Israel also has a more compressed distribution of firm productivity than any of the other countries shown – possibly because Israeli firms tend to be more advanced in terms of the technology they are using, but also because Israel is more competitive than the average transition country.4 The ratio of the 90th to the 10th percentiles of the log of labour productivity – a measure of variation in productivity across firms – ranges from 1.19 in Israel to 1.59 in Tajikistan. In most EBRD countries and regions this ratio tends to be higher for services than it is for manufacturing. Within manufacturing, the productivity spread tends to be lowest in high-tech sectors, which face strong competitive pressure to innovate and reduce costs. The spread is highest among providers of services, which (unlike producers of manufactured goods) do not face such strong competition from imports.

There is evidence that the performance of sectors which produce or are heavily reliant on information and communication technology (ICT) and their ability to innovate and adopt technology are important drivers of cross-country differences in aggregate productivity.5

ICT-intensive sectors are characterised by high levels of labour productivity, and this holds for the transition region as well. The largest productivity premiums for these sectors relative to other manufacturing industries can be found in central Europe and the Baltic states (CEB), south-eastern Europe (SEE), and eastern Europe and the Caucasus (EEC). Within the EEC region, this is particularly true of Armenia and Azerbaijan, two countries with a strong focus on ICT in their innovation policies.6 However, in most countries differences between the productivity levels of individual firms are also large within ICT-intensive sectors. Thus, even in these sectors, it seems that many firms have ample scope for improving their productivity.

There may be reasons why the correlation observed between innovation and productivity is weaker than the true underlying impact that innovation has on productivity. For example, if poorly performing firms find themselves under greater pressure to innovate, innovation may appear to be linked to poor short-term performance, despite improving firms’ productivity in the longer run.

In order to deal with such issues appropriately, we need a more comprehensive analysis of the relationship between innovation and firms’ productivity that accounts for factors that may affect both firms’ productivity and the decision to innovate. To this end, this chapter uses a well-known model devised by Crépon, Duguet and Mairesse (known as the “CDM model” – see Chart 2.1.1) that links R&D, innovation and labour productivity.10 The model controls for other factors that can affect R&D, innovation and labour productivity, such as a firm’s size and age, the skills of the workforce, the level of competition and the type of industry (see Box 2.1 for details).

Once these factors are taken into account, the impact that innovation has on productivity becomes stronger.11 Product innovation is associated with a 43 per cent increase in labour productivity, and this effect has a high degree of statistical significance. This suggests that a firm with median labour productivity would move from the 50th to the 60th percentile of the labour productivity distribution after introducing a new product. Labour productivity also benefits from the implementation of process innovations. Although this effect is smaller (with the introduction of new processes being associated with a 20 per cent increase in labour productivity), it is also statistically significant. A firm with median labour productivity would move from the 50th to the 55th percentile of the labour productivity distribution after introducing a new process. These effects are somewhat stronger than those found for developed economies, but they are comparable to those observed in developing economies.12

Interestingly, the increase in labour productivity is smaller for firms that engage in product and process innovation simultaneously than it is for those that engage exclusively in either product or process innovation. This can be explained by the fact that simultaneous product and process innovation is more complex and takes longer to be fully reflected in increased labour productivity, while BEEPS data only enable us to look at the short-term impact of new products and processes.

The estimated effects are stronger when using self-reported measures of innovation than when using cleaned measures. In the case of product innovation, the estimated improvement in productivity is 69 per cent when a self-reported measure of innovation is used, compared with a 43 per cent improvement when using a cleaned measure. This could be because almost a quarter of all self-reported product innovations and 11 per cent of all self-reported process innovations were in fact either organisational or marketing innovations (see Box 1.1), which nevertheless result in increased turnover per worker. Indeed, the increase in labour productivity associated with self-reported organisational and/or marketing innovation is estimated at 67 per cent.13 Organisational and marketing innovations are probably less risky and costly for firms than technological innovations and, given these high productivity yields, it is perhaps surprising that less than a third of all BEEPS firms engage in either. This could be due to a lack of information on new organisational and marketing methods, scepticism regarding their effectiveness or resistance to change within organisations.14

These effects are fairly sizeable, but they are not as large when placed in the context of the labour productivity distribution. A low-tech manufacturing firm with median labour productivity would move from the 50th to the 82nd percentile of the labour productivity distribution after introducing a new product. A higher-tech manufacturing firm, on the other hand, would move from the 50th to the 69th percentile of the labour productivity distribution.17

