Tag: Innovation

Gender Diversity and Firm Innovation in Post-Communist Economies

Posted on February 17, 2025 | Policy Brief

diverse group of professionals, including men and women, engaged in an innovation-focused business meeting, promoting gender diversity and collaboration.

This policy brief examines how gender diversity in key organizational positions—owners and employees—affects firm innovation outcomes in post-communist economies. Utilizing Business Environment and Enterprise Performance Survey (BEEPS) data, we analyze the impact of gender diversity through the Doing-Using-Interacting innovation framework. Our findings suggest that gender diversity enhances innovation through two primary channels: managerial practices (Doing) and technology adoption (Using). Policymakers and business leaders in post-communist settings must recognize these pathways and develop strategies to harness the benefits of diversity-driven innovation.

Why Gender Diversity Matters

Gender diversity has emerged as a crucial factor in shaping organizational innovation and performance. Previous research has highlighted the significant role of gender in managerial practices and decision-making processes and demonstrated that a balanced gender composition, particularly in leadership roles, positively impacts an enterprise’s performance (Ruiz-Jiménez and Fuentes-Fuentes, 2016; Tonoyan and Boudreaux, 2023). Gender diversity can enhance problem recognition and problem-solving capabilities, which are critical for innovation. Moreover, gender-diverse teams exhibit superior decision-making, creativity, and adaptability, which contribute to the development of innovative products and strategies (Tonoyan, Boudreaux, 2023; Østergaard et al., 2011). Conversely, homogeneous teams often suffer from limited idea generation, weaker interpersonal dynamics, and lack of constructive conflict, leading to missed opportunities for innovation. However, the impact of gender diversity on innovation is not uniformly positive and depends on how diversity is managed within organizations. Factors such as industry type, organizational culture, team dynamics, and institutional context influence whether gender diversity enhances or hinders innovation (Joshi et al., 2015, Machokoto et al., 2020).

Recently, a sizable literature has been devoted to understanding the role of gender diversity as an innovation driver in emerging economies, where firm innovation remains lower than in advanced economies (Chkir et al., 2021), and gender diversity practices differ from those in the developed world. In particular, research has established that also in emerging economies firms with gender-diverse ownership or top management demonstrate higher innovation output and that the impact of gender diversity on innovation is stronger in less-advanced emerging economies (Machokoto et al., 2023, Tonoyan, Boudreaux, 2023). As concerns the impact of gender diversity among employees the results on innovation in an emerging country context have been mixed (see e.g., Na and Shin, 2019 and Madison, et al., 2022). However, the empirical channels through which gender diversity influences innovation in emerging economies are still not well understood.

This brief contributes to this understanding in the particular context of post-communist economies. It examines the impact of gender diversity on innovation within a DUI (learning-by-doing, by-using, and by-interacting) framework. This framework, introduced by Jensen et al. (2007), highlights the critical role of experiential and interaction-based learning in fostering innovation, and is particularly relevant in contexts with limited R&D resources, such as post-communist economies.

Our results show that gender diversity enhances innovation by strengthening learning-by-doing and learning-by-using processes. These insights can help shape policies and workplace strategies that promote gender equality and, in turn, foster innovation in these economies.

DUI and Gender Diversity

Traditionally, innovation has been closely linked to the STI (Science, Technology, and Innovation) framework. The STI mode emphasizes that innovation is driven by science and technology and is based on R&D, scientific human capital that increases a company’s absorptive capacity, research infrastructure, and connections with scientific partners Instead, the Doing-Using-Interacting (DUI) mode is based on non-scientific innovation drivers including practice, experience, experimentation, specialization in production, product customization, interaction and network. The DUI mode refers to the exchange of experiences and know-how that involve a large component of tacit knowledge. It is particularly relevant in contexts with limited R&D resources, such as post-communist economies, where practical and collaborative approaches are essential for innovation.

