Are Science Cities Fostering Firm Innovation?

PRELIMINARY AND INCOMPLETE – DO NOT CITE

Evidence from Russia’s Regions 1

Helena Schweiger Paolo Zacchia European Bank for Reconstruction and University of California – Berkeley Development [email protected] [email protected]

February 2015

Abstract:

Using a mixture of data from the recent regionally representative Business Environment and Enterprise Performance Survey (BEEPS) in Russia as well as municipal level data, we find that there are significant differences in innovation activity of firms and cities across the Russian regions. We investigate whether this could be explained by the proximity to science cities – towns with a high concentration of research and development facilities, as well as human capital. They were selected and given this status in a largely random way during Soviet times. We match the science and non-science cities on their historic and geographical characteristics and compare the innovation activity (R&D, product and process innovation) of firms in science cities with the innovation activity of similar-in-the-observables firms in similar, non-science cities. Preliminary evidence suggests that firms located in science cities were more likely to engage in product and process innovation than firms located in similar, non-science cities. The results have important policy implications because many countries in the world, including developing countries, have been introducing science parks or other areas of innovation, viewing them as a type of silver bullet with the capability of dramatically improving a country’s ability to compete in the global economy and help the country to grow.

JEL Classification: O33, O38, O14 Keywords: Innovation, Russia, science cities

1 We would like to thank Natalya Volchkova, Sergei Guriev and Maria Gorban for helpful discussions and Irina Capita, Jan Lukšič, Alexander Stepanov and Maria Vasilenko for excellent research assistance. The views expressed in this paper are our own and do not necessarily represent those of the institutions of affiliation.

1 Introduction There is general consensus that innovation is essential for accelerating the growth of the economy. Governments can support innovation indirectly by implementing reforms to foster innovation or directly through public investment in science and basic research (OECD 2007). The mix between the two varies by country, but over the last 50 years, it included various types of place-based policies 2 - science, technology, research, or science and technology parks, technology centres, and granting cities the status of a science city in a number of countries 3 - with the aim of promoting a culture of competitiveness and innovation of the firms located there, stimulating technological spillovers and ultimately contributing to faster economic growth.

There is evidence that innovation activity is more spatially concentrated than population and economic activity (see Carlino and Kerr 2014 for an overview). Localised knowledge spillovers are often considered – along with labor market pooling and intermediate goods provision cost-type advantage – as one of the theoretical explanations for the existence of agglomeration economies, dating back at least to Marshall (1890). 4 It is important to note that, despite the fact that (localized) knowledge spillovers are most often related to agglomeration externalities, there is no reason not to assume that they are also playing a role in more limited settings especially where research is very concentrated.

A challenge in identifying typically unobservable local knowledge spillovers is to disentangle them from the technological spillovers that arise between firms that are technologically close but not necessarily spatially close – particularly so because technologically similar firms tend to locate near one another. In addition, this is a natural case where the econometric “reflection” endogeneity problem (Manski 1993) kicks in – it is hard to tell ‘who spills on whom’ and whether the productivity differentials are not just caused by common, for instance localized, shocks. Moretti (2004) hints at

2 Place –based policies, which are defined as specific government or otherwise external interventions – be it in the form of directed financial support for economic activities or investment, or in the form of special regulations – that are directed to specific, well-defined geographical areas. Such policies are typically either directed to the poorest areas in the case of the advanced economies, in order for these to catch up with the rest of the country, or to the areas that are considered to have the best potential for growth in the case of developing countries or transition economies, as it is often thought that this may at some point spur growth in surrounding areas as well as the rest of the country. 3 International Association of Science Parks and Areas of Innovation (IASP), which was established in 1984, has 398 members in 73 countries, covering all continents apart from Antarctica ( http://www.iasp.ws/facts-and- figures , 9 February 2015). Science city status has been awarded by national governments to cities in China, Germany, Japan, Philippines, Russia, Spain, Sweden, Switzerland, United Arab Emirates and UK (Wikipedia - http://en.wikipedia.org/wiki/Science_City). 4 While theories are old, careful empirical studies that quantify the sources of agglomeration economies are much more recent. An excellent example is a study by Greenstone et al. (2010), who employ a matching-like methodology that compares locations where a very large plant has opened to the locations that were very close to be chosen in their place. They find very large agglomeration productivity externalities - an effect of around 10 per cent increase - that result from the opening of a “Million Dollar Plant” (MDP), and wide heterogeneity across locations in the estimated effect. In addition, firms/plants belonging to a close technological class seem to enjoy more the opening of the MDP, hinting at a central role for knowledge exchange in generating the externalities. A notable analysis that attempts to quantify patterns of industrial coagglomeration is Ellison et al. (2010), who are able to assess the impact of all three main theories of agglomeration economies. 2

the presence of localized knowledge spillovers by showing that productivity at the local level depends on the relative level of education of the whole workforce.

Jaffe et al. (1993) and Lychagin et al. (forthcoming) address the issue with a different perspective. Using U.S. patent data, the former find that citation patterns are locally clustered. Lychagin et al. in particular attempt to separately distinguish three kinds of knowledge spillovers using US data: horizontal spillovers across product-market rivals, technology spillovers across firms that are close in the technological space, and geographical-locational spillovers. They find that in terms of correlations, geographical spillovers matter as much as technology spillovers once the latter are controlled for. However, their study is mostly descriptive, as they do not control for endogeneity of R&D; hence it does not really attack the two aforementioned endogeneity problems.

The literature on the evaluation of place-based policies is to a large extent even less developed. Relatively recent examples of this category of studies are the papers by Wang (2013), Wren and Taylor (1999), Albouy (2012), Papke (1996), Busso et al. (2013), Ham et al. (2011), Neumark and Kolko (2010). Most of these are focused on the short-run. An exception is the study by Kline and Moretti (2014), who assess the long-run impact of a large-scale place-based policy enacted in the US at the times of the New Deal, namely the Tennessee Valley Authority (TVA) program. Their empirical results, even if they are consistent with the presence of agglomeration economies, do not seem to confirm the existence of strong non-linearities that would strongly motivate large place-based policies.

Knowledge-focused place-based policies and their assessment have received much less attention, not only because they are far less common. Moreover, innovation and its outcomes are much harder to measure even in conventional, not geographically focused studies; in addition, slightly different analytical frameworks are needed. Empirical evidence on the performance of science parks as one available knowledge-focused place-based policy instrument is mixed. Rather than functioning as “seedbeds” of innovation, Felsenstein (1994) suggested they are closer to functioning as “enclaves” of innovation. Westhead (1997) found no statistically significant differences in R&D outputs of new technology-based firms located within and outside of the science parks in the United Kingdom. Lindelöf and Löfsten (2003) found that new technology-based firms located within science parks in Sweden placed more emphasis on innovative activities than their outside counterparts. Yang et al. (2009), on the other hand, find that new technology-based firms in a Taiwanese science park invest more efficiently in innovation than those outside of the science park. 5

In this paper we contribute to the literature by evaluating the legacy of innovation-oriented place- based policies in the former Soviet Union on firm- and municipal-level innovation in present-day Russia. The former Soviet Union was in a way a pioneer of such policies, with public investment in science and basic research approach. The model of innovation followed by the Soviet authorities since the early 1930s was the creation of “special-regime enclaves intended to promote innovation” (Cooper 2012). The enclaves first appeared as secret research and development laboratories (so- called Experimental Design Bureaus or more commonly, sharashkas ) in the Soviet Gulag labour camp

5 See also Westhead and Storey (1994) and Siegel et al. (2003). 3

system. 6 This was later followed by science cities, closed cities, academic towns and to a certain extent also by cities closed to foreigners. In present-day Russia, there are 14 locations officially recognised as naukograds (science cities).

To identify the effect of these “innovation enclaves” on present-day firm- and municipal-level innovation, we exploit the fact that the locations of these cities were chosen for their geographical characteristics (with several purpose-built from scratch) rather than pre-existing comparative advantages in terms of R&D facilities, industries and level of education of their inhabitants. We apply coarsened exact matching on population and the number of research and development institutes in 1959 to form strata of municipalities selected to host “innovation enclaves” with similar municipalities that were not. We then regress innovation outcomes at the firm- and municipality- level using OLS on different definitions of science cities (science cities, closed cities and cities restricted to foreigners, academic towns), controlling for the strata obtained in this way.

At the firm-level, we use the unique data from the fifth round of the European Bank for Reconstruction and Development (EBRD) – World Bank (WB) Business Environment and Enterprise Performance Survey (BEEPS V) to analyse differences in R&D, product and process innovation, while at the municipality-level, we use the data on location of inventors in PATSTAT to look into differences in patenting. Our hypothesis is that the localised knowledge spillovers are due to the persistence of human capital accumulated in science cities over time. We find some evidence that firms located in science cities were more likely to engage in innovative activity than similar firms located in similar, non-science cities.

Our paper shares some similarities with Siegel et al. (2003), who compare the research productivity measured in terms of the number of new products or services, patents and copyrights of firms located on university science parks in the UK to that of comparable (“observationally equivalent”) firms located outside university science parks. They find that firms located on university science parks have slightly higher research productivity. Another related paper is Falck et al. (2010); the authors there evaluate the cluster-oriented policy introduced in Bavaria in 1999 in terms of innovation activities using a difference-in-difference-in-differences method, comparing the performance of firms in Bavaria with performance of firms in other states before and after the introduction of the policy and performance of firms in target industries with the performance of firms in non-target industries in Bavaria. They find that the policy increased the likelihood of firms becoming innovators in target industries by 4.6-5.7 percentage points.

The rest of the paper is organised as follows. The next section introduces a brief historical background of the innovation system in Russia. Section 3 provides a description of our dataset.

6 Gulag is the acronym for Chief Administration of Corrective Labour Camps and Colonies, the Soviet Union government agency that administered the main Soviet forced labour camp systems during the Stalin era, from the 1930s to the 1950s. The gulag is recognised as a major instrument in political repression in the Soviet Union, and according to some estimates, only about a third of gulag prisoners were convicts – the rest were convicted by simplified procedures and other forms of extrajudicial punishment. Approximately 14 million people passed through the Gulag labour camp system from 1929 to 1953, a further 6-7 million were deported and exiled to remote areas of the Soviet Union and 4-5 million passed through labour colonies. 4

Section 4 presents the setup and methodology. Section 5 estimates the impact of science cities on firm-level measures of innovation. Section 6 concludes.

2 Background The former Soviet Union was in a way a pioneer of the public investment in science and basic research place-based policy approach, supported by its leadership’s obsession with security considerations and secrecy, and allocation of all resources - including human - according to the Party’s set of priorities. The best quality resources were allocated to the defence industry, followed by iron and steel, energy and chemicals – sectors considered vital to the country’s national security. Around two-thirds of research and development spending was for military purposes, and almost all the country’s high-technology industry was within the military sector (Cooper 2012).

The model of innovation followed by the Soviet authorities since the early 1930s was the creation of “special-regime enclaves intended to promote innovation” (Cooper 2012). The enclaves first appeared as secret research and development laboratories (so-called Experimental Design Bureaus or more commonly, sharashkas ) in the Soviet Gulag labour camp system. The scientists and engineers at a sharashka were prisoners picked from various camps and prisons, assigned to work on scientific and technological problems for the state – a task at which they were quite successful.7 This was later followed by science cities, closed cities, academic towns and to a certain extent also by cities closed to foreigners. We discuss each of them briefly below, and identify the locations in Appendix A.

