Abstract
The Selected Issues paper describes the nexus between household wealth, saving, and consumption, and provides estimates for the medium-term path of household saving and consumption. The paper also discusses to what extent the credit market frictions are holding back Ireland's economic recovery. Under current macroeconomic assumptions, the savings rate is expected to decline. Households have rapidly accumulated debt during boom times, and incomes and asset values have declined severely during the crisis. The Executive Board welcomes the country’s efforts toward economic recovery.
II. Access to credit, debt overhang, and Economic Recovery: The Irish Case1
A. Introduction
1. After a prolonged boom, particularly in property-related and financial sector activities, bank credit has been falling steadily, raising concerns of a credit crunch.2 A credit crunch is a reduction in the general availability of credit or a sudden tightening of the conditions required to obtain credit. There are several reasons why lenders might curtail lending. Supply shocks include (i) a capital shortfall, reducing lending capacity when raising capital is costly; (ii) an increase in collateral requirements, resulting from a decline in the value of the collateral used by the banks to secure the loans; and (iii) increased uncertainty about borrowers’ creditworthiness, resulting in an increase in asymmetric information between borrower and lender. Demand shocks include an increase in aggregate uncertainty and a reduction in aggregate demand such as those arising from tighter monetary conditions or a reduction in asset prices and net wealth.
2. This chapter will assess to what extent credit market frictions are holding back the economic recovery. This will include an analysis of whether the decline in credit is primarily driven by supply or demand factors and an analysis of the significance of debt overhang. The emphasis of the analysis is on small-and medium-sized enterprises (SME) which play an important role in the Irish economy and tend to be financially constrained. Access to finance of households will also be analyzed, since they have much in common with SMEs: both are numerous—complicating debt restructuring—and both carry substantial real estate assets with funds borrowed from banks. Moreover, many new firms grow out of households, starting as sole traders with initial investments derived from home equity.
3. The analysis concludes with an assessment of government policy to improve SME access to finance. This will include options to increase the availability of bank financing, such as through credit guarantees and enhancements. The chapter proceeds as follows. Section B analyzes the overall credit conditions for firms and households. Section C assesses whether credit is primarily driven by supply or demand factors. Section D discusses several options to improve access to finance for SMEs. Section E concludes.
B. Overall Credit Conditions
4. Credit has been falling steadily, following a prolonged credit boom. Ireland’s credit boom was large even compared to that in other recent banking crises. A relaxation in credit standards led to excessive risk taking by banks.3 Credit to the private sector continues to contract at stable rates, with credit to households contracting the most.
5. Banks’ cross-border exposures have contracted more rapidly than their domestic loan portfolios. Cross-border lending by Irish credit institutions (primarily to the U.K.) has fallen from a peak of 150 percent of GDP to just above 80 percent of GDP. The decline in private credit from the domestic banking sector has been much slower than the deleveraging of banks’ credit exposures abroad, indicating that the impact of the deleveraging process is disproportionally falling abroad, consistent with the design of the deleveraging plans of the intervened banks.
Real Private Credit, 2000-2011 1/
(2000 = 100)
Citation: IMF Staff Country Reports 2012, 265; 10.5089/9781475510461.002.A002
Source: IMF’s International Financial Statistics database.1/ Real private credit is credit outstanding to the private sector adjusted for CPI inflation. Year 2000 values are set at 100. Systemic banking crises include ongoing systemic banking crises in Western Europe (Austria, Belgium, Germany, Greece, Iceland, Netherlands, Spain, and United Kingdom), with systemic banking crises as defined in Laeven and Valencia (2012). Borderline crisis cases, as defined as in Laeven and Valencia (2012), include France, Italy, Portugal, and Switzerland. Denmark and Luxembourg are excluded due to missing data. Country averages.Real Private Credit, 2000-2011 1/
(2000 = 100)
Citation: IMF Staff Country Reports 2012, 265; 10.5089/9781475510461.002.A002
Source: IMF’s International Financial Statistics database.1/ Real private credit is credit outstanding to the private sector adjusted for CPI inflation. Year 2000 values are set at 100. Systemic banking crises include ongoing systemic banking crises in Western Europe (Austria, Belgium, Germany, Greece, Iceland, Netherlands, Spain, and United Kingdom), with systemic banking crises as defined in Laeven and Valencia (2012). Borderline crisis cases, as defined as in Laeven and Valencia (2012), include France, Italy, Portugal, and Switzerland. Denmark and Luxembourg are excluded due to missing data. Country averages.Real Private Credit, 2000-2011 1/
(2000 = 100)
Citation: IMF Staff Country Reports 2012, 265; 10.5089/9781475510461.002.A002
Source: IMF’s International Financial Statistics database.1/ Real private credit is credit outstanding to the private sector adjusted for CPI inflation. Year 2000 values are set at 100. Systemic banking crises include ongoing systemic banking crises in Western Europe (Austria, Belgium, Germany, Greece, Iceland, Netherlands, Spain, and United Kingdom), with systemic banking crises as defined in Laeven and Valencia (2012). Borderline crisis cases, as defined as in Laeven and Valencia (2012), include France, Italy, Portugal, and Switzerland. Denmark and Luxembourg are excluded due to missing data. Country averages.
Foreign claims and private credit of Irish banks, 2004-2011
(Percent of GDP)
Citation: IMF Staff Country Reports 2012, 265; 10.5089/9781475510461.002.A002
Sources: CBI and CSO.1/ Foreign (non-resident) claims by domestic Irish banks on an ultimate risk basis by, where domestic Irish banks are defined as those banks guaranteed by the Irish Government under the Credit Institutions (Eligible Liabilities Guarantee) Scheme 2009.2/ Domestic credit extended to the private sector.Foreign claims and private credit of Irish banks, 2004-2011
(Percent of GDP)
Citation: IMF Staff Country Reports 2012, 265; 10.5089/9781475510461.002.A002
Sources: CBI and CSO.1/ Foreign (non-resident) claims by domestic Irish banks on an ultimate risk basis by, where domestic Irish banks are defined as those banks guaranteed by the Irish Government under the Credit Institutions (Eligible Liabilities Guarantee) Scheme 2009.2/ Domestic credit extended to the private sector.Foreign claims and private credit of Irish banks, 2004-2011
(Percent of GDP)
Citation: IMF Staff Country Reports 2012, 265; 10.5089/9781475510461.002.A002
Sources: CBI and CSO.1/ Foreign (non-resident) claims by domestic Irish banks on an ultimate risk basis by, where domestic Irish banks are defined as those banks guaranteed by the Irish Government under the Credit Institutions (Eligible Liabilities Guarantee) Scheme 2009.2/ Domestic credit extended to the private sector.6. The credit squeeze has mostly affected property-related sectors and loans for house purchases. Among businesses, loans to property-related sectors have been particularly hard hit, against a backdrop of falling house prices and overcapacity of newly constructed properties for commercial and residential real estate, although a significant part of this decline reflects the transfer of €74 billion in large distressed property development and commercial real estate assets from banks to the National Asset Management Agency during 2010. Credit to households for house purchases and credit to corporations in non-property sectors has also fallen, although to a smaller extent, and many have argued that this decline is evidence of a credit crunch driven by supply factors.4
Loans Outstanding to Irish Residents by Sector 1/
(Billions of euros)
Citation: IMF Staff Country Reports 2012, 265; 10.5089/9781475510461.002.A002
Source: Central Bank of Ireland.1/ All resident credit institutions, excluding financial intermediation.2/ Credit to Irish resident private-sector enterprises in property-related sectors, including construction, hotels and restaurants, and real estate, land and development activities.3/ Credit to Irish resident private households for house purchase.Loans Outstanding to Irish Residents by Sector 1/
(Billions of euros)
Citation: IMF Staff Country Reports 2012, 265; 10.5089/9781475510461.002.A002
Source: Central Bank of Ireland.1/ All resident credit institutions, excluding financial intermediation.2/ Credit to Irish resident private-sector enterprises in property-related sectors, including construction, hotels and restaurants, and real estate, land and development activities.3/ Credit to Irish resident private households for house purchase.Loans Outstanding to Irish Residents by Sector 1/
(Billions of euros)
Citation: IMF Staff Country Reports 2012, 265; 10.5089/9781475510461.002.A002
Source: Central Bank of Ireland.1/ All resident credit institutions, excluding financial intermediation.2/ Credit to Irish resident private-sector enterprises in property-related sectors, including construction, hotels and restaurants, and real estate, land and development activities.3/ Credit to Irish resident private households for house purchase.Corporate sector
7. SME credit is flat and new lending has slowed further. New lending to SMEs is running at about half the amount prior to the crisis. While SMEs account for much of employment and economic activity,5 their reliance on bank financing is relatively modest (excluding their significant real estate related investments) and has been curtailed from already low levels since the crisis.
