Industrial policies
supporting firms

The inability of young firms to grow fast enough can hinder their transformation into large firms, which tend to be more productive and innovative. Larger firms (especially those with over 100 employees) are the primary drivers of job reallocation (both job creation and job destruction) in the EBRD regions. Those larger firms often pay better wages, attract workers from smaller companies during crises and benefit from economies of scale. Moreover, the presence of large firms – particularly “superstar” firms whose markups are above the average for their industries (exporting domestic firms or multinationals, for instance) – can generate positive spillover effects at a local level, such as productivity increases in firms that supply to large firms entering a new market.1

Over the past two decades, EBRD economies have made greater use of direct state assistance when seeking to address the challenges faced by young firms (although the overall level of such assistance remains low compared with more advanced economies). This chapter reveals that direct state assistance often lacks differentiation and targets firms indiscriminately – a problem that is not unique to EBRD economies and is also prevalent in more economically advanced regions. Countries could benefit from making their industrial policies more targeted, addressing the specific challenges faced by young firms and designing interventions that support their growth and scaling-up processes.

The second dataset used is the EBRD-World Bank Business Environment and Enterprise Performance Survey (BEEPS), which has covered more than 50,000 firms across 44 countries over 14 years, offering insights into firms’ financial situations, innovation practices and business obstacles. While this is a survey-based dataset and only covers a subsample of the firm population, it provides novel insights into innovation practices, business obstacles and other aspects of firms’ circumstances that are otherwise difficult to observe.

Third, Eurostat’s Structural Business Statistics (SBS) contain official aggregate data for all EU member states over time. Although Eurostat provides high-quality statistics, these data are only available at an aggregate level and are limited to EU countries.

The fourth dataset, Worldscope, is a comprehensive financial database that provides detailed and standardised financial information on publicly listed companies worldwide. Worldscope is used to describe “superstar” firms in the EBRD regions and compare them with their peers in other emerging market economies.

Larger firms are often more productive for a variety of reasons.6 First, they can exploit economies of scale and spread costs over larger amounts of output, resulting in lower average costs per unit of production. Second, better managerial practices are associated with higher levels of productivity: since larger firms attract top managers, their productivity can also be explained by their superior managerial practices.7 More generally, larger firms are able to pay higher wages and are therefore able to attract and retain more skilled workers, which in turn increases their productivity. Third, larger firms can take advantage of a more specialised labour force, which can increase efficiency and productivity.8 Lastly, larger firms tend to invest more in R&D and are more likely to adopt advanced technologies, which make production processes more productive and efficient (as the next section will show).

If promising new firms are to reap the benefits of operating at scale (such as increased productivity and innovation), they must scale up swiftly without internal or external constraints. This section looks at (i) whether young firms in the EBRD regions face more severe frictions than their counterparts in advanced economies, (ii) which frictions affect them most, and (iii) the importance of such firms for the generation of employment in the EBRD regions.

These questions are addressed using Orbis data for the period 2016-21, with firms classified on the basis of their age and size. “Young” firms are five years old or less, while “mature” firms are more than five years old. Firms are classified on the basis of size using the following commonly applied criteria: “micro-firms” have nine employees or fewer; “SMEs” have between 10 and 99 employees; and “large” firms employ 100 people or more. Table 4.1 summarises this classification.

One indication that younger firms may face constraints on their growth is their significantly higher average return to capital compared with mature large firms (see Chart 4.6). If younger firms were not facing constraints on their growth, their average return to capital would be comparable to that of firms that had grown in size over longer periods.12 As Chart 4.6 shows, this is not the case. In fact, after accounting for sector, year and country fixed effects, young firms (of all sizes) exhibit significantly higher average returns to capital (measured as the log of the ratio of operating revenues to total assets) relative to mature large firms. This suggests that, both in the EBRD regions and (to a lesser extent) in comparator countries such as Portugal, there are potential gains to be reaped from reallocating more capital to younger firms.