The term “science city” was first introduced in 1991 in Zhukovsky, Moscow oblast, as a formal term for towns with high concentration of research and development facilities, associated with the scientific and technical development. The term was used in connection with the firm of the Union for the development of science cities (Ruchnov and Zaytseva 2011). However, de-facto science cities (naukograds) began to develop around strategically important (military) research centres from the mid-1930s, and more followed after the Second World War (Table A.3 in Appendix A). The former Soviet Union was not a science cities pioneer – the first science city was established in 1937 in Peenemünde, Germany (Ruchnov and Zaytseva 2011) – but it has implemented the idea to a much larger extent.

In the 1940s, the system of closed cities appeared as an extension of the naukograd system, to protect the secrecy of the nuclear weapons programme (Cooper 2012). Closed cities (Table A.1 in Appendix A) were often built from scratch (often by forced labour in gulags), specifically for the purpose of co-location of scientific research centres, training institutes and manufacturing facilities. The main objective of closed cities was to develop nuclear weapons, missile technology, aircraft and electronics (high-tech sectors), and while there were very few resources after the Second World War, they were set aside to build the prototypes of science cities.

7 Famous sharashka inmates included Sergey Korolyov (aircraft and rocket designer), rocket engine designer Valentin Glushko, aircraft designers Andrei Tupolev, Vladimir Petlyakov, Vladimir Myasishchev, Leonid Kerber, geneticist and radiobiologist Nikolay Timofeev-Ressovsky, inventor of the straight-flow boiler Leonid Ramzin, pioneer of aeronautics and space flight Yuri Kondratyuk (http://en.wikipedia.org/wiki/Sharashka). 5

Because of the nature of research in the Cold War environment and to maintain security and privacy, closed cities were often located in remote areas situated deep in the Urals and Siberia, 8 out of reach of enemy bombers, and represented only on classified maps. However, their location was chosen for their geographical characteristics and they were typically built close to rivers and lakes, which were used to provide the large amounts of water needed for heavy industry and nuclear technology.

Local citizens not living there had to have special permission to travel there, were subject to document checks and security checkpoints, and explicit permission was required for them to visit, while foreigners were prohibited from entering them. To relocate to the closed city, a security clearance by the KGB 9 was required. The residents were required to keep their place of residence secret – this lack of freedom was often compensated by better pay and housing conditions and a better choice of goods in retail trade than elsewhere in the country.

In addition to closed cities that even Soviet citizens were not allowed to visit without proper authorisation, there was a second category of cities that were freely accessible to Soviet citizens, but closed for unauthorised access to foreigners – we classify them as cities restricted to foreigners (Table A.2 in Appendix A). These include some of the regional capitals, such as Nizhny Novgorod, Omsk, Perm, Samara, Vladivostok, Krasnoyarsk and Ufa.

The policy of closing cities underwent major changes in the late 1980s and early 1990s. Some, such as Perm, were opened before the fall of the Soviet Union, others remained closed until 1992. There are currently still 42 publicly acknowledged closed cities in present day Russia, although they have been renamed as “closed administrative-territorial formations” (ZATO – “zakrytye administrativno- territorialnye obrazovaniya”) in 1993.

Another category of research-focused place-based policies were the so-called Akademgorodoks (academic towns). During the Second World War, many factories were evacuated from the European part of the Soviet Union beyond Ural, fostering industrial development of Siberia. In the 1950s, the Soviet government decided to do the same with science and to establish the Siberian branch of the Soviet Academy of Sciences. The first Akademgorodok was founded near Novosibirsk in 1957. It was a model scientific community designed to foster theoretical and applied research in the natural sciences, technology and economics. Living conditions were better than the Soviet standard, with well-supplied shops, comfortable apartments and abundant cultural opportunities. At its peak, it was home to 65,000 scientists and their families. Other Akademgorodoks were created in Irkutsk (1988), Krasnoyarsk (1965) and Tomsk (1972). 10

The Soviet Union spent a considerable share of GDP on R&D, but its technology lagged behind that of the developed market economies, with the lag especially acute in electronics and computing. There was no competition, the civil economy lacked the quality resources, the investment process was biased towards large-scale projects, and the small enterprise sector was non-existent.

8 Mikhailova (2012) analysed the spatial distortions in Russian economy inherited from the Soviet system. 9 KGB stands for the Committee for State Security, the main security agency for the Soviet Union. 10 http://en.wikipedia.org/wiki/Akademgorodok_(Tomsk). 6

Public funding of science and basic research dried up with the collapse of the Soviet Union, and many scientists lived in poverty. Although the Russian government has been implementing a variety of models of state programmes designed to support economic development and modernisation via technological innovation and commercialisation of innovations since the early 1990s, a major policy shift can be observed from about 2002 onwards (Wade 2012). The programmes include science cities, innovation technology centres, technology parts, incubators, Special Economic Zones, and most recently the Skolkovo innovation centre near Moscow city. 11 However, Russia’s R&D spending amounts to only about 1 per cent of GDP, and almost 75 per cent of R&D is currently conducted by public organisations, with the funding coming mainly from the federal budget. The Soviet legacies are still very apparent in the present day Russia, where about 75 per cent of all R&D organisations are state-owned. Innovation by enterprises is weak - owing both to the Soviet legacies as well as to weak incentives to invest in innovation (EBRD 2012).

In our analysis, we exclude closed or restricted cities with only military presence without any research facilities. We look separately at the impact of science, academic, closed and restricted cities. We use the term science city to refer to any and all of these categories in what follows.

3 Data and descriptive statistics Besides the database with information on science, closed, academic and restricted cities constructed based on the publicly available information and described in the previous section and Appendix A, we use two additional data sources: BEEPS V for Russia, conducted between August 2011 and October 2012 and historic municipal level database.

3.1 BEEPS V Russia BEEPS is an enterprise survey whose objective is to gain an understanding of firms' perception of the environment in which they operate in order to be able to assess the constraints to private sector growth and enterprise performance. 12

For the first time 37 Russian regions were covered, at least one in each federal district. The survey was primarily targeted at top managers (CEOs), but in reality the respondents often included accountants or operations managers. A total of 4,220 face-to-face interviews were completed, on average 114 interviews per region (see Table 1 for details). The interviews lasted on average 54 minutes. The database contains GPS coordinates of the firm’s location, based on which we can determine the municipality (raion) in which the firm is located.

Also for the first time, BEEPS V Russia included an Innovation Module, with the aim to obtain a better understanding of innovation - not only product innovation, but also process, organisation and marketing innovation, as well as R&D and protection of innovation. The main questionnaire contained questions that determined eligibility for participation in the Innovation module, which was based on the third edition of the Oslo Manual. The so-called filtering questions were asked with

11 Refer to Wade (2012) and EBRD (2012) for more details. 12 It covers topics related to infrastructure, sales and supplies, degree of competition, land and permits, crime, finance, business-government relations, labour and establishment performance. BEEPS is implemented by private contractors, using face-to-face interviews in the country's official language(s). 7

the help of show cards, which contained examples of the relevant innovations. The Innovation Module can be merged with the main BEEPS data and with Bureau van Dijk's Orbis database. At present, the database is cross-sectional in nature.

In Russia, 1927 firms (45.7 per cent) reported at least one innovation activity in the last three years and only 30 of them refused to complete the Innovation Module. There was a wide variation in the prevalence of innovation activity across regions, ranging from 21.7 per cent of firms in Murmansk to 74.2 per cent of firms in Stavropol. 13 Refusal to participate in the Innovation Module occurred in 16 out of 37 regions and was mostly concentrated in Kemerovo, Kirov, Leningrad and Lipetsk. This was mostly due to the inadequately experienced interviewers in these regions. 14

The firms were only asked the relevant parts of the Innovation Module, which in turn collected more detailed information on how the firms innovate, the level of innovativeness and how important innovation is for the firms, as well as on R&D spending and patents and for manufacturing companies with at least 50 employees also data on management practices. Firms were asked to specify their main innovative product and process. The interviews lasted on average 18.5 minutes.

More than 90 per cent of Innovation Module interviews were completed face-to-face immediately after the main questionnaire; 5.6 per cent were completed during a follow-up phone call, and the rest during a second face-to-face visit or immediately after completing section H in the main questionnaire.

3.2 Historic data on Russian municipalities We constructed a municipal-level dataset for the municipalities in regions covered in BEEPS V Russia and regions that have at least one science city, broadly defined, based on the information available on the website of the Russian Statistical Office (Goskomstat) as well as in historical regional statistical digests and similar sources. For some of the regions, the data start in 1939, but for most of the municipalities the coverage becomes better around 1959-1961, so we use this period for matching purposes.

The dataset contains several indicators, with the coverage best for total population. Other indicators available are the number of research and development institutes, number of general educational institutions (excluding tertiary education), employment and average wages by sector, and value of manufacturing goods sold. No data is available for several of the currently closed cities (ZATOs), and for a few of the regions, coverage is not very good.

3.3 Innovation in Russia Many countries across the world conduct innovation surveys (see, e.g., Mairesse and Mohnen 2010). Goskomstat began to conduct annual innovation surveys in 1994, closely aligning its approach on the European Union's Community Innovation Survey (CIS). The latest surveys have been undertaken in

13 See also the section on descriptive statistics and measurement error. 14 The EBRD and World Bank representatives conducted training for the regional managers, who in turn were required to train the interviewers in their regions. In some of the regions, the turnover of interviewers as well as regional managers was relatively high and could not be overcome by the provision of training materials. 8

accordance with the requirements of CIS-2008 and use the definitions outlined in the third edition of the Oslo Manual. Data collection is mandatory for enterprises.

Table 2 shows that there are significant differences between the percentage of innovative enterprises based on the EBRD-WB BEEPS V Russia and Goskomstat, with the BEEPS V Russia estimate on average 4.2-times higher than Goskomstat. Pairwise and rank correlation coefficients are 0.27 and 0.21, respectively, and neither is statistically significant. 15 Both pairwise and rank correlation coefficients are significant between Goskomstat estimates and BEEPS V estimates for product and process innovation only: 0.34 (p-value 0.041) and 0.35 (p-value 0.035), respectively.

In contrast to the BEEPS V Innovation Module, the Goskomstat innovation survey coverage differs by sector and size of the enterprise. It covers large and medium-sized industrial enterprises (in mining, manufacturing, production and distribution of electricity, gas and water 16 ), a number of industries in the services sector such as communications, computing and IT and others (technical testing, research and certification, market analysis, business and management consulting, etc.). For small enterprises (those with up to 100 employees and revenue of less than 400 million roubles), shorter biennial innovation surveys are carried out. 17 BEEPS V Innovation module covers the same sectors everywhere, and is the same for eligible enterprises, regardless of their size. There are some differences in the wording used for manufacturing and services firms, but the overlap is large and methodology used the same everywhere, resulting in a greater comparability of the BEEPS V innovation data across the group of countries where BEEPS V is applied.