Outstanding SME Credit 1/
(Billions of euros)
Citation: IMF Staff Country Reports 2012, 265; 10.5089/9781475510461.002.A002
Source: Central Bank of Ireland.1/ All resident credit institutions, excluding real estate and financial intermediation.Outstanding SME Credit 1/
(Billions of euros)
Citation: IMF Staff Country Reports 2012, 265; 10.5089/9781475510461.002.A002
Source: Central Bank of Ireland.1/ All resident credit institutions, excluding real estate and financial intermediation.Outstanding SME Credit 1/
(Billions of euros)
Citation: IMF Staff Country Reports 2012, 265; 10.5089/9781475510461.002.A002
Source: Central Bank of Ireland.1/ All resident credit institutions, excluding real estate and financial intermediation.8. With bank financing curtailed, firms increasingly resort to internal sources of finance. Firms have been borrowing less from banks, against a backdrop of increased borrowing costs and reduced growth opportunities. Bank debt has been replaced mainly by internal equity (including retained earnings). Trade credit has been stable throughout crisis and debt securities play a minor role as most firms do not have access to market financing.
9. As a result, financial leverage of firms has decreased. Firm leverage has come down, restoring corporate balance sheets. According to national financial accounts data, the ratio of financial liabilities to equity reached a peak of 1.8 in March 2008, and then fell to 0.85 in December 2011. This pattern can also be seen in firm-level financial statements data which unlike national financial accounts are not biased by borrowing by multinationals and other large firms. Leverage for the median firm (which is a small firm) has fallen to 46 percent of equity, with the usage of bank debt showing a similar decline. The data also indicate that trade credit and other non-debt liabilities play an important role in the financing of SMEs, together with internal financing from retained earnings.
Financial leverage of non-financial companies 1/
Citation: IMF Staff Country Reports 2012, 265; 10.5089/9781475510461.002.A002
Source: Amadeus database, Bureau Van Dijk.1/ Median value across firms. Sample of 3,599 non-financial firms.2/ Ratio of total liabilities (excl. equity) to total equity.3/ Ratio of debt (bank loans and bonds) to total equity.Financial leverage of non-financial companies 1/
Citation: IMF Staff Country Reports 2012, 265; 10.5089/9781475510461.002.A002
Source: Amadeus database, Bureau Van Dijk.1/ Median value across firms. Sample of 3,599 non-financial firms.2/ Ratio of total liabilities (excl. equity) to total equity.3/ Ratio of debt (bank loans and bonds) to total equity.Financial leverage of non-financial companies 1/
Citation: IMF Staff Country Reports 2012, 265; 10.5089/9781475510461.002.A002
Source: Amadeus database, Bureau Van Dijk.1/ Median value across firms. Sample of 3,599 non-financial firms.2/ Ratio of total liabilities (excl. equity) to total equity.3/ Ratio of debt (bank loans and bonds) to total equity.10. Despite an overall decline in corporate debt, an increasing number of firms are facing difficulties covering interest payments on debt. Interest coverage ratios have declined, with the interest coverage for the median firm having decreased from 6.9 in 2002 to 0.8 in 2009, and with an increasing number of firms not generating sufficient income to cover interest payments on outstanding debt. The interest coverage of construction and real estate firms has deteriorated more sharply than that of other firms. Moreover, the interest coverage is markedly lower for SMEs, with a median of 0.8 compared to 1.9 for large firms. The decline in firm profitability associated with depressed demand is playing an important role in the reduction in interest coverage ratio. This suggests that financing constraints are particularly important among SMEs and in property-related sectors.
Interest coverage of non-financial companies 1/
Citation: IMF Staff Country Reports 2012, 265; 10.5089/9781475510461.002.A002
Source: Amadeus database, Bureau Van Dijk.1/ Ratio of earnings before interest and taxes (EBIT) to interest expenses. Median value across firms. Sample of 3,599 non-financial firms.Interest coverage of non-financial companies 1/
Citation: IMF Staff Country Reports 2012, 265; 10.5089/9781475510461.002.A002
Source: Amadeus database, Bureau Van Dijk.1/ Ratio of earnings before interest and taxes (EBIT) to interest expenses. Median value across firms. Sample of 3,599 non-financial firms.Interest coverage of non-financial companies 1/
Citation: IMF Staff Country Reports 2012, 265; 10.5089/9781475510461.002.A002
Source: Amadeus database, Bureau Van Dijk.1/ Ratio of earnings before interest and taxes (EBIT) to interest expenses. Median value across firms. Sample of 3,599 non-financial firms.11. To gauge the relevance of financing constraints for SMEs, a standard dynamic investment model is estimated. Following Bond and Meghir (1994), a dynamic investment model with financial factors and debt is estimated as
where I/K is the ratio of firm investment to fixed capital, S is net sales, CF is operating cash flow, D is total debt, i denotes firm i, and t denotes year t. The sample consists of manufacturing firms only covering the period 2002 to 2010.
12. The investment regression is estimated using the Arellano-Bond (1991) one-step GMM estimator for models with lagged dependent variables and fixed effects. All possible levels of the endogenous investment variable are used to form instruments for the difference equation and the first-difference of each exogenous variable is used to form instruments for the level equation. All regressions include firm and year-fixed effects.
13. Regressions are estimated separately for small versus large firms, with differential effects for the crisis period (Table 1). The first regression restricts the sample to unconstrained firms, defined as firms with total assets in a given year in the top seven deciles of the annual asset size distribution, while the remaining two regressions restrict the sample to constrained firms, defined as firms with total assets in a given year in the bottom three of the annual asset size distribution. The final regression also includes an interaction between the ratio of cash flow to fixed assets and a banking crisis dummy variable that takes a value of one for the years 2008 onwards and zero otherwise.
Financial Constraints, Firm Size, and Banking Crisis, 2002–10 1/
Regressions estimated using the Arellano-Bond (1991) one-step GMM estimator for models with lagged dependent variables and fixed effects. Regression in column (1) restricts sample to unconstrained firms, defined as firms with total assets in a given year in the top seven deciles of the annual asset size distribution, while the regressions in columns (2) and (3) restrict sample to constrained firms, defined as firms with total assets in a given year in the bottom three of the annual asset size distribution. Regression in column (3) also includes an interaction between the ratio of cash flow to fixed assets and a banking crisis dummy variable that takes a value of one for the years 2008 onwards and zero otherwise. Regressions include year-fixed effects. Standard errors are reported between brackets. ***, **, and * denote significance at the 1%, 5%, and 10% level, respectively.
