Malta: Selected Issues

International Monetary Fund. European Dept.

doi:10.5089/9781475580082.002.A001

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Malta: Selected Issues

Author: International Monetary Fund. European Dept.

Publication Date:: 21 Feb 2017

eISBN:: 9781475580082

Language:: English

Keywords:: ISCR; CR; firm; price; financial condition; interest expenses; net investment; firms' tendency; medium-sized enterprise; lending amount; Financial statements; Income shocks; Manufacturing; Business enterprises

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Malta: Selected Issues

Abstract

Malta: Selected Issues

The Non-Financial Corporate Sector in Malta: Balance Sheet Vulnerabilities and Impact on Innovation¹

Non-financial corporations’ debt is relatively high in Malta and poses a number of challenges. This paper uses firm-level data to examine the vulnerability of Maltese firms, measured by their exposure and resilience to shocks. In addition, the paper investigates the effect of firms’ balance sheets on investment in innovation. The results indicate that, while the financial health of medium and large firms has improved in recent years, vulnerabilities remain in the construction sector and for small and medium enterprises (SMEs). Furthermore, the analysis finds that firms with weaker balance sheets tend to invest less in innovation, even during “good times”. Policy implications are three-fold: (i) accelerating the restructuring of corporate balance sheets of highly leveraged, but viable firms, (ii) improving the insolvency framework to allow a fast exit of non-viable companies, and (iii) expanding corporate funding options for SMEs including via non-bank financing alternatives.

A. Exposure of Maltese Non-Financial Corporations to Shocks

1. Non-financial corporate leverage is high in Malta compared with European peers. The risk for overleveraged firms is that companies are restricted from further investment and instead concentrate on paying off loans. Reducing private debts becomes more difficult to sustain when the economy slows down. In two alternative measures, Malta debt appears high: Debt-to-Assets is close to 150 percent and debt-to-equity is about 160 percent at end 2015. Following a rapid increase in indebtedness, the deleveraging process started in 2012 with debt-to-assets declining by about 15 percentage points between 2012 and 2015. Still, NFC leverage remains higher than its pre-crisis average.

2. Inter-company loans represent a large share of debt, thereby providing a cushion.² The high reliance on inter-company loans is a dampening factor given their relative stability and limited impact on the domestic banking system. Inter-company lending amounts to 46 percent of NFC total debt and has increased significantly since 2010 when the bank deleveraging process started. Intra-company debt has risen over the years and represents more than half the stock of total inter-company debt. However, it is worth mentioning that the reliance on inter-company loans is very likely to be a unique characteristic of larger corporations compared with smaller local firms with limited external finance options.

3. In this paper, we rely on the Orbis BvD database, which provides a rich source of firm-level data over a reasonable time span. The dataset covers firm-level data for Malta with more than 5,000 firms over the period 2007-2013.³ Despite its appeal, there are a number of limitations, leading to some caution when interpreting the results. Not all the firms report data on all the variables leading to substantial data gaps. Attrition is also important as not the same pool of firms is observed every year. The sample is dominated by SMEs. For the purpose of the study, the financial sector, gambling activities, agriculture, electricity and water utilities are excluded from the sample due to the limited number of firms recorded in these sectors in the database. Overall, the number of firms for which we can compute relevant financial ratios of interest varies between years and peaks at 127 in the year 2012 in the case of the computation of the interest coverage ratio (EBITDA divided by interest expenses) and 828 firms in the case of leverage ratio (total debt divided by assets). Given the limited coverage of firms reporting the number of employees, the breakdown of firms by size is achieved through the sales volume following the standard breakdown of firms by size in the EU: Microenterprises are defined as enterprises whose annual turnover does not exceed EUR 2 million; Small enterprises whose annual turnover does not exceed EUR 10 million; medium-sized enterprises have an annual turnover not exceeding EUR 50 million; large enterprises have an annual turnover exceeding EUR 50 million. Despite some of its limitations, the Orbis dataset mimics broader regularities observed in official register data from the NSO: dominant role of smaller firms, including within sectors.⁴

4. Aggregate measures of leverage mask considerable sectoral and size heterogeneities. Firm-level data (Orbis, BvD) for Malta suggest that the average leverage ratio (measured by firm debt in percent of total assets) is higher for SMEs compared to large firms, in line with previous works (Grima and Vella, 2014; IMF, 2014). Consistent with IMF (2014), firm-level data indicate that the construction sector and real estate exhibit the highest leverage ratios, followed by the manufacturing and the service sector.