In the EBRD regions, mature micro-firms – the firms with the weakest growth – account for more than half of the total business landscape, whereas young micro-firms make up one-third of all firms (see Chart 4.8). Mature SMEs account for a further 12 per cent, young SMEs make up about 2 per cent, mature large firms account for approximately 1 per cent, and young large firms make up just 0.1 per cent. Given their importance for job creation, policies should focus on young firms, which make up a small percentage of total firms, but account for a larger share of total job creation.

The revenue shares of top 5 and top 15 firms have been growing in recent years, both in the EBRD regions and in other emerging markets (see Chart 4.9). In the EBRD regions, the median revenue share of top 15 firms increased from 5 per cent in 2005 to 11 per cent in 2022, while the median revenue share of top 15 firms in other emerging markets rose from 13 per cent in 2005 to 22 per cent in 2022. The rise of large firms brings both benefits and challenges, which have significant policy implications. On the one hand, large firms tend to be more productive and invest heavily in R&D, stimulating innovation. On the other hand, however, they operate as oligopolists, leading to increases in market concentration. This dominance creates barriers to the entry of new market participants, undermining the competitive environment that fosters dynamic economic activity. What is more, idiosyncratic shocks that affect those very large firms can spread throughout the entire economy, causing large aggregate shocks to GDP and impacting all firms.16 These features of the growth of large firms have important policy implications. Policymakers should balance the benefits of large firms’ innovation and productivity with the need to prevent excessive market concentration. When implementing industrial policies, the need for anti-trust regulations and support for SMEs should also be taken into consideration.

Looking at the ultimate owners of publicly listed firms in emerging markets, we can see that the increase in the aggregate revenue share of top firms has been driven mostly by privately owned enterprises, while the revenue share of state-owned enterprises (SOEs) has been relatively stable (see Chart 4.10). This is true of both EBRD economies and other emerging markets, although the distribution of the revenue share of privately owned enterprises is broader in other emerging markets.

However, looking at firm markups, the aggregate markup for SOEs is still much higher than the equivalent figure for privately owned enterprises (see Chart 4.11). SOEs in the EBRD regions exhibit significantly higher markups than their counterparts in other emerging markets, while private firms’ markups are similar across the two groups of economies. The significant increase seen in the markups of SOEs in the EBRD regions has been driven mostly by the mining sector in recent years and by the transport sector and public utilities in the period before that. This disparity in markups may reflect differing levels of competition and market efficiency, particularly for SOEs in EBRD regions, highlighting the need for policy interventions to enhance the competitiveness of markets.

There are several emerging markets and developing economies where the top five exporters account for a significant percentage of total exports. In the Kyrgyz Republic, for example, the top five exporting firms account for 48 per cent of total exports. In Zambia, meanwhile, the equivalent figure is a striking 82 per cent – the highest figure in the group of comparator countries (see Chart 4.12). Similar trends can be observed in some advanced economies: in France, for example, export champions in export-intensive sectors make a major contribution tototal exports.17

The rise of “superstar” firms in the EBRD regions and other emerging markets and developing economies presents both opportunities and challenges. The increasing concentration of revenue among the top firms signals a shift towards a situation where more market power is in the hands of a few private actors. While this could help to drive efficiency and innovation as a result of economies of scale, it also raises concerns about market competition, especially in sectors where SOEs continue to maintain higher markups despite stable revenue shares. In addition, the heavy reliance on a few dominant exporters underscores the need for policies that broaden the export base, reduce barriers to market entry and enhance economic resilience.