Financial Constraints, Firm Size, and Banking Crisis, 2002–10 1/
| Dependent variable: I/Kit | (1) Unconstrained firms | (2) Constrained firms | (3) Constrained and Crisis |
|---|---|---|---|
| I/Kit–1 | 0.228*** | -0.187** | -0.187** |
| (0.0577) | (0.0891) | (0.0893) | |
| -0.102 | 0.0758 | 0.0773 | |
| (0.0798) | (0.154) | (0.155) | |
| S/Kit–1 | -0.00049 | -0.00271 | -0.00278 |
| (0.00125) | (0.00182) | (0.00184) | |
| CF/Kit–1 | 0.0261* | 0.0510** | 0.0498** |
| (0.0141) | (0.0205) | (0.0212) | |
| -0.0000515 | 0.000175 | 0.000183 | |
| (0.000128) | (0.000174) | (0.000178) | |
| CF/Kit–1 × Crisist-1 | 0.00579 | ||
| (0.0297) | |||
| Number of observations | 903 | 235 | 235 |
| Number of firms | 293 | 99 | 99 |
| Arellano-Bond test for zero autocorrelation in first-differenced errors (H0: no autocorrelation): | |||
| —First-order (p-value) | 0.00 | 0.00 | 0.00 |
| —Second-order (p-value) | 0.70 | 0.69 | 0.69 |
| Sargan test of overidentifying restrictions (H0: overidentifying restrictions are valid): | |||
| —Chi-squared (p-value) | 0.23 | 0.10 | 0.10 |
Regressions estimated using the Arellano-Bond (1991) one-step GMM estimator for models with lagged dependent variables and fixed effects. Regression in column (1) restricts sample to unconstrained firms, defined as firms with total assets in a given year in the top seven deciles of the annual asset size distribution, while the regressions in columns (2) and (3) restrict sample to constrained firms, defined as firms with total assets in a given year in the bottom three of the annual asset size distribution. Regression in column (3) also includes an interaction between the ratio of cash flow to fixed assets and a banking crisis dummy variable that takes a value of one for the years 2008 onwards and zero otherwise. Regressions include year-fixed effects. Standard errors are reported between brackets. ***, **, and * denote significance at the 1%, 5%, and 10% level, respectively.
Financial Constraints, Firm Size, and Banking Crisis, 2002–10 1/
| Dependent variable: I/Kit | (1) Unconstrained firms | (2) Constrained firms | (3) Constrained and Crisis |
|---|---|---|---|
| I/Kit–1 | 0.228*** | -0.187** | -0.187** |
| (0.0577) | (0.0891) | (0.0893) | |
| -0.102 | 0.0758 | 0.0773 | |
| (0.0798) | (0.154) | (0.155) | |
| S/Kit–1 | -0.00049 | -0.00271 | -0.00278 |
| (0.00125) | (0.00182) | (0.00184) | |
| CF/Kit–1 | 0.0261* | 0.0510** | 0.0498** |
| (0.0141) | (0.0205) | (0.0212) | |
| -0.0000515 | 0.000175 | 0.000183 | |
| (0.000128) | (0.000174) | (0.000178) | |
| CF/Kit–1 × Crisist-1 | 0.00579 | ||
| (0.0297) | |||
| Number of observations | 903 | 235 | 235 |
| Number of firms | 293 | 99 | 99 |
| Arellano-Bond test for zero autocorrelation in first-differenced errors (H0: no autocorrelation): | |||
| —First-order (p-value) | 0.00 | 0.00 | 0.00 |
| —Second-order (p-value) | 0.70 | 0.69 | 0.69 |
| Sargan test of overidentifying restrictions (H0: overidentifying restrictions are valid): | |||
| —Chi-squared (p-value) | 0.23 | 0.10 | 0.10 |
Regressions estimated using the Arellano-Bond (1991) one-step GMM estimator for models with lagged dependent variables and fixed effects. Regression in column (1) restricts sample to unconstrained firms, defined as firms with total assets in a given year in the top seven deciles of the annual asset size distribution, while the regressions in columns (2) and (3) restrict sample to constrained firms, defined as firms with total assets in a given year in the bottom three of the annual asset size distribution. Regression in column (3) also includes an interaction between the ratio of cash flow to fixed assets and a banking crisis dummy variable that takes a value of one for the years 2008 onwards and zero otherwise. Regressions include year-fixed effects. Standard errors are reported between brackets. ***, **, and * denote significance at the 1%, 5%, and 10% level, respectively.
14. Estimates based on this dynamic investment model show that small firms are particularly financially constrained but not more so during the crisis. Estimates of the dynamic investment model with financial factors and debt suggests that financial factors such as cash flow play an important role in the investment decisions of especially small firms (Table 1). This suggests that small firms in Ireland are financially constrained. Differential estimates for the crisis period since 2008 indicate that these financing constraints for small firms have not become more severe during the crisis. These results suggest that the decline in investment by especially small firms during the crisis is primarily driven by demand factors.
15. Small firms finance reduced investment predominantly with internal finance. Firm level data also indicate that firm indebtedness came down since the crisis. At the same time, investment and operating cash flow also shrunk markedly, reflecting lower aggregate demand (Table 2).
Firm investment, sales, cash flow, and debt: 2002-2010 1/
(median values across firms)
I/K is investment to fixed capital, S is net sales, CF is operating cash flow, and D is total debt. Median values across firms. Sample of 392 manufacturing firms.
Firm investment, sales, cash flow, and debt: 2002-2010 1/
(median values across firms)
| Year | I/K | S/K | CF/K | D/K |
|---|---|---|---|---|
| 2002 | 0.066 | 4.657 | 0.173 | 1.435 |
| 2003 | -0.013 | 4.459 | 0.180 | 1.364 |
| 2004 | -0.043 | 4.604 | 0.236 | 1.450 |
| 2005 | 0.072 | 5.263 | 0.344 | 1.468 |
| 2006 | 0.109 | 6.126 | 0.347 | 1.655 |
| 2007 | 0.108 | 5.929 | 0.375 | 1.754 |
| 2008 | 0.091 | 5.601 | 0.243 | 1.542 |
| 2009 | 0.064 | 4.805 | 0.167 | 1.292 |
I/K is investment to fixed capital, S is net sales, CF is operating cash flow, and D is total debt. Median values across firms. Sample of 392 manufacturing firms.
Firm investment, sales, cash flow, and debt: 2002-2010 1/
(median values across firms)
| Year | I/K | S/K | CF/K | D/K |
|---|---|---|---|---|
| 2002 | 0.066 | 4.657 | 0.173 | 1.435 |
| 2003 | -0.013 | 4.459 | 0.180 | 1.364 |
| 2004 | -0.043 | 4.604 | 0.236 | 1.450 |
| 2005 | 0.072 | 5.263 | 0.344 | 1.468 |
| 2006 | 0.109 | 6.126 | 0.347 | 1.655 |
| 2007 | 0.108 | 5.929 | 0.375 | 1.754 |
| 2008 | 0.091 | 5.601 | 0.243 | 1.542 |
| 2009 | 0.064 | 4.805 | 0.167 | 1.292 |
I/K is investment to fixed capital, S is net sales, CF is operating cash flow, and D is total debt. Median values across firms. Sample of 392 manufacturing firms.
16. Survey data show that lack of demand for products, not access to finance, is the most pressing problem for SMEs. Results from the Survey on the Access to Finance of small and medium-sized Enterprises (SAFE) show that the inability to find customers is the most pressing problem for SMEs. Access to finance follows as the second most pressing problem. Access to finance is particularly problematic for SMEs and comparable to other euro area economies and has deteriorated somewhat since the previous time the survey was conducted. Rejection rates on loan applications have somewhat improved since the previous survey and are only marginally worse than in the rest of the euro area, although other studies point to higher rejection rates when including loan overdraft applications and using different samples.6 At the same time, a survey by Mazars (2012) finds that reduced or impaired collateral values have a negative impact on the ability for SMEs to obtain bank financing. At the same time, a survey by Mazars (2012) finds that reduced or impaired collateral values have a negative impact on the ability for SMEs to obtain bank financing. Overall, these survey results suggest that demand factors play an important role in the lack of credit for SMEs, although collateral constraints and balance sheet distress from property exposures may also be undermining the availability of credit.