We argue that gender diversity within organizations can significantly enhance the DUI drivers of innovation by introducing varied perspectives, experiences, and collaborative dynamics. Learning-by-doing involves acquiring knowledge and skills through hands-on experience, routines, and iterative problem-solving in daily work activities. Gender-diverse teams can contribute to this process by offering a wider range of practical insights and approaches.  Learning-by-using driver focuses on the utilization and adaptation of technologies, machines, and equipment, as well as analyzing user feedback and customizing products to meet diverse needs. Gender diversity may enhance this aspect by integrating varied user experiences and preferences into the innovation process. Women and men may bring different insights into how technologies are used and adapted, leading to more comprehensive analyses of user needs and improved product development strategies. Learning-by-interacting occurs through communication among supply chain actors.  Innovation can also be a result of interactions, networks, informal relationships and organizational collaborations within and between organizations. Gender-diverse teams are better equipped to build inclusive relationships and foster trust within these networks. Varied communication styles and interpersonal dynamics enhance collaborative problem-solving and knowledge exchange. Diversity not only facilitates stronger connections with external stakeholders but also improves internal coordination, making organizations more adaptable and innovative.

The Relevance of DUI in Post-Communist Economies

Post-communist economies share a common institutional history of centrally planned systems, which shaped their innovation landscapes. The collapse of the Soviet Union triggered major economic, social, and technological transformations. While the Soviet model had a strong science and technology sector, it prioritized large-scale projects over market-driven innovation. Its linear innovation model focused on R&D but overlooked user needs, market dynamics, and interactive learning.

During the transition, these economies faced significant challenges, including limited financial capital, weak innovation management, and outdated technology. However, they retained a highly educated workforce, which became a key asset for innovation. Many post-communist economies now operate behind the technology frontier and rely heavily on imported technologies, making it essential to adopt innovation models that emphasize practical, collaborative learning over traditional R&D investments (Apanasovich et al., 2016; Marozau et al., 2021).

Most in-country analyses on modes of innovation have primarily focused on developed market economies. However, a study on Belarus (Apanasovich et al., 2016) found that the DUI mode is more effective than the STI mode in generating product innovation. This suggests that firms in post-communist economies may benefit more from hands-on, experience-based innovation than R&D-driven strategies. In this context, the DUI mode of innovation thus plays a crucial role by facilitating technology adoption, adaptation, and productivity growth. Gender diversity, in turn, may further enhance the effectiveness of the DUI drivers, as argued above.

Data and Method

Our analysis is based on a dataset of 2,871 enterprises across 22 post-communist countries from BEEPS (EBRD, 2020). BEEPS is one of the most comprehensive firm-level datasets available for post-communist economies, providing rich information on innovation, gender diversity, and institutional constraints.

The study utilizes generalized structural regression models with an ordinal output variable to assess the relationships between gender diversity, DUI drivers, R&D activities, and product innovation (see Figure 1). Innovation output is categorized by novelty as; no innovation (0), new-to-firm innovation (1), or new-to-market innovation (2). The indicators of DUI drivers used in the empirical specification follow Alhusen et al. (2021) and Apanasovich (2016). In particular, Doing represents managerial practices, including performance monitoring, employee awareness of production targets, performance-based incentives, strategic planning, and quality certifications. Using captures firms’ investments in innovation-enabling resources, such as purchasing new or upgraded machinery, licensing foreign technologies, and implementing formal employee training programs. Interacting reflects the extent of collaboration with external partners, including business memberships, trade associations, supplier relationships, and managerial stakeholder meetings.

Figure 1. Generalized structural equation model

We also consider R&D activities (RnD), measured through expenditures on acquiring external knowledge, in-house research and development, and contracted R&D engagements. Additionally, gender diversity is incorporated as a key explanatory variable, using the Blau index (Blau, 1977; Tonoyan & Boudreaux, 2023) to measure diversity in firm ownership (Blau_owners) and workforce composition (Blau_empl), where 0 indicates no diversity and 0.5 represents a balanced gender representation. These two variables were incorporated one by one (Model 1 and Model 2) and together (Model 3).