So far, this chapter has shown that large firms generally demonstrate greater productivity and innovation. Those companies play a crucial role in economic dynamics, accounting for a significant proportion of both job creation and job destruction. However, young firms drive most net job creation and exhibit stronger growth than their mature counterparts. Ideally, successful young firms should be able to scale up rapidly in order to capitalise on economies of scale. However, evidence suggests that young firms in many EBRD economies face constraints, as indicated by their unusually high returns to capital. BEEPS data point to several challenges that these firms encounter, including competition from the informal sector, corruption, inefficient legal systems and infrastructure barriers (such as unreliable electricity supply). The most effective way for policy to support promising young firms would be to reduce these barriers. This could involve improving infrastructure, ensuring reliable access to electricity, combating corruption and reforming the justice system to enhance the efficiency of the courts. Such measures would create a business environment that was more conducive to young firms thriving, growing and contributing to economic growth.

In addition, governments can also employ more active industrial policies, especially when there are indications that promising young firms with projects with high net present values are financially constrained or lack the collateral required to obtain credit from private banks. The next section of this chapter looks at the question of state support for firms, comparing the EBRD regions with advanced and emerging market economies. It also addresses the need for governments to target firms irrespective of size, focusing on the companies with the greatest growth potential. Box 4.1 discusses the EBRD’s Star Venture programme, which is an example of how to stimulate the startup ecosystem in emerging market economies.

The analysis in this section is based primarily on the Global Trade Alert database, which provides information on state interventions affecting trade in goods and services, foreign investment and labour force migration (see Chapter 1 for more details). In order to identify direct state assistance, the GTA database was filtered to look only at the intervention types listed in Table 4.2, with the analysis covering 23 EBRD economies plus comparator countries over the period 2009-23.19 Furthermore, firm-specific policies were filtered out, in order to prevent the inclusion of direct state assistance that only targeted one specific firm. A two-pronged approach was used for this: first, all firm-specific policies as identified by the GTA database were excluded; and second, policy descriptions were fed into ChatGPT in order to remove any other firm-specific policies from the dataset.20

The resulting dataset included 705 direct state assistance policies in EBRD economies over the period 2009-23 (which accounted for 12.18 per cent of the total GTA sample for those economies over that period).

Some of those studies show that policies have a positive effect on the employment and investment levels of the beneficiary firms – which implies that they can, in principle, address major constraints on firms’ growth (such as credit constraints), especially for SMEs. Horváth and Lang (2021) found that Hungary’s Funding for Growth Programme, a large-scale subsidised loan programme implemented by the country’s central bank, had resulted in increases in employment, productivity and investment in SMEs that had benefited from the policy. Similarly, Beņkovskis et al. (2019) found that Latvian firms benefiting from the support of the European Regional Development Fund saw immediate increases in their employment, turnover and capital stock per employee, while productivity growth did not come until two years later.

There is a growing body of evidence suggesting that while government support can boost the performance of individual firms, this may come at the cost of displacing competitors’ growth. There are studies in various countries illustrating this trade-off. In China, for instance, Cai and Szeidl (2024) found that firms benefiting from government loan programmes improved their performance, but at the expense of their competitors. Similarly, Rotemberg (2019) observed that subsidies for small firms in India led indirectly to losses for ineligible competitors. Ru (2018), meanwhile, showed that loans from the China Development Bank to SOEs had crowded out private firms in the same industries, while crowding in downstream private firms, especially more efficient ones.

Other research has examined the negative spillover effects that state assistance has on ineligible firms in the relevant sectors or clusters. For example, Blonigen (2016) discovered that sector-specific aid could harm the export competitiveness of downstream sectors, and Du et al. (2023) noted that while subsidised firms in China experienced productivity boosts, non-subsidised firms in the relevant clusters saw a weakening of productivity growth.

