15 developed in MENA yet. Therefore, while many SME units seem to be developing more sophisticated lending techniques, in most cases they are still conducting relationship lending, although in a more effective way.

We also look at how the quality of financial infrastructure in a country affects SME lending. _Cover_ is a variable that represents the volume of credit information provided to lenders by public credit registries or private credit bureaus. This variable is based on information from the World Bank‟s Doing Business Database and is defined by the highest coverage ratio of either the public registry or the private bureau.<sup>11</sup> The _Legal_ variable measures the quality of the legal and contractual framework, and is represented by several alternative legal indicators from the World Bank‟s Doing Business database. We use: (i) the legal rights index that measures the degree to which collateral and bankruptcy laws protect the rights of lenders, (ii) the time to register a property, (iii) the time to enforce a contract and (iv) the time to close a business. All these indicators capture the strength of creditor rights and the risks of SME lending.

Finally, we look at the impact of a particular type of government intervention – partial credit guarantee schemes (PCGs). These schemes have become a popular tool to stimulate SME lending in many countries, and 10 MENA countries have already introduced such schemes.<sup>12</sup> _Guar_ is a variable that measures the size of PCGs through the ratio of outstanding guarantees to GDP. Table 7 provides information on the stock of outstanding guarantees (in % of GDP) of MENA PCGs. Some schemes are still very young and have not yet accumulated a significant volume of guarantees.

Our analysis includes all the countries listed in Table 1 except for Libya (where we could not verify the data). Most banks reported information for at least two years in the 2007-2009 period, allowing us to build a panel of about 240 observations. The appendix tables provide descriptive statistics of the variables used in the regression, the correlation matrix and the definitions and sources of the variables.

For estimation we first use Ordinary Least Squares (OLS), where we relax the assumptions of independence and homoskedasticy and report two sets of standard errors: Huber/White robust standard errors and robust standard errors corrected for possible intra-bank correlation. We realize the potential endogeneity of the partial credit guarantee variable – the volume of outstanding guarantees reflects at least partly the demand for guarantees from banks lending to SMEs. Therefore, we also use Two State Least Squares (2SLS) and employ the lag of outstanding guarantees and the median statutory coverage ratios of the guarantee schemes as instrumental variables (IVs). The coverage ratio is the share of the loan which is guaranteed and is one of the key statutory rules of a guarantee scheme. Coverage ratios remained constant during the estimation period and it can be argued that they only affected SME lending through

> 11 We also used the sum of coverage of credit registry and credit bureau and our results did not change significantly. We prefer to use the maximum coverage indicator, as the sum can result in significant double counting of coverage. 12 Saadani, Arvai, and Rocha (2010) provide an assessment of PCGs in MENA.  The IFC (2010) provides an overall assessment of policy interventions that have been introduced to induce more lending.