Our control variables include enterprise age (lnAge), firm size (lnSize), employee education levels (Univ_degree), foreign direct investment (FDI), CEO experience (LnCEO_experience), and whether the enterprise operates in the manufacturing sector. The Global Innovation Index (GII) score is used to account for the broader national innovation environment.

Results

The results of our empirical analysis are provided in Table 1 below.

The DUI drivers and the explicit R&D measure consistently show a positive and statistically significant relationship with innovation output. Gender diversity significantly enhances the DUI drivers that fuel innovation. Ownership diversity positively influences the Using driver by promoting technology adoption and employee training. Workforce diversity strengthens the Doing driver by improving managerial practices, such as performance monitoring and quality assurance. This suggests that a gender diverse workforce is better equipped to absorb, integrate, and apply knowledge – enhancing creativity and problem solving – ultimately fostering a more innovative work environment.

Table 1. Structural Regression Model Results

Additionally, our results indicate that larger and older firms, as well as those with foreign equity exhibit higher levels of DUI activity, underscoring also the role of organizational characteristics for innovation.

Conclusion

This policy brief highlights the role of gender diversity for firm innovation in post-communist economies. Our findings indicate that gender diversity enhances key innovation processes through the DUI drivers. Specifically, workforce diversity strengthens managerial practices (Doing), while ownership diversity promotes technology adoption and employee training (Using). These insights suggest that gender diversity indirectly contributes to innovation by improving decision-making, knowledge absorption, and organizational learning. By implementing policies that support inclusive leadership and workforce development, post-communist economies can unlock the potential of diverse teams, strengthening their competitiveness and innovation capacity in the global market.

Workforce development initiatives should focus on offering leadership and innovation training to diversify teams. To create gender equal opportunities, family-friendly workplace policies, such as childcare support and flexible work hours, could be implemented. Mentorship programs could also enhance women’s representation at decision-making levels. Importantly, policies in post-communist economies should go beyond traditional R&D approaches by fostering experiential and interaction-based learning and promoting teamwork practices that leverage diverse perspectives to maximize the impact of diversity on innovation.

References

Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.

Trade Induced Technological Change: Did Chinese Competition Increase Innovation in Europe?

Posted on April 29, 2019 | Policy Brief

20190428 Trade Induced Technological Change Image 01

The last 30 years has witnessed a shift of the world’s manufacturing core from Europe and North America to China. A key question is what impact this has had on manufacturing workers in other developed economies, and also on innovation, patenting, IT adoption, and productivity growth. While a rigorous data analysis on these variables for developing economies, particularly in Eastern Europe, is not yet available, this brief examines the impact of the rise of China on innovation in Western Europe, and also reviews the evidence on the impact of the rise of China generally. Recent research by Bloom, Draca, and Van Reenen (2016) found that Chinese competition induced a rise in patenting, IT adoption, and TFP by 30% of the total increase in Europe in the early 2000s. Yet, we find numerous problems with the Bloom et al. analysis, and, overall, we do not find convincing evidence that Chinese competition increased innovation in Europe.

Few events have inspired the ire of economists as much as Brexit and the rise of Donald Trump, two events seen as related as both were a seeming reaction to both globalization and slowing economic growth, particularly as some (such as Trump himself) saw the former as a key cause of the latter. Both Brexit and the trade war spawned by Trump do seem to have had negative economic effects – US equities have suffered every time the trade war has escalated, while anecdotal reports and more sophisticated economic analyses seem to suggest that Brexit has cost the UK jobs.

And yet, there is a need for policy makers and economists to hold two ideas in our heads simultaneously: Trump’s trade war and Brexit may be policy disasters, and yet globalization can create both winners and losers, even if it is clear that, generally speaking, the overall gains are likely positive and large. This is likely also true of the rise of China – one of the most dramatic events in international economics in the past 50 years. Figure 1 shows the increase in trade with China from the early 1980s to 2017, a period in which US imports from China grew from 7 to 476 billion dollars.

