In this context, a firm’s leverage position would matter for its investment decisions. In particular, while financial deepening could help diversify firms’ funding options and boost productivity levels, excess leverage would more than offset these benefits by raising vulnerabilities and amplifying firms’ sensitivity to income and interest shocks (Bernanke and Gertler, 1989).

Empirical literature and methodology

15. The empirical relationship between corporate leverage and investment has been widely tested in the literature, including for European countries. Building on seminal work by Fazzari et al. (1988) and Bernanke et al. (1999), Vermeulen (2000) finds evidence of a financial accelerator effect in Germany, France, Italy, and Spain over the period 1983–1997 showing that weak balance sheets tend to amplify the impact of adverse shocks on firm investment, especially during downturns and for smaller firms. Goretti and Souto (2013) confirm these results using aggregated firm-level data for 21 sectors of activity and eight Euro area countries (Austria, Belgium, France, Germany, Italy, Netherlands, Portugal and Spain) over the post-Euro adoption period, 2000–2010.

16. Building on Goretti and Souto (2013) and earlier empirical work, this section of the paper follows a panel-data approach to test the hypothesis that firms’ investment decisions are indeed affected by their balance sheet positions. The baseline specification for the investment equation is as follows:

The dependent variable IKit is the investment-to-capital ratio of firm i at time t. The debt overhang variable D is in turn proxied by a standard leverage measure, debt to equity, as well as the ICR. The latter is calculated as the ratio of EBITDA to interest payments, as described in Section II. The specification includes the lagged sales-to-capital ratio SK to control for standard sales-accelerator effects.

17. The coefficient δ is the parameter measuring the sensitivity of the investment rate with respect to changes in the debt overhang variable. Rejecting the null hypothesis that the coefficient δ is equal to zero (as suggested by the perfect capital market theory) would indicate that firms’ investment decisions are affected by their balance sheet position. Moreover, the coefficient should present a negative sign if debt overhang is proxied by the debt-to-equity ratio, while the sign should turn positive if the ICR is used instead.

18. Since the specification introduces lags of the dependent variable to control for possible endogeneity, the standard fixed effect estimator would be inconsistent. In order to address this issue while still allowing for a dynamic model, we use the GMM two-step system estimator by Blundell and Bond (1998), applying the STATA module developed by Roodman (2003).

Database and balance-sheet diagnostics

19. For the analysis in this section, we make use of firm-level micro data from the Orbis database by Bureau van Dijck, focusing on companies in Bulgaria and other nine NMS, based on data availability.8 The dataset covers the period 2004–2013, although firms’ coverage improves in more recent years. In the case of Bulgaria, the dataset includes over 200,000 companies in 2013, including a large share of micro and small enterprises (96 percent of total), across different industries (with about 85 percent of companies in non-tradable sectors). The summary table below presents summary statistics across all firms in the dataset for the variables to be used in the econometric specification.

Orbis Dataset: Summary Statistics of Regression Variables
Obs.MeanStd. Dev.25th Perc.Median75th Perc.
IK: Investment to capital ratioNMS2,409,0232.501546.000.000.110.42
Bulgaria154,5501.7932.090.020.140.51
SK: Sales to capital ratioNMS3,256,27835.621640.711.504.9316.33
Bulgaria203,05029.77383.231.413.7211.76
DE: Debt to equity ratioNMS1,482,3132.505.980.200.621.86
Bulgaria209,8312.235.300.200.601.77
ICR: Interest coverage ratioNMS822,04028.1880.743.008.0021.67
Bulgaria166,60014.0736.832.475.4213.00
Sources: Orbis database and staff own calculations.
Sources: Orbis database and staff own calculations.

20. While the granularity of the dataset allows for greater insights at firm and industry level, a word of caution is needed in interpreting results at country level, given the definitional and coverage differences (including a highly unbalanced pane of data, characterized by several gaps, due to firms’ entry/exit and structural breaks in coverage) compared to the aggregate Eurostat national accounts data.9 In particular, in the case of Bulgaria, on average total debt owed by firms in the Orbis sample accounts for about 35 percent of the aggregate corporate-sector debt calculated using sectoral accounts. Accordingly, the dataset rather presents a snapshot of the behavior of selected firms by size and sectors in the region and/or each country.

