Chart 4.7 depicts the geographical distribution of banking activity across both emerging Europe (see 4.7a) and Russia (see 4.7b). These maps are based on information collected by the EBRD on the geographic coordinates of over 137,786 bank branches. These branches span 1,737 different locations and are owned by over 600 different banks.12 Bank branches are fairly evenly distributed across much of emerging Europe, with greater concentration in capital cities and other urban areas. Russia’s bank branches are concentrated in the south-west of the country, particularly in and around Moscow, as this is where Russia’s economic activity is concentrated.

These detailed branch data are used to construct two indicators for each town or city where BEEPS firms were interviewed. First, the Herfindahl-Hirschman index (HHI) – a measure of market concentration – is calculated for the local area. This HHI index is the sum of the squares of the market shares of the banks in the area, where these market shares are expressed as the share of total branches that is owned by each bank. The index ranges from 0 to 1, with higher values indicating a decrease in competition and an increase in banks’ local market power. The HHI index for emerging Europe as a whole is 0.15, indicating that most local banking markets are only moderately concentrated. Variation across towns and cities is considerable, however.

On the one hand, a concentrated banking market (with a high HHI index) can facilitate long-term lending relationships and thus reduce credit constraints, particularly for more opaque firms. On the other hand, it can also be argued that such concentration may stifle inter-bank competition and thus reduce the supply of credit to firms. While scholars have found evidence for both of these opposing ideas, recent research has tried to reconcile them.

Italian data show, for instance, that while market power (that is to say, higher levels of concentration) can boost firm creation, particularly in opaque industries, at some point the banks’ excessive market power starts to have a negative impact on firm creation. Similarly, cross-country evidence shows that while bank concentration promotes growth in sectors that are dependent on external finance, the overall relationship between bank concentration and economic growth is a negative one.13

The second town/city-level banking indicator that is used in this chapter is the percentage of bank branches that are owned by foreign banks. On average, 39 per cent of the bank branches in a given town or city are foreign-owned. A higher percentage of foreign ownership may reduce small firms’ access to credit if domestic banks possess a comparative advantage in terms of reduced information asymmetries in relation to local firms. This may be because they share a common language and culture or have a better knowledge of local legal and accounting institutions. Such factors may make it easier for domestic banks to base lending decisions on soft data when it comes to smaller local firms, as they have developed long-term relationships with these companies. On the other hand, however, foreign banks may be better at applying transaction technologies that use hard data (such as credit scoring) or collateral-based methods when lending to small businesses. In this case, the presence of foreign banks may actually benefit such firms.

An important assumption in this analysis is that these three local banking variables (or “instruments”) are exogenous in the sense that they only affect firm-level innovation through their impact on the probability of firms being credit-constrained. While plausible, this exclusion restriction could be violated if the location of bank branches is related to local factors that are also correlated with firm-level innovation. While the validity of this assumption cannot be tested directly, there is some evidence to suggest that the concentration and composition of the local banking markets in this dataset are largely exogenous.

First, there is very little correlation between changes in the number of bank branches in a given region between 2002 and 2011 and innovative activity in that region in 2012. Second, the three instruments are not related to firms’ credit demand. Thus, the structure of local credit markets does not appear to have responded in a systematic manner to local demand for external finance on the part of innovative firms. Third, in order to further mitigate endogeneity concerns, (unreported) town/city-level regressions were run where the dependent variable was either “HHI of town/city” or “share of foreign banks”.

This allows us to see the extent to which a battery of town/city-level firm characteristics (and industry fixed effects) can explain local banking structures. If the local banking structure is driven by the composition of the local business sector, we should find significant relationships between firm characteristics (averaged at town/city level) and that banking structure. In this case, there is no significant relationship between, on the one hand, the percentage of small firms, the percentage of large firms, the percentage of sole proprietorships, the percentage of privatised firms, the percentage of exporters or the percentage of audited firms and, on the other hand, bank concentration or the presence of foreign banks. Thus, it appears that the concentration and composition of these banking markets are unrelated to a large set of observable characteristics of the local business sector.

In addition to the three instrumental variables, various other firm and town/city-level characteristics are also included (but not shown for reasons of brevity). This ensures that the analysis carefully controls for other possible determinants of credit constraints. At firm level, these are: the firm’s age and size; whether the firm’s accounts are audited; whether the firm regularly trains its staff; whether it is quality-certified; whether it operates at national level; whether it expects sales to increase; whether it was previously state-owned; and the experience (in years) of the main manager.

At town/city level, these control variables include the size of the relevant town or city and whether that area is the main business centre in the country. They also include the BEEPS V firms’ average responses to questions on: their use of high-speed internet; the frequency of power outages; their assessment of local security, business licensing policies and political instability; and the quality of local courts and the education of the workforce. In addition, they include country and industry fixed effects, so that the analysis effectively compares firms within the same geographical area and within the same industrial sector. This controls for unobservable characteristics common to firms in the same industry or country.

Column 1 shows that a higher HHI index (reflecting a more concentrated local banking market) is associated with a lower probability of a firm being credit-constrained, everything else being equal. In terms of economic magnitude, the coefficient implies that a 1-standard-deviation increase in local bank concentration reduces the probability of a firm being credit-constrained by 5.4 percentage points.

At the same time, however, credit constraints are higher in areas with very concentrated banking markets. This non-linear effect is in line with the literature cited above and shows that while banks’ local market power can help firms to access credit, this only holds up to a certain point. When inter-bank competition declines too much, access to credit starts to suffer.