Many countries - developed and developing - conduct innovation surveys applying the guidelines of the Oslo Manual. The definitions and concepts related to innovation are evolving constantly in an effort to better understand and measure the innovation activities. However, despite the best efforts, methodology used differs from country to country, making it difficult to compare the results. Moreover, BEEPS Innovation Module experience shows that even when a uniform methodology is used in conjunction with providing examples of innovative products, processes, organisational and marketing methods, there is still scope for significant measurement errors. In BEEPS, the firms were first asked whether they have introduced any new products, processes, organisational and marketing methods. Those who said yes were asked for more details about these new products, processes, organisational and marketing methods. Crucially, for new products and processes, they were asked to name the main new innovative product or process. We analysed the verbatim answers and compared them to the firm's main product overall as well as against the definitions of product and process innovations. We discovered that only 51.9 per cent of companies that said they

15 P-value for the pairwise correlation coefficient is 0.107 and p-value for the rank correlation coefficient is 0.208. 16 Except trading electric power and trading gas fuel supplied through distribution network. 17 Goskomstat has started the process of evaluating and adapting the innovation questionnaire, in cooperation with the World Bank. Cognitive test of Goskomstat's current questions and questions and approach used in BEEPS V concluded that the questionnaire currently used (Form No 4) should be simplified in terminology and design and that face-to-face interviews have on the whole more advantages than mailing the questionnaires, but they are of course more costly. In addition, providing terminological explanations and cards with examples helped the respondents to understand the questions better, which improved the quality of the answers. The Innovation Module of BEEPS V, while not perfect, has used most of these recommendations. 9

introduced new products did product innovation and only 59.7 per cent of companies that said they introduced new processes met the definition of process innovation. We also corrected the indicator for R&D spending in the last three years based on the answers in the Innovation module.

There was a significant variation across regions on all of these measures (see Figure B.1 in Appendix B), which could reflect both the competence of interviewers as well as understanding of the respondents. 18 We are not able to do the same for organisational and marketing innovation, since we did not ask the respondents to name the main organisational and marketing innovations.

In analysis that follows, we focus on actual product and process innovation and R&D spending measures. As suggested by Table 2, there is a wide variation in innovation activity across Russian regions. Figure 1 shows the bar charts of product and process innovation and R&D activity. A few of observations emerge:

• Ranking of regions depends on the measure of innovation activity: Moscow city is in first place on product innovation and R&D, but not on process innovation; Primorsky territory is in the last place on product innovation and R&D, but not on process innovation.

• Regions that rank high on one of the innovation activity measures tend to rank high on the other two innovation activity measures.

• There is variation in innovation activity among regions in the same federal district, and while regions in the European part of Russia tend to be more innovative, there are innovative regions in the Asian part of Russia, too.

Large firms tend to be more innovative than medium-sized and small firms, with the difference particularly striking for R&D expenditure. We do not find significant differences in innovation activity by firm age, though young firms (less than 5 years old) tend to be less innovative, and those between 5 and 19 years somewhat more innovative than old firms. Share of innovative firms is about two times higher in manufacturing than in services. Within manufacturing, share of firms engaged in product innovation and R&D is, as expected, the highest in high-tech industries and lowest in low-tech industries; differences are not very pronounced for process innovation. Share of firms engaged in R&D among exporters (either direct or indirect) is four times higher than among non-exporters; share of product and process innovators is likewise higher among exporters.

Interestingly, share of firms engaged in R&D is lower among foreign-owned firms, but that could be due to them having R&D facilities outside Russia. However, share of product innovators is also lower among foreign-owned firms and there are no significant differences in the share of process innovators among foreign- and domestic-owned firms. Share of firms engaged in R&D is almost four times higher among private firms than state-owned firms; the latter also have a lower share of product and process innovators.

18 The regions with the highest number of errors in the data discovered during the data cleaning process are Irkutsk, Kaluga, Kirov, Lipetsk, and Kirov. However, we cannot say that the data for these regions is less reliable. 10

Share of firms engaged in R&D is much higher among firms whose main market is national or international, than among those that compete primarily on the regional market, and the same is in general true for product and process innovation.

Why are certain regions more innovative (as measured by the share of firms engaged in product or process innovation and R&D) than others? Historical organisation factors described in section 2 offer one possible explanation, which we explore in more detail in this paper - Figure 2 contains choropleth maps of the same, adding the location of broadly-defined non-military science cities. Table 3 shows the percentage of innovative firms by city status, where city status is measured as science city, closed city, city restricted to foreigners, academic town and city hosting a Russian Academy of Science regional centre. There are statistically significant differences on all three measures of innovation (product and process innovation, and spending on R&D) between science and non-science cities and academic and non-academic towns. The share of innovative firms is almost twice as high in science towns as it is in non-science towns, but it is lower in academic towns. Share of firms engaged in process innovation is statistically significantly higher in cities that were restricted to foreigners than in non-restricted cities.

The above differences in means could be due to differences in sector composition, ownership and export orientation of the firms in each type of cities, so we next explore whether the effect remains after we control for these as well as other firm characteristics. We describe the methodology we use in the next section.

4 Methodology Given the diversity in both the origin and the level of our data sources – the municipal data are paired with innovation data that are collected at the firm level – we assess the effects of science cities on innovation both at the firm and at the municipality level.

In both approaches, the object of estimation (and, consequently, the associated econometric and endogeneity problems) will differ. However, we employ the same identification strategy in both: we match science cities to other non-science cities that look similar conditional on the observable variables. In other words, we use a matching procedure.

The two approaches are discussed in greater extent below.

4.1 Firm-level analysis All our analyses that are based on firms as the unit of observation are cross-sectional and display the following structure:

(1) = + ∙ + + + where is a specific outcome variable that is characteristic of structure S within a specified mode M, is our main regressor of interest – a dummy variable to indicate whether the a firm lies in the proximity of a science city or not, is a set of structural variables that are model-dependent and is a set of extra controls, typically fixed effects aimed at removing the common characteristics of groups of observations, such as firms in the same industry. The outcomes we analyse are indicators 11

for product and process innovation and R&D spending. As indicated in Section 2, we define science city indicator in five different ways: as an indicator for a strict definition of science cities, academic towns, closed cities and cities restricted to foreigners.

The function defines the structure of the model and the characteristics of the error component will vary accordingly.∙ For instance, if is a binary outcome, and can vary to allow for a probit, a logit or a linear probability model to be estimated on the∙ data. Table 4 summarizes the principal models and relative set of structural control variables. Our preference will usually fall on linear models of the following kind:

(2) = + ∙ + + + A characteristic common to all firm-level models is that the main variation of interest happens at a higher level – the municipality or raion – than our units of observations, which are taken at the firm level. On the one hand, this supports identification: if one assumes that the treatment dummy is not correlated with the firm-level structural variables, any “structural” source of bias will likely not affect the main coefficient of interest. Formally this amounts to saying that , which is easily tenable in our setting. The correlation between various measures of capital, ∙ size, = and 0 firm age with various definitions of science city is typically very small and insignificant.

On the other hand, a more relevant endogeneity problem may arise due to the presence of potentially unobserved firms’ characteristics that are correlated with both the outcome and the municipality status. Since cities’ locations are static and exogenously given at the time when Russia started its transition to a market economy, this amounts to the possibility that firms of a specific kind have endogenously chosen to locate close to a science city, for instance because it makes innovative activity less costly and/or more profitable, due to the presence of localized spillovers, for example.

With our cross-sectional data, possible solutions to this issue are instrumental variables or a matching-type strategy. We opted for the latter, which we believe is more transparent given the setting at hand. In particular, a matching strategy should be able to identify firms which are located in science cities and counterpart “control” firms that are similar in terms of their observables characteristics, but are located in non-science cities that are in turn similar to their partner’s science cities in terms of municipal-level observables. We exploit the fact that the locations of these cities were chosen for their geographical characteristics (with several purpose-built from scratch) rather than pre-existing comparative advantages in terms of R&D facilities, industries and level of education of their inhabitants, and match the municipalities on selected observable municipal-level variables in 1959, the earliest year for which such data exist for majority of our sample.

Matching on municipal-level observables, however, entails relevant practical difficulties when trying to apply the most standard procedures combining firm-level and municipal data. For instance, trying to estimate the propensity score with any specification so to achieve at the same time common support and covariates’ balance for the municipal level controls will be very hard, given the stratified

random sampling design of BEEPS.19 For this reason we have employed a variation of the coarsened exact matching (CEM) procedure as in Iacus et al. (2008).

The spirit of coarsened matching is to pre-process the data in order to form coarsened groups of observations, or strata, which are characterized by a close overlapping of the conditional distributions of covariates for the treated and the non-treated observations. In contrast to the typical classical matching, where observations are paired, this procedure pairs the strata; this requires no parametric assumptions on the probability of receiving the treatment conditional on the covariates. However, different choices of covariates sets or restriction on the strata-definition algorithm may induce loss of statistical information.

The variation of the procedure that we adopt in this study is based on the following idea: we first use coarsened exact matching to construct groups of municipalities that are similar in their conditional joint distribution of municipal-level covariates. Then, we use the strata constructed in this way to characterize groups of similar science vs. non-science cities and run the estimation at the firm level. It is important that at this point firms’ specific structural variables are balanced across groups within strata.

In the estimation stage, we will attempt to correct for the endogenous location problem by a dual approach that is allowed by the structure of our data: we will remove stratum fixed effects – effectively assuming this controls for much of the residual potential place-induced endogeneity – and at the same time we will appropriately reweight firm-level observations to gauge an appropriate measure of the causal effect that appropriately takes into account the differential number of treated and non-treated observations for each stratum. In particular, the weights are attributed as follows.

Let SC denote the set of firms in science cities, their number being given by ; similarly, denote firms in non-science cities as NS , totalling , with c indexing strata, so that and refer to number of firms in science vs. non-science cities in a specific stratum. Then, weights are assigned as:

1, ∈ (3) = , ∈ and 0 otherwise (whereby the observation belongs to an “unmatched” stratum, where there is no overlap between science and non-science cities). Notice that the particular structure of our data – with the variation happening at a higher level than our fundamental unit of observation – allows for multiple approaches to simultaneously correct for potential sources of endogeneity. Intuitively, this is due to the fact that like our fundamental variation of interest, the correlates of the likely unobserved sources of endogeneity also vary at a higher level, thus allowing for a higher number of degrees of freedom in the model(s).

19 The crucial issue is that cities in our sample vary in the number of firms. Intuitively, it is the municipal level controls that carry most of the predictive power for the science city status, and trying to estimate the propensity score at the firm level will magnify differences in all the moments of covariates’ distributions. 13

4.2 Municipal-level analysis The effect(s) of interest at the municipal level, such as patenting, could be estimated at increasing degrees of complexities with methods relying on the CIA assumption and thus on a “selection on observables” approach. This time, however, the selection problem does not focus on potential endogenous choice of treatment at exogenously given science cities location (as it was the case for firms), but on the possibility that science cities locations have been selected to begin with – by the Soviet government – for some intrinsic ex-ante advantage of theirs. To characterize, one could start from a simple municipal level cross-sectional regression that controls for the observed selection determinants:

(4) = + + + + As long as our cross-sectional determinants of science cities choice remove all the source of endogeneity between science cities and the error term, the parameter of interest is identified. The set of additional controls depends on the structural model of choice and hence it is related to the choice of our outcome variable . If, for instance, the outcome is patents per population, contains the number of research and development institutions and number of scientists.

Unobserved determinants of the output measure would bias the entire regression as well as the parameter of interest. A possible solution in the absence of full panel data is to use long differences for our structural variables in the regression, in the spirit of Kline and Moretti (2014). Such long differences (of the kind and , t > s) should be taken for the structural variables only, as the selection controls − remain cross-sectio − nal like our treatment variable. Proper instruments would be constructed from the set of variables according to standard procedures. The actual interpretation of the estimated effect is dependent on the time interval spanned by the long differences: it is reasonable to assess economic effects that are typical of market economy at least since the start of the transition era.

In addition to this, a lot more of statistical methods relying on the CIA can be applied to more robustly estimate the effect, from, again, coarsened exact matching to propensity score based matching and propensity score reweighting. Note though that the total number of degrees of freedom in the estimation of the model is usually around 200, as we are only working with a number of science cities that is slightly higher than 100, and cross-sectional data.