Figure 1. Chinese Imports (in logs, deflated)

Source: World Bank WITS

The academic literature tends to show that this impact, the rise of China, may have cost the US as much as 2.2 million jobs directly (Autor et al.), and as much as 3 million jobs once all input-output and local labor market effects are included. While approximate, these numbers are large enough for the China shock to have played a role in the initial onset of “secular stagnation” – the growth slowdown which began around 2000 for many advanced nations, including the US and Europe. In addition, Autor et al. (forthcoming) found that Chinese competition also resulted in a decline in patent growth. In the European context, however, other authors have found that although China did do some damage to certain sectors, overall, it does not appear to have been quite as damaging, particularly in Germany, which also benefitted from exporting increased machine tools to the Chinese manufacturing sector. And, in a seminal paper, Bloom, Draca, and Van Reenen (2016) find that Chinese competition actually led to an increase in patents, IT adoption, and productivity in Europe from 1996 to 2005, along accounting for nearly 30% of the increase. This is important, as it implies that without the rise of competition with China, the slowdown in European growth would have been even more pronounced than it was. It also implies that, far from being a source of stagnation, Chinese competition has been a source of strength. It also makes it more likely that the slowdown in growth since 2000 was caused by supply-side factors, such as new inventions becoming more difficult over time, as is perhaps the leading explanation among economists, notably Northwestern University business professor, Robert Gordon (2017), and also supported by others (see this VoxEU Ebook featuring a “who’s who?” among economists). It would also be evidence that contradicts the “Bernanke Hypothesis” that the former US Fed Chair first laid out in a 2005 speech at Jackson Hole, in which he suggested that international factors – particularly the savings glut and US trade deficit – were behind falling interest rates in the US. Since then, Ben Bernanke has followed up with a series of blog posts suggesting that these international factors were the cause of the initial onset of secular stagnation.

Figure 2. European Growth Relative to Trend

Source: World Bank WDI

In this brief, I present new research in which my coauthor and I test the robustness of the research finding that China had a positive impact on innovation in Europe (Campbell and Mau, 2019). We find that these findings are very sensitive to controls for time trends and other slight changes in specification. We also find that the number of patents matched to firms in the sample shrinks over the sample period (from 1996 to 2005). Overall, we conclude that, unfortunately, it is unlikely that the rise led to a significant increase in innovation in Europe, although more research is needed. Our research also sheds light on the so-called “replication crisis” currently gripping the social sciences, as researchers begin to realize that many published findings are not robust.

Trade-Induced Technical Change?

Bloom, Draca, and Van Reenen (2016) – hereafter BDV – tried to isolate the impact of the rise of China on Europe using several methods, using firm-level data for Europe. They placed each firm in a 4-digit sector, where they measured imports from China over time. First, they just looked at changes in patents, IT, and total factor productivity (TFP) at the firm level for sectors in which Chinese imports increased a lot vs. other sectors. But, because economists are always weary of the difficulty of isolating a causal relationship from non-experimental data, the authors, worrying that the sectors which saw increases in Chinese imports might differ systematically from the others, the authors also used what is called an instrumental variable. That is, they used the fact that when China joined the WTO in 2001, they also negotiated a reduction in textile quotas. Thus, BDV reason that textile sectors which had tightly binding quotas prior to removal were likely to have had fast growth in Chinese imports after China’s accession to the WTO. Thus, they end up comparing textile sectors in which the quotas were binding to sectors in which they were not binding. We went back and compared the evolution of patents in these same groups (sectors with binding textile quotas vs. not binding) below in Figure 3.

Figure 3. Patent Growth in China-Competing Sectors (Quota Group) vs. Other Sectors

Notes: The vertical red lines are dates when textile quotas were removed. The blue line shows the evolution of patents in the sectors without binding quotas (non-competing sectors), and the red line is the evolution of patents in the China-competing sectors. The dotted lines are 2 standard deviation error bounds.