The U-shaped relationship between local bank concentration and firms’ credit constraints is depicted in Chart 4.8. On the horizontal axis all towns and cities have been allocated to one of five categories with increasing levels of bank concentration. The vertical axis indicates the average percentage of credit-constrained firms in each of those categories. The chart shows that while firms are initially less credit-constrained in more concentrated credit markets (that is to say, those with a higher HHI index), this effect is then reversed once a critical concentration threshold is crossed.

The negative coefficient for the variable “share of foreign banks” in Table 4.2 shows that a higher percentage of foreign-owned bank branches in a firm’s town/city is also associated with less binding credit constraints. Foreign banks may be better placed than domestic banks when it comes to overcoming agency problems and lending to firms. This effect is fairly substantial. A 1-standard-deviation increase in this variable reduces the probability of a firm being credit-constrained by 5.6 percentage points. This reflects the fact that foreign banks are not disadvantaged relative to domestic banks when lending to small and medium-sized businesses. If anything, their presence in the area has had a positive impact on the ability of firms to access external funding. The negative relationship between the percentage of foreign banks and the intensity of credit constraints is also shown graphically in Chart 4.9.

If the positive impact that local bank concentration has on credit constraints reflects the increased ability of banks to build relationships with firms (as economic theory would suggest), this impact should be stronger for relatively opaque firms, for whom such lending relationships are most important.

Columns 2 to 7 in Table 4.2 provide evidence to support this assertion. These columns show interaction terms between the HHI index and various firm-level characteristics. They show that bank concentration reduces credit constraints particularly strongly for smaller firms (column 2), younger firms (column 3), firms without any quality certification (column 4) and unaudited firms (column 5). Together, these findings suggest that, across the transition region, moderately concentrated credit markets may, to some extent, alleviate credit constraints for opaque businesses.

For instance, column 2 shows that a 1-standard-deviation increase in bank concentration reduces the probability of being credit-constrained by 9.4 percentage points for the smallest firms in the sample. For the average firm the reduction is only 5.4 percentage points. That impact becomes progressively smaller for larger and older firms. When a firm reaches 245 employees or 23 years of age, bank concentration starts to have a negative impact on access to credit, indicating that larger and older firms benefit from inter-bank competition.

Columns 6 and 7 of Table 4.2 explore this idea further. In each column the firm sample is split into two groups. The impact that credit market concentration has on credit constraints is then estimated for each of these groups separately. Column 6 distinguishes between firms in high-tech and low-tech industries. High-tech industries are characterised by larger information asymmetries and more severe agency problems between borrowers and lenders. This reflects both the inherent riskiness of high-tech investments and the fact that in high-tech industries collateral is typically intangible. It is therefore easier for firms in low-tech industries to obtain financing via arm’s-length lending techniques, and this type of lending tends to perform better in less concentrated lending markets. The data support this theory, revealing that the impact that local bank concentration has on credit constraints in high-tech industries is almost twice the size of that seen for low-tech industries.

Next, column 7 distinguishes between industries with high (above median) and low (below median) dependence on external finance. Dependence on external finance is calculated by averaging, for each industry, the percentage of working capital that firms in that industry derive from sources other than internal funds and retained earnings (as reported by firms in the BEEPS V survey). As expected, the data show that the impact of bank concentration is more pronounced in industries that are heavily reliant on external funding.

But how exactly does the ability to borrow from a bank help firms to become more innovative? Chart 4.10 provides some initial information on this subject, showing that firms change the way they innovate once credit constraints are loosened. The red bars show the breakdown of innovation strategies for credit-constrained firms, whereas the green bars show the breakdown for firms without financial constraints. Overall, the main strategies that firms use to introduce new technologies involve: (i) the exploitation and implementation of their own ideas; (ii) the licensing (or informal imitation) of products and processes developed by other firms (typically competitors); and (iii) the development of new products and the upgrading of production processes in cooperation with suppliers, clients and academic institutions.

When comparing the red and green bars, one key difference is the fact that unconstrained firms appear to find it easier to use (and pay for) external ideas. The percentage of firms that are reliant on their own ideas declines from 55 to 49 per cent in the case of product innovation and from 55 to 46 per cent in the case of process innovation. This decline is mirrored by increases in cooperation with suppliers and – more frequently – the licensing of products and processes developed by other firms. This suggests that access to credit may allow firms to access and implement external know-how more quickly and more easily. It also indicates that bank credit can help to facilitate the spread of technology across firms.

Table 4.4 looks at these issues in more detail, applying a similar statistical framework to Table 4.3. More specifically, the table takes a set of innovation outcomes and looks at whether they are affected by firms’ access to credit.

One striking result is that access to credit allows firms not only to introduce products and processes that are new to the firms themselves, but also to adopt products and processes that are new to the main markets where the firms sell their goods or services. This is particularly true of firms that serve a local market, but somewhat less clear-cut for firms that operate at national level. Access to credit helps firms to introduce technologies that are already available elsewhere in the country (or abroad) but are not yet available in their own local markets.

In line with some of the findings of Chart 4.10, this suggests that easier access to credit may help technologies to spread within countries and across local markets. Thus, policies that reduce credit constraints may have collateral benefits in the form of greater intranational diffusion of technology and a gradual reduction in regional growth disparities.15

Importantly, Table 4.4 also indicates a notable limitation of bank credit. In keeping with some of the arguments that were set out at the start of this chapter, data for the transition region show that there is no correlation between easier access to bank credit and in-house innovation in the form of R&D.

This suggests that while bank credit may help firms to adopt existing products and processes that have been developed elsewhere – technologies that are new to those firms and, in many cases, new to local and even national markets – it does little to boost original R&D by firms.