Access to finance of SMEs, April 2011 to March 2012
Access to finance of SMEs, April 2011 to March 2012
| Most pressing problem (%) | Loan application in past 6 months (%) | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Country | Firm type | Period | Number of firms | Finding customers | Competition | Access to finance | Production/labor costs | Skilled staff | Regulation | Applied but was rejected |
| Ireland | SMEs | Oct 2011 – Mar 2012 | 485 | 35.3 | 12.5 | 24.7 | 10.1 | 5.9 | 5.1 | 17.1 |
| Ireland | Large | Oct 2011 – Mar 2012 | 15 | 34.0 | 33.6 | 2.8 | 12.2 | 2.8 | 12.2 | 0.0 |
| Euro area | SMEs | Oct 2011 – Mar 2012 | 6969 | 26.9 | 12.5 | 17.3 | 13.8 | 13.7 | 7.4 | 13.2 |
| Ireland | SMEs | Apr 2011 – Sep 2011 | 484 | 35.9 | 12.3 | 20.6 | 10.5 | 4.0 | 5.0 | 22.6 |
Access to finance of SMEs, April 2011 to March 2012
| Most pressing problem (%) | Loan application in past 6 months (%) | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Country | Firm type | Period | Number of firms | Finding customers | Competition | Access to finance | Production/labor costs | Skilled staff | Regulation | Applied but was rejected |
| Ireland | SMEs | Oct 2011 – Mar 2012 | 485 | 35.3 | 12.5 | 24.7 | 10.1 | 5.9 | 5.1 | 17.1 |
| Ireland | Large | Oct 2011 – Mar 2012 | 15 | 34.0 | 33.6 | 2.8 | 12.2 | 2.8 | 12.2 | 0.0 |
| Euro area | SMEs | Oct 2011 – Mar 2012 | 6969 | 26.9 | 12.5 | 17.3 | 13.8 | 13.7 | 7.4 | 13.2 |
| Ireland | SMEs | Apr 2011 – Sep 2011 | 484 | 35.9 | 12.3 | 20.6 | 10.5 | 4.0 | 5.0 | 22.6 |
Firm Demographics by Sector, 2006-2010
Citation: IMF Staff Country Reports 2012, 265; 10.5089/9781475510461.002.A002
Source: CSO.1/ Number of firm births divided by total number of firms.Firm Demographics by Sector, 2006-2010
Citation: IMF Staff Country Reports 2012, 265; 10.5089/9781475510461.002.A002
Source: CSO.1/ Number of firm births divided by total number of firms.Firm Demographics by Sector, 2006-2010
Citation: IMF Staff Country Reports 2012, 265; 10.5089/9781475510461.002.A002
Source: CSO.1/ Number of firm births divided by total number of firms.17. And firm creation outside property-related sectors remains robust. National statistics on firm demographics indicate that there are increasingly fewer new firms. This is especially the case for small firms, firms that tend to be more financially constrained. Moreover, there have been an increasing number of firm deaths, especially among small firms. With depressed home prices it has become more difficult to finance a new firm using home equity, which has hampered job creation. However, while the firm entry rate has come down somewhat since 2009, it is relatively stable outside the construction and real estate sectors.
Household sector
18. As house prices have halved from their peak, housing affordability indicators have returned to their historical level. House prices have halved from their 2007 peak and are now back at 2001 levels. Such steep declines in house prices are rare, but they have previously occurred following real estate busts even in advanced economies. 7 While affordability indicators indicate that house prices have reached historical levels, with house prices about ten times per capita disposable income, some overshooting of house price declines is normal following real estate boom-bust cycles.
19. But while interest burdens have improved, the household debt overhang remains severe, with household debt high in international comparison. Interest payments on household debt have fallen markedly relative to disposable income, mainly because of reduced ECB interest rates. Nevertheless, household debt remains high, also by international standards, at about 220 percent of disposable household income, having decreased only marginally from its peak.
20. As a result, new lending for house purchases has halted. Demand for mortgage loans has been curtailed due to a debt overhang—especially affecting first-time buyers and buy-to-let investors during the 2004–08 boom—and expectations of further house price declines. Supply of mortgage loans has also been limited by legacy problems and the high cost of funding at banks.
Loans for House Purchases
(Billions of euros)
Citation: IMF Staff Country Reports 2012, 265; 10.5089/9781475510461.002.A002
Source: Irish Banking Federation/PWC.Loans for House Purchases
(Billions of euros)
Citation: IMF Staff Country Reports 2012, 265; 10.5089/9781475510461.002.A002
Source: Irish Banking Federation/PWC.Loans for House Purchases
(Billions of euros)
Citation: IMF Staff Country Reports 2012, 265; 10.5089/9781475510461.002.A002
Source: Irish Banking Federation/PWC.21. The malaise in the property market is also negatively affecting some SMEs. A large fraction of SMEs have been active in property-related sectors, e.g., commercial real estate such as retail space as well as buy-to-let residential property. Following the decline in commercial property and house prices, many of these SMEs are in financial distress. The viability of a large number of SMEs in Ireland is therefore dependent on a revival of the property market or on some workout of their property-related debt.
Banking sector
22. On the supply side, bank lending has been curtailed due to legacy problems and high funding costs. Bank asset quality has deteriorated with a growing number of mortgage loans and SME credits in arrears, increasing provisioning for bad loans. Moreover, around half of mortgages have been issued with rates that track the ECB benchmark rate, reducing net interest margins as monetary conditions loosened. Indeed, banks’ cash flow from net interest revenues is currently insufficient to cover provisioning expenses. At the same time, banks’ funding costs remain high, with high deposit interest rates and fees on the Eligible Liabilities Guarantee (ELG) scheme, increasing the marginal cost to lend.
C. Lending Standards and Credit Conditions Disentangled
23. Lending standards are stable but credit demand conditions remain weak, suggesting that the reduced lending activity is primarily demand driven. Data from the ECB bank lending survey show that lending standards for corporate and households have stabilized, while credit demand especially for corporate continues to fall.8 But, as lending standards and credit demand conditions are driven by common factors, such as economic conditions, it is difficult to infer a causal interpretation based on lending survey data (in the absence of exogenous shifts in the supply of credit).
Changes in Credit Standards
(2006Q1=100, + = tightening)
Citation: IMF Staff Country Reports 2012, 265; 10.5089/9781475510461.002.A002
Source: Central Bank of Ireland.Changes in Credit Standards
(2006Q1=100, + = tightening)
Citation: IMF Staff Country Reports 2012, 265; 10.5089/9781475510461.002.A002
Source: Central Bank of Ireland.Changes in Credit Standards
(2006Q1=100, + = tightening)
Citation: IMF Staff Country Reports 2012, 265; 10.5089/9781475510461.002.A002
Source: Central Bank of Ireland.
Changes in Credit Demand
(2006Q1=100)
Citation: IMF Staff Country Reports 2012, 265; 10.5089/9781475510461.002.A002
Source: Central Bank of Ireland.Changes in Credit Demand
(2006Q1=100)
Citation: IMF Staff Country Reports 2012, 265; 10.5089/9781475510461.002.A002
Source: Central Bank of Ireland.Changes in Credit Demand
(2006Q1=100)
Citation: IMF Staff Country Reports 2012, 265; 10.5089/9781475510461.002.A002
Source: Central Bank of Ireland.24. To disentangle whether changes in lending standards or credit demand conditions are driving loan growth, regression analysis of bank lending survey responses is used.9 To gauge the importance of supply-side constraints for credit growth, regressions of loan growth are estimated where demand is purged from supply factors, and vice versa. These regressions use ECB bank lending survey responses to changes in lending standards and credit demand conditions as proxies for changes in supply and demand factors, respectively. These regressions are estimated separately for lending to corporates and households. Purging demand from supply factors, and vice versa, allows for an estimate of upper and lower bounds of the effect of supply-side factors on credit growth. While this approach is subject to criticism, primarily because it assumes that the loan survey responses are accurate and exogenous, it offers some guidance on the relative importance of supply and demand factors.
25. Regressions are first estimated using data on the bank lending survey for corporations. The basic regression model is
where the dependent variable is the growth rate of loans to non-financial corporations in a given quarter. ΔS denotes the change in the supply of credit to corporate, measured as the change in lending standards over the past three months on loans or credit lines to enterprises.10 Higher numbers denote a relaxation in standards, i.e., an increase in supply. ΔD denotes the demand for credit from corporate, measured as the change in demand for loans or credit lines to enterprises over the past three months. Higher numbers denote an increase in demand.
26. To purge demand factors from supply factors and obtain an lower-bound estimate of the effect of supply-side factors on credit growth, the regression model is adjusted as
where Ŝt denotes the residual of a country-specific OLS regression of S on D for corporates.