What is immediately obvious in Figure 3 is that patents are declining rapidly over the whole period in both groups. The overall level of patents was falling in both groups for the full period. There is a 95.8% decline in patenting for the China-competing group, vs. a 96.2% decline for firms in the non-competing (“No quota”) group. By 2005, average patents per firm are close to zero in both groups (.04 in the China-competing sectors vs. .11 in the others). However, in the “No quota” group, the initial level of patents – close to three per firm per year – was much larger than in the quota group. Since patents are falling rapidly in both groups but bounded by zero, the level of the fall in patents in the non-quota group is larger, but one can easily see that much of this decline happens before quotas are removed. If we control for simple time trends, the effect goes away. Also, given the tendency of patents to decline, we can also remove the correlation between Chinese competition and patent growth in some specifications by simply controlling for the lagged level of patents. The overall declining share of patents in the BDV data also raises questions about data selection issues, as patents granted in the BDV data in the later years were a smaller share of the total patents actually granted in reality.

BDV also look at the impact of the rise of China on IT adoption. However, here they proxied IT adoption by computers per worker, but they did not collect enough data to control for pre-trends properly in the data, so we cannot be sure whether this correlation is causal or not. (For what it is worth, on the data we do have, from 2000 to 2007, including trends in the data renders the apparent correlation between Chinese import growth and computers-per-worker insignificant.)

Lastly, BDV look at the impact of the rise of China on TFP growth. Here, unlike before, we find that their measure is robust across various estimation methodologies. However, when we look at changes in a commonly used alternative measure of productivity, value-added per worker, instead of TFP (as TFP needs to be calculated using strong assumptions about the functional form of technology), we find no impact (see Figure 4 below).

Figure 4. Value-Added per worker Growth: China-competing sectors vs. others

Figure 4 above compares the evolution of value-added per worker in the most China-competing sectors vs. the others. Trends look similar for firms in either group of sectors (China-competing or otherwise), and we do not find a correlation. We also do not find that Chinese competition led to an increase in profits, nor an increase in sales per worker (in fact, we found a significant decrease in most specifications).

Conclusion

All in all, we find that the BDV findings suggesting that the rise of China had a large impact on innovation in Europe is not robust. However, in most specifications, we also don’t find a negative impact as did Autor et al. (forthcoming) for the US. This might have to do with data quality, although it does seem to be closer to other work, such as Dauth et al. (2014), which suggests that the rise of China had a smaller impact in Germany than in the US.

We also felt it was a bit alarming that a simple plot of  the trends in patents for China-competing and not-competing sectors was enough to seriously question the conclusions of BDV, as their paper was published in the Review of Economic Studies, a top 5 journal in academic economics. If influential articles published in the most fancy journals can exhibit such mistakes, this underscores the extent which the profession of economics may suffer from many published “false-positive” results. The reasons why this could be the case are obvious: researchers are under pressure to find significant results, as top journals don’t often publish null results, and replication is exceedingly rare in a field in which one needs to make friends to publish. However, there are signs that replication is becoming more mainstream, and as it does, we can certainly hope that voters around the world will turn back to science.

References

  • Autor, D., D. Dorn, G. H. Hanson, G. Pisano, and P. Shu. Forthcoming. Foreign Competition and Domestic Innovation: Evidence from US Patents. Forthcoming: AEJ:Insights.
  • Bloom, N., M. Draca, and J. Van Reenen. 2016. “Trade Induced Technical Change? The Impact of Chinese Imports on Innovation, IT and Productivity.” The Review of Economic Studies 83 (1): 87–117.
  • Campbell, Douglas and Mau, Karsten. 2019.. Trade Induced Technological Change: Did Chinese Competition Increase Innovation in Europe?”, mimeo
  • Dauth, W., S. Findeisen, and J. Suedekum. 2014. “The Rise of the East and the Far East: German Labor Markets and Trade Integration.” Journal of the European Economic Association 12 (6): 1643–1675.
  • Gordon, R.J., 2017. The rise and fall of American growth: The US standard of living since the civil war (Vol. 70). Princeton University Press.

Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.