The economies in the EBRD regions are no more reliant on state assistance than many other economies. As Chart 4.14 shows, state assistance accounts for a significantly higher percentage of total industrial policies in advanced economies such as Spain and France, where that figure exceeds 50 per cent. This is well above the 35 per cent seen in Lithuania, which is the highest level in the EBRD regions. At the other end of the spectrum, Armenia has the lowest figure, with state assistance accounting for just 2 per cent of all industrial policies. Overall, use of state assistance in the EBRD regions tends, on average, to be lower than in wealthier countries such as Spain and France as a percentage of total industrial policies, but higher than in emerging market economies such as India, China and Brazil, where state assistance accounts for just 7 per cent of all industrial policies. It should be noted, however, that the number of state assistance policies is far lower in the EBRD regions than it is in other emerging markets and advanced economies. China, for example, implemented 389 state assistance policies between 2009 and 2023, while Spain and France both implemented more than 550. The economies of the EBRD regions averaged 47 state assistance policies each over that period, compared with averages of 74 and 180 for other emerging markets and advanced economies respectively.

EBRD economies in the EU make greater use of state assistance than other EBRD economies. Indeed, in 2023 less than 10 per cent of the total number of state assistance policies in the EBRD regions were in economies outside the EU. However, a significant percentage of the state assistance policies that are used in EBRD economies in the EU have some form of EU involvement through the European Commission, the European Investment Bank (EIB), the European Agricultural Fund for Rural Development (EAFRD), the European Investment Fund (EIF), the European Maritime, Fisheries and Aquaculture Fund (EMFAF), the European Agricultural Guarantee Fund (EAGF) and other supranational EU policies. This reflects the fact that EBRD economies in the EU are heavily reliant on external international support when directing state assistance to their firms (see Chart 4.15).

A closer look at the specific instruments employed in the EBRD regions reveals that direct grants and loans are the most commonly used forms of state assistance. However, their relative popularity has shifted over time, as Chart 4.16 shows. In 2023, direct grants accounted for a substantial 81 per cent of all state assistance policies, following significant increases in their use over time. Conversely, loans now make up less than 8 per cent of all state assistance policies, pointing to a decline in their use.

Within the category of direct grants, we can see that financial grants and production subsidies feature prominently. Indeed, as Chart 4.17 illustrates, financial grants account for 55 per cent of all state assistance provided. In the category of tax-based advantages, tax or social insurance relief is the most common form of state assistance.

These trends underscore the evolving nature of state assistance in the EBRD regions, with governments showing a clear preference for direct grants, especially in the form of financial grants and production subsidies. While loans and tax-based advantages still have a role to play, their relative importance has diminished over time.

Startups with strong growth potential are vital for market economies owing to their innovation, rapid scaling, job creation and revenue generation. The entrepreneurs behind such firms are typically well educated and driven, requiring less help with basic business skills and more specialist advice on refining their business models and attracting investment. Studies show that, in addition to financing, entrepreneurial know-how, management skills and market access are also essential for successful scaling.26

The Star Venture programme provides tailored technical assistance to high-potential startups, offering business know-how, mentorship and access to risk capital. This support focuses on refining business models, improving product-market fit and positioning firms for external investment.

In order to qualify for support, startups must have a marketable product or service, demonstrate strong growth potential and be less than 10 years old. Startups are recruited through public calls for applications, after which EBRD staff and consultants shortlist candidates on the basis of funding and capacity. Shortlisted startups pitch to judges, who score them in six areas, and the top-scoring firms are invited to join the programme. Once they have been selected, startups gain access to a network of mentors, investors and business tools. Over the past four years, Star Venture has supported more than 250 tech startups and 33 local accelerators across 26 economies, with a budget of €25.7 million provided by 12 donors.

This box assesses the causal effect of the Star Venture programme by comparing the performance of participating startups with that of a similar group of startups that were shortlisted but not selected. A quasi-experimental method (a regression discontinuity design) is used to distinguish between the effects of the programme itself and the impact of the initial selection process. The comparison focuses on startups that are near the cut-off point for selection, which is determined by the cohort’s capacity. (For example, if eight startups are admitted, the cut-off point is after position 8 in the score-based ranking.)

The analysis uses data on 327 shortlisted startups across 23 cohorts in 11 countries/regions. Of those shortlisted startups, 155 were selected to participate in the Star Venture programme, while the other 172 firms were ultimately rejected. The cohorts in question embarked on the programme between 2019 and 2022.