21. The firms included in the dataset tend to show on average higher leverage levels (debt to equity ratios) over the sample period than those reported in the Eurostat sectoral accounts for non-financial corporations, as presented in the previous section. However, this is subject to large standard deviations, with leverage levels ranging from 20 percent of equity in firms’ lower quartile to up to 170 percent in the upper quartile. The same applies to the Bulgarian firms where the median leverage value of around 60 percent of equity—below Eurostat aggregate levels—hides significant differences at firm-level within the sample. In particular, leverage is on average higher for firms of smaller size as well as operating in the non-tradable sector, notably utilities.

Bulgaria: Firms’ Indebtedness by Size and Sector, 2013

(percent of equity, firms’ average)

Sources: Orbis database; and IMF staff calculations.

Bulgaria: Firms’ Indebtedness by Size, 2013

(percent of equity, firms’ average)

Sources: Orbis database; and IMF staff calculations.

Bulgaria: Firms’ Indebtedness by Sector, 2013

(percent of equity, firms’ average)

Sources: Orbis database; and IMF staff calculations.

22. While interest coverage ratios tend to appear adequate on average, the sizable standard deviations indicate that several firms in the sample are under liquidity pressures. In particular, in 2013, in Bulgaria almost 20 percent of the firms in the sample (accounting for 32 percent of total NFC assets in the sample) had an ICR below the precautionary threshold of 2, used to identify “debt at risk.” Among them, 10 percent of firms had an ICR lower than 1, i.e., they did not generate enough gross operating income to cover their interest burden. Consistently with the higher leverage levels, debt at risk appears concentrated in SMEs (19 percent of total firms). Nevertheless, while a smaller number of large firms present debt at risk, this accounts for a large share (21 percent) of assets in the sample. Firms in the more indebted non-tradable sector are also subject to greater liquidity risks. However, a more in depth analysis by industry shows that manufacturing actually accounts for the bulk of debt at risk (9 percent of assets), followed by utilities and sales, among non-tradables (7 percent of assets each).

Bulgaria: Firms with Debt at Risk by Size and Sector, 2013

(percent of total firms within each category)

Sources: Orbis database; and IMF staff calculations.

Bulgaria: Firms with Debt at Risk by Sector, 2013

(Percent)

Sources: Orbis database; and IMF staff calculations.

Bulgaria: Firms with Debt at Risk by Size, 2013

(Percent)

Sources: Orbis database; and IMF staff calculations.

Econometric results

23. The empirical results for the NMS panel find evidence of a negative sensitivity of firms’ investment-to-capital ratio to corporate debt overhang, as defined above, after controlling for sales performance and lagged investment behavior. The estimated coefficients in the regression are significant and enter with the expected sign, in line with the literature.10 Specifically, higher debt overhang is found to reduce investment in the NMS in the sample, with an impact ranging from 1 to 6 percent depending on whether debt overhang is proxied by higher debt-to-equity leverage or lower capacity to repay (i.e., the perfect capital markets hypothesis that δ is equal to zero is rejected).

NMS: Corporate Debt Overhang and Investment Ratio

D=DED=ICR
Constant1.97***0.44***
IKit-10.07**−0.03***
SKit-1−0.04***0.01***
Dit-1−0.06***0.01***
AR(1) test−1.68*−3.34***
AR(2) test0.99−1.05
Obs.1,800,892686,110
Notes: Dynamic panel data with GMM two-step system estimator. ***, **, * indicate significance at 1, 5, and 10 percent level.
Notes: Dynamic panel data with GMM two-step system estimator. ***, **, * indicate significance at 1, 5, and 10 percent level.