5 Results

5.1 Firm-level results using probit We first estimate the impact of science cities on innovation outcomes ignoring the potentially unobserved firms’ characteristics that are correlated with both the outcome and the municipality status. Tables 5 and 6 contain the estimated average marginal effects from probit models with innovation input and output indicators as dependent variables.

We do not find any statistically significant differences in the likelihood of spending on R&D between firms located in science cities and firms located in non-science cities, regardless of how science cities

are defined (Table 5). In fact, only firms located in strictly defined science cities (naukograds) are 6 percentage points more likely to have introduced process innovation in 2008-2010 than firm located outside these cities (Table 6, column (5); the average marginal effect is significant at 10 per cent level of significance). The average marginal effects for the other measures of science cities are mostly positive, but not statistically significant.

Control variables are also interesting. In both tables, larger firms are 2-2.5 percentage points more likely to be innovative on all three dimensions we look at: product and process innovation as well as R&D. Firms exposed to more competition are also more likely to be innovative. Direct or indirect exporters are about 3.5 and 6.8 percentage points more likely to engage in product innovation and R&D spending, respectively, than firms that do not export at all. Firms competing primarily on the local or national market, on the other hand, are 12.5 and 23 percentage points more likely to have introduced a new process, respectively, than firms competing primarily on the international market. This is somewhat counterintuitive, but could be due to the fact that the share of firms competing primarily on the international market is very low in our sample. It is also interesting to note that firms in 50 per cent or more state ownership are less likely to engage in innovative activities. However, the average marginal effects are negative and significant only for process innovation: the likelihood of such firms to engage in it is almost 10 percentage points lower than for majority privately-owned firms. Finally, percentage of permanent, full-time employees with a completed university degree is always positive, but significant at 1 per cent level of significance only in the case of R&D spending.

Estimates in Tables 5 and 6 suggest that science cities generally have no impact on innovative activity of firms. However, given that there are differences not only among firms, but also among locations, the estimates are likely biased. We turn next to the application of our full-fledged matching method to correct for potential determinants of endogeneity.

5.2 Firm-level results using municipal-level coarsened exact matching As discussed in section 4.1, we are coarsening municipal level variables to form strata of cities that were similar in 1959, the earliest year for which we can find municipal-level data for most of the observations in our sample. For each possible definition of science cities we then drop strata for which there is no overlap between science and non-science cities (no common support). We then use OLS to regress innovation activity indicators on different definitions of science cities, controlling for strata fixed effects and reweighting for the relative proportion of science cities firms against non- science cities firms in each stratum. Standard errors are clustered at the municipality level. We argue that this procedure gets as close as possible to capture the causal effect of being close to a science city for a firm.

We construct the strata by increasingly including different municipal-level variables in the coarsening algorithm. By order of inclusion:

1. Number of survey firms in the municipality, with a hierarchic priority; 2. Number of research and development institutes (RDI) in the municipality in 1959; 3. Municipal area population according to the 1959 census;

4. Economic region; 20 5. Municipal manufacturing output around 1960.

The hierarchic inclusion of the number of surveyed firms is done to ensure that the number of ‘treatment’ and ‘control’ firms is relatively balanced, conditional on the relative proportion of science cities, within each stratum. Sample size decreases by almost 50 per cent when we include municipal level manufacturing output due to data availability.

Table 7 shows the statistic, a measure of overall imbalance, based on the difference between the multidimensionalℒ histogram of all pre-treatment covariates in the treatment group and that in the control group (Iacus et al. 2008). Larger values of indicate larger imbalance between the groups. Prior to applying CEM, is typically greater thanℒ 0.3, and close to 1 in several cases; after CEM, it usually falls in the 0.0-0.3ℒ range. However, in some cases, increases after CEM – in particular, for closed cities and academic towns when we use CEM on ℒthe number of firms, number of RDI and population.

The firm-level structural variables that are employed in each model (as listed in Table 4) are balanced across strata in the models that we estimate.21 We believe that the best balance between bias and variance is provided here by three simple levels of coarsening (conditional on the same subdivision by the number of surveyed firms): number of RDI, number of RDI and population and number of RDI, population and economic region. The latter, however, may in some cases dramatically reduce the amount of usable information.

Tables 8-10 display the results from the three structural models summarised in Table 4. Twelve estimates are displayed for each model: three for each definition of science city that is adopted, one with coarsening by the number of RDI, one by the number of RDI and population and one by the number of RDI, population and economic region.

Table 8 shows no statistically discernible differential impact of science cities on R&D activity; only the coefficient on cities with restrictions on foreigners in the past is positive and statistically significant in one of the cases. This is not surprising given that probably Russia, like other countries not lying on the technological frontier – at least for commercialized industrial products – is characterized by innovation-driving institutions that are not based on formal R&D activity and diffused patenting, perhaps because of weak or weakly enforced EPRs.

More interesting results appear in Tables 9 and 10, which represents the impact of science cities on product and process innovation, respectively. Firms located in strictly defined science cities

20 Economic regions are defined as groups of federal subjects that share the following characteristics: common economic and social goals and participation in development programs; relatively similar economic conditions and potential; similar climatic, ecological, and geological conditions; similar methods of technical inspection of new construction; similar methods of conducting customs oversight; and overall similar living conditions of the population. There are 12 such regions in Russia; we group Central, Northwestern and Kaliningrad economic regions to increase the number of matched observations, so we end up with 10 economic regions. 21 The tests for the balancing property of firm-level controls are available on request, for each definition of science cities. 16

(naukograds) were more than 6 percentage points more likely to introduce new products in 2008- 2010 than firms in comparable locations (columns 1-3), while firms located in academic towns were at least 7 percentage points less likely to do so than firms in comparable locations (columns 4-6). Firms located in closed cities were also less likely to introduce new products than firms in comparable locations.

The results for process innovation (Table 10) differ somewhat: firms in science cities were more likely to introduce new processes, too, though the coefficients are now statistically significant only at the 10 per cent level (columns 1-3). The results are somewhat stronger for firms in cities that were restricted to foreigners in the past, although the magnitude of the coefficients is smaller and suggests about 2.5 percentage points higher likelihood of introducing a new process (columns 10- 12). Spending on R&D increased the likelihood of engaging in product and process innovation substantially in the sample as a whole, ranging from a 14.3 to 34 percentage points increase.

Comparison of results for science cities and academic towns is particularly interesting. There are no statistically significant differences between the firms located in either of these locations and firms located in municipalities that were similar in 1959 in terms of the probability of spending on R&D. However, while firms in science cities were more likely to engage in product and process innovation, firms in academic towns were statistically significantly less likely to introduce new products. This may highlight the importance of strong links between science and industry for favourable innovation outcomes.

Science cities were set up to have both research and development facilities as well as industry in the same location; the focus in academic towns was purely on the latter. On the one hand, interregional labour mobility in Russia is low by international standards and is also lower than it used to be in the Soviet Union (Andrienko and Guriev 2004). To a large extent, this is due to a significant part of worker compensation paid in-kind rather than in cash, argue Friebel and Guriev (2005), and is also related to the geographical concentration of industrial activity, a Soviet-era legacy.

On the other hand, brain drain from Russia has been substantial since the collapse of the Soviet Union – especially so among scientists, researchers and professors. Whole laboratories relocated to the USA, Israel or Europe, and by the beginning of the 2000s, nearly all the top names from Soviet science field were working outside of Russia. The estimates of the number of Russian scientists and researchers working outside of Russia range between 25,000-100,000 (Chyernich and Grusdeva 2011).

However, many stayed in Russia, but left science and pursued other career options, including business (Moody 1996, Ganguli 2014). Reliable numbers on these transitions do not exist, but it may have been easier to emigrate for pure academic scientists than for those working in more applied research and development institutions, who on the other hand may have found it relative easier to move to business and implement their ideas there. Our findings could be interpreted as evidence for the latter.

5.3 Municipality-Level Analysis We expect to have preliminary results in mid-2015. 17

5.4 Robustness checks In section 5.2, we reported results using coarsening by the number of RDI, by the number of RDI and population and by the number of RDI, population and economic region. In Table 11, we add an additional coarsening variable, the municipal-level manufacturing output around 1960. The number of matched observations that can be used in the regression drops significantly, but the results remain broadly robust.

In addition to the models including CEM strata fixed effects and firm-level structural variables only, we also estimate models where we include additional firm-level and interview characteristics, as an additional check on our results. Table 12 reports the estimates from the model that uses CEM on the number of firms, number of RDIs, population and economic region, with additional firm-level controls for ownership, exporter status, main market and percentage of employees with a completed university degree. They are broadly similar as their counterparts in columns (3), (6),(9) and (12) in Tables 8, 9 and 10 in terms of sign and statistical significance as well as magnitude for the most part.

6 Conclusion We use BEEPS V data and municipal level data for Russia to evaluate the impact of Soviet era science cities on present-day firm-level innovation. We match the science and non-science cities on their characteristics in 1959, and compare the innovation activity of firms located in science cities with the innovation activity of similar-in-the-observables firms in similar, non-science cities. Specifically, we look at R&D expenditure, product and process innovation. We find some evidence that firms in science cities (naukograds) are more likely to engage in product and process innovation than firms located in similar, non-science cities, while firms located in academic towns are less likely to engage in product innovation than firms located in non-academic towns.

We do not find, however, any evidence that firms in science cities, academic towns, closed cities or cities restricted to foreigners fare any better in terms of the likelihood of engaging in R&D. This is somewhat puzzling, but may be related to the relative lack of funding and the level of intellectual property protection. Our interpretation of the facts is that Russia has much potential for a lively innovation activity in its private sector, also thanks to the heritage of past Soviet-era and generally public research investments.

However, appropriate commercialization and appropriation of the benefits of innovation in the Russian economy seems to be stifled by other determinants at present, perhaps of the institutional kind – ranging from the quality of the business environment to the degree of intellectual property protection. As Russia is attempting to shift from an extraction-dependent to an advanced innovation-driven, high value-added diversified economy, it should then formulate policies that, besides stimulating the growth of new clusters of research and production activity, also nurture the pre-existing research facilities and resources, in order to support them to drive a successful economic performance in the modern market economy.

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Tables

Table 1: BEEPS V Russia sample breakdown

Region Not eligible Eligible, completed Eligible, refusal Total % eligible Central 607 511 6 1124 46.0 Belgorod 89 31 0 120 25.8 Kaluga 69 50 2 121 43.0 Kursk 51 36 0 87 41.4 Lipetsk 68 49 4 121 43.8 Moscow City 45 76 0 121 62.8 Moscow Oblast 54 68 0 122 55.7 Smolensk 43 28 0 71 39.4 Tver 61 59 0 120 49.2 Voronezh 57 64 0 121 52.9 Yaroslavl 70 50 0 120 41.7 Far Eastern 171 163 0 334 48.8 Khabarovsk 50 72 0 122 59.0 Primorsky Krai 73 47 0 120 39.2 Sakha (Yakutia) 48 44 0 92 47.8 North Caucasian 31 89 0 120 74.2 Stavropol Krai 31 89 0 120 74.2 Northwestern 310 168 6 484 36.0 Kaliningrad 68 54 0 122 44.3 Leningrad 61 55 4 120 49.2 Murmansk 94 24 2 120 21.7 St. Petersburg 87 35 0 122 28.7 Siberian 420 281 8 709 40.8 Irkutsk 74 56 1 131 43.5 Kemerovo 65 52 7 124 47.6 Krasnoyarsk 53 36 0 89 40.4 Novosibirsk 82 41 0 123 33.3 Omsk 68 52 0 120 43.3 Tomsk 78 44 0 122 36.1 Southern 210 117 1 328 36.0 Krasnodar 57 30 1 88 35.2 Rostov 85 35 0 120 29.2 Volgograd 68 52 0 120 43.3 Urals 98 101 0 199 50.8 Chelyabinsk 46 33 0 79 41.8 Sverdlovsk 52 68 0 120 56.7 Volga 446 467 9 922 51.6 Bashkortostan 31 75 0 106 70.8 Kirov 99 28 7 134 26.1

Mordovia 70 49 1 120 41.7 Nizhni Novgorod 41 41 0 82 50.0 Perm 41 79 0 120 65.8 Samara 36 84 0 120 70.0 Tatarstan 73 47 0 120 39.2 Ulyanovsk 55 64 1 120 54.2 Total 2293 1897 30 4220 45.7 Source: EBRD-World Bank BEEPS V Russia.