27. To purge supply factors from demand factors and obtain an upper-bound estimate of the effect of supply-side factors on credit growth, the regression model is adjusted as
where
28. Regressions are estimated using OLS and include quarter fixed effects (Table 4). The sample consists of quarterly loan growth and survey data from December 2002 to March 2012. Regressions in columns (1) to (2) and columns (5) to (6) pertain to results for Ireland and regressions in columns (3) and (4) to the euro area. The regression in column (3) gives an upper bound of the effect of supply on loan growth because it removes supply factors from demand and therefore attaches maximum weight to supply factors, while the regression in column (4) gives a lower bound on the effect of supply on loan growth because it removes demand factors from supply and therefore attaches maximum weight to demand factors.
Supply and demand of loans to non-financial companies, Dec 2002 – Mar 2012
Notes: Dependent variable is the growth rate of loans to non-financial corporations in a given quarter. Supply to corporates is the change in lending standards over the past three months on loans or credit lines to enterprises. Higher numbers denote a relaxation in standards, i.e., an increase in supply. Demand from corporates is the change in demand for loans or credit lines to enterprises over the past three months. Higher numbers denote an increase in demand. Supply to corporates—residual is the residual of a country-specific OLS regression of Supply to corporates on Demand from corporates. Demand from corporates—residual is the residual of a country-specific OLS regression of Demand from corporates on Supply to corporates. Regressions are estimated using OLS and include quarter fixed effects. Sample consists of quarterly data over the period Dec 2002 to March 2012. Regressions in Columns (1)-(2) and (5)-(6) pertain to results for Ireland and regressions in Columns (3)-(4) to Euro area. Regression in column (3) gives an upper bound of the effect of supply on loan growth because it takes out supply factors from demand and attaches maximum weight to supply. Regression in column (4) gives a lower bound on the effect of supply on loan growth because it takes out demand factors from supply and attaches maximum weight to demand. Robust standard errors are reported in parentheses. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.
Supply and demand of loans to non-financial companies, Dec 2002 – Mar 2012
| Dependent variable: Growth rate of loans to non-financial companies | (1) Ireland | (2) Ireland | (3) Euro area | (4) Euro area | (5) Ireland | (6) Ireland |
|---|---|---|---|---|---|---|
| VARIABLES | ||||||
| Supply to corporates | 0.0271 | -0.000983 | 0.0265 | |||
| (0.025) | (0.0107) | (0.0244) | ||||
| Demand from corporates | 0.0326** | 0.0381*** | 0.0345*** | |||
| (0.0121) | (0.0117) | (0.0115) | ||||
| Demand from corporates—residual | 0.0748*** | |||||
| (0.0214) | ||||||
| Supply to corporates—residual | -0.0976** | |||||
| (0.0453) | ||||||
| Constant | -0.066 | -0.0795** | 0.015 | -0.101*** | -0.0713 | -0.0891** |
| (0.0636) | (0.0341) | (0.0308) | (0.0363) | (0.0637) | (0.0347) | |
| Quarter fixed effects | Y | Y | Y | Y | Y | Y |
| Observations | 37 | 37 | 37 | 37 | 37 | 37 |
| R-squared | 0.028 | 0.125 | 0.035 | 0.264 | 0.201 | 0.201 |
Notes: Dependent variable is the growth rate of loans to non-financial corporations in a given quarter. Supply to corporates is the change in lending standards over the past three months on loans or credit lines to enterprises. Higher numbers denote a relaxation in standards, i.e., an increase in supply. Demand from corporates is the change in demand for loans or credit lines to enterprises over the past three months. Higher numbers denote an increase in demand. Supply to corporates—residual is the residual of a country-specific OLS regression of Supply to corporates on Demand from corporates. Demand from corporates—residual is the residual of a country-specific OLS regression of Demand from corporates on Supply to corporates. Regressions are estimated using OLS and include quarter fixed effects. Sample consists of quarterly data over the period Dec 2002 to March 2012. Regressions in Columns (1)-(2) and (5)-(6) pertain to results for Ireland and regressions in Columns (3)-(4) to Euro area. Regression in column (3) gives an upper bound of the effect of supply on loan growth because it takes out supply factors from demand and attaches maximum weight to supply. Regression in column (4) gives a lower bound on the effect of supply on loan growth because it takes out demand factors from supply and attaches maximum weight to demand. Robust standard errors are reported in parentheses. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.
Supply and demand of loans to non-financial companies, Dec 2002 – Mar 2012
| Dependent variable: Growth rate of loans to non-financial companies | (1) Ireland | (2) Ireland | (3) Euro area | (4) Euro area | (5) Ireland | (6) Ireland |
|---|---|---|---|---|---|---|
| VARIABLES | ||||||
| Supply to corporates | 0.0271 | -0.000983 | 0.0265 | |||
| (0.025) | (0.0107) | (0.0244) | ||||
| Demand from corporates | 0.0326** | 0.0381*** | 0.0345*** | |||
| (0.0121) | (0.0117) | (0.0115) | ||||
| Demand from corporates—residual | 0.0748*** | |||||
| (0.0214) | ||||||
| Supply to corporates—residual | -0.0976** | |||||
| (0.0453) | ||||||
| Constant | -0.066 | -0.0795** | 0.015 | -0.101*** | -0.0713 | -0.0891** |
| (0.0636) | (0.0341) | (0.0308) | (0.0363) | (0.0637) | (0.0347) | |
| Quarter fixed effects | Y | Y | Y | Y | Y | Y |
| Observations | 37 | 37 | 37 | 37 | 37 | 37 |
| R-squared | 0.028 | 0.125 | 0.035 | 0.264 | 0.201 | 0.201 |
Notes: Dependent variable is the growth rate of loans to non-financial corporations in a given quarter. Supply to corporates is the change in lending standards over the past three months on loans or credit lines to enterprises. Higher numbers denote a relaxation in standards, i.e., an increase in supply. Demand from corporates is the change in demand for loans or credit lines to enterprises over the past three months. Higher numbers denote an increase in demand. Supply to corporates—residual is the residual of a country-specific OLS regression of Supply to corporates on Demand from corporates. Demand from corporates—residual is the residual of a country-specific OLS regression of Demand from corporates on Supply to corporates. Regressions are estimated using OLS and include quarter fixed effects. Sample consists of quarterly data over the period Dec 2002 to March 2012. Regressions in Columns (1)-(2) and (5)-(6) pertain to results for Ireland and regressions in Columns (3)-(4) to Euro area. Regression in column (3) gives an upper bound of the effect of supply on loan growth because it takes out supply factors from demand and attaches maximum weight to supply. Regression in column (4) gives a lower bound on the effect of supply on loan growth because it takes out demand factors from supply and attaches maximum weight to demand. Robust standard errors are reported in parentheses. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.
29. The economic effect of demand-side factors for lending to corporates is substantial. Based on the estimates reported in column (6), a one standard deviation increase in Demand from corporates implies an increase in loan growth of non-financial companies of 2.2 percentage points. This is substantial given that it amounts to about one-third the standard deviation in loan growth of non-financial companies of 5.9 percent.
30. Similar regressions are estimated using bank lending survey responses on lending to households (Table 5). The dependent variable in these regressions is the growth rate of loans to households for house purchase in a given quarter. Supply to households is the change in lending standards over the past three months on loans to households for house purchase, with higher numbers denoting a relaxation in standards (an increase in supply). Demand from households is the change in demand for loans to households for house purchase over the past three months, with higher numbers denoting an increase in demand. Otherwise, the regressions are similar to those for corporations.
Supply and demand of household loans for house purchase, Dec 2002 – Mar 2012
Notes: Dependent variable is the growth rate of loans to households for house purchase in a given quarter. Supply to households is the change in lending standards over the past three months on loans to households for house purchase. Higher numbers denote a relaxation in standards, i.e., an increase in supply. Demand from households is the change in demand for loans to households for house purchase over the past three months. Higher numbers denote an increase in demand. Supply to households—residual is the residual of a country-specific OLS regression of Supply to households on Demand from households. Demand from households—residual is the residual of a country-specific OLS regression of Demand from households on Supply to households. Regressions are estimated using OLS and include quarter fixed effects. Sample consists of quarterly data over the period Dec 2002 to March 2012. Regressions in Columns (1)-(2) and (5)-(6) pertain to results for Ireland and regressions in Columns (3)-(4) to Euro area. Regression in column (3) gives an upper bound of the effect of supply on loan growth because it takes out supply factors from demand and attaches maximum weight to supply. Regression in column (4) gives a lower bound on the effect of supply on loan growth because it takes out demand factors from supply and attaches maximum weight to demand. Robust standard errors are reported in parentheses. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.