24. Bulgaria-specific results differ significantly depending on the sample period. While the ICR measure is significant throughout 2004–2013, suggesting that a firm’s liquidity risk, as proxied by its capacity to repay, matters for its funding and investment decisions, the same does not seem to apply to the debt-to-equity measure. Interestingly, results become significant once the regression sample is restricted to only consider the post-crisis period. The explanation underpinning these results is likely to be two-fold. On one side, the global crisis has heightened funding constraints, as the ample liquidity and credit appetite of the pre-EU accession years receded (as discussed in Section II). In this context, solvency considerations would become more binding. At the same time, the results are likely affected by the underlying firms’ coverage of the Orbis database, since the number of firms available in the sample nearly doubles after 2010 with a more differentiated composition by both size and sectors.11

BGR: Corporate Debt Overhang and Investment Ratio

D=DED=ICR
Full sample2004-102011-13Full sample2004-102011-13
Constant0.287***0.184***0.369***0.246***0.249***0.223***
IKit-10.034***0.077***−0.0020.033***0.065***0.011**
SKit-10.058***0.012***0.005***0.006***0.010***0.005***
Dit-10.0030.255−0.677***0.003***0.007***0.002***
AR(1) test−11.72***−7.11***−7.91***−10.89***−5.46***−8.66***
AR(2) test3.38***2.19*2.06*2.71**1.452.86**
Hansen test72.62115.0628.0571.6266.7136.88
Obs.75,99530,19145,80468,96818,59750,371
Notes: Dynamic panel data with GMM two-step system estimator. ***, **, * indicate significance at 1, 5, and 10 percent level.
Notes: Dynamic panel data with GMM two-step system estimator. ***, **, * indicate significance at 1, 5, and 10 percent level.

25. Robustness tests by sector and size also provide interesting insights depending on the selected sample period. Before 2010, higher corporate leverage appears to negatively affect with a significant sign only the micro firms in the sample, consistent with expected higher funding constraints. Across industries, the same applies to the utilities sector, which earlier diagnostic tests identified as more leveraged. Results for the post-crisis period show a consistent negative relationship between leverage and investment across sectors and/or by firm’s size. Nevertheless, these results remain subject to the same important caveats highlighted above, also given more limited data availability at each disaggregated level.12

D. Policy Implications and Conclusions

26. Bulgaria’s corporate sector is among the most leveraged among NMS, pointing to important liquidity risks, which could in turn raise solvency concerns in presence of severe macro-financial shocks. Moreover, available data suggest great heterogeneity at the firm level, with a significant number of firms presenting interest coverage levels below precautionary thresholds. With corporate debt accounting for over 55 percent of banks’ domestic loans in Bulgaria, this is mirrored by a sustained high level of corporate NPLs in the country.13

27. The paper also confirms previous evidence in the literature of an important drag on investment and growth engendered by high corporate sector debt overhang. In particular, the empirical results for the NMS sample—based on firm-level data—show evidence of a negative relationship between firms’ investment-to-capital ratios and their debt burdens over the sample period. In Bulgaria, the relationship between investment and leverage is found to hold consistently by firm’s size and across sectors only in the post-crisis period, likely due to the higher liquidity constraints and risk aversion post-crisis, but possibly also to coverage issues in the database.

Bulgaria: Bank loans by creditor

(BGN billion)

Sources: ECB; and IMF staff calculations.

Bulgaria: Non-Performing Loans

(Percent of total loans)

Sources: BNB; and IMF staff calculation.

28. While it is critical to advance corporate deleveraging in highly indebted countries to unlock credit and investment, this process has yet to start in earnest in most NMS, including Bulgaria. Moreover, lessons from other country episodes characterized by sizable corporate adjustment suggest that this process tends to be protracted and, if conducted through a generalized withholding of credit to all firms, can generate significant macro-financial spillovers through heightened risks of corporate bankruptcies and rising NPLs in banks’ balance sheets.14

29. To mitigate these potential costs, the policy mix needs to be supportive of an orderly and efficient adjustment process, aimed at restoring corporate productivity and growth. A self-reinforcing institutional framework needs to be in place to prevent continued build-up of imbalances in specific segments of the economy. As discussed in the Annex, in past and ongoing country experiences this has been supported by a broad range of policy initiatives, including an efficient corporate debt restructuring framework, the promotion of alternative funding sources, as well as macro-prudential tools and tax measures to promote firms’ long-term viability.

Annex I. Cross-Country Policy Initiatives to Support an Orderly Deleveraging Process

While the specific policy toolkit to address corporate debt overhang rests on each country’s specific needs and evolving circumstances, a review of cross-country experiences with corporate deleveraging in the region can offer important lessons on the range of policy tools potentially available to support an orderly and efficient corporate adjustment process, as well as to prevent continued build-up of corporate imbalances and support firms’ long-term viability. A non-exhaustive summary of these policy initiatives is presented below.