Table 2: Innovative firms as share of the population, BEEPS V Russia and GOSKOMSTAT 2012

BEEPS V Russia, % GOSKOMSTAT 2012, % Region Mean Std. Err 95% Confidence Interval Belgorod 17.4 3.7 10.2 24.6 12.0 Kaluga 39.3 5.8 28.0 50.6 11.9 Kursk 34.5 6.2 22.4 46.6 9.3 Lipetsk 42.3 5.9 30.7 54.0 9.7 Moscow City 60.2 7.5 45.6 74.8 13.2 Moscow Oblast 45.2 5.6 34.1 56.3 8.2 Smolensk 27.2 6.2 15.1 39.3 5.1 Tver 40.8 6.3 28.4 53.1 6.2 Voronezh 48.2 6.1 36.4 60.1 10.7 Yaroslavl 33.3 5.2 23.1 43.4 12.0 Khabarovsk 50.3 5.5 39.5 61.1 12.9 Primorsky Krai 40.0 6.6 27.1 53.0 9.9 Sakha (Yakutia) 44.6 6.4 32.2 57.1 9.0 Stavropol Krai 72.6 4.8 63.1 82.0 7.5 Kaliningrad 43.0 5.3 32.6 53.3 3.0 Leningrad 42.9 5.4 32.2 53.5 11.1 Murmansk 22.3 4.4 13.6 31.0 7.1 St. Petersburg 21.1 3.7 13.8 28.5 14.6 Irkutsk 37.9 5.3 27.6 48.2 7.0 Kemerovo 43.6 6.6 30.7 56.5 7.3 Krasnoyarsk 34.7 5.7 23.6 45.7 12.7 Novosibirsk 26.9 4.2 18.6 35.2 9.6 Omsk 40.0 5.2 29.9 50.1 10.7 Tomsk 34.4 4.9 24.8 44.0 20.7 Krasnodar 29.3 5.7 18.2 40.4 6.7 Rostov 25.8 4.4 17.2 34.4 10.4 Volgograd 45.3 5.0 35.4 55.1 9.5 Chelyabinsk 40.6 6.3 28.4 52.9 12.5 Sverdlovsk 49.8 6.3 37.4 62.2 16.1 Bashkortostan 67.6 5.2 57.4 77.8 14.9 Kirov 25.5 4.7 16.3 34.7 9.6 Mordovia 37.6 4.7 28.4 46.9 11.7 Nizhni Novgorod 49.1 6.1 37.2 61.0 20.4 Perm 62.9 4.8 53.4 72.3 22.7 Samara 68.5 4.7 59.2 77.8 14.9 Tatarstan 36.6 4.9 27.0 46.1 15.6 Ulyanovsk 51.8 4.9 42.1 61.5 9.7 Source: EBRD-World Bank BEEPS V Russia and GOSKOMSTAT.

Table 3: Descriptive statistics for innovation by city status

Yes No Number Mean Number Mean p-value Product innovation (h1c) Science city 185 0.232 4035 0.124 0.001 Closed city 97 0.124 4123 0.129 0.877 Restricted city 604 0.154 3616 0.125 0.063 Academic town 412 0.068 3808 0.136 0.000 Process innovation (h3c) Science city 185 0.254 4035 0.135 0.000 Closed city 97 0.186 4123 0.139 0.197 Restricted city 604 0.184 3616 0.133 0.003 Academic town 412 0.109 3808 0.144 0.035 Product and process innovation (h1h3c) Science city 185 0.362 4035 0.209 0.000 Closed city 97 0.247 4123 0.215 0.441 Restricted city 604 0.275 3616 0.206 0.000 Academic town 412 0.155 3808 0.222 0.001 Spending on R&D (h6c) Science city 185 0.168 4035 0.108 0.035 Closed city 97 0.093 4123 0.111 0.570 Restricted city 604 0.116 3616 0.110 0.658 Academic town 412 0.066 3808 0.116 0.000 Source: EBRD-World Bank BEEPS V Russia.

Table 4: Principal models and relative set of structural control variables

R&D Product Process Model indicator innovation innovation indicator indicator Log (Employment) X X X Log (Age) X X X R&D indicator X X

Table 5: R&D spending average marginal effects

Dependent variable R&D spending indicator (1) (2) (3) (4) Science city in the past indicator 0.028

(0.029)

Non-military closed city indicator -0.013

(0.041)

Restrictions on foreigners in the past 0.028

indicator, excluding military (0.032)

Academic town indicator -0.027

(0.036)

50% or more foreign owned 0.002 0.001 0.003 0.001 (0.027) (0.027) (0.027) (0.027) 50% or more state owned -0.048 -0.047 -0.048 -0.047 (0.031) (0.031) (0.031) (0.031) Direct or indirect exporter 0.067*** 0.068*** 0.069*** 0.068*** (0.018) (0.019) (0.019) (0.018) Main market: local -0.006 -0.007 -0.006 -0.007 (0.037) (0.037) (0.037) (0.037) Main market: national 0.064 0.064 0.064 0.064 (0.048) (0.048) (0.048) (0.048) Log(number of employees) 0.025*** 0.025*** 0.025*** 0.025*** (0.004) (0.004) (0.004) (0.004) % of full time employees who completed 0.001*** 0.001*** 0.001*** 0.001*** a university degree (0.000) (0.000) (0.000) (0.000) Firm age -0.000 -0.000 -0.000 -0.000 (0.000) (0.000) (0.000) (0.000) Region fixed effects YES YES YES YES 2-digit industry fixed effects YES YES YES YES Number of observations 4178 4178 4178 4178 chi2 591.1 590.1 590.9 590.5 Pseudo R2 0.202 0.202 0.202 0.202 Source: EBRD-World Bank BEEPS V Russia.

Notes: * = significant at the 10% level, ** = significant at the 5% level, * = significant at the 1% level. All columns are estimated by probit, shown are average marginal effects with standard errors in brackets below coefficient. The sample consists of all firms with available data. Besides the listed variables, all regressions included a constant, an indicator for firms with missing % of employees with a completed university degree, and an indicator for firms with missing information on the main market for their main product.

Table 6: Product and process innovation average marginal effects Dependent variable Product innovation Process innovation (1) (2) (3) (4) (5) (6) (7) (8) Science city in the past indicator 0.037 0.060*

(0.029) (0.033)

Non-military closed city indicator -0.051 0.038

(0.036) (0.055)

Restrictions on foreigners in the past 0.003 -0.011

indicator, excluding military (0.028) (0.029)

Academic town indicator -0.024 0.052

(0.042) (0.068)

50% or more foreign owned -0.014 -0.015 -0.014 -0.015 0.003 0.003 0.002 0.002 (0.028) (0.028) (0.028) (0.028) (0.033) (0.033) (0.033) (0.033) 50% or more state owned -0.024 -0.023 -0.023 -0.022 -0.096*** -0.095*** -0.095*** -0.095*** (0.041) (0.041) (0.041) (0.041) (0.025) (0.026) (0.026) (0.026) Direct or indirect exporter 0.035** 0.036** 0.037** 0.036** 0.014 0.016 0.015 0.016 (0.018) (0.018) (0.018) (0.018) (0.018) (0.018) (0.018) (0.018) Main market: local 0.005 0.004 0.005 0.005 0.126 0.126 0.125 0.126 (0.044) (0.044) (0.044) (0.044) (0.114) (0.081) (0.081) (0.081) Main market: national 0.035 0.034 0.035 0.034 0.233** 0.233** 0.231** 0.232** (0.049) (0.049) (0.049) (0.049) (0.114) (0.114) (0.114) (0.114) Log(number of employees) 0.020*** 0.020*** 0.020*** 0.020*** 0.024*** 0.024*** 0.024*** 0.024*** (0.004) (0.004) (0.004) (0.004) (0.005) (0.005) (0.005) (0.005) % of full time employees who 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 completed a university degree (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Firm age -0.000 -0.000 -0.000 -0.000 -0.000 -0.000 -0.000 -0.000 (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Region fixed effects YES YES YES YES YES YES YES YES 2-digit industry fixed effects YES YES YES YES YES YES YES YES Number of observations 4178 4178 4178 4178 4178 4178 4178 4178 chi2 753.1 752.6 751.2 751.5 533.1 529.7 529.3 529.9 Pseudo R2 0.234 0.233 0.233 0.233 0.157 0.156 0.156 0.156

Source: EBRD-World Bank BEEPS V Russia.

Table 7: Measure of global imbalance, statistic, prior to and after CEM Science cities, Restricted to Science cities, strict Closed cities foreigners Academic towns broad Pre- Pre- Pre- Pre- Pre- Variables included in CEM CEM CEM CEM CEM CEM CEM CEM CEM CEM CEM Number of firms 0.231 0.134 0.322 0.083 0.379 0.097 0.862 0.000 0.331 0.086 Number of firms and economic region 0.480 0.241 0.517 0.130 0.498 0.000 0.961 0.000 0.436 0.000 Number of firms and RDI 0.298 0.222 0.328 0.281 0.424 0.000 0.862 0.333 0.338 0.065 Number of firms, RDI and economic 0.573 0.269 0.672 0.202 0.543 0.000 0.961 0.000 0.512 0.000 region Number of firms, RDI and population 0.298 0.222 0.328 0.582 0.424 0.000 0.862 0.872 0.338 0.069

Number of firms, RDI, population and 0.573 0.269 0.672 0.320 0.543 0.000 0.961 0.000 0.512 0.000 economic region Number of firms, RDI and 0.390 0.186 0.422 0.619 0.439 0.000 0.856 0.375 0.256 0.045 manufacturing output Number of firms, RDI, economic 0.496 0.167 0.643 0.517 0.665 0.000 0.962 0.000 0.359 0.019 region and manufacturing output Number of firms, RDI, population and 0.390 0.186 0.422 0.619 0.439 0.000 0.856 0.500 0.256 0.045 manufacturing output Number of firms, RDI, population, economic region and manufacturing 0.496 0.167 0.643 0.530 0.665 0.000 0.962 0.000 0.359 0.019 output Source: EBRD-World Bank BEEPS V Russia.

Notes: RDI stands for (the number of) research and development institutes.