This variation in estimated returns to innovation can be explained by differences in the probability of introducing new products and the level of competitive pressures faced. Firms in high-tech manufacturing sectors are more likely to introduce new products (see Chart 2.3) and more likely to compete in national or international markets (as opposed to local markets). While these competitive pressures may explain why firms have greater incentives to introduce new products, they may also limit returns to innovation because such firms tend to be fairly productive in the first place. In low-tech manufacturing sectors, on the other hand, most innovations come from suppliers of equipment and materials,18 so low-tech firms’ ability to innovate depends crucially on their ability to adapt their production processes and the adaptability of their employees.19 The relatively small number of firms that manage to adapt and introduce new products successfully may manage to capture a larger market share as a result of their innovations, thereby increasing their output per worker. Some innovations by firms in low-tech manufacturing sectors may be due to firms moving production from China to eastern Europe owing to rising wage costs in China and the increasing cost of fossil fuels.20

In a management field experiment looking at large Indian textile firms, improved management practices resulted in a 17 per cent increase in productivity in the first year through improvements in the quality of products, increased efficiency and reduced inventories.23 This suggests that improving management practices may be a relatively low-cost and low-risk way of boosting firms’ productivity across the transition region.

BEEPS V includes a subset of questions on management practices taken from the Management, Organisation and Innovation (MOI) survey conducted by the EBRD and the World Bank.24 These questions look at core management practices relating to operations, monitoring, targets and incentives. They range from dealing with machinery breakdowns to factors determining the remuneration of workers. On the basis of firms’ answers, the quality of their management practices can be assessed and given a rating, which can then be used to explain productivity levels (see Box 2.2 for details).

Estimates suggest that improving the average firm’s management practices from the median to the top 12 per cent is associated with a 12 per cent increase in labour productivity, everything else being equal (see Table 2.3). The estimated impact on productivity is larger still when process innovation is also accounted for (standing at 19 per cent). Despite these sizeable effects, estimated returns to better management practices tend to be somewhat lower than returns to innovation, regardless of the type of innovation.

There are significant differences across regions in terms of the role played by improved management practices in boosting firms’ productivity. In EU member states, candidate countries and potential candidate countries (in other words, the CEB and SEE regions), where the quality of management practices tends to be higher, returns to further improvements in management practices are lower than returns to process innovation (see Chart 2.4). In the SEE region, process innovation is associated with an increase in labour productivity of more than 150 per cent. This may be largely due to the upgrading of production facilities with the aim of being more competitive in the EU market.

On the other hand, in less developed countries, where the quality of management is generally lower, returns to better management practices are much higher than returns to process innovation. In the EEC region, for example, better management practices are associated with a 40 per cent increase in labour productivity, whereas the introduction of a new process is associated with a mere 6 per cent increase. In Russia returns to better management and process innovations are estimated at 32 and 2 per cent respectively.

These findings raise the question of why firms in these regions (and less developed countries more generally) do not adopt better management practices. The recent management field experiment looking at large Indian textile firms suggests that this may be due to information barriers. Firms might not have heard of some management practices, or they may be sceptical regarding their impact.25 Improvements to certain management practices – particularly those relating to underperforming employees, pay or promotions – may also be hampered by regulations or a lack of competition (since competition could force badly managed firms to exit the market).

Training programmes covering basic operations (such as inventory management and quality control) could be helpful, but suitable consultancy or training services offering such products may not exist in a given market or may be geared towards large firms, making them too expensive for SMEs.26

The EBRD’s Business Advisory Services (BAS) and Enterprise Growth Programme (EGP) promote good management practices in micro, small and medium-sized enterprises (MSMEs) in the transition region, providing direct support to individual enterprises.27 Box 3.4 analyses links between the use of consultancy services, innovation, management practices and productivity in the transition region.

Examining the relationship between innovation and economic performance at the country level poses its own challenges, as many factors will affect a country’s growth and, at the same time, be related to the country’s ability to innovate. In an effort to overcome this problem, the analysis below focuses on the performance of individual industries. It seeks to explain differences between the average rates of export growth of industries with different levels of innovation intensity (as defined in Chapter 1) across various countries over the period 1990-2010.29

The growth rates of industries’ exports can be affected by a number of country-level characteristics (such as macroeconomic conditions or political stability), as well as a number of industry-level characteristics. For instance, industries which cater for consumer demand in emerging markets may grow faster.

