Back Matter
Author: Nir Klein

References

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Annexes

Data

Data on SMEs shares in employment and value added is extracted from the European Commission’s database for its annual reports on small and medium size enterprises in the EU (http://ec.europa.eu/enterprise/policies/sme/facts-figures-analysis/performance-review/index_en.htm#h2-5). Data on credit to the private non-financial sector is obtained from the BIS database (http://www.bis.org/statistics/credtopriv.htm). For, Romania, Slovenia, Malta, Lithuania, and Luxemburg the data is taken from Haver. Data on real GDP, GDP deflator, and public debt, is taken from the IMF’s World Economic Outlook database. Bank lending surveys and data on the VIX and interest rate spreads is taken from Haver, and Bank of Lithuania.

Bank lending survey25

The survey questions are phrased in terms of changes over the past three months, apart from Lithuania were the survey refers to a period of six months.

For the lending standards to SMEs, the analysis uses the responses to the following question 26, 27: “Over the past three months, how have your bank’s credit standards as applied to the approval of loans or credit lines to enterprises changed?”

For the UK, the analysis uses the responses to the following question (with an opposite sign): “How has the proportion of loan applications from medium private nonfinancial corporations being approved changed?”

For the loan demand by SMEs, the analysis uses the responses to the following question: “Over the past three months, how has the demand for loans or credit lines to enterprises changed at your bank, apart from normal seasonal fluctuations?”

For the UK, the analysis uses the responses to the following question: “How has demand for lending from medium private nonfinancial corporations changed?”

In the context of credit standards, the net percentage is defined as the difference between the sum of the percentages of banks responding “tightened considerably” and “tightened somewhat”, and the sum of the percentages of banks responding “eased considerably” and “eased somewhat”. Regarding demand for loans, the net percentage is defined as the difference between the sum of the percentages of banks responding “increased considerably” and “increased somewhat”, and the sum of the percentages of banks responding “decreased considerably” and “decreased somewhat”.

Figure 1A.
Figure 1A.

Economic Recovery and the Prevalence of SMEs (excl. Greece)

Citation: IMF Working Papers 2014, 098; 10.5089/9781498303590.001.A999

Sources: WEO and the EC’s annual report on SMEs in the EU in 2011/12.
Figure 2A.
Figure 2A.

Economic Recovery and the Prevalence of SMEs (excl. Greece)

Citation: IMF Working Papers 2014, 098; 10.5089/9781498303590.001.A999

Sources: WEO and the EC’s annual report on SMEs in the EU in 2011/12.
Figure 3A.
Figure 3A.

Banks’ Lending Standards applied to SMEs and demand for credit by SMEs, Euro Area

Citation: IMF Working Papers 2014, 098; 10.5089/9781498303590.001.A999

Source: ECB.
Table 1A.

Summary Statistics, Panel VAR variables

article image
Table 2A.

Correlation Matrix

article image
Table 3A.

Composition of the two-subsamples

article image
Figure 4A.
Figure 4A.

Impulse Responses to Adverse Credit Supply Shock Applied to SMEs,1,2

High share sub-sample

Citation: IMF Working Papers 2014, 098; 10.5089/9781498303590.001.A999

1 A shock of one standard deviation.2 The shaded bands indicate 95 confidence intervals generated by Monte-Carlo with 300 repititions.Source: IMF staff’s estimations.
Figure 5A.
Figure 5A.

Impulse Responses to Adverse Credit Supply Shock Applied to SMEs,1,2

Low share sub-sample

Citation: IMF Working Papers 2014, 098; 10.5089/9781498303590.001.A999

1 A shock of one standard deviation.2 The shaded bands indicate 95 confidence intervals generated by Monte-Carlo with 300 repititions.Source: IMF staff’s estimations.
Figure 6A.
Figure 6A.
Figure 6A.

Bank Lending Surveys: Credit Standards applied to SMEs

Citation: IMF Working Papers 2014, 098; 10.5089/9781498303590.001.A999

Source: Haver and IMF staff’s estimations.
1

My thanks to Costas Christou, Asmaa ElGanainy, Balazs Csonto, Vahram Stepanyan, Estelle Xue Liu, Carolina Osorio Buitron, Johannes Wiegand, Pelin Berkmen, Ali Al-Eyd, Fabian Valencia, Vanessa Redak, David Stenzel, the participants at the IMF’s European Department seminar, and the participants at the workshop at the National Bank of Hungary for helpful discussions and comments. All remaining errors are mine.

