Main variables and methodology

The main variable of interest is the occurrence of financing constraints. This measure is a firm’s assessment of obstacles to its operations due to difficulties of accessing finance. It refers to both the availability of finance and the cost of finance. I transform the ordinal scale from the survey (0 = “no obstacle”, 4=”very severe obstacle”) into a binary variable, which takes the value of 1 if a firm reports financing constraints that fall into categories 3 or 4. Using the binary variable imposes a less restrictive functional form on the estimations to isolate the presence of financing constraints.

While most of the empirical studies use various measures of financing constra ints derived from the optimal investment patterns, or from the relationship between the cash-flows and investments (Fazzari et al., 1988), the self-reported estimate is obtained directly from a firm’s management. The advantage of using this measure is that it is a direct representation of financing constraints rather than a proxy and it is not endogenous to other firm characterist ics. However, because it is reported by a firm itself, it might be biased in the sense that firms are more likely to report that they are financially constrained when they are not. The problem is mitigated by the fact that the surveys are anonymous and in the empirical analyses I control for the trustworthiness estimate of the interviewees.

I focus on two main variables that estimate the extent of credit information sharing in

an economy. First, the scope of credit information is an index that measures the depth of the

information and its accessibility in both public credit registry and private credit bureau. The scope of credit information consists of 6 components: data on both firms and individuals is distributed; both positive and negative credit information is available and distributed; data from

retailers and utility companies is distributed in addition to data from financial institutions; at least 2 years of historical data is distributed; data on loan amounts below 1% of income per capita is distributed; by law, borrowers have the right to access their data in the largest credit

bureau or registry in the economy. Second, the scale of credit information represents the

coverage of the credit reporting agencies measured as a percentage of adults and firms relative to the adult population. This variable reports the number of individuals and firms listed in a public credit or private registry with current information on repayment history, unpaid debts, or credit outstanding. I calculate the overall scale as the maximum of the scale of credit registry and credit bureau.

I include firm- level variables to control for the heterogeneity in firm size, age, growth, transparency (presence of audited statements), government ownership, and the legal form of an establishment. At the country level I control for the overall growth options in an economy (using a proxy GDP per capita growth), pricing stability (inflation rate), financial developme nt (stock market capitalization), and the quality of the institutional and legal environment. I use four variables related to the structure of banking systems that might influence the access to finance: prevalence of bank finance (proportion of firms using banks to finance investments), bank concentration, riskiness of the banking sector (bank z-score), and credit market regulat io n.

Table 4.1 presents the summary statistics of the main variables. On average, 26% of all firms are financially constrained across all countries in the sample, but there is a large variatio n in the presence of financing constraints (44% standard deviation). The mean coverage of credit information sharing systems is 36% of firms and individuals as a proportion of population in a country. The majority of the credit registries and bureaus capture at least 4 out of the 6 dimensions of credit information sharing. The most frequent users of credit information data are banks, followed by non-bank financial institutions, and retailers/traders. Private credit bureaus tend to collect a greater amount and more detailed information compared to public credit registries. The largest difference is apparent in the availability of data about liabilit ies and financial positions of borrowers where private credit bureaus exert considerably more ongoing effort in data collection on current financial standing of borrowers. A typical company in the dataset is a small, privately held, limited liability company with less than 50 employees and with average age of around 10 years.

The purpose of the empirical analysis is to explain the cross-sectional differences in the relationship between credit information sharing systems and the occurrence of financ ing constraints. In particular, I investigate whether and how the credit information scope and scale relate to a firm’s perceived obstacles in accessing external finance. The empirical analysis is

based on a repeated cross-sectional setup with probit estimations where the dependent variable is the binary indicator of financing constraints. Each estimation includes industry and year- fixed effects to isolate the time-invariant component in cross-country differences. Because the characteristics of credit information sharing systems are fairly stable over time, I do not include country-fixed effects in the baseline analyses. I report results using robust standard errors adjusted for clustering at the country-level. In each estimation I control for the perceived truthfulness of the interviewee.

4.4. Empirical results

4.4.1 In which countries are firms more likely to experience financing constraints?