Supply and demand of household loans for house purchase, Dec 2002 – Mar 2012
| Dependent variable: Growth rate of household loans for house purchase | (1) Ireland | (2) Ireland | (3) Euro area | (4) Euro area | (5) Ireland | (6) Ireland |
|---|---|---|---|---|---|---|
| VARIABLES | ||||||
| Supply to households | 0.0583*** | 0.0556*** | 0.0579*** | |||
| (0.0174) | (0.0100) | (0.0184) | ||||
| Demand from households | 0.0378*** | 0.0199*** | 0.0376*** | |||
| (0.0105) | (0.0037) | (0.0109) | ||||
| Demand from households—residual | 0.0258** | |||||
| (0.0123) | ||||||
| Supply to households—residual | 0.0354 | |||||
| (0.0209) | ||||||
| Constant | -0.178*** | -0.111*** | -0.147*** | -0.0459*** | -0.176** | -0.111*** |
| (0.0647) | (0.0376) | (0.0288) | (0.0119) | (0.0681) | (0.0388) | |
| Quarter fixed effects | Y | Y | Y | Y | Y | Y |
| Observations | 37 | 37 | 37 | 37 | 37 | 37 |
| R-squared | 0.223 | 0.237 | 0.57 | 0.448 | 0.273 | 0.273 |
Notes: Dependent variable is the growth rate of loans to households for house purchase in a given quarter. Supply to households is the change in lending standards over the past three months on loans to households for house purchase. Higher numbers denote a relaxation in standards, i.e., an increase in supply. Demand from households is the change in demand for loans to households for house purchase over the past three months. Higher numbers denote an increase in demand. Supply to households—residual is the residual of a country-specific OLS regression of Supply to households on Demand from households. Demand from households—residual is the residual of a country-specific OLS regression of Demand from households on Supply to households. Regressions are estimated using OLS and include quarter fixed effects. Sample consists of quarterly data over the period Dec 2002 to March 2012. Regressions in Columns (1)-(2) and (5)-(6) pertain to results for Ireland and regressions in Columns (3)-(4) to Euro area. Regression in column (3) gives an upper bound of the effect of supply on loan growth because it takes out supply factors from demand and attaches maximum weight to supply. Regression in column (4) gives a lower bound on the effect of supply on loan growth because it takes out demand factors from supply and attaches maximum weight to demand. Robust standard errors are reported in parentheses. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.
Supply and demand of household loans for house purchase, Dec 2002 – Mar 2012
| Dependent variable: Growth rate of household loans for house purchase | (1) Ireland | (2) Ireland | (3) Euro area | (4) Euro area | (5) Ireland | (6) Ireland |
|---|---|---|---|---|---|---|
| VARIABLES | ||||||
| Supply to households | 0.0583*** | 0.0556*** | 0.0579*** | |||
| (0.0174) | (0.0100) | (0.0184) | ||||
| Demand from households | 0.0378*** | 0.0199*** | 0.0376*** | |||
| (0.0105) | (0.0037) | (0.0109) | ||||
| Demand from households—residual | 0.0258** | |||||
| (0.0123) | ||||||
| Supply to households—residual | 0.0354 | |||||
| (0.0209) | ||||||
| Constant | -0.178*** | -0.111*** | -0.147*** | -0.0459*** | -0.176** | -0.111*** |
| (0.0647) | (0.0376) | (0.0288) | (0.0119) | (0.0681) | (0.0388) | |
| Quarter fixed effects | Y | Y | Y | Y | Y | Y |
| Observations | 37 | 37 | 37 | 37 | 37 | 37 |
| R-squared | 0.223 | 0.237 | 0.57 | 0.448 | 0.273 | 0.273 |
Notes: Dependent variable is the growth rate of loans to households for house purchase in a given quarter. Supply to households is the change in lending standards over the past three months on loans to households for house purchase. Higher numbers denote a relaxation in standards, i.e., an increase in supply. Demand from households is the change in demand for loans to households for house purchase over the past three months. Higher numbers denote an increase in demand. Supply to households—residual is the residual of a country-specific OLS regression of Supply to households on Demand from households. Demand from households—residual is the residual of a country-specific OLS regression of Demand from households on Supply to households. Regressions are estimated using OLS and include quarter fixed effects. Sample consists of quarterly data over the period Dec 2002 to March 2012. Regressions in Columns (1)-(2) and (5)-(6) pertain to results for Ireland and regressions in Columns (3)-(4) to Euro area. Regression in column (3) gives an upper bound of the effect of supply on loan growth because it takes out supply factors from demand and attaches maximum weight to supply. Regression in column (4) gives a lower bound on the effect of supply on loan growth because it takes out demand factors from supply and attaches maximum weight to demand. Robust standard errors are reported in parentheses. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.
31. The economic effects of supply and demand-side factors for lending to households are comparable. Based on the regression estimates reported in column (5), a one standard deviation increase in Supply to households implies an increase in loan growth of 2.0 percentage points. Similarly, based on results in column (6), a one standard deviation increase in Demand from households implies an increase in household loan growth for house purchase of 2.1 percentage points. Both of these effects are substantial given the standard deviation of loan growth of household loans for home purchase of 5.4 percent.
32. Overall, these regressions suggest that supply factors play a more important role in lending to households than in lending to corporates. Moreover, demand factors play a similar role in lending to households and lending to firms. Importantly, these results are for the corporate sector as a whole and may not prove a firm basis for inference of the relevance of supply factors for lending to SMEs.
D. Options for dealing with reduced access to finance for SMEs 11
33. Access to finance for SMEs is limited given several market failures that may require government intervention. SME financing is curtailed given the increased uncertainty and information asymmetries about borrower creditworthiness as well as the tighter collateral constraints arising from the sharp decline in asset prices. In particular, for SMEs with outstanding mortgage financing, it is more difficult to secure new bank credits due to reduced or impaired collateral values of property-related assets. Given the possibility of a recovery of asset values, banks provide forbearance rather than loan workouts, but this limits the scope for making new loans.
34. Credit guarantees or subsidies on SME loans can in principle stimulate SME financing. Credit guarantees can help alleviate collateral constraints demanded by banks and loan subsidies can reduce the cost of borrowing. Until recently, Ireland was one of the few OECD countries without some form of loan guarantee scheme. Appendix Table 1 gives an overview of selected government-supported SME programs.
35. However, the international experience with SME lending schemes is mixed.12 In reviewing a broad sample of credit guarantee schemes, Levitsky (1997) concludes that “there is no consensus that credit guarantee schemes are an effective or economical way of widening access to formal bank credits for SMEs”. Moreover, the historical experience shows that credit guarantee schemes can only be effective when there are competent, financially sound banks, with adequate staff to effectively screen and monitor SME loans. Credit guarantees have a mixed track record in promoting credit growth in part because there is a risk of misallocation (overextension) to SMEs that do not need it or have alternative sources. A proper design of the scheme can limit this risk. In particular, the more effective schemes are (i) targeted to those sectors that are most several financially constrained and (ii) operated on a commercial basis. The recently announced Temporary Partial Credit Guarantee Scheme of Ireland embeds these design elements. It excludes property-related sectors where growth opportunities are limited; it targets term financing where collateral constraints are most binding; and it operates on commercial basis through on-lending by commercial banks.
36. Government support for SMEs will need to be complemented with progress in improving the operational capacity of banks to work out loans. The restructuring of SMEs on a case-by-case basis is resource intensive yet important to ensure that where a viable core business exists, that it has the possibility to invest and grow, and contribute to broader economic recovery. Considering the number of SMEs, it would not be appropriate to rely principally on court-based bankruptcy procedures. Rather, banks will need to build their capacity to design and implement work outs though out-of-court workout processes. Drawing on international expertise may well be needed to help major banks build capacity in this area.