5. Profitability is unevenly distributed across firm size and sectors. Return on assets and return on equities are low for smaller and medium-sized firms in Malta as opposed to large firms. This in part may reflect the impact of elevated debt service payments on the profitability of highly leveraged SMEs. Construction, where SMEs are prevalent, has notably a lower return on assets, again consistent with excessive leverage.

6. Weaker firm balance sheets are weighing on banks. Sectors with elevated indebtedness are reporting higher NPLs ratios. Recent data show a concentration of NPL ratios well above the country’s average in the mining and in the construction sectors. The manufacturing sector is also reporting significant NPLs along with the real estate sector. Most of the service sectors show opposite figures, consistent with their limited leverage and relatively higher profitability ratios.

7. One way to assess the exposure of NFCs to risks is through their Interest Coverage Ratio (ICR). The ICR is measured by the ratio between the firms’ earnings before interest, taxes, depreciation and amortization (EBITDA), and the firms’ interest expenses.⁵ Firms whose EBITDA is less than interest payments due (i.e. ICRs of less than 1) are referred as being in “technical default”. In such situations, many of these firms can survive for some time by selling assets to meet their debt obligations, but if their ICRs remain below 1 for a sustained period, they will eventually run out of assets and actual defaults will ensue. A firm with an ICR between 1 and 2 is generally regarded as being at “heightened risk”.

8. There is a considerable heterogeneity in the distribution of the ICR across firms. Micro and small firms present relatively low ICR levels compared with medium-size and large corporations. In terms of sectors, the median firm in the construction sector is seen as already in “technical default” with an ICR dropping from about 2 in 2007 to below 1 in 2012-13. It is worth noting that the time coverage of the dataset does not take into account possible improvements in some sectors (e.g. construction, real estate) after the year 2013, boosted by rising property prices. The manufacturing sector also posts a median ICR which dropped to below 2, suggesting rising risks. The real estate and service sectors experienced a strengthening of ICR values. In the next section, we test for the resilience of the NFC sector to both income and interest shocks to better assess the sources of vulnerabilities.

B. Resilience of Maltese Non-Financial Corporations to Shocks

9. This section tests for the resilience of NFCs in Malta to adverse shocks. Three types of shocks are considered: (i) an interest rate shock; (ii) a profit shock; and (iii) an interest rate-profit combined shock. In this static exercise, we define all three shocks on the basis of end-2013 balance sheets.

Interest rate shock. We use the calculated effective interest rate of each firm at end-2013 (i_eff,t-1), and we apply a 400bp shock, which is broadly consistent with the ECB sensitivity analysis of house prices and interest rate shocks in Malta (see Central Bank of Malta, 2013). In addition, and in line with the importance of intra-company loans in Malta, we assume that only 40 percent of the 2013 debt stock (Debt_t-1) will be rolled-over with a higher interest rate:
$I n t e r e s t e x p e n s e_{i_{-} s h o c k, t} = \frac{i_{e f f, t} + 4}{100} \cdot 0.4 \cdot D e b t_{t - 1} + \frac{i_{e f f, t}}{100} \cdot 0.6 \cdot D e b t_{t - 1}$
The ICR in the interest rate shock scenario is then given by:⁶
$I C R_{i_{-} s h o c k, t} = \frac{E B I T D A_{t}}{I n t e r e s t e x p e n s e_{i_{-} s h o c k, t}}$
Profit shock. This scenario simulates an economic downturn, leading to lower profitability. Lower profits are derived by applying a negative shock to the firms’ added value (15 percent, which is about five times the decline in NFC nominal value added observed in 2009), while holding the costs of employees constant at their baseline level. The rigidity in the costs reflects firms’ tendency to hoard labor in the short run at least until the magnitude and length of the shock become clearer. The ICR in this scenario is then given by:
$\begin{matrix} E B I T D A_{p r o f i t_{-} s h o c k, t} = A d d e d v a l u e_{t} - C o s t s o f e m p l o y e e s_{t} \\ \begin{matrix} = A d d e d v a l u e_{t - 1} * 0.85 - C o s t s o f e m p l o y e e s_{t - 1} \\ I C R_{{p r o f i t}_{-} s h o c k, t} = \frac{E B I T D A_{p r o f i t_{-} s h o c k, t}}{I n t e r e s t e x p e n s e_{t}} \end{matrix} \end{matrix}$
Combined interest rate and profit shock. This shock combines the two shocks that are discussed above to affect the numerator and the denominator. The ICR in this shock is given by:
$\begin{matrix} I C R_{{c o m b i n e d}_{-} s h o c k, t} = \frac{E B I T D A_{p r o f i t_{-} s h o c k, t}}{I n t e r e s t e x p e n s e_{i_{-} s h o c k, t}} \end{matrix}$