In addition, certain industries may grow faster in countries with specific characteristics. In particular, better economic institutions may enable the exports of innovation-intensive industries to grow more rapidly.

Indeed, poor economic institutions – high incidence of corruption, weak rule of law, burdensome red tape, and so on – can substantially increase the cost of introducing new products and greatly increase the uncertainty of returns to investment in new products and technologies. As a result, risk-adjusted returns to innovation may look less attractive when economic institutions are weak. This will primarily affect industries where the introduction of new products and technologies is essential in order to maintain the competitiveness of exports, so firms tend to introduce new products more frequently – in other words, innovation-intensive industries.

The BEEPS results provide some support for this view. Firms that have introduced a new product in the last three years regard all aspects of their immediate business environment as a greater constraint on their operations than firms that do not innovate. Such differences between innovative and non-innovative firms’ perception of their business environment are particularly large when it comes to the skills of the workforce, corruption and customs and trade regulations (as discussed in more detail in Chapter 3).

In order to examine the relationship between the quality of economic institutions and the growth of innovative industries, we can look at growth rates for the exports of various industries in various countries.30 These can be explained by country fixed effects (roughly corresponding to the average growth rates of total exports in individual countries) and industry fixed effects (namely the average growth rates of global exports for individual industries), as well as the initial exports of a given industry in a given country, expressed as a percentage of that country’s total goods exports. In addition, regressions include interaction terms between the innovation intensity of a given industry and a country-level characteristic: either the quality of economic institutions or the level of financial development. A positive and significant coefficient for the interaction term between innovation intensity and the quality of economic institutions would imply that innovation-intensive exports grow relatively fast compared with other exports in countries that have superior economic institutions.

The quality of economic institutions is measured using the average of four of the World Bank’s Worldwide See Kaufmann et al. (2009) for a discussion.”]31[/tooltip] These indicators range from -2.5 to 2.5, with higher values corresponding to stronger underlying economic institutions. Financial development is captured by the ratio of private-sector credit to GDP (as reported in the World Bank’s Global Financial Development Database)32 and primarily reflects the level of development of banking services. In order to see whether these same factors influence the incidence of innovation in advanced economies and emerging/developing economies, the relevant coefficients were allowed to vary between the two groups of countries.33

The results are presented in Table 2.4. They suggest that the exports of innovation-intensive industries do grow faster relative to other exports in countries with stronger economic institutions and that this effect is statistically significant. These estimates also indicate that the impact the quality of institutions has on the relative performance of innovation-intensive exports is greater in emerging/developing economies than it is in advanced economies (where the quality of economic institutions tends to be higher).

In order to understand the magnitude of this effect, we can look at one industry which is in the top 25 per cent in terms of innovation intensity (for instance, pharmaceuticals) and another which is in the bottom 25 per cent (such as basic metals). A 1-standard-deviation improvement in the quality of economic institutions (say, from the level of Albania to that of Poland) will boost the average growth rate of the exports of the more innovation-intensive industry, pharmaceuticals, by an extra 0.35 percentage point a year relative to the growth rate of basic metals. In the case of emerging markets, the extra growth premium for the more innovation-intensive industry stands at 0.95 percentage point a year. This is a sizeable difference, given that the median rate of growth across all industries and countries in the sample is around 8 per cent.

The specifications reported in columns 3 and 4 suggest a similar relationship with financial development, with the exports of innovation-intensive industries also growing faster relative to other exports in countries with higher credit-to-GDP ratios. This reflects the fact that industries that are more innovation-intensive may be more reliant on the availability of credit in order to fund investment in the development of new products (as discussed in more detail in Chapter 4 of this report).

Another important source of labour productivity gains is improvements in the quality of management. In less developed transition countries, where the quality of management is generally poor, returns to improvements in management are high, while returns to process innovation are generally low.This suggests that management practices need to be improved before new processes can lead to sizeable productivity gains. In contrast, in the CEB and SEE regions, where management practices tend to be better, returns to the introduction of new processes exceed returns to further improvements in management.

Cross-country analysis of the exports of various industries suggests that industries involving higher levels of innovation are able to grow faster, thereby driving overall economic growth – provided that the business environment is accommodative. These estimates also imply that the quality of the business environment is particularly important for the development of innovation-intensive industries. The results suggest that improvements in the quality of economic institutions are associated with increases in the innovation intensity of exports and output over time as innovation-intensive industries grow faster and their relative contribution to the country’s exports rises. Chapter 3 examines the relationship between the quality of the business environment and firm-level innovation in more detail.