37. The government could also explore ways to facilitate the securitization of SME loans. However, liquidity premia currently demanded by market participants even on senior tranches, plus the inability of the Irish government to offer substantial credit enhancements on such securitizations given the low sovereign credit rating, imply that, at least for the moment, the market for securitization of SME loans is limited.
E. Conclusions
38. The analysis presented indicates weak lending is mostly demand-driven, although supply factors play in role in mortgage lending and pockets of SME lending. While analysis points to a tightening of lending standards and significant financing constraints at SMEs, the sharp decline in household and corporate lending appears primarily demand driven.
39. A small-scale and well-targeted credit guarantee program can help SME financing. This would relieve financing constraints for SMEs with profitable growth opportunities. But care is needed that guaranteed credit does not flow to SMEs that do not need it or that have alternative sources of finance.
40. The restructuring of bad loans needs to be speeded up. More intense efforts by banks to work out distressed loans are urgently needed. This will repair private sector balance sheets and reduce debt overhang, improve prospects for economic recovery and sound lending opportunities, and ultimately help restore the viability of banks.
Selected government schemes to support SME lending
Selected government schemes to support SME lending
| Country | Name of scheme | Start of scheme | Overview of scheme | Size of the scheme | Benefits of scheme | Access to scheme | Uptake of the scheme | Impact of scheme |
|---|---|---|---|---|---|---|---|---|
| Ireland | Temporary Partial Credit Guarantee Scheme | Aug 2012 | The scheme provides a 75% state guarantee to banks against losses on qualifying loans to firms. | The scheme will facilitate up to €150 million of additional lending to eligible SMEs lending per annum. | Improved access to finance for SMEs. In return, recipient businesses pay an annual guarantee premium of 2% on the outstanding balance of the loan. | Loans to eligible firms from participating banks. Excluded from the scheme: (1) Agriculture, horticulture, fisheries, and property-related activities; (2) refinancing of existing debts; and (3) overdrafts. Loan decision by lender and on commercial basis. | Not applicable | Not applicable |
| EU | SME Guarantee Facility | Jan 2007 | The scheme provides a guarantee of up to 50% on loans or other debt finance granted by EU financial intermediaries to SMEs with the aim to improve access to debt finance for SMEs. The scheme is administered by the European Investment Fund (EIF). (Under the same program, EIF also provides guarantees for securitized SME financing instruments in the form of AAA-rated credit enhancements in SME securitizations.) | Under the program, a total of € 550 million of guarantees is available to financial intermediaries. | Improved access to finance for SMEs. | Guarantees are granted to EU financial intermediaries for lending to SMEs. Final beneficiaries must be SMEs, i.e., enterprises which employ fewer than 250 employees and which have an annual turnover not exceeding EUR 50 million, and/or an annual balance sheet total not exceeding EUR 43 million. Guarantees cover portfolios of mid- to long-term financing such as loans and/or lease transactions with a term of at least 18 months. | By June 2012, 201,561 loans had been provided to businesses under the CIP program, with guarantees totaling € 307 million. | Significant uptake, but program size relatively small. |
| Mexico | Punto Final program | Dec 1998 | Debt relief program targeted at mortgage holders, agribusiness, and small and medium-sized enterprises. The program offered subsidies to borrowers to pay back their bank loans. For every three pesos of new loans extended by the banks, the government would assume an additional one peso of discount. The program thus combined loss sharing between government and banks with an incentive to restart lending. | Not applicable | The program offered large subsidies (up to 60 percent of the book value of the loan) to borrowers to pay back their bank loans. The discounts depended on the sector, the amount of the loan, and on whether the bank restarted lending to the sector. | Mortgage holders, agribusiness, and small and medium-sized enterprises | Significant. | The program was successful in terms of rapid debt relief but at very large cost to the taxpayer. |
| U.K. | The National Loan Guarantee Scheme (NLGS) | March 2012 | The scheme provides banks with guarantees to issue unsecured debt, enabling participating banks to borrow at cheaper rates and lend to smaller businesses at lower rates. | The scheme provides banks with up to £20 billion of guarantees to issue unsecured debt. | Helps businesses by reducing the cost of bank loans under the scheme by 1 percentage point. | Businesses with a turnover of not more than £250 million (increased on June 26, 2012 from an initial maximum turnover of £50 million) are eligible for the scheme and should apply for loans at participating banks. | By June 26, 2012, over 10,000 cheaper loans worth over £1.5bn had been offered to businesses. | Major uptake during initial months of the scheme. |
| U.S. | Term Asset-Backed Securities Loan Facility (TALF) | Nov 2008 | Funding facility intended to make credit available to consumers and businesses on more favorable terms by facilitating the issuance of asset-backed securities (ABS)—including those collateralized by small business loans—and by improving the market conditions for ABS more generally. | Initially, the program would lend up to $200 billion on a non-recourse basis to holders of certain AAA-rated ABS. Subsequently, this amount was raised to up to $1 trillion. | Credit extensions in the form of non-secured loans with a term of up to five years to holders of eligible asset-backed securities (ABS). | All U.S. persons that own eligible collateral may participate in the TALF. Eligible collateral initially included U.S. dollar-denominated ABS backed by student loans, auto loans, credit card loans, and loans guaranteed by the Small Business Administration (SBA), and with a credit rating in the highest investment-grade rating category from two or more approved rating agencies and do not have a credit rating below the highest investment-grade rating category from a major rating agency. Credit exposures underlying eligible ABS must be newly or recently originated exposures to U.S.-domiciled obligors. Subsequently, the list of eligible collateral for TALF loans was expanded. Haircuts are applied to eligible collateral. TALF loans awarded to borrowers each month based on a competitive, sealed bid auction process. | By the time the TALF program ended, June 30, 2010, a total of $71.1 billion of loans were made under TALF. Out of this total amount, $2.2 billion were loans collateralized by small business loans. | While TALF helped prevent a shutdown of the securitization market by improving its market and funding liquidity conditions, loans extended to small business under the program were relatively small. |
Selected government schemes to support SME lending
| Country | Name of scheme | Start of scheme | Overview of scheme | Size of the scheme | Benefits of scheme | Access to scheme | Uptake of the scheme | Impact of scheme |
|---|---|---|---|---|---|---|---|---|
| Ireland | Temporary Partial Credit Guarantee Scheme | Aug 2012 | The scheme provides a 75% state guarantee to banks against losses on qualifying loans to firms. | The scheme will facilitate up to €150 million of additional lending to eligible SMEs lending per annum. | Improved access to finance for SMEs. In return, recipient businesses pay an annual guarantee premium of 2% on the outstanding balance of the loan. | Loans to eligible firms from participating banks. Excluded from the scheme: (1) Agriculture, horticulture, fisheries, and property-related activities; (2) refinancing of existing debts; and (3) overdrafts. Loan decision by lender and on commercial basis. | Not applicable | Not applicable |
| EU | SME Guarantee Facility | Jan 2007 | The scheme provides a guarantee of up to 50% on loans or other debt finance granted by EU financial intermediaries to SMEs with the aim to improve access to debt finance for SMEs. The scheme is administered by the European Investment Fund (EIF). (Under the same program, EIF also provides guarantees for securitized SME financing instruments in the form of AAA-rated credit enhancements in SME securitizations.) | Under the program, a total of € 550 million of guarantees is available to financial intermediaries. | Improved access to finance for SMEs. | Guarantees are granted to EU financial intermediaries for lending to SMEs. Final beneficiaries must be SMEs, i.e., enterprises which employ fewer than 250 employees and which have an annual turnover not exceeding EUR 50 million, and/or an annual balance sheet total not exceeding EUR 43 million. Guarantees cover portfolios of mid- to long-term financing such as loans and/or lease transactions with a term of at least 18 months. | By June 2012, 201,561 loans had been provided to businesses under the CIP program, with guarantees totaling € 307 million. | Significant uptake, but program size relatively small. |