Table 8: R&D Spending, coarsened matching approach

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

-0.038 -0.038 -0.021 Science city in the past indicator (0.047) (0.047) (0.054)

-0.013 -0.013 -0.049 Academic town indicator (0.016) (0.016) (0.029)

-0.012 -0.073 -0.113 Non-military closed city indicator (0.061) (0.053) (0.107)

0.036** 0.024 -0.023 Restrictions on foreigners in the past indicator (0.018) (0.019) (0.027)

Log (number of employees), 0.022** 0.022** 0.033** 0.024*** 0.024*** 0.041** 0.036*** 0.032** 0.042 0.033*** 0.030*** 0.046*** BEEPS (0.010) (0.010) (0.013) (0.006) (0.006) (0.012) (0.007) (0.012) (0.025) (0.005) (0.006) (0.008) 0.013 0.013 0.014 -0.000 -0.000 0.000 -0.003 -0.006 0.017 0.002 0.002 0.005 Log (firm age) (0.019) (0.019) (0.018) (0.006) (0.006) (0.010) (0.020) (0.023) (0.028) (0.006) (0.007) (0.008) Stratification by RDIs YES YES YES YES YES YES YES YES YES YES YES YES Stratification by population NO YES YES NO YES YES NO YES YES NO YES YES Stratification by economic NO NO YES NO NO YES NO NO YES NO NO YES region Stratum fixed effects YES YES YES YES YES YES YES YES YES YES YES YES Industry fixed effects YES YES YES YES YES YES YES YES YES YES YES YES Number of observations 823 823 503 1,780 1,780 840 668 600 236 3,262 3,056 1,203 Adjusted R2 0.178 0.178 0.225 0.058 0.058 0.140 0.152 0.127 0.128 0.088 0.082 0.157

Source: EBRD-World Bank BEEPS V Russia.

Table 9: Product innovation, coarsened matching approach

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

0.064** 0.064** 0.075** Science city in the past indicator (0.028) (0.028) (0.032)

-0.071*** -0.071*** -0.081*** Academic town indicator (0.017) (0.017) (0.023)

0.047 -0.058*** -0.087*** Non-military closed city indicator (0.054) (0.017) (0.021)

0.046* 0.010 -0.027 Restrictions on foreigners in the past indicator (0.028) (0.023) (0.031)

Log (number of employees), 0.029*** 0.029*** 0.032** 0.006 0.006 0.004 0.009 0.033* 0.053*** 0.007 0.013*** 0.008 BEEPS (0.010) (0.010) (0.015) (0.007) (0.007) (0.013) (0.015) (0.019) (0.018) (0.005) (0.005) (0.010) -0.038 -0.038 0.008 0.006 0.006 -0.001 -0.016 -0.010 -0.023 0.007 0.005 -0.003 Log (firm age) (0.028) (0.028) (0.018) (0.008) (0.008) (0.009) (0.024) (0.033) (0.027) (0.007) (0.007) (0.010) Spent on R&D in the last 3 0.208*** 0.208*** 0.225*** 0.297*** 0.297*** 0.340*** 0.186*** 0.162*** 0.233*** 0.251*** 0.263*** 0.326*** years (0.054) (0.054) (0.070) (0.056) (0.056) (0.094) (0.048) (0.056) (0.085) (0.039) (0.042) (0.076) Stratification by RDIs YES YES YES YES YES YES YES YES YES YES YES YES Stratification by population NO YES YES NO YES YES NO YES YES NO YES YES Stratification by economic NO NO YES NO NO YES NO NO YES NO NO YES region Stratum fixed effects YES YES YES YES YES YES YES YES YES YES YES YES Industry fixed effects YES YES YES YES YES YES YES YES YES YES YES YES Number of observations 823 823 503 1,780 1,780 840 668 600 236 3,262 3,056 1,203 Adjusted R2 0.271 0.271 0.327 0.201 0.201 0.297 0.179 0.266 0.358 0.186 0.209 0.283 Source: EBRD-World Bank BEEPS V Russia.

Table 10: Process Innovation, coarsened matching approach

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

0.066* 0.066* 0.072* Science city in the past indicator (0.038) (0.038) (0.040)

-0.002 -0.002 -0.015 Academic town indicator (0.018) (0.018) (0.017)

0.018 -0.086 0.119* Non-military closed city indicator (0.055) (0.061) (0.066)

0.051** 0.025* 0.026** Restrictions on foreigners in the past indicator (0.022) (0.015) (0.013)

Log (number of employees), 0.011 0.011 0.017 0.013* 0.013* 0.016* 0.002 0.035* 0.034 0.017** 0.021*** 0.027*** BEEPS (0.012) (0.012) (0.018) (0.006) (0.006) (0.008) (0.018) (0.019) (0.030) (0.007) (0.007) (0.007) 0.017 0.017 0.026 0.009 0.009 0.006 0.023 0.005 0.026 0.016 0.010 0.007 Log (firm age) (0.015) (0.015) (0.024) (0.011) (0.011) (0.014) (0.024) (0.015) (0.035) (0.010) (0.009) (0.012) Spent on R&D in the last 3 0.246*** 0.246*** 0.226** 0.208*** 0.208*** 0.237*** 0.143** 0.120 0.085 0.194*** 0.211*** 0.227*** years (0.064) (0.064) (0.097) (0.028) (0.028) (0.023) (0.062) (0.091) (0.135) (0.025) (0.026) (0.038) Stratification by RDIs YES YES YES YES YES YES YES YES YES YES YES YES Stratification by population NO YES YES NO YES YES NO YES YES NO YES YES Stratification by economic NO NO YES NO NO YES NO NO YES NO NO YES region Stratum fixed effects YES YES YES YES YES YES YES YES YES YES YES YES Industry fixed effects YES YES YES YES YES YES YES YES YES YES YES YES Number of observations 823 823 503 1,780 1,780 840 668 600 236 3,262 3,056 1,203 Adjusted R2 0.149 0.149 0.177 0.118 0.118 0.190 0.083 0.101 0.092 0.124 0.140 0.196 Source: EBRD-World Bank BEEPS V Russia.

Table 11: R&D spending, product and process innovation, coarsened matching approach, stratifying on municipal manufacturing output

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) 0.001 0.088** 0.111*** Science city in the past indicator (0.058) (0.039) (0.036)

-0.044 -0.085* -0.003 Academic town indicator (0.047) (0.028) (0.024)

-0.064 -0.070 0.061 Non-military closed city indicator (0.088) (0.049) (0.046)

-0.043 -0.097*** 0.033* Restrictions on foreigners in the past indicator (0.042) (0.035) (0.019)

0.029** 0.046* 0.043*** 0.044** 0.047** -0.001 0.018 -0.006 0.013 0.020 0.064*** 0.053*** Log (number of employees), BEEPS (0.014) (0.015) (0.015) (0.020) (0.022) (0.023) (0.024) (0.013) (0.025) (0.017) (0.018) (0.014) 0.013 0.002 0.038 0.015 -0.011 -0.018 0.009 -0.032** 0.034 -0.003 0.035 -0.003 Log (firm age) (0.026) (0.016) (0.024) (0.022) (0.023) (0.009) (0.028) (0.015) (0.035) (0.020) (0.040) (0.024) 0.236*** 0.420** 0.234** 0.401*** 0.171 0.278** -0.105 0.201*** Spent on R&D in the last 3 years (0.076) (0.080) (0.091) (0.089) (0.108) (0.054) (0.099) (0.059)

Stratification by RDIs YES YES YES YES YES YES YES YES YES YES YES YES Stratification by population YES YES YES YES YES YES YES YES YES YES YES YES Stratification by economic region YES YES YES YES YES YES YES YES YES YES YES YES Stratification by manuf. output YES YES YES YES YES YES YES YES YES YES YES YES Stratum fixed effects YES YES YES YES YES YES YES YES YES YES YES YES Industry fixed effects YES YES YES YES YES YES YES YES YES YES YES YES Number of observations 410 406 212 349 410 406 212 349 410 406 212 349 Adjusted R2 0.186 0.208 0.112 0.202 0.351 0.366 0.363 0.396 0.188 0.212 0.158 0.251 Source: EBRD-World Bank BEEPS V Russia.

Table 12: R&D spending, product and process innovation, coarsened matching approach, controlling for additional firm-level variables R&D Product innovation Process innovation (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) -0.040 0.073** 0.054 Science city in the past indicator (0.051) (0.030) (0.037)

-0.042* -0.076*** -0.009 Academic town indicator (0.023) (0.018) (0.020)

-0.112 -0.083*** 0.097* Non-military closed city indicator (0.114) (0.021) (0.058)

-0.015 -0.027 0.034** Restrictions on foreigners in the past indicator (0.023) (0.030) (0.015)

Log (number of employees), 0.030** 0.043** 0.041 0.045*** 0.025* 0.004 0.049** 0.006 0.024 0.017* 0.037 0.029*** BEEPS (0.014) (0.017) (0.027) (0.010) (0.013) (0.012) (0.022) (0.010) (0.022) (0.009) (0.032) (0.008) 0.010 -0.010 0.025 -0.003 -0.003 -0.005 -0.030 -0.007 0.018 0.008 0.038 0.009 Log (firm age) (0.019) (0.011) (0.028) (0.009) (0.022) (0.012) (0.037) (0.012) (0.025) (0.017) (0.033) (0.013) 0.213*** 0.312*** 0.220** 0.303*** 0.203** 0.242*** 0.039 0.227*** Spent on R&D in the last 3 years (0.076) (0.082) (0.089) (0.067) (0.097) (0.025) (0.126) (0.042)

Stratification by RDIs YES YES YES YES YES YES YES YES YES YES YES YES Stratification by population YES YES YES YES YES YES YES YES YES YES YES YES Stratification by economic region YES YES YES YES YES YES YES YES YES YES YES YES Extra firm-level variables YES YES YES YES YES YES YES YES YES YES YES YES Stratum fixed effects YES YES YES YES YES YES YES YES YES YES YES YES Industry fixed effects YES YES YES YES YES YES YES YES YES YES YES YES Number of observations 503 840 236 1,203 503 840 236 1,203 503 840 236 1,203 Adjusted R2 0.249 0.221 0.164 0.219 0.338 0.306 0.359 0.291 0.184 0.189 0.134 0.196 Source: EBRD-World Bank BEEPS V Russia.

Notes: * = significant at the 10% level, ** = significant at the 5% level, * = significant at the 1% level. All columns are estimated by OLS, with standard errors clustered by municipality in brackets below coefficient. Extra firm-level variables include an indicator for at least 50 per cent foreign ownership, an indicator for at least 50 per cent state ownership, indicator for direct or indirect exporter status, indicators for the local and national market as the main market, respectively, % of full-time employees with a completed university degree, an indicator for firms with missing % of full-time employees with a completed university degree, an indicator for firms with missing information on the main market for their main product and a constant.

Figures Figure 1: Product and process innovation and R&D spending across Russian regions

Introduced new products in the last 3 years Proportion of establishments .3

0 Tver Kirov Perm Kursk Omsk Irkutsk Tomsk Kaluga Rostov Lipetsk Samara Belgorod Yaroslavl Mordovia Tatarstan Voronezh Leningrad Smolensk Volgograd Kemerovo Krasnodar Ulyanovsk Murmansk Sverdlovsk Kaliningrad Novosibirsk Khabarovsk Chelyabinsk Krasnoyarsk Moscow City Bashkortostan Sakha (Yakutia) Moscow Region Nizhni Novgorod Saint Petersburg Stavropol Territory Primorsky Territory

Regional average National average

Introduced new processes in the last 3 years Proportion of establishments .4

0 Tver Kirov Perm Kursk Omsk Irkutsk Tomsk Kaluga Rostov Lipetsk Samara Belgorod Mordovia Yaroslavl Tatarstan Voronezh Leningrad Smolensk Volgograd Kemerovo Krasnodar Ulyanovsk Murmansk Sverdlovsk Kaliningrad Novosibirsk Khabarovsk Chelyabinsk Krasnoyarsk Moscow City Bashkortostan Sakha (Yakutia) Moscow Region Nizhni Novgorod Saint Petersburg Stavropol Territory Primorsky Territory

Regional average National average

Spending on R&D in the last 3 years Proportion of establishments .5

0 Tver Kirov Perm Kursk Omsk Irkutsk Tomsk Kaluga Rostov Lipetsk Samara Belgorod Mordovia Yaroslavl Tatarstan Voronezh Leningrad Smolensk Volgograd Kemerovo Krasnodar Ulyanovsk Murmansk Kaliningrad Sverdlovsk Novosibirsk Khabarovsk Chelyabinsk Krasnoyarsk Moscow City Bashkortostan Sakha (Yakutia) Moscow Region Nizhni Novgorod Saint Petersburg Stavropol Territory Primorsky Territory

Regional average National average

Source: Own calculations based on EBRD-WB BEEPS V Russia.