10. The sensitivity analysis suggests that Maltese corporations, particularly SMEs, are vulnerable to adverse macroeconomic changes. In particular:

A 400bp interest rate shock would push the median SMEs’ ICR from 3 in the baseline to just above 2. For large firms, the shock lowers the median ICR but it remains well above the risk thresholds. This can suggest the limited vulnerability of large firms either due to their relatively low leverage and the liquidity of their assets. The most vulnerable group therefore appears to be SMEs, reflecting their high leverage and debt service payments. In terms of sectors, the interest rate shock would push the median ICR in the manufacturing sector to the “heightened risk” category (ICR between 1 and 2). The service sector seems to withstand such a shock, whereas the decline in the ICR is substantial for the real estate sector.
A profit shock would push the ICR of the median SMEs down to below 2, suggesting the severity of the shock for the balance sheet of SMEs compared to the interest rate shock. For large corporations, the profit shock has a similar effect on the median ICR than the interest shock, suggesting the importance of income shocks on the Maltese economy. In terms of sectors, the income shock has a smaller effect on the real estate sector compared with the interest rate shock, suggesting elevated leverage in the sector. Interestingly, the shock pushes the median firm in the service sector very close to the “heightened risk” status with an ICR at just 2. This suggests the key role of income and profitability shocks on the service sector despite its moderate leverage.
Finally, a combined shock of tighter financial conditions and lower growth would have a sizable impact on firms’ balance sheets, and thus likely to push many firms into a vulnerable situation. In particular, in the combined shock scenario, the median ICR for SMEs decreases to levels close to “technical default” whereas large firms remain able to withstand the shocks. Importantly, all sectors (except the real estate) will fall into the “heightened risk” category in presence of a combined income and interest shocks, although the construction sector and to some extent the manufacturing will be in “technical default” under the combined shock scenario.

C. Firms’ Leverage and Investment in Innovation

11. We investigate the effect of leverage on firm decisions on investment in innovation. The Orbis database provides rich data on the breakdown into tangible and intangible fixed assets. Due to limited data coverage on firm R&D spending levels, changes in intangible fixed assets provide a reasonable proxy for innovation, though intangible assets are still subject to measurement challenges.⁷ We follow previous works on the determinants of firm investment using micro data (IMF, 2016) and fit the percent change in intangible fixed assets on firm leverage and other characteristics. Conditional on other factors that explain firm investment, a higher leverage is expected to be negatively associated with investment:

(\frac{I_{i s, t}}{K_{i s, t - 1}}) = β_{1} (\frac{D E B T}{A S S E T S})_{i s, t - 1} + β_{2} (S A L E S E G R O)_{i s, t} + {X^{'}}_{i s, t - 1} Θ + α_{i} + α_{s, t} + \in_{i s, t}

Here, I is the firm’s net investment in intangible fixed assets, K its intangible fixed assets, DEBT its total debt, ASSETS its total assets, SALESGRO its real sales growth (a proxy for firm-specific demand shocks).⁸ All ratios and growth rates are expressed in percent. The vector X includes the natural log of total assets (following Kalemli-Ozcan and others, 2015). i indexes firms and t indexes years. Each firm belongs to a particular sector s. αi denotes the set of firm-specific effects which capture time-invariant unobservable factors at the firm level, while α_x,t denotes the set of sector-year-specific fixed effects that capture common shocks to firms belonging to the same sector in a given year. The latter set of fixed effects helps control for aggregate sectoral demand or policy-induced shocks, as well cross-sectional dependence between firms.

