A clear picture – albeit only a preliminary one – is beginning to emerge as regards the relationship between access to credit and innovative activity: firms that innovate tend to be those that apply for a loan and are granted one. Firms that do not demand a loan in the first place are the least likely to innovate, probably because their lack of interest in borrowing coincides with a lack of innovative capacity.

For those firms that have managed to obtain a bank loan – a third of all firms interviewed – Table 4.1 also contains information on the type of bank that lent to them. Only 17 per cent of firms borrowed from a state bank, 29 per cent from a private domestic bank and 54 per cent from a foreign bank. Is innovative activity affected by the type of bank that a firm borrows from?

Table 4.1 suggests not (as does unreported additional analysis). There is some evidence that the clients of state and foreign banks innovate more, but these differences are fairly small and statistically weak, and they disappear when controlling for other firm-level characteristics.

Chart 4.4 shows product and process innovation among credit-constrained and unconstrained firms in selected transition countries. In almost all countries unconstrained firms innovate more than credit-constrained firms.

Chart 4.5 plots data for the same set of countries. Here, the horizontal axis measures the percentage of firms that are credit-constrained, while the vertical axis indicates the difference between the innovative activities of unconstrained and constrained firms. That difference is a rough indicator of the aggregate sensitivity of innovation to firms’ credit constraints in any given country. It indicates the extent to which reducing credit constraints could boost firm-level innovation, given the current economic, political and institutional framework in the country.

The chart shows that in some countries (such as Azerbaijan and Ukraine) credit constraints remain rife among firms. However, in these countries there is also little difference between credit-constrained and unconstrained firms in terms of their innovative behaviour. Consequently, it may be that access to credit has little impact on innovation in these countries, with other constraints – such as an inadequately educated workforce or corruption (see Chapter 3) – having more effect. In contrast, in countries such as Belarus, Lithuania, Romania and Russia, not only are there large numbers of credit-constrained firms but access to bank loans seems to have a relatively large impact in terms of unleashing innovation.

Chart 4.6 indicates that even within countries, at town or city level, there is a strong negative correlation between firms’ credit constraints and innovative activity. The remainder of this chapter looks at this relationship in more detail.

There are two main reasons for conducting this additional analysis. First, a more rigorous investigation is needed to control for other firm-level characteristics, so that the “everything else being equal” condition holds as much as possible. Second, the strong negative correlation between credit constraints and innovation does not necessarily indicate that credit constraints cause a decline in innovation. It could be that causation runs the other way – that is to say, it may be that when firms innovate successfully, banks are more amenable to financing them, thereby reducing credit constraints. One way to address this concern is to consider only credit constraints that are driven by external non-firm-specific factors. To this end, the remainder of this chapter focuses on the impact of credit constraints stemming from exogenous variation in the local banking landscape that surrounds each BEEPS firm.