Figure 2: Intensity of product and process innovation and R&D spending and location of firms in science, academic, closed and cities closed to foreigners in the past Intensity of sh1cr and broader science cities

(.174348,.2154841] (.1332108,.174348] (.0920736,.1332108] (.0509364,.0920736] [.0098002,.0509364] No data No Yes

Intensity of sh3cr and broader science cities

(.2794516,.3436053] (.2152969,.2794516] (.1511421,.2152969] (.0869874,.1511421] [.0228337,.0869874] No data No Yes

Intensity of sh6cr and science cities

(.208906,.260938] (.156873,.208906] (.1048399,.156873] (.0528069,.1048399] [.0007748,.0528069] No data No Yes Source: EBRD-WB BEEPS V Russia and Appendix A.

Appendix A: Closed cities, cities restricted to foreigners and science cities in the Soviet Union and Russia

Table A.1: Closed cities No. Founded Year Closed Year Location Oblast closed now? opened Specialization in the past Industry 2 Sibirsky Altai Krai 1979 1979 Y Strategic Missile Troops Military 3 Uglegorsk Amur 1961 1965 Y Spaceport "Svobodny", near InterContinental Ballistic Military Missile base 4 Mirny Arkhangelsk 1957 1966 Y State experimental cosmodrome, ballistic weapon Aerospace 5 Severodvinsk Arkhangelsk 1936 1950 N ? Naval base Military 6 Znamensk Astrakhan 1948 1948 Y Strategic Missile Troops Military 7 Mezhgorye Bashkortostan 1979 1979 Y Construction Construction 9 Lokomotivny Chelyabinsk 1974 1974 Y Strategic Missile Troops Military 11 Ozyorsk Chelyabinsk 1945 1945 Y Rosatom city, nuclear power development Nuclear 12 Snezhinsk Chelyabinsk 1957 1957 Y Rosatom city, nuclear weapon development Nuclear 13 Tryokhgorny Chelyabinsk 1952 1952 Y Rosatom city, equipment for nuclear weapon/power Nuclear production 19 Bechevinka Kamchatka 1960 1960 N 1996 Naval base Military 20 Shipunsky Kamchatka ? ? N 1996 Strategic Missile Troops Military 21 Vilyuchinsk Kamchatka 1968 1968 Y Naval base Military 22 Vulkanny Kamchatka 1955 1969 N 1999 Strategic Missile Troops Military 23 Zavoyko Kamchatka 1840 1960 N 1985 Naval base Military 24 Bolshaya Kartel Khabarovsk 1939-1940 1939- N 1989 Strategic Missile Troops Military 1940 27 Lyevintsi Kirov 1928 end of N 2004 Biological weapons (defence and production) Biochemistry 1970s 28 Omutninsk Kirov 1773 ?1960 N ? Biological weapons Biochemistry 29 Pervomaysky Kirov 1959 1959 Y Strategic Missile Troops Military 34 Kedrovy Krasnoyarsk 1965 1965 N 2007 Strategic Missile Troops Military 37 Podgorny Krasnoyarsk 1953 1953 ?Y 2008? Rosatom city Nuclear 38 Solnechny Krasnoyarsk 1965 1965 Y Strategic Missile Troops Military

39 Zelenogorsk Krasnoyarsk 1956 1956 Y Rosatom city, uranium enrichment Nuclear development 40 Zheleznogorsk Krasnoyarsk 1950 1954 Y Rosatom city, production of weapon-grade plutonium Nuclear development 44 Sosnovy Bor Leningrad 1958 1961 Y Nuclear power plant Nuclear energy 45 Gudim Magadan 1958 1958 N 1998 Strategic Missile Troops Military 48 Alachkovo Moscow Oblast ? ? N ? Military communications Military 53 Chernetskoe Moscow Oblast 1968 1968 N 2006 Strategic Missile Troops Military 60 Ivanteyevka Moscow Oblast 1564/1938 ?1954 N ?2004 Production and testing of biological weapons and Chemistry and forestry protection 65 Krasnoznamensk Moscow Oblast 1950 1950 Y Spacecraft automated control system Military, Ballistic 67 Lyubuchany Moscow Oblast 1572 ?1973 N ? Production and testing of biological weapons and Biology protection 68 Makarovo Moscow Oblast 1954 1954 N ? Strategic Missile Troops Military 70 Molodyozhny Moscow Oblast 1964 1964 Y Strategic Missile Troops Military 71 Obolensk Moscow Oblast 1975 1975 N 1994 Production and testing of biological weapons and Biology protection 74 Prioksk Moscow Oblast ? ? N 1998 Military communication Military 75 Protvino Moscow Oblast 1960 1960 N ? Rosatom city, Proton accelerator, Institute of High High Energy Physics Energy Physics 79 Sergiyev Posad Moscow Oblast 1337 ?1950 N ?1991/1 Rosatom city, Central physics institute of the Ministry of Physics 992 Defence 82 Vaulovo Moscow Oblast 1954 1954 N ? Military communications 83 Venyukovo Moscow Oblast ? ? N ? Military construction 84 Vlasikha Moscow Oblast 1646 ?1941 Y Strategic Missile Troops Military 85 Voskhod Moscow Oblast 1965 1965 Y Military communication Military 86 Yubileyny Moscow Oblast 1939 1946 N 1989 Research Institute of the Ministry of Defence Ballistics, Aerospace (development of rocket and aerospace technologies) 89 Zvyozdny gorodok Moscow Oblast 1960 1960 Y Roskosmos, space training center Aerospace 90 Aleksandrovsk Murmansk ? ? Y Naval base Military 93 Ostrovnoy Murmansk 1611 1981 Y Naval base Military 94 Severomorsk Murmansk 1896 1951 Y Naval base Military 95 Vidyayevo Murmansk 1958 1958 Y Naval base Military 96 Zaozyorsk Murmansk 1958 1958 Y Naval base Military 40

101 Nizhny Novgorod Nizhny Novgorod 1221 1959 N ? Military research and production facilities Military 102 Sarov Nizhny Novgorod 1310 1947 Y Rosatom city, nuclear weapon development Nuclear Physics 104 Koltsovo Novosibirsk 1979 1979 N ? Production and testing of biological weapons and Biology protection 109 Komarovsky Orenburg ? ?1961 Y Strategic Missile Troops Military 110 Kuznetsk-12 Penza ? ? N ? Military communications 111 Zarechny Penza 1954 1954 Y Rosatom city, production of nuclear weapon parts Nuclear and Others 114 Zvyozdny Perm 1961 1961 Y Strategic Missile Troops Military 115 Bolshoy Kamen Primorsky 1947 1947 Y Military shipyard - submarines, Naval base Military, Shipbuilding 116 Fokino 22 Primorsky 1891 1980 Y Naval base Military 118 Salsk-7 Rostov ? ? Y Military city, now abolished Military 121 Mikhailovsky Saratov 1942 1942 Y Chemical weapons storage Military 123 Shikhany Saratov 1928 ? Y Defence against chemical weapons attacks Military 124 Svetly Saratov 1964 1964 Y Strategic Missile Troops Military 125 Voskhod Saratov ? ? Y Military communications 126 Lermontov Stavropol 1953 1953 N 1967 Rosatom city, uranium ore extraction Nuclear 127 Lesnoy Sverdlovsk 1947 1947 Y Rosatom city, radio R&D institute, nuclear production Nuclear Production 129 Novouralsk Sverdlovsk 1941 1941 Y Rosatom city Chemical, Nuclear 130 Svobodny Sverdlovsk 1960 1965 Y Strategic Missile Troops Military 131 Uralsky Sverdlovsk 1960 1960 Y Strategic Missile Troops Military 135 Seversk Tomsk 1949 1949 Y Rosatom city, production of nuclear weapon details Chemical, Nuclear 136 Ozyorny Tver 1972 1972 Y Strategic Missile Troops Military 138 Solnechny Tver 1947 1947 Y Rocket equipment development Aerospace 139 Tobolsk Tyumen 1587 ?1944 N ? Production and testing of biological weapons and Biology protection 145 Raduzhny Vladimir 1971 1971 Y Laser and electro-optical weapon systems Military (laser systems of weapons) 149 Gorny Zabaykalsky 1965 1965 Y Strategic Missile Troops Military 151 Yasnaya Zabaykalsky 1904 1960 N 1994 Strategic Missile Troops Military

22 Incl. Tikhookeansky, Domashlino, Krim, Rudnevo, Askold, Dunay, Razboynik, Temr, Yuzhnorechensk, Putyatin, Abrek, Pavlovsk.

Table A.2: Cities restricted to foreigners in the Soviet Union and Russia

No. Location Oblast Founded Restricted Year Year Specialization in the past Industry now? begin ended 8 Ufa Bashkortostan 1574 N Aviation and space instruments Aerospace 15 Zlatoust Chelyabinsk 1754 N Military and craft centre Various 17 Baltiysk Kaliningrad 1626 N Naval base Military 25 Komsomolsk-na- Khabarovsk 1932 N 1959 1993 Light and heavy industry Various Amure 30 Pechora Komi 1940 N Strategic Missile Troops Military 33 Dudinka Krasnoyarsk 1667 Y (except 2001 1991 Cargo processing, shipping Belarus) 35 Krasnoyarsk Krasnoyarsk 1628 N Major center of the gulag system, large plants (aluminum, metallurgic, base metals), radar station 36 Nrilsk Krasnoyarsk 1920s/1935 Y (except 2001 1991 Nickel, copper, cobalt, platinum, palladium, coal Mining and smelting ore Belarus) 42 Kronshtadt Leningrad 1704 N 1993 Naval base Military 46 Magadan Magadan 1929 N Mining, geology Several 47 Zelenograd Moscow City 1958 N Electronics and radio equipment Electronics, microelectronics and computer industry 52 Chekhov Moscow Oblast 1175 N 56 Dubna Moscow Oblast 1956 N Nuclear research, aerospace Nuclear Physics, Aerospace 87 Zheleznodorozhny Moscow Oblast 1861 N Aerodynamic Institute, Research Institute of Building Several Ceramics, electromechanical plant 88 Zhukovsky Moscow Oblast 1933 N Research/industrial complex; aerodynamics Aerohydromechanics 92 Olenegorsk Murmansk 1949 N Strategic Missile Troops, iron ore extraction and Military processing

97 Arzamas Nizhny Novgorod 1552 N Development and production of avionics, wheeled Military equipment armored vehicles 99 Dzerzhinsk Nizhny Novgorod 1606 N Chemical production Chemical 100 Nizhny Novgorod Nizhny Novgorod 1221/1932 N ?1991 Aircraft construction, shipbuilding, large machinery, weapons, ammunition 107 Omsk Omsk 1716 N Rocket, tank building, aviation 112 Perm Perm 1647 N Automobile plant, then military industry - weapons, chemical plant, receiving evacuees and businesses 117 Vladivostok Primorsky 1860 N Naval base Military 120 Samara Samara 1586 N ?1990 Space center and other companies of the aerospace industry 122 Saratov Saratov 1590 N Aerospace, military equipment, etc Military equipment 140 Izhevsk Udmurtia 1760 N Mass production of weapons 141 Kambarka Udmurtia 1767 N Storage of chemical weapon Cheamical weapon 148 Rybinsk Yaroslavl 1071 N Aircraft engine, military instruments and shipbuilding Several 150 Krasnokamensk Zabaykalsky 1967 N Uranium extraction Military