On Economics of Innovation Subsidies in Russia

Posted on October 9, 2017 | Policy Brief

20171008 On Economics of Innovation Subsidies in Russia Image 01

Following the general agreement that innovation is a source of economic growth, the Russian government has provided various stimuli to foster domestic innovation. One of the mechanisms of innovation policy is research subsidies. This policy brief starts off with a discussion of the theoretical predictions and empirical evidence, which relates the economic incentives of research subsides to innovation and growth. We then address the potential adverse effects of focusing innovation subsidies mainly on large public companies in Russia. Finally, we attempt to establish a link between the innovation rate and market competition within Russian industries.

Overview

According to data from the Russian Statistical Agency, the R&D intensity – measured by R&D expenditure as percent of sales – increases with company size. Companies with 50 to 500 employees spend 1% of their sales on R&D, while the R&D intensity varies from 2 to 5% of sales for larger businesses (see Figure 1). The size non-neutrality of R&D in Russia contradicts the findings in the theoretical and empirical literature, which hold for companies in the developed countries (Cohen, 2010). An explanation may be the excessive government support to public companies in Russia, and in particular, to larger public corporations. A positive consequence of such policies is that public corporations come ahead of private companies, not only in R&D intensity, but also in innovation rates (see Figures 2–3).

However, government support towards innovation does not necessarily have a positive impact on overall economic activity. The purpose of this brief is to discuss the unwanted effects of the government policy in the form of research subsidies, both in theory and in an application to public companies and corporations in Russia. We base our analysis on the outcomes of the 2014–2017 micro surveys by the Analytical Center under the Government of the Russian Federation.

The role of government

Fighting under-provision of innovation

According to the seminal paradigm of the endogenous growth models with technological change, companies are engaged in quality competition, and their innovations are explained by a rational decision to raise profits through expanding the markets for existing products or entering markets for new products (Schumpeter, 1942; Romer, 1990; Grossman and Helpman, 1991; Kletter and Kortum, 2004). The innovation becomes one of the causes of economic growth, which is proved in empirical applications for developed countries, such as the U.S., Japan and the Netherlands (Akcigit and Kerr, 2010; Lentz and Mortensen, 2008; Grossman, 1990).

Figure 1. Innovation rate and R&D intensity by company size (number of employees)

Source: Indicators of Innovation in the Russian Federation: 2017. Tables 2.4, 2.16, Data for 2015. Innovative rate is % of companies involved in innovative activity.

However, the technological change is closely linked to knowledge disclosure, which means that new products become vulnerable to imitation, and that the non-rival character of knowledge causes an under-provision of innovation on the market (Arrow, 1962). The argument supports the cause for government policies through the system of intellectual property rights on the legal side, and research subsidies as an economic mechanism (Rockett, 2010; Hall and Lerner, 2010). Research subsidies are expected to have a positive effect on innovation rate, as is empirically shown for the U.S. in Acemoglu et al. (2016) and Wilson (2009). However, the impact on economic growth is ambiguous (Acemoglu et al., 2013; Grossman, 1990).

Figure 2. Innovation rate and R&D intensity by ownership

Source: Indicators of Innovation in the Russian Federation: 2017. Tables 2.6, 2.17, Data for 2015, public corporations are different from organizations by regional/federal government.

Figure 3. Share of public funds in R&D financing, % of company budget

Notes: Indicators of Innovation in the Russian Federation: 2017. Table 1.13; Innovation Development Programmes of Russian State-Owned Companies, Fig.4.

Unwanted effects of subsidies

Two concerns are associated with subsidization of innovation. First, while research subsidies may stimulate innovation among the targeted companies, the growth effect is likely to be heterogeneous across companies in the industry or economy, leading to a neutral or even negative overall effect. For instance, the increased innovation rate in subsidized large incumbents may curb entry of new (and more productive) firms, so the net outcome is deceleration of growth in the economy (Acemoglu et al., 2013). Research subsidies may even cause a shrinking of the high-tech sectors: if skilled labor moves from manufacturing to research labs, manufacturing may experience a shortage of labor, resulting in the net effect being a decrease in production (Grossman, 1990).

Another extreme of subsidizing entrants, in view of antitrust policies, occurs when former entrants change their market status to incumbents: now they face lower profits relative to newer entrants and hence, become less incentivized in their economic activity (Segal and Whinston, 2007).