2

The European Commission’s Annual Report on European SMEs, 2012/13.

3

This evidence, however, in not conclusive. Beck et al (2005) provide extensive literature review about some mixed results in this area.

4

This stream of studies is closely related to the seminal work of Rajan and Zingales (1998) who illustrated that the link between financial development and economic growth is also a function of the dependency on external funds. In particular, they showed that industries that are relatively more in need of external finance (measured by investment not covered by retained earnings) grow disproportionally faster in countries with more developed financial markets.

5

The underlying assumption is that internal and external funds are not perfect substitutes, in part because informational asymmetries.

6

The assumption that small firms are more dependent on domestic bank financing has been used in the literature. See for instance Dell’Ariccia et al. (2008).

7

In most countries, the SMEs’ share showed little variance over time.

8

The EU classifies SMEs as firms with less than 250 employees and an annual turnover of €50 million or a balance sheet total of €43 million. In the bank lending surveys, an enterprise is normally considered large if its annual net turnover is more than €50.

9

The analysis excludes Croatia, which officially joined the EU in July 1, 2013.

10

Exclusion of Greece from Figure 6 does not change the slope of the trend line. See Figures 1A and 2A in the Annex.

11

Since the focus of this work is on GDP growth, we use, from this point onward, the share of SMEs’ value added in the non-financial business sector as indication for the SMEs’ prevalence.

12

External debt was not found to have a significant impact in the GDP growth estimations.

13

Since comparable database on credit stock of SMEs is not available for all EU countries, we use BIS data on credit to non-financial corporations (from all sectors). This data does not provide a breakdown of lending by currencies, thus can lead to valuation problems for countries in which foreign currency lending is extensive (e.g. Hungary).

14

We use bank lending surveys of the following countries: Austria, Cyprus, France, Germany, Hungary, Italy, Lithuania, Luxemburg, Malta, the Netherlands, Poland, Portugal, Slovenia, Spain, and the UK. See Annex for more details.

15

The net percentage is defined as the difference between the sum of the percentages of banks responding “tightened considerably” and “tightened somewhat”, and the sum of the percentages of banks responding “eased considerably” and “eased somewhat”.

16

Bassett et al (2013) use bank level responses and also controlled for bank-specific factors such as the bank size, stock market returns, change in loan loss provisions and change in net interest margin.

17

IMF (2013) uses a similar approach, though it does not include the banks’ responses about the change in demand for loans but variables that affect demand such as real GDP forecast, and stock market volatility.

18

For Slovenia, Malta, Cyprus, and the UK, the sample is shorter due to data limitations. For Hungary, bank lending survey data is available in semi-annual frequency in 2003q1-2008q4 and in quarterly frequency in 2009q1-2012q4. The missing data points for 2003q1-2008q4 were populated by the average of the preceding and subsequent quarters.

19

This transformation preserves the orthogonality between the transformed variables and lagged regressors. The estimation uses lagged regressors as instruments and estimate the coefficient by GMM methodology.

20

Monte Carlo simulations are used to generate the confidence intervals.

21

Qualitatively, the results remain unchanged to different ordering and lags.

22

The relatively high impact on GDP growth may result from the composition of the sample, which comprises of several crisis and near-crisis cases, and possibility that the adverse shock to SMEs may also capture the tightening of credit conditions to other segments in the economy such as households and large firms.

23

The shock to the credit standard under the three alternative specifications is broadly the same.

24

Luxemburg registered a sharp decline of SMEs share in value added to below the sample median in 2004, but since it remained above the sample median for the rest of the period it is classified as a “High share” country.

25

We use the bank lending survey of Austria, Cyprus, France, Germany, Hungary, Italy, Lithuania, Luxemburg, Malta, the Netherlands, Poland, Portugal, Slovenia, Spain, and the UK.

26

Responses for Lithuania were taken from the Bank of Lithuania’s website.

27

In Hungary, the responses refer to micro and small enterprises.

Small and Medium Size Enterprises, Credit Supply Shocks, and Economic Recovery in Europe
Author: Nir Klein