  • Corporate restructuring framework. Effective insolvency regimes are pivotal to an orderly deleveraging process, by targeting the re-organization of the financial and operational structure of distressed but still viable firms, as well as the liquidation of non-viable ones. Enforcement and foreclosure processes are also essential to enable an effective realization of collateral in case of debtor distress. Policy approaches to corporate restructuring tend to vary depending on country-specific circumstances and the severity of the problem at hand. In particular, past country initiatives have ranged from government-sponsored market-based models, including the introduction of guidelines for voluntary out-of-court debt workouts in Iceland, Latvia, Portugal and Romania (along the so-called London Approach), 1 to more intrusive government-directed models, such as the establishment of committees with strong powers including binding arbitration (Thailand, Korea) or centralized asset management companies.2 Since the global crisis, Bulgaria has taken steps to strengthen its legal framework for insolvency resolution by amending its corporate insolvency legislation to address deficiencies identified earlier, including by IMF staff, such as limiting the backdating of insolvencies (to three years) and clarifying the rules for the set-off and for avoidance of certain transactions. Nevertheless, judicial bottlenecks to timely and predictable insolvency proceedings remain a concern.3 Voluntary restructuring is not specifically regulated in Bulgaria, although this option is available to the creditors and the debtor. While information on the number of successfully completed restructuring plans or out-of court settlements is not available, the number of insolvency proceedings in Bulgaria has increased significantly in recent years, from 390 in 2011 to 1,339 in 2012, as reported in the latest COFACE Bankruptcy Report.
  • Supervisory activity and banks’ NPL management. Intense supervision remains a central tool to secure banks’ recognition of losses and promote prompt recourse to debt restructuring. Independent AQRs (and stress testing) of banks, and subsequent actions to ensure capital shortfalls are replenished in a timely manner, have proved effective in recent cases, e.g., Spain, in providing the necessary conditions for promoting effective and speedy balance sheet clean-up. These efforts can be complemented by further supervisory actions (e.g., Ireland and Portugal) to ensure banks’ debt recovery and restructuring capacity and processes are adequate to manage NPLs, including with external support by independent workout specialists. In some cases, NPL management can be guided by specific guidelines, as in the recent case of Romania. In Bulgaria, the Bulgarian National Bank (BNB) relies on two macro-prudential capital instruments—a capital conservation buffer of 2.5 percent and the systemic risk buffer of 3 percent—to ensure credit institutions sustain full coverage of NPLs (net of impairments) with own funds exceeding 8 percent in terms of total capital adequacy ratio. In addition, micro-prudential measures have been introduced at the individual banks’ level to address the high NPLs for the affected banks.
  • Targeted tax incentives. In the past, time-bound tax incentives—over 2–3 years—have been introduced by governments (e.g., Thailand, and more recently, Iceland and Latvia) to accelerate corporate debt restructuring. Moreover, targeted tax measures, with limited budget implications, can also help strengthen balance sheets by limiting the distortions resulting from the different tax treatment of debt versus equity. For example, “thin capitalization rules” can be introduced to limit the amount of interest expenditure deductions allowed for over-leveraged firms, while minimizing any undesired impact on capital investment. Allowances for new corporate equity (the so-called ACE) can also be effective in enhancing tax neutrality, while avoiding pro-cyclicality, along recent experiences in Latvia and Italy. Thin capitalization rules apply in Bulgaria, if the company’s liabilities exceed three times its equity.
  • Access to Funding Sources. Continued access to funding sources by viable yet over-indebted firms throughout a deleveraging process is critical to ensure a gradual adjustment and prevent liquidity pressures deteriorating into solvency problems. In some cases, including Italy and Portugal, governments have provided guarantees to special bank credit lines to help alleviate firms’ high credit risk premia and collateral requirements (often associated with banks’ unwillingness to take further NFC assets in their balance sheets in the context of already high corporate leverage). This is especially relevant in the case of SMEs. Nevertheless, past experience (e.g., Korea) has also highlighted how these programs need to be well-targeted (e.g., firms with strong credit scores) and limited in time given the underlying fiscal and moral hazard risks they can generate. Efforts to diversify firms’ funding sources, notably from debt instruments to credit, are also necessary. In this context, country authorities’ efforts to enhance transparency and information sharing on the corporate sector, e.g., through the development of public and private credit bureaus, can support firms’ access to new funding sources by allowing investors to properly assess the credit standing of new potential clients. In Bulgaria, SMEs benefit from equity and debt financial instruments financed by EU funds under different programs. In particular, guarantee schemes are extended by the National Guarantee Fund to banks for loans towards investment projects under the EU Rural Development Program and OP “Development of the Fishery sector”. Moreover, both equity and debt instruments are financed by the JERAMIE initiative under the EU OP “Competitiveness”.
  • Macro-prudential tools. Macro-prudential measures can help secure the health of firms’ balance sheets and prevent the materialization of new imbalances going forward. A broad set of tools has been considered in some countries to avoid build-up of corporate risks in specific niches of the economy. Beyond standard balance sheet tools (in line with the Basel III requirements), sectoral capital requirements, or variable risk weights, have been applied in the past by supervisors to target specific sectors showing signs of exuberance, by requiring banks to hold additional capital buffers.4 While the BNB currently relies on the capital conservation and systemic risk buffers as mentioned above in terms of macro-prudential instruments, sectoral limits and measures under Pillar II could be possibly developed, if warranted, going forward.
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    VermeulenP.2000Business Fixed Investment: Evidence of a Financial Accelerator in Europe. ECB Working Paper No. 37November.