Second, innovation policy (for instance, in the form of subsidies) may sometimes not even increase the innovation rate. Indeed, incumbents have no incentives to innovate in order to keep their market power or to prevent entry of higher quality firms in industries with non-perfect competition (Rockett, 2010; Qian, 2007).

Both mechanisms are likely to hold for Russian industries, where the protection of large public corporations has led to low competition, various forms of distortions on the market and hence, weak incentives to innovate.

Potential adverse effects in Russia

Large companies are likely to attract public attention owing to their obvious advantages in spreading fixed costs of innovations (Cohen,

2010). Russia is no exception to the phenomenon, so public corporations, which are commonly of a large size, received government subsidies. However, the subsidy is primarily used for acquiring new technologies and perfecting design, rather than conducting R&D (See Figure 4 with comparison available for communications and IT industry). The fact points to a possibility of a small effect of innovations on growth of public companies. Only if the research subsidy is spent on delegating the R&D research to specialized firms, with a subsequent acquiring of the resulting technology, the existing policy of supporting public corporations may induce their growth and/or growth of the corresponding industry.

Figure 4. Structure of spending the research subsidy in communications and IT in 2013, %

Notes: Indicators of Innovation in the Russian Federation: 2017. Table 1.134 Innovation Development Programmes of Russian State-Owned Companies, Fig.3.

In an attempt to formally assess the effect of innovation subsidies on company growth, we focus on the time profiles of the common proxies for company size: sales, profits and employment (Akcigit et al., 2017; Akcigit and Kerr, 2010; Acemoglu et al., 2013). The macroeconomic literature predicts that innovation becomes one of the channels for an increase of each of the three variables through a rise in quality. Motivated by this literature, the micro-data analysis “On the Interaction of the Elements of the Innovation Infrastructure”, conducted by the Analytical Center under the Government of the Russian Federation (2014), asked companies to assess their changes in sales, profits and employment in response to the innovation subsidy. As a result, the outcomes of the above analysis allow for a comparative assessment of the impact of the government’s innovation subsidy for public and private companies.

In particular, the results point to higher growth across private companies owing to research subsidies: the percent of private companies with new employees is higher than that of public companies. Similarly, the percentage of private companies that increased market share or raised profits/export due to subsidies exceed those of the public companies (see Figure 5). Here, we interpret new hires as employment growth and increase of market share as a potential indicator of sales growth.

Figure 5. Economic activity owing to research subsidies, % of companies

Source: Analytical Center under the Government of the Russian Federation, 2014. Fig.22

The innovation activity in private Russian companies lead to a higher prevalence of new products in comparison with public companies. The fact goes in line with a more important role of research and development in the innovative activity of private Russian companies (see Figure 4).

Finally, we attempt to establish a link between the innovation rate and market competition at the level of Russian industries. For this purpose, we use the results of the annual surveys “An assessment of the competitiveness in Russia”, conducted in 2015–2017 by the Analytical Center across 650–1500 companies from 84 Russian regions. The respondents were asked if they implemented R&D as a strategy for raising their competitiveness. We use the percentage of firms doing R&D as a proxy for the innovation rate. Competition in the industry was evaluated by respondents on a five-point scale (no competition, weak, median, high and very high), and we combine the prevalence of the two top categories as a proxy for competition in the industry.

Figure 6. Competition and R&D in Russian industries, % of firms

Source: Analytical Center under the Government of the Russian Federation, 2017, pp.8, 18.

The results show that innovative activity in the form of R&D or product modification is observed in industries with relatively high competition in Russia – for instance, in machinery and electric/electronic equipment (Figure 6). At the same time, industries where competition is not as high (e.g. woodworking, construction) show absence of either type of innovation. The findings go in line with the economic theory about market competition being a prerequisite for the rational choice of companies about innovation. Moreover, if the purpose of government subsidies is to foster innovation, the effective allocation of subsidies would imply the focus on Russian industries with high competition – here various forms of innovation do play a role in the company strategy on the market.