The causal effect of the Star Venture programme is assessed by comparing outcomes for participating startups one and two years after joining with equivalent outcomes for startups that were not selected, using funding, employment and numbers of LinkedIn followers as indicators of success. The results indicate that participation in the programme leads to substantial improvements in key business metrics. In terms of funding, participation in the programme results in startups securing an average of US$ 1.34 million more in funding within one year of joining, with that figure rising to US$ 2.17 million after two years. On average, participating startups also recruit 12 employees more within one year of joining, with that employment growth remaining robust in the second year. And in terms of market reach, participation in the programme results, on average, in startups achieving 3,577 LinkedIn followers more (based on data as at March 2024), pointing to enhanced market visibility and improvements in brand recognition and market access.

These outcomes highlight the programme’s effectiveness in facilitating the financial and operational scaling of high-potential startups. A summary of the main results can be found in Chart 4.1.1.

The Star Venture programme provides robust evidence that structured, tailored business assistance can play a pivotal role in the growth of startups in emerging markets. Its combination of strategic business training, targeted advisory support and mentorship has proven to be particularly effective in helping startups to overcome growth challenges and achieve scalability. These findings offer valuable insights for the design and implementation of entrepreneurship support programmes in similar contexts.

Against this backdrop, the privatisation of SOEs in the former East Germany after reunification offers important lessons. One of the largest and most rapid privatisation programmes in history, this required the creation of a new agency – the Treuhand – to implement industrial restructuring and privatisation within a very short time frame. The Treuhand’s mandate required it to ensure “efficiency and competitiveness” through “closure […] of companies that cannot be restructured”. In-depth analysis of contemporary and archival firm-level data by Mergele et al. (2024) reveals three key findings about the Treuhand’s performance:

1. Using initial labour productivity as a simple measure of “competitiveness”, more productive firms were (i) more likely to be privatised (rather than liquidated), (ii) privatised faster, (iii) sold for higher prices and (iv) more likely to be acquired by buyers in the former West Germany. The agency relied on internal firm rating scores assigned with the help of management consultants, which were an important predictor of liquidation decisions (as demonstrated in Chart 4.2.1), even after controlling for other factors.
2. Firms with higher initial productivity that were not liquidated by the agency were more likely to survive in the long run (up to 20 years post-privatisation). The internal firm rating scores can also help to predict survival post-privatisation.
3. Using a machine learning approach to compare the Treuhand’s actual choices with counterfactual scenarios involving the liquidation of different sets of firms, the actual outcomes of the privatisation programme can be benchmarked against those alternative scenarios. This analysis suggests that while the Treuhand successfully avoided the worst possible outcomes (that is to say, it did not target the firms with the lowest predicted probability of survival), it did not achieve the best possible results, either (that is to say, it failed to target the firms with the highest predicted probability of survival).

These findings contain three lessons for the design and implementation of industrial policies for firms. First, they suggest that the agency was generally able to identify unviable firms and select them for liquidation. Thus, government agencies may be able to pursue their mandated objectives even in the face of potential pressure from political interest groups. Second, they show that the Treuhand’s internal firm ratings proved to be valuable inputs when deciding which enterprises to privatise and liquidate. Moreover, the Treuhand’s central office achieved better privatisation outcomes than regional branches, pointing to the importance of having access to detailed information and centralised institutional expertise. And third, they indicate that the rapid pace of privatisation may have come at the expense of achieving the best possible outcomes in terms of retaining viable firms and maintaining local ownership.

These insights from Germany’s historical experience of privatisation highlight some of the challenges of implementing large-scale industrial policies and privatisation programmes. They demonstrate the importance of building institutional capacity, establishing careful firm selection processes and balancing speed with the need to ensure optimal long-term outcomes. Even then, policymakers need to recognise the practical difficulties not only of picking winners, but also of letting losers go.