Table A.3: Science cities

No. Location Oblast Founded Year Naukograd Year Specialization in the past Industry status status obtained 1 Biysk Altai Krai 1718 1957 Y 2005 Evacuation of factories during WWII, military- General, Research, industrial complex, research institute, chemical Pharmaceutical plant, defence and production of high-tech products 4 Mirny Arkhangelsk 1957 1966 N State experimental cosmodrome, ballistic Aerospace weapon 6 Znamensk Astrakhan 1948 1962 N Strategic Missile Troops Military 10 Miass Chelyabinsk 1773 1955 N Aerospace Machinery, Ballistics 11 Ozyorsk Chelyabinsk 1945 1945 N Rosatom city, nuclear power development Nuclear 12 Snezhinsk Chelyabinsk 1957 1957 N Rosatom city, nuclear weapon development Nuclear 13 Tryokhgorny Chelyabinsk 1952 1952 N Rosatom city, equipment for nuclear Nuclear weapon/power production 14 Ust-Katav Chelyabinsk 1758 1942 N Vehicles Vehicles, Automobiles 16 Akademgorodok Irkutsk 1949 1988 N Scientific center General Research 18 Obninsk Kaluga 1946 1946 Y 2000 Nuclear physics and nuclear energy , Nuclear physics and meteorology , radiology , radiation chemistry nuclear energy , and geophysics meteorology , radiology , radiation chemistry and geophysics 31 Krasnodar-59 Krasnodar ? ? N 32 Akademgorodok Krasnoyarsk 1944 1965 N Krasnoyarsk Siberian Branch of RAS, forestry, General Research computer simulation, etc. 39 Zelenogorsk Krasnoyarsk 1956 1956 N Rosatom city, uranium enrichment Nuclear development 40 Zheleznogorsk Krasnoyarsk 1950 1954 N Rosatom city, production of weapon-grade Nuclear development plutonium 41 Gatchina Leningrad 1928 1956 N Nuclear power Nuclear physics 43 Primorsk Leningrad 1268 1948 N Port/space center Port / space center 44 Sosnovy Bor Leningrad 1958 1962 N Nuclear power plant Nuclear energy 44

47 Zelenograd Moscow City 1958 1958 N Electronics and radio equipment Electronics, microelectronics and computer industry 49 Avtopoligon Moscow Oblast 1964 1964 N Research and development of experimental Automobile industry methodology testing of vehicles and components 50 Balashikha Moscow Oblast 1830 1942 N Major industrial center with industries in Various metallurgy, aviation industry, cryogenic technology, machinery, and other fields. 51 Beloozersky Moscow Oblast 1961 1961 N Aircraft equipment Aerospace 54 Chernogolovka Moscow Oblast 1710 1956 Y 2008 Scientific center (chemical physics) Chemical 55 Dolgoprudny Moscow Oblast 1931 1951 N General research Aerospace, Physics 56 Dubna Moscow Oblast 1956 1956 Y 2001 Nuclear research, aerospace Nuclear Physics, Aerospace 57 Dzerzhinsky Moscow Oblast 1938 1956 N Thermal power plant, solid rocket fuel and Energy, Military, satellite development Technology 58 Fryazino Moscow Oblast 1584 1953 Y 2003 Electronics, working for the military Microwave Electronics 59 Istra Moscow Oblast 1589 1946 N Electromechanics - VNIIEM (electrical equipment Electromechanics missile systems, space vehicles) 61 Khimki Moscow Oblast 1850 1950 N R&D in Defense and ballistic missiles, space Ballistics, Aerospace industry 62 Klimovsk Moscow Oblast 1882 1940 N Production of ammunition, concrete structures Military 63 Korolyov Moscow Oblast 1938 1946 Y 2001 Artilery development, research institutes, space Aerospace, others industry 64 Krasnoarmeysk Moscow Oblast 1928 1934 N Defense industry research and production Nuclear Physics 65 Krasnoznamensk Moscow Oblast 1950 1950 N Spacecraft automated control system Military, Ballistic 66 Lytkarino Moscow Oblast 1939 1957 N Aviation, machinery, optical glass production Various 69 Mendeleyevo Moscow Oblast 1957 1965 N Scientific Research Institute for Physical- Metereology, Engineering and Radiotechnical Metrology telecommunications 71 Obolensk Moscow Oblast 1975 1975 N Production and testing of biological weapons and Biology protection 72 Orevo Moscow Oblast 1954 1954 N Aerospace, experimental range of the Moscow Aerospace Physical-Technical Institute, Dmitrov branch of

Bauman Moscow State Technical University 73 Peresvet Moscow Oblast 1948 1948 N Aerospace equipment test center Aerospace 75 Protvino Moscow Oblast 1960 1960 Y 2008 Rosatom city, Proton accelerator, Institute of High Energy Physics High Energy Physics 76 Pushchino Moscow Oblast 1956 1966 Y 2005 Institute of Biophysics, Academy of Sciences of Biotechnology the USSR / Pushchino Scientific Center of Biological Research 77 Remmash Moscow Oblast 1957 1957 N Scientific testing center for aerospace industry Ballistics, Aerospace 78 Reutov Moscow Oblast 1492- 1940 Y 2003 aerospace and rocket equipment production, Ballistics, Aerospace 1495 Aerospace department of the Bauman Moscow State Technical University 80 Tomilino Moscow Oblast 1894 1961 N Scientific research, development, and production Aerospace, Various center; aerospace, diamond tools, electronics 81 Troitsk Moscow Oblast 1617 1977 Y 2007 Several research institutes: terrestrial Nuclear, Physics magnetism, nuclear research, magnetic laboratory, geophysical, high pressure physics, etc 86 Yubileyny Moscow Oblast 1939 1950 N Research Institute of the Ministry of Defence Ballistics, Aerospace (development of rocket and aerospace technologies) 87 Zheleznodorozhny Moscow Oblast 1861 1952 N Aerodynamic Institute, Research Institute of Several Building Ceramics, electromechanical plant 88 Zhukovsky Moscow Oblast 1933 1947 Y 2007 Research/industrial complex; aerodynamics Aerohydromechanics 89 Zvyozdny gorodok Moscow Oblast 1960 1960 N Roskosmos, space training center Aerospace 98 Balakhna (Pravdinsk) Nizhny Novgorod 1932 1941 N Electronics and radio equipment production Military 99 Dzerzhinsk Nizhny Novgorod 1606 1930 N Chemical production Chemical 102 Sarov Nizhny Novgorod 1310 1947 N Rosatom city, nuclear weapon development Nuclear Physics 103 Akademgorodok Novosibirsk 1957 1957 N Several research institutes, Nvosibirsk State General Research University 104 Koltsovo Novosibirsk 1979 1979 Y 2003 Production and testing of biological weapons and Biology protection 105 Krasnoobsk Novosibirsk 1970 1978 N Siberian Branch of Agricultural Sciences Agronomy, Biology

106 Nvosibirsk-49 Novosibirsk ? ? N 108 Omsk-5 Omsk ? ? N 111 Zarechny Penza 1954 1958 N Rosatom city, production of nuclear weapon Nuclear and Others parts 113 Perm-6 Perm ? ? N 119 Petergof Saint Petersburg 1711 1960 Y 2005 Main home to the St. Petersburg State General Research University, several research institutes 127 Lesnoy Sverdlovsk 1947 1954 N Rosatom city, radio R&D institute, nuclear Nuclear Production production 128 Nizhnyaya Salda Sverdlovsk 1760 1958 N Research institute for exploration of outer space, Aerospace and space station production Mechanics 129 Nvouralsk Sverdlovsk 1941 1949 N Rosatom city Chemical, Nuclear 132 Zarechny Sverdlovsk 1955 1955 N Nuclear power station, Institute of Reactor Nuclear and Others Materials 134 Akademgorodok Tomsk 1972 1972 N Siberian division of the RAS, Institute of General Research atmospheric optics, petroleum chemistry, etc 135 Seversk Tomsk 1949 1949 N Rosatom city, production of nuclear weapon Chemical, Nuclear details 137 Redkino Tver 1843 1950 N Pilot-Experimental Chemical Plant Chemical 138 Solnechny Tver 1947 1951 N Rocket equipment development Aerospace 142 Dimitrovgrad Ulyanovsk 1698 1956 N Scientific Research Institute of Atomic Reactor Nuclear, Automobile 144 Melenki Vladimir 1709 ? N Mechanics, textile, light industry etc. Mechanics/Textile/Foo d industry 145 Raduzhny Vladimir 1971 1971 N Laser and electro-optical weapon systems Military 146 Borok Yaroslavl 1807 1956 N Several biology research institutes Hydro-biology 147 Pereslavl-Zalessky Yaroslavl 1152 1964 N Cinema film factory Various

Source: Wikipedia (Russian), Union of science cities of Russia website, several other sources.

Appendix B: Comparison between actual and reported product and process innovation and R&D spending

Figure B.1: Ratio between actual and reported product and process innovation and R&D spending

Introduced new products in the last 3 years Ratio: actual/reported 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 Tver Kirov Perm Kursk Omsk Irkutsk Tomsk Kaluga Rostov Lipetsk Samara Belgorod Mordovia Yaroslavl Tatarstan Voronezh Leningrad Smolensk Volgograd Kemerovo Krasnodar Ulyanovsk Murmansk Sverdlovsk Kaliningrad Novosibirsk Khabarovsk Chelyabinsk Krasnoyarsk Moscow City Bashkortostan Sakha Sakha (Yakutia) Moscow Region Nizhni Novgorod Nizhni Saint Saint Petersburg Stavropol Stavropol Territory Primorsky Territory

Regional average National average

Introduced new processes in the last 3 years Ratio: actual/reported 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 Tver Kirov Perm Kursk Omsk Irkutsk Tomsk Kaluga Rostov Lipetsk Samara Belgorod Yaroslavl Mordovia Tatarstan Voronezh Leningrad Smolensk Volgograd Kemerovo Krasnodar Ulyanovsk Murmansk Sverdlovsk Kaliningrad Novosibirsk Khabarovsk Chelyabinsk Krasnoyarsk Moscow Moscow City Bashkortostan Sakha Sakha (Yakutia) Moscow Moscow Region Nizhni Novgorod Nizhni Saint Saint Petersburg Stavropol Stavropol Territory Primorsky Territory

Regional average National average

Spending on R&D in the last 3 years Ratio: actual/reported 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 Tver Kirov Perm Kursk Omsk Irkutsk Tomsk Kaluga Rostov Lipetsk Samara Belgorod Yaroslavl Mordovia Tatarstan Voronezh Leningrad Smolensk Volgograd Kemerovo Krasnodar Ulyanovsk Murmansk Sverdlovsk Kaliningrad Novosibirsk Khabarovsk Chelyabinsk Krasnoyarsk Moscow Moscow City Bashkortostan Sakha Sakha (Yakutia) Moscow Moscow Region Nizhni Novgorod Nizhni Saint Saint Petersburg Stavropol Stavropol Territory Primorsky Territory

Regional average National average

Source: Own calculations based on EBRD-WB BEEPS V Russia.