Conclusion

Our analysis outlines the theoretical foundations for the potential adverse effects of innovation policies in the form of research subsidies. The unwanted outcomes may relate to heterogeneity of companies and absence of the association between innovation activity and growth on non-competitive markets.

We offer the empirical evidence, which points to the undesired effects of subsidizing public companies in Russia. For instance, compared to the overall Russian sector of communications and IT, the innovative activity in public corporations has a weaker association with research and development. Additionally, compared to private companies, the innovations may result in smaller prevalence of increased exports, profits or new hires, as well as in a less frequent development of new products by public companies in Russia.

References

  • Acemoglu, D., Akcigit, U., Bloom, N., Kerr, W. R., 2013. “Innovation, reallocation and growth”, National Bureau of Economic Research Working paper, No. 18993.
  • Acemoglu, D., Akcigit, U., Hanley, D., Kerr, W. (2016). Transition to clean technology. Journal of Political Economy, Volume 124(1), pages 52-104.
  • Akcigit, U., Kerr, W. R., 2010. “Growth through heterogeneous innovations” National Bureau of Economic Research Working Paper, No. 16443.
  • Analytical Center under the Government of the Russian Federation, 2014. “On the Interaction of the Elements of the Innovation Infrastructure”, Analytical report, in Russian.
  • Analytical Center under the Government of the Russian Federation, 2015-2017. “An Assessment of the Competitiveness in Russia”, Analytical reports, in Russian.
  • Arrow, K., 1962. “Economic welfare and the allocation of resources for invention”, In The Rate and Direction of Inventive Activity: Economic and Ssocial Factors, Princeton University Press, pages 609-626.
  • Cohen, W. M., 2010. “Fifty years of empirical studies of innovative activity and performance”, Handbook of the Economics of Innovation, Volume 1, pages 129-213.
  • Grossman, G. M., Helpman, E., 1991. “Quality ladders in the theory of growth”, The Review of Economic Studies, Volume 58(1), pages 43-61.
  • Grossman, G.M., 1990. ”Explaining Japan’s innovation and trade”, BOJ Monetary and Economic Studies, Volume 8(2), pages 75-100.
  • Hall, B. H., Lerner, J., 2010. “The financing of R&D and innovation”, Handbook of the Economics of Innovation, Volume 1, pages 609-639.
  • Indicators of Innovation in the Russian Federation: 2017. N. Gorodnikova, L. Gokhberg, K. Ditkovskiy et al.; National Research University Higher School of Economics, in Russian.
  • Innovation Development Programmes of Russian State-Owned Companies: Interim Results and Priorities, 2015. M. Gershman, T. Zinina, M. Romanov et al.; L. Gokhberg, A. Klepach, P. Rudnik et al. (eds.), National Research University Higher School of Economics, in Russian.
  • Klette, T. J., Kortum, S., 2004. “Innovating firms and aggregate innovation”, Journal of Political Economy, Volume 112(5), pages 986-1018.
  • Lentz, R., Mortensen, D.T., 2008. “An empirical model of growth through product innovation”, Econometrica, Volume 76(6), pages 1317–1373.
  • Qian, Y., 2007. “Do national patent laws stimulate domestic innovation in a global patenting environment? A cross-country analysis of pharmaceutical patent protection, 1978–2002”, The Review of Economics and Statistics, Volume 89(3), pages 436-453.
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Does Immigration Help Diffuse Knowledge? Evidence from Russian Scientists

Posted on November 24, 2014 | Policy Brief

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Author: Ina Ganguli, SITE.

Immigration is a hotly contested policy issue in many countries. Often, the debate centers on whether immigration is ‘good’ or ‘bad’ for the receiving country. A growing literature in economics focuses on understanding whether immigrants can be beneficial for a receiving economy by helping spread knowledge and increasing innovative activities. In this policy brief, I discuss new evidence showing that immigrants can be an important channel for diffusing knowledge across national borders. Drawing upon the influx of Russian scientists to the United States after the end of the Soviet Union, I present compelling evidence that immigrants contributed to cross-border knowledge flows, which are the basis for innovation and ultimately economic growth.