1Prepared by Manuela Goretti. This Selected Issues Paper draws on the analysis and methodologies presented in the forthcoming Spring 2015 REI Thematic Chapter on “Private Sector Indebtedness, Balance Sheet Repair, And The Real Economy” and the IMF Working Paper “Macro-Financial Implications of Corporate (De)Leveraging in the Euro Area Periphery”, WP/13/154.
2Throughout the paper, the New Member States group comprises Bulgaria, Czech Rep., Croatia, Hungary, Poland, and Romania, among non-Euro area members, and Estonia, Latvia, Lithuania, Slovenia, and Slovakia, among Euro-area members.
3The stock of debt is defined as the sum of loans and debt securities.
4Estimates are based on IIP information for direct investment in debt instruments from non-residents, as of end-2013.
5Please refer also to the 2015 EC Country Report for Bulgaria for a discussion of corporate indebtedness and deleveraging.
7National Accounts are compiled in accordance with the European System of Accounts (ESA). Figures are collected and transmitted to Eurostat by the National Statistical Institutes of each EU Member State. The non-financial corporation sector comprises all private and public corporate enterprises that produce goods or provide non-financial services to the market.
8Among the NMS identified earlier, Lithuania is the only country excluded from the analysis due to data gaps.
9The econometric analysis focuses on solvent companies with positive equity and excludes extreme values in the dataset by trimming observations below/above the 1st/99th percentile for each regression variable to reduce the effect of possibly spurious outliers.
10The magnitude of the coefficients is smaller than estimated by Goretti and Souto (2013), ranging from 20 to 30 percent. This can be explained by the different datasets (and definitional differences) as well as the relatively lower leverage levels of the NMS sample compared to the EA periphery one.
11The unbalanced characteristics of the dataset across the sample period are also reflected in weak diagnostic tests.
12Robustness test results are omitted for brevity but available upon request.
13Bad and restructured loans in the NFC sector account for 65 percent of the total, as of end-2014.
14See Pomerleano and Shaw (2005), Mc Kinsey (2012), and Goretti and Souto (2013) for a review of past corporate deleveraging episodes.
2See Liu and Rosenberg (2013) and Laryea (2010) for more details on corporate debt restructuring in past cases.
3See also the EC’s Cooperation and Verification Mechanism Report for a broader discussion of issues in the judicial system in Bulgaria.
4Past examples include the use of higher risk weights on commercial real estate loans in Australia in 2004 and on corporate lending in India in 2005–2006. See also Bank of England (2011) for a broader review of macro-prudential tools.

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