| Mexico | Punto Final program | Dec 1998 | Debt relief program targeted at mortgage holders, agribusiness, and small and medium-sized enterprises. The program offered subsidies to borrowers to pay back their bank loans. For every three pesos of new loans extended by the banks, the government would assume an additional one peso of discount. The program thus combined loss sharing between government and banks with an incentive to restart lending. | Not applicable | The program offered large subsidies (up to 60 percent of the book value of the loan) to borrowers to pay back their bank loans. The discounts depended on the sector, the amount of the loan, and on whether the bank restarted lending to the sector. | Mortgage holders, agribusiness, and small and medium-sized enterprises | Significant. | The program was successful in terms of rapid debt relief but at very large cost to the taxpayer. |
| U.K. | The National Loan Guarantee Scheme (NLGS) | March 2012 | The scheme provides banks with guarantees to issue unsecured debt, enabling participating banks to borrow at cheaper rates and lend to smaller businesses at lower rates. | The scheme provides banks with up to £20 billion of guarantees to issue unsecured debt. | Helps businesses by reducing the cost of bank loans under the scheme by 1 percentage point. | Businesses with a turnover of not more than £250 million (increased on June 26, 2012 from an initial maximum turnover of £50 million) are eligible for the scheme and should apply for loans at participating banks. | By June 26, 2012, over 10,000 cheaper loans worth over £1.5bn had been offered to businesses. | Major uptake during initial months of the scheme. |
| U.S. | Term Asset-Backed Securities Loan Facility (TALF) | Nov 2008 | Funding facility intended to make credit available to consumers and businesses on more favorable terms by facilitating the issuance of asset-backed securities (ABS)—including those collateralized by small business loans—and by improving the market conditions for ABS more generally. | Initially, the program would lend up to $200 billion on a non-recourse basis to holders of certain AAA-rated ABS. Subsequently, this amount was raised to up to $1 trillion. | Credit extensions in the form of non-secured loans with a term of up to five years to holders of eligible asset-backed securities (ABS). | All U.S. persons that own eligible collateral may participate in the TALF. Eligible collateral initially included U.S. dollar-denominated ABS backed by student loans, auto loans, credit card loans, and loans guaranteed by the Small Business Administration (SBA), and with a credit rating in the highest investment-grade rating category from two or more approved rating agencies and do not have a credit rating below the highest investment-grade rating category from a major rating agency. Credit exposures underlying eligible ABS must be newly or recently originated exposures to U.S.-domiciled obligors. Subsequently, the list of eligible collateral for TALF loans was expanded. Haircuts are applied to eligible collateral. TALF loans awarded to borrowers each month based on a competitive, sealed bid auction process. | By the time the TALF program ended, June 30, 2010, a total of $71.1 billion of loans were made under TALF. Out of this total amount, $2.2 billion were loans collateralized by small business loans. | While TALF helped prevent a shutdown of the securitization market by improving its market and funding liquidity conditions, loans extended to small business under the program were relatively small. |
References
Arellano, M. and S. Bond (1991), “Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations,” Review of Economic Studies, vol. 58(2), pp. 277–97.
Bannock and Partners (1997), Credit Guarantee Schemes for Small Business Lending: A Global Perspective. London: Graham Bannock and Partners.
Bond, S. and C. Meghir (1994), “Dynamic Investment Models and the Firm’s Financial Policy,” Review of Economic Studies, vol. 61(2), pp. 197–222.
Claessens, S., S. Djankov, and A. Mody (2003), Resolution of Financial Distress: An International Perspective on the Design of Bankruptcy Laws, WBI Development Studies, World Bank.
Dell’Ariccia, G., D. Igan, and L. Laeven (2012), “Credit Booms and Lending Standards: Evidence from the Subprime Mortgage Market,” Journal of Money, Credit and Banking, vol. 44(3), pp. 367–84.
Drees, B. and C. Pazarbasioglu (1998), “The Nordic Banking Crisis: Pitfalls in Financial Liberalization,” IMF Occasional Paper No. 161, International Monetary Fund, April 20, 1998.
Hagan, S., E. Kalter, and R. Weeks-Brown (2003), “Corporate Debt Restructuring in the Wake of Economic Crisis,” In: Managing Financial Crises: Recent Experience and Lessons for Latin America, IMF Occasional Paper No. 217, pp. 84–100.
Hoelscher, D. and M. Quintyn (2003), “Managing Systemic Banking Crises,” IMF Occasional Paper No. 224, 2003.
Holton, S. and F. McCann, (2012), “SME Credit Supply and Demand: Comparisons Across Surveys and Countries,” Central Bank of Ireland Economic Letters No. 8.
Kennedy, B. (2011), “Using the Bank Lending Survey to Understand the Recent Disruption to Financial Markets: An Overview,” Central Bank of Ireland Quarterly Bulletin 03, pp. 83–108, July 2011.
Laeven, L. and T. Laryea (2009), “Principles of Household Debt Restructuring”, IMF Staff Position Note No. 09/15, June 26, 2009.
Laeven, L. and F. Valencia (2012), “Systemic Banking Crises Database: An Update”, IMF Working Paper No. 12/163, International Monetary Fund.
Laryea, T. (2010), “Approaches to Corporate Debt Restructuring in the Wake of Financial Crises”, IMF Staff Position Note No. 10/02, January 26, 2010.
Lawless, M. and F. McCann (2011), “Credit Access for Small and Medium Firms: Survey Evidence for Ireland,” Research Technical Paper No. 11/RT/11, Central Bank of Ireland.
Lawless, M. and F. McCann (2012), “Determinants of Default: Evidence from a Sector-Level Panel of Irish SME Loans,” CBI Research Technical Working Paper No. 3/RT/12, Central Bank of Ireland.
Lawless, M., F. McCann, and T. McIndoe Calder (2012), “SMEs in Ireland: Stylised Facts from the Real Economy and Credit Market,” Mimeo, Central Bank of Ireland.
Levitsky, J. (1997), “Credit Guarantee Schemes: An International Review,” Small Enterprise Development, vol. 8(2), pp. 4–17.
Mazars (2012), “SME Lending Demand Study,” July 2012.
Valencia, F. (2012), “Credit Developments in the Euro Area: Demand vs. Supply,” mimeo, International Monetary Fund.
Prepared by Luc Laeven (RES). The author gratefully acknowledges comments received from Craig Beaumont, Fergal McCann and Jochen Andritzky.
The Irish authorities have done much analysis in this area, with several recent working papers by the Central Bank of Ireland suggesting that there is a credit crunch in Ireland, with supply factors playing an important role in holding back bank lending to SMEs. See, for example, Kennedy (2011) and Lawless and McCann (2011).
For evidence of a link between deteriorating lending standards during the boom period and subsequent loan delinquencies in Ireland, see Lawless and McCann (2012). And for evidence of a similar pattern in the US subprime mortgage market, see Dell’ Ariccia, Igan, and Laeven (2012).
See, for example, Kennedy (2011), Lawless and McCann (2011), and Lawless et al. (2012).
According to national statistics, SMEs account for 52 percent of economic activity, where small firms are those employing less than 50 employees and medium firms those with 50 to 250 employees. The SME sector also accounts for a large fraction of employment (with about 72 percent of private sector employees).
For example, Holton and McCann (2012) find that the Irish SME rejection rate is double the euro area average and second only to Greece. Their analysis considers bank loans and overdrafts as well as a wider range of credit constrained firms as rejected. Similarly, Mazars (2012) surveys a much larger sample of Irish firms and finds a rejection rate of 28 percent when considering bank loan and overdraft applications.
For example, Norway experienced similar house price declines during its crisis in the early 1990s (Drees and Pazarbasioglu, 1998).
It should be noted that the number of banks responding to the BLS in each quarter in Ireland is very small.
This approach is similar to that in Valencia (2012).
The survey responses on lending standards and credit demand conditions are effectively lagged one period in the regression analysis. For example, the results reported in the April 2012 bank lending survey relate to changes during the first quarter of 2012 and expectations of changes in the second quarter of 2012. This survey was conducted between 23 March and 5 April 2012.
For more details on principles of corporate debt restructuring, including SMEs, see Laryea (2010), Hagan et al. (2003), Hoelscher and Quintyn (2003), and Claessens et al. (2003). Since SMEs share characteristics with the restructuring of household debt (both are small and numerous, complicating restructuring, and both rely heavily on property as collateral for loans), see also Laeven and Laryea (2009).
See Bannock and Partners (1997) for a review of SME credit guarantee schemes around the world.