12. The results show that weaker firm balance sheets are associated with lower investment in innovation. The effect of elevated firm leverage on investment in innovation is negative and statistically significant. On average, net investment in intangible fixed assets is 6 percentage points lower for a 10 percentage points exogeneous rise in leverage. The findings that pre-existing leverage has a negative effect on investment is consistent with the previous empirical literature (such as IMF, 2016; Kalemi-Ozcan and others, 2015, and Lang and others, 1996), although the estimated effect here on intangible fixed assets is much larger in absolute terms. The results are robust to controlling for the maturity structure of firm debt (measured here by the share of long-term debt in total debt). Similar qualitative results are found when we replace the leverage ratio by the indicator of firm financial stress (the ICR). The results show (despite a much smaller regression sample) that a higher ICR (lower stress) is associated with a higher investment in intangibles.

13. Higher leverage and lower debt servicing capacity are associated with a lower investment response to demand (real sales growth). The chart below shows how the marginal effect of sales growth depends on the firm’s leverage: high leverage weakens the firm’s investment response to demand growth. Going from the 25^th percentile to the 90^th percentile of firm leverage (from about 13 percent to 66 percent leverage) is associated with a 3.5 percentage points lower investment response to a 10 percentage point rise in sales growth. We also find that lower financial stress (higher ICR) helps increase the reaction of investment to positive demand shocks. Both of these findings are consistent with the financial constraints hypothesis and previous work (IMF, 2016); more highly leveraged firms are less able to respond to demand shocks.

D. Conclusions

14. Maltese SMEs, particularly in construction and manufacturing, appear vulnerable to shocks. In this paper, we showed that (i) smaller firms are in general heavily leveraged, less profitable and less resilient to shocks; (ii) vulnerabilities in the construction and manufacturing are elevated. The analysis also indicates that higher leverage weighs on firms’ investment (in innovation) and makes investment less responsive to positive demand shocks. Policies to promote speedier restructuring of corporate balance sheets for distressed, but viable firms, while facilitating a fast exit for unviable cases would enhance the resilience of SMEs to shocks.

15. Supporting firm growth, including through enhanced innovation activity, would also strengthen resilience to shocks. The results indicate that innovation is significantly lower among SMEs, reflecting in part their weak financial health. Strengthening the balance sheets of weak, but viable, firms, while expanding SMEs’ non-bank and equity financing options would help ease financing constraints for R&D activities. Additional ways to boost innovation could include enhanced collaboration with the academia, higher internationalization of SMEs, and greater direct public sector’s support, particularly given that existing tax incentives are less effective for small firms that lack sufficient funds to invest in R&D.

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Prepared by Christian Ebeke (EUR).

Inter-company debt refers to debt between all companies. Intra-company debt refers to debt between companies of a same group.

The time coverage of the dataset will not take into account possible improvements in some sectors (e.g. construction, real estate) over the most recent years.

https://nso.gov.mt/en/News_Releases/View_by_Unit/Unit_B4/Business_Registers/Pages/Business-Demographics.aspx

The Orbis dataset has a good coverage of firm debt level but limited information on interest expenses making the computation of the ICR challenging. To deal with large missing values on the ICR, we proceeded in two steps. First we estimated the interest expenses for the missing cells using the average effective rate (interest expenses over lagged debt) at the firm size-time level. Then we used it to approximate the missing interest expenses and then the ICR. A number of procedures were applied to deal with outliers in the dataset (e.g. ICR values were bounded at 0 and 50). Only observations on which the information on EBITDA, Debt, interest expenses, and cost of employees were kept to compute the ICR and produce the shock analyses. We end up with more than 700 observations for the ICRs over the period 2007-2013.

We adjusted the computed ICR values to 0 if EBITDA is negative and to 50 if the observed ICR is above this value.

Among other, intangible assets include patent, trademarks, business methodologies.

The sample spans the years 2007-2013 and covers 192 firms (and about thousand observations) for which we have information on investment in intangible assets and other variables. Investment in intangible fixed assets is measured by the annual increase in the stock of intangible fixed assets over lagged stock of intangible fixed assets. We removed outliers in line with Cleary (1999). The cutoff values are 200 and -200 for investment/net fixed asset, 100 and -100 for real sales growth, and 100 and 0 for leverage.

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