# **5. Empirical evidence**

# **5.1. Data**

In order to examine the determinants of the debt maturity structure of firms in countries at different levels of development, we construct a novel database including a number of institutional, macro and micro variables, as briefly illustrated below.

We measure the debt maturity structure of a firm by the ratio of short-term debt (i.e. debt obligations with maturity less than one year) to total debt in the firm’s balance sheet. We calculate averages of this ratio across firms for each country, including all countries for which we have data for at least 20 firms, and these firms represent more than 20 percent of the stock market capitalization in that country (source: WorldScope). Our sample covers a total of 14,178 publicly listed firms in 27 industrial economies and 18 developing economies over the period 1994-2004.<sup>23</sup> As postulated in Section 4.2, improvements in credit information quality are likely to increase the maturity horizon at which financial institutions feel safe to lend. We employ three measures of credit information quality: (i) the existence of a public registry or a private bureau, (ii) the extent of coverage of individuals and companies by public registries or private bureaus and (iii) the quality of accounting standards reported in La Porta, Lopez-de-Silanes, Shleifer and Vishny (1998) (hereafter LLSV).<sup>24</sup> The main sources for these data are the 23 The industrial economies in our sample are Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Greece, Hong Kong (China), Ireland, Israel, Italy, Japan, the Republic of Korea, Luxembourg, Netherlands, New Zealand, Norway, Portugal, Singapore, Spain, Sweden, Switzerland, Taiwan (China), United Kingdom, and United States. The developing countries are Argentina, Brazil, Chile, China, Czech Republic, Hungary, India, Indonesia, Malaysia, Mexico, Pakistan, Peru, Philippines, Poland, Russian Federation, South Africa, Taiwan, China, Thailand, and Turkey.

24 The credit bureau coverage variables have more cross-country and time-series variation, but may be predominantly driven by the coverage of retail borrowers rather than corporate loans. This problem is less pronounced for Public Credit Registries, as most of them have loan cut-off minimum amounts, usually excluding retail or small business loans. The indicator variables for the existence of private or public credit bureaus are more likely to reflect the availability of credit information on both individuals and firms, but show less variation than coverage variables as they are binary dummies. The indicator and coverage variables of private credit bureaus are highly correlated with the “Credit Information Index” reported in the World Bank/IFC “Doing Business” database, which measures the extent of credit information available on individuals and firms through either public or private credit registries (correlation coefficients are 0.79 and 0.42 respectively and statistically significant). Additionally, we find that countries with higher coverage of private credit bureaus also have higher percentage of firms rated by either S&P or Moody's (the correlation coefficient is 0.38 and statistically significant). We also use a measure of the quality of accounting standards across countries, which captures more directly the availability of credit information about firms. However, this variable shows little time-series variation. As each of these proxies for the quality of credit information have their own advantages and disadvantages, we will employ several of them in alternative specifications of our baseline regression. It is interesting to note that our main results remain unchanged whichever